[ { "id": "kgCWJ5v0ltIAPa7Q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.013, 0.329, 0.024, 0.444]?" }, { "from": "gpt", "value": "Market Share." } ], "id_raw": 0 }, { "id": "5tzdsT1kbHV2RCfL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.098, 0.079, 0.167, 0.427]?" }, { "from": "gpt", "value": "2021 bar for Energy sector." } ], "id_raw": 0 }, { "id": "N3Gzjd7YeZj269uj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of {the subtitle} at [0.401, 0.019, 0.642, 0.041]?" }, { "from": "gpt", "value": "Industry Market Share Comparison." } ], "id_raw": 0 }, { "id": "EgOZ1ggNpL3sgWmr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by [0.911, 0.055, 0.982, 0.113]?" }, { "from": "gpt", "value": "This is the legend section, which includes {2020}[0.948, 0.062, 0.977, 0.080] and {2021}[0.948, 0.088, 0.977, 0.106]." } ], "id_raw": 0 }, { "id": "2mS1lILle37IVpcP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nIs {the 2020 bar for 'Healthcare'}[0.357, 0.627, 0.426, 0.726] taller than {the 2021 bar for 'Healthcare'}[0.357, 0.477, 0.426, 0.627]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 0 }, { "id": "Pmoz1tt7kjDeYkj7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nAre {the 2020 bars} taller or shorter than {the 2021 bars} in general?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 0 }, { "id": "cYPXDEaGMFLMXdhg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nIn the legend, is the {2020 label}[0.948, 0.062, 0.977, 0.080] above or below the {2021 label}[0.948, 0.088, 0.977, 0.106]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 0 }, { "id": "lSpASCYgh3zFCuCX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nIs the y-coordinate of the top side of {Energy 2020 bar}[0.098, 0.427, 0.167, 0.726] higher or lower than that of the bottom side of {Energy 2021 bar}[0.098, 0.079, 0.167, 0.427]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 0 }, { "id": "bqosDHDHvLxZctgC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.911, 0.055, 0.982, 0.113]." }, { "from": "gpt", "value": "This region contains the legend of the graph. It holds two labels with their corresponding color indicators. The first label is {2020}, denoted by the color blue with coordinates [0.948, 0.062, 0.977, 0.080], and the second label is {2021}, denoted by the color orange, with coordinates [0.948, 0.088, 0.977, 0.106]." } ], "id_raw": 0 }, { "id": "06J7fIeppSNnCbel", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the area from [0.013, 0.329, 0.024, 0.444]." }, { "from": "gpt", "value": "This is the location of the Y-axis label of the chart, which is called \"Market Share\"." } ], "id_raw": 0 }, { "id": "Sj1d3u7h5O7EbUkw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what belongs to the region of [0.444, 0.676, 0.513, 0.726]?" }, { "from": "gpt", "value": "This region contains the 2020 bar for the Consumer_Staples sector, the height of the bar represents the market share value." } ], "id_raw": 0 }, { "id": "zNDisX33aMS1al1h", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat are included within [0.826, 0.650, 0.950, 0.700] region?" }, { "from": "gpt", "value": "This region includes the lower portion of the 2021 bars for Real_Estate[0.789, 0.676, 0.859, 0.701] and Communication_Services [0.876, 0.651, 0.945, 0.701]." } ], "id_raw": 0 }, { "id": "oO8yu17seAm7qTo2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the vertical axis at coordinates [0.015, 0.259, 0.029, 0.706]?" }, { "from": "gpt", "value": "The label is \"Equivalent CO2 Emissions (thousands)\"." } ], "id_raw": 1 }, { "id": "U9xh0lNelhnozvKx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at [0.140, 0.329, 0.309, 0.634]?" }, { "from": "gpt", "value": "It is orange." } ], "id_raw": 1 }, { "id": "uEwCN5G4gCEulFSx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nWhich category does the bar within [0.352, 0.443, 0.521, 0.863] represent?" }, { "from": "gpt", "value": "It represents the \"Road Traffic\" category." } ], "id_raw": 1 }, { "id": "kxwk67iEqMGDFtph", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis located at [0.505, 0.952, 0.580, 0.975]?" }, { "from": "gpt", "value": "The label is \"Categories\"." } ], "id_raw": 1 }, { "id": "SzHivaNnje6JGjwx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Urban Greening bar for 2021}[0.140, 0.329, 0.309, 0.634] taller than {the Urban Greening bar for 2020}[0.140, 0.062, 0.309, 0.329]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1 }, { "id": "NgO5o3KM8xlszeOR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the Waste Management bar for 2021}[0.564, 0.291, 0.733, 0.634] or {the Air Condition Usage bar for 2021}[0.775, 0.367, 0.945, 0.748]?" }, { "from": "gpt", "value": "The Air Condition Usage bar for 2021." } ], "id_raw": 1 }, { "id": "p5wm7JF956yTFCyZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Road Traffic bar for 2021}[0.352, 0.443, 0.521, 0.863] towards the right of the {Urban Greening bar for 2020}[0.140, 0.062, 0.309, 0.329]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1 }, { "id": "P7nbInOKEJZClRKI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nAre there more CO2 Emissions for {Waste Management in 2021}[0.564, 0.291, 0.733, 0.634] or {Waste Management in 2020}[0.564, 0.062, 0.733, 0.291]?" }, { "from": "gpt", "value": "2021." } ], "id_raw": 1 }, { "id": "3BAZ8x0Wsvac4VFu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in [0.107, 0.815, 0.226, 0.891]." }, { "from": "gpt", "value": "This is the legend section of the chart. It contains two items. The first item, with text position at [0.151, 0.824, 0.220, 0.847], is labeled \"Year 2020\" and has a color marker in light blue at [0.112, 0.826, 0.140, 0.842]. The second item, with text position at [0.151, 0.859, 0.220, 0.882], is labeled \"Year 2021\" and has a color marker in orange at [0.112, 0.861, 0.140, 0.877]." } ], "id_raw": 1 }, { "id": "PZUIYTtgN0M9Yeud", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area [0.564, 0.062, 0.733, 0.634]?" }, { "from": "gpt", "value": "This area contains two bars representing CO2 Emissions for the category \"Waste Management\" in the years 2020 and 2021. The bar representing 2020 is located at [0.564, 0.062, 0.733, 0.291] and the bar representing 2021 is located at [0.564, 0.291, 0.733, 0.634]." } ], "id_raw": 1 }, { "id": "OXgSDPJjtAwajTxB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the coordinates [0.791, 0.919, 0.929, 0.942]." }, { "from": "gpt", "value": "There is a text label corresponding to the category \"Air Condition Usage\" within these coordinates." } ], "id_raw": 1 }, { "id": "U0EBd80I4SsZmd0b", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within [0.396, 0.919, 0.477, 0.942]?" }, { "from": "gpt", "value": "There's an x-axis label indicating the category \"Road Traffic\"." } ], "id_raw": 1 }, { "id": "s5L97LI5IUt7ZQZt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis label}[0.015, 0.318, 0.029, 0.647] called?" }, { "from": "gpt", "value": "Investment in million dollars." } ], "id_raw": 2 }, { "id": "eJy1QkD0F5Pc2iT0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the {subtitle}[0.319, 0.025, 0.736, 0.055] of the chart?" }, { "from": "gpt", "value": "Investment pattern in EdTech and Online Learning." } ], "id_raw": 2 }, { "id": "HBltB8Cp2lVHdcwP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat investment sector is mentioned in {xtick}[0.704, 0.919, 0.813, 0.942]?" }, { "from": "gpt", "value": "Online Learning." } ], "id_raw": 2 }, { "id": "B0EqV8TSDWOyMwAB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat color does the {first legend item}[0.122, 0.083, 0.185, 0.106] represent?" }, { "from": "gpt", "value": "{First Year}[0.083, 0.085, 0.111, 0.101]." } ], "id_raw": 2 }, { "id": "BS9Ba6jnKLrkvOs4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nIs {the investment in Education Technology in the first year}[0.112, 0.615, 0.482, 0.903] higher or lower compared to {the investment in Education Technology in the second year}[0.112, 0.230, 0.482, 0.615]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 2 }, { "id": "8Ls8NvXkdaSHNpkK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nAre {the investments in the first year}[0.112, 0.615, 0.482, 0.903], [0.574, 0.551, 0.943, 0.903] overall higher or lower than {the investments in the second year}[0.112, 0.230, 0.482, 0.615],[0.574, 0.102, 0.943, 0.551]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 2 }, { "id": "nUWN1Fnp3TkekdrJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhich investment sector has a higher investment in the first year, {Education Technology}[0.112, 0.615, 0.482, 0.903] or {Online Learning}[0.574, 0.551, 0.943, 0.903]?" }, { "from": "gpt", "value": "Online Learning." } ], "id_raw": 2 }, { "id": "0dYpEEet1A1zD57t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhich sector has seen a greater increase in investment from the {first year}[0.122, 0.083, 0.185, 0.106] to the {second year}[0.122, 0.118, 0.207, 0.141], {Education Technology}[0.112, 0.615, 0.482, 0.903], [0.112, 0.230, 0.482, 0.615] or {Online Learning}[0.574, 0.551, 0.943, 0.903], [0.574, 0.102, 0.943, 0.551]?" }, { "from": "gpt", "value": "Online Learning." } ], "id_raw": 2 }, { "id": "5tnDqGuqFoOr5AU6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found at these coordinates [0.078, 0.074, 0.212, 0.151]." }, { "from": "gpt", "value": "The coordinates represent a legend. Two color-text labels are present. The first handle has a blue color at [0.083, 0.085, 0.111, 0.101] and is labelled as First Year at [0.122, 0.083, 0.185, 0.106]. The second handle is in orange at [0.083, 0.120, 0.111, 0.136], and is labelled as Second Year at [0.122, 0.118, 0.207, 0.141]." } ], "id_raw": 2 }, { "id": "iEAdNycBU4C9Bbti", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.112, 0.102, 0.943, 0.903]?" }, { "from": "gpt", "value": "In this region, there are four bars that signify the investments in Education Technology and Online Learning for the first and second years. The 'First Year' bars:[0.112, 0.615, 0.482, 0.903] and [0.574, 0.551, 0.943, 0.903]. The 'Second Year' bars:[0.112, 0.230, 0.482, 0.615] and [0.574, 0.102, 0.943, 0.551]." } ], "id_raw": 2 }, { "id": "1VeCh7W9JnnecySO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is within these bounding coordinates [0.221, 0.919, 0.813, 0.942]." }, { "from": "gpt", "value": "The region contains two x-ticks, which are the labels for the two investment sectors. The Education Technology label is at coordinates [0.221, 0.919, 0.373, 0.942] and the Online Learning label is at coordinates [0.704, 0.919, 0.813, 0.942]." } ], "id_raw": 2 }, { "id": "CW5mfgf0u6MTfIPB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nDiscuss the content displayed within the region [0.043, 0.381, 0.061, 0.405]." }, { "from": "gpt", "value": "Within these coordinates, we have the Y-axis tick label '80' displayed." } ], "id_raw": 2 }, { "id": "G8oBgUBWq8sYOXqR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat content is being represented at coordinates [0.132, 0.062, 0.493, 0.600]?" }, { "from": "gpt", "value": "The bar represents the time spent on Social Media for Current Year." } ], "id_raw": 3 }, { "id": "dHCd7FjRtzfVD2VN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label indicated by the text coordinates [0.880, 0.824, 0.966, 0.847]?" }, { "from": "gpt", "value": "The label is \"Current Year\" in the legend." } ], "id_raw": 3 }, { "id": "mxp1X8Z4ou7a830N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the text [0.308, 0.025, 0.768, 0.055] in the figure indicate?" }, { "from": "gpt", "value": "The text is the subtitle of the chart, \"Time spent on Social Media and Digital Communication\"." } ], "id_raw": 3 }, { "id": "ZYUcQttNkU1OeN2f", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for the \"Current Year\"[0.132, 0.062, 0.493, 0.600] have a higher or lower value than the \"Previous Year\"[0.132, 0.600, 0.493, 0.863] for Social Media usage?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 3 }, { "id": "kTyrN5vTAeJdAav8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nFor \"Digital Communication\", is the time spent in the \"Previous Year\"[0.583, 0.243, 0.944, 0.646] lesser or greater than the \"Current Year\"[0.583, 0.062, 0.944, 0.243]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 3 }, { "id": "Kb9tLGNc3FoxOnsX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nIs the color #87ceeb representing {\"Current Year\"}[0.880, 0.824, 0.966, 0.847] or {\"Previous Year\"}[0.880, 0.859, 0.972, 0.882] in the legend?" }, { "from": "gpt", "value": "Current Year." } ], "id_raw": 3 }, { "id": "mc0DSKWvfsv6rwhm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat content is represented in the figure within the boundary [0.836, 0.815, 0.978, 0.891]?" }, { "from": "gpt", "value": "It's the legend section defining the color indicators for \"Current Year\" ({handle color for Current Year}[0.841, 0.826, 0.869, 0.842], {Current Year label}[0.880, 0.824, 0.966, 0.847]) and \"Previous Year\" ({handle color for Previous Year}[0.841, 0.861, 0.869, 0.877], {Previous Year label}[0.880, 0.859, 0.972, 0.882])." } ], "id_raw": 3 }, { "id": "rFvHyJmJ4MBxinPa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.035, 0.987, 0.081, 1.010]." }, { "from": "gpt", "value": "It's the label for y-axis, specified as \"-1600\"." } ], "id_raw": 3 }, { "id": "yKi8evpL3R8u8RWC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat details are there in the area [0.583, 0.062, 0.944, 0.646]?" }, { "from": "gpt", "value": "This area contains two bars. The lower bar ({Current Year bar}[0.583, 0.062, 0.944, 0.243]) is for the \"Current Year\" indicates the time spend on \"Digital Communication\", and the upper bar ({Previous Year bar}[0.583, 0.243, 0.944, 0.646]) is for the \"Previous Year\", for the same sector." } ], "id_raw": 3 }, { "id": "ehz7AL259kqfi5yv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate refers to the \"Previous Year\" in the legend?" }, { "from": "gpt", "value": "{the \"Previous Year\"}[0.143, 0.859, 0.235, 0.882]" } ], "id_raw": 5 }, { "id": "y3zEnl6cdUskiauO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nIn the chart, what is found within the coordinates [0.559, 0.062, 0.731, 0.227]?" }, { "from": "gpt", "value": "It is the bar representing the \"Child Development\" of the \"Current Year\"." } ], "id_raw": 5 }, { "id": "zlTkAKgnugTPHbrz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the object bounded by the coordinates [0.044, 0.147, 0.081, 0.171] represent?" }, { "from": "gpt", "value": "It represents the y-axis label, \"-200\"." } ], "id_raw": 5 }, { "id": "sTseQ7h6uYKgcjW6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar related to the \"Current Year\" under \"Child Development\"[0.559, 0.062, 0.731, 0.227] shorter than the bar related to the \"Previous Year\" under \"Child Development\"[0.559, 0.227, 0.731, 0.453]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 5 }, { "id": "mwSBv0LFEi1fDIKy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nIs the \"Previous Year\" bar for \"Income Inequality\"[0.346, 0.439, 0.517, 0.863] taller than the \"Current Year\" bar for \"Income Inequality\"[0.346, 0.062, 0.517, 0.439]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 5 }, { "id": "KHgLKEOVBHUBNugN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nAre the bars labelled as \"Current Year\" generally taller than the bars labelled as \"Previous Year\"?" }, { "from": "gpt", "value": "No." } ], "id_raw": 5 }, { "id": "c105B5AQj2IkkSkE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the region [0.098, 0.815, 0.240, 0.891]." }, { "from": "gpt", "value": "The region contains two labels indicating the color coding of the bars. {The blue label}[0.104, 0.826, 0.131, 0.842] corresponds to \"Current Year\" and {the orange label}[0.104, 0.861, 0.131, 0.877] corresponds to \"Previous Year\"." } ], "id_raw": 5 }, { "id": "PxtM0YgfkyEqJttA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what exists within the box [0.035, 0.901, 0.081, 0.924]?" }, { "from": "gpt", "value": "The coordinate features the y-axis label \"-1800\"." } ], "id_raw": 5 }, { "id": "iDkWWMvoa6TiOQlo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.346, 0.062, 0.517, 0.863]?" }, { "from": "gpt", "value": "It consists of two bars. The lower one represents the \"Current Year\" for the \"Income Inequality\" sector with coordinates [0.346, 0.062, 0.517, 0.439] and the upper one represents the \"Previous Year\" for the same sector with coordinates [0.346, 0.439, 0.517, 0.863]." } ], "id_raw": 5 }, { "id": "Td2LXVkMe6bfJfCM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.937, 0.824, 0.972, 0.847] in the chart?" }, { "from": "gpt", "value": "It is a legend text for the year 2020." } ], "id_raw": 6 }, { "id": "WsV68MoaesqHColU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nWhich department can be found at [0.350, 0.919, 0.387, 0.942]?" }, { "from": "gpt", "value": "The department at these coordinates is the Sales department." } ], "id_raw": 6 }, { "id": "Sr5OREKzC0T3306f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat range of performance can be found at the coordinates [0.035, 0.575, 0.081, 0.598]?" }, { "from": "gpt", "value": "The performance range for these coordinates is between -1500 and 0." } ], "id_raw": 6 }, { "id": "dyeukq08hcUxCFcV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Sales bar for 2020}[0.301, 0.062, 0.436, 0.245] higher than the {Sales bar for 2019}[0.301, 0.245, 0.436, 0.499]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 6 }, { "id": "WdJLTInPNKRhqDdh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nIs the {color handle for 2020} [0.898, 0.826, 0.926, 0.842] above the {color handle for 2019} [0.898, 0.861, 0.926, 0.877]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 6 }, { "id": "sgZuuOWxhikqKYM6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nAmong the labels {Marketing}[0.165, 0.919, 0.234, 0.942], {Sales}[0.350, 0.919, 0.387, 0.942], {Development}[0.491, 0.919, 0.585, 0.942], and {Operations}[0.669, 0.919, 0.746, 0.942], which one is furthest to the right?" }, { "from": "gpt", "value": "Operations." } ], "id_raw": 6 }, { "id": "FJOuwgtfEPIp6nMY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nDescribe what is inside the box delimited by [0.132, 0.062, 0.267, 0.523]." }, { "from": "gpt", "value": "Inside this box, there is a blue bar representing the 2020 performance of the Marketing department." } ], "id_raw": 6 }, { "id": "Bp6aXV0jVCI7jtCy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.091, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "This region comprises the entire plot of the bar chart. It contains all the department's performance bars for 2020 and 2019. The x-axis labels representing the different departments and the y-axis labels representing the performance shift can also be found here." } ], "id_raw": 6 }, { "id": "Dda1AnlaymKBpS5r", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents within the coordinates [0.132, 0.523, 0.267, 0.863]?" }, { "from": "gpt", "value": "Within these coordinates, we find an orange vertical bar that represents the 2019 performance of the Marketing department." } ], "id_raw": 6 }, { "id": "o7JHpBPN3N9gX7Hs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the graph placed at [0.351, 0.025, 0.725, 0.055]?" }, { "from": "gpt", "value": "The title is Human History and Civilization Development." } ], "id_raw": 7 }, { "id": "jxP30KgeXLQ5Ro2I", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the area represented by the handle at [0.104, 0.826, 0.131, 0.842]?" }, { "from": "gpt", "value": "The color is blue, representing the Current Century." } ], "id_raw": 7 }, { "id": "XcBep3qwvBOonJMA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat y-axis label is located at [0.015, 0.220, 0.029, 0.745]?" }, { "from": "gpt", "value": "The label is Advancements in Civilization (arbitrary units)." } ], "id_raw": 7 }, { "id": "wphh9PSeO01sxETg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled in coordinates [0.329, 0.919, 0.408, 0.942]?" }, { "from": "gpt", "value": "The label is Bronze Age." } ], "id_raw": 7 }, { "id": "4M30ARssHZqMtR33", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Stone Age in the Current Century}[0.132, 0.062, 0.267, 0.216] taller than {the bar for Stone Age in the Previous Century}[0.132, 0.216, 0.267, 0.386]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 7 }, { "id": "rH2Z3vTQvUwjoAXp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nAre {the Modern Era bars}[0.809, 0.062, 0.944, 0.863] wider than {the Iron Age bars}[0.470, 0.062, 0.606, 0.420]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 7 }, { "id": "XmfTOuiJoMjIqJKM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nDo {the bars representing the Previous Century} contain more data points than {the bars representing the Current Century}?" }, { "from": "gpt", "value": "No." } ], "id_raw": 7 }, { "id": "juIwQa8ANGBrflJ1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {the Previous Century's Middle Ages}[0.640, 0.318, 0.775, 0.658] taller than the bar for {the Current Century's Middle Ages}[0.640, 0.062, 0.775, 0.318]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 7 }, { "id": "pmdoWJeN4ijcziMv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the region [0.098, 0.815, 0.266, 0.891]?" }, { "from": "gpt", "value": "This region contains a legend with two items. The first item at [0.142, 0.824, 0.254, 0.847] is labeled as Current Century and the color handle at [0.104, 0.826, 0.131, 0.842] is blue. The second item at [0.142, 0.859, 0.261, 0.882] is labeled as Previous Century and the color handle at [0.104, 0.861, 0.131, 0.877] is orange." } ], "id_raw": 7 }, { "id": "1oEZDuN6ZrECCjzA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the area defined by [0.301, 0.062, 0.436, 0.284]?" }, { "from": "gpt", "value": "There are two vertical bars in this area. One is a blue bar at [0.301, 0.062, 0.436, 0.147] representing the value for the Bronze Age in the Current Century. Another is an orange bar at [0.301, 0.147, 0.436, 0.284] representing the value for the Bronze Age in the Previous Century." } ], "id_raw": 7 }, { "id": "9L20YfKkBlbY3sz7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nProvide details about the area [0.351, 0.025, 0.725, 0.055]." }, { "from": "gpt", "value": "This area contains the chart's subtitle: \"Human History and Civilization Development\"." } ], "id_raw": 7 }, { "id": "p7eZ3IPoFaLLcAMF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.035, 0.053, 0.081, 0.076] represent?" }, { "from": "gpt", "value": "These coordinates mark the position of '0' on the y-axis." } ], "id_raw": 7 }, { "id": "81QikVhHT9v88CcG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nWhat text is represented by the coordinates [0.046, 0.528, 0.072, 0.552]?" }, { "from": "gpt", "value": "The y-axis label 400." } ], "id_raw": 8 }, { "id": "FW2J0GT7uP7gM3LN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label present at the coordinates represented by [0.391, 0.919, 0.460, 0.942]?" }, { "from": "gpt", "value": "The x-axis label \"Education\"." } ], "id_raw": 8 }, { "id": "YQYckAywW152uTJA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nWhat is marked at the coordinates [0.555, 0.262, 0.728, 0.738] in the bar chart?" }, { "from": "gpt", "value": "It's the Blue Bar representing '2021' policy impact in the Healthcare area." } ], "id_raw": 8 }, { "id": "TR4QoJWE4UZftJTS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nWhich bar {2021 bar for Healthcare}[0.555, 0.262, 0.728, 0.738] or the {2021 bar for Transportation}[0.771, 0.062, 0.944, 0.738] is taller?" }, { "from": "gpt", "value": "The 2021 bar for Transportation." } ], "id_raw": 8 }, { "id": "mls5JpJledG41b6R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\n{2021 bars for Education and Healthcare}[0.339, 0.312, 0.512, 0.738],[0.555, 0.262, 0.728, 0.738], which one is closer to the x-axis?" }, { "from": "gpt", "value": "2021 bar for Healthcare." } ], "id_raw": 8 }, { "id": "UQzdDq2TndroRZIh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nAre {2020 bars}[0.123, 0.187, 0.296, 0.638],[0.339, 0.312, 0.512, 0.638],[0.555, 0.262, 0.728, 0.863],[0.771, 0.062, 0.944, 0.563] comparatively shorter than {2021 bars}[0.123, 0.187, 0.296, 0.738],[0.339, 0.312, 0.512, 0.738],[0.555, 0.262, 0.728, 0.738],[0.771, 0.062, 0.944, 0.738]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 8 }, { "id": "bs85tzzhUzJrGDaC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region given by [0.089, 0.074, 0.174, 0.151]?" }, { "from": "gpt", "value": "This region contains the legend of the bar chart, with two entries. The first entry has a blue color label located at [0.095, 0.085, 0.122, 0.101], and the corresponding text \"2021\" at [0.134, 0.083, 0.169, 0.106]. The second entry has an orange color label at [0.095, 0.120, 0.122, 0.136] with the corresponding text \"2020\" at [0.134, 0.118, 0.169, 0.141]." } ], "id_raw": 8 }, { "id": "YmRpsTwF3UVMBPuk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the region [0.064, 0.729, 0.072, 0.752]?" }, { "from": "gpt", "value": "The y-axis label \"0\" is in this region." } ], "id_raw": 8 }, { "id": "Oazn3cXWvCMUZQrp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within [0.082, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "This region contains all the bars, labels, legend and title of the chart. It includes the x-axis labels for \"Housing\", \"Education\", \"Healthcare\", and \"Transportation\" at respective coordinates; the y-axis labels for values ranging from \"-400\" to \"1400\" at respective coordinates. The blue and orange bars representing the policy impact for different socioeconomic areas in the years 2021 and 2020 and their respective locations. The title of the chart \"Socioeconomic Policy Making: 2020 vs 2021\" is located at [0.350, 0.025, 0.717, 0.055]." } ], "id_raw": 8 }, { "id": "8eHbaefMFxigdWgC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nIn the right bottom corner, unit is described for the y-axis. Can you provide the coordinate of this label?" }, { "from": "gpt", "value": "The unit label or y-axis label, named \"Intensity or Impact\", is located at [0.015, 0.373, 0.029, 0.592]." } ], "id_raw": 9 }, { "id": "TGSgsJOroQPb7zvL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat's the text at position [0.142, 0.118, 0.226, 0.141]?" }, { "from": "gpt", "value": "The text at the given position is \"Modern Age\"." } ], "id_raw": 9 }, { "id": "t4DoF28TsdZqq2FY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nCan you identify the coordinates for the bar referring to 'Modern Age' under the category of 'Global Cooperation'?" }, { "from": "gpt", "value": "The coordinates for the bar referring to 'Modern Age' under the category of 'Global Cooperation' are [0.773, 0.169, 0.944, 0.222]." } ], "id_raw": 9 }, { "id": "W48oH2xqSuEO4UxO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat's located at [0.104, 0.120, 0.131, 0.136] on the chart?" }, { "from": "gpt", "value": "It is the color handle for {the Modern Age}[0.104, 0.120, 0.131, 0.136], which is colored in blue." } ], "id_raw": 9 }, { "id": "PD7XRsBAIjixvTdu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Feudal System' bar for the 'Middle Ages'}[0.132, 0.062, 0.303, 0.436] higher than {the same for the 'Modern Age'}[0.132, 0.436, 0.303, 0.863]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 9 }, { "id": "VWbcZHfGxuuMDJzy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Birth of Nations' bar for 'Middle Ages'}[0.346, 0.062, 0.517, 0.329] lower or higher than {the same one for 'Modern Age'}[0.346, 0.329, 0.517, 0.436]?" }, { "from": "gpt", "value": "It is lower." } ], "id_raw": 9 }, { "id": "9KjUvvejMYoXfNOD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nIs the {the Modern Age bar for 'Technology Advancements'}[0.559, 0.116, 0.730, 0.302] higher or lower than {the Middle Ages bar for the same category}[0.559, 0.062, 0.730, 0.116]?" }, { "from": "gpt", "value": "It is higher." } ], "id_raw": 9 }, { "id": "S2xfN25GShjrQQJc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nIn comparison, is the impact of {Middle Ages}[0.142, 0.083, 0.227, 0.106] on our civilization stronger or weaker than that of {Modern Age}[0.142, 0.118, 0.226, 0.141]?" }, { "from": "gpt", "value": "Stronger." } ], "id_raw": 9 }, { "id": "AZVDALXIP3Rm1hRo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe [0.132, 0.062, 0.303, 0.436]." }, { "from": "gpt", "value": "In this vertical rectangle region, there is {a bar color-coded for Middle Ages}[0.132, 0.062, 0.303, 0.436] under 'Feudal System' heading on the x-axis. It indicates the intensity or impact of 'Feudal System' during the Middle Ages." } ], "id_raw": 9 }, { "id": "50mm8b4J6gS08DfC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat can I see within [0.559, 0.062, 0.730, 0.302]?" }, { "from": "gpt", "value": "Within this region, two overlapping bars could be identified. One of the bars represents the impact of 'Technology Advancement' in the {Middle Ages}[0.559, 0.062, 0.730, 0.116]; the other one represents the same historical aspect during the {Modern Age}[0.559, 0.116, 0.730, 0.302]. The bar representing Modern Age is longer, suggesting a higher impact of 'Technology Advancement' during that era." } ], "id_raw": 9 }, { "id": "l0p7hLcV8zcnSzdU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nTell me about the content in the area [0.132, 0.062, 0.944, 0.863]." }, { "from": "gpt", "value": "This region includes most of the chart content. Here you will find both the 'Middle Ages' and 'Modern Age' bar graphs for all categories: 'Feudal System', 'Birth of Nations', 'Technology Advancements', 'Global Cooperation'. The bars representing each era are overlapped, with 'Middle Ages' bar starting from y=0 and 'Modern Age' bar starting from the end of 'Middle Ages' bar." } ], "id_raw": 9 }, { "id": "O6Swclh4SLjHG42q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the region [0.098, 0.074, 0.232, 0.151]?" }, { "from": "gpt", "value": "It includes a legend key for two categories. The {first one}[0.142, 0.083, 0.227, 0.106] is the 'Middle Ages' which is color-coded in red, and the {second one}[0.142, 0.118, 0.226, 0.141] is the 'Modern Age' which is color-coded in blue." } ], "id_raw": 9 }, { "id": "MWGcyVChXt53ovHU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the main element located at coordinates [0.353, 0.025, 0.711, 0.055]?" }, { "from": "gpt", "value": "Subtitle of the chart, namely \"Health and Medical Field Data Presentation\"." } ], "id_raw": 11 }, { "id": "b7nI7I0x1UlhsJsP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat color do you assign at the coordinates [0.092, 0.120, 0.120, 0.136]?" }, { "from": "gpt", "value": "Blue (#1e90ff)." } ], "id_raw": 11 }, { "id": "b0NMIJuPYeZCQWuW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat category is the bar at [0.121, 0.676, 0.258, 0.903] representative of?" }, { "from": "gpt", "value": "It represents \"Emergency Services\"." } ], "id_raw": 11 }, { "id": "ErlttbXiYDvmoiKn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled within the range of [0.119, 0.919, 0.260, 0.942]?" }, { "from": "gpt", "value": "The text label \"Emergency Services\"." } ], "id_raw": 11 }, { "id": "mccZj4bFLySh46No", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for the \"Current Year\" or the \"Previous Year\" reveal higher spending for 'Emergency Services'?" }, { "from": "gpt", "value": "Previous Year." } ], "id_raw": 11 }, { "id": "ap6dIhhaW6rALp1W", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for 'Pediatric Care'}[0.464, 0.555, 0.601, 0.903] higher than the {bar for 'Primary Care'}[0.292, 0.601, 0.429, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 11 }, { "id": "R2P0pqkoJy1h6ZHk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nIs the {legend icon for 'Previous Year'}[0.092, 0.120, 0.120, 0.136] to the left or right of the {legend icon for 'Current Year'}[0.092, 0.085, 0.120, 0.101] ?" }, { "from": "gpt", "value": "Neither. They are vertically aligned." } ], "id_raw": 11 }, { "id": "i5lCh6QZiLLPLKDD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value, the {bar for 'Public Health' for the current year}[0.807, 0.495, 0.944, 0.903] or the {bar for 'Public Health' for the previous year}[0.807, 0.102, 0.944, 0.495]?" }, { "from": "gpt", "value": "The bar representing the previous year." } ], "id_raw": 11 }, { "id": "nTlAIT3GMzjAr320", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the zone [0.086, 0.074, 0.229, 0.151]." }, { "from": "gpt", "value": "This zone contains a legend with two elements. The element at coordinates [0.092, 0.085, 0.120, 0.101] is a red (#ff6347) square representing 'Current Year', and the corresponding label is at coordinates [0.131, 0.083, 0.217, 0.106]. The second element at coordinates [0.092, 0.120, 0.120, 0.136] is a blue (#1e90ff) square representing 'Previous Year', with the corresponding label at coordinates [0.131, 0.118, 0.223, 0.141]." } ], "id_raw": 11 }, { "id": "kK6Dejofluv2xENM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the area limited by these coordinates [0.079, 0.062, 0.985, 0.903]." }, { "from": "gpt", "value": "The area spans the entire chart, including the x-axis labeled 'Healthcare Fields', the y-axis labeled 'Medical Expenses (in million dollars)', the legend located in the bottom left, and a set of bars representing various healthcare categories for 'Current Year' and 'Previous Year'." } ], "id_raw": 11 }, { "id": "lzhTWzyJ2JPu24uq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents inside [0.121, 0.435, 0.258, 0.676]?" }, { "from": "gpt", "value": "It is a blue bar that corresponds to the 'Previous Year' category and represents 'Emergency Services'. It indicates a spending of approximately between 1500 to 3100 million dollars." } ], "id_raw": 11 }, { "id": "GRaeiMq71lg9jasP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.015, 0.272, 0.029, 0.693] contain?" }, { "from": "gpt", "value": "It has a vertical text label reading \"Medical Expenses (in million dollars)\" which serves as the label for the Y-axis." } ], "id_raw": 11 }, { "id": "nqLNU6uYmOP5pkkT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nWhat type of data is represented by the coordinates [0.132, 0.405, 0.303, 0.713] in the chart?" }, { "from": "gpt", "value": "It's a bar for Education in the previous year." } ], "id_raw": 12 }, { "id": "QgcDiW5TbMb6N8dQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is at [0.015, 0.262, 0.029, 0.703]?" }, { "from": "gpt", "value": "This pair of coordinates contains the y-axis label, which is \"Expenditures (in thousands of dollars)\"." } ], "id_raw": 12 }, { "id": "upYiIcgckIXKwVS0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [0.346, 0.062, 0.517, 0.469]?" }, { "from": "gpt", "value": "These coordinates represent a bar for Healthcare in the current year." } ], "id_raw": 12 }, { "id": "ymSil3yF9Ar3YV3f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.142, 0.824, 0.228, 0.847]?" }, { "from": "gpt", "value": "It is the legend label for the current year." } ], "id_raw": 12 }, { "id": "qKujzvtJA9hvYJ9Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nIs the bar for current year's expenditures in Education at [0.132, 0.062, 0.303, 0.405] higher or lower than the same bar for the previous year at [0.132, 0.405, 0.303, 0.713]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 12 }, { "id": "LtnFySjBMPotUS82", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nIs the {Current year's bar for Healthcare}[0.346, 0.062, 0.517, 0.469] taller than {Previous year's bar for Healthcare}[0.346, 0.469, 0.517, 0.863]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 12 }, { "id": "DcqdRQd6FK6yMQz5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nIs {the expenditures of Welfare in the Current Year}[0.773, 0.062, 0.944, 0.426] lesser than {Healthcare expenditures in the Previous Year} [0.346, 0.469, 0.517, 0.863]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 12 }, { "id": "ePNiWL0WaCLKHHgm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nAre expenditures in the {current year for Transportation}[0.559, 0.062, 0.730, 0.362] higher than expenditures in the {previous year for Education}[0.132, 0.405, 0.303, 0.713]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 12 }, { "id": "VH8pJHI385yBpu37", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area defined by the coordinates [0.132, 0.062, 0.303, 0.713]." }, { "from": "gpt", "value": "This region holds two vertical bars. The first bar, found at [0.132, 0.062, 0.303, 0.405], represents expenditures in Education for the current year while the second bar at [0.132, 0.405, 0.303, 0.713] denotes the same for the previous year. The two bars represent a significant reduction in Education allocation." } ], "id_raw": 12 }, { "id": "CMSXCy6WsIWvbkD1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nWhat is occurring within the coordinates [0.559, 0.062, 0.730, 0.692]?" }, { "from": "gpt", "value": "This selected area includes two bars indicating expenditure on Transportation in the current year [0.559, 0.062, 0.730, 0.362] and the previous year [0.559, 0.362, 0.730, 0.692]. The second bar is longer, indicating a higher expenditure on Transportation in the previous year." } ], "id_raw": 12 }, { "id": "rrI2XZylD17P97NI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region in [0.773, 0.062, 0.944, 0.820]?" }, { "from": "gpt", "value": "The region holds two bars which denote the expenditure on Welfare in the current year [0.773, 0.062, 0.944, 0.426] and the previous year [0.773, 0.426, 0.944, 0.820]. The second bar is longer which means that the expenditures in the previous year for Welfare was higher." } ], "id_raw": 12 }, { "id": "UTN2NOX1NNJjdLss", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what's in the region [0.098, 0.815, 0.240, 0.891]?" }, { "from": "gpt", "value": "This region contains two labels within a legend. {The Current Year label}[0.142, 0.824, 0.228, 0.847], represented as the label colour blue and at [0.104, 0.826, 0.131, 0.842], and the {Previous Year label}[0.142, 0.859, 0.235, 0.882], represented as the label colour orange and at [0.104, 0.861, 0.131, 0.877]." } ], "id_raw": 12 }, { "id": "NMJf0mIGFWaFMInk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the x-axis labels?" }, { "from": "gpt", "value": "Mobile Learning Tech: [0.144, 0.919, 0.290, 0.942], Gamification: [0.387, 0.919, 0.475, 0.942], E-Learning: [0.608, 0.919, 0.682, 0.942], Virtual Training: [0.806, 0.919, 0.911, 0.942]." } ], "id_raw": 13 }, { "id": "SdK8ja66dkV0NGHs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the first bar in the legend at [0.098, 0.815, 0.240, 0.891]?" }, { "from": "gpt", "value": "The first bar color is blue, as indicated by its color label [0.104, 0.826, 0.131, 0.842]." } ], "id_raw": 13 }, { "id": "kYQfBGaOloS7QPKE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at coordinates [0.559, 0.062, 0.730, 0.443] represent in terms of its sector and year?" }, { "from": "gpt", "value": "The bar represents the sector \"E-Learning\" for the \"Current Year\"." } ], "id_raw": 13 }, { "id": "No5hbBdAeBJZSELR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Current Year's spending in Mobile Learning Tech}[0.132, 0.062, 0.303, 0.313] higher than the {Previous Year's spending in the same sector}[0.132, 0.313, 0.303, 0.646]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 13 }, { "id": "fcPrUKyIZRLqTtgv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nWhich sector has a higher spending in the current year, {Mobile Learning Tech}[0.132, 0.062, 0.303, 0.313] or {Gamification}[0.346, 0.062, 0.517, 0.211]?" }, { "from": "gpt", "value": "Mobile Learning Tech." } ], "id_raw": 13 }, { "id": "sB7IrHQd5PndeYHD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Current Year's spending in E-Learning}[0.559, 0.062, 0.730, 0.443] more than the {Previous Year's spending in the same sector}[0.559, 0.443, 0.730, 0.863]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 13 }, { "id": "06LjrNMbH5OnJl8H", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the bars in the area [0.132, 0.062, 0.303, 0.646]." }, { "from": "gpt", "value": "The area includes two bars. The first bar in blue, located at [0.132, 0.062, 0.303, 0.313], represents the \"Current Year's\" spending in the \"Mobile Learning Tech\" sector. The second bar in orange, located at [0.132, 0.313, 0.303, 0.646], displays the \"Previous Year's\" spending in the same sector." } ], "id_raw": 13 }, { "id": "XuMVRvOuIw4slhcf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.034, 0.980, 0.081, 1.003]?" }, { "from": "gpt", "value": "The region contains the y-axis label \"-1600\"." } ], "id_raw": 13 }, { "id": "SCHTnlLpFfO7yfOy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.098, 0.815, 0.240, 0.891]." }, { "from": "gpt", "value": "The area contains a legend with two entries. The first color-text pair is a blue handle at [0.104, 0.826, 0.131, 0.842] with the text \"Current Year\" at [0.142, 0.824, 0.228, 0.847], indicating that the blue bars represent current year's spending. The second color-text pair has an orange handle at [0.104, 0.861, 0.131, 0.877] with the text \"Previous Year\" at [0.142, 0.859, 0.235, 0.882], indicating that the orange bars represent the previous year's spending." } ], "id_raw": 13 }, { "id": "169NQ5shbqxbzz5p", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.044, 0.687, 0.079, 0.710] represent?" }, { "from": "gpt", "value": "It represents the y-axis label \"4000\"." } ], "id_raw": 14 }, { "id": "c7acHY90Egsil17r", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at [0.082, 0.02, 0.910, 0.055]?" }, { "from": "gpt", "value": "This coordinate range corresponds to the subtitle \"Fitness and Sports Health Trends\"." } ], "id_raw": 14 }, { "id": "0PhcBlgMBIqSVVBG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the object marked by [0.937, 0.118, 0.972, 0.141] represent?" }, { "from": "gpt", "value": "This is for the text \"2020\" in the legend." } ], "id_raw": 14 }, { "id": "qPpfMoITOxlz8CfE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at location [0.616, 0.919, 0.672, 0.942]?" }, { "from": "gpt", "value": "The label is \"Running\"." } ], "id_raw": 14 }, { "id": "yDEI764vBs0Atzro", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nIs the {2020 bar for Yoga}[0.129, 0.231, 0.301, 0.593] taller than the {2019 bar for Yoga}[0.129, 0.593, 0.301, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 14 }, { "id": "MVNSX0eT7SOSVAMH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nIs the {2019 bar for Biking}[0.773, 0.670, 0.944, 0.903] to the left or right of the {2020 bar for Biking}[0.773, 0.412, 0.944, 0.670]?" }, { "from": "gpt", "value": "They share the same horizontal position, so neither left nor right." } ], "id_raw": 14 }, { "id": "Q8hYMQdnkI4Qp94O", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nIs the height of {2019 bar for Running}[0.558, 0.645, 0.730, 0.903] higher than {2019 bar for Weightlifting}[0.344, 0.515, 0.515, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 14 }, { "id": "q225Nuqf1SsZckHV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, the {2019 bar for Yoga}[0.129, 0.593, 0.301, 0.903] or the {2020 bar for Weightlifting}[0.344, 0.102, 0.515, 0.515]?" }, { "from": "gpt", "value": "The 2020 bar for Weightlifting is taller." } ], "id_raw": 14 }, { "id": "J2Asd3PyY2dlwpzA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\ncan you provide information on the section of the chart [0.893, 0.074, 0.978, 0.151]?" }, { "from": "gpt", "value": "This area is where the legend of the chart is. It includes the label \"2019\" with the associated color at [0.898, 0.085, 0.926, 0.101] and label \"2020\" with its color at [0.898, 0.120, 0.926, 0.136]. The label \"2019\" refers to the color #87ceeb, and \"2020\" refers to the color #f4a460." } ], "id_raw": 14 }, { "id": "136UCRVqA9Zh9e5N", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the area [0.387, 0.906, 0.600, 0.932]?" }, { "from": "gpt", "value": "Within this range are the x-axis labels for \"Weightlifting\" at [0.385, 0.919, 0.473, 0.942] and \"Running\" at [0.616, 0.919, 0.672, 0.942]." } ], "id_raw": 14 }, { "id": "hX1pcNsqsAiu9bl5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.034, 0.061, 0.079, 0.923]?" }, { "from": "gpt", "value": "This is the region for the y-axis scale with tick labels from \"0\" at [0.070, 0.894, 0.079, 0.917] up to \"18000\" at [0.035, -0.036, 0.079, -0.013]." } ], "id_raw": 14 }, { "id": "AWGbqRIA6GwiJJsU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the area between [0.126, 0.072, 0.301, 0.903]." }, { "from": "gpt", "value": "Within these coordinates, there are two bars representing 2019 & 2020 participation for the \"Yoga\" activity. For \"2019\", the bar coordinates are [0.129, 0.593, 0.301, 0.903] and for \"2020\", the bar coordinates are [0.129, 0.231, 0.301, 0.593]." } ], "id_raw": 14 }, { "id": "igdMGTB5J2RBUgjm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object with coordinates [0.055, 0.613, 0.064, 0.630] on the chart?" }, { "from": "gpt", "value": "This is the label '0' on the Y-axis." } ], "id_raw": 28 }, { "id": "WSO6slZItfbWUbwh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 3rd tick on the x-axis?" }, { "from": "gpt", "value": "[0.454, 0.927]" } ], "id_raw": 28 }, { "id": "u5jnsALRJPqksPmF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the tick at [0.606, 0.927] represent?" }, { "from": "gpt", "value": "The category 'Worker Training Programs'." } ], "id_raw": 28 }, { "id": "VPMVs779t8Etv1El", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat policy is associated with the bar at [0.868, 0.144, 0.944, 0.620]?" }, { "from": "gpt", "value": "Debt Relief." } ], "id_raw": 28 }, { "id": "hjk4Lv20ctmOCi1i", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing 'Heavy Taxation'[0.115, 0.323, 0.190, 0.620] higher or lower than the bar representing 'Reduced Tariffs'[0.266, 0.234, 0.341, 0.620]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 28 }, { "id": "wR9opU2VlbSoK4o9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nRelative to the bar for 'Heavy Taxation'[0.115, 0.323, 0.190, 0.620], where is the bar for 'Debt Relief'[0.868, 0.144, 0.944, 0.620] placed?" }, { "from": "gpt", "value": "To the right." } ], "id_raw": 28 }, { "id": "IkDmOGh9Bzyj4uK9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nAmong policies 'Unemployment Benefits'[0.718, 0.085, 0.793, 0.620] and 'Subsidies for SMEs'[0.416, 0.174, 0.492, 0.620], which one has higher effectiveness?" }, { "from": "gpt", "value": "Unemployment Benefits." } ], "id_raw": 28 }, { "id": "D71L87bWVDhZMemT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nBetween 'Reduced Tariffs' bar[0.266, 0.234, 0.341, 0.620] and 'Debt Relief' bar[0.868, 0.144, 0.944, 0.620], which one extends above the 0 level on the y axis?" }, { "from": "gpt", "value": "Debt Relief." } ], "id_raw": 28 }, { "id": "t0DmXQ6QqrG8DRyG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.115, 0.323, 0.190, 0.620]?" }, { "from": "gpt", "value": "This area contains the negative pink bar denoting the policy effectiveness for 'Heavy Taxation' and the coordinates for the effectiveness level are [0.144, 0.340, 0.161, 0.358] which is '50'." } ], "id_raw": 28 }, { "id": "48gOSGYOT2UOfES9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents at [0.046, 0.078, 0.064, 0.096]." }, { "from": "gpt", "value": "This region contains the label '90' on the Y-axis, indicating an effectiveness level of 90%." } ], "id_raw": 28 }, { "id": "eLargdr6KXtxhKlq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the area [0.115, 0.620, 0.190, 0.739]?" }, { "from": "gpt", "value": "Within this region, you can find the positive pink bar that corresponds to the policy 'Heavy Taxation', and its effectiveness level is marked at coordinates [0.596, 0.311, 0.613, 0.328], which is '-20'." } ], "id_raw": 28 }, { "id": "FfUqaClItU77LY93", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained in the region [0.515, 0.939, 0.694, 0.957]?" }, { "from": "gpt", "value": "This region contains the label 'Worker Training Programs' on the X-axis." } ], "id_raw": 28 }, { "id": "NJkm7kn7gmbDLLn9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.053, 0.062, 0.985, 0.903] in the chart?" }, { "from": "gpt", "value": "This is the primary plot area of the bar chart which contains all the individual bars representing data." } ], "id_raw": 30 }, { "id": "iILTTwK5iAcnHlBy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first color handle in the legend?" }, { "from": "gpt", "value": "[0.427, 0.085, 0.455, 0.101]" } ], "id_raw": 30 }, { "id": "4VZXAD9iGFK5Oabk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.489, 0.952, 0.550, 0.975]?" }, { "from": "gpt", "value": "Quarters." } ], "id_raw": 30 }, { "id": "sZxtEJGMlEMdYCRu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nWhat category is represented at [0.096, 0.195, 0.126, 0.903] and [0.126, 0.102, 0.157, 0.903]?" }, { "from": "gpt", "value": "Career Development and Workplace Trends are represented respectively." } ], "id_raw": 30 }, { "id": "N6jBR7QlLec46w2w", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nIs {the Career Development bar}<{Career Development bar at Q1}[0.096, 0.195, 0.126, 0.903]> taller than {the Workplace Trends bar}<{Workplace Trends bar at Q1}[0.126, 0.102, 0.157, 0.903]> in Q1?" }, { "from": "gpt", "value": "No." } ], "id_raw": 30 }, { "id": "c1QYaiSLJcCGkpKT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nComparing {Career Development and Workplace Trends bars at Q2}[0.183, 0.237, 0.213, 0.903] and [0.213, 0.187, 0.244, 0.903], which trend saw a greater increase?" }, { "from": "gpt", "value": "Workplace Trends." } ], "id_raw": 30 }, { "id": "xR5t5whXKEsRje9j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nIs {the Career Development}[0.466, 0.083, 0.611, 0.106] trend typically higher than the {Workplace Trends}[0.466, 0.118, 0.589, 0.141]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 30 }, { "id": "mEfef1pfGGnrOLrp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nIs the color for {Career Development}[0.466, 0.083, 0.611, 0.106] in the legend the same as the color for {Career Development bar at Q1}[0.096, 0.195, 0.126, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 30 }, { "id": "QiBiKKMEPx016gFR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.422, 0.074, 0.616, 0.151]." }, { "from": "gpt", "value": "It is a legend containing two entries. The first entry consists of a color handle with coordinates [0.427, 0.085, 0.455, 0.101] of color '#5878a3' representing the label 'Career Development' at coordinates [0.466, 0.083, 0.611, 0.106]. The second entry consists of a color handle with coordinates [0.427, 0.120, 0.455, 0.136] of color '#e59344' representing the label 'Workplace Trends' at coordinates [0.466, 0.118, 0.589, 0.141]." } ], "id_raw": 30 }, { "id": "rakEc2bBsVpETQMI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.035, 0.388, 0.043, 0.411]." }, { "from": "gpt", "value": "This area displays a tick label on the y-axis, marking the value '6'." } ], "id_raw": 30 }, { "id": "z88AivEFNxEnFyPj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.183, 0.00, 0.250, 1.0]?" }, { "from": "gpt", "value": "This region contains two bar elements. The first bar corresponds to the value of 'Career Development' at Q2, whose coordinates are [0.183, 0.237, 0.213, 0.903]. The second bar corresponds to the value of 'Workplace Trends' at Q2, whose coordinates are [0.213, 0.187, 0.244, 0.903]." } ], "id_raw": 30 }, { "id": "Wm3ZznxRTn5Ls1jB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.277, 0.025, 0.761, 0.055]?" }, { "from": "gpt", "value": "This area contains the chart's subtitle which says \"Career and Workplace Development Trends over 2.5 Years\"." } ], "id_raw": 30 }, { "id": "eu4EBMw9kINPvb91", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat technology name does the first text on x-axis represent according to its coordinates?" }, { "from": "gpt", "value": "{The technology name}[0.125, 0.903, 0.219, 0.931] represents \"Solar Panels\"." } ], "id_raw": 31 }, { "id": "Dm3AeT8T0sLB749c", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the rating for {the Development Rate}[0.821, 0.183, 0.966, 0.211] of {the \"Electric Vehicles\"}[0.742, 0.113, 0.771, 0.883]?" }, { "from": "gpt", "value": "4.2." } ], "id_raw": 31 }, { "id": "7AvVnlvx7ft3kKOF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the graph's y-axis represent according to the label's coordinates?" }, { "from": "gpt", "value": "The {y-axis}[0.017, 0.427, 0.032, 0.531] represents \"Ratings\"." } ], "id_raw": 31 }, { "id": "ucDUCFX7QUGiwLfd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Stability bar}[0.314, 0.187, 0.343, 0.883] of Wind Turbines higher than {the Stability bar}[0.457, 0.333, 0.486, 0.883] of Tidal Power?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 31 }, { "id": "xDvQiKMx7odwZE7G", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nComparing the {Market Acceptance bar}[0.628, 0.370, 0.657, 0.883] for Geothermal Plants and {Market Acceptance bar}[0.771, 0.242, 0.799, 0.883] for Electric Vehicles, which one is higher?" }, { "from": "gpt", "value": "Electric Vehicles." } ], "id_raw": 31 }, { "id": "ySsS8qB9yIWkdkm8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nAre {the Efficiency bars}[[0.115, 0.187, 0.144, 0.883],[0.258, 0.333, 0.286, 0.883],[0.400, 0.517, 0.429, 0.883],[0.543, 0.242, 0.571, 0.883],[0.685, 0.150, 0.714, 0.883],[0.828, 0.425, 0.856, 0.883]] generally higher than {the Market Acceptance bars}[[0.201, 0.425, 0.229, 0.883],[0.343, 0.333, 0.372, 0.883],[0.486, 0.608, 0.514, 0.883],[0.628, 0.370, 0.657, 0.883],[0.771, 0.242, 0.799, 0.883],[0.913, 0.480, 0.942, 0.883]]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 31 }, { "id": "A0DooErxmHhWtzaE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects and information exist in the area [0.821, 0.100, 0.896, 0.128]?" }, { "from": "gpt", "value": "The text of the label \"Efficiency\" for the legend is located in this region. It refers to the red bar in the chart." } ], "id_raw": 31 }, { "id": "QLDtkbmhmDYcA5qe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nCould you please describe the content of the area [0.300, 0.100, 0.600, 0.883]?" }, { "from": "gpt", "value": "The area contains multiple bars that represent the Efficiency, Stability, Development Rate, and Market Acceptance of different technologies including Wind Turbines, Tidal Power, and Geothermal Plants." } ], "id_raw": 31 }, { "id": "4iJr0H1go5o4DsY9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area with coordinates [0.261, 0.903, 0.369, 0.931]?" }, { "from": "gpt", "value": "The area contains the technology name \"Wind Turbines\"." } ], "id_raw": 31 }, { "id": "7XFxmcW3kfUVDGdh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the project represented by the coordinates [0.251, 0.903, 0.371, 0.931] on the x-axis labels?" }, { "from": "gpt", "value": "Slow Expansion." } ], "id_raw": 32 }, { "id": "tEL6YclWEFuldRqg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at these coordinates [0.864, 0.183, 0.877, 0.211] in the legend?" }, { "from": "gpt", "value": "IP." } ], "id_raw": 32 }, { "id": "rga6SSlk8mGhFixJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the vertical label located at these coordinates [0.017, 0.427, 0.032, 0.531]?" }, { "from": "gpt", "value": "Ratings." } ], "id_raw": 32 }, { "id": "vaTtxU8kKBpmarJy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nWhat color does the legend describe at [0.821, 0.144, 0.851, 0.164]?" }, { "from": "gpt", "value": "The legend at these coordinates corresponds to the color #e59344, which is a shade of orange." } ], "id_raw": 32 }, { "id": "iLjobeG4MB186YRP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Life bar of the Rapid Decline project}[0.521, 0.729, 0.605, 0.883] taller or shorter than {the Infrastructure bar of the Slow Expansion project}[0.185, 0.421, 0.269, 0.883]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 32 }, { "id": "yZhWjc6UuEA4tYEb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Commercial bar of the Rapid Decline project}[0.857, 0.575, 0.941, 0.883] to the left or right of {the Ecosystem bar of the Slow Expansion project}[0.353, 0.113, 0.437, 0.883]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 32 }, { "id": "qcpc4mUfTybg6LiW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nDoes the {Life bar of the Slow Expansion project}[0.101, 0.267, 0.185, 0.883] lie to the left or right of {the Infrastructure bar}[0.185, 0.421, 0.269, 0.883]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 32 }, { "id": "bKAN0Demu1Ady8ct", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Commercial bar of the Rapid Decline project}[0.857, 0.575, 0.941, 0.883] darker or lighter than {the Ecosystem bar of the Rapid Decline project}[0.773, 0.729, 0.857, 0.883]?" }, { "from": "gpt", "value": "Darker." } ], "id_raw": 32 }, { "id": "0uAX7OSDgtermqHD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region from [0.814, 0.089, 0.976, 0.306]?" }, { "from": "gpt", "value": "This region contains the chart's legend, with each entry represented by a color and corresponding label, such as {Life}[0.864, 0.100, 0.892, 0.128] representing #5878a3 and {Commercial}[0.864, 0.267, 0.956, 0.295] representing #bb3f3f." } ], "id_raw": 32 }, { "id": "VfQoeEwkwlmUCsYc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area with coordinates [0.101, 0.267, 0.941, 0.883]?" }, { "from": "gpt", "value": "This area contains all the bars of both the Slow Expansion and Rapid Decline projects, representing various categories such as Life, Infrastructure, IP, Ecosystem, and Commercial." } ], "id_raw": 32 }, { "id": "gLEDXGIgBT3EzLGz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the area [0.038, -0.052, 0.048, -0.024]?" }, { "from": "gpt", "value": "This area contains the y-axis label for the value \"6\", which is located outside of the grid of the bar chart." } ], "id_raw": 32 }, { "id": "LAs7Nt1SUAdyiJ9u", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at position [0.386, 0.030, 0.656, 0.066]?" }, { "from": "gpt", "value": "The title of the chart, \"Innovation Project Evaluation\", is in this area." } ], "id_raw": 32 }, { "id": "UNtDl5mWhMw5sCjr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.017, 0.439, 0.032, 0.519] in the chart?" }, { "from": "gpt", "value": "Y-axis label, which is \"Y-Axis\"." } ], "id_raw": 33 }, { "id": "gEvdfC1r50zblk5p", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the text inside the second legend item?" }, { "from": "gpt", "value": "[0.779, 0.142, 0.913, 0.170]" } ], "id_raw": 33 }, { "id": "TTrdnxM55mZyjZZH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the {x-axis label} at [0.488, 0.942, 0.574, 0.970]?" }, { "from": "gpt", "value": "It is labeled as \"Companies\"." } ], "id_raw": 33 }, { "id": "j9wh6g1Z7KLwBEJf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat parameter or category is inscribed at [0.779, 0.225, 0.958, 0.253]?" }, { "from": "gpt", "value": "It is labeled {Employee Performance}[0.779, 0.225, 0.958, 0.253]." } ], "id_raw": 33 }, { "id": "31UZwXGoAhOkeoex", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar}[0.120, 0.743, 0.148, 0.802] depict a higher Sales Growth (%) than the {bar}[0.403, 0.802, 0.432, 0.824]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 33 }, { "id": "vHid5Szswp5UJOQo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nDo the {bars of group \"Investment in R&D ($ M)\"}[[0.176, 0.111, 0.205, 0.802],[0.318, 0.180, 0.347, 0.802],[0.460, 0.385, 0.488, 0.802],[0.602, 0.251, 0.630, 0.802],[0.744, 0.212, 0.772, 0.802],[0.885, 0.490, 0.914, 0.802]] have a higher total value than the {bars of group \"Market Share (%)\"}[[0.148, 0.634, 0.176, 0.802],[0.290, 0.670, 0.318, 0.802],[0.432, 0.701, 0.460, 0.802],[0.573, 0.675, 0.602, 0.802],[0.715, 0.679, 0.744, 0.802],[0.857, 0.721, 0.885, 0.802]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 33 }, { "id": "lNYwo65bNEJWTfAU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nDoes {Quick Transfer}'s {Investment in R&D bar}[0.318, 0.180, 0.347, 0.802] have a greater value than {Direct Deliver's Investment in R&D bar}[0.602, 0.251, 0.630, 0.802]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 33 }, { "id": "zWAs5oArayEP6l3f", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nWhich is higher,{Sales Growth(%) for Momentum Movers}[0.687, 0.752, 0.715, 0.802] or {Investment in R&D of Solid Transit}[0.460, 0.385, 0.488, 0.802]?" }, { "from": "gpt", "value": "{Investment in R&D of Solid Transit}[0.460, 0.385, 0.488, 0.802] is higher." } ], "id_raw": 33 }, { "id": "5zn5g1HRzH2ut0PP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.730, 0.089, 0.976, 0.264]?" }, { "from": "gpt", "value": "This is a legend area with four pairs of color-text labels denoting parameters. The first pair {Sales Growth (%)}[0.779, 0.100, 0.912, 0.128] is colored blue, the second pair {Market Share (%)}[0.779, 0.142, 0.913, 0.170] is orange, the third, {Investment in R&D ($ M)}[0.779, 0.183, 0.969, 0.211] is red, and the fourth, {Employee Performance}[0.779, 0.225, 0.958, 0.253] is mint green." } ], "id_raw": 33 }, { "id": "TsLpMmnaq78VWLE6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.079, 0.075, 0.983, 0.883]." }, { "from": "gpt", "value": "This is the main chart area, containing six groups of four bars each. Each group represents a company, and each bar within a group corresponds to a different indicator or parameter - {Sales Growth (%)}, {Market Share (%)}, {Investment in R&D ($ M)}, and {Employee Performance}." } ], "id_raw": 33 }, { "id": "PdWwpgck9e2X941M", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat bars are located within [0.261, 0.718, 0.318, 0.802]?" }, { "from": "gpt", "value": "There are two bars in this area. The {Sales Growth (%) bar}[0.261, 0.718, 0.290, 0.802] which is blue, and the {Market Share (%) bar}[0.290, 0.670, 0.318, 0.802] which is colored orange." } ], "id_raw": 33 }, { "id": "IkJh7ia0RUDk7scr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat content can you find within the area [0.263, 0.903, 0.374, 0.931]?" }, { "from": "gpt", "value": "There is {a label for the company \"Quick Transfer\"}[0.263, 0.903, 0.374, 0.931]." } ], "id_raw": 33 }, { "id": "9RAKxDgUEjLTWqkN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label represented by these coordinates [0.267, 0.919, 0.338, 0.942]?" }, { "from": "gpt", "value": "It's {Chemistry}[0.267, 0.919, 0.338, 0.942], a research field on the x-axis." } ], "id_raw": 34 }, { "id": "xmJZrHs6XVdTQRIv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at coordinates [0.155, 0.195, 0.207, 0.903]?" }, { "from": "gpt", "value": "The bar at {these coordinates}[0.155, 0.195, 0.207, 0.903] is colored by '#d62728', indicating it represents 2021 data." } ], "id_raw": 34 }, { "id": "0slxc29UVWlsYPm4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the contents at these coordinates: [0.937, 0.083, 0.972, 0.106]?" }, { "from": "gpt", "value": "The contents at these coordinates {coordinates}[0.937, 0.083, 0.972, 0.106] are the legend label \"2020\"." } ], "id_raw": 34 }, { "id": "C1cXGXXvKicIAD6n", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nDo the bars of {'Mathematics'}[{2020 Math bar}[0.546, 0.102, 0.597, 0.903], {2021 Math bar}[0.597, 0.119, 0.649, 0.903]] indicate an increase or decrease in acceptance rates from 2020 to 2021?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 34 }, { "id": "qb8LGKX7CY7IlvE4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nIs {the Psychology bar for 2021}[0.744, 0.153, 0.796, 0.903] higher or lower than {the Psychology bar for 2020}[0.693, 0.161, 0.744, 0.903]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 34 }, { "id": "QGeTE1KG6UI1A4KM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nAre {the research fields} from 2021[[0.155, 0.195, 0.207, 0.903],[0.303, 0.212, 0.354, 0.903],[0.450, 0.262, 0.501, 0.903],[0.597, 0.119, 0.649, 0.903],[0.744, 0.153, 0.796, 0.903],[0.892, 0.237, 0.943, 0.903]] placed to the right or left of those from 2020[[0.104, 0.144, 0.155, 0.903],[0.251, 0.203, 0.303, 0.903],[0.398, 0.237, 0.450, 0.903],[0.546, 0.102, 0.597, 0.903],[0.693, 0.161, 0.744, 0.903],[0.840, 0.229, 0.892, 0.903]] in the graph?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 34 }, { "id": "vXtOElWU5QbxkGxC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.893, 0.074, 0.978, 0.151]." }, { "from": "gpt", "value": "This area includes the legend, with two entries showing color and year correlates. The first entry is colored blue, labeled '2020'[0.937, 0.083, 0.972, 0.106]. The second entry is colored red, labeled '2021'[0.937, 0.118, 0.972, 0.141]." } ], "id_raw": 34 }, { "id": "4nGydjaiozqYUMiz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the area [0.043, 0.894, 0.052, 0.917]?" }, { "from": "gpt", "value": "The y-axis label '0' is present in the coordinates {0}[0.043, 0.894, 0.052, 0.917]." } ], "id_raw": 34 }, { "id": "gpuDNmtVQHABuFh5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects in the regional space from [0.298, 0.212, 0.354, 0.903] to [0.546, 0.102, 0.597, 0.903]?" }, { "from": "gpt", "value": "This area includes two bars of Chemistry for both years: {2020 Chemistry bar}[0.251, 0.203, 0.303, 0.903] and {2021 Chemistry bar}[0.303, 0.212, 0.354, 0.903] as well as two bars for Biology {2020 Biology bar}[0.398, 0.237, 0.450, 0.903] and {2021 Biology bar}[0.450, 0.262, 0.501, 0.903] and two bars for Mathematics {2020 Mathematics bar}[0.546, 0.102, 0.597, 0.903] and {2021 Mathematics bar}[0.597, 0.119, 0.649, 0.903]." } ], "id_raw": 34 }, { "id": "McdPvqZAyH6myrzv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the large numerical y-axis scale at the coordinate point [0.038, 0.370, 0.058, 0.398]?" }, { "from": "gpt", "value": "It's a scale point at \"15\"." } ], "id_raw": 35 }, { "id": "wVvblBSGZfxEU6uF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the second legend marker at the coordinate [0.828, 0.144, 0.859, 0.164] represent?" }, { "from": "gpt", "value": "It represents the 'Impact' in the risk assessment." } ], "id_raw": 35 }, { "id": "VcjLbK5IhN1UHs0l", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nWhat is associated with the x-axis labeled object at coordinates [0.342, 0.903, 0.447, 0.931]?" }, { "from": "gpt", "value": "It's labeled as \"Financial Risk\"." } ], "id_raw": 35 }, { "id": "BIpE7z1PY6FgT5Jv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nRegarding the bar at [0.778, 0.632, 0.803, 0.883], what does it represent?" }, { "from": "gpt", "value": "The bar illustrates the 'Risk Score' for the 'Natural Disaster' risk factor." } ], "id_raw": 35 }, { "id": "VMtHonAsH2YU7k23", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nIs {the Risk Score for Financial Risk}[0.382, 0.482, 0.431, 0.883] higher or lower than {the Impact for Legal Issues}[0.506, 0.716, 0.531, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 35 }, { "id": "xUSB5LfNbTfEOfxD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nAre the bars representing the \"Impact\" value generally taller than the ones representing the \"Risk Score\"?" }, { "from": "gpt", "value": "No." } ], "id_raw": 35 }, { "id": "NsXnCj4NuIggR5Mr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nAmong {the Risk Score for Market Fluctuation}[0.654, 0.567, 0.679, 0.883] and {the Impact for Cybersecurity Breach}[0.877, 0.726, 0.902, 0.883], which one is lower?" }, { "from": "gpt", "value": "The Risk Score for Market Fluctuation." } ], "id_raw": 35 }, { "id": "Xr9pLPPNcjjIWPCS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nIn the relation between {the Risk Score for Natural Disaster}[0.778, 0.632, 0.803, 0.883] and {the Probability for Natural Disaster}[0.729, 0.833, 0.753, 0.883], which one is higher?" }, { "from": "gpt", "value": "The Probability for Natural Disaster." } ], "id_raw": 35 }, { "id": "aE3zOS9MZ3x7Mf4z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nPlease discuss all the elements within the region [0.765, 0.350, 0.925, 0.875]." }, { "from": "gpt", "value": "The area contains four bars: the Probability, Impact, and Risk Score for 'Natural Disaster' and 'Cybersecurity Breach'. Specific coordinates: \n1. {Probability for Natural Disaster}[0.729, 0.833, 0.753, 0.883], \n2. {Impact for Natural Disaster}[0.753, 0.716, 0.778, 0.883], \n3. {Risk Score for Natural Disaster}[0.778, 0.632, 0.803, 0.883], \n4. {Probability for Cybersecurity Breach}[0.852, 0.756, 0.877, 0.883], \n5. {Impact for Cybersecurity Breach}[0.877, 0.726, 0.902, 0.883], \n6. {Risk Score for Cybersecurity Breach}[0.902, 0.285, 0.927, 0.883]." } ], "id_raw": 35 }, { "id": "xXqrbP3FebLpMYAD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found in the space defined by [0.250, 0.705, 0.500, 0.900]." }, { "from": "gpt", "value": "This area contains part of the 'Probability' and the entire 'Impact' bars for 'Human Error' and 'Financial Risk'. Specifically:\n1. {Probability for Human Error}[0.233, 0.743, 0.258, 0.883], \n2. {Impact for Human Error}[0.258, 0.733, 0.283, 0.883], \n3. {Probability for Financial Risk}[0.357, 0.800, 0.382, 0.883], \n4. {Impact for Financial Risk}[0.382, 0.723, 0.407, 0.883]." } ], "id_raw": 35 }, { "id": "WtAVy5TYK3ENpOSg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region from [0.822, 0.089, 0.960, 0.223]." }, { "from": "gpt", "value": "This region contains a legend with three entries: 'Probability', 'Impact', and 'Risk Score'. Each entry is composed of a colored marker and a text label. Their specific coordinates are:\n1. {Probability marker}[0.828, 0.102, 0.859, 0.122], {Probability text}[0.872, 0.100, 0.954, 0.128],\n2. {Impact marker}[0.828, 0.144, 0.859, 0.164], {Impact text}[0.872, 0.142, 0.925, 0.170],\n3. {Risk Score marker}[0.828, 0.186, 0.859, 0.205], {Risk Score text}[0.872, 0.183, 0.952, 0.211]." } ], "id_raw": 35 }, { "id": "ic6ft8uxRr2ye0gW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nWhat elements can be found in the range [0.110, 0.730, 0.310, 0.900]?" }, { "from": "gpt", "value": "Within this range, three unique bars can be observed. They are the {Probability, Impact and Risk Score for Equipment Failure}[0.110, 0.375, 0.184, 0.883] and {Probability and part of the Impact for Human Error}[0.233, 0.734, 0.283, 0.883]." } ], "id_raw": 35 }, { "id": "5tnbOuVuHBg30Haa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the text in the bounding box [0.308, 0.030, 0.735, 0.066] represent?" }, { "from": "gpt", "value": "Subplot title, it's called \"Trends in Fitness and Sports in 2021 and 2022\"." } ], "id_raw": 41 }, { "id": "3Iuhl0L0gFMGpRzi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the content placed at [0.501, 0.942, 0.541, 0.970]?" }, { "from": "gpt", "value": "It is the x-axis label, named \"Years\"." } ], "id_raw": 41 }, { "id": "nde19AwX7nvZksKv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the subject of the label at coordinates [0.017, 0.427, 0.032, 0.531]?" }, { "from": "gpt", "value": "Y-axis Label, the label is \"Ratings\"." } ], "id_raw": 41 }, { "id": "ZcqtSb5RVvhVU2IP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell what is denoted by the text at [0.878, 0.142, 0.944, 0.170]?" }, { "from": "gpt", "value": "It is part of the legend, and the label is \"Strength\"." } ], "id_raw": 41 }, { "id": "1WbI736r3mL47SiK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nHow does {the bar for \"Cardio\" in 2022}[0.101, 0.113, 0.194, 0.883] compare to {the bar for \"Cardio\" in 2021}[0.568, 0.284, 0.661, 0.883] in term of height?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 41 }, { "id": "O5JFpNtkXmoaZQ8W", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nBetween {the Strength bar for 2021}[0.661, 0.199, 0.755, 0.883] and {the Mindfulness bar for 2022}[0.288, 0.456, 0.381, 0.883], which one is taller?" }, { "from": "gpt", "value": "Strength bar for 2021." } ], "id_raw": 41 }, { "id": "fCehySpG8Glw1zjh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for Flexibility in 2022}[0.381, 0.370, 0.475, 0.883] have a higher or lower rating than {the bar for Flexibility in 2021}[0.848, 0.284, 0.941, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 41 }, { "id": "in9rTPE51sTvY1nY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nWhich is rated higher, {the Strength for 2022}[0.194, 0.113, 0.288, 0.883] or {the Mindfulness for 2022}[0.288, 0.456, 0.381, 0.883]?" }, { "from": "gpt", "value": "Strength for 2022." } ], "id_raw": 41 }, { "id": "FYAsR6MsAGoj2QTn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within the coordinates [0.828, 0.089, 0.976, 0.264]?" }, { "from": "gpt", "value": "Within this area, there is a legend containing labels for four categories represented in the bar chart. The categories are: {Cardio}[0.878, 0.100, 0.928, 0.128] colored in yellow, {Strength}[0.878, 0.142, 0.944, 0.170] colored in blue, {Mindfulness}[0.878, 0.183, 0.969, 0.211] colored in pink and {Flexibility}[0.878, 0.225, 0.952, 0.253] colored in green." } ], "id_raw": 41 }, { "id": "l4vw21AthAAtlTT9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.101, 0.113, 0.475, 0.883] encompass?" }, { "from": "gpt", "value": "It consists of 4 bar graphs representing different fitness categories for the year 2022. The specific categories are {Cardio}[0.101, 0.113, 0.194, 0.883], {Strength}[0.194, 0.113, 0.288, 0.883], {Mindfulness}[0.288, 0.456, 0.381, 0.883], and {Flexibility}[0.381, 0.370, 0.475, 0.883]." } ], "id_raw": 41 }, { "id": "WWVUYogpanMeEFNW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.017, 0.427, 0.032, 0.531]?" }, { "from": "gpt", "value": "This area contains the y-axis label of the chart which is labeled as {Ratings}[0.017, 0.427, 0.032, 0.531]." } ], "id_raw": 41 }, { "id": "Geo3B3OesEReuY1x", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat could you say about the area spanning from [0.101, 0.113, 0.941, 0.883]?" }, { "from": "gpt", "value": "The area includes the bars representing the fitness category ratings for both the years, 2022 and 2021, for each of the categories, namely, Cardio, Strength, Mindfulness, and Flexibility." } ], "id_raw": 41 }, { "id": "94PLlgwJhpHJnQqY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the y-axis?" }, { "from": "gpt", "value": "{Ratings}[0.058, 0.453, 0.074, 0.557]" } ], "id_raw": 42 }, { "id": "PNoVD4DoMc7M51UH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar for Public_Figure6's Neutral Stance?" }, { "from": "gpt", "value": "[0.816, 0.564, 0.840, 0.890]" } ], "id_raw": 42 }, { "id": "DDmQ8PolMY0UkuDh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Public Figure} at the x-axis located at [0.761, 0.909, 0.871, 0.938]?" }, { "from": "gpt", "value": "Public_Figure6" } ], "id_raw": 42 }, { "id": "Beh1tj8ebj0QaBTQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat indicators or categories are shown in [0.727, 0.145, 0.806, 0.299]?" }, { "from": "gpt", "value": "{Liberal Stance}[0.727, 0.145, 0.837, 0.173] and {Conservative Stance}[0.727, 0.187, 0.886, 0.215] and {Neutral Stance}[0.727, 0.229, 0.842, 0.257] and {No Stance}[0.727, 0.271, 0.806, 0.299]" } ], "id_raw": 42 }, { "id": "7klzm4SJHuY7VvPZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nIs the {rating of liberal stance for Public_Figure1}[0.160, 0.157, 0.185, 0.890] higher than {rating of conservative stance for Public_Figure1}[0.185, 0.360, 0.209, 0.890]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 42 }, { "id": "eKKZvLmcWcWXlmiB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nIs {Public_Figure5's rating of no stance}[0.719, 0.360, 0.743, 0.890] higher than {Public_Figure6's rating of no stance}[0.840, 0.401, 0.865, 0.890]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 42 }, { "id": "ENbdIzDRKKYnjjw2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nIs {Public_Figure3's rating of Neutral Stance}[0.452, 0.564, 0.476, 0.890] the same as {Public_Figure3's rating of No Stance}[0.476, 0.238, 0.500, 0.890]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 42 }, { "id": "x4BTO2EsYLjotldo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nBetween {Public_Figure4's rating of Liberal Stance}[0.525, 0.442, 0.549, 0.890] and {Public_Figure5's rating of Liberal Stance}[0.646, 0.238, 0.670, 0.890], which one is higher?" }, { "from": "gpt", "value": "Public_Figure5." } ], "id_raw": 42 }, { "id": "iRXCYz3WMKpCxJ32", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.677, 0.134, 0.892, 0.310]." }, { "from": "gpt", "value": "The zone contains the legend of the chart, with four distinct stance labels: The \"Liberal Stance\" is represented by the blue color with code '#5878a3'[0.683, 0.148, 0.714, 0.167]; The \"Conservative Stance\" is depicted in an orange hue, coded with '#e59344'[0.683, 0.189, 0.714, 0.209]; The \"Neutral Stance\" is represented by the color '#d1605e'[0.683, 0.231, 0.714, 0.251]; The \"No Stance\" is reflected by the color '#85b6b2'[0.683, 0.273, 0.714, 0.293]." } ], "id_raw": 42 }, { "id": "GFGR3Hf4CHJOIHPj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.154, 0.909, 0.871, 0.938]." }, { "from": "gpt", "value": "The area [0.154, 0.909, 0.871, 0.938] contains all public figure labels on the x-axis, they are;\n1. \"Public_Figure1\": [0.154, 0.909, 0.264, 0.938],\n2. \"Public_Figure2\": [0.275, 0.909, 0.385, 0.938],\n3. \"Public_Figure3\": [0.397, 0.909, 0.507, 0.938],\n4. \"Public_Figure4\": [0.518, 0.909, 0.628, 0.938],\n5. \"Public_Figure5\": [0.640, 0.909, 0.750, 0.938],\n6. \"Public_Figure6\": [0.761, 0.909, 0.871, 0.938]." } ], "id_raw": 42 }, { "id": "p7PCKborgYjfxdpw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.403, 0.320, 0.476, 0.890]?" }, { "from": "gpt", "value": "Within this area, there are 3 bars representing the ratings of Public_Figure3's political stances:\n1. The bar for Liberal Stance, with coordinates [0.403, 0.320, 0.427, 0.890].\n2. The bar for Conservative Stance, with coordinates [0.427, 0.401, 0.452, 0.890].\n3. The bar for Neutral Stance, with coordinates [0.452, 0.564, 0.476, 0.890]." } ], "id_raw": 42 }, { "id": "UyRsxDDx17hFdCJT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.154, 0.157, 0.379, 0.890]?" }, { "from": "gpt", "value": "A set of color-coded bars corresponding to ratings for Public_Figure1 and Public_Figure2 for each stance is visible in these coordinated. More specifically:\n1. Public_Figure1's ratings for Liberal Stance[0.160, 0.157, 0.185, 0.890], Conservative Stance[0.185, 0.360, 0.209, 0.890], Neutral Stance[0.209, 0.523, 0.233, 0.890], and No Stance[0.233, 0.483, 0.257, 0.890]\n2. Public_Figure2's ratings for Liberal Stance[0.282, 0.401, 0.306, 0.890], Conservative Stance[0.306, 0.197, 0.330, 0.890], Neutral Stance[0.330, 0.646, 0.355, 0.890], and No Stance[0.355, 0.279, 0.379, 0.890]" } ], "id_raw": 42 }, { "id": "iZGYv2647pvjoRGB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at coordinates [0.500, 0.952, 0.550, 0.975]?" }, { "from": "gpt", "value": "The x-axis label is \"Periods\"." } ], "id_raw": 43 }, { "id": "vvESmTJJvXVISnZj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label at coordinates [0.015, 0.417, 0.029, 0.548]?" }, { "from": "gpt", "value": "The y-axis label is \"Percentage\"." } ], "id_raw": 43 }, { "id": "rfFOqhKaQxLiJHG7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the subtitle in the coordinates [0.386, 0.025, 0.664, 0.055]?" }, { "from": "gpt", "value": "The subtitle is \"Key Economic Indicators for 2021\"." } ], "id_raw": 43 }, { "id": "qFfQ2Req4waP1Qug", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the indicators shown in coordinates [0.072, 0.745, 0.265, 0.891]?" }, { "from": "gpt", "value": "The indicators are {Unemployment Rate}[0.116, 0.754, 0.259, 0.777], {GDP Growth Rate}[0.116, 0.789, 0.237, 0.812], {Inflation Rate}[0.116, 0.824, 0.209, 0.847], and {Interest Rate}[0.116, 0.859, 0.206, 0.882]." } ], "id_raw": 43 }, { "id": "L97CSjju06wI6OnT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Unemployment Rate bar in Q4_2021}[0.804, 0.314, 0.839, 0.635] taller than {the GDP Growth Rate bar in Q4_2021}[0.839, 0.100, 0.873, 0.635]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 43 }, { "id": "DdpDsd7ZHkQCrWYJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Inflation Rate bar in Q1_2021}[0.351, 0.444, 0.385, 0.635] shorter than {the Interest Rate bar in Q4_2021}[0.908, 0.616, 0.943, 0.635]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 43 }, { "id": "Infayw0sEiWCjlcn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Unemployment Rate bar in YE2020}[0.106, 0.116, 0.141, 0.635] higher than {the Unemployment Rate bar in Q4_2021}[0.804, 0.314, 0.839, 0.635]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 43 }, { "id": "WoFd78xRtyqK94OX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nIs {the GDP Growth Rate bar in Q3_2021}[0.664, 0.162, 0.699, 0.635] the same height as {the GDP Growth Rate bar in Q4_2021}[0.839, 0.100, 0.873, 0.635]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 43 }, { "id": "Z0ATVk5B6I540a60", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.106, 0.116, 0.141, 0.635]." }, { "from": "gpt", "value": "It's a blue bar representing the Unemployment Rate in YE2020, with a value of 6.8%." } ], "id_raw": 43 }, { "id": "jiee1E5EGZQ9omdo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.386, 0.025, 0.664, 0.055]." }, { "from": "gpt", "value": "It's the title of the bar chart, which reads \"Key Economic Indicators for 2021\"." } ], "id_raw": 43 }, { "id": "sQ5AbbdFla3dYpjY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.839, 0.100, 0.873, 0.635]?" }, { "from": "gpt", "value": "There is a single orange-colored bar representing the GDP Growth Rate for Q4_2021 with a value of 7.0%." } ], "id_raw": 43 }, { "id": "YLgXDAtoQaS0dLwh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.281, 0.169, 0.316, 0.635]?" }, { "from": "gpt", "value": "You can see a bar representing the Unemployment Rate for Q1_2021, which is 6.1% in blue color." } ], "id_raw": 43 }, { "id": "NlN6FZPoHIOLSqwt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nWhat do the bars represent that are within the coordinates [0.115, 0.209, 0.207, 0.883] and [0.574, 0.113, 0.666, 0.883]?" }, { "from": "gpt", "value": "These bars represent the Interaction ratings of Facebook and Instagram respectively." } ], "id_raw": 44 }, { "id": "ORHwwc1HLb5XbImA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what specific object is located at [0.873, 0.183, 0.950, 0.211]?" }, { "from": "gpt", "value": "It's a legend entry, marked as \"Popularity\"." } ], "id_raw": 44 }, { "id": "ysGAX899EiVvphST", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nWhat's plotted at the coordinates [0.262, 0.903, 0.335, 0.931]?" }, { "from": "gpt", "value": "It's the x-axis label for Facebook." } ], "id_raw": 44 }, { "id": "WlGEUczxKsaPKEKr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller between {the 'Facebook' Interaction bar}[0.115, 0.209, 0.207, 0.883] and {the 'Instagram' Interaction bar}[0.574, 0.113, 0.666, 0.883]?" }, { "from": "gpt", "value": "The Instagram Interaction bar is taller." } ], "id_raw": 44 }, { "id": "QM4G0oXaEsOXBL7V", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Popularity bar for Instagram}[0.758, 0.209, 0.850, 0.883] higher than {the Trust Bar for Instagram}[0.850, 0.402, 0.942, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 44 }, { "id": "QhsRutBYoeyhPHFP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nIs {the interaction bar of Instagram}[0.574, 0.113, 0.666, 0.883] higher than {the connectivity bar of Instagram}[0.666, 0.209, 0.758, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 44 }, { "id": "1Y45ce24tweV9DGL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is in the area [0.823, 0.089, 0.976, 0.264]?" }, { "from": "gpt", "value": "This area represents the legend of the chart. It clearly defines four parameters - Interactions, Connectivity, Popularity, and Trust, which are identified by different colors." } ], "id_raw": 44 }, { "id": "hNU1iJaabZFV4Lii", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nWhat's contained in the area specified by the coordinates [0.115, 0.209, 0.483, 0.883]?" }, { "from": "gpt", "value": "In this area, you can find four bars corresponding to Facebook's ratings on Interactions, Connectivity, Popularity, and Trust respectively." } ], "id_raw": 44 }, { "id": "SsrGMuTWosvGzxR1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nWhat are the characteristics of the region [0.574, 0.113, 0.942, 0.883]?" }, { "from": "gpt", "value": "This region contains four bars that symbolize Instagram's ratings on Interactions, Connectivity, Popularity and Trust." } ], "id_raw": 44 }, { "id": "96dI82fsyPQoGbBf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is presented in the data?" }, { "from": "gpt", "value": "bar chart." } ], "id_raw": 45 }, { "id": "ZEI8VgrINVpcCTfy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent?" }, { "from": "gpt", "value": "Entrepreneurial skills." } ], "id_raw": 45 }, { "id": "s7xgSyZ0YSIQgJgq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat specific text does {the subtitle}[0.453, 0.019, 0.698, 0.041] contain?" }, { "from": "gpt", "value": "Entrepreneurial Skill Volatility." } ], "id_raw": 45 }, { "id": "sKPhE6vPRhQxkvoE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis indicator show at {the point}[0.564, 0.964, 0.588, 0.981]?" }, { "from": "gpt", "value": "\u25b2%." } ], "id_raw": 45 }, { "id": "8G1QA00xgwNh2a85", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nConsidering their heights, is {the bar representing \"Marketing\" Skill}[0.576, 0.777, 0.910, 0.887] higher or lower than {the bar representing \"Public Speaking\" Skill}[0.576, 0.639, 0.834, 0.749]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 45 }, { "id": "ICETB2bt3ftG23VU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Risk Taking\" Skill}[0.410, 0.501, 0.576, 0.611] exhibit a positive or negative change?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 45 }, { "id": "Yx24DMPHczpG3rNR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Networking\"}[0.576, 0.225, 0.706, 0.335] and {\"Product Development\"}[0.576, 0.363, 0.612, 0.473], which entrepreneurial skill has a higher variance?" }, { "from": "gpt", "value": "Networking." } ], "id_raw": 45 }, { "id": "Tq0mA9YzfCrgHxlD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Strategic Planning\" Skill}[0.502, 0.087, 0.576, 0.197] extending to the left or right of the y-axis?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 45 }, { "id": "bWY5BQ3yUrO7xRY4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what you see within the coordinate [0.175, 0.047, 0.976, 0.927]." }, { "from": "gpt", "value": "The area contains a bar chart plotting the volatility of different entrepreneurial skills. The skills are represented on the y-axis and include \"Marketing\", \"Public Speaking\", \"Risk Taking\", \"Product Development\", \"Networking\", and \"Strategic Planning\". Each skill has a corresponding bar on the chart that illustrates its volatility in percentage. Positive and negative percentages are represented by bars extending to the right and left of the y-axis, respectively." } ], "id_raw": 45 }, { "id": "MMtY2tAkEaJ0g3SE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.576, 0.777, 0.910, 0.887]?" }, { "from": "gpt", "value": "The region has a bar representing the volatility of the \"Marketing\" skill. It shows a positive percentage of 16.7%, indicated by the text located at [0.920, 0.823, 0.964, 0.841], which means that the skill demonstrated an increase." } ], "id_raw": 45 }, { "id": "bCVHFaRo1KndE2PA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the area [0.410, 0.501, 0.576, 0.611]?" }, { "from": "gpt", "value": "There is a single bar that represents the \"Risk Taking\" skill. The bar's negative direction, and the negative value of -8.3% written at [0.360, 0.547, 0.400, 0.565], indicates a decrease in the skill." } ], "id_raw": 45 }, { "id": "6qPsNuUqUx2kVTBF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe what can be seen in the region [0.056, 0.687, 0.166, 0.705]." }, { "from": "gpt", "value": "A label indicating the entrepreneurial skill of \"Public Speaking\" is located in this region." } ], "id_raw": 45 }, { "id": "oAAoQ5nAfKJFJsXb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.452, 0.025, 0.619, 0.055]?" }, { "from": "gpt", "value": "It's the subtitle, which reads \"Social Media Impact.\"" } ], "id_raw": 46 }, { "id": "jeRIgBW8H4G2uf7a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label at [0.492, 0.952, 0.579, 0.975] represent?" }, { "from": "gpt", "value": "The x-axis label represents \"Impact(%) \u25b2\"." } ], "id_raw": 46 }, { "id": "eF1GgjzZ7An4ldLN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the value represented by the bar at [0.536, 0.737, 0.756, 0.865]?" }, { "from": "gpt", "value": "The value is \"12.5%\" according to the text at [0.774, 0.789, 0.818, 0.813]." } ], "id_raw": 46 }, { "id": "Vbv7ETwXWBboc63q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.187, 0.578, 0.536, 0.705]?" }, { "from": "gpt", "value": "This region contains the bar for Twitter, which has a value of \"-19.8%\" as indicated by the text at [0.120, 0.630, 0.169, 0.653]." } ], "id_raw": 46 }, { "id": "TBosWoIW0kHlpFhH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Facebook bar}[0.536, 0.737, 0.756, 0.865] longer or shorter than {the Instagram bar}[0.536, 0.419, 0.664, 0.546]?" }, { "from": "gpt", "value": "Longer." } ], "id_raw": 46 }, { "id": "ST3AFyq2oANDUTOe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nWhich is greater in impact, {the Snapchat bar}[0.494, 0.260, 0.536, 0.387] or {the Twitter bar}[0.187, 0.578, 0.536, 0.705]?" }, { "from": "gpt", "value": "The Twitter bar." } ], "id_raw": 46 }, { "id": "FXR0Pde8LhnIMpg3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nIs the {TikTok bar}[0.536, 0.100, 0.858, 0.228] showing a positive or negative impact?" }, { "from": "gpt", "value": "Positive." } ], "id_raw": 46 }, { "id": "TfbM7p0RlReFZxQI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nWhich is to the left, {the -20 x-axis tick}[0.169, 0.919, 0.198, 0.942] or {the 15 x-axis tick}[0.791, 0.919, 0.809, 0.942]?" }, { "from": "gpt", "value": "The -20 x-axis tick." } ], "id_raw": 46 }, { "id": "3JbLV0zZKM27Byye", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nTell me about the area from [0.095, 0.062, 0.976, 0.903]." }, { "from": "gpt", "value": "This is the main body of the bar chart illustrating the impact of various social media platforms. It contains labels for percentage impact along the x-axis, the names of the platforms along the y-axis, and horizontally aligned bars to represent the impact. The subsection contains five bars, each of which corresponds to a social media platform." } ], "id_raw": 46 }, { "id": "ZXC18d7aLWQHcMF6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area between [0.020, 0.155, 0.086, 0.178]." }, { "from": "gpt", "value": "This little area contains the y-axis label for the TikTok bar." } ], "id_raw": 46 }, { "id": "iKEICaEiNPMuJgC6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.187, 0.578, 0.536, 0.705]?" }, { "from": "gpt", "value": "It's an area of the chart containing the Twitter impact bar that shows a negative reading for impact. It is highlighted in red to indicate a negative value." } ], "id_raw": 46 }, { "id": "JBFiZnU31Swa6z76", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.436, 0.312, 0.476, 0.335]?" }, { "from": "gpt", "value": "This area contains the text \"-2.4%\", which relates to the Snapchat bar. It indicates the negative impact percentage of Snapchat." } ], "id_raw": 46 }, { "id": "jLSDZfaIvCAOZrZl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat's the title of the chart?" }, { "from": "gpt", "value": "The title is \"Age Distribution in Different Demographic Groups\" located at [0.328, 0.019, 0.743, 0.041]." } ], "id_raw": 47 }, { "id": "XPVAS7cCL51sAH8I", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nWhere is the label '%\u25b2' located?" }, { "from": "gpt", "value": "It is located at [0.524, 0.964, 0.548, 0.981]." } ], "id_raw": 47 }, { "id": "pKFOIdnXlYSDvlql", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the bar at coordinates [0.536, 0.777, 0.906, 0.887]?" }, { "from": "gpt", "value": "The label is \"Youths\"." } ], "id_raw": 47 }, { "id": "edGZyJxjiW4WaMT5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Adults\" at [0.360, 0.639, 0.536, 0.749] to the left or right of the zero line?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 47 }, { "id": "lPr4ZZibdV06ebCB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Seniors\"}[0.536, 0.501, 0.642, 0.611] longer than {the bar representing \"Children\"}[0.536, 0.225, 0.686, 0.335]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 47 }, { "id": "zDgrrYBiBrQpDwZq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nBy observing {the bar representing \"Seniors\"}[0.536, 0.501, 0.642, 0.611] and {the bar representing \"Teenagers\"}[0.272, 0.087, 0.536, 0.197], who has the higher value?" }, { "from": "gpt", "value": "Seniors." } ], "id_raw": 47 }, { "id": "eCyWTR2dkLD2yNAX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.536, 0.777, 0.906, 0.887]." }, { "from": "gpt", "value": "There is a white bar representing the \"Youths\" category with a value of around 8.4%, the value text located at [0.928, 0.823, 0.963, 0.841] indicates it as '8.4%'." } ], "id_raw": 47 }, { "id": "OqLoEyBG8tfaUq29", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.536, 0.225, 0.686, 0.335]?" }, { "from": "gpt", "value": "There is a white bar within the given region that corresponds to \"Children\". The value of it, as given at [0.708, 0.271, 0.743, 0.289], is '3.4%'." } ], "id_raw": 47 }, { "id": "jmNkj8IkZTtgV1et", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat elements can be observed within the region [0.184, 0.363, 0.536, 0.473]?" }, { "from": "gpt", "value": "Within this region there is a white bar corresponding to the \"Veterans\" category. The bar shows a value of approximately -8.0%, as indicated by the text located at [0.122, 0.409, 0.162, 0.427], suggesting a decline in this category." } ], "id_raw": 47 }, { "id": "DLDnripYxaUgzkGG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text specified at [0.611, 0.170, 0.646, 0.187]?" }, { "from": "gpt", "value": "The text is '0.9%'." } ], "id_raw": 48 }, { "id": "wLlH5zdC4PulUmpz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the caption that corresponds to the coordinates [0.376, 0.019, 0.733, 0.041]?" }, { "from": "gpt", "value": "The caption is 'Rural and Urban Development Comparison'." } ], "id_raw": 48 }, { "id": "itOg6Uo4cILas4Ea", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is placed at the coordinates [0.732, 0.503, 0.767, 0.521]?" }, { "from": "gpt", "value": "This is where the text '7.3%' is located." } ], "id_raw": 48 }, { "id": "FUVRxLWaUZ82876f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the y label for the bar at [0.287, 0.253, 0.575, 0.387]?" }, { "from": "gpt", "value": "The y label is 'Upward Trajectory'." } ], "id_raw": 48 }, { "id": "rVYKgipTGwpkQFmb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nComparing the bar at [0.575, 0.754, 0.825, 0.887] and the bar at [0.451, 0.754, 0.575, 0.887], which one is located on the right side?" }, { "from": "gpt", "value": "{The bar}[0.575, 0.754, 0.825, 0.887] is located on the right side." } ], "id_raw": 48 }, { "id": "JNbV11l6CzFvwL2a", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nIs the bar at [0.575, 0.087, 0.592, 0.220] taller than the bar at [0.188, 0.087, 0.575, 0.220]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 48 }, { "id": "qAYErfPgwUO4LFIK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nComparing the bar at [0.575, 0.253, 0.921, 0.387] and the bar at [0.287, 0.253, 0.575, 0.387], which one is longer?" }, { "from": "gpt", "value": "The bar on the right, located at [0.575, 0.253, 0.921, 0.387], is longer." } ], "id_raw": 48 }, { "id": "AKTH35TbFvm4mz4r", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nLooking at the plot, is the \"Abrupt Increase\" represented by the bar at [0.575, 0.420, 0.713, 0.554] larger or smaller than the \"Stable Decline\" represented by the bar at [0.575, 0.087, 0.592, 0.220]?" }, { "from": "gpt", "value": "Larger." } ], "id_raw": 48 }, { "id": "MY2aN5VwyoPWfU10", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area represented by the coordinates [0.018, 0.647, 0.142, 0.664]?" }, { "from": "gpt", "value": "This area contains the label \"Sustained Growth\" on the y-axis." } ], "id_raw": 48 }, { "id": "gHRLfa7Rykcgfh1m", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinate [0.575, 0.087, 0.592, 0.220]?" }, { "from": "gpt", "value": "Within these coordinates, there is a bar that represents \"Stable Decline\" in the data, with a text annotation of '0.9%'." } ], "id_raw": 48 }, { "id": "rRqlxLtggpypIS5q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is inside the area marked by [0.130, 0.120, 0.179, 0.137]?" }, { "from": "gpt", "value": "This area contains the text '-20.4%', which is a data label." } ], "id_raw": 48 }, { "id": "gfAp4tLp35o9zj0O", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the region that goes from the [0.441, 0.754, 0.575, 0.887] to the [0.575, 0.754, 0.825, 0.887]?" }, { "from": "gpt", "value": "Both of these coordinates contain bars, with the first one marked as a negative change of '-6.5%' and the second one as a positive change of '13.2%'." } ], "id_raw": 48 }, { "id": "jL8xukI9p5h90AGJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nWhat label can be found at the coordinate [0.529, 0.639, 0.581, 0.749]?" }, { "from": "gpt", "value": "The label at this coordinate is \"Strategic Planning\"." } ], "id_raw": 49 }, { "id": "pH3GOf0H5tkxFEoa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar}[0.581, 0.501, 0.873, 0.611] in the chart?" }, { "from": "gpt", "value": "The color of the bar is white." } ], "id_raw": 49 }, { "id": "JlVR70YzJl1F2R1X", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the bar on the y-axis}[0.581, 0.501, 0.873, 0.611] which is situated between 0.00 and 7.40?" }, { "from": "gpt", "value": "The value is 7.4%." } ], "id_raw": 49 }, { "id": "JopTUe0LIPTUkoyJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Revenue Growth\"}[0.581, 0.087, 0.909, 0.197] higher or lower than {the bar representing \"Operational Excellence\"}[0.581, 0.501, 0.873, 0.611]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 49 }, { "id": "2BOaqJw5IlayjYWJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar indicating a value of \"-0.5%\"}[0.561, 0.363, 0.581, 0.473] to the right or left of {the y-axis}?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 49 }, { "id": "VauOLMlDT9Bfx47U", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nWho has the greater value, {Operational Excellence}[0.581, 0.501, 0.873, 0.611] or {Revenue Growth}[0.581, 0.087, 0.909, 0.197]?" }, { "from": "gpt", "value": "Revenue Growth." } ], "id_raw": 49 }, { "id": "caRUfboTfct9SMpg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.355, 0.225, 0.581, 0.335]." }, { "from": "gpt", "value": "It's a bar representing \"Resource Management\" with a value of \"-5.7%\" positioned to the left of the y-axis. The value text is colored red." } ], "id_raw": 49 }, { "id": "VRbLEaVL5g8aHSAf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.497, 0.777, 0.581, 0.887]?" }, { "from": "gpt", "value": "You can find a white bar representing the \"Customer Satisfaction\", which has a value of \"-2.1%\", sitting to the left of the y-axis. The value label is colored red." } ], "id_raw": 49 }, { "id": "BgS5CiUbgmjVbSZJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nDiscuss what you see when you look at the box bounded by [0.185, 0.047, 0.976, 0.927]." }, { "from": "gpt", "value": "This region includes the entire chart with 6 bars representing different organizational performance categories: \"Customer Satisfaction\", \"Strategic Planning\", \"Operational Excellence\", \"People Leadership\", \"Resource Management\", and \"Revenue Growth\". It also includes an x-axis representing the percentage change and a y-axis listing the six categories." } ], "id_raw": 49 }, { "id": "FPEFWNCNJsVMrjA2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is depicted in the JSON structured information?" }, { "from": "gpt", "value": "The chart is a bar chart." } ], "id_raw": 50 }, { "id": "Ex9G2TdOCgJEnYm8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the data represent according to the subplot's subtitle {the subplot's subtitle}[0.375, 0.019, 0.703, 0.041]?" }, { "from": "gpt", "value": "The subtitle reads \"Market Trends in the Real Estate Sector\", so the data represents changes in various segments of the real estate sector." } ], "id_raw": 50 }, { "id": "Q3BXUkddFd8IAS9g", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label {the x-axis label}[0.527, 0.964, 0.551, 0.981]?" }, { "from": "gpt", "value": "The x-axis label is \"\u25b2%\", which may symbolize the percentage change in various real estate sectors." } ], "id_raw": 50 }, { "id": "SgPH271hjBGbtayx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nDoes the {Warehouse bar}[0.393, 0.253, 0.845, 0.387] have a positive or a negative change?" }, { "from": "gpt", "value": "Positive." } ], "id_raw": 50 }, { "id": "SGs5f9yI0FT6xO17", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nAre {Industrial bar}[0.393, 0.420, 0.589, 0.554] growth percentage smaller than {Warehouse bar}[0.393, 0.253, 0.845, 0.387]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 50 }, { "id": "O1G9LFF0vUXsS6Uy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nIs {Retail bar}[0.393, 0.587, 0.460, 0.720] higher compared to {Residential bar}[0.248, 0.754, 0.393, 0.887]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 50 }, { "id": "zwfMq4n97ZBnCvVr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the x-axis range from [0.746, 0.927] to [0.976, 0.927]." }, { "from": "gpt", "value": "There are two x-axis labels: \"15\" and \"20\", corresponding to the percentage changes represented on the x-axis. There is also part of the Warehouse bar showing a trend of 15.5% growth in this sector." } ], "id_raw": 50 }, { "id": "RDGNi5NBXAZD7CnY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the bars and their associated data in the region {region}[0.3, 0.1, 0.6, 0.8]." }, { "from": "gpt", "value": "Two bars are present in this area. The first represents {Retail}[0.393, 0.587, 0.460, 0.720] with a growth of 2.3%, and the second represents {Industrial}[0.393, 0.420, 0.589, 0.554] with a growth of 6.7%." } ], "id_raw": 50 }, { "id": "4LaHP5cUJ3jaQ23A", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the y-axis region from [0.102, 0.820] to [0.102, 0.153]?" }, { "from": "gpt", "value": "This region includes y-axis labels and corresponding bars: {Residential}[0.248, 0.754, 0.393, 0.887], {Retail}[0.393, 0.587, 0.460, 0.720], {Industrial}[0.393, 0.420, 0.589, 0.554], {Warehouse}[0.393, 0.253, 0.845, 0.387] and {Office}[0.373,0.087,0.393,0.220]." } ], "id_raw": 50 }, { "id": "2PE4mpOX4aHaCN2J", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.478, 0.754, 0.573, 0.887]?" }, { "from": "gpt", "value": "A white bar representing the value change of \"Freedom of Speech\"." } ], "id_raw": 51 }, { "id": "mygXdFRtF1IZlKvd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text} at [0.955, 0.311, 0.985, 0.329] imply?" }, { "from": "gpt", "value": "It indicates a 20% increase." } ], "id_raw": 51 }, { "id": "zwjjRjI21vl2UPPJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the text} at [0.514, 0.145, 0.554, 0.162]?" }, { "from": "gpt", "value": "It represents a 0.5% decrease." } ], "id_raw": 51 }, { "id": "vf8OlabKxquo5oeI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Environmental Protection' value bar}[0.573, 0.253, 0.945, 0.387] longer than {the 'Family Values' value bar}[0.573, 0.587, 0.820, 0.720]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 51 }, { "id": "mVqpF7QpddMhc7pK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Freedom of Speech' value bar}[0.478, 0.754, 0.573, 0.887] showing an increase or decrease in percentage?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 51 }, { "id": "kqbpRqhlNNyMWMfG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nIs the 'Environmental Protection' value change higher or lower than the 'Family Values' value change?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 51 }, { "id": "C8WfOZK2AXodFoMy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area in [0.201, 0.047, 0.969, 0.927]." }, { "from": "gpt", "value": "This area describes the main plot with several subcomponents. It includes the 'x' axis label with \"\u25b2% of Perception Change\" at coordinates [0.496, 0.964, 0.673, 0.981] and the 'y' axis labels including 'Freedom of Speech',[0.057, 0.814, 0.191, 0.831], 'Family Values',[0.096, 0.647, 0.191, 0.664], 'Religious Freedom', [0.064, 0.480, 0.191, 0.498], 'Environmental Protection', [0.015, 0.313, 0.191, 0.331], and 'Gender Equality', [0.079, 0.147, 0.191, 0.164]. It also contains 5 bars each representing a difference for each of the cultural values." } ], "id_raw": 51 }, { "id": "X1X5b3VXaXrjwIqC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.201, 0.654, 0.820, 0.720]?" }, { "from": "gpt", "value": "A white bar, which displays the value change for 'Family Values', along with a text '[0.830, 0.645, 0.874, 0.662]' indicating a 13.3% increase." } ], "id_raw": 51 }, { "id": "T4p87a9JgQvf2j8V", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nDescribe what's in the region [0.015, 0.313, 0.191, 0.331]." }, { "from": "gpt", "value": "This region contains the y-axis label for 'Environmental Protection'." } ], "id_raw": 51 }, { "id": "UT8cxgmOjGR5Js8X", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis represent in the chart according to the label at [0.540, 0.964, 0.563, 0.981]?" }, { "from": "gpt", "value": "Percent change (%\u25b2)." } ], "id_raw": 52 }, { "id": "zG7ARgsnRJiXKs3z", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the coordinates [0.732, 0.645, 0.767, 0.662]?" }, { "from": "gpt", "value": "The text \"7.5%\", the growth of Asia region." } ], "id_raw": 52 }, { "id": "w5tCbHkvogUqk8cc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat's located at the coordinates [0.127, 0.820, 0.117, 0.831]?" }, { "from": "gpt", "value": "The label \"South America\" on Y-axis." } ], "id_raw": 52 }, { "id": "n8o9h6uI9Gb5C7uC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nIs {the \"% \u25b2\" of Asia}[0.551, 0.587, 0.711, 0.720] higher or lower than the {the \"% \u25b2\" of Europe}[0.551, 0.253, 0.768, 0.387]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 52 }, { "id": "K7SRNoMNkwZV7ddR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nIs {the \"% \u25b2\" of South America}[0.452, 0.754, 0.551, 0.887] more or less than 0?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 52 }, { "id": "xYd8Rrz9a1yzThLz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nWhich region between {South America}[0.452, 0.754, 0.551, 0.887] and {Africa}[0.309, 0.420, 0.551, 0.554] has witnessed a more drastic decrease in \"% \u25b2\"?" }, { "from": "gpt", "value": "Africa." } ], "id_raw": 52 }, { "id": "c6XISMfNiPBsUzaO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area between [0.309, 0.420, 0.551, 0.554]." }, { "from": "gpt", "value": "It is a bar segment representing Africa. It's labeled with \"-11.4%\", indicating a decrease, with the exact label at location [0.239, 0.478, 0.288, 0.496]." } ], "id_raw": 52 }, { "id": "S5Qu8xoCjtd2PoRG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nKindly describe the region covered between the coordinates [0.452, 0.754, 0.551, 0.887]." }, { "from": "gpt", "value": "This area consists of a bar segment that represents South America. The bar label reads \"-4.7%\", indicating a decrease, and is located at [0.391, 0.812, 0.430, 0.829]." } ], "id_raw": 52 }, { "id": "VV3bub8zRpiCIaoz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [0.551, 0.253, 0.768, 0.387]?" }, { "from": "gpt", "value": "The area represents the Europe region with a positive \"% \u25b2\" of \"10.2%\" as shown by the label at [0.789, 0.311, 0.833, 0.329]." } ], "id_raw": 52 }, { "id": "zRpfC8vFUT8qs5q2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart as seen at [0.350, 0.019, 0.756, 0.041]?" }, { "from": "gpt", "value": "The title in the mentioned coordinates is \"Educational Resource Distribution and Utilization\"." } ], "id_raw": 54 }, { "id": "OPrcvOUqFvoJoQPi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at coordinates [0.455, 0.777, 0.572, 0.887] represent?" }, { "from": "gpt", "value": "It's the bar representing percentage of Students." } ], "id_raw": 54 }, { "id": "ZvqW2ObpJUrtf9xN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat category is shown at [0.070, 0.825, 0.131, 0.843] on the Y-axis?" }, { "from": "gpt", "value": "The category \"Students\"." } ], "id_raw": 54 }, { "id": "FLutbyA9pi7WvScM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing the {Expenditure}[0.231, 0.501, 0.455, 0.611] higher or lower than zero?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 54 }, { "id": "nyhqVVp1KUGJ0sOW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Scholarships bar}[0.455, 0.087, 0.929, 0.197] higher or lower than the {Research Output bar}[0.455, 0.363, 0.632, 0.473]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 54 }, { "id": "NAlVTrOoiJBr5ChJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nAre the {Facilities bar}[0.427, 0.225, 0.455, 0.335] and the {Professors bar}[0.455, 0.639, 0.503, 0.749] represented on the same side of the X-axis (\u0394%)?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 54 }, { "id": "QSsScD0afk49q4gy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the rectangle of [0.455, 0.777, 0.572, 0.887]?" }, { "from": "gpt", "value": "A single white bar which represents the category \"Students\" with a 5.6% change. The percentage change label for this bar is located to the right of the bar at coordinates [0.582, 0.823, 0.617, 0.841]." } ], "id_raw": 54 }, { "id": "t1GozhbdGD3WRcmD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat type of information can be found in the region defined by [0.350, 0.019, 0.756, 0.041]?" }, { "from": "gpt", "value": "The title of the chart, \"Educational Resource Distribution and Utilization\", is located in this region." } ], "id_raw": 54 }, { "id": "G7zuyOlC7owrYS9T", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.231, 0.501, 0.455, 0.611]?" }, { "from": "gpt", "value": "This region contains a bar which represents the category \"Expenditure\". This bar is drawn on the negative side of the x-axis, implying a decrease. The label showing a change of \"-10.7%\", is to the left of the bar and located at [0.172, 0.547, 0.220, 0.565]." } ], "id_raw": 54 }, { "id": "V12AvCNkZPSSQv1k", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the text within the coordinates [0.270, 0.812, 0.319, 0.829] represent?" }, { "from": "gpt", "value": "It represents the percent change for {'Mass Tourism'}[0.340, 0.754, 0.555, 0.887], which is -10.2%." } ], "id_raw": 55 }, { "id": "A07YECuo0xl6sV6a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.779, 0.019, 0.326, 0.041]?" }, { "from": "gpt", "value": "The chart's subtitle: \"Tourism and Hospitality Industry Performance Analysis\"." } ], "id_raw": 55 }, { "id": "DGGt5cxvsdodmI5U", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the xlabel at [0.463, 0.964, 0.642, 0.981]?" }, { "from": "gpt", "value": "Performance Increase \u25b2%." } ], "id_raw": 55 }, { "id": "b4pMPUvZzl8B4dGR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nDoes {Dark Tourism}[0.555, 0.587, 0.677, 0.720] have a higher or lower percent increase than {Eco-Tourism}[0.555, 0.420, 0.820, 0.554]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 55 }, { "id": "dlqMZdFWIVmsz7tR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nIs {Sports Tourism}[0.161, 0.253, 0.555, 0.387] to the left or right of the zero mark on the x-axis (0.555, 0.927)?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 55 }, { "id": "mXkC9mB6irYMHlHe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nBetween {Mass Tourism}[0.340, 0.754, 0.555, 0.887] and {Digital Tourism}[0.555, 0.087, 0.782, 0.220], which category had a bigger performance increase?" }, { "from": "gpt", "value": "Digital Tourism." } ], "id_raw": 55 }, { "id": "MYGPkXwQZfpJugq4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the layout in the region [0.025, 0.147, 0.118, 0.831]." }, { "from": "gpt", "value": "This region includes all the labels for each y-tick on the y-axis, which are, in order: \"Mass Tourism\"[0.025, 0.147, 0.118, 0.164], \"Dark Tourism\"[0.027, 0.647, 0.118, 0.664], \"Eco-Tourism\"[0.036, 0.480, 0.118, 0.498], \"Sports Tourism\"[0.016, 0.313, 0.118, 0.331] and \"Digital Tourism\"[0.015, 0.147, 0.118, 0.164]." } ], "id_raw": 55 }, { "id": "w92k5Gv48QwsjAhi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within [0.340, 0.754, 0.555, 0.887]?" }, { "from": "gpt", "value": "This region corresponds to the {'Mass Tourism'}[0.340, 0.754, 0.555, 0.887] bar, which extends from the left to right, indicating a performance decrease of -10.2%." } ], "id_raw": 55 }, { "id": "RBpT4l306u5x0m6g", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.119, 0.939, 0.985, 0.957]?" }, { "from": "gpt", "value": "This region includes labels for each x-tick on the x-axis, which are in increments of 5 from -20 to 20." } ], "id_raw": 55 }, { "id": "CBe2n7rnZIqqJ54i", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.343, 0.812, 0.383, 0.829] represent in the chart?" }, { "from": "gpt", "value": "It represents the decrease percentage of users (-7.5%) for {the platform Khan Academy}[0.394, 0.754, 0.553, 0.887]." } ], "id_raw": 56 }, { "id": "I3eCKZ6BZA36GiGu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of y-axis at coordinates [0.056, 0.647, 0.119, 0.664]?" }, { "from": "gpt", "value": "The label is \"Coursera\"." } ], "id_raw": 56 }, { "id": "xoBiyg2CROF94B3P", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat information is located at [0.855, 0.645, 0.899, 0.662] in the chart?" }, { "from": "gpt", "value": "It's the increase percentage (13.8%) of users for {the platform Coursera} [0.553, 0.587, 0.845, 0.720]." } ], "id_raw": 56 }, { "id": "nYf3uAXneBoXEUCm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nIs the growth rate of {EdX}[0.209, 0.420, 0.553, 0.554] higher or lower than that of {Udacity}[0.553, 0.253, 0.680, 0.387]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 56 }, { "id": "5gqqHQLqeENCpIDR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nDoes {Khan Academy}[0.394, 0.754, 0.553, 0.887] have a higher or lower growth rate than {CareerFoundry}[0.533, 0.087, 0.553, 0.220]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 56 }, { "id": "pr800sGN6upkZWF1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nAre {Coursea's}[0.553, 0.587, 0.845, 0.720] changes more or less drastic than {CareerFoundry's}[0.533, 0.087, 0.553, 0.220] changes?" }, { "from": "gpt", "value": "More." } ], "id_raw": 56 }, { "id": "dgaKYqu28IZVq4x3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.394, 0.754, 0.553, 0.887] include?" }, { "from": "gpt", "value": "It includes a white bar representing the user growth of Khan Academy from 2019-2020, which decreased by 7.5%, indicated by a red text label marked '-7.5%' located at [0.343, 0.812, 0.383, 0.829]." } ], "id_raw": 56 }, { "id": "KnalPujtjSBo5LU9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.553, 0.587, 0.845, 0.720]." }, { "from": "gpt", "value": "This area shows a white upward bar representing Coursera, signifying the platform's user growth. The growth rate is 13.8%, indicated by a '13.8%' text label in black, precisely located at [0.855, 0.645, 0.899, 0.662]." } ], "id_raw": 56 }, { "id": "lP930KrXLYfARthQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what the area [0.209, 0.420, 0.553, 0.554] contains?" }, { "from": "gpt", "value": "This area contains a horizontal white bar associated with EdX. It represents a decrease in user growth, indicated by a '-16.2%' text label in red at the coordinate [0.150, 0.478, 0.199, 0.496]." } ], "id_raw": 56 }, { "id": "KBt0efr54yoWuJBP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the main object with coordinates [0.138, 0.754, 0.455, 0.887] represent in the chart?" }, { "from": "gpt", "value": "It represents the change in the Dance industry." } ], "id_raw": 57 }, { "id": "U5gZ1BFfLxQ47eiS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the label corresponding to the Visual Arts bar?" }, { "from": "gpt", "value": "[0.015, 0.313, 0.089, 0.331]." } ], "id_raw": 57 }, { "id": "swqwGQGS7yh2L5RR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text}[0.691, 0.311, 0.726, 0.329] refer to?" }, { "from": "gpt", "value": "It represents the percentage change in the Visual Arts industry, which is 9.7%." } ], "id_raw": 57 }, { "id": "W6vINY9xo3hpLksn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the {Film Bar}[0.358, 0.087, 0.455, 0.220]?" }, { "from": "gpt", "value": "The value of the Film bar is -4.2%." } ], "id_raw": 57 }, { "id": "GfoWICL8eWLxeYpa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nIs {the value of Theatre} [0.455, 0.587, 0.927, 0.720] higher or lower than {that of Visual Arts} [0.455, 0.253, 0.679, 0.387]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 57 }, { "id": "nFaHH51FJcaHi85R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the Music bar}[0.330, 0.420, 0.455, 0.554] show a decrease or an increase in percentage?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 57 }, { "id": "02usAnwyYeaAk9Mb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater change: {the Dance industry}[0.138, 0.754, 0.455, 0.887] or {the Theatre industry}[0.455, 0.587, 0.927, 0.720]?" }, { "from": "gpt", "value": "Theatre." } ], "id_raw": 57 }, { "id": "LymCZWGCbO3yjoCg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.330, 0.420, 0.455, 0.554]." }, { "from": "gpt", "value": "It includes a red bar indicating the percentage change for Music industry, which is -5.4% located at [0.278, 0.478, 0.318, 0.496]." } ], "id_raw": 57 }, { "id": "0vInY2aNCEj6MOq8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.793, 0.939, 0.811, 0.957]." }, { "from": "gpt", "value": "It contains the x-axis label \"15%\"." } ], "id_raw": 57 }, { "id": "rF5KW4QYQRD2hSlj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.138, 0.754, 0.455, 0.220]?" }, { "from": "gpt", "value": "It includes the bars representing the changes in Dance, Theatre, Visual Arts, and Film industries, which are located at [0.138, 0.754, 0.455, 0.887], [0.455, 0.587, 0.927, 0.720], [0.455, 0.253, 0.679, 0.387] and [0.358, 0.087, 0.455, 0.220] respectively." } ], "id_raw": 57 }, { "id": "fL65acYE1kBJt81M", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the item at [0.504, 0.754, 0.937, 0.887] in the bar chart?" }, { "from": "gpt", "value": "It's a bar representing Tesla's market research." } ], "id_raw": 58 }, { "id": "YZh5YqWRWlmRt6X3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat object resides within the coordinates [0.165, 0.645, 0.200, 0.662]?" }, { "from": "gpt", "value": "That is a text label indicating a value of -20%." } ], "id_raw": 58 }, { "id": "gOaAVjS5a8oKeP2O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what occupies [0.036, 0.480, 0.061, 0.498]?" }, { "from": "gpt", "value": "It's the y-axis label for the company \"Lyft\"." } ], "id_raw": 58 }, { "id": "b2V1RlqRhD2lPwBN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.510, 0.964, 0.533, 0.981]?" }, { "from": "gpt", "value": "It is the x-axis label denoting the percentage change (\u25b2%)." } ], "id_raw": 58 }, { "id": "hXoofyzJ4OrhHQFP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nIs {Toyota's bar}[0.359, 0.253, 0.504, 0.387] positive or negative compared to {Lyft's bar}[0.504, 0.420, 0.576, 0.554]?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 58 }, { "id": "41yQVECnUC5ZC7L5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat is higher, {Ford's bar}[0.504, 0.087, 0.720, 0.220] or {Lyft's bar}[0.504, 0.420, 0.576, 0.554]?" }, { "from": "gpt", "value": "Ford's bar." } ], "id_raw": 58 }, { "id": "eJWhOTHQ1tNM2eO7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nDoes {Uber's bar}[0.215, 0.587, 0.504, 0.720] indicate a positive or negative value?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 58 }, { "id": "75tUceporNbYnp05", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nBetween {Tesla}[0.504, 0.754, 0.937, 0.887] and {Ford}[0.504, 0.087, 0.720, 0.220], which one has a higher value?" }, { "from": "gpt", "value": "Tesla." } ], "id_raw": 58 }, { "id": "HgB5kjR0WZ41nziZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area [0.504, 0.087, 0.720, 0.220]?" }, { "from": "gpt", "value": "There is a bar representing Ford's market research data along with its percentage text marker denoting a 15% increase." } ], "id_raw": 58 }, { "id": "GM19K9bkzgPOYrLV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.215, 0.587, 0.504, 0.720]?" }, { "from": "gpt", "value": "The area contains a bar representing Uber's market research data and a text marker indicating a decrease by 20%." } ], "id_raw": 58 }, { "id": "lYg9dqTOrmLlSPpY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.070, 0.047, 0.973, 0.927] encompass?" }, { "from": "gpt", "value": "It encompasses the entire bar chart, including the x and y axis labels, the bars representing market research data for each automotive and transportation company \u2014 Tesla, Uber, Lyft, Toyota, and Ford, and percentage indicators." } ], "id_raw": 58 }, { "id": "SLBd0ma3PMqhDrqs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nHow does region [0.499, 0.939, 0.508, 0.957] relate to the chart?" }, { "from": "gpt", "value": "This small region contains the zero value marker on the x-axis, which delineates positive and negative percentage changes in the visualization." } ], "id_raw": 58 }, { "id": "qlwjjz0OUPi3nwaO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the main object located at [0.523, 0.777, 0.734, 0.887]?" }, { "from": "gpt", "value": "The label is Health & Sanitation." } ], "id_raw": 59 }, { "id": "Mgu3gwXkGfKZ6nrc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat's the value indicated by the text at the coordinates [0.671, 0.547, 0.702, 0.565]?" }, { "from": "gpt", "value": "The value is 10%." } ], "id_raw": 59 }, { "id": "7zWmmq2A94kraiqH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the department indicated by the coordinates [0.025, 0.411, 0.161, 0.429]?" }, { "from": "gpt", "value": "The department's name is Community Service." } ], "id_raw": 59 }, { "id": "dBns5sV5uYYkgd4u", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nIs the percentage change of {Education department}[0.241, 0.639, 0.523, 0.749] positive or negative?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 59 }, { "id": "ihWAjHGb3ZwILDgM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nIs the change in {Public Safety department}[0.417, 0.087, 0.523, 0.197] higher or lower than 0?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 59 }, { "id": "jx2yeopgUlYXIisa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nComparing {Health & Sanitation department}[0.523, 0.777, 0.734, 0.887] and {Environment & Parks department}[0.523, 0.225, 0.946, 0.335], which one has a higher increase in the local government expenditure?" }, { "from": "gpt", "value": "Environment & Parks department." } ], "id_raw": 59 }, { "id": "ckF2lybcehJz0sRu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region [0.523, 0.677, 0.972, 0.887]?" }, { "from": "gpt", "value": "Within these coordinates, we have a positive bar for the Health & Sanitation department with a growth rate of 15%. The bar's coordinates are [0.523, 0.777, 0.734, 0.887], and the corresponding text label '15%' is located at [0.742, 0.823, 0.772, 0.841]." } ], "id_raw": 59 }, { "id": "tR9G6EwbkQkDdVwb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.241, 0.639, 0.523, 0.749]." }, { "from": "gpt", "value": "This area covers the Education department whose bar indicates a negative change of -20%. The coordinates of the bar are [0.241, 0.639, 0.523, 0.749] and the text label '-20%' is at [0.198, 0.685, 0.234, 0.703]." } ], "id_raw": 59 }, { "id": "ZHNqozSTYygU3rTL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat\u2019s inside the area [0.523, 0.225, 0.946, 0.335]?" }, { "from": "gpt", "value": "This area represents the Environment & Parks department, which has experienced a positive change. Specifically, there's a bar indicating a positive change of 30% with coordinates [0.523, 0.225, 0.946, 0.335], and a text label '30%' at [0.953, 0.271, 0.984, 0.289]." } ], "id_raw": 59 }, { "id": "ZXumZkygdTwAyU2Z", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.093, 0.495, 0.164, 0.519] refer to?" }, { "from": "gpt", "value": "{Liberalism}[0.093, 0.495, 0.164, 0.519]." } ], "id_raw": 76 }, { "id": "aznt1DvCiOWVbtK6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nCan you identify the element at coordinates [0.957, 0.819, 0.983, 0.842]?" }, { "from": "gpt", "value": "{Value label}[0.957, 0.819, 0.983, 0.842]." } ], "id_raw": 76 }, { "id": "TLnUALgxEhxBRHDW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [0.957, 0.574, 0.983, 0.598]?" }, { "from": "gpt", "value": "{Text of the value}[0.957, 0.574, 0.983, 0.598], which is 8.1 for Conservatism." } ], "id_raw": 76 }, { "id": "Ce9yqNmeLTpFKWI9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Traditionalism\"}[0.174, 0.798, 0.377, 0.863] higher than {the bar for \"Spirituality\"}[0.174, 0.065, 0.248, 0.130]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 76 }, { "id": "MAQtOkxeYRNr0IFZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nDo {the green bar}[0.587, 0.554, 0.957, 0.619] and {the blue bar}[0.174, 0.554, 0.248, 0.619] for the same category have the same value?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 76 }, { "id": "jazoJM9fQO1MrINf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nWhich is larger, {the value for \"Globalism\" in Metric 1}[0.174, 0.635, 0.507, 0.700] or {the value for \"Globalism\" in Metric 2}[0.587, 0.635, 0.678, 0.700]?" }, { "from": "gpt", "value": "Metric 1." } ], "id_raw": 76 }, { "id": "kYZ2ZZ9fjKnzrReR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the space described by coordinates [0.587, 0.716, 0.815, 0.782] contain?" }, { "from": "gpt", "value": "It contains {a green bar}[0.587, 0.716, 0.815, 0.782] representing the Culture Perspective 'Modernism' with a value change labelled as 5." } ], "id_raw": 76 }, { "id": "kAEKw0uhKGJNVtsq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat is within the boundaries [0.067, 0.821, 0.164, 0.845]?" }, { "from": "gpt", "value": "A {Cultural Perspective label}[0.067, 0.821, 0.164, 0.845] that reads 'Traditionalism'." } ], "id_raw": 76 }, { "id": "Utt4SoOIlyoSzUCk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart elements bounded by [0.015, 0.342, 0.029, 0.586]." }, { "from": "gpt", "value": "The area includes {the vertical bar's Y-axis label}[0.015, 0.342, 0.029, 0.586] which states 'Cultural Perspectives'." } ], "id_raw": 76 }, { "id": "edsLA6DahJeFGAVO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the y-axis} at [0.015, 0.446, 0.029, 0.492]?" }, { "from": "gpt", "value": "Type." } ], "id_raw": 77 }, { "id": "PZAARbGJmxDwjf2a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the bar corresponding to \"Spa Services\"} at [0.203, 0.145, 0.527, 0.212]?" }, { "from": "gpt", "value": "Pink." } ], "id_raw": 77 }, { "id": "pGd2nGnkq2bH6jLm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat value does {the text next to the \"Cruise Bookings\" bar} at [0.510, 0.418, 0.532, 0.438] indicate?" }, { "from": "gpt", "value": "18." } ], "id_raw": 77 }, { "id": "MjcF9vALNMbxbtLu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nIs {the frequency bar for \"Restaurant Revenue\" in Season 1}[0.203, 0.311, 0.544, 0.378] higher than {the frequency bar for \"Event Ticket Sales\" in Season 1}[0.203, 0.228, 0.561, 0.295]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 77 }, { "id": "mOBCNF00xhuE08wz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nAre {the values in Season 1}[0.203, 0.021, 0.579, 0.917] generally higher than {the values in Season 2}[0.604, 0.021, 0.980, 0.917] for all categories?" }, { "from": "gpt", "value": "No." } ], "id_raw": 77 }, { "id": "wenn1z0aSr3MJmnm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Hotel Bookings\" in Season 1}[0.203, 0.727, 0.408, 0.793] located closer to the {left edge of its subplot}[0.203, 0.021, 0.579, 0.917] than {the corresponding bar in Season 2}[0.604, 0.727, 0.805, 0.793]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 77 }, { "id": "uRHhDMWrn8sUqQd5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat are some details about the region [0.203, 0.021, 0.579, 0.917]?" }, { "from": "gpt", "value": "The region contains all the bars for Season 1 spread across various categories such as \"Resort Rentals\", \"Hotel Bookings\", \"Adventure Tours\", etc., up to \"Museum Visits\". Each bar is paired with a numerical value displayed next to it, signifying frequency counts for each category in Season 1." } ], "id_raw": 77 }, { "id": "1MuiPeFEMPpxcU36", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found within [0.600, 0.931, 1.042, 0.951]." }, { "from": "gpt", "value": "This area contains the x-axis labels for the Season 2 subplot with values ranging from 0 to 30 in increments, assisting in reading the frequency values corresponding to each category in Season 2." } ], "id_raw": 77 }, { "id": "S831YcIegOMP2fr9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nExplain what can be observed in [0.203, 0.083, 0.464, 0.850]." }, { "from": "gpt", "value": "This vertical section holds various bars for different categories, spanning most of the visit and booking types for Season 1, from \"Adventure Tours\" to \"Cruise Bookings\". Each bar is accompanied by a label indicating its exact value, facilitating a comparison of different categories." } ], "id_raw": 77 }, { "id": "uPsmh6SYYVQ9Y3PN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.112, 0.029, 0.779]?" }, { "from": "gpt", "value": "The Y-axis label \"Cybersecurity and Data Privacy Issues\"." } ], "id_raw": 80 }, { "id": "Jo4nQyon7VA87hro", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nCan you tell me where the value text of the \"Password Attacks\" bar for Frequency Metric 2 is situated?" }, { "from": "gpt", "value": "Yes, the text is at coordinates [0.761, 0.769, 0.810, 0.804]." } ], "id_raw": 80 }, { "id": "Ab1oZBMllfDRLN0F", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the label located at coordinates [0.068, 0.546, 0.158, 0.581] denote?" }, { "from": "gpt", "value": "It's the label for \"Ransomware\"." } ], "id_raw": 80 }, { "id": "qGyxQ2IaLnjG1Quo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat is described at the coordinates [0.271, 0.927, 0.432, 0.963]?" }, { "from": "gpt", "value": "It's the label for the x-axis on the left subplot, named as \"Frequency Metric 1 (%)\"." } ], "id_raw": 80 }, { "id": "qDmkNcTZxWKKKSdn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Password Attacks}[0.168, 0.757, 0.437, 0.817] from Frequency Metric 1 bigger than {its corresponding bar from Frequency Metric 2}[0.586, 0.757, 0.761, 0.817]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 80 }, { "id": "bgqGoCKqATgQ6fge", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar of Ransomware from Frequency Metric 2}[0.586, 0.529, 0.723, 0.590] have higher value than {the bar of Data Breach from Frequency Metric 2}[0.586, 0.453, 0.844, 0.514]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 80 }, { "id": "xG94SFmULMtx3Jbi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Insider Threats from Frequency Metric 1}[0.168, 0.302, 0.451, 0.363] lower than {the bar for Insider Threats from Frequency Metric 2}[0.586, 0.302, 0.890, 0.363]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 80 }, { "id": "XaQSOFnxtJ61dy3n", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nWhich bar is longer between {the Frequency Metric 1 bar for IoT Vulnerabilities}[0.168, 0.226, 0.506, 0.287] and {the Frequency Metric 2 bar for the same topic}[0.586, 0.226, 0.711, 0.287]?" }, { "from": "gpt", "value": "Frequency Metric 1." } ], "id_raw": 80 }, { "id": "ejPg5J1HSzesncci", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the text presented at coordinates [0.271, 0.927, 0.432, 0.963] and [0.690, 0.927, 0.850, 0.963]?" }, { "from": "gpt", "value": "The text at coordinates [0.271, 0.927, 0.432, 0.963] indicates the label for the X-axis on the left subplot, which is \"Frequency Metric 1 (%)\". While the text at [0.690, 0.927, 0.850, 0.963] is the x label for the right subplot, which is \"Frequency Metric 2 (%)\"." } ], "id_raw": 80 }, { "id": "sLyf3ZFCzLuGxBj9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the section covering [0.583, 0.757, 0.816, 0.817]?" }, { "from": "gpt", "value": "This area contains the Frequency Metric 2 bar for \"Password Attacks\". The bar sits at [0.586, 0.757, 0.761, 0.817], and the accompanying value text \"12.2%\" is at [0.761, 0.769, 0.810, 0.804]." } ], "id_raw": 80 }, { "id": "HcbmjgwLuIDVbmNP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat do the objects in the area [0.582, 0.681, 0.941, 0.741] represent?" }, { "from": "gpt", "value": "This region encompasses the Frequency Metric 2 bar for \"Phishing\" that ranges [0.586, 0.681, 0.827, 0.741], accompanied by the value text \"16.8%\" located at [0.827, 0.694, 0.876, 0.729]." } ], "id_raw": 80 }, { "id": "vTOKBDyRaoBmVEvq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area [0.035, 0.243, 0.158, 0.278]." }, { "from": "gpt", "value": "There is a y-axis label within this region. The label \"IoT Vulnerabilities\" is located within these coordinates." } ], "id_raw": 80 }, { "id": "qyvOPRA5yj14JgWt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nWhat can we see at the coordinate [0.529, 0.560, 0.467, 0.590]?" }, { "from": "gpt", "value": "The part of the bar in the left panel of the bar chart representing the value for \"Carbon Footprint\" in Frequency Metric 1." } ], "id_raw": 83 }, { "id": "Eifo79z2IapN04FQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of {the text}[0.559, 0.769, 0.599, 0.804]?" }, { "from": "gpt", "value": "It indicates a value \"8.2%\", which is the value of the \"Investment\" category under Frequency Metric 1." } ], "id_raw": 83 }, { "id": "85T9IkzwYOqxuXHN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at [0.796, 0.769, 0.835, 0.804]?" }, { "from": "gpt", "value": "This refers to the text \"4.5%\", a Frequency Metric 2 value for the \"Investment\" category." } ], "id_raw": 83 }, { "id": "uBXvjEbDfEFotJLh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nFrom the category \"Supply Chain Management\", is the {value for Frequency Metric 1}[0.232, 0.453, 0.432, 0.514] greater or less than the {value for Frequency Metric 2}[0.618, 0.453, 0.796, 0.514]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 83 }, { "id": "twgWDpbVKcDNzJ1g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nIn the Frequency Metric 1, Is the {value of \"Investment\"}[0.232, 0.757, 0.559, 0.817] higher than the {value of \"Employee Welfare\"}[0.232, 0.605, 0.479, 0.666]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 83 }, { "id": "1Aqa5zJ7U04JiVSo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nIs the {value for \"Equal Opportunities\" in Frequency Metric 2}[0.618, 0.302, 0.756, 0.363] higher or lower than the {value for \"Community Development\" in Frequency Metric 2}[0.618, 0.150, 0.824, 0.211]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 83 }, { "id": "l9IX4WJktqOlCZKy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the left panel of the chart from [0.232, 0.037, 0.576, 0.854]?" }, { "from": "gpt", "value": "The left panel represents the bar chart for Frequency Metric 1 (%). The x-axis represents Frequency Metric 1 in percentage, while the y-axis includes various categories starting from \"Investment\" on top to \"Sustainable Raw Materials\" at the bottom. Each category has a corresponding bar, with the length of each bar representing the numerical value of Frequency Metric 1 for each category." } ], "id_raw": 83 }, { "id": "H9pE2kg68vfzk4CH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the region in [0.710, 0.927, 0.871, 0.963] contain?" }, { "from": "gpt", "value": "It contains the x-axis label for the right side of the bar chart, which is \"Frequency Metric 2 (%)\"." } ], "id_raw": 83 }, { "id": "ZA2j7bw7B4seVEw0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area of the chart within the coordinates [0.618, 0.037, 0.962, 0.854]?" }, { "from": "gpt", "value": "This region represents the right part of the bar chart, where Frequency Metric 2 (%) is plotted against the same categories as Frequency Metric 1. A similar pattern is followed with bars indicating the corresponding values, with bar lengths vary according to their values." } ], "id_raw": 83 }, { "id": "VIm0Fl8qbu7NbQ9h", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the first chart?" }, { "from": "gpt", "value": "Innovation Ecosystem[0.015, 0.304, 0.029, 0.609]." } ], "id_raw": 84 }, { "id": "YlpsDuQf3DXvvEKP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the bars in the second chart?" }, { "from": "gpt", "value": "Light blue[0.596, 0.030, 0.962, 0.883]." } ], "id_raw": 84 }, { "id": "VrCjkTGrSmubK6B7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the text near the bar that represents 'Blockchain' in the first chart?" }, { "from": "gpt", "value": "7.9%[0.511, 0.720, 0.551, 0.748]." } ], "id_raw": 84 }, { "id": "PST2aZ7G4DNt2n4l", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar for 'Artificial Intelligence' longer in the first or second chart?" }, { "from": "gpt", "value": "Second[0.596, 0.781, 0.945, 0.845], [0.187, 0.781, 0.536, 0.845]." } ], "id_raw": 84 }, { "id": "tqa3OhdZ4aDf9mO9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for 'Drones Tech' have a higher percentage rate in the first or second chart?" }, { "from": "gpt", "value": "First[0.187, 0.385, 0.372, 0.449], [0.596, 0.385, 0.766, 0.449]." } ], "id_raw": 84 }, { "id": "quj5pUhS4zSCXv9R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nAre 'Cloud Computing' bars in descending order from the first to the second chart?" }, { "from": "gpt", "value": "No[0.187, 0.544, 0.433, 0.607], [0.596, 0.544, 0.830, 0.607]." } ], "id_raw": 84 }, { "id": "MIHxwtIsPTdcP3hm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nWhat is described in the x-axis area of the second chart?" }, { "from": "gpt", "value": "Percentages ranging from 0 to 8, positioned at [0.591, 0.883, 0.968, 0.931]." } ], "id_raw": 84 }, { "id": "zFoVKobvZWTQqiYe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the y-axis area of the first chart?" }, { "from": "gpt", "value": "Innovation sectors such as 'Artificial Intelligence', 'Blockchain', etc., labeled between coordinates [0.035, 0.069, 0.177, 0.830]." } ], "id_raw": 84 }, { "id": "JPkgaH59Nj6ovDtv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nWhat information is present in the region covering both bar charts?" }, { "from": "gpt", "value": "Two bar graphs comparing different technology sectors across 'Rate1 (%)' and 'Rate2 (%)' with corresponding values indicated at the end of each bar, ranging from coordinates [0.187, 0.781, 0.945, 0.845] to [0.596, 0.781, 0.945, 0.845]." } ], "id_raw": 84 }, { "id": "GHgpXRwANympXKj9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar with coordinates [0.568, -0.229, 0.586, 0.935]?" }, { "from": "gpt", "value": "It's the VR category bar with an Outlook Score of 0.8." } ], "id_raw": 96 }, { "id": "fpflqBv54AYl3VPI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nTell me what [0.686, 0.025, 0.824, 0.055] is." }, { "from": "gpt", "value": "It's the subtitle for the right subplot, which says \"Focus on Lower End\"." } ], "id_raw": 96 }, { "id": "aBIoE6zCwZuJyNfA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat lies at [0.329, 0.630, 0.347, 0.935] in the chart?" }, { "from": "gpt", "value": "A bar representing the IoT category with an Outlook Score of 0.35." } ], "id_raw": 96 }, { "id": "gisZAsPAzyo90HMk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.506, 0.418, 0.518, 0.580] refer to in the chart?" }, { "from": "gpt", "value": "It's the label of the y-axis in the right subplot named \"Outlook Score\"." } ], "id_raw": 96 }, { "id": "vuVm4oyGj3enf2nI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Blockchain bar}[0.619, -0.083, 0.636, 0.935] in the subplot taller or shorter than the {Quantum Computing bar}[0.772, 0.353, 0.789, 0.935]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 96 }, { "id": "GicHRZnVIOAxfuRj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 'AI' bar}[0.670, 0.062, 0.687, 0.935] to the left or right of {the '5G' bar}[0.721, 0.208, 0.738, 0.935] in the second chart?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 96 }, { "id": "gONOwu971LPJ8XSd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the bar marked 'VR'}[0.074, 0.237, 0.092, 0.935] or {the bar marked 'Cloud Computing'}[0.431, 0.717, 0.448, 0.935] in the first chart?" }, { "from": "gpt", "value": "VR bar." } ], "id_raw": 96 }, { "id": "A0sWRnFMqoSUPiYT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nBetween {AI}[0.176, 0.412, 0.194, 0.935] and {VR}[0.074, 0.237, 0.092, 0.935], which category in the first chart has a higher Outlook Score?" }, { "from": "gpt", "value": "VR." } ], "id_raw": 96 }, { "id": "WxNrNu7SWcxS9zub", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed within the region [0.549, 0.062, 0.961, 0.935]?" }, { "from": "gpt", "value": "This region comprises the second subplot which focuses on the lower end of the outlook score among emerging technologies. There are 8 bars representing different technologies: VR, Blockchain, AI, 5G, Quantum Computing, IoT, Big Data, and Cloud Computing. Each bar height corresponds to their outlook scores." } ], "id_raw": 96 }, { "id": "v9naDFXQBAY4c8dy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of the region [0.055, 0.062, 0.467, 0.935]?" }, { "from": "gpt", "value": "This region contains the first subplot which presents an overall outlook of emerging technologies. It shows the scores of 8 different technologies: VR, Blockchain, AI, 5G, Quantum Computing, IoT, Big Data, and Cloud Computing. The higher the bar, the higher the outlook score." } ], "id_raw": 96 }, { "id": "2dwjtLdE991Ez6mn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.329, 0.952, 0.346, 0.975]" }, { "from": "gpt", "value": "It's the 'IoT' label on the x-axis of the second subplot." } ], "id_raw": 96 }, { "id": "F2XI0pzzXiIcYWe6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nHow would you describe what is happening in [0.673, 0.952, 0.738, 0.975]?" }, { "from": "gpt", "value": "This region contains the '5G' label on the x-axis of the second subplot." } ], "id_raw": 96 }, { "id": "q90Vocxc8ZTAPLuL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nWhat text is in the bound box with coordinates [0.330, 0.020, 0.670, 0.056]?" }, { "from": "gpt", "value": "E-commerce and Retail Industry Analysis." } ], "id_raw": 105 }, { "id": "WMhPVsbLLE0jjO1C", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the text written inside {the box} at [0.224, 0.146, 0.328, 0.182]?" }, { "from": "gpt", "value": "E-commerce." } ], "id_raw": 105 }, { "id": "Z37yuaDt7n0iakiX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nWhat text is in position [0.918, 0.912, 0.947, 0.940]?" }, { "from": "gpt", "value": "May." } ], "id_raw": 105 }, { "id": "PfI47JJ176N7sFfJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nComparing {the bar representing January in E-commerce} at [0.079, 0.398, 0.145, 1.594] and {the one representing January in Traditional Retail} at [0.572, 0.738, 0.637, 1.594], which one is higher?" }, { "from": "gpt", "value": "E-commerce." } ], "id_raw": 105 }, { "id": "FnqeBBsrIuzZulgg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nIn March, was {E-commerce}[0.243, 0.381, 0.309, 1.594] more profitable than {Traditional Retail}[0.736, 0.786, 0.801, 1.594]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 105 }, { "id": "FvmM4KVaz4nEpA1H", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nIs the {May sales of E-commerce}[0.407, 0.276, 0.473, 1.594] more than that of {Traditional Retail in May}[0.900, 0.723, 0.965, 1.594]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 105 }, { "id": "e9gnV56Qwm8SbXkT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nCan you give a description of the area [0.060, 0.191, 0.493, 0.893]?" }, { "from": "gpt", "value": "It is the chart of E-Commerce sales over five months, from January to May. The sales are represented by blue bars, and each month's sales is labeled on the x-axis." } ], "id_raw": 105 }, { "id": "Xo22C2j1rZZXSPvz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements within the region [0.552, 0.191, 0.985, 0.893]." }, { "from": "gpt", "value": "This region contains a bar chart - Traditional Retail. It represents the sales data of traditional retail from January to May. It displays blue bars for each month's sales values plotted on the y-axis, and the month labels on the x-axis." } ], "id_raw": 105 }, { "id": "0oZwy3fsbLYec7Nc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.079, 0.398, 0.145, 1.594]?" }, { "from": "gpt", "value": "There is a blue bar that represents the sales of E-commerce in January." } ], "id_raw": 105 }, { "id": "6uGwAZkyYJuloSNW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart in coordinates [0.391, 0.020, 0.609, 0.056]?" }, { "from": "gpt", "value": "\"Energy Use and Resource Consumption\"" } ], "id_raw": 106 }, { "id": "JJmvUrIb2Z54arNq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the label in coordinates [0.171, 0.912, 0.226, 0.940] represent in the x-axis?" }, { "from": "gpt", "value": "\"Natural Gas\"" } ], "id_raw": 106 }, { "id": "ky7bc2enCLO5KCTa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the text content indicated by the coordinates [0.211, 0.146, 0.348, 0.182] on the sub-plot?" }, { "from": "gpt", "value": "It represents the subtitle \"Annual Energy Use (GW)\"." } ], "id_raw": 106 }, { "id": "0IBDsnYzMNRhXapp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Coal}[0.084, 0.752, 0.149, 0.893] higher or lower than the bar representing {Hydro}[0.329, 0.871, 0.394, 0.893]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 106 }, { "id": "WxiOdeIduD7XFejC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nOn the resource consumption chart, is {Natural Gas bar}[0.661, 0.577, 0.726, 0.893] to the left or right of the bar representing {Nuclear}[0.742, 0.735, 0.807, 0.893]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 106 }, { "id": "4txsFYrZ1rY2v1z0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nComparing {Coal}[0.084, 0.752, 0.149, 0.893] and {Other Renewables}[0.410, 0.877, 0.475, 0.893] on the Annual Energy Use chart, which is higher?" }, { "from": "gpt", "value": "Coal." } ], "id_raw": 106 }, { "id": "Y1GoQVH99owSe6RL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the coordinates [0.065, 0.191, 0.495, 0.893]." }, { "from": "gpt", "value": "This region represents the subplot labelled \"Annual Energy Use (GW)\". It includes individual bar plots for \"Coal\", \"Natural Gas\", \"Nuclear\", \"Hydro\", and \"Other Renewables\" along the x-axis and values ranging from 0 to 200,000 on the y-axis with the ylabel as 'Billions of MWh'." } ], "id_raw": 106 }, { "id": "EwJOoTuage8dMjh5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region with coordinates [0.560, 0.191, 0.990, 0.893]?" }, { "from": "gpt", "value": "This region represents the subplot titled \"Resource Consumption (TJ)\". It comprises bar plots representing \"Coal\", \"Natural Gas\", \"Nuclear\", \"Hydro\", and \"Other Renewables\" along the x-axis. The y-values range from 0 to 200,000 with the ylabel as 'Trillions of BTUs'." } ], "id_raw": 106 }, { "id": "ff2v8IRcaT2cYWtt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.579, 0.331, 0.644, 0.893] cover?" }, { "from": "gpt", "value": "This region covers the bar plot for \"Coal\" in the subplot \"Resource Consumption (TJ)\", indicating a range of 0 - 160,000." } ], "id_raw": 106 }, { "id": "hOHPCXSJ7kt34gKH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nWhat color is the bar for {Medical Gloves in June}[0.887, 0.429, 0.908, 0.893]?" }, { "from": "gpt", "value": "Dark blue." } ], "id_raw": 107 }, { "id": "sBJxUrPZsfb2qjfu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the chronological point marked just before {the April tick}[0.072, 0.912, 0.084, 0.940] on the x-axis?" }, { "from": "gpt", "value": "February." } ], "id_raw": 107 }, { "id": "9WY9XE6sDrzGro7j", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nReferencing the bar with sales figures closest to 50000 for {Medical Masks}[0.240, 0.680, 0.390, 0.893], which month does it correspond to?" }, { "from": "gpt", "value": "{December}[0.369, 0.371, 0.390, 0.893]." } ], "id_raw": 107 }, { "id": "lrW0sEi96UYTTTIm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nWhich one is greater: {the December sales for Disinfectants}[0.170, 0.314, 0.191, 0.893] or {the December sales for Medical Gloves}[0.964, 0.309, 0.985, 0.893]?" }, { "from": "gpt", "value": "Disinfectants." } ], "id_raw": 107 }, { "id": "7k8un6WRWoispdHN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nIs {the April bar for Ventilators}[0.663, 0.583, 0.684, 0.893] taller than {the August bar for Testing Kits}[0.516, 0.327, 0.537, 0.893]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 107 }, { "id": "mJpKRuCANiEOslXa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nWhich has fewer sales in June, {Disinfectants}[0.093, 0.420, 0.114, 0.893] or {Medical Masks}[0.292, 0.490, 0.312, 0.893]?" }, { "from": "gpt", "value": "Medical Masks." } ], "id_raw": 107 }, { "id": "pdlp53jA7GCDEPNl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region encompassing the sales numbers for Disinfectants and Medical Masks for February and April [0.034, 0.362, 0.287, 0.893]?" }, { "from": "gpt", "value": "There are four bars representing sales of Disinfectants and Medical Masks for February and April, with two of the bars colored light green and the other two turquoise, indicating different sales figures for these months and products." } ], "id_raw": 107 }, { "id": "Z7wXekeG8ZhHCWax", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found in the plot area [0.431, 0.359, 0.588, 0.893] that includes the y-axis and part of the bars for Testing Kits." }, { "from": "gpt", "value": "This area includes the y-axis with multiple horizontal grid lines and labels ranging from 0 to 70000 in increments of 10000. Additionally, there are six red bars corresponding to the monthly sales of Testing Kits for all months available in the dataset." } ], "id_raw": 107 }, { "id": "BfvY3rXWLF30Cwlw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nProvide a description for the chart elements located within [0.828, 0.180, 0.993, 0.951]." }, { "from": "gpt", "value": "The region contains the fifth subplot's y-axis, with tick labels and tick marks spaced vertically, a title labeled \"Medical Gloves\" at the top, and a series of dark blue bars representing monthly sales data for the entire year." } ], "id_raw": 107 }, { "id": "9VUhM4TXIvdr97TO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.416, 0.020, 0.584, 0.056] in the chart?" }, { "from": "gpt", "value": "It's the chart title named {Risk Assessment Across Areas}[0.416, 0.020, 0.584, 0.056]." } ], "id_raw": 108 }, { "id": "shwxCSqAIzYpo45p", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with the bar located at [0.708, 0.552, 0.784, 0.893]?" }, { "from": "gpt", "value": "It corresponds to {Threat Level}[0.708, 0.552, 0.784, 0.893]." } ], "id_raw": 108 }, { "id": "XppeQO8ZN4xMgYFF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nWhich parameter do {coordinates}[0.139, 0.912, 0.225, 0.940] refer to?" }, { "from": "gpt", "value": "It refers to the x-axis label {Vulnerability Level}[0.139, 0.912, 0.225, 0.940]." } ], "id_raw": 108 }, { "id": "6ENgz7saUl4NiNZl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing {Risk Rating}[0.900, 0.239, 0.977, 0.893] have a higher or lower range value than the bar representing {Threat Level}[0.708, 0.552, 0.784, 0.893]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 108 }, { "id": "iiqXs83uhL4XJdm3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nIs the {Risk Area 2 subtitle}[0.481, 0.146, 0.543, 0.182] to the left or right of the {Risk Area 3 subtitle}[0.811, 0.146, 0.873, 0.182]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 108 }, { "id": "qKkyiaesxm6uDEQx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nIs the {Vulnerability Level bar}[0.804, 0.579, 0.880, 0.893] taller than the {Threat Level bar}[0.708, 0.552, 0.784, 0.893]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 108 }, { "id": "L4uIAZnJSzzjrDsx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.694, 0.191, 0.990, 0.893]." }, { "from": "gpt", "value": "This region comprises of a subplot for \"Risk Area 3\", containing three bars corresponding to \"Threat Level\", \"Vulnerability Level\" and \"Risk Rating\" of respective coordinates {Threat Level bar}[0.708, 0.552, 0.784, 0.893], {Vulnerability Level bar}[0.804, 0.579, 0.880, 0.893], and {Risk Rating bar}[0.900, 0.239, 0.977, 0.893]. It also contains labels for the X and Y axis as well as Y gridlines." } ], "id_raw": 108 }, { "id": "o6euH9B7pZCZWW35", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.364, 0.191, 0.660, 0.893]?" }, { "from": "gpt", "value": "This is the second subplot with the title {Risk Area 2}[0.481, 0.146, 0.543, 0.182], and it contains three bars representing the \"Threat Level\", \"Vulnerability Level\", and \"Risk Rating\" with coordinates as {Threat Level bar}[0.378, 0.639, 0.454, 0.893], {Vulnerability Level bar}[0.474, 0.650, 0.550, 0.893], {Risk Rating bar}[0.570, 0.396, 0.647, 0.893]." } ], "id_raw": 108 }, { "id": "ZW3oUSwJYoxLB9qD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nWhat's within the boundaries of [0.034, 0.191, 0.330, 0.893]?" }, { "from": "gpt", "value": "This region contains the first subplot for \"Risk Area 1\", having three bars for \"Threat Level\", \"Vulnerability Level\", and \"Risk Rating\", with coordinates {Threat Level bar}[0.048, 0.731, 0.124, 0.893], {Vulnerability Level bar}[0.144, 0.758, 0.220, 0.893], {Risk Rating bar}[0.240, 0.596, 0.317, 0.893] respectively. It also contains labels for the X and Y axis with Y gridlines." } ], "id_raw": 108 }, { "id": "Uf7tBixOK6hsWEPp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the chart title}[0.401, 0.020, 0.599, 0.056]?" }, { "from": "gpt", "value": "Entrepreneurship Ecosystem trends." } ], "id_raw": 109 }, { "id": "zh7IhEyqvwKauKOB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate range for {the bar labeled \"VC Funding\" showing \"Erratic Fluctuations\"}[0.232, 0.423, 0.301, 0.893]?" }, { "from": "gpt", "value": "0.00 to 3345." } ], "id_raw": 109 }, { "id": "AaHvQ9XVrwbadQyt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the tickmark located at [0.847, 0.893] on the x-axis of the subplot labeled \"Continuous Decrease\" signify?" }, { "from": "gpt", "value": "Acquired Startups." } ], "id_raw": 109 }, { "id": "Zm6AYEMeiypQtjzH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nIs {the graph labeled \"Top-Tier Tech Talent\" under \"Erratic Fluctuations\"}[0.404, 0.798, 0.472, 0.893] higher than the same graph under {the \"Continuous Decrease\"}[0.899, 0.776, 0.967, 0.893] section?" }, { "from": "gpt", "value": "No." } ], "id_raw": 109 }, { "id": "Apjr79i4rewSC1fv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"VC Funding\"}[0.232, 0.423, 0.301, 0.893] in the \"Erratic Fluctuations\"subplot taller than {the bar representing the same category in the \"Continuous Decrease\" subplot}[0.727, 0.250, 0.796, 0.893]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 109 }, { "id": "YH4o7iwPcTR30Aa4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nDo {the Startup Formation values}[0.060, 0.890, 0.129, 0.893], [0.555, 0.890, 0.624, 0.893] increase or decrease from \"Erratic Fluctuations\" to \"Continuous Decrease\"?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 109 }, { "id": "2s030tuZGS7Gemt4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.535, 0.191, 0.988, 0.893]" }, { "from": "gpt", "value": "The area contains a chart that signifies \"Continuous Decrease\". It has 5 bars indicating Startup Formation, Patent Applications, VC Funding, Acquired Startups, and Top-Tier Tech Talent respectively. Each bar's height represents a statistical measurement, with corresponding values located on the y-axis." } ], "id_raw": 109 }, { "id": "KKlVTfV4SSQ5dB9S", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area [0.040, 0.191, 0.493, 0.893]?" }, { "from": "gpt", "value": "This is the \"Erratic Fluctuations\" subplot, featuring five bars. Each bar represents a category: Startup Formation, Patent Applications, VC Funding, Acquired Startups, and Top-Tier Tech Talent. The height of each bar corresponds to the measure in that category." } ], "id_raw": 109 }, { "id": "qUrIFoAOO1ciT2Iy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.702, 0.146, 0.820, 0.182]?" }, { "from": "gpt", "value": "This region contains the title of the subplot, \"Continuous Decrease\"." } ], "id_raw": 109 }, { "id": "G06dMbSvF7jhjwT6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object labeled with 'Developing' in the Europe Migration subplot?" }, { "from": "gpt", "value": "It's a green bar with coordinates [0.119, 0.679, 0.191, 0.808]." } ], "id_raw": 110 }, { "id": "36dRs4uWnw3olh8N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nWhere is the first tick mark on the x-axis located in the Africa Migration subplot?" }, { "from": "gpt", "value": "[0.463, 0.873]" } ], "id_raw": 110 }, { "id": "VSBBnj764J0ZU3WZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.666, 0.635, 0.742, 0.686] in the America Migration subplot represent?" }, { "from": "gpt", "value": "It represents the bar for 'Developed' region, indicating a value of 0.70." } ], "id_raw": 110 }, { "id": "BY3DG4y6vXIsizKb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar marked with 'Developed'}[0.031, 0.575, 0.102, 0.679] taller than {the bar marked with 'Developing'}[0.119, 0.679, 0.191, 0.808] in the Europe Migration subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 110 }, { "id": "ssqZnAkW3pMrHZ1A", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nAre {the green bar}[0.318, 0.679, 0.389, 0.776] values lower than {the red bar}[0.516, 0.271, 0.588, 0.679] in the Africa Migration subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 110 }, { "id": "GydqJnkC0pXw0Ant", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nIn the Oceania Migration subplot, does {the bar for Developed}[0.825, 0.666, 0.896, 0.679] reach a higher value than {the bar for Developing}[0.913, 0.420, 0.985, 0.679]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 110 }, { "id": "aA94MFtGNkEFmtjG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects and their positions in the region [0.007, 0.795, 0.813, 0.932]." }, { "from": "gpt", "value": "The region contains y-axis labels and x-axis values from various subplots. For the Europe Migration subplot, the y-axis label '\u22122' at [0.007, 0.795, 0.018, 0.830]; in the Asia Migration subplot, the x-axis 'Developed' label at [0.246, 0.897, 0.283, 0.932]; in the America Migration subplot, the x-axis 'Developing' label at [0.731, 0.897, 0.770, 0.932]." } ], "id_raw": 110 }, { "id": "29xut6cMFjO6Xo5N", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nExplain the contents and layout within the area [0.402, 0.550, 0.613, 0.959]." }, { "from": "gpt", "value": "In this area, we see the y-axis label and y-scale tick marks for the Africa Migration subplot. The y-axis label is at [0.402, 0.550] and ticks marks for '6', '4', '2', '0', '-2', and '-4' are observed within the range ending at [0.415, 0.959]." } ], "id_raw": 110 }, { "id": "QoKufCq584DN1qeK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nCan you identify and specify the contents found in the coordinates [0.600, 0.536, 0.706, 0.932] in the America Migration subplot?" }, { "from": "gpt", "value": "This area encases the y-scale labels '-4', '-2', '0', '2', '4', '6' along with their respective tick coordinates ending at [0.613, 0.932], showing all levels of migration values for America." } ], "id_raw": 110 }, { "id": "euaBEYy8nPagb1R5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text} with coordinates [0.321, 0.020, 0.679, 0.056] represent?" }, { "from": "gpt", "value": "This represents the title of the chart, which is \"Environmental and Climate Change Impact\"." } ], "id_raw": 112 }, { "id": "ROmjCPtiyLJD1F4U", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the first year listed on the X-axis of the chart?" }, { "from": "gpt", "value": "The first year listed is 1975 at coordinates [0.073, 0.912, 0.109, 0.940]." } ], "id_raw": 112 }, { "id": "wj0PfWB3MJwIJLTh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of CO2 Emission in 1995 according to the left subplot chart?" }, { "from": "gpt", "value": "The value of CO2 Emission in 1995 is 19, based on the bar coordinates [0.204, 0.626, 0.260, 0.893]." } ], "id_raw": 112 }, { "id": "MnmRMqJFiARKo1MW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nIn 2025, was the CO2 Emission higher or lower than Ocean Acidification?" }, { "from": "gpt", "value": "The CO2 Emission was higher, as reflected by {\"CO2 bar in 2025\"}[0.416, 0.331, 0.472, 0.893] and {\"Ocean Acidification bar in 2025\"}[0.908, 0.800, 0.965, 0.893]." } ], "id_raw": 112 }, { "id": "YEGY6ySZ4NwwIt1F", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nBetween 1975 and 1985, did the CO2 Emissions decrease or increase?" }, { "from": "gpt", "value": "The CO2 Emissions increased, as seen by the 1975 bar [0.063, 0.682, 0.119, 0.893] being lower than the 1985 bar [0.133, 0.654, 0.190, 0.893]." } ], "id_raw": 112 }, { "id": "JaV8MV9cIVtqW1L5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nWhich was higher in 2005, CO2 Emissions or Ocean Acidification?" }, { "from": "gpt", "value": "CO2 Emissions were higher, as seen in {\"CO2 bar in 2005\"}[0.274, 0.542, 0.331, 0.893] in comparison with the {\"Ocean Acidification bar in 2005\"}[0.767, 0.794, 0.823, 0.893]." } ], "id_raw": 112 }, { "id": "hj07Ajw8bVIuJkaC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nCan you provide a brief summary of the chart at coordinates [0.042, 0.191, 0.492, 0.893]?" }, { "from": "gpt", "value": "This region represents a bar chart showcasing data of CO2 Emissions over time from 1975 to 2025. The bars represent the amount of CO2 Emissions and the x-axis represents the years. For instance, the bar for 1975 at [0.063, 0.682, 0.119, 0.893] shows lower emissions than the bar for 2025 at [0.416, 0.331, 0.472, 0.893], suggesting an increase in emissions over this period." } ], "id_raw": 112 }, { "id": "qJwt1KANzsYxtHkK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.535, 0.191, 0.985, 0.893]." }, { "from": "gpt", "value": "This area houses a bar chart that details the ocean acidification from 1975 to 2025. The y-axis measures the level of ocean acidification, and the x-axis signifies the corresponding year. For instance, ['ocean acidification bar in 1975'][0.555, 0.789, 0.612, 0.893] denotes a higher level of ocean acidification compared to ['ocean acidification bar in 2025'][0.908, 0.800, 0.965, 0.893], indicating a decrease in the levels of ocean acidification over this time period." } ], "id_raw": 112 }, { "id": "xZy9NKHviMxXHOrn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat is visible in the chart at coordinates [0.145, 0.676, 0.201, 0.892]?" }, { "from": "gpt", "value": "Within these coordinates, there is a bar from the CO2 Emissions chart that represents the year 1985. The bar indicates a CO2 Emissions value of 17." } ], "id_raw": 112 }, { "id": "iCLVQUG9pSXvNEWl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.708, 0.302, 0.827, 0.893] in the chart?" }, { "from": "gpt", "value": "It's the blue bar in the subplot \"Region 3 Evaluation\" representing the \"PolicyEffectiveness\"." } ], "id_raw": 113 }, { "id": "XIdgnQkFhtj0mP8f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nWhere is the Y-axis label in Region 2 located?" }, { "from": "gpt", "value": "[0.336, 0.542, 0.336, 0.542]" } ], "id_raw": 113 }, { "id": "m1K3Ko94JeMczvJE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat item does {the label}[0.663, 0.906, 0.732, 0.930] refer to on the x-axis?" }, { "from": "gpt", "value": "it refers to \"inequality\"." } ], "id_raw": 113 }, { "id": "KVglzDtrVy0ahTW2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat text is within {the box}[0.366, 0.020, 0.634, 0.056] in the plot?" }, { "from": "gpt", "value": "The text is \"Government Policy and Effectiveness Evaluation\"." } ], "id_raw": 113 }, { "id": "poRupvezZjufFicj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nFor Region 3, is {the bar showing PolicyEffectiveness} [0.708, 0.302, 0.827, 0.893] longer than {the bar showing PolicyDuration} [0.857, 0.875, 0.977, 0.893]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 113 }, { "id": "Tiu6EwKkQG4DsqTC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar indicating 'PolicyEffectiveness'} [0.048, 0.370, 0.167, 0.893] in Region 1 have a larger value than {the bar for 'PolicyEffectiveness'} [0.378, 0.449, 0.497, 0.893] in Region 2?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 113 }, { "id": "FNtsTGS0VTWG4FRQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nIs {the x-axis label 'PolicyDuration'}[0.883, 0.912, 0.950, 0.940] in subplot 3 at the same position as {the 'PolicyDuration'}[0.223, 0.912, 0.290, 0.940] in subplot 1?" }, { "from": "gpt", "value": "No." } ], "id_raw": 113 }, { "id": "Boc8e3K2KJkqVQmA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nAre the Y-axis labels in Region 2 and Region 3 located at the same coordinates?" }, { "from": "gpt", "value": "No." } ], "id_raw": 113 }, { "id": "QHVsVsgsxqxCkj87", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects found in the area [0.694, 0.191, 0.990, 0.893]." }, { "from": "gpt", "value": "This area represents the subplot for Region 3 Evaluation, in which I can see two bars: one blue bar representing 'PolicyEffectiveness' with coordinates [0.708, 0.302, 0.827, 0.893], and the other one representing 'PolicyDuration' with coordinates [0.857, 0.875, 0.977, 0.893]. There is also a subplot title named 'Region 3 Evaluation' at [0.787, 0.146, 0.897, 0.182] and a y-axis label at [0.666, 0.542, 0.666, 0.542]. On the x-axis, the labels for 'PolicyEffectiveness' and 'PolicyDuration' are found at [0.723, 0.912, 0.811, 0.940] and [0.883, 0.912, 0.950, 0.940] respectively." } ], "id_raw": 113 }, { "id": "DDUcRD9o2aiqHaNH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nDescribe what is enclosed by the coordinates [0.034, 0.191, 0.330, 0.893]." }, { "from": "gpt", "value": "The region encloses the subplot for Region 1 Evaluation, where a red bar represents 'PolicyEffectiveness' at [0.048, 0.370, 0.167, 0.893] and another red bar represents 'PolicyDuration' at [0.197, 0.871, 0.317, 0.893]. It also has a subplot title named 'Region 1 Evaluation' at [0.127, 0.146, 0.237, 0.182] and different y-axis labels for values ranging from 0 to 100." } ], "id_raw": 113 }, { "id": "YXj2ZmWyw15YEfdZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.364, 0.191, 0.660, 0.893]?" }, { "from": "gpt", "value": "The coordinates define the area for the subplot \"Region 2 Evaluation\". Inside it, there are two green bars representing 'PolicyEffectiveness' and 'PolicyDuration', located at [0.378, 0.449, 0.497, 0.893] and [0.527, 0.880, 0.647, 0.893] respectively. Additionally, the subplot title 'Region 2 Evaluation' is located at [0.457, 0.146, 0.567, 0.182], and there are various labels on the y-axis representing values from 0 to 100." } ], "id_raw": 113 }, { "id": "NNBwALZc8on4LTCs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you see within the region [0.366, 0.020, 0.634, 0.056]?" }, { "from": "gpt", "value": "Within this region, there is a title of the whole chart, \"Government Policy and Effectiveness Evaluation\". The complete chart-title coordinates are [0.366, 0.020, 0.634, 0.056]." } ], "id_raw": 113 }, { "id": "Amu6U30uPAxPx033", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat words at [0.369, 0.020, 0.631, 0.056] on top of the chart?" }, { "from": "gpt", "value": "The title \"Manufacturing Production Efficiency Evaluation\"" } ], "id_raw": 114 }, { "id": "QGmS69VOh4CcPfrJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar in the first subplot at [0.048, 0.460, 0.092, 1.477] represent?" }, { "from": "gpt", "value": "It represents the threading process in December with a value of 87." } ], "id_raw": 114 }, { "id": "w76QaLPHxOVhYy5T", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the second bar in the second subplot at [0.434, 0.752, 0.478, 1.477]?" }, { "from": "gpt", "value": "It represents the drilling process in January with a value of 62." } ], "id_raw": 114 }, { "id": "PQ4abdZeYmrXCMVh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Assembly\" bar in January}[0.546, 0.401, 0.591, 1.477] higher than {the \"Assembly\" bar in February}[0.876, 0.366, 0.921, 1.477]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 114 }, { "id": "IxGns1Pymc5iRlQ9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nAre the {February bars}[[0.708, 0.623, 0.752, 1.477], [0.764, 0.717, 0.808, 1.477], [0.820, 0.483, 0.864, 1.477], [0.876, 0.366, 0.921, 1.477], [0.932, 0.530, 0.977, 1.477]] generally higher or lower than the {January bars}[[0.378, 0.600, 0.422, 1.477], [0.434, 0.752, 0.478, 1.477], [0.490, 0.460, 0.534, 1.477], [0.546, 0.401, 0.591, 1.477], [0.602, 0.565, 0.647, 1.477]]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 114 }, { "id": "xfVbUeibjnzUKRZc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nDoes the {Finishing process in February}[0.932, 0.530, 0.977, 1.477] have a higher value than the {Sawing process in January}[0.490, 0.460, 0.534, 1.477]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 114 }, { "id": "9rFTFLnTtpjXNzJl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.034, 0.191, 0.330, 0.893]." }, { "from": "gpt", "value": "This region contains the first chart subplot. It is a bar chart representing the process efficiency in December. The processes include threading, drilling, sawing, assembly, and finishing." } ], "id_raw": 114 }, { "id": "17Gp8isilllrD1J4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.364, 0.191, 0.660, 0.893]?" }, { "from": "gpt", "value": "This region contains the second subplot, a bar chart representing the process efficiency in January. Like the first subplot, this chart also includes threading, drilling, sawing, assembly, and finishing." } ], "id_raw": 114 }, { "id": "molc7R7GO3qexA56", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained within [0.694, 0.191, 0.990, 0.893]?" }, { "from": "gpt", "value": "This region contains the third subplot of the chart. It is a bar chart representing the process efficiency in February. It assesses five processes: threading, drilling, sawing, assembly, and finishing." } ], "id_raw": 114 }, { "id": "RaK1zi8tFbu8W9FC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the subject of the chart figure with coordinates [0.317, 0.020, 0.683, 0.056]?" }, { "from": "gpt", "value": "Ecological Environment Sustainability Assessment Across Regions." } ], "id_raw": 115 }, { "id": "vql9bpnATon28iRF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the subtitle for the second chart where the bar data is focused on Region 2?" }, { "from": "gpt", "value": "[0.479, 0.146, 0.528, 0.182]." } ], "id_raw": 115 }, { "id": "Xmyk5udUlk5htsIs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the {Bar}[0.794, 0.359, 0.829, 0.893] in the last chart figure (subplot_2)?" }, { "from": "gpt", "value": "Soil Health." } ], "id_raw": 115 }, { "id": "1ieb89fgFyo84CDb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.881, 0.464, 0.916, 0.893] represent in the last subplot?" }, { "from": "gpt", "value": "Carbon Footprint in the {Bar}[0.881, 0.464, 0.916, 0.893]." } ], "id_raw": 115 }, { "id": "KXgstNRNun2KEtaJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nIs the {Soil Health bar for Region 3}[0.794, 0.359, 0.829, 0.893] higher or lower than the {Soil Health bar for Region 1}[0.134, 0.401, 0.169, 0.893]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 115 }, { "id": "6WO5MKIkWTXH4lZ1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the {Air Quality bar for Region 1}[0.047, 0.436, 0.082, 0.893] or the {Air Quality bar for Region 3}[0.707, 0.296, 0.742, 0.893]?" }, { "from": "gpt", "value": "Region 3." } ], "id_raw": 115 }, { "id": "tmA0zYo9zKgJYEOQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nDoes the {Water Quality bar for Region 1}[0.090, 0.507, 0.125, 0.893] have a higher or lower level than the {Water Quality bar for Region 2}[0.420, 0.415, 0.455, 0.893]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 115 }, { "id": "7LbAUddAAwWmY5ku", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhich value is larger for Forestation Levels, {Region 2}[0.595, 0.289, 0.630, 0.893] or {Region 3}[0.925, 0.254, 0.960, 0.893]?" }, { "from": "gpt", "value": "Region 3." } ], "id_raw": 115 }, { "id": "dFbKaOJqtcnB7FKk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.034, 0.191, 0.313, 0.893]." }, { "from": "gpt", "value": "This area primarily contains the subplot for Region 1, with axes highlighting ecological aspects such as air quality, water quality, soil health, biodiversity, carbon footprint, and forestation levels. The specific bar values for each aspect are encoded in the bars with coordinates like {Air Quality}<{Bar}[0.047, 0.436, 0.082, 0.893]>." } ], "id_raw": 115 }, { "id": "NlIOPGXO4dAERVdx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the main features contained in area [0.364, 0.191, 0.643, 0.893]?" }, { "from": "gpt", "value": "This area primarily comprises the subplot for the Region 2, featuring bars representing air quality, water quality, soil health, biodiversity, carbon footprint, and forestation levels. Specific categories can be referenced such as {Biodiversity}<{Bar}[0.508, 0.387, 0.543, 0.893]>." } ], "id_raw": 115 }, { "id": "wjflsgoA0xdLMa7y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.694, 0.191, 0.973, 0.893]?" }, { "from": "gpt", "value": "The area forms the compartment for the Region 3's chart, representing several ecological factors through bars, including {Carbon Footprint}<{Bar}[0.881, 0.464, 0.916, 0.893]> and others. It shows measured levels of air quality, water quality, soil health, biodiversity, carbon footprint, and forestation levels for the Region 3." } ], "id_raw": 115 }, { "id": "IE8k0ZCVuVj53Lnn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in area [0.047, 0.436, 0.082, 0.893]." }, { "from": "gpt", "value": "A single green bar within the chart for Region 1 is depicted, representing the measured level for {Air Quality}[0.047, 0.436, 0.082, 0.893] within that region." } ], "id_raw": 115 }, { "id": "tpwQWGB7m9antodN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the text content within the coordinates [0.268, 0.020, 0.732, 0.056]?" }, { "from": "gpt", "value": "The content is \"Global Trends and Changes Observation (2010 vs 2020)\"." } ], "id_raw": 116 }, { "id": "E9Id2IBzncOANRIj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed within [0.063, 0.277, 0.180, 0.893] in the first subplot?" }, { "from": "gpt", "value": "A {Blue bar}[0.063, 0.277, 0.180, 0.893] representing CO2 Emissions for the year 2010." } ], "id_raw": 116 }, { "id": "Jmx78KdvetrmiYvq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat is described in the coordinates [0.701, 0.745, 0.818, 0.893] from the second subplot?" }, { "from": "gpt", "value": "This is a {red bar}[0.701, 0.745, 0.818, 0.893] representing Sea Level for the year 2011." } ], "id_raw": 116 }, { "id": "KTYbWBfcITUDUp4P", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat is labelled at the coordinate [0.234, 0.912, 0.301, 0.940] in the first subplot?" }, { "from": "gpt", "value": "The label is \"Sea Level\"." } ], "id_raw": 116 }, { "id": "EQsj5p2YaEjgg5va", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nIn the subplot for 2010, is the {bar indicating CO2 Emissions}[0.063, 0.277, 0.180, 0.893] taller than the {bar indicating Forest Cover}[0.355, 0.338, 0.472, 0.893]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 116 }, { "id": "SeaYkGha6Qq11Zjz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nIn the subplot for 2020, does the {bar representing Sea Level}[0.701, 0.745, 0.818, 0.893] have a higher placement compared to the {bar for CO2 Emissions}[0.555, 0.254, 0.672, 0.893]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 116 }, { "id": "HjxJHKoNPWHbPlQr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nIn the subplot for 2010, is the {bar for CO2 Emissions}[0.063, 0.277, 0.180, 0.893] positioned to the left or right of the {bar for Forest Cover}[0.355, 0.338, 0.472, 0.893]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 116 }, { "id": "zb2bs7mU7N1NbVEb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nAre the {bars}[0.063, 0.277, 0.180, 0.893], [0.209, 0.835, 0.326, 0.893], [0.355, 0.338, 0.472, 0.893] in the subplot for 2010 oriented in ascending order from left to right?" }, { "from": "gpt", "value": "No." } ], "id_raw": 116 }, { "id": "KHicBohDOIg7h4ay", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between [0.042, 0.191, 0.492, 0.893]?" }, { "from": "gpt", "value": "It's the area of the first subplot showing the data for the year 2010. In this region, there are three blue bars showing the CO2 Emissions, Sea Level, and Forest Cover. The heights of these bars indicate the data values for each respective category." } ], "id_raw": 116 }, { "id": "qI7GBeQS6qwegGoi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the region [0.509, 0.912, 0.525, 0.208]?" }, { "from": "gpt", "value": "This region includes nine y-axis ticks labeling values from 0 to 40, placed vertically, for the second subplot." } ], "id_raw": 116 }, { "id": "nigABlbbEEzxjeVY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are situated within the coordinates [0.535, 0.191, 0.985, 0.893]?" }, { "from": "gpt", "value": "This is the area containing the second subplot. In this region, there are three red bars representing CO2 Emissions, Sea Level, and Forest Cover for the year 2020. The heights of these bars indicate the data values for each category." } ], "id_raw": 116 }, { "id": "zObxJtMtTD4DJUeW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nDescribe what is visible in the area [0.563, 0.912, 0.665, 0.940]." }, { "from": "gpt", "value": "This area contains the x-axis label for the 'CO2 Emissions' in the second subplot, serving to indicate the corresponding red bar." } ], "id_raw": 116 }, { "id": "33EDtEnlU9lvRUYx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "The title of the chart is \"Artificial Intelligence and Automation Trends Across Regions\", located at the coordinates {title}[0.334, 0.020, 0.666, 0.056]." } ], "id_raw": 117 }, { "id": "sohZB2TUb3H0UN2n", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the data represented by the bar with coordinates [0.813, 0.563, 0.858, 0.893]?" }, { "from": "gpt", "value": "The bar represents the \"AI Patents\" category for \"Region 3\", with a value of 4.7." } ], "id_raw": 117 }, { "id": "G4YVCHvG0TdFDsb6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the first subplot?" }, { "from": "gpt", "value": "The y-axis label of the first subplot is missing as indicated by the empty value {y-axis label}[0.006, 0.542, 0.006, 0.542]." } ], "id_raw": 117 }, { "id": "kmQUFytM2lcL5Z4X", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nIs the value of \"AI Patents\" higher in {Region 1}[0.153, 0.647, 0.198, 0.893] or {Region 2}[0.483, 0.359, 0.528, 0.893]?" }, { "from": "gpt", "value": "Region 2." } ], "id_raw": 117 }, { "id": "XKQ57cds4HhCoxxZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nIs {Region 3's \"AI Research\"}[0.702, 0.619, 0.746, 0.893] higher than {Region 1's \"Automation Use\"}[0.097, 0.507, 0.142, 0.893]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 117 }, { "id": "dEjgGzmsWOV8yotq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nBetween {Region 1}[0.153, 0.647, 0.198, 0.893], {Region 2}[0.483, 0.359, 0.528, 0.893], and {Region 3}[0.813, 0.563, 0.858, 0.893], which has the highest \"AI Patents\" value?" }, { "from": "gpt", "value": "Region 2." } ], "id_raw": 117 }, { "id": "oAm13GBVkycCePlu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within the area [0.688, 0.191, 0.983, 0.893]?" }, { "from": "gpt", "value": "This area corresponds to the third subplot of the chart, it contains the y-axis labels which are numbers indicating the value range, as well as the x-axis labels indicating the categories of artificial intelligence and automation trends, such as \"AI Research\", \"Automation Use\", \"AI Patents\", \"AI Education\", and \"AI Investments\". Each category is represented as a bar. This subplot is titled as \"Region 3 Trends\", located at {title of subplot}[0.792, 0.146, 0.880, 0.182]." } ], "id_raw": 117 }, { "id": "VAitHSqSvkC7Qbkb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the x-axis of the entire graph?" }, { "from": "gpt", "value": "The x-axis stretches from the coordinates [0.028, 0.191] to [0.983, 0.893], and labels for the x-axis are in the form of categories such as \"AI Research\", \"Automation Use\", \"AI Patents\", \"AI Education\", and \"AI Investments\"." } ], "id_raw": 117 }, { "id": "6ZsLL4PSaEtdRWxn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the details within the area [0.358, 0.191, 0.653, 0.893]?" }, { "from": "gpt", "value": "This area represents the second subplot of the chart titled \"Region 2 Trends\" located at the coordinates {title of subplot}[0.462, 0.146, 0.550, 0.182]. It contains bars depicting values related to \"AI Research\", \"Automation Use\", \"AI Patents\", \"AI Education\", and \"AI Investments\" in Region 2. The highest bar is represented by \"AI Patents\" with the coordinate {AI Patents bar}[0.483, 0.359, 0.528, 0.893]." } ], "id_raw": 117 }, { "id": "DXKGo1EZxMrBfMKf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat color is the bar for {the month of January in the Bonds subplot}[0.378, 0.510, 0.396, 1.786]?" }, { "from": "gpt", "value": "Turquoise." } ], "id_raw": 118 }, { "id": "bKxgYWOu6jAbvGCn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis in {the subplot for Commodities}[0.666, 0.542, 0.666, 0.542]?" }, { "from": "gpt", "value": "The y-axis label is not specified." } ], "id_raw": 118 }, { "id": "jnVLGVwe6MnAigwW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the style of the grid in {the Equity subplot}[0.034, 0.191, 0.330, 0.893]?" }, { "from": "gpt", "value": "Dashed." } ], "id_raw": 118 }, { "id": "oJN79fJ2KSzvpWXr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nIs {the value bar for March in Equity}[0.093, 0.580, 0.111, 1.786] higher than {the October bar for Commodities}[0.913, 0.655, 0.931, 1.786]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 118 }, { "id": "zFlPwcTDdAPCCKlX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nIn {the subplot for Bonds}, does {the bar for June}[0.492, 0.452, 0.510, 1.786] have a greater value than {the bar for November}[0.606, 0.283, 0.624, 1.786]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 118 }, { "id": "FBjRtN1yPrnPOUVl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nIs {the first y-tick label in Bonds}[0.346, 0.882, 0.358, 0.910] smaller than {the last y-tick label in Commodities}[0.676, 0.116, 0.688, 0.144]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 118 }, { "id": "KQICBilhp78Aw4pJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within {the Bonds subplot}[0.364, 0.191, 0.660, 0.893]." }, { "from": "gpt", "value": "This region contains various monthly bars depicting financial performance. Each bar, from January to December, represents different value levels, starting from 100.00 to 121.50, with colors transitioning from lighter turquoise to darker as values increase." } ], "id_raw": 118 }, { "id": "GheTbzlOoysc2EBs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in {the Equities subplot area}[0.034, 0.191, 0.330, 0.893]?" }, { "from": "gpt", "value": "There are multiple green bars representing monthly performances from January to December. The bar heights start high in January and decrease throughout the year, indicating a downward trend in values." } ], "id_raw": 118 }, { "id": "UkIfxYKdTF6YhfxA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in {the Commodities subplot}[0.694, 0.191, 0.990, 0.893]." }, { "from": "gpt", "value": "The area consists of red bars representing monthly data from January to December, each indicating different levels of commodity values. The plot includes y-axis labels for specific value markers with a dashed grid background enhancing visibility." } ], "id_raw": 118 }, { "id": "nU59en3yMN2nEnBD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the object labeled 'Supply' on the y-axis in the first subplot?" }, { "from": "gpt", "value": "[0.075, 0.100, 0.134, 0.114]" } ], "id_raw": 120 }, { "id": "WuqSC9L1n6BYCFzh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bars showing 'Current Year' in both subplots?" }, { "from": "gpt", "value": "Orange" } ], "id_raw": 120 }, { "id": "x3uq2hC75kjgxMgM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of the x value for the bar representing the 'Risk' category of 'Current Year' in the second subplot?" }, { "from": "gpt", "value": "'0.00 -> 15.00'," } ], "id_raw": 120 }, { "id": "Oe0qhn4K4TPxSLD3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the bars with the coordinates '[0.522, 0.546, 0.775, 0.586]' in the second subplot?" }, { "from": "gpt", "value": "Risk" } ], "id_raw": 120 }, { "id": "LnX38rU8E90J8cSn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nIs the length of {the 'Opportunity' bar of the 'Current Year'}[0.522, 0.596, 0.893, 0.637] greater than the length of {the 'Risk' bar in 'Current Year'}[0.522, 0.546, 0.775, 0.586] in the second subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 120 }, { "id": "bJ28ro6yxx9R2bmN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Demand' bar in 'Current Year'}[0.522, 0.901, 0.792, 0.941] longer than {the 'Supply' bar in 'Current Year'}[0.522, 0.850, 0.758, 0.891] in the second subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 120 }, { "id": "oCThGBgu44AXx3SP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nDo {the bars of 'Current Year'}[0.522, 0.901, 0.792, 0.941] in the first subplot and the second subplot have the same color?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 120 }, { "id": "MOjDXrYzWAYQhTqa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Competition' category}[0.522, 0.647, 0.724, 0.688] in the second subplot higher than {the 'Deficit' category bar}[0.522, 0.749, 0.690, 0.789]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 120 }, { "id": "rlPQDQMiCriRDpP4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects present in the region [0.147, 0.526, 0.981, 0.961]." }, { "from": "gpt", "value": "The region corresponds to the second subplot. It contains 8 bars depicting 'Current Year' data for each of 8 categories: 'Demand', 'Supply', 'Surplus', 'Deficit', 'Market Share', 'Competition', 'Opportunity', and 'Risk'. Each bar has individual coordinates. Similarly, the second subplot has a x-axis with range labels and a y-axis with category labels. The subplot's title is 'Current Year'." } ], "id_raw": 120 }, { "id": "vPneJOdDqHJ19gxx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.522, 0.238, 0.943, 0.278] in the second subplot contain?" }, { "from": "gpt", "value": "The area [0.522, 0.238, 0.943, 0.278] contains the orange bar that represents the category 'Market Share' in 'Current Year', with the x range of '0.00 -> 25.00'." } ], "id_raw": 120 }, { "id": "Crm0Px0vg8OpdBla", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the coordinates [0.147, 0.015, 0.981, 0.450]." }, { "from": "gpt", "value": "These coordinates correspond to the first subplot. It contains green and orange bars, each depicting 'Previous Year' and 'Current Year' data respectively for each of 8 categories: 'Demand', 'Supply', 'Surplus', 'Deficit', 'Market Share', 'Competition', 'Opportunity', and 'Risk'. Each bar has individual coordinates. The x-axis is number-based while y-axis is labeled with categories." } ], "id_raw": 120 }, { "id": "JVGD1fMryDizTJ5u", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area [0.522, 0.238, 0.606, 0.278]?" }, { "from": "gpt", "value": "This area contains the green bar representing the 'Market Share' category for 'Previous Year' in the first subplot, with an x range of '0.00 -> 5.00'." } ], "id_raw": 120 }, { "id": "ClmKhHsHM8QNx8O1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.548, 0.838, 0.869, 0.898]?" }, { "from": "gpt", "value": "It's a bar representing Google's technology score of 9 points." } ], "id_raw": 127 }, { "id": "B3s8b1LkdhsJYENI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the text at coordinates [0.869, 0.856, 0.925, 0.880]?" }, { "from": "gpt", "value": "It is the \"9 points\" indicating the technology score of Google." } ], "id_raw": 127 }, { "id": "DNcyZ3SGe9ZbjHO8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the second bar chart starting at [0.656, 0.025, 0.868, 0.055]?" }, { "from": "gpt", "value": "The title is \"Technology of Companies\"." } ], "id_raw": 127 }, { "id": "v8x9h8LenthySIFx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nWhich company, from those represented in [0.090, 0.155, 0.447, 0.216] to [0.090, 0.838, 0.340, 0.898] in the Innovation category, achieved a higher score than Apple, located at [0.090, 0.762, 0.269, 0.822]?" }, { "from": "gpt", "value": "Google, with coordinates [0.090, 0.838, 0.340, 0.898]." } ], "id_raw": 127 }, { "id": "xGboJcasR7VyMK4m", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing SpaceX's innovation score at [0.090, 0.231, 0.447, 0.291] have a higher score or lower score than Google shown at [0.090, 0.838, 0.340, 0.898]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 127 }, { "id": "xWtjB4M4BpsGMc06", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nWhich company between {Microsoft}[0.090, 0.686, 0.162, 0.747] and {Facebook}[0.090, 0.458, 0.305, 0.519] has a higher score in the Innovation Category?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 127 }, { "id": "khxr3dlXr3eadP6f", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained in the area marked by [0.548, 0.534, 0.583, 0.595]?" }, { "from": "gpt", "value": "It contains a bar representing Tesla's technology score. The bar is labeled with '1 points' to signify Tesla's score in the technology category, and the coordinates for that text are [0.583, 0.553, 0.639, 0.576]." } ], "id_raw": 127 }, { "id": "yTehKIjnp1bGVruv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nDescribe what is shown in the region [0.548, 0.118, 0.976, 0.935]." }, { "from": "gpt", "value": "This region contains a bar chart titled \"Technology of Companies\" and 10 bars representing the technology scores of different companies. The companies include Google, Apple, Microsoft, Amazon, Tesla, Facebook, Netflix, Twitter, SpaceX, and Uber, with scores ranging from 1 to 9 points." } ], "id_raw": 127 }, { "id": "SHD3O2pwpnKSU8Yw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nWhat can we find in the region [0.090, 0.118, 0.519, 0.935}?" }, { "from": "gpt", "value": "This region includes a bar chart with the title \"Innovation of Companies\". There are 10 bars representing the innovation scores of different companies including Google, Apple, Microsoft, Amazon, Tesla, Facebook, Netflix, Twitter, SpaceX, and Uber. Their scores range from 2 points to 10 points." } ], "id_raw": 127 }, { "id": "F9RubNYmRvUYuAqm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar in the Revised plot for the policy \"Guaranteed Income\" in Human-authored data?" }, { "from": "gpt", "value": "The color is \"#ffff99\" for {the corresponding bar}[0.783, 0.807, 0.875, 0.897]." } ], "id_raw": 128 }, { "id": "QUBp81A6iZVupi7e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat policy is represented by the bar with coordinates [0.365, 0.582, 0.415, 0.672] in the Original plot?" }, { "from": "gpt", "value": "The policy represented is \"Immigration Control\"." } ], "id_raw": 128 }, { "id": "vu1TI0J0P7K5Er5w", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the bar group with coordinates [0.369, 0.807, 0.415, 0.897] in the Original plot?" }, { "from": "gpt", "value": "The label is \"Synthetic Data\"." } ], "id_raw": 128 }, { "id": "ZM2luhhgvTaIcNHx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nWhich policy is represented by the second label on the y-axis?" }, { "from": "gpt", "value": "The policy is \"Renewable Energy Encouragement\" as indicated by the label at {coordinates}[0.015, 0.731, 0.259, 0.754]." } ], "id_raw": 128 }, { "id": "U7dAmziFC1ZQ5QJ3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nIn the Revised plot, is the human-authored data bar for \"Renewable Energy Encouragement\" taller or shorter than the synthetic data bar?" }, { "from": "gpt", "value": "The {human-authored data bar}[0.783, 0.695, 0.898, 0.785] is taller than the {synthetic data bar}[0.692, 0.695, 0.783, 0.785]." } ], "id_raw": 128 }, { "id": "YIsJoC1F1eQZc1ET", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nIn the Original plot, is the \"Tax Hike\" bar for synthetic data negative or positive?" }, { "from": "gpt", "value": "Negative, as can be seen from their {coordinates}[0.342, 0.133, 0.415, 0.223]." } ], "id_raw": 128 }, { "id": "jkmxfsWHk7S0Iys3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nIn the Revised plot, is the \"Public Health\" bar for synthetic data longer than the bar for human-authored data?" }, { "from": "gpt", "value": "No. The {synthetic data bar}[0.724, 0.470, 0.783, 0.560] is shorter than the {human-authored data bar}[0.783, 0.470, 0.935, 0.560]." } ], "id_raw": 128 }, { "id": "2igqaesKa0LANEPE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nIn the Original plot, does the \"Free Education\" policy show a greater negative impact in the synthetic data or the human-authored data?" }, { "from": "gpt", "value": "The synthetic data shows a greater negative impact as indicated by their {bar}[0.351, 0.358, 0.415, 0.448]." } ], "id_raw": 128 }, { "id": "diIOPmUgbjBxFRm1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.597, 0.095, 0.975, 0.935]?" }, { "from": "gpt", "value": "The region includes the right-hand side plot labeled \"Revised\" which contains bars depicting policies in color \"#ffff99\". These policies include \"Guaranteed Income\", \"Renewable Energy Encouragement\", \"Immigration Control\", \"Public Health\", \"Free Education\", \"Early Retirement\", and \"Tax Hike\". Each of these policies has two bars representing human-authored data and synthetic data respectively." } ], "id_raw": 128 }, { "id": "oh6jHQZvZTNmosGT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nDescribe what's happening in the region at [0.268, 0.095, 0.617, 0.935]." }, { "from": "gpt", "value": "The region encompasses the left side plot labeled \"Original\" which includes seven policies, \"Guaranteed Income\", \"Renewable Energy Encouragement\", \"Immigration Control\", \"Public Health\", \"Free Education\", \"Early Retirement\", and \"Tax Hike\". Each of these includes a pair of bars represented data of two types: Synthetic and Human-authored. Every pair of bars represents different values, with the Synthetic Data bars showing values less than 0 while Human-authored data bars represent values larger than 0." } ], "id_raw": 128 }, { "id": "0HQgJmZmFXscwjdO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.783, 0.582, 0.921, 0.672]?" }, { "from": "gpt", "value": "There is a single object within this range: The bar representing human-authored data for the \"Immigration Control\" policy from the \"Revised\" plot." } ], "id_raw": 128 }, { "id": "dk6k477E4WBbLO6l", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within the region [0.415, 0.807, 0.875, 0.897]?" }, { "from": "gpt", "value": "Inside this region, there is a single bar from the \"Revised\" plot, representing the human-authored data for the \"Guaranteed Income\" policy." } ], "id_raw": 128 }, { "id": "vH5BnmJoI9VjpBXK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat's labeled at the coordinate [0.399, 0.025, 0.441, 0.055] in the chart?" }, { "from": "gpt", "value": "Year 2017." } ], "id_raw": 129 }, { "id": "qYjEnNMGoellRHmx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the color stands for at coordinates [0.803, 0.796, 0.924, 0.898]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 129 }, { "id": "b9sVgN8cVGm8UJ61", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nWhich development type does the coordinates [0.056, 0.710, 0.228, 0.733] refer to?" }, { "from": "gpt", "value": "Residential development." } ], "id_raw": 129 }, { "id": "FKoAlOggYyAOYKH5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nIs the improvement in {Public space}[0.278, 0.796, 0.420, 0.898] greater in 2017 or 2018?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 129 }, { "id": "zCQVjDHOpgkzuGAa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nComparing {Transport systems}[0.307, 0.539, 0.420, 0.642] and {Social development}[0.347, 0.155, 0.420, 0.257] in 2017, which one has a greater decline?" }, { "from": "gpt", "value": "Transport systems." } ], "id_raw": 129 }, { "id": "bxNV8RDEF0lOUYYJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nIn terms of increase in 2018, is {Economic development}[0.803, 0.283, 0.884, 0.386] greater than {Residential development}[0.803, 0.668, 0.904, 0.770]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 129 }, { "id": "qalto7hTTw7Ffmo3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat's inside the area bounded by [0.238, 0.118, 0.985, 0.935]?" }, { "from": "gpt", "value": "In this region, there are 2 subplots for the years 2017 and 2018. The 2017 subplot features 6 bars indicating the decrease in different areas ranging from -15% to -35%. The 2018 subplot also has 6 bars but indicates the increase in same areas ranging from 20% to 35%." } ], "id_raw": 129 }, { "id": "QbdDA2zdBsarF6Eq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be found between these coordinates [0.621, 0.118, 0.985, 0.935]?" }, { "from": "gpt", "value": "The right subplot of chart is situated here. It indicates the increases across various areas of development in the year 2018." } ], "id_raw": 129 }, { "id": "UiTyuV55yOxppHGh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.238, 0.118, 0.602, 0.935]?" }, { "from": "gpt", "value": "The left subplot of the chart is positioned in this region. It illustrates the decreases in different areas of development for 2017." } ], "id_raw": 129 }, { "id": "gv385Z7SlFrNFgr7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at this specific coordinate [0.000, 0.345, 0.020, 0.655] represent in the chart?" }, { "from": "gpt", "value": "This text refers to the label Y-axis, titled \"Leadership Scores (%)\"." } ], "id_raw": 135 }, { "id": "zfHaWapRkXBBYaiU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nWhere is the label of the X-axis located for the 'Leadership' category in the bar chart?" }, { "from": "gpt", "value": "The 'Leadership' category for the X-axis is located at this coordinate [0.478, 0.952, 0.562, 0.975]." } ], "id_raw": 135 }, { "id": "lHAn8d1Ej7tZ3Hrx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {the orange bar}[0.848, 0.665, 0.941, 0.935] represent on the item in the legend?" }, { "from": "gpt", "value": "The orange bar represents 'Process Oriented' skill in the legend." } ], "id_raw": 135 }, { "id": "x2ELu6aD7a0ekb5S", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nCan you specify the coordinates for the 'Team Approach' label in the legend?" }, { "from": "gpt", "value": "The 'Team Approach' label in the legend is positioned at [0.850, 0.083, 0.969, 0.106]." } ], "id_raw": 135 }, { "id": "7ZT0cy4s0VQiUJpw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Communication}[0.099, 0.332, 0.192, 0.448] higher than {the bar representing Strategic Thinking}[0.848, 0.313, 0.941, 0.448] in the 'Team Approach' plot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 135 }, { "id": "6Zpl1XGOZ2DubeWI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nConsidering {the bar representing Planning}[0.286, 0.294, 0.380, 0.448] and {the bar representing Leadership}[0.473, 0.352, 0.567, 0.448] in 'Team Approach', which one is taller?" }, { "from": "gpt", "value": "The bar representing Planning." } ], "id_raw": 135 }, { "id": "yVKufTn1IWVqvZBh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for Communication}[0.286, 0.878, 0.380, 0.935] in 'Process Oriented' higher or lower than the {bar for Leadership}[0.473, 0.800, 0.567, 0.935]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 135 }, { "id": "MsnEOGXON6pj7s2F", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nBetween the {bar representing Leadership}[0.473, 0.800, 0.567, 0.935] and {bar representing Strategic Thinking}[0.848, 0.665, 0.941, 0.935] in 'Process Oriented', which has a higher value?" }, { "from": "gpt", "value": "Strategic Thinking." } ], "id_raw": 135 }, { "id": "9nJow8FUrHwsIHwq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nWhat details can you provide about the area of [0.801, 0.074, 0.976, 0.116]?" }, { "from": "gpt", "value": "This area is the legend section of the chart. The Process Oriented category is represented by the color #8d44c8 and is located at [0.796, 0.573, 0.827, 0.589] for the color and [0.839, 0.571, 0.969, 0.594] for the label." } ], "id_raw": 135 }, { "id": "1aRkRbe8DU6HgSOF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nCan you tell me more about what is in the space [0.057, 0.550, 0.983, 0.935]?" }, { "from": "gpt", "value": "The space encompasses the entirety of the 'Process Oriented' bar chart. This includes leader labels, bars representing different categories (Communication, Planning, Leadership, Decision Making, Strategic Thinking), and the related values on the Y-axis labels." } ], "id_raw": 135 }, { "id": "wGpghDtOnjJI78MH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is located in the region [0.286, 0.294, 0.380, 0.448]?" }, { "from": "gpt", "value": "This is the area of the bar that represents the 'Planning' subdivision in the 'Team Approach' category. The bar signifies the range of 0 to 40." } ], "id_raw": 135 }, { "id": "IHXxm5rYaBD894mj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell us about the content in the area [0.057, 0.062, 0.983, 0.448]?" }, { "from": "gpt", "value": "This region includes the entire 'Team Approach' subplot, which contains labels for y-ticks, bars representing scores in different fields such as Communication, Planning, Leadership, Decision Making, and Strategic Thinking. It also includes a subtitle at [0.449, 0.025, 0.591, 0.055]." } ], "id_raw": 135 }, { "id": "8ycHDLDNfQVtwqTM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary object at [0.000, 0.227, 0.020, 0.773]?" }, { "from": "gpt", "value": "The primary object is the chart title, called 'Performance Scores of Tech Companies'" } ], "id_raw": 136 }, { "id": "zXWdfdVgw9BLomdW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the text label for Microsoft in the 'Innovative Businesses' category?" }, { "from": "gpt", "value": "The label is at [0.134, 0.464, 0.204, 0.487]" } ], "id_raw": 136 }, { "id": "ESCLq8IYd31BDEhR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nAt which position can I find the indicator for 'Facebook' in the second subplot?" }, { "from": "gpt", "value": "'Facebook' is located at [0.835, 0.952, 0.907, 0.975]." } ], "id_raw": 136 }, { "id": "Ta4kwL2IkPleXw7d", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nWhere is the '70' on the y-axis in the 'Traditional Businesses' category located?" }, { "from": "gpt", "value": "It is located at [0.026, 0.695, 0.046, 0.718]." } ], "id_raw": 136 }, { "id": "DZWqervY5PGKp6YW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Microsoft for 'Innovative Businesses'}[0.099, 0.101, 0.239, 0.448] taller than {the bar representing Microsoft for 'Traditional Businesses'}[0.099, 0.665, 0.239, 0.935]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 136 }, { "id": "M1A7Br5efAJH05ea", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Facebook bar in the 'Innovative Businesses'}[0.801, 0.109, 0.941, 0.448] placed higher than {the Facebook grouping in the 'Traditional Businesses'}[0.801, 0.681, 0.941, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 136 }, { "id": "ZHEIhSfOOSLTXuxK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nCompared to {the Apple bar in 'Innovative Businesses'}[0.450, 0.120, 0.590, 0.448], where is {the Apple bar in 'Traditional Businesses'}[0.450, 0.723, 0.590, 0.935] positioned?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 136 }, { "id": "Dw0LlqukmFEi6FU1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nIn terms of score, how does {the Microsoft bar in 'Innovative Businesses'}[0.099, 0.101, 0.239, 0.448] compare with {the Microsoft bar in 'Traditional Businesses'}[0.099, 0.665, 0.239, 0.935]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 136 }, { "id": "EvMbHn8Ebt5MbSCt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region defined by [0.057, 0.550, 0.983, 0.935]." }, { "from": "gpt", "value": "This region contains the subplot titled 'Traditional Businesses' including the x and y axis labels and their corresponding values, tick marks, and bars representing the scores of Microsoft [0.099, 0.665, 0.239, 0.935], Apple [0.450, 0.723, 0.590, 0.935] and Facebook [0.801, 0.681, 0.941, 0.935]." } ], "id_raw": 136 }, { "id": "cHEZyabzlidpTuEd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is located in the region [0.057, 0.062, 0.983, 0.448]?" }, { "from": "gpt", "value": "The zone has a subplot titled 'Innovative Businesses'. It contains x and y labels along with their corresponding values, tick marks, and bars representing Microsoft [0.099, 0.101, 0.239, 0.448], Apple [0.450, 0.120, 0.590, 0.448] and Facebook [0.801, 0.109, 0.941, 0.448] scores." } ], "id_raw": 136 }, { "id": "Rl1iOq6ugToOj0bf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region bounded by [0.134, 0.464, 0.204, 0.487]?" }, { "from": "gpt", "value": "This region contains the x-axis label 'Microsoft' for the 'Innovative Businesses' subplot." } ], "id_raw": 136 }, { "id": "qkLDNuXmlwJIVlXR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the area [0.418, 0.512, 0.622, 0.542]?" }, { "from": "gpt", "value": "The sub-plot title 'Traditional Businesses' is anchored in this region." } ], "id_raw": 136 }, { "id": "7b5yhNewcZR2ZMtX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the {subtitle} in the first subplot at [0.499, 0.025, 0.657, 0.055]?" }, { "from": "gpt", "value": "Cultural Heritage." } ], "id_raw": 137 }, { "id": "iaawJTiabhe9evGy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhere is the label for \"60%\" on the x-axis in the second subplot?" }, { "from": "gpt", "value": "[0.568, 0.919, 0.588, 0.942]." } ], "id_raw": 137 }, { "id": "CKalkVV02Pbic9mR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhere is the {bar for 'Evolving Integration'} in the first subplot?" }, { "from": "gpt", "value": "[0.188, 0.163, 0.773, 0.230]." } ], "id_raw": 137 }, { "id": "994qguZ20o81Y3At", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat percentage does the {pink bar in the second subplot for 'Progressive Heritage'} at [0.188, 0.735, 0.513, 0.802] represent?" }, { "from": "gpt", "value": "50%." } ], "id_raw": 137 }, { "id": "HjOlAQacNbKR0xp3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhich is longer, the {bar for 'Manual Integration'} in the first subplot [0.188, 0.332, 0.383, 0.399] or the {bar for 'Manual Integration'} in the second subplot [0.188, 0.819, 0.578, 0.887]?" }, { "from": "gpt", "value": "Second subplot." } ], "id_raw": 137 }, { "id": "4s8wwFwBgiEuTKUB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nAre the {labels on the y-axis of the first subplot}[[0.031, 0.356, 0.177, 0.380], [0.017, 0.272, 0.177, 0.295], [0.021, 0.187, 0.177, 0.211], [0.023, 0.103, 0.177, 0.126]] the same as those on the y-axis of the second subplot[[0.031, 0.844, 0.177, 0.867], [0.017, 0.759, 0.177, 0.783], [0.021, 0.675, 0.177, 0.698], [0.023, 0.590, 0.177, 0.614]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 137 }, { "id": "tMNLmStMenSkvgTW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for 'Regulative Heritage'} at [0.188, 0.078, 0.839, 0.146] longer than the bar for {Evolving Integration} at [0.188, 0.163, 0.773, 0.230]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 137 }, { "id": "uzjV4M3XURdc8yHu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nCompare {the bar for 'Progressive Heritage'} in the first subplot at [0.188, 0.247, 0.643, 0.315] and the second subplot at [0.188, 0.735, 0.513, 0.802], which one is longer?" }, { "from": "gpt", "value": "First subplot." } ], "id_raw": 137 }, { "id": "F54GGdY1isGmY9d1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.188, 0.819, 0.578, 0.887] in the second subplot?" }, { "from": "gpt", "value": "This region contains the pink bar for 'Manual Integration' in the second subplot, representing a percentage of 60." } ], "id_raw": 137 }, { "id": "rcAotFkuya575hSq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.346, 0.083, 0.679, 0.113] represent?" }, { "from": "gpt", "value": "This region partly contains the title \"Strategies\", but it does not contain the full title." } ], "id_raw": 137 }, { "id": "uDnSIwUWQR01iQvL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.188, 0.062, 0.969, 0.415]." }, { "from": "gpt", "value": "The region contains the first subplot titled \"Cultural Heritage\", a horizontal bar chart with the y-axis labelled with strategies: 'Manual Integration', 'Progressive Heritage', 'Evolving Integration', and 'Regulative Heritage', and the x-axis represents the percentage from 0 to 120. The blue bars represent the respective percentages for each strategy." } ], "id_raw": 137 }, { "id": "zdFNlyYkP1Y0np77", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.030, 0.429, 0.050, 0.571] contain?" }, { "from": "gpt", "value": "The region contains the label \"Strategies\" oriented vertically." } ], "id_raw": 137 }, { "id": "e89HG5tloIQpYNFo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the item located at [0.000, 0.346, 0.020, 0.654] represent?" }, { "from": "gpt", "value": "It is the Y-axis label, titled \"Degree of Change (%)\"." } ], "id_raw": 140 }, { "id": "zokSZWyQzyR7LbiJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.409, 0.448]?" }, { "from": "gpt", "value": "It's a tick mark on the x-axis, corresponding to the label \"Erratic behavior\"." } ], "id_raw": 140 }, { "id": "0BaVDuj1o7uSWvCL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the object represented by [0.542, 0.704, 0.720, 0.935] mean in the subplot_1?" }, { "from": "gpt", "value": "It is a bar in the bar chart representing the \"Marginal growth\" category for \"Cultural Change\" with a value of 60." } ], "id_raw": 140 }, { "id": "M9r9b2UsHpllXEyb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the element located at coordinates [0.764, 0.274, 0.941, 0.448] in subplot_0 signify?" }, { "from": "gpt", "value": "It represents a bar representing the category \"Irregular variations\" in \"Social Change\" with a value of 45." } ], "id_raw": 140 }, { "id": "XmGx3837INRhdJ1B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Upward trend category}[0.099, 0.332, 0.276, 0.448] in subplot_0 longer than the bar of {Erratic behavior category}[0.321, 0.294, 0.498, 0.448]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 140 }, { "id": "x7LdvY73SC0nTBXP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhich one is taller, the bar at {Erratic behavior for cultural change}[0.321, 0.800, 0.498, 0.935] or the bar at {Irregular variations for cultural change}[0.764, 0.743, 0.941, 0.935]?" }, { "from": "gpt", "value": "Erratic behavior." } ], "id_raw": 140 }, { "id": "cqucFOUGGjO68N8l", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nAre bars of {Upward trend}[0.099, 0.332, 0.276, 0.448] and {Erratic behavior}[0.321, 0.294, 0.498, 0.448] in subplot_0 shorter than the bar of {Marginal growth}[0.542, 0.178, 0.720, 0.448] in the same subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 140 }, { "id": "6MXmog07TyPJtkVE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nAmong the values for {Social Change} and {Cultural Change}, which one shows a higher degree of change in the {Marginal growth} category for events located at {Marginal growth for social change}[0.542, 0.178, 0.720, 0.448] and {Marginal growth for cultural change}[0.542, 0.704, 0.720, 0.935]?" }, { "from": "gpt", "value": "Cultural Change." } ], "id_raw": 140 }, { "id": "VrLXRFKmKlRC7q0i", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat information is available in the area located at coordinates [0.057, 0.062, 0.983, 0.448]?" }, { "from": "gpt", "value": "It covers the area of the first subplot (subplot_0), which includes a bar chart showing degrees of change (%) for different categories ('Upward trend', 'Erratic behavior', 'Marginal growth', 'Irregular variations') for 'Social Change'." } ], "id_raw": 140 }, { "id": "xdPj86MlyFk5qAJC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.027, 0.849, 0.046, 0.872]?" }, { "from": "gpt", "value": "This area contains a label on the y-axis ticks for subplot_1, which marks the value 20." } ], "id_raw": 140 }, { "id": "TdgU9vCkpITDxccu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements in the region [0.057, 0.550, 0.983, 0.935]." }, { "from": "gpt", "value": "This segment embodies the second subplot (subplot_1), containing a bar graph that presents degrees of change (%) for various categories ('Upward trend', 'Erratic behavior', 'Marginal growth', 'Irregular variations') pertaining to 'Cultural Change'." } ], "id_raw": 140 }, { "id": "B63mUV21N95voDoq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.764, 0.274, 0.941, 0.448] in subplot_0?" }, { "from": "gpt", "value": "A single bar in the bar chart is located in the highlighted area. It denotes the \"Irregular variations\" category with a value of 45 for \"Social Change\"." } ], "id_raw": 140 }, { "id": "tzrh8919Z8eMxElM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.000, 0.238, 0.023, 0.762]?" }, { "from": "gpt", "value": "It's the Y-axis label, indicating \"Popularity of fashion trends (%)\"." } ], "id_raw": 143 }, { "id": "ReZu7qlllcdJ4V9T", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object located at [0.681, 0.256, 0.748, 0.438] in the chart?" }, { "from": "gpt", "value": "Yes, it's a bar from the group \"Hippie Bohemian\", represented in the color #e19e9c and it signifies the popularity of \"Soaring Eagle Style\" trend, which is 50%." } ], "id_raw": 143 }, { "id": "Fcj713y8BaZkfAYb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.019, 0.549, 0.052, 0.577] in the sub-chart pertains to?" }, { "from": "gpt", "value": "It represents a label on the y-axis of the second sub-chart, which states the value \"100\"." } ], "id_raw": 143 }, { "id": "EQGn73qKnzhi1AdG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Graveyard Chic'}[0.681, 0.669, 0.748, 0.923] from {the Punk Rocker group} taller than {the bar for the 'Gothic Frills'}[0.872, 0.632, 0.940, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 143 }, { "id": "MGQUk3ZffPshKcae", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for '60s Fashion'}[0.106, 0.220, 0.173, 0.438] in the {Hippie Bohemian group} shorter than {the bar for the '80s Fashion'}[0.297, 0.365, 0.364, 0.438] in the same group?" }, { "from": "gpt", "value": "No." } ], "id_raw": 143 }, { "id": "s3eTgyOL46TYlbhn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nComparing the {Gothic Frills trend bar}[0.872, 0.111, 0.940, 0.438] for {Hippie Bohemian} and {Punk Rocker}[0.872, 0.632, 0.940, 0.923], which group holds more popularity for this fashion trend?" }, { "from": "gpt", "value": "Hippie Bohemian." } ], "id_raw": 143 }, { "id": "cAbg6ZkT2g6Qkh6S", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements within [0.106, 0.220, 0.173, 0.438]." }, { "from": "gpt", "value": "This region contains a bar that is part of the Hippie Bohemian group. It is colored #e19e9c and represents the '60s Fashion trend which holds a popularity of 60%." } ], "id_raw": 143 }, { "id": "hwoWmJdLum59TjYX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the characteristics of the region [0.681, 0.669, 0.748, 0.923]?" }, { "from": "gpt", "value": "This region encapsulates a bar indicating 'Soaring Eagle Style' fashion trend from the 'Punk Rocker' group. It has a popularity of 70%." } ], "id_raw": 143 }, { "id": "1cV1hkpAVwX72A00", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the box defined by coordinates [0.455, 0.942, 0.590, 0.970]?" }, { "from": "gpt", "value": "This set of coordinates contains a label on the x-axis marking the 'Graveyard Chic' fashion trend in the 'Punk Rocker' sub-chart." } ], "id_raw": 143 }, { "id": "0pMdOMxCncnrwEk9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.000, 0.285, 0.020, 0.715]?" }, { "from": "gpt", "value": "This is the Y-axis label, named \"Energy Production Changes(%)\"." } ], "id_raw": 144 }, { "id": "RqYUlHhxr9LhkGEC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text \"Nuclear Energy\" in the X-axis labels?" }, { "from": "gpt", "value": "[0.838, 0.464, 0.956, 0.487]" } ], "id_raw": 144 }, { "id": "7GyXUeN5oO8CqsB3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within coordinates [0.099, 0.139, 0.187, 0.448]?" }, { "from": "gpt", "value": "It is a bar representing the production of \"Solar Energy\" in the \"Erratic decline\" scenario, having a value of 80." } ], "id_raw": 144 }, { "id": "q2g9q7ycuqJ56zQw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the coordinates [0.838, 0.571, 0.969, 0.594]?" }, { "from": "gpt", "value": "This is the text label \"Gradual increase\" for the green color in the legend." } ], "id_raw": 144 }, { "id": "WtzzTbQIreEMbZjs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nIs the solar energy bar of {Erratic decline}[0.099, 0.139, 0.187, 0.448] taller than the solar energy bar of {Gradual increase}[0.099, 0.781, 0.187, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 144 }, { "id": "4N7Q4yvuDI7kSNLD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend of the energy source in {the bar}[0.853, 0.665, 0.941, 0.935]?" }, { "from": "gpt", "value": "Gradual increase." } ], "id_raw": 144 }, { "id": "T18AeE2Stv7gnd38", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nIs {Wind energy}[0.597, 0.952, 0.695, 0.975] closer to the {Solar energy}[0.094, 0.952, 0.192, 0.975] or {Nuclear energy}[0.838, 0.952, 0.956, 0.975] on the x-axis?" }, { "from": "gpt", "value": "Solar energy." } ], "id_raw": 144 }, { "id": "BM2XAYYCYLfSSsuG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.057, 0.550, 0.983, 0.935]." }, { "from": "gpt", "value": "This region mainly includes a bar chart showing the \"Gradual increase\" scenario. It shows bars representing different energy sources, namely {Solar Energy}[0.099, 0.781, 0.187, 0.935], {Hydro Energy}[0.350, 0.858, 0.438, 0.935], {Wind Energy}[0.602, 0.743, 0.690, 0.935], and {Nuclear Energy}[0.853, 0.665, 0.941, 0.935]." } ], "id_raw": 144 }, { "id": "XkP0WB8i3tvTxx0k", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.099, 0.139, 0.187, 0.448]." }, { "from": "gpt", "value": "Within this region, there is a bar representing the Solar Energy's production change, labeled as \"Erratic decline\" with a value of 80." } ], "id_raw": 144 }, { "id": "dJGUkMnmj8LJnw6L", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.094, 0.952, 0.192, 0.975]?" }, { "from": "gpt", "value": "It contains the X-axis label \"Solar Energy\"." } ], "id_raw": 144 }, { "id": "rFfMtxMUg6Gg7aNM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at [0.000, 0.376, 0.020, 0.624] represent?" }, { "from": "gpt", "value": "The y-axis label represents the Progress Rate (%)." } ], "id_raw": 145 }, { "id": "yaD8b6xkLrWduSNs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the title of the second chart?" }, { "from": "gpt", "value": "[0.723, 0.025, 0.808, 0.055]" } ], "id_raw": 145 }, { "id": "WGmQpSPjOLXl2hYa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat's the category at the first x-axis label in the second chart?" }, { "from": "gpt", "value": "{Manual Planning}[0.554, 0.752, 0.648, 0.893]." } ], "id_raw": 145 }, { "id": "8zcEyJ8e8E4rtoQs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the second bar in the second chart?" }, { "from": "gpt", "value": "{The orange bar in Technology Assisted}[0.651, 0.197, 0.716, 0.735] in the second chart has a progress rate of 80%." } ], "id_raw": 145 }, { "id": "XpUoXbAHXve1MIug", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Automated Planning'}[0.241, 0.130, 0.307, 0.735] in the first chart higher than {the bar for 'Automated Planning'} in the second chart [0.733, 0.332, 0.799, 0.735] ?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 145 }, { "id": "PjhIOaSJYEkQnnow", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Government Intervention'} in the first chart [0.324, 0.466, 0.390, 0.735] shorter than the {the bar for 'Government Intervention'} in the second chart [0.815, 0.197, 0.881, 0.735]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 145 }, { "id": "SriusimxlW5pNSfP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nWhich bar in the second chart is the highest, {the bar for 'Manual Planing'}[0.568, 0.264, 0.634, 0.735] or {the bar for 'Technology Assisted'}[0.651, 0.197, 0.716, 0.735]?" }, { "from": "gpt", "value": "Technology Assisted." } ], "id_raw": 145 }, { "id": "O9BleriUl4N4OMOE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nAre the progress rates for \"Automated Planning\" in both charts the same?" }, { "from": "gpt", "value": "No." } ], "id_raw": 145 }, { "id": "tgT3z6GBtLBRFlaV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat elements are in the area [0.548, 0.062, 0.983, 0.735]?" }, { "from": "gpt", "value": "This area contains the second subplot, which is a bar chart. The title of the chart is \"Economy\"[0.723, 0.025, 0.808, 0.055]. In terms of content, the y-axis has labels ranging from 0 to 100 with an interval of 20, while the x-axis labels are \"Manual Planning\"[0.554, 0.752, 0.648, 0.893], \"Technology Assisted\"[0.623, 0.752, 0.744, 0.933], \"Automated Planning\"[0.705, 0.752, 0.827, 0.935], \"Government Intervention\"[0.774, 0.752, 0.923, 0.975], and \"Total\"[0.912, 0.752, 0.949, 0.807]. The bar with the highest progress rate is the bar for \"Technology Assisted\"[0.651, 0.197, 0.716, 0.735], reaching 80%." } ], "id_raw": 145 }, { "id": "L5DX97w57u6GjQ8g", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.077, 0.332, 0.142, 0.735]." }, { "from": "gpt", "value": "This area contains one single bar in the first subplot, specifically the bar representing \"Manual Planning\". The bar reaches a progress rate of 60." } ], "id_raw": 145 }, { "id": "VfaOafLPMWavEPiH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat elements are located within the coordinates [0.568, 0.264, 0.634, 0.735]?" }, { "from": "gpt", "value": "The region encompasses a single bar depicting the progress rate for \"Manual Planing\" in Economy. This bar reaches a progress rate of 70%." } ], "id_raw": 145 }, { "id": "ld1lZRt4r0hkZqOe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you see within the region [0.733, 0.332, 0.799, 0.735]?" }, { "from": "gpt", "value": "I can see a single bar in the second subplot of the bar chart. This bar represents the progress rate for \"Automated Planning\" in Economy, and achieves a rate of 60%." } ], "id_raw": 145 }, { "id": "yTk2Hu98ObwQuUh6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.000, 0.307, 0.020, 0.693] represent?" }, { "from": "gpt", "value": "It is the title of the y-axis, named Tourist Attractiveness Score." } ], "id_raw": 146 }, { "id": "eyyxKoqetXECoYk2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.888, 0.074, 0.976, 0.116] correspond to?" }, { "from": "gpt", "value": "These coordinates encompass the legend box which provides the color coding for bars representing USA." } ], "id_raw": 146 }, { "id": "Du5FOkmOBRAfNgIx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat is referred to by the coordinates [0.099, 0.869, 0.167, 0.935] in the second subplot?" }, { "from": "gpt", "value": "These coordinates correspond to the bar representing the Sightseeing score of 60 for Canada." } ], "id_raw": 146 }, { "id": "mCtOxVNp2tiaSDgs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nIs {the Sightseeing bar for USA}[0.099, 0.371, 0.167, 0.448] taller than {the Sightseeing bar for Canada}[0.099, 0.869, 0.167, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 146 }, { "id": "YIL7lemgfpYrPEFM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nIs {the total score bar for Canada}[0.873, 0.594, 0.941, 0.935] to the left or right of {the total score bar for USA}[0.873, 0.101, 0.941, 0.448]?" }, { "from": "gpt", "value": "They are at the same position." } ], "id_raw": 146 }, { "id": "yM8tS8Pdi0MxDWBQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nCompared to {the Gastronomy bar for Canada}[0.293, 0.842, 0.360, 0.935], is {the Gastronomy bar for USA}[0.293, 0.360, 0.360, 0.448] taller or shorter?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 146 }, { "id": "PCgIDmCSn6Q8xh4k", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nDescribe what is contained in the area [0.057, 0.062, 0.983, 0.448]." }, { "from": "gpt", "value": "This area includes the subplot for USA tourist attractiveness scores in different categories. It displays five bars representing Sightseeing, Gastronomy, Adventure, Nature, and Total scores, each marked on the X-axis, with a legend at the top right." } ], "id_raw": 146 }, { "id": "TaAuCrIbYecyfhbh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat's inside the area [0.017, 0.108, 0.046, 0.131]?" }, { "from": "gpt", "value": "It contains the y-axis label '300' for the USA subplot." } ], "id_raw": 146 }, { "id": "DLYQzNHEUbudP3OS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects fall within [0.279, 0.360, 0.360, 0.448]?" }, { "from": "gpt", "value": "This region contains a single object, the Gastronomy bar for USA which shows a score of 80." } ], "id_raw": 146 }, { "id": "AHoi1sm0EqW4HSmb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the text in the coordinates [0.000, 0.170, 0.020, 0.830] refer to?" }, { "from": "gpt", "value": "It is the chart's title, called \"Technological Industry Development Trends (%)\"." } ], "id_raw": 148 }, { "id": "ENDbFnyl0lsSMFOT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the respective color of {the first bar}[0.099, 0.197, 0.187, 0.448] and {the last bar}[0.853, 0.773, 0.941, 0.935] in AI Technology subplot?" }, { "from": "gpt", "value": "Both bars are denoted by the color #e19e9c." } ], "id_raw": 148 }, { "id": "5066bLCEu25xYyrq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label at [0.864, 0.952, 0.931, 0.975] say?" }, { "from": "gpt", "value": "The x-axis label is \"VerifNER\"." } ], "id_raw": 148 }, { "id": "nizRNV6SdPEJsjgM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nWhich bar is higher, {the \"Manual Mapping\" bar in AI Technology subplot}[0.099, 0.197, 0.187, 0.448] or {the \"Manual Mapping\" bar in FinTech subplot}[0.099, 0.627, 0.187, 0.935]?" }, { "from": "gpt", "value": "The \"Manual Mapping\" bar in FinTech subplot." } ], "id_raw": 148 }, { "id": "XzZ5A32avJxNW8uR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nIs {the \"LLM-revision\" bar}[0.350, 0.170, 0.438, 0.448] in AI Technology subplot taller or shorter than {the \"LLM-revision w/CoT\" bar}[0.602, 0.255, 0.690, 0.448] in the same subplot?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 148 }, { "id": "FCwZ8J5rSHyTiaa2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nComparing {the \"VerifNER\" bar in AI Technology subplot}[0.853, 0.224, 0.941, 0.448] and {the \"VerifNER\" bar in FinTech subplot}[0.853, 0.773, 0.941, 0.935], which one is taller?" }, { "from": "gpt", "value": "The \"VerifNER\" bar in FinTech subplot." } ], "id_raw": 148 }, { "id": "rAuSnkWkuJQDrLs3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.057, 0.062, 0.983, 0.448]?" }, { "from": "gpt", "value": "This region contains the first subplot, which includes a title \"AI Technology\" at [0.456, 0.025, 0.584, 0.055], four bars with labels (Manual Mapping, LLM-revision, LLM-revision w/CoT, VerifNER), and an x-axis and y-axis with tick labels. The bars indicate the percentage development trends of AI Technology for different mapping methods." } ], "id_raw": 148 }, { "id": "WD1XqBR1Vz9yDiSd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nDescribe what's in the area [0.057, 0.550, 0.983, 0.935]." }, { "from": "gpt", "value": "This area contains the second subplot, which is similar in structure to the first one. It comprises a title \"FinTech\" at [0.486, 0.512, 0.554, 0.542], four bars labeled Manual Mapping, LLM-revision, LLM-revision w/CoT, VerifNER, and axes with tick labels. The bars depict the percentage development trends of FinTech for different mapping strategies." } ], "id_raw": 148 }, { "id": "FUOEfq21LrAqZ03K", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat is present within the region [0.099, 0.197, 0.187, 0.935]?" }, { "from": "gpt", "value": "Two bars are present: the first bar in AI Technology subplot with the range '0.00 -> 65.00' in coordinates [0.099, 0.197, 0.187, 0.448] and the first bar in FinTech subplot with the range '0.00 -> 80.00' in coordinates [0.099, 0.627, 0.187, 0.935]. Both bars represent Manual Mapping method in their respective fields." } ], "id_raw": 148 }, { "id": "0jv46WsRkSFWcMNf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object symbolized by {the rectangles}[0.099, 0.217, 0.219, 0.448]?" }, { "from": "gpt", "value": "{the rectangles}[0.099, 0.217, 0.219, 0.448] symbolizes a bar representing \"Traditional Learning\" with performance in Mathematics." } ], "id_raw": 149 }, { "id": "feJh9DSjaS2P9ElH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the legend handle for E-Learning}[0.773, 0.120, 0.804, 0.136] have?" }, { "from": "gpt", "value": "{The legend handle}[0.773, 0.120, 0.804, 0.136] for E-Learning is colored light orange." } ], "id_raw": 149 }, { "id": "A0W2bcudNfK7Accn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.083, 0.464, 0.235, 0.487] on the x-axis represent?" }, { "from": "gpt", "value": "The label[0.083, 0.464, 0.235, 0.487] represents \"Traditional Learning\"." } ], "id_raw": 149 }, { "id": "QnX51BbTRb6tjkEE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar labeled \"Traditional Learning\"}[0.099, 0.217, 0.219, 0.448] taller than {the bar labeled \"MOOCs\"}[0.821, 0.101, 0.941, 0.448] for Mathematics?" }, { "from": "gpt", "value": "No." } ], "id_raw": 149 }, { "id": "sKDlnv6hviTKuDDM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nIs {the first label on the x-axis}[0.083, 0.464, 0.235, 0.487] further left than {the second label on the x-axis}[0.359, 0.464, 0.441, 0.487]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 149 }, { "id": "TeLOJj5wmYmGd6OW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar with a success rate of 95%}[0.821, 0.569, 0.941, 0.935] located in {the segment for the Science discipline}[0.057, 0.550, 0.983, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 149 }, { "id": "qi1iMd1FTAjIc2FQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area covered by [0.057, 0.062, 0.983, 0.448]." }, { "from": "gpt", "value": "It contains various bars representing the field of Mathematics, broken down by sub-categories like Traditional Learning, E-Learning, Blended Learning, and MOOCs. The labels for each category are located between [0.083, 0.464, 0.235, 0.487], [0.359, 0.464, 0.441, 0.487], [0.573, 0.464, 0.708, 0.487], and [0.853, 0.464, 0.909, 0.487] respectively, with color labels for each learning type in the legend section [0.767, 0.074, 0.976, 0.220]." } ], "id_raw": 149 }, { "id": "yRNwfps8zfVmGOvr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.767, 0.074, 0.976, 0.220]?" }, { "from": "gpt", "value": "This area encompasses the legend of the chart, which entails labels for each learning type plotted within the chart. Specific labels include Traditional Learning, E-Learning, Blended Learning, and MOOCs, with corresponding color handles each located at [0.773, 0.085, 0.804, 0.101], [0.773, 0.120, 0.804, 0.136], [0.773, 0.155, 0.804, 0.171], and [0.773, 0.190, 0.804, 0.206]." } ], "id_raw": 149 }, { "id": "eZI9lontSnPVHOhS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe in the region [0.099, 0.217, 0.941, 0.448]?" }, { "from": "gpt", "value": "This region comprises an array of bars related to Mathematics' success rate for different learning models such as Traditional Learning, E-Learning, Blended Learning, and MOOCs. Each category's end bar coordinates are [0.219, 0.448], [0.460, 0.448], [0.701, 0.448], and [0.941, 0.448] respectively, indicating their final value on the graph." } ], "id_raw": 149 }, { "id": "vKuuEFGCToS8KKtK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.112, 0.525, 0.148, 184.387] in the chart?" }, { "from": "gpt", "value": "It is a bar in the category of 'Early integration' representing the 'Rapid Expansion' category." } ], "id_raw": 150 }, { "id": "aA3yIWdFi06jkPsl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second text in the legend?" }, { "from": "gpt", "value": "[0.473, 0.058, 0.605, 0.082]" } ], "id_raw": 150 }, { "id": "qB5ElVxzuZE4SIQ2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {ylabel} at [0.035, 0.332, 0.066, 0.355]?" }, { "from": "gpt", "value": "The label is '1e-2'." } ], "id_raw": 150 }, { "id": "nLNp2J5sLELqq1Nn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat indicators or categories are shown in [0.246, 0.049, 0.809, 0.091]?" }, { "from": "gpt", "value": "{Early integration}[0.291, 0.058, 0.407, 0.082], {Further integration}[0.473, 0.058, 0.605, 0.082], and {System integration}[0.671, 0.058, 0.804, 0.082]." } ], "id_raw": 150 }, { "id": "1YGIvJY5J6xaQROe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Early integration' bar for 'Gradual Improvement'}[0.257, 0.341, 0.293, 184.387] taller than {the 'System integration' bar for 'Rapid Expansion'}[0.907, 0.525, 0.943, 184.387]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 150 }, { "id": "a2ojkrTsZaZGH5ji", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nAre the bars under the 'Early integration' category generally higher than those under the 'System integration' category?" }, { "from": "gpt", "value": "Uncertain, because the bar heights differ depending on the specific category." } ], "id_raw": 150 }, { "id": "nqvu5ojMhL3W7gcn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Further integration' bar for 'Pronounced Decline'}[0.727, 0.710, 0.763, 184.387] taller than {the 'System integration' bar for 'Pronounced Decline'}[0.763, 0.157, 0.799, 184.387]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 150 }, { "id": "lt5oYu5zK4ZmdVcU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nBetween {the 'Further integration' bars} and {the 'System integration' bars}, which set of bars is higher?" }, { "from": "gpt", "value": "The comparison is subjective to the categories in which the bars lie. There isn't a definite answer." } ], "id_raw": 150 }, { "id": "LzoKlBGK3xznlKWT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.246, 0.049, 0.809, 0.091]." }, { "from": "gpt", "value": "This area includes the legend of the chart, featuring three categories: {Early integration}[0.252, 0.061, 0.280, 0.077], {Further integration}[0.435, 0.061, 0.462, 0.077] and {System integration}[0.632, 0.061, 0.660, 0.077]. Each category is associated with a unique color code." } ], "id_raw": 150 }, { "id": "rtGpDN3gfDHlZ14t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.071, 0.129, 0.985, 0.737]." }, { "from": "gpt", "value": "This is the main region of the chart where all the bar data are plotted. Several bars are placed under each of the labels along the x-axis representing different categories of growth. Each color of the bars corresponds to a type of integration shown in the legend." } ], "id_raw": 150 }, { "id": "XFthpsGmFFk0uBHG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.112, 0.525, 0.148, 184.387]?" }, { "from": "gpt", "value": "There is one purple bar which indicates the value of the 'Rapid Expansion' category in the 'Early integration' set." } ], "id_raw": 150 }, { "id": "hRYCD3Ucv29OCizY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.401, 0.157, 0.437, 184.387]?" }, { "from": "gpt", "value": "Within this area, there's a purple bar that represents the 'Erratic Growth' category in the 'Early integration' set." } ], "id_raw": 150 }, { "id": "RtUHc8lQp7ibbDJu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the legend corresponding to the handle color '#3c0f64'?" }, { "from": "gpt", "value": "{RBF_INT}[0.398, 0.029, 0.455, 0.053]" } ], "id_raw": 151 }, { "id": "euHleczFhxg0TT5Q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be noticed from the coordinates [0.474, 0.831, 0.516, 1092.651]?" }, { "from": "gpt", "value": "It is the bar of {Unemployment_Rate} for {RBF_INT}[0.473, 0.831, 0.516, 1092.651]." } ], "id_raw": 151 }, { "id": "MkhwTgJzkVaU52hx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.833, 0.919, 0.927, 0.942]?" }, { "from": "gpt", "value": "The label for the x-axis item, which is {Stock_Market}[0.833, 0.919, 0.927, 0.942]." } ], "id_raw": 151 }, { "id": "nX18ajlU8atmwvzi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, the {RBF_INT}'s GDP bar[0.131, 0.264, 0.174, 1092.651] or the {RBF_POL}'s GDP bar[0.174, 0.128, 0.217, 1092.651]?" }, { "from": "gpt", "value": "{RBF_POL}'s GDP bar." } ], "id_raw": 151 }, { "id": "y6nyCkENf7u2bxmX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nWhich indicator has the highest value for {RBF_COM}?" }, { "from": "gpt", "value": "{Interest_Rate}[0.730, 0.683, 0.773, 1092.651]." } ], "id_raw": 151 }, { "id": "4qzFfmwuf3ULIqGB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nIs the {RBF_POL}'s Unemployment_Rate bar[0.516, 0.873, 0.559, 1092.651] located to the left or right of the {RBF_INT}'s Unemployment_Rate bar[0.473, 0.831, 0.516, 1092.651]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 151 }, { "id": "ZBUEBMdOPZgYdW7m", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.353, 0.020, 0.722, 0.062]." }, { "from": "gpt", "value": "The region contains a legend with three labeled handles. They are: \n1. {RBF_INT}[0.398, 0.029, 0.455, 0.053] with color '#3c0f64' represented by a color box at [0.359, 0.031, 0.387, 0.048]; \n2. {RBF_POL}[0.522, 0.029, 0.583, 0.053] with color '#ae4256' represented by a color box at [0.483, 0.031, 0.510, 0.048], and \n3. {RBF_COM}[0.650, 0.029, 0.716, 0.053] with color '#e47f37' represented by a color box at [0.611, 0.031, 0.638, 0.048]." } ], "id_raw": 151 }, { "id": "UHulci8eJPZGU68q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.131, 0.264, 0.174, 1092.651] consist of?" }, { "from": "gpt", "value": "It consists of a bar representing the GDP for {RBF_INT}[0.131, 0.264, 0.174, 1092.651]." } ], "id_raw": 151 }, { "id": "SHywCfNsT5UJY4X6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.663, 0.919, 0.755, 0.942]?" }, { "from": "gpt", "value": "The x-axis label for the category of {Interest_Rate}[0.663, 0.919, 0.755, 0.942]." } ], "id_raw": 151 }, { "id": "fxUi9W9v3nl9MO0n", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinate [0.015, 0.432, 0.029, 0.569] signify?" }, { "from": "gpt", "value": "This is the label for the Y-axis, which represents the 'Return Rate'." } ], "id_raw": 152 }, { "id": "bVczi4MhksG7sdvH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat's at the coordinates [0.302, 0.031, 0.330, 0.048]?" }, { "from": "gpt", "value": "This is the color label of the investment method 'Tech Startup' in the legend." } ], "id_raw": 152 }, { "id": "WGtWGuUGPKrzNJxV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the coordinate [0.341, 0.029, 0.428, 0.053]?" }, { "from": "gpt", "value": "It's the text label 'Tech Startup' in the legend." } ], "id_raw": 152 }, { "id": "SEK1GzmYB9WpCqm6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the object at [0.633, 0.968, 0.696, 0.990]?" }, { "from": "gpt", "value": "It's an x-axis label, that signifies 'Slow Expansion'." } ], "id_raw": 152 }, { "id": "ELNjR54ekm6wsE71", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {Real Estate in Substantial decrease}[0.266, 0.313, 0.296, 573.254] or {Tech Startup in Substantial decrease}[0.235, 0.473, 0.266, 573.254]?" }, { "from": "gpt", "value": "Tech Startup in Substantial decrease." } ], "id_raw": 152 }, { "id": "jXndVt0GyEYDHTwe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nDoes the {Crypto Currency bar during Slow expansion}[0.420, 0.839, 0.451, 573.254] perform better than the {Real Estate bar during Slow expansion}[0.358, 0.701, 0.389, 573.254]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 152 }, { "id": "NKQLtiyyavpSumaT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nBetween {Real Estate in Upward trend}[0.512, 0.327, 0.543, 573.254] and {Crypto Currency in Upward trend}[0.543, 0.199, 0.574, 573.254], which one experienced greater return rates?" }, { "from": "gpt", "value": "Real Estate." } ], "id_raw": 152 }, { "id": "JLB4K7Rop5eHlTll", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nIs the return rate for {Tech Startup during Gradual recovery}[0.605, 0.646, 0.635, 573.254] higher than it was during {Substantial decrease}[0.235, 0.473, 0.266, 573.254]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 152 }, { "id": "VMNjdU6MND3Ia1Jp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.296, 0.020, 0.759, 0.062]." }, { "from": "gpt", "value": "This area contains the chart's legend with three sets of color-text labels. The sets represent 'Tech Startup' with a purplish color label at [0.302, 0.031, 0.330, 0.048], 'Real Estate' with a reddish color label at [0.456, 0.031, 0.483, 0.048], and 'Crypto Currency' with a brownish color label at [0.601, 0.031, 0.628, 0.048]." } ], "id_raw": 152 }, { "id": "HDUJidxOZE0IpxDC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.123, 0.919, 0.193, 0.942]." }, { "from": "gpt", "value": "It contains the x-axis label 'Sharp rise'." } ], "id_raw": 152 }, { "id": "ubFSxrx0tOIbU7QB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.235, 0.473, 0.266, 573.254]?" }, { "from": "gpt", "value": "There's a vertical bar representing the return rate of a 'Tech Startup' under the condition of 'Substantial decrease'." } ], "id_raw": 152 }, { "id": "6hVnDH1iaTxl0Zhz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.112, 0.356, 0.142, 573.254]?" }, { "from": "gpt", "value": "In this area, you can see a bar representing a 'Tech Startup' during a 'Sharp rise' scenario in the bar chart." } ], "id_raw": 152 }, { "id": "ViC8hzn5WEYjHixD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label in the chart with coordinates [0.490, 0.952, 0.565, 0.975]?" }, { "from": "gpt", "value": "Continents." } ], "id_raw": 153 }, { "id": "ynyVXIGyCSKWwz0K", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the y-axis label with coordinates [0.015, 0.466, 0.029, 0.536]?" }, { "from": "gpt", "value": "log L2." } ], "id_raw": 153 }, { "id": "HrgLbAcpwUSGmLNM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the bar}[0.112, 0.116, 0.148, 240.664]?" }, { "from": "gpt", "value": "It represents the \"Spatial Expansion\" for North America." } ], "id_raw": 153 }, { "id": "XvwjO8scYlOZvoiv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content presented at the coordinates [0.287, 0.029, 0.411, 0.053]?" }, { "from": "gpt", "value": "Spatial Expansion {label}<{the handle}[0.248, 0.031, 0.276, 0.048]>." } ], "id_raw": 153 }, { "id": "8IwF7NIUqJz64BNj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Africa in group 0}[0.401, 0.133, 0.437, 240.664] taller or shorter than {the bar for Africa in group 1}[0.437, 0.603, 0.474, 240.664]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 153 }, { "id": "8zkgdEj5muiTQO0y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nAre {the Asia bars of group 0 and 1}[[0.546, 0.122, 0.582, 240.664][0.582, 0.628, 0.618, 240.664]] higher or lower than {the Europe's bars of group 0 and 1}[[0.690, 0.116, 0.727, 240.664][0.727, 0.621, 0.763, 240.664]]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 153 }, { "id": "cd6zABap5ItJ5Qhj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Spatial Decrease in Australia}[0.871, 0.635, 0.907, 240.664] and {Spatial Improvement in Australia}[0.907, 0.502, 0.943, 240.664], which one is higher in value?" }, { "from": "gpt", "value": "Spatial Improvement." } ], "id_raw": 153 }, { "id": "VxYRL1PNpctAgEQk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nOf the {labels} [0.287, 0.029, 0.411, 0.053], [0.477, 0.029, 0.595, 0.053], [0.662, 0.029, 0.808, 0.053]. Which one appears first?" }, { "from": "gpt", "value": "Spatial Expansion." } ], "id_raw": 153 }, { "id": "GduVIQFDpX6OqAhT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.242, 0.020, 0.813, 0.062]." }, { "from": "gpt", "value": "This region contains 3 pairs of color-text labels within the legend. They are Spatial Expansion colored purple {label}[0.287, 0.029, 0.411, 0.053]{handle}[0.248, 0.031, 0.276, 0.048], Spatial Decrease colored red {label}[0.477, 0.029, 0.595, 0.053]{handle}[0.439, 0.031, 0.466, 0.048], Spatial Improvement colored orange {label}[0.662, 0.029, 0.808, 0.053]{handle}[0.623, 0.031, 0.651, 0.048]." } ], "id_raw": 153 }, { "id": "qHmJcVVccAGlxmob", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the coordinates [0.112, 0.116, 0.148, 240.664]?" }, { "from": "gpt", "value": "This region represents a bar {the bar}[0.112, 0.116, 0.148, 240.664] showing the value of \"Spatial Expansion\" for \"North America\" in the bar chart." } ], "id_raw": 153 }, { "id": "zjegPLaha91mudcJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within [0.401, 0.133, 0.474, 240.664]?" }, { "from": "gpt", "value": "There are two vertical bars within this region. The first bar is colored purple {the bar}[0.401, 0.133, 0.437, 240.664], represents the \"Spatial Expansion\" for Africa. The second bar is colored red {the bar}[0.437, 0.603, 0.474, 240.664], representing the \"Spatial Decrease\" for Africa. Both bars are from the bar chart and refer to Africa." } ], "id_raw": 153 }, { "id": "HyahOLqVTrrGqSfD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in [0.858, 0.919, 0.920, 0.942]." }, { "from": "gpt", "value": "It contains the x-axis label \"Australia\" {label}[0.858, 0.919, 0.920, 0.942]." } ], "id_raw": 153 }, { "id": "id0Lxz4Sr1GA0oVA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.015, 0.435, 0.029, 0.566]?" }, { "from": "gpt", "value": "It's the Y-axis label, which represents \"Percentage\"." } ], "id_raw": 154 }, { "id": "zhv44C9liPLWwqln", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinate of the legend text 'Fast Food Industry'?" }, { "from": "gpt", "value": "Yes, the coordinates are [0.455, 0.029, 0.582, 0.053]." } ], "id_raw": 154 }, { "id": "fdmLki6oQIk0z1Ck", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.501, 0.952, 0.546, 0.975]?" }, { "from": "gpt", "value": "Trends." } ], "id_raw": 154 }, { "id": "IMyPn4qIG9jgNVBh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat categories are outlined in [0.251, 0.020, 0.795, 0.062]?" }, { "from": "gpt", "value": "The area refers to the legend consisting of {Tech Industry}[0.296, 0.029, 0.388, 0.053], {Fast Food Industry}[0.455, 0.029, 0.582, 0.053], {Auto Industry}[0.649, 0.029, 0.789, 0.053]." } ], "id_raw": 154 }, { "id": "greAsXiLSWlQ555H", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nIs the {bar of Fast Food Industry with coordinates [0.383, 0.874, 0.439, 825.220] for Continuous Decline} higher or shorter than the {bar of Automobile Industry with coordinates [0.439, 0.480, 0.495, 825.220] for the same trend}?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 154 }, { "id": "QhlCrq2Jcz0RzBwT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar representing \"Automobile Industry\"}[0.887, 0.625, 0.943, 825.220] in \"Sustained Decline\" hold a higher percentage than the {bar representing \"Fast Food Industry\"}[0.831, 0.625, 0.887, 825.220] in the same category?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 154 }, { "id": "F6LPIJaQvxWjT6Ux", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing the \"Tech Industry\"}[[0.103, 0.376, 0.159, 825.220], [0.327, 0.625, 0.383, 825.220], [0.551, 0.176, 0.607, 825.220], [0.775, 0.874, 0.831, 825.220]] generally higher or lower than the {bars representing the \"Fast Food Industry\"}[[0.159, 0.231, 0.215, 825.220], [0.383, 0.874, 0.439, 825.220], [0.607, 0.128, 0.663, 825.220], [0.831, 0.625, 0.887, 825.220]]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 154 }, { "id": "GWOjPivGaKbhcLSP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nBetween {Fast Food Industry}[[0.159, 0.231, 0.215, 825.220], [0.383, 0.874, 0.439, 825.220], [0.607, 0.128, 0.663, 825.220], [0.831, 0.625, 0.887, 825.220]] and {Automobile Industry}[[0.215, 0.296, 0.271, 825.220], [0.439, 0.480, 0.495, 825.220], [0.663, 0.231, 0.719, 825.220], [0.887, 0.625, 0.943, 825.220]], which industry shows a higher value for \"Continous Rise\" category?" }, { "from": "gpt", "value": "Fast Food Industry." } ], "id_raw": 154 }, { "id": "ATiPwNNWBZaIzjgc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content inside [0.251, 0.020, 0.795, 0.062]?" }, { "from": "gpt", "value": "This area represents the chart legend and includes three color-text labels. The first label, \"Tech Industry,\" is assigned the color #3c0f64 at [0.257, 0.031, 0.285, 0.048] with text coordinates [0.296, 0.029, 0.388, 0.053]; the second label, \"Fast Food Industry\", is assigned the color #ae4256 at [0.416, 0.031, 0.444, 0.048] with text coordinates [0.455, 0.029, 0.582, 0.053]; the third label, \"Automobile Industry\", is assigned the color #e47f37 at [0.610, 0.031, 0.638, 0.048] with text coordinates [0.649, 0.029, 0.789, 0.053]." } ], "id_raw": 154 }, { "id": "EAyUtdsZuNgVPnCQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the region [0.103, 0.376, 0.159, 825.220]?" }, { "from": "gpt", "value": "The region contains a bar of the Tech Industry which represents the trend \"Minimal Decline\" with a value of 0.04." } ], "id_raw": 154 }, { "id": "pg9vO2DbMThvQ6Lu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.523, 0.077, 0.523, 0.077]?" }, { "from": "gpt", "value": "There isn't any significant detail or object located in this specific coordinate. It seems to be an empty space in the chart." } ], "id_raw": 154 }, { "id": "3SY5Yzndl5BxHZBF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe what's within [0.797, 0.919, 0.922, 0.942]." }, { "from": "gpt", "value": "This area contains a label \"Sustained Decline\" on the X-axis." } ], "id_raw": 154 }, { "id": "8fPabcC7bbdJcWdu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the object with coordinates [0.615, 0.031, 0.642, 0.048] in the chart?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 155 }, { "id": "NRnCkmJM95mXa1xd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the category located at the coordinates [0.827, 0.919, 0.955, 0.942]?" }, { "from": "gpt", "value": "Sports & Outdoors." } ], "id_raw": 155 }, { "id": "gdrY2sdtT1nxZsZY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the text labeled at {coordinates}[0.402, 0.029, 0.461, 0.053]?" }, { "from": "gpt", "value": "Q1 2021." } ], "id_raw": 155 }, { "id": "PApUIn1OkgAnW9pD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the sales revenue represented at {coordinates}[0.661, 0.874, 0.697, 2130.183]?" }, { "from": "gpt", "value": "200,000." } ], "id_raw": 155 }, { "id": "XhsCs2y6GyJCQ7qb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nAre the sales revenues represented by {Q3 bars}{'bar_coordinates': '[0.202, 0.532, 0.237, 2130.183]'}, {'bar_coordinates': '[0.379, 0.765, 0.414, 2130.183]'}, {'bar_coordinates': '[0.555, 0.213, 0.591, 2130.183]'}, {'bar_coordinates': '[0.732, 0.786, 0.768, 2130.183]'}, {'bar_coordinates': '[0.909, 0.548, 0.944, 2130.183]'}] higher than those represented by {Q2 bars}{'bar_coordinates': '[0.166, 0.427, 0.202, 2130.183]'}, {'bar_coordinates': '[0.343, 0.687, 0.379, 2130.183]'}, {'bar_coordinates': '[0.520, 0.128, 0.555, 2130.183]'}, {'bar_coordinates': '[0.697, 0.745, 0.732, 2130.183]'}, {'bar_coordinates': '[0.874, 0.581, 0.909, 2130.183]'}?" }, { "from": "gpt", "value": "No." } ], "id_raw": 155 }, { "id": "DF2VolEW4Gmy4ped", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nIs {the Q1 2021 bar}[0.485, 0.236, 0.520, 2130.183] taller than {the Q3 2021 bar}[0.555, 0.213, 0.591, 2130.183]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 155 }, { "id": "skKvSm7ZQSxbAE2u", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nIs the {Q1 2021 bar for Electronics}[0.131, 0.501, 0.166, 2130.183] higher than the {Q3 2021 bar for Fashion}[0.379, 0.765, 0.414, 2130.183]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 155 }, { "id": "aw8pdCJNIUONz2uf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nBetween the bars representing {Q1 2021}[0.402, 0.029, 0.461, 0.053] and {Q2 2021}[0.528, 0.029, 0.587, 0.053], which one has a higher sales revenue at the Fashion category?" }, { "from": "gpt", "value": "Q2 2021." } ], "id_raw": 155 }, { "id": "TY4Zewl3r5ERK1xg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.357, 0.020, 0.718, 0.062]." }, { "from": "gpt", "value": "It's the legend of the chart with three categories: Q1 2021 with color code at [0.363, 0.031, 0.391, 0.048] in purple, Q2 2021 with color code at [0.489, 0.031, 0.517, 0.048] in red, and Q3 2021 with color code at [0.615, 0.031, 0.642, 0.048] in orange." } ], "id_raw": 155 }, { "id": "6zk1o8Sic4BPjsS6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.090, 0.090, 0.985, 0.911]." }, { "from": "gpt", "value": "This area displayed the actual plot of the chart. It includes 3 sets of colored bars representing sales revenues in Q1, Q2, and Q3 of 2021 respectively for five sales categories, including Electronics, Fashion, Grocery, Health & Beauty, and Sports & Outdoors." } ], "id_raw": 155 }, { "id": "smHT3z66nsbS3Lpa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.485, 0.236, 0.591, 2130.183]?" }, { "from": "gpt", "value": "This area features three bars indicating the sales revenues of the Grocery category across three time periods. Specifically, Q1 2021 (purple), Q2 2021 (red), and Q3 2021 (orange)." } ], "id_raw": 155 }, { "id": "6BOd80EwwbvplKnI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.657, 0.919, 0.772, 0.942]?" }, { "from": "gpt", "value": "There is a labeled text \"Health & Beauty\", which represents a sales category on the chart." } ], "id_raw": 155 }, { "id": "oBYQZq9UErOYxQRM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at [0.015, 0.407, 0.029, 0.529]?" }, { "from": "gpt", "value": "Y-axis label, called log Impact." } ], "id_raw": 156 }, { "id": "eWOf7yn4EMB38MV4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the indicator \"Change in Forests\" in the legend?" }, { "from": "gpt", "value": "It is located at [0.725, 0.185, 0.849, 0.209]." } ], "id_raw": 156 }, { "id": "ZoPfiIW02whCQ5zv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {this item in the legend}[0.686, 0.118, 0.714, 0.134]?" }, { "from": "gpt", "value": "That is the color of the \"Emissions from Agriculture\" bars." } ], "id_raw": 156 }, { "id": "pqvgrhdX07KoTg6x", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the representations of [0.269, 0.919, 0.311, 0.942] and [0.747, 0.919, 0.783, 0.942]?" }, { "from": "gpt", "value": "These are the x-axis labels \"Urban\" and \"Rural\" respectively." } ], "id_raw": 156 }, { "id": "iVwgni30KzkSVlxj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nHow does the height of {the \"Urban\" \"Greenhouse Gases\" bar}[0.112, 0.065, 0.183, 804.689] compare to {the \"Rural\" \"Greenhouse Gases\" bar}[0.587, 0.308, 0.658, 804.689]?" }, { "from": "gpt", "value": "The \"Urban\" bar is taller." } ], "id_raw": 156 }, { "id": "8ijzjEzvJ3NVOTwq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Emissions from Deforestation\" in Urban}[0.183, 0.143, 0.254, 804.689] taller than {the one in Rural}[0.658, 0.486, 0.730, 804.689]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 156 }, { "id": "z6jwERV49rJd48Pf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nRegarding \"Industrial Process\" emissions, is the {Urban bar}[0.326, 0.308, 0.397, 804.689] higher than the {Rural bar}[0.801, 0.486, 0.872, 804.689]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 156 }, { "id": "pQZ0t4ONlIrkiKWj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nConsidering the {Rural bar for \"Change in Forests\"}[0.872, 0.871, 0.943, 804.689] and {Urban bar for \"Greenhouse Gases\"}[0.112, 0.065, 0.183, 804.689] which one is taller?" }, { "from": "gpt", "value": "The Urban bar for \"Greenhouse Gases\" is taller." } ], "id_raw": 156 }, { "id": "ghM5ohrmCOBC9HEZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content between [0.681, 0.037, 0.978, 0.218]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It includes the color reference and the corresponding indicator for \"Greenhouse Gases\", \"Emissions from Deforestation\", \"Emissions from Agriculture\", \"Emissions from Industrial Processes\", and \"Change in Forests\"." } ], "id_raw": 156 }, { "id": "lHndUdKq8lTsbjia", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.112, 0.065, 0.183, 804.689] contain?" }, { "from": "gpt", "value": "It contains the bar representing the \"Urban\" area's \"Greenhouse Gases\" emissions." } ], "id_raw": 156 }, { "id": "98YKKZOJ3QsQnPdS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat is within [0.183, 0.143, 0.254, 804.689]?" }, { "from": "gpt", "value": "This region contains the bar representing \"Emissions from Deforestation\" in the \"Urban\" area." } ], "id_raw": 156 }, { "id": "kERK7fcQbWLtfDt4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat details can you give about the region [0.326, 0.308, 0.397, 804.689]?" }, { "from": "gpt", "value": "This area has a bar that corresponds to the \"Emissions from Industrial Processes\" in the \"Urban\" area." } ], "id_raw": 156 }, { "id": "dNHIyreM9wP4xr4A", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.517, 0.952, 0.566, 0.975]?" }, { "from": "gpt", "value": "The label is {Indices}[0.517, 0.952, 0.566, 0.975]." } ], "id_raw": 157 }, { "id": "phJlQpPWtirMADoH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the item labeled \"Initial\" in the legend?" }, { "from": "gpt", "value": "The coordinates are [0.492, 0.030, 0.531, 0.053]." } ], "id_raw": 157 }, { "id": "GkjC8UnB5SX0W7h4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis?" }, { "from": "gpt", "value": "The label is {log Change}[0.015, 0.443, 0.029, 0.574]." } ], "id_raw": 157 }, { "id": "u9rnw0tEtOdz8JiW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the category at coordinate [0.673, 0.919, 0.753, 0.942]?" }, { "from": "gpt", "value": "The category is {Commodity}[0.673, 0.919, 0.753, 0.942]." } ], "id_raw": 157 }, { "id": "61oNtXjIbGDy3yEc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Initial bar for Equity}<{Initial bar for Equity}[0.139, 0.755, 0.199, 885.059]> higher than {the Final bar for Equity}<{Final bar for Equity}[0.199, 0.137, 0.259, 885.059]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 157 }, { "id": "EaCHHUUBKMTksWMm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nBetween the bar representing {Commodity} in the Initial and Final states, which is taller?" }, { "from": "gpt", "value": "{Final state}[0.713, 0.137, 0.773, 885.059]." } ], "id_raw": 157 }, { "id": "Hvx4DQ1qAfk2SKos", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Initial bar for Bond}<{Initial bar for Bond}[0.310, 0.645, 0.370, 885.059]> similar in height to {the Final bar for Futures}<{Final bar for Futures}[0.885, 0.163, 0.945, 885.059]>?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 157 }, { "id": "mH4IBD1t54KwMYxQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Initial bar for Forex}<{Initial bar for Forex}[0.482, 0.559, 0.542, 885.059]> taller than {the Final bar for Commodity}<{Final bar for Commodity}[0.713, 0.137, 0.773, 885.059]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 157 }, { "id": "GrzuFepAZIqzFjSP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what you see in the coordinates [0.448, 0.021, 0.635, 0.063]?" }, { "from": "gpt", "value": "This area is the legend of the chart. There seems to be two items here: {Initial}[0.492, 0.030, 0.531, 0.053] associated with a {color handle}[0.454, 0.032, 0.481, 0.048] and {Final}[0.598, 0.030, 0.630, 0.053] with another {color handle}[0.559, 0.032, 0.587, 0.048]. The color handle of Initial is #234361(blue) and for Final is #bebf2b(yellow)." } ], "id_raw": 157 }, { "id": "IJEwUOv1o26CYF16", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the labels in the area [0.072, 0.103, 0.093, 0.942]." }, { "from": "gpt", "value": "This area contains the labels for the y- coordinates, ranging from 0.2[0.072, 0.902, 0.093, 0.925] to 1.6[0.071, 0.103, 0.093, 0.126]." } ], "id_raw": 157 }, { "id": "wZEiiS1jzBvtkw94", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.653, 0.137, 0.885, 885.059]?" }, { "from": "gpt", "value": "There are two bars in this region: the {Initial bar for Commodity}[0.653, 0.489, 0.713, 885.059] and the {Final bar for Commodity}[0.713, 0.137, 0.773, 885.059]." } ], "id_raw": 157 }, { "id": "BBJfRXoa3eJUdEqf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat elements are included in the coordinates area [0.370, 0.137, 0.430, 885.059]?" }, { "from": "gpt", "value": "There's a single bar in this specified area, which is designated to the category {Bond} for the {Final}[0.370, 0.137, 0.430, 885.059] data." } ], "id_raw": 157 }, { "id": "HsZQnjcXy8RRPIEx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinate [0.503, 0.952, 0.548, 0.975]?" }, { "from": "gpt", "value": "The x-axis label, called Trends." } ], "id_raw": 158 }, { "id": "qlNZdoPUXaCbqMEi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.035, 0.792, 0.061, 0.815]?" }, { "from": "gpt", "value": "The y-axis tick label, labeled 100." } ], "id_raw": 158 }, { "id": "7ZhEXFiDrtwRk4bq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the Trend at coordinate [0.564, 0.919, 0.713, 0.942]?" }, { "from": "gpt", "value": "Pronounced decrease." } ], "id_raw": 158 }, { "id": "3BiOM71L4wWpdRt4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of this color in the legend at [0.534, 0.031, 0.562, 0.048] ?" }, { "from": "gpt", "value": "{Predictions color} at [0.534, 0.031, 0.562, 0.048]." } ], "id_raw": 158 }, { "id": "CF75IjoyRAKu5a0j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Pronounced decrease\" Forecasts}[0.559, 0.603, 0.638, 0.911] higher or lower than {the bar for \"Pronounced decrease\" Predictions}[0.638, 0.691, 0.717, 0.911]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 158 }, { "id": "Mm9EOuPvfGaTGZqt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nWhich bar is to the left of the { bar for \"Upward trajectory\" Predictions} at [0.412, 0.129, 0.492, 0.911]?" }, { "from": "gpt", "value": "{The bar for \"Upward trajectory\" Forecasts} at [0.333, 0.163, 0.412, 0.911]." } ], "id_raw": 158 }, { "id": "Lmx4b0UzEDgtpeM2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nDid the \"Continuous decrease\" of the {Forecast bar}[0.108, 0.361, 0.187, 0.911] occur before the {Predictions bar}[0.187, 0.196, 0.266, 0.911]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 158 }, { "id": "idSvyBjXjkSbyphl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nBetween {Forecast bar for \"Erratic decline\"}[0.785, 0.548, 0.864, 0.911] and {Predictions bar for \"Erratic decline\"}[0.864, 0.515, 0.943, 0.911], which bar represents a higher value?" }, { "from": "gpt", "value": "{Predictions bar for \"Erratic decline\"}[0.864, 0.515, 0.943, 0.911]." } ], "id_raw": 158 }, { "id": "Ohny4qtmMmmJeWiS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat items are included in [0.396, 0.020, 0.655, 0.062]?" }, { "from": "gpt", "value": "There are two items in the legend. One is {Forecast}[0.440, 0.029, 0.506, 0.053] with color {coordinate}[0.401, 0.031, 0.429, 0.048], and the other one is {Predictions}[0.573, 0.029, 0.649, 0.053] with color {coordinate}[0.534, 0.031, 0.562, 0.048]." } ], "id_raw": 158 }, { "id": "i92X1K3VpHIABwJo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents at region [0.050, 0.690, 0.067, 0.920]." }, { "from": "gpt", "value": "This region includes three y-axis labels: 200 at [0.035, 0.682, 0.061, 0.705], 300 at [0.035, 0.572, 0.061, 0.595], and 400 at [0.035, 0.462, 0.061, 0.485]." } ], "id_raw": 158 }, { "id": "nnuAy8zXw1HKHic6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nDescribe what lies within the coordinate space [0.785, 0.515, 0.943, 0.911]." }, { "from": "gpt", "value": "There are two bars within this area. One is the {Forecast for \"Erratic decline\"}[0.785, 0.548, 0.864, 0.911], and the other is the {Prediction for \"Erratic decline\"}[0.864, 0.515, 0.943, 0.911]." } ], "id_raw": 158 }, { "id": "AqeHVmKBnCHVylnI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat features are found within the region [0.108, 0.129, 0.266, 0.911]?" }, { "from": "gpt", "value": "This region includes two bars representing the \"Continuous decrease\" trend. Specifically, {Forecast bar}[0.108, 0.361, 0.187, 0.911] and {Predictions bar}[0.187, 0.196, 0.266, 0.911]." } ], "id_raw": 158 }, { "id": "JUqNY4LY78qQuLyJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the x-axis?" }, { "from": "gpt", "value": "Subcultures." } ], "id_raw": 160 }, { "id": "nJFA8ZFUxspOUaOM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the y-axis?" }, { "from": "gpt", "value": "Number of people." } ], "id_raw": 160 }, { "id": "OsAUk4woIHJQnefg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the subculture represented at the coordinates [0.553, 0.594, 0.593, 0.933]?" }, { "from": "gpt", "value": "Hip-Hop." } ], "id_raw": 160 }, { "id": "2IiPm4emgwqrfcRi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat subcultures are represented in the chart?" }, { "from": "gpt", "value": "{Punk}[0.081, 0.368, 0.121, 0.933], {Gothic}[0.239, 0.529, 0.278, 0.933], {Lolita}[0.396, 0.449, 0.435, 0.933], {Hip-Hop}[0.553, 0.594, 0.593, 0.933], {CyberPunk}[0.711, 0.626, 0.750, 0.933], and {Hippies}[0.868, 0.255, 0.907, 0.933]." } ], "id_raw": 160 }, { "id": "LwYmjvLqHtZ6omJN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nIs the Punk bar groupId_bars_of_group_0 higher or lower than the Punk bar groupId_bars_of_group_1?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 160 }, { "id": "k8icTNDaNCpaODdE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nDoes the bar of Punk groupId_bars_of_group_1 have a color closer to purple or red?" }, { "from": "gpt", "value": "Red" } ], "id_raw": 160 }, { "id": "gPz40poLG4sB36Tn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nWhich bar is wider, {the Hip-Hop bar of Punk}[0.553, 0.594, 0.593, 0.933] or {the Hip-Hop bar of Gothic}[0.593, 0.610, 0.632, 0.933]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 160 }, { "id": "DHB84DfTaYZJOWH4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nWhich subculture is more popular between {Gothic}[0.239, 0.529, 0.278, 0.933] and {CyberPunk}[0.711, 0.626, 0.750, 0.933]?" }, { "from": "gpt", "value": "CyberPunk." } ], "id_raw": 160 }, { "id": "bUQdE0E1vL8pbITR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what is visible in the region [0.396, 0.449, 0.932, 0.933]." }, { "from": "gpt", "value": "This region includes four subcultures namely Lolita, Hip-Hop, CyberPunk, and Hippies. In particular, it includes six bars: {Lolita bar for Punk} [0.396, 0.449, 0.435, 0.933], {Hip-Hop bar for Punk} [0.553, 0.594, 0.593, 0.933], {CyberPunk bar for Punk} [0.711, 0.626, 0.750, 0.933], {Hippies bar for Punk} [0.868, 0.255, 0.907, 0.933], {Hip-Hop bar for Gothic} [0.593, 0.610, 0.632, 0.933], {CyberPunk bar for Gothic} [0.750, 0.344, 0.789, 0.933], {Hippies bar for Gothic} [0.907, 0.384, 0.947, 0.933]." } ], "id_raw": 160 }, { "id": "af6YB3e9hTUY1muv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.081, 0.288, 0.317, 0.933] contain?" }, { "from": "gpt", "value": "This region contains the bars for the Punk and Gothic subcultures. Specifically, it includes the {Punk bar for Punk} [0.081, 0.368, 0.121, 0.933], the {Gothic bar for Punk} [0.239, 0.529, 0.278, 0.933] and the {Punk bar for Gothic} [0.121, 0.287, 0.160, 0.933]" } ], "id_raw": 160 }, { "id": "VglSV0iyOOYEF7lu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nWhich objects are found in the area [0.023, 0.119, 0.035, 0.782]?" }, { "from": "gpt", "value": "This area contains the y-axis labels which indicate the number of people in each subculture. The labels include: {1K} [0.023, 0.765, 0.035, 0.782], {2K} [0.023, 0.603, 0.035, 0.621], {3K} [0.023, 0.442, 0.035, 0.459], {4K} [0.023, 0.280, 0.035, 0.298] and {5K} [0.023, 0.119, 0.035, 0.136]." } ], "id_raw": 160 }, { "id": "i6pIycIDOdw8OfCw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what is visible in [0.023, 0.119, 0.947, 0.957]." }, { "from": "gpt", "value": "The whole bar chart is within these coordinates, depicting the popularity of different subcultures, including Punk and Gothic. The x-axis represents the subcultures and the y-axis measures in thousands of people." } ], "id_raw": 160 }, { "id": "aW4bTXB8iWaBEPce", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the label associated with the coordinates [0.355, 0.952, 0.406, 0.975] represent?" }, { "from": "gpt", "value": "{This}<[0.355, 0.952, 0.406, 0.975]> is the x-axis label, which is 'Cuisine'." } ], "id_raw": 161 }, { "id": "jlpiqgPeofvw8gHZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat color does the box at coordinates [0.846, 0.048, 0.874, 0.064] represent?" }, { "from": "gpt", "value": "The color code at {these coordinates}<[0.846, 0.048, 0.874, 0.064]> represents the 'Popular index'." } ], "id_raw": 161 }, { "id": "ZWkMACxzbRkLakPC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the data represented in the box with four coordinates [0.297, 0.602, 0.331, 268.273]?" }, { "from": "gpt", "value": "{The box}<[0.297, 0.602, 0.331, 268.273]> represents the effort level for 'Indian' cuisine." } ], "id_raw": 161 }, { "id": "AE0yF8tbWrsryCck", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nIs {the effort level for Italian cuisine}[0.099, 0.065, 0.133, 268.273] higher than {the effort level for Chinese cuisine}[0.198, 0.334, 0.232, 268.273]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 161 }, { "id": "kCRuP5VDot54iH56", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nIs {the popular index for Indian cuisine}[0.331, 0.334, 0.366, 268.273] equal to {the popular index for Chinese cuisine}[0.232, 0.334, 0.267, 268.273]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 161 }, { "id": "KBeKgZwczyJHEPtW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Vietnamese\"}[0.494, 0.602, 0.529, 268.273] and {\"Thailand\"}[0.593, 0.065, 0.628, 268.273], which cuisine requires a higher effort level?" }, { "from": "gpt", "value": "Vietnamese." } ], "id_raw": 161 }, { "id": "WGo2A7Ij42OP5jhQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the area [0.071, 0.025, 0.691, 0.911]" }, { "from": "gpt", "value": "The area is the main plot area. It contains six bars for each of the two bar groups. They represent different cuisine types and their corresponding effort levels and popularity indexes." } ], "id_raw": 161 }, { "id": "FMa7GqvQ0M2DC6Rh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.698, 0.037, 0.985, 0.078]?" }, { "from": "gpt", "value": "The region contains the legend of the bar chart, with two items: {Effort level}[0.742, 0.046, 0.818, 0.069] in a color denoted by the box at [0.703, 0.048, 0.731, 0.064], and {Popular index}[0.885, 0.046, 0.979, 0.069] in a color denoted by the box at [0.846, 0.048, 0.874, 0.064]." } ], "id_raw": 161 }, { "id": "s7RrBY7UIRUloQJN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the layout and information present in the region [0.035, 0.056, 0.066, 0.079]." }, { "from": "gpt", "value": "This region includes labels for the Y-axis, scaling in log form. The labels are {\"1e-4\"}[0.035, 0.862, 0.066, 0.885], {\"1e-3\"}[0.035, 0.593, 0.066, 0.616], {\"1e-2\"}[0.035, 0.325, 0.066, 0.348] and {\"1e-1\"}[0.035, 0.056, 0.066, 0.079]." } ], "id_raw": 161 }, { "id": "Ms0q84ZvDxO0uqE5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label in the chart?" }, { "from": "gpt", "value": "The Y-axis label is \"Relative Scale\", located at {the Y-axis label}[0.015, 0.419, 0.029, 0.582]." } ], "id_raw": 162 }, { "id": "POPOpl5QJSV5ci5Y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nWhere can the label \"Contemporary Art\" be found on the x-axis?" }, { "from": "gpt", "value": "The label \"Contemporary Art\" is found at coordinates {the label \"Contemporary Art\"}[0.102, 0.919, 0.228, 0.942]." } ], "id_raw": 162 }, { "id": "1w44DVjaDmV1lbHq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the color with coordinates [0.356, 0.031, 0.384, 0.048] in the legend?" }, { "from": "gpt", "value": "The specified color represents \"Relative Growth\"." } ], "id_raw": 162 }, { "id": "DmSyD38r66tCMkLi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the category located at [0.258, 0.919, 0.363, 0.942] on the x-axis?" }, { "from": "gpt", "value": "It's the \"Classical Music\" category." } ], "id_raw": 162 }, { "id": "BLs4ehpTyEriRAxf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nIs the bar of \"Relative Growth\" at {the bar of \"Relative Growth\"}[0.114, 0.372, 0.165, 555.438] higher than the bar of \"Engagement Level\" at {the bar of \"Engagement Level\"}[0.165, 0.128, 0.216, 555.438] for \"Contemporary Art\"?" }, { "from": "gpt", "value": "No." } ], "id_raw": 162 }, { "id": "zKyCyzlxiROPJc4V", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Film\"}[0.842, 0.874, 0.893, 555.438] in \"Relative Growth\" farther to the right than {the category \"Film\"}[0.878, 0.919, 0.907, 0.942] in \"Engagement Level\"?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 162 }, { "id": "Yco4mLcrZKdHb4hb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nBetween \"Relative Growth\" in {the category \"Modern Dance\"}[0.405, 0.220, 0.456, 555.438] and \"Engagement Level\" in {the category \"Modern Dance\"}[0.456, 0.343, 0.507, 555.438], which bar is higher?" }, { "from": "gpt", "value": "\"Engagement Level\"." } ], "id_raw": 162 }, { "id": "eaKmpKpV0NkBOkYF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Engagement Level\" of \"Literature\"{the bar representing \"Literature\"}[0.747, 0.318, 0.798, 555.438] taller than the bar representing \"Engagement Level\" of \"Film\"{the bar representing \"Film\"}[0.893, 0.485, 0.944, 555.438]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 162 }, { "id": "N5FypayadQolYE3g", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.350, 0.020, 0.707, 0.062]?" }, { "from": "gpt", "value": "The area contains two legend entries. \"Relative Growth\" is represented by the color at {the color of \"Relative Growth\"}[0.356, 0.031, 0.384, 0.048], and \"Engagement Level\" is represented by the color at {the color of \"Engagement Level\"}[0.533, 0.031, 0.561, 0.048]." } ], "id_raw": 162 }, { "id": "OaaPdXNVFkHDCKS4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nHow would you describe what is in the following region [0.114, 0.372, 0.216, 555.438]?" }, { "from": "gpt", "value": "In this region, there are two bars. The first bar representing \"Relative Growth\" for \"Contemporary Art\" spans from {the first bar}[0.114, 0.372, 0.165, 555.438], and the second bar representing the \"Engagement Level\" for \"Contemporary Art\" spans from {the second bar}[0.165, 0.128, 0.216, 555.438]." } ], "id_raw": 162 }, { "id": "Nwwb3oNBiB286qai", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within the area [0.842, 0.874, 0.944, 555.438]?" }, { "from": "gpt", "value": "The region consists of two bars. The first bar representing \"Relative Growth\" for \"Film\" at {the first bar}[0.842, 0.874, 0.893, 555.438] and the second bar representing \"Engagement Level\" for \"Film\" at {the second bar}[0.893, 0.485, 0.944, 555.438]." } ], "id_raw": 162 }, { "id": "0pX3bkvaQj8KmGvv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nProvide detail on the region from [0.258, 0.919, 0.363, 0.942]." }, { "from": "gpt", "value": "In this area, there is a single element: a label for the category \"Classical Music\" on the X-axis." } ], "id_raw": 162 }, { "id": "9KLM20eC8V7fq5LS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the object in coordinates [0.407, 0.029, 0.466, 0.053]?" }, { "from": "gpt", "value": "It's the label for the blue bar, named as \"Startups\"." } ], "id_raw": 163 }, { "id": "o8cw3AWP10Sz2Rif", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the title of the chart located at [0.346, 0.083, 0.679, 0.113]?" }, { "from": "gpt", "value": "Unfortunately, the title of the chart t is cut off in the given region, it is partially \"art Showing Food Security Trends\"." } ], "id_raw": 163 }, { "id": "TCxI5n2h3vTNcpvc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat is at this coordinate [0.024, 0.902, 0.033, 0.925]?" }, { "from": "gpt", "value": "It's the y-axis label, \"0\"." } ], "id_raw": 163 }, { "id": "7ZGJfD3B9GrFI5hh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the object at [0.015, 0.120, 0.033, 0.144]?" }, { "from": "gpt", "value": "It's the label of the highest y-axis value, \"10\"." } ], "id_raw": 163 }, { "id": "9mulBd3bNVJYXTfr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller in the \"Cybersecurity\" category: the {Startups}[0.313, 0.677, 0.395, 0.911] bar or the {R&D Investments}[0.395, 0.364, 0.476, 0.911] bar?" }, { "from": "gpt", "value": "The {R&D Investments}[0.395, 0.364, 0.476, 0.911] bar is taller." } ], "id_raw": 163 }, { "id": "m0Yo1gp9Nvdan6lR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nIs the {AI bar for Startups}[0.546, 0.442, 0.628, 0.911] taller or shorter than the {Blockchain bar for Startups}[0.779, 0.286, 0.860, 0.911]?" }, { "from": "gpt", "value": "The {AI bar for Startups}[0.546, 0.442, 0.628, 0.911] is shorter." } ], "id_raw": 163 }, { "id": "1f45SydtQecfOpGD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nBetween the {Blockchain bars}[[0.779, 0.286, 0.860, 0.911], [0.860, 0.442, 0.942, 0.911]], which one represents higher investment?" }, { "from": "gpt", "value": "The bar for {R&D Investments}[0.860, 0.442, 0.942, 0.911] is higher." } ], "id_raw": 163 }, { "id": "jxEP4gF2omi3DDhg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Startups bar for IoT}[0.081, 0.833, 0.162, 0.911] higher or lower than the {R&D Investments bar for IoT}[0.162, 0.755, 0.244, 0.911]?" }, { "from": "gpt", "value": "It is higher." } ], "id_raw": 163 }, { "id": "rDqJC9ME3iJfHSTf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects inside the area [0.363, 0.020, 0.660, 0.062]?" }, { "from": "gpt", "value": "This area contains the legend for the chart, which includes two pairs of label and color patches. The first pair is the text \"Startups\" with coordinates [0.407, 0.029, 0.466, 0.053] and a blue color patch with coordinates [0.369, 0.031, 0.396, 0.048]. The second pair is the text \"R&D Investments\" with coordinates [0.533, 0.029, 0.654, 0.053] and a color patch in orange with coordinates [0.494, 0.031, 0.522, 0.048]." } ], "id_raw": 163 }, { "id": "8Ze6UN5qYCTjblIA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the region between [0.15, 0.83, 0.24, 0.91]?" }, { "from": "gpt", "value": "Two bars are located in this region. The blue bar is for \"Startups\" with coordinates [0.081, 0.833, 0.162, 0.911], and the orange bar corresponds to \"R&D Investments\" with coordinates [0.162, 0.755, 0.244, 0.911]. Both bars belong to the category \"IoT\"." } ], "id_raw": 163 }, { "id": "PnMNL0sbGdPzZfox", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the components inside the area [0.79, 0.27, 0.94, 0.91]?" }, { "from": "gpt", "value": "There are two vertically oriented bars in this area, representing the \"Blockchain\" category for the two trends. The blue bar ([0.779, 0.286, 0.860, 0.911]) represents \"Startups\" and the orange bar ([0.860, 0.442, 0.942, 0.911]) represents \"R&D Investments\"." } ], "id_raw": 163 }, { "id": "UbST3fjv3TC1WUFP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the area [0.62, 0.42, 0.70, 0.91]?" }, { "from": "gpt", "value": "Two bars are located in this area, represent the \"AI\" category. The blue bar ([0.546, 0.442, 0.628, 0.911]) corresponds to \"Startups\" and the orange bar ([0.628, 0.129, 0.709, 0.911]) shows \"R&D Investments\"." } ], "id_raw": 163 }, { "id": "Xq8nqao5uraGL2ci", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label shown at the left side of the chart, specifically at [0.012, 0.337, 0.024, 0.665]?" }, { "from": "gpt", "value": "The label is 'Amount / Publications Count'." } ], "id_raw": 164 }, { "id": "46Lw0Av2ryRCy4cV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar at coordinates [0.452, 0.821, 0.490, 301.558]?" }, { "from": "gpt", "value": "The bar represents the count of 'Research Publications' in the field of 'Autonomous Vehicles'." } ], "id_raw": 164 }, { "id": "ooLHaGeff8sRxcPI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the field highlighted at [0.855, 0.919, 0.960, 0.942] on the x-axis?" }, { "from": "gpt", "value": "The highlighted field is 'Space Exploration'." } ], "id_raw": 164 }, { "id": "ZCTQoG8cSvroddSu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nWhich is larger, the amount of 'Investments' in 'AI Research' represented by the bar at [0.111, 0.169, 0.149, 301.558] or the 'Research Publications' in the same field represented by the bar at [0.149, 0.797, 0.187, 301.558]?" }, { "from": "gpt", "value": "'Investments'." } ], "id_raw": 164 }, { "id": "3ipCHZE2ryqE4dDF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing 'Quantum Computing' at position [0.262, 0.125, 0.300, 301.558] longer than the bar representing 'Space Exploration' at position [0.870, 0.132, 0.908, 301.558]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 164 }, { "id": "ou6k57PLSZjruoTP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nBetween the bars located at [0.262, 0.125, 0.300, 301.558] and [0.566, 0.178, 0.604, 301.558], which one represents a higher amount of 'Investments'?" }, { "from": "gpt", "value": "The bar at [0.262, 0.125, 0.300, 301.558] which represents 'Quantum Computing'." } ], "id_raw": 164 }, { "id": "7eIl3L5cCiCyA7Xa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the region [0.381, 0.020, 0.676, 0.062]?" }, { "from": "gpt", "value": "In this region, there is a legend containing two entries. The first entry 'Investments' is marked with color '#1f77b4', located at [0.385, 0.031, 0.409, 0.048] and the corresponding text is at [0.418, 0.029, 0.489, 0.053]. The second entry 'Research Publications' is marked with color '#d62728', located at [0.512, 0.031, 0.535, 0.048] and the associated text is at [0.545, 0.029, 0.671, 0.053]." } ], "id_raw": 164 }, { "id": "DEbIgY1hGtjXsZxI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what is present in the area [0.069, 0.090, 0.988, 0.911]." }, { "from": "gpt", "value": "The area is the entire chart area. It contains multiple bars representing 'Investments' and 'Research Publications' in various fields such as 'AI Research', 'Quantum Computing', 'Autonomous Vehicles', 'Genomic Research', 'Nanotechnology', and 'Space Exploration'. Each field has two bars - one for 'Investments' and the other for 'Research Publications'." } ], "id_raw": 164 }, { "id": "xS6cNsmqiwmrWQv8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area [0.115, 0.919, 0.183, 0.942]?" }, { "from": "gpt", "value": "In this area, there is a label on the x-axis representing the field named 'AI Research'." } ], "id_raw": 164 }, { "id": "Fv3U1Twhse9y8Kly", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the bar associated with \"Environment Protection\" and labeled \"Ours\"}[0.729, 0.110, 0.807, 0.461]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 165 }, { "id": "fNv6ftGFIb6zk3lZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the value on the {bar associated with \"Random\" in \"Educational Funds\"}[0.574, 0.884, 0.651, 0.952] denote?" }, { "from": "gpt", "value": "65.0." } ], "id_raw": 165 }, { "id": "eU1TGpSjfpQJcALZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest {y-tick label}[0.510, 0.530, 0.542, 0.548] displayed on the chart?" }, { "from": "gpt", "value": "400." } ], "id_raw": 165 }, { "id": "aL4XCddOouiV7VXM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Ours\" in \"Healthcare Campaigns\"}[0.238, 0.218, 0.316, 0.461] higher than {the bar representing \"Random\" in \"Food Drives\"}[0.083, 0.828, 0.161, 0.952]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 165 }, { "id": "vLQHt50kJGPEJgFi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the one associated with \"Random\" in \"Educational Funds\"}[0.574, 0.884, 0.651, 0.952] or {the one associated with \"SOTA\" in \"Food Drives\"}[0.394, 0.913, 0.471, 0.952]?" }, { "from": "gpt", "value": "SOTA." } ], "id_raw": 165 }, { "id": "231ic64PSLcaNd56", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nAre {the bars under the subtitle \"Environment Protection\"}[0.646, 0.019, 0.890, 0.041] of greater value than those under {the subtitle \"Food Drives\"}[0.216, 0.509, 0.339, 0.532]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 165 }, { "id": "yr5F3snB1pIBx4i4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the grouping in the region [0.554, 0.537, 0.981, 0.952]." }, { "from": "gpt", "value": "This region contains a set of three blue bars, representing the \"Random\", \"Ours\", and \"SOTA\" categories with corresponding values placed above each bar; text values are {Random}[0.593, 0.871, 0.632, 0.888], {Ours}[0.749, 0.860, 0.787, 0.877], and {SOTA}[0.898, 0.834, 0.948, 0.852], under the subtitle \"Educational Funds\"." } ], "id_raw": 165 }, { "id": "w5kCL3G3I90MCMfx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nCan you delineate what is within [0.064, 0.047, 0.491, 0.461]?" }, { "from": "gpt", "value": "The defined area displays a set of blue bars, representing the categories \"Random,\" \"Ours,\" and \"SOTA\" for \"Healthcare Campaigns\" with each bar having a text label on top indicating the numerical value they correspond to; for instance, the value for \"Ours\" is within coordinates [0.253, 0.204, 0.302, 0.222]." } ], "id_raw": 165 }, { "id": "MkrFGKWViQXKwGRV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the space [0.554, 0.047, 0.981, 0.461] encompass?" }, { "from": "gpt", "value": "This space contains blue bars corresponding to categories \"Random,\" \"Ours,\" and \"SOTA\" for the \"Environment Protection\" campaign, with values placed atop each bar, for example, {Ours}[0.743, 0.096, 0.793, 0.114] and the subtitle for \"Environment Protection\" located above the bars." } ], "id_raw": 165 }, { "id": "7XVQFy9kHlTykZPJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.191, 0.473, 0.212, 0.491] in subplot_0?" }, { "from": "gpt", "value": "T1." } ], "id_raw": 166 }, { "id": "MN7bYMTWR8k9LWOY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the first bar of 'Erratic Decline' in subplot_0?" }, { "from": "gpt", "value": "[0.119, 0.212, 0.284, 0.254]" } ], "id_raw": 166 }, { "id": "01Q7EdTLhTXhr5TQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.044, 0.634, 0.066, 0.651] in subplot_1 represent?" }, { "from": "gpt", "value": "50" } ], "id_raw": 166 }, { "id": "Rhe11QqiKPw2efhG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat is occupied by {the red bar}[0.776, 0.556, 0.940, 0.744] in subplot_1?" }, { "from": "gpt", "value": "{The data point}[0.776, 0.556, 0.940, 0.744] represents the 'Steady Increase' trend at time 'T3' with a value of 91.2." } ], "id_raw": 166 }, { "id": "PN3DqHoiltiQI1e4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"T1\"}[0.119, 0.579, 0.284, 0.744] in subplot_1 higher or lower than {the bar representing \"T2\"}[0.448, 0.570, 0.612, 0.744]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 166 }, { "id": "Xefb4dYHlu3g4OO8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nWhich one is taller among these two bars: {the T2 bar of Erratic Decline in subplot_0}[0.448, 0.221, 0.612, 0.254] and {the T2 bar of Steady Increase in subplot_1}[0.448, 0.570, 0.612, 0.744]?" }, { "from": "gpt", "value": "{The T2 bar of Steady Increase in subplot_1}[0.448, 0.570, 0.612, 0.744]." } ], "id_raw": 166 }, { "id": "w4w4bUTbKX5y5F0e", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nDoes the value of the 'Steady Increase' trend rise or fall from {T1}[0.119, 0.579, 0.284, 0.744] to {T3}[0.776, 0.556, 0.940, 0.744] under subplot_1?" }, { "from": "gpt", "value": "Rise." } ], "id_raw": 166 }, { "id": "Xwnv5u3GvE001hwu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nAre the bars in {subplot_1} generally higher than those in {subplot_0}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 166 }, { "id": "91NpsDR580dAkwSf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.078, 0.537, 0.981, 0.952]." }, { "from": "gpt", "value": "This is the area for subplot_1, which has a subtitle \"Under Steady Increase\" at [0.412, 0.509, 0.648, 0.532], also contains 3 bars representing 'Steady Increase' trend at different times; for example, the first bar with value 79.7 at time T1 is displayed at [0.119, 0.579, 0.284, 0.744]." } ], "id_raw": 166 }, { "id": "XpYjzYT9B6vwxY0W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.078, 0.047, 0.981, 0.461]." }, { "from": "gpt", "value": "This is subplot_0. It includes a subtitle \"Under Erratic Decline\" at [0.419, 0.019, 0.641, 0.041], the y-axis with numbers ranging from -100 to 100 and an x-axis with 3 labels ('T1', 'T2', 'T3'). In this subplot, there are three bars representing different times ('T1', 'T2', 'T3') under 'Erratic Decline' trend; for example, the bar for 'T1' is displayed at [0.119, 0.212, 0.284, 0.254] with a value of 20.3." } ], "id_raw": 166 }, { "id": "e3popQeMSSFQrhFJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.119, 0.236, 0.940, 0.254] in subplot_0?" }, { "from": "gpt", "value": "There is one object; a blue bar representing the 'Erratic Decline' trend at T3, with a value of 8.8 and coordinate range of [0.776, 0.236, 0.940, 0.254]." } ], "id_raw": 166 }, { "id": "ab3mLo9OPjhWxHOq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.412, 0.509, 0.648, 0.532] in subplot_1?" }, { "from": "gpt", "value": "There is a subtitle \"Under Steady Increase\" located." } ], "id_raw": 166 }, { "id": "E17ip30t4rAFvq5f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.087, 0.254, 0.164, 0.280]?" }, { "from": "gpt", "value": "{Orange Prices bar of Year 1}[0.087, 0.254, 0.164, 0.280]." } ], "id_raw": 168 }, { "id": "POpPw1kkCFJxa8VJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by the text at [0.414, 0.272, 0.452, 0.290]?" }, { "from": "gpt", "value": "The value {17.5}[0.414, 0.272, 0.452, 0.290] for {Orange Prices of Year 3}[0.394, 0.254, 0.471, 0.299]." } ], "id_raw": 168 }, { "id": "FuyK864TjwOgJUM5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.252, 0.964, 0.306, 0.981] correspond to?" }, { "from": "gpt", "value": "The {Year 2 x-axis label}[0.252, 0.964, 0.306, 0.981]." } ], "id_raw": 168 }, { "id": "wyecvdwGwN53yGiv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the value at the coordinates [0.106, 0.253, 0.144, 0.271] or at [0.260, 0.268, 0.298, 0.285]?" }, { "from": "gpt", "value": "The value at {0.106, 0.253, 0.144, 0.271}[0.106, 0.253, 0.144, 0.271]." } ], "id_raw": 168 }, { "id": "eWj31ikrdEGXbX4x", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nAre {the apple prices bars}[0.087, 0.098, 0.164, 0.254] more varied than {the orange prices bars}[0.577, 0.254, 0.654, 0.870] from Year 1 to Year 3?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 168 }, { "id": "3A2H006USpg3AKMQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {subtitle}[0.165, 0.509, 0.393, 0.532] imply a greater proportion of apples to oranges compared to the subtitle at [0.655, 0.019, 0.884, 0.041]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 168 }, { "id": "WdyBnvqiSlTZNjCm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of [0.558, 0.537, 0.962, 0.871]." }, { "from": "gpt", "value": "The area contains the {Year 3 bars and labels}[0.885, 0.734, 0.962, 0.744] for apple and orange prices with the respective values of 4.00 for apples and 49.00 for oranges, alongside their value labels positioned just above the bars." } ], "id_raw": 168 }, { "id": "p5YEa8STi2BbdVa2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.067, 0.254, 0.471, 0.498]?" }, { "from": "gpt", "value": "The x-axis line with {x-ticks}[0.125, 0.461], [0.279, 0.461], [0.433, 0.461] and the {labels \"Year 1\", \"Year 2\", \"Year 3\"}[0.099, 0.473, 0.152, 0.491], [0.252, 0.473, 0.306, 0.491], [0.406, 0.473, 0.459, 0.491] for each tick mark, as well as the bars and the values for apple and orange prices for each year." } ], "id_raw": 168 }, { "id": "0zAajzTw3riw0L7Y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the section at [0.019, 0.195, 0.055, 0.472]." }, { "from": "gpt", "value": "This area includes the {left y-axis with labels}[0.019, 0.195, 0.055, 0.213], [0.019, 0.247, 0.055, 0.264] indicating prices between \u221220 and 40, and the {vertical grid lines}[0.067, 0.202] corresponding to these values." } ], "id_raw": 168 }, { "id": "tFjfv6b5nULbW8s2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat indicates the year \"2022\" in the Cloud Computing subplot?" }, { "from": "gpt", "value": "{The x-axis label}[0.250, 0.964, 0.294, 0.981]." } ], "id_raw": 169 }, { "id": "GubdwDC5kGXyLOFR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nWhich color represents bars in the subplot for the \"Blockchain\" technology?" }, { "from": "gpt", "value": "{Blue}[0.563, 0.109, 0.643, 0.461]." } ], "id_raw": 169 }, { "id": "GnTZXXM8vpzVV1pJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at the coordinates [0.741, 0.964, 0.784, 0.981]?" }, { "from": "gpt", "value": "The x-axis label \"2022\"." } ], "id_raw": 169 }, { "id": "fNTAJ5dOPULZdcDR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nWhich year column is the tallest in {the cloud computing subplot}[[0.073, 0.557, 0.152, 0.952], [0.232, 0.630, 0.312, 0.952], [0.391, 0.650, 0.471, 0.952]]?" }, { "from": "gpt", "value": "2021." } ], "id_raw": 169 }, { "id": "gVpcfdZ9PrZAVAse", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nIs {the 2023 bar for Artificial Intelligence}[0.391, 0.066, 0.471, 0.461] higher than {the 2021 bar for Machine Learning}[0.563, 0.557, 0.643, 0.952]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 169 }, { "id": "VTTCp7NvyqOr0JgK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for 2022 in Machine Learning}[0.723, 0.587, 0.802, 0.952] exceed {the bar for 2021 in Blockchain}[0.563, 0.109, 0.643, 0.461]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 169 }, { "id": "Xbmb7zem5EtFsyxo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.053, 0.537, 0.491, 0.952] encompass?" }, { "from": "gpt", "value": "This area includes the Cloud Computing subplot, incorporating years 2021 to 2023, with bars representing values, each year marked on the x-axis, and value scale on the y-axis." } ], "id_raw": 169 }, { "id": "99G4EOx7AMFPsJBl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within [0.544, 0.047, 0.981, 0.461]." }, { "from": "gpt", "value": "This region holds the Blockchain subplot; it contains bars for years 2021 to 2023, showing different values related to the impact of Blockchain technology. Each bar is colored blue and has its corresponding value labeled overhead." } ], "id_raw": 169 }, { "id": "HJfH8k6gFewblrxn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nIdentify and describe the contents in [0.544, 0.537, 0.981, 0.952]." }, { "from": "gpt", "value": "The region showcases the Machine Learning subplot, indicating the y-axis ranging from 0 to 50 and x-axis labeled with years 2021, 2022, and 2023. Blue bars represent Machine Learning metric values by year, with respective value labels attached to the top of each bar." } ], "id_raw": 169 }, { "id": "plFNng6Gbr92WXej", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.009, 0.171, 0.009, 0.171] in the \"Painting\" subplot?" }, { "from": "gpt", "value": "There is no label indicated for the y-axis in that position." } ], "id_raw": 170 }, { "id": "9H3QGIQVCDVhjvKY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nCan you determine what {the highest y-tick label}[0.024, 0.063, 0.041, 0.077] states in the \"Architecture\" subplot?" }, { "from": "gpt", "value": "90" } ], "id_raw": 170 }, { "id": "bAtJGVvkpcdzo1NT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat is located at {the chart area}[0.051, 0.037, 0.246, 0.305] in the layout?" }, { "from": "gpt", "value": "This is the \"Painting\" subplot area displaying bar graphs comparing 'Random', 'Ours', and 'SOTA'." } ], "id_raw": 170 }, { "id": "UIIJoteH0IicJIDZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nIs {the performance score bar for 'Random'}[0.060, 0.136, 0.090, 0.352] higher than {the score bar for 'Ours'}[0.134, 0.153, 0.163, 0.352] in the \"Painting\" subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 170 }, { "id": "GKlfmjf8q1v9wKIx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nWhich has the earlier x-tick position, {the 'Random' label}[0.538, 0.643, 0.596, 0.657] or {the 'Ours' label}[0.625, 0.643, 0.657, 0.657] in the \"Glass\" category?" }, { "from": "gpt", "value": "[0.538, 0.643, 0.596, 0.657]" } ], "id_raw": 170 }, { "id": "j6S3KSLv22bHrKAu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nIs {the color of 'SOTA' bar}[0.947, 0.141, 0.976, 0.352] the same as {the 'Random' bar}[0.799, 0.131, 0.828, 0.352] in the \"Architecture\" subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 170 }, { "id": "D0VTjcGnhG7ZgNLb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the features found within the region [0.544, 0.366, 0.739, 0.633] in the layout." }, { "from": "gpt", "value": "This is the \"Glass\" subplot area. It includes y-axis tick labels from '10' to '100', x-axis labels 'Random', 'Ours', and 'SOTA', and colored bars indicating performance scores for each category." } ], "id_raw": 170 }, { "id": "vccEQeiM1gCKNdco", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat elements are in the area [0.790, 0.694, 0.985, 0.961]?" }, { "from": "gpt", "value": "This region covers part of the \"Digital Art\" subplot, featuring bars representing performance numbers for 'Random', 'Ours', and 'SOTA', as well as corresponding x-tick and y-tick labels from '0' to '100'." } ], "id_raw": 170 }, { "id": "tvUNNBaZnxEpjlqy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the bounded region [0.297, 0.366, 0.493, 0.633]?" }, { "from": "gpt", "value": "This boundary encases the \"Ceramics\" subplot, showing visual elements like y-axis labels from '10' to '100', x-axis category labels for 'Random', 'Ours', and 'SOTA', including their respective performance bars in a specific color." } ], "id_raw": 170 }, { "id": "rKPNFAQ2AHLLj1jf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the background color of {the plot}[0.067, 0.047, 0.491, 0.461]?" }, { "from": "gpt", "value": "The background color is white." } ], "id_raw": 171 }, { "id": "Lgu8kCzHJ9JnLe0s", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at {coordinates}[0.644, 0.509, 0.896, 0.532] in the chart?" }, { "from": "gpt", "value": "A subtitle reading \"Funds:Attendance = 1:2\"." } ], "id_raw": 171 }, { "id": "7eSSIeuMKe4X09ix", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the label shown at {the y-tick}[0.019, 0.841, 0.055, 0.859]?" }, { "from": "gpt", "value": "Black." } ], "id_raw": 171 }, { "id": "yu3msbvmVXWWwRJa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nIs {the first bar of 2018}[0.087, 0.183, 0.164, 0.254] higher than {the last bar of 2020}[0.885, 0.744, 0.962, 0.821]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 171 }, { "id": "vPS3Lq7YvS7jtdoY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar label for 2019 in subplot_1}[0.597, 0.332, 0.635, 0.349] represent a larger or smaller value compared to {the bar label for 2020 in subplot_1}[0.751, 0.167, 0.789, 0.184]?" }, { "from": "gpt", "value": "Larger." } ], "id_raw": 171 }, { "id": "EuyMjiFfsHYQc5C6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nIs {the attendance bar of 2018}[0.577, 0.254, 0.654, 0.358] positioned above or below {the funds bar of 2018}[0.577, 0.188, 0.654, 0.254]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 171 }, { "id": "v7neAhT9RLKoQZbv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the scene within {the bounds}[0.558, 0.537, 0.981, 0.952]." }, { "from": "gpt", "value": "This area comprises two histograms, each containing bars for the years 2018, 2019, and 2020, one histogram representing funds received and the other representing attendance. The bars are colored in shades of blue and red respectively." } ], "id_raw": 171 }, { "id": "F7vRbZCZhwNbLWp6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the area within {coordinates}[0.067, 0.537, 0.491, 0.952] depict?" }, { "from": "gpt", "value": "This region visualizes a sequence of horizontal bars sorted sequentially from 2018 to 2020, juxtaposing funds received against attendance, displayed in a histogram format with a backdrop and grid lines enhancing readability." } ], "id_raw": 171 }, { "id": "ZUW0bUMVwRevnMBV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat information can be gathered from {the region}[0.067, 0.047, 0.491, 0.461]?" }, { "from": "gpt", "value": "The region shows bars that represent the quantitative relationship between funds received and attendance for the years 2018, 2019, and 2020 with corresponding values listed above each bar, set against a simple white background and clearly marked y-grid lines." } ], "id_raw": 171 }, { "id": "jO8l6ePHskOubMmh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled at the coordinates [0.113, 0.689, 0.135, 0.707] in the chart?" }, { "from": "gpt", "value": "The number 25 is labeled, representing a value." } ], "id_raw": 172 }, { "id": "AASpr1kLr8aYpBRS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nIdentify the value displayed at the label position [0.818, 0.606, 0.840, 0.624]." }, { "from": "gpt", "value": "The value is 65." } ], "id_raw": 172 }, { "id": "YpvGnnSK3zxXHgSt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.220, 0.292, 0.242, 0.309] signify?" }, { "from": "gpt", "value": "It signifies the value 30." } ], "id_raw": 172 }, { "id": "GWWZvFYE4t37w4L8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar at}[0.588, 0.693, 0.641, 0.744] higher than {the one at}[0.909, 0.568, 0.963, 0.744]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 172 }, { "id": "jgMJj6D6S3PJdOom", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the bar marked at}[0.097, 0.202, 0.151, 0.254] or {the one marked at}[0.204, 0.161, 0.258, 0.254]?" }, { "from": "gpt", "value": "{the one marked at}[0.204, 0.161, 0.258, 0.254]." } ], "id_raw": 172 }, { "id": "Av6UPhFcOJJNuThs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar starting from}[0.802, 0.610, 0.855, 0.744] have a higher endpoint than {the bar at}[0.695, 0.651, 0.748, 0.744]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 172 }, { "id": "UiqKX7RpsMRnazoh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the area bounded by [0.569, 0.047, 0.981, 0.254]." }, { "from": "gpt", "value": "This region contains several bars from multiple groups:\n1. {Efficient group bar for \"Trend 1\"}[0.588, 0.202, 0.641, 0.254],\n2. {Efficient group bar for \"Trend 2\"}[0.695, 0.161, 0.748, 0.254],\n3. {Inefficient group bar for \"Trend 1\"}[0.588, 0.254, 0.641, 0.368],\n4. {Inefficient group bar for \"Trend 2\"}[0.695, 0.254, 0.748, 0.316]." } ], "id_raw": 172 }, { "id": "uKPwP6LOJiae6Fyp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within [0.078, 0.537, 0.491, 0.952]?" }, { "from": "gpt", "value": "This region contains several bars along with their value labels, specifically:\n1. A blue bar representing \"Trend 1\" along with the value 25 shown at top[0.097, 0.693, 0.151, 0.744],\n2. An orange bar representing \"Trend 1\" displaying the negative value 65 above it[0.097, 0.744, 0.151, 0.858]." } ], "id_raw": 172 }, { "id": "tmOxnspFJXLNeIvF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nWhat elements are positioned between [0.569, 0.498, 0.981, 0.744]?" }, { "from": "gpt", "value": "This section encompasses multiple bars representing Trend Group 2 of varying efficiencies:\n1. A blue colored bar for \"Trend 1\" with a value 25 indicated at the top [0.588, 0.651, 0.641, 0.744],\n2. An orange colored bar for \"Trend 1\" showing the negative value 65[0.588, 0.744, 0.641, 0.858]." } ], "id_raw": 172 }, { "id": "9FBuudVOQvql3xfF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.569, 0.727, 0.626, 0.740] in the chart?" }, { "from": "gpt", "value": "It's the x-axis label, representing \"Random\"." } ], "id_raw": 179 }, { "id": "xcMV7k56WNJZGZ6U", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at coordinates [0.229, 0.388, 0.309, 0.472]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 179 }, { "id": "HUuaAUrNvmTWPWvD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-value range shown by {the bar}[0.875, 0.653, 0.954, 0.718]?" }, { "from": "gpt", "value": "33.9." } ], "id_raw": 179 }, { "id": "WVhwS2KbwzjfonNI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar labeled 'Ultimate Safe' Random}[0.071, 0.940, 0.150, 0.965] taller than {the bar labeled 'Ultimate Safe' Ours}[0.229, 0.939, 0.309, 0.965]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 179 }, { "id": "LHnqe85Bk8yYsv7Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nAre the scores for {Ours}[0.275, 0.468, 0.370, 0.481] always higher than {SOTA}[0.564, 0.468, 0.666, 0.481] across all subplots?" }, { "from": "gpt", "value": "No." } ], "id_raw": 179 }, { "id": "NxeBjojEmkIfwcbb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nDoes {Secure Bonds stage Random Bar}[0.071, 0.679, 0.150, 0.718] reach higher than {Secure Bonds stage SOTA Bar}[0.388, 0.687, 0.467, 0.718]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 179 }, { "id": "a7z4Vr61HQtyKXYh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the area {range}[0.538, 0.034, 0.974, 0.225]." }, { "from": "gpt", "value": "This region contains a subplot with the category 'Balanced'. It includes three bars representing the 'Random', 'Ours', and 'SOTA' strategies, which are colored blue." } ], "id_raw": 179 }, { "id": "81ILQjQZ1aiycdF2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region {rectangle}[0.051, 0.527, 0.487, 0.718]?" }, { "from": "gpt", "value": "The area includes a subplot with various blue bars representing different investing strategies under the 'Secure Bonds' category, and it includes labels for 'Random', 'Ours', and 'SOTA'." } ], "id_raw": 179 }, { "id": "epQ6ksLswRXeCAfb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe all objects located within the area {region}[0.538, 0.281, 0.974, 0.472]." }, { "from": "gpt", "value": "This part of the chart showcases a subplot labeled 'Stable Growth' containing blue-colored bars covering different strategies from 'Random', 'Ours', to 'SOTA', each with corresponding numeric labels indicating their respective values." } ], "id_raw": 179 }, { "id": "2JHFmBcaoCsI7HiU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object at the coordinates [0.108, 0.118, 0.248, 0.141]?" }, { "from": "gpt", "value": "The label of this object is Fund A Performance." } ], "id_raw": 180 }, { "id": "kHdRvhJVUIeT7HIC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat color is the object at the coordinates [0.070, 0.155, 0.097, 0.171]?" }, { "from": "gpt", "value": "The color of this object is orange (#d98763)." } ], "id_raw": 180 }, { "id": "47JCCPv0ylzRcKWX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat is written at the coordinates [0.352, 0.025, 0.690, 0.055]?" }, { "from": "gpt", "value": "The text \"Financial Market Fluctuations and Trends\" is written in this area." } ], "id_raw": 180 }, { "id": "rO8iCH7Oz1Rc9elR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the scale at the y-axis label [0.015, 0.334, 0.029, 0.672]?" }, { "from": "gpt", "value": "The word \"Percent Increase in Value (%)\" is visible." } ], "id_raw": 180 }, { "id": "iqSKA3UhQGFnV3X4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {Fund A Performance}<{the bar_1}[0.117, 0.650, 0.150, 0.944]> and {Fund B Performance}<{the bar_2}[0.150, 0.709, 0.182, 0.944]> for ABC Corporation?" }, { "from": "gpt", "value": "Fund A Performance is higher." } ], "id_raw": 180 }, { "id": "RuC29rQOXHbNLlA5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nAre {Fund A Performance bars}<{A_performance_for_YZA}[0.860, 0.180, 0.892, 0.944]> generally higher than {Fund B Performance bars}<{B_performance_for_YZA}[0.892, 0.209, 0.925, 0.944]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 180 }, { "id": "SZulRCZGBSYDgNRF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhich performance is higher for STU Enterprises, {Fund A Performance}<{A_performance_for_STU}[0.674, 0.297, 0.707, 0.944]> or {Fund B Performance}<{B_performance_for_STU}[0.707, 0.356, 0.739, 0.944]>?" }, { "from": "gpt", "value": "Fund B Performance." } ], "id_raw": 180 }, { "id": "wS6Y2HQVQvsS8xnC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhich fund performed better for DEF Enterprises, {Fund A Performance}<{A_performance_for_DEF}[0.210, 0.591, 0.243, 0.944]> or {Fund B Performance}<{B_performance_for_DEF}[0.243, 0.503, 0.275, 0.944]>?" }, { "from": "gpt", "value": "Fund B Performance" } ], "id_raw": 180 }, { "id": "n1PbJumnnw42F20P", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the region [0.117, 0.650, 0.182, 0.944]?" }, { "from": "gpt", "value": "In this region, two objects can be found:\n- 1. {Fund-A bar for ABC Corporation}[0.117, 0.650, 0.150, 0.944], a bar of Fund A performance for ABC corporation; \n- 2. {Fund-B bar for ABC Corporation}[0.150, 0.709, 0.182, 0.944], a bar of Fund B performance for ABC corporation." } ], "id_raw": 180 }, { "id": "J205yzHI8QeVqjdW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the content represent in [0.035, 0.200, 0.052, 0.223]?" }, { "from": "gpt", "value": "Represents the y-axis label of \"25\"." } ], "id_raw": 180 }, { "id": "sPSGCNPOKbmgJ1zb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements represented in the bounding box of coordinates [0.064, 0.074, 0.254, 0.185]?" }, { "from": "gpt", "value": "This region contains two elements: \n- 1. The color label {handle}[0.070, 0.120, 0.097, 0.136], which is blue, associated with Fund A Performance, and \n- 2. The color label {handle}[0.070, 0.155, 0.097, 0.171], which is orange, associated with Fund B Performance." } ], "id_raw": 180 }, { "id": "qIUFapgkan3dx3y3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the area [0.210, 0.591, 0.275, 0.944]." }, { "from": "gpt", "value": "This region contains the performance bars for DEF Enterprises. The green bar represents Fund A performance {A_performance_for_DEF}[0.210, 0.591, 0.243, 0.944] with a height corresponding to a 12% increase. The orange bar represents Fund B performance {B_performance_for_DEF}[0.243, 0.503, 0.275, 0.944] with a height corresponding to a 15% increase." } ], "id_raw": 180 }, { "id": "hyXA4QZqVw4bKne7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y-axis in the chart?" }, { "from": "gpt", "value": "Development Score {ylabel}[0.015, 0.389, 0.029, 0.617]." } ], "id_raw": 181 }, { "id": "18z8EPWYJsYeV7aV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar represented by \"Hardware\" data in the legend?" }, { "from": "gpt", "value": "Green {label Hardware color}[0.866, 0.120, 0.894, 0.136]." } ], "id_raw": 181 }, { "id": "SaUowtew1lVW5Qan", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhich year is given by the first X-axis label?" }, { "from": "gpt", "value": "10 years ago {label 10 years ago}[0.250, 0.952, 0.341, 0.975]." } ], "id_raw": 181 }, { "id": "qqZM9SVxYgXf2fDB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat value is represented by the bar at [0.445, 0.426, 0.525, 0.944]?" }, { "from": "gpt", "value": "70.5 {Hardware data for Now}[0.445, 0.426, 0.525, 0.944]." } ], "id_raw": 181 }, { "id": "lg1qkPNXhnS0VWzp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Hardware bar for \"Now\"}[0.445, 0.426, 0.525, 0.944] higher than the {Software bar for \"Now\"}[0.525, 0.464, 0.606, 0.944]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 181 }, { "id": "ooH1w3ToixXrpMbR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Software bar for \"10 years ago\"}[0.296, 0.669, 0.376, 0.944] to the right of the {Hardware bar for \"10 years ago\"}[0.215, 0.608, 0.296, 0.944]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 181 }, { "id": "HGF2GGl4tD9UfUlb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nIs {Software}[0.905, 0.153, 0.967, 0.176] listed above or below {Hardware}[0.905, 0.118, 0.973, 0.141] in the legend?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 181 }, { "id": "g7C2yCluYduh5Gvj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhich prediction is greater, {Hardware in Future Predictions}[0.675, 0.277, 0.755, 0.944] or {Software in Future Predictions}[0.755, 0.317, 0.836, 0.944]?" }, { "from": "gpt", "value": "Hardware" } ], "id_raw": 181 }, { "id": "Qr9z7E8MBPay4piY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.861, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "This area contains the legend of the chart, which contains two elements: \"Hardware\" labeled in green at {label Hardware color}[0.866, 0.120, 0.894, 0.136], and \"Software\" labeled in orange, at {label Software color}[0.866, 0.155, 0.894, 0.171]." } ], "id_raw": 181 }, { "id": "0TpbbNtM4mxzxbr3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the region [0.066, 0.062, 0.985, 0.944]?" }, { "from": "gpt", "value": "The content within these coordinates includes all the bars of the chart, the x and y axis with their labels and tick marks, and the subtitle \"Future Technological Development Prospects\" {subplot subtitle}[0.339, 0.025, 0.712, 0.055]." } ], "id_raw": 181 }, { "id": "Pj9wjNpLerKvhXO5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the area [0.755, 0.044, 0.836, 0.944]?" }, { "from": "gpt", "value": "This area contains the bar chart and labels for Software's future predictions, with a value of 85.3 {Software data for Future Predictions}[0.755, 0.317, 0.836, 0.944]." } ], "id_raw": 181 }, { "id": "0T27s7wT0DD8nvcv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nTell me about the region [0.215, 0.608, 0.296, 0.944]." }, { "from": "gpt", "value": "This region contains the Hardware development score for 10 years ago, with a value of 45.7 {Hardware data for 10 years ago}[0.215, 0.608, 0.296, 0.944]." } ], "id_raw": 181 }, { "id": "Wwhu1DUGeLBcn2gM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object} at coordinates [0.848, 0.153, 0.973, 0.176]?" }, { "from": "gpt", "value": "Legend label 'Risk Management'." } ], "id_raw": 182 }, { "id": "3BT8R5CH2NHK2dyM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhat is described by the coordinates [0.043, 0.788, 0.061, 0.811]?" }, { "from": "gpt", "value": "Y-axis label '20'." } ], "id_raw": 182 }, { "id": "q3LfjjdBYLbPGJEc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the element at [0.126, 0.369, 0.157, 0.392] indicate?" }, { "from": "gpt", "value": "The score '74.1' of the Risk Assessment for Scenario A." } ], "id_raw": 182 }, { "id": "EZJtviDTVOslLHAj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.853, 0.083, 0.928, 0.106] denote?" }, { "from": "gpt", "value": "The title 'Categories' of the legend." } ], "id_raw": 182 }, { "id": "QSx1utEBZ0bc6iwh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher score, {Scenario A Risk Assessment bar} [0.126, 0.399, 0.158, 0.944] or {Scenario A Risk Management bar} [0.158, 0.586, 0.190, 0.944]?" }, { "from": "gpt", "value": "Risk Assessment." } ], "id_raw": 182 }, { "id": "ve3Cp3BCBfMPktbe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nDoes {Scenario D Risk Assessment bar} [0.401, 0.244, 0.433, 0.944] have a higher or lower score than {Scenario D Risk Management bar} [0.433, 0.281, 0.466, 0.944]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 182 }, { "id": "RtM7y9fsy8uXMu2C", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nIn the legend, is the color of {Risk Assessment} [0.809, 0.120, 0.837, 0.136] before or after the color of {Risk Management} [0.809, 0.155, 0.837, 0.171]?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 182 }, { "id": "7zjTQHKThIxSnwd0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nComparing the y-axis coordinates of the {Scenario H Risk Management bar} [0.801, 0.364, 0.833, 0.944] and {Scenario H Risk Assessment bar} [0.769, 0.298, 0.801, 0.944], which score is lower?" }, { "from": "gpt", "value": "Risk Assessment." } ], "id_raw": 182 }, { "id": "Mu9yXiAOyT2B2q30", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the information contained in [0.803, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "This region contains a subplot legend titled 'Categories' with two items. The first item is for 'Risk Assessment' with its correspondent color box at [0.809, 0.120, 0.837, 0.136]. The second listed item is 'Risk Management' with its color box at [0.809, 0.155, 0.837, 0.171]." } ], "id_raw": 182 }, { "id": "1Iozvi28nVhWGF8B", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within [0.401, 0.244, 0.466, 0.944]?" }, { "from": "gpt", "value": "In this region, there are 2 bars. One bar with coordinates [0.401, 0.244, 0.433, 0.944] represents the Risk Assessment score for 'Scenario D' which is 95.3. The other bar with coordinates [0.433, 0.281, 0.466, 0.944] represents the Risk Management score for 'Scenario D', which is 90.2." } ], "id_raw": 182 }, { "id": "mOpLMdaflv1U8g4n", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhat content falls within [0.121, 0.952, 0.195, 0.975]?" }, { "from": "gpt", "value": "The X-axis label 'Scenario A' is within these coordinates." } ], "id_raw": 182 }, { "id": "NBdQAPmfwsoOxA8U", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the content represented in the region [0.02, 0.88, 0.1, 0.96]?" }, { "from": "gpt", "value": "The region contains a Y-axis label with text 'Scores' at [0.015, 0.465, 0.029, 0.541]" } ], "id_raw": 182 }, { "id": "ofEr3Ay1vnqxU7G3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.343, 0.025, 0.702, 0.055] in the chart?" }, { "from": "gpt", "value": "This is the subtitle of the plot, which is \"GDP Growth and Inflation rates over a year\"." } ], "id_raw": 183 }, { "id": "Ru2MzbyiT6fDn6fe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color corresponding to {Inflation Rate (%) handle} at [0.072, 0.818, 0.100, 0.834]?" }, { "from": "gpt", "value": "The color corresponding to the Inflation Rate (%) handle is orange (#d98763)." } ], "id_raw": 183 }, { "id": "r6eJ715SD3425UYa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the bar at [0.248, 0.341, 0.273, 0.461]?" }, { "from": "gpt", "value": "The value of the bar is 1.5, which represents the GDP Growth Rate (%) in March." } ], "id_raw": 183 }, { "id": "4dovdhQvBB9t1ers", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nIs the {February bar for GDP Growth Rate (%)}[0.177, 0.230, 0.202, 0.461] higher than the {February bar for Inflation Rate (%)}[0.202, 0.317, 0.227, 0.461] in the chart?" }, { "from": "gpt", "value": "No." } ], "id_raw": 183 }, { "id": "8vrUrPfqVEh8Kdg6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nIs the {December bar for Inflation Rate (%)}[0.914, 0.134, 0.939, 0.461] higher than the {December bar for GDP Growth Rate (%)}[0.889, 0.190, 0.914, 0.461]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 183 }, { "id": "8XNWKlETQ5tSHwxa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, the {June bar for Inflation Rate (%)}[0.487, 0.262, 0.512, 0.461] or the {June bar for GDP Growth Rate (%)}[0.462, 0.461, 0.487, 0.716]?" }, { "from": "gpt", "value": "The bar for GDP Growth Rate (%)." } ], "id_raw": 183 }, { "id": "il1rqUhguM9qwPgp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the information within [0.106, 0.166, 0.131, 0.461]." }, { "from": "gpt", "value": "These coordinates contain a bar representing the GDP Growth Rate (%) for January and its value is 3.7." } ], "id_raw": 183 }, { "id": "6fAUXkxEVxPs889H", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region from [0.034, 0.931, 0.055, 0.954]?" }, { "from": "gpt", "value": "The region contains the y-axis label -6." } ], "id_raw": 183 }, { "id": "1HZoLYFGsxAbCJEP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.067, 0.737, 0.266, 0.848]?" }, { "from": "gpt", "value": "The region houses the bar plot's legend titled \"Economic Indicators\". The two entries are GDP Growth Rate (%) with a representative color of green(#69b3a2) and Inflation Rate (%) represented by orange(#d98763)." } ], "id_raw": 183 }, { "id": "7gsUrbKfJW0bPAtu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the figure, mainly positioned at [0.075, 0.062, 0.985, 0.875], represent?" }, { "from": "gpt", "value": "It's an axes region that includes a bar chart representing International Trade Trends." } ], "id_raw": 184 }, { "id": "UUqiRXyntobSb87L", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the text in location [0.422, 0.025, 0.638, 0.055] is?" }, { "from": "gpt", "value": "It shows the text \"International Trade Trends\", which is the subtitle of the graph." } ], "id_raw": 184 }, { "id": "Y8BGKdaaGeI7USNB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis enclosed by coordinates [0.015, 0.333, 0.029, 0.604]?" }, { "from": "gpt", "value": "The label is \"Trade Value (in Billions)\" for the y-axis." } ], "id_raw": 184 }, { "id": "W5HLSEwPChz8056f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color and meaning of the square box at [0.887, 0.120, 0.915, 0.136] in the legend?" }, { "from": "gpt", "value": "The box at [0.887, 0.120, 0.915, 0.136] is green and represents \"Export\"." } ], "id_raw": 184 }, { "id": "rG7N8blytey0yuhM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the Export trade value for China}[0.316, 0.130, 0.348, 0.875], or {the Import trade value for China}[0.348, 0.252, 0.380, 0.875]?" }, { "from": "gpt", "value": "Export." } ], "id_raw": 184 }, { "id": "w3CD3SJy7HdxaBH8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nIs {the Export trade value for Germany}[0.225, 0.265, 0.257, 0.875] higher than {its Import trade value}[0.257, 0.340, 0.288, 0.875]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 184 }, { "id": "398Q7pu4HAPOyQHm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Export}[0.134, 0.367, 0.166, 0.875] for the USA located to the left or right of its {Import}[0.166, 0.333, 0.197, 0.875]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 184 }, { "id": "QuPgiqm3kiwNAwSx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nWhich is larger, {the Export trade value for India}[0.771, 0.299, 0.803, 0.875], or {the Import trade value for India}[0.803, 0.326, 0.835, 0.875]?" }, { "from": "gpt", "value": "Export." } ], "id_raw": 184 }, { "id": "mDBru6zdDiTWAMhC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.882, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "The area is a legend box entitled \"Trade Type\". It contains two items, each consisting of a colored square and a label. The first item possesses a green square at [0.887, 0.120, 0.915, 0.136] and is labeled as \"Export\" at [0.926, 0.118, 0.972, 0.141]. The second item has a brown square at [0.887, 0.155, 0.915, 0.171] and is labeled as \"Import\" at [0.926, 0.153, 0.973, 0.176]." } ], "id_raw": 184 }, { "id": "2qmDIXGnohINdXam", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects inside [0.043, 0.053, 0.070, 0.889]." }, { "from": "gpt", "value": "This area encompasses y-axis labels and tick marks, displaying values from 0 to 1200 in increments of 200." } ], "id_raw": 184 }, { "id": "NQRk3aMMIby45Zh7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat details are included in the region [0.134, 0.367, 0.197, 0.875]?" }, { "from": "gpt", "value": "This region includes two bars. The first, colored in green, with boundaries [0.134, 0.367, 0.166, 0.875], represents exports from USA, and the vertical height of the bar indicates the trade value. The second bar, colored brown, with boundary [0.166, 0.333, 0.197, 0.875], signifies imports into USA, again with vertical height representing the trade value." } ], "id_raw": 184 }, { "id": "qFCuEwhUxLeWXYB8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.329, 0.883, 0.367, 0.947]?" }, { "from": "gpt", "value": "This region contains the label \"China\" which signifies that the related bars represent trade data for China." } ], "id_raw": 184 }, { "id": "ZuF6mlNZeOTlZfR7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of data that the chart is showing in [0.317, 0.025, 0.742, 0.055]?" }, { "from": "gpt", "value": "It's showing 'Time Series Data Comparison: Bitcoin vs Ethereum'." } ], "id_raw": 185 }, { "id": "cRn2eVJjTekU5ywL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate for the first text label for Ethereum in the legend?" }, { "from": "gpt", "value": "[0.899, 0.153, 0.967, 0.176]." } ], "id_raw": 185 }, { "id": "EMSEUqGEltzJJa5c", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate positioned at [0.035, 0.739, 0.070, 0.762]?" }, { "from": "gpt", "value": "It refers to '2000' on the y-axis." } ], "id_raw": 185 }, { "id": "wqCFAjRz9FsGo4j8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar data represented by [0.682, 0.356, 0.944]?" }, { "from": "gpt", "value": "It represents Bitcoin's value for the year 2020." } ], "id_raw": 185 }, { "id": "Z5KNOupef20gWuqf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nIs the Ethereum's bar for 2017 [0.226, 0.929, 0.279, 0.944] higher than Bitcoin's bar for 2017 [0.173, 0.748, 0.944]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 185 }, { "id": "Vcjd5UYpr2uXeIq0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nCompare the performance of {Bitcoin} with {Ethereum} for 2020." }, { "from": "gpt", "value": "Bitcoin performed better." } ], "id_raw": 185 }, { "id": "aEyO8cBNKm4Dbw3q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nIn 2019, which cryptocurrency {Bitcoin}[0.477, 0.160, 0.530, 0.944] or {Ethereum}[0.530, 0.846, 0.583, 0.944] had the higher value?" }, { "from": "gpt", "value": "Bitcoin." } ], "id_raw": 185 }, { "id": "GYydR98klZaQeFDb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nBetween {Bitcoin} and {Ethereum} in 2021, which one had a lower value?" }, { "from": "gpt", "value": "Bitcoin." } ], "id_raw": 185 }, { "id": "sTu9AcA9GnKS70Qn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the legend items within the coordinates [0.849, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "The legend comprises two items:\n1. Bitcoin, represented by an orange color at [0.860, 0.120, 0.888, 0.136],\n2. Ethereum, represented by a purple color at [0.860, 0.155, 0.888, 0.171]." } ], "id_raw": 185 }, { "id": "Jotj1RnH4bJ96X0j", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nPlease detail the axis text within the region [0.209, 0.952, 0.851, 0.975]." }, { "from": "gpt", "value": "This region covers all the x-axis label which represents the years: 2017 at [0.209, 0.952, 0.244, 0.975], 2018 at [0.360, 0.952, 0.396, 0.975], 2019 at [0.512, 0.952, 0.547, 0.975], 2020 at [0.664, 0.952, 0.699, 0.975], and 2021 at [0.816, 0.952, 0.851, 0.975]." } ], "id_raw": 185 }, { "id": "CC1wjzncFHd5RWWU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat figures are in the area [0.360, 0.748, 0.735, 0.944]?" }, { "from": "gpt", "value": "The figures within these coordinates are the Bitcoin and Ethereum bars for the years 2018, 2019, and 2020, along with their respective label texts." } ], "id_raw": 185 }, { "id": "vfQY4kW0tbC3i11f", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the coordinates [0.035, 0.151, 0.070, 0.174]." }, { "from": "gpt", "value": "Within the specified region, there is a y-axis label indicating the value '8000'." } ], "id_raw": 185 }, { "id": "BzM1HNFOGQQdva02", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart information when read from coordinates [0.015, 0.423, 0.029, 0.500]?" }, { "from": "gpt", "value": "This is a Y-axis label indicating the attribute \"Scores\"." } ], "id_raw": 186 }, { "id": "AaZvaYJVVBxi4XyQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the details noted at [0.120, 0.222, 0.149, 0.860]?" }, { "from": "gpt", "value": "This identifies a blue bar representing the US's Free Trade score of 80." } ], "id_raw": 186 }, { "id": "DakUmbL9MvKn4NJB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nWhat text is specified by the coordinates [0.863, 0.118, 0.935, 0.141]?" }, { "from": "gpt", "value": "This denotes the text label \"Free Trade\" in the legend." } ], "id_raw": 186 }, { "id": "51XmRNPh55Kb14mY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the coordinate [0.397, 0.869, 0.411, 0.925] refer to?" }, { "from": "gpt", "value": "It refers to the X axis label \"India\"." } ], "id_raw": 186 }, { "id": "4l359EwwxNaxOjvK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for {Free Trade - Germany}[0.287, 0.190, 0.316, 0.860] have a higher or lower score compared to {Free Trade - China}[0.204, 0.142, 0.233, 0.860]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 186 }, { "id": "jWpYKazeRCF0a7ev", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nHow is the {Global Sourcing score for France}[0.651, 0.350, 0.680, 0.860] in relation to the {Global Sourcing score for Japan}[0.734, 0.214, 0.764, 0.860]?" }, { "from": "gpt", "value": "The score for France is lower than Japan's." } ], "id_raw": 186 }, { "id": "eirq6ts8FMW05qoB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nAre the positions of {Australia - Free Trade}[0.538, 0.222, 0.567, 0.860] and {Australia - Global Sourcing}[0.567, 0.302, 0.596, 0.860] side by side on the graph?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 186 }, { "id": "f6JiTfaU3H5UYuRO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nIs {Global Sourcing - US}[0.149, 0.302, 0.179, 0.860] higher than {Free Trade - US}[0.120, 0.222, 0.149, 0.860]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 186 }, { "id": "ttCi1zZwZNalcShT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.819, 0.074, 0.978, 0.185]." }, { "from": "gpt", "value": "This region contains the legend where two metrics are defined: first, {Free Trade}[0.863, 0.118, 0.935, 0.141] marked in blue color [0.824, 0.120, 0.852, 0.136], and second, {Global Sourcing}[0.863, 0.153, 0.973, 0.176] marked in orange color [0.824, 0.155, 0.852, 0.171]." } ], "id_raw": 186 }, { "id": "kZ1unt9FN5yzgZNt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nWhat is found within the bounded region [0.560, 0.869, 0.574, 0.971]?" }, { "from": "gpt", "value": "Within these bounds, the label \"Australia\" is evident which is a marker on the x-axis." } ], "id_raw": 186 }, { "id": "FLcpC6vLRJQGI0bR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nProvide some details about the region [0.371, 0.862, 0.400, 0.860]." }, { "from": "gpt", "value": "This region contains a bar and a value. The displayed value is 66. The bar represents Global Sourcing in India." } ], "id_raw": 186 }, { "id": "XEA2Vzowu8EABKwY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.287, 0.190, 0.316, 0.860]?" }, { "from": "gpt", "value": "This region presents a blue bar representing Germany's Free Trade metric with a score of 84." } ], "id_raw": 186 }, { "id": "0EhRso4TqAtFCXox", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the text found at [0.386, 0.019, 0.656, 0.041]?" }, { "from": "gpt", "value": "Building and Real Estate Market Trends." } ], "id_raw": 187 }, { "id": "w25X7z4T32B432Aj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat color is associated with the legend detail at [0.849, 0.088, 0.965, 0.106]?" }, { "from": "gpt", "value": "The label at [0.849, 0.088, 0.965, 0.106], Rental Market Trend, is associated with the color at [0.816, 0.090, 0.840, 0.102], which is green." } ], "id_raw": 187 }, { "id": "uFAeCfx7jOnVpz8q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.362, 0.396, 0.366, 0.795] represent?" }, { "from": "gpt", "value": "The bar represents the Commercial Building Trend and the coordinate of the text label is [0.360, 0.801, 0.372, 0.981]." } ], "id_raw": 187 }, { "id": "eWO89FwsqjfQCq6o", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the coordinate [0.744, 0.595, 0.780, 0.795]?" }, { "from": "gpt", "value": "The bar at [0.744, 0.595, 0.780, 0.795] represents the Rental Market Trend of the Hotel/ Motel property type." } ], "id_raw": 187 }, { "id": "LDP1yje88P1vqRzc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of the Vacant Land in Rental Market Trend}[0.537, 0.496, 0.573, 0.795] higher than {the bar of the Vacant Land in the Property Market Trend}[0.573, 0.381, 0.609, 0.795]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 187 }, { "id": "2EePsIB73Oe0qNdm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of the Recreational Land in the Rental Market Trend}[0.848, 0.645, 0.884, 0.795] lower than {the bar of the Recreational Land in the Property Market Trend}[0.884, 0.346, 0.920, 0.795]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 187 }, { "id": "kCKweMamKjNOs7q6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nComparing the position of {the bar of the Industrial Property in the Rental Market Trend}[0.433, 0.446, 0.469, 0.795] and {the bar of the Industrial Property in the Property Market Trend}[0.469, 0.346, 0.506, 0.795], which is to the right?" }, { "from": "gpt", "value": "{The bar of the Industrial Property in the Property Market Trend}[0.469, 0.346, 0.506, 0.795] is to the right." } ], "id_raw": 187 }, { "id": "7EUTLYzBC6ld1E7k", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is higher, {the bar of the Single Family Home in the Rental Market Trend}[0.122, 0.296, 0.158, 0.795] or {the bar of the Single Family Home in the Property Market Trend}[0.158, 0.181, 0.195, 0.795]?" }, { "from": "gpt", "value": "{The bar of the Single Family Home in the Property Market Trend}[0.158, 0.181, 0.195, 0.795] is higher." } ], "id_raw": 187 }, { "id": "jHgnEBjr76WSQvhj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.812, 0.055, 0.982, 0.139]?" }, { "from": "gpt", "value": "This area[0.812, 0.055, 0.982, 0.139] is the chart legend with two items. The first item is the Rental Market Trend which is green in color, and the color box is at [0.816, 0.090, 0.840, 0.102]. The second item is the Property Market Trend denoted by an orange color, the color box is at [0.816, 0.116, 0.840, 0.128]." } ], "id_raw": 187 }, { "id": "gP81wSXv5wQxATqH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the region [0.537, 0.496, 0.609, 0.795]?" }, { "from": "gpt", "value": "The area [0.537, 0.496, 0.609, 0.795] consists of two bars. The first bar at [0.537, 0.496, 0.573, 0.795] is colored green and represents the Rental Market Trend for the Vacant Land. The second bar at [0.573, 0.381, 0.609, 0.795] is colored orange and represents the Property Market Trend for the Vacant Land." } ], "id_raw": 187 }, { "id": "eYf89M3CdhQ3zjTE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the information contained in the area [0.122, 0.181, 0.195, 0.795]?" }, { "from": "gpt", "value": "This area [0.122, 0.181, 0.195, 0.795] contains two bars. The first bar at [0.122, 0.296, 0.158, 0.795], colored green, represents the Rental Market Trend of Single Family Homes. The second bar at [0.158, 0.181, 0.195, 0.795] colored orange, represents the Property Market Trend of Single Family Homes." } ], "id_raw": 187 }, { "id": "aAExWQKYKz8Umhcm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects found in the region [0.226, 0.346, 0.298, 0.795]?" }, { "from": "gpt", "value": "The region [0.226, 0.346, 0.298, 0.795] covers two bars. The green bar at [0.226, 0.346, 0.262, 0.795] represents the Rental Market Trend for Apartment Building. The orange bar at [0.262, 0.396, 0.298, 0.795] denotes the Property Market Trend for Apartment Building." } ], "id_raw": 187 }, { "id": "mhBIBqpL32saohvg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is depicted by the JSON structured information?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 188 }, { "id": "tD2670T5FunpyTh2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart indicated by the coordinates [0.301, 0.019, 0.741, 0.041]?" }, { "from": "gpt", "value": "Entrepreneurship and Innovation Ecosystem across Major cities." } ], "id_raw": 188 }, { "id": "Wrs4xJ4OojxtJdga", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {the legend item}[0.896, 0.116, 0.919, 0.128] indicate?" }, { "from": "gpt", "value": "It represents the color used for the 'Hubs' data in the bar chart." } ], "id_raw": 188 }, { "id": "rvtHXHWiuAbS2M8M", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nComparing {the bar of 'Startups' in 'San Francisco'}[0.110, 0.267, 0.140, 0.878] and {the bar of 'Hubs' in 'San Francisco'}[0.140, 0.184, 0.169, 0.878], which one is higher?" }, { "from": "gpt", "value": "Hubs." } ], "id_raw": 188 }, { "id": "C4enqIlznBJ2Pl0s", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Berlin' Startups bar} [0.534, 0.587, 0.564, 0.878] taller or shorter than {the 'Berlin' Hubs bar}[0.564, 0.490, 0.593, 0.878]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 188 }, { "id": "1k7j1doQIyElFeFG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the startups bar of Paris}[0.619, 0.618, 0.648, 0.878] and {the startups bar of Beijing}[0.364, 0.427, 0.394, 0.878], which one is taller?" }, { "from": "gpt", "value": "Beijing." } ], "id_raw": 188 }, { "id": "yGV11fF7CBHj6M1c", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region described by the coordinates [0.891, 0.055, 0.982, 0.139]?" }, { "from": "gpt", "value": "It is the chart legend. The first item {Startups} with color indicator [0.896, 0.090, 0.919, 0.102] and label at [0.928, 0.088, 0.977, 0.106], and the second item {Hubs} with color indicator [0.896, 0.116, 0.919, 0.128] and label at [0.928, 0.115, 0.957, 0.132]." } ], "id_raw": 188 }, { "id": "PBOty765FtzqZFtE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the region [0.110, 0.267, 0.169, 0.878]?" }, { "from": "gpt", "value": "There are two bars representing the 'Startups' and 'Hubs' metrics for San Francisco. {The Startups bar}[0.110, 0.267, 0.140, 0.878] and {The Hubs bar}[0.140, 0.184, 0.169, 0.878]." } ], "id_raw": 188 }, { "id": "hJUMxQFjqllVs7pt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat information is conveyed by the coordinates [0.043, 0.872, 0.051, 0.889] to [0.029, -0.024, 0.051, -0.007] region?" }, { "from": "gpt", "value": "These regions represent the Y-axis labels, indicating the count (in thousands) which range from 0 to 140." } ], "id_raw": 188 }, { "id": "iA8N1pwhWT7Nmqud", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis?" }, { "from": "gpt", "value": "The label for the y-axis is \"Performance Scores\" given by {y-axis label}[0.015, 0.387, 0.029, 0.618]." } ], "id_raw": 190 }, { "id": "aQVYadVLDcqFUZp7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the component labelled at the coordinates [0.250, 0.025, 0.800, 0.055]?" }, { "from": "gpt", "value": "Those coordinates correspond to the subtitle of the chart, which is \"Healthcare Performance in Human Health & Disease Management\"." } ], "id_raw": 190 }, { "id": "L5wDcjquAFQtrXK2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the blue bar at [0.126, 0.279, 0.158, 0.944] represent?" }, { "from": "gpt", "value": "The blue bar at these coordinates represents the performance score of 98 for Non-Aggregation method in Depression Recovery." } ], "id_raw": 190 }, { "id": "l09fVB7soE9KYeQV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nIs the score for the Aggregation method in Depression Recovery higher than that for the Non-Aggregation method?" }, { "from": "gpt", "value": "No, {Non-Aggregation}[0.126, 0.279, 0.158, 0.944] has a higher score than {Aggregation}[0.158, 0.537, 0.190, 0.944]." } ], "id_raw": 190 }, { "id": "CwFG77RCHh7BnN50", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nBetween Alzheimer Management and Oncology, which department has a higher score in the Non-Aggregation method?" }, { "from": "gpt", "value": "Alzheimer Management with the score located at {Non-Aggregation for Alzheimer Management}[0.217, 0.557, 0.250, 0.944] has a lower Non-Aggregation score than Oncology located at {Non-Aggregation for Oncology}[0.309, 0.333, 0.342, 0.944]." } ], "id_raw": 190 }, { "id": "bNkcQXaVAuOClh39", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nBetween Vision and Nephrology, which received a higher Aggregation score?" }, { "from": "gpt", "value": "Vision with the Aggregation score at {Aggregation for Vision}[0.709, 0.218, 0.741, 0.944] has a higher score than Nephrology at {Aggregation for Nephrology}[0.801, 0.605, 0.833, 0.944]." } ], "id_raw": 190 }, { "id": "ufhwPf76AbD7MT7u", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within [0.073, 0.074, 0.240, 0.185]?" }, { "from": "gpt", "value": "This region contains a legend which differentiates between two methods of healthcare performance measurement: Non-Aggregation and Aggregation, represented by two color-text labels. The Non-Aggregation method is represented by a green color denoted at {Non-Aggregation hangle color}[0.078, 0.120, 0.106, 0.136], and Aggregation is represented by an orange color denoted at {Aggregation hangle color}[0.078, 0.155, 0.106, 0.171]." } ], "id_raw": 190 }, { "id": "zMimkLuO8o3H3ErI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nPlease detail the contents within [0.309, 0.952, 0.374, 0.975]." }, { "from": "gpt", "value": "This region marks the category \"Oncology\" on the x-axis. The coordinates for \"Oncology\" are {Oncology label}[0.309, 0.952, 0.374, 0.975]." } ], "id_raw": 190 }, { "id": "Z0SVtTYzbcoNIKBC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat features are within the scope [0.525, 0.266, 0.558, 0.944]?" }, { "from": "gpt", "value": "Within these coordinates, there is an orange bar observing the Aggregation method's score in Cardiology. The score is 100, and the coordinates of the score are {Aggregation score for Cardiology}[0.433, 0.266, 0.466, 0.944]." } ], "id_raw": 190 }, { "id": "qF6y5pUsUHYeONFY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.426, 0.029, 0.579]?" }, { "from": "gpt", "value": "That is the y-axis label, named People Count." } ], "id_raw": 191 }, { "id": "FUobtvRISpUlhurn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label in the chart that corresponds to the coordinates [0.357, 0.025, 0.694, 0.055]?" }, { "from": "gpt", "value": "The label at {these coordinates}[0.357, 0.025, 0.694, 0.055] is the subtitle \"Population Migration and Mobility Trends\"." } ], "id_raw": 191 }, { "id": "qecgVKv1ARpgK5Kl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the square at [0.846, 0.120, 0.874, 0.136]?" }, { "from": "gpt", "value": "The color of the square at {these coordinates}[0.846, 0.120, 0.874, 0.136] is #69b3a2 (a shade of green)." } ], "id_raw": 191 }, { "id": "47aQLgD81hk9gCSv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nWhich color corresponds to the higher population trend, the color at {In_Country's color label}[0.846, 0.120, 0.874, 0.136] or the color at {Out_Country's color label}[0.846, 0.155, 0.874, 0.171]?" }, { "from": "gpt", "value": "The color at {Out_Country's color label}[0.846, 0.155, 0.874, 0.171], which is a shade of orange." } ], "id_raw": 191 }, { "id": "zyuKxZnhS7ZvGSLW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nCompare {the color corresponding to In_Country}[0.846, 0.120, 0.874, 0.136] and {the color corresponding to Out_Country}[0.846, 0.155, 0.874, 0.171], which one represents a trend that had a higher population in 2010?" }, { "from": "gpt", "value": "{The color corresponding to Out_Country}[0.846, 0.155, 0.874, 0.171] represents a trend with a higher population in 2010." } ], "id_raw": 191 }, { "id": "xuLPq4zknH7OISdX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nDoes {In_Country\u2019s bar in 2017}[0.711, 0.274, 0.741, 2.001] have a higher population than {Out_Country\u2019s bar in 2017}[0.741, 0.344, 0.771, 2.001]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 191 }, { "id": "wkA2i5WI2Ej6VaGN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the configuration in the region [0.841, 0.074, 0.978, 0.186]." }, { "from": "gpt", "value": "This area includes a legend displaying the trends for 'In_Country' and 'Out_Country'. For 'In_Country', a green color label exists at [0.846, 0.120, 0.874, 0.136] and the text label reads 'In_Country' at [0.885, 0.118, 0.960, 0.142]. For 'Out_Country', an orange color label is present at [0.846, 0.155, 0.874, 0.171] and the associated text label reading 'Out_Country' is at [0.885, 0.153, 0.973, 0.177]." } ], "id_raw": 191 }, { "id": "555wGedhbbks7dWm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects inside the region [0.566, 0.062, 0.685, 2.001]?" }, { "from": "gpt", "value": "Two bars are located within this region:\n1. The green bar represents {In_Country in 2016}[0.625, 0.415, 0.655, 2.001] with a population count of 90.\n2. The orange bar represents {Out_Country in 2016}[0.655, 0.397, 0.685, 2.001] with a population count of 91." } ], "id_raw": 191 }, { "id": "BuBnUfxnIg4c3Cjx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.035, 0.053, 0.061, 0.076]." }, { "from": "gpt", "value": "The area represents the y-axis label {\u2018110\u2019}[0.035, 0.053, 0.061, 0.076] noting a people count of 110." } ], "id_raw": 191 }, { "id": "tkffS2pQTpGyMyFk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.073, 0.074, 0.257, 0.185]?" }, { "from": "gpt", "value": "It's the legend for the chart, showcasing the two types of investments: {Risky Investments}[0.117, 0.118, 0.244, 0.141] and {Stable Investments} [0.117, 0.153, 0.251, 0.176]." } ], "id_raw": 192 }, { "id": "WWlaf46b0nI9mh4T", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart element at coordinates [0.035, 0.133, 0.061, 0.157]?" }, { "from": "gpt", "value": "It's a y-axis label for the value 200." } ], "id_raw": 192 }, { "id": "vFwEJUwOCdDNyo7U", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the element within [0.120, 0.463, 0.149, 0.944]?" }, { "from": "gpt", "value": "It is a blue bar {blue bar}[0.120, 0.463, 0.149, 0.944] representing the value of Risky Investments for Portfolio 1, which is 120.0." } ], "id_raw": 192 }, { "id": "F74Ygsdp4AlI2RP5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nIs the {Risky Investments bar for Portfolio 3}[0.287, 0.303, 0.316, 0.944] taller than corresponding {Stable Investments bar}[0.316, 0.603, 0.346, 0.944]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 192 }, { "id": "0ePpaSUpST8LzSxG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nIn the series of Portfolios, is the {Risky Investments bar for Portfolio 10}[0.872, 0.142, 0.901, 0.944] higher than the {Stable Investments bar for Portfolio 10}[0.901, 0.263, 0.931, 0.944]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 192 }, { "id": "Pv31VoIslY5n5kRM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nAre {Risky Investments bars} generally taller than {Stable Investments bars} in the portfolios?" }, { "from": "gpt", "value": "Not necessarily, it varies in different portfolios." } ], "id_raw": 192 }, { "id": "en39PpVtXII3ts8Q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the information within the bounding coordinates [0.073, 0.074, 0.257, 0.185]." }, { "from": "gpt", "value": "Within these coordinates lies the legend of the chart. It details the associations of the colors to the different investment types: {Risky Investments}[0.117, 0.118, 0.244, 0.141] is represented by the color blue [0.078, 0.120, 0.106, 0.136]; {Stable Investments}[0.117, 0.153, 0.251, 0.176] is represented by the color orange [0.078, 0.155, 0.106, 0.171]." } ], "id_raw": 192 }, { "id": "MF2IbScd6gyGjyic", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat information can be gathered from the region [0.120, 0.463, 0.149, 0.944]?" }, { "from": "gpt", "value": "The region includes a blue bar which represents the value (120.0) for Risky Investments in Portfolio 1." } ], "id_raw": 192 }, { "id": "AzfSgGMSHUSinYKr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content inside the region [0.066, 0.062, 0.985, 0.944]." }, { "from": "gpt", "value": "This is the main plotting area of the chart, it includes all portfolio values for both Risky and Stable Investments. Each portfolio is represented with 2 bars; one for each investment type, with blue bars for Risky Investments and orange bars for Stable Investments." } ], "id_raw": 192 }, { "id": "XE8ZqZ1XScmLIINA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the text within the coordinate [0.365, 0.025, 0.686, 0.055]?" }, { "from": "gpt", "value": "It's \"Social Inequality and Rights Protection\" which is the subtitle of the chart." } ], "id_raw": 193 }, { "id": "AHopxrIMKcx04LjL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar}[0.265, 0.071, 0.372, 0.944] that refers to the Wealthiest 10% in terms of Income Inequality?" }, { "from": "gpt", "value": "The color is light green, specifically #69b3a2." } ], "id_raw": 193 }, { "id": "ZhuLdamXIDU0d8T5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label {[0.015, 0.404, 0.029, 0.602]} stand for?" }, { "from": "gpt", "value": "The label refers to \"Percentile Scores\"." } ], "id_raw": 193 }, { "id": "ZGlgSlCC1BxhUCBZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Wealthiest 10% under Income Inequality}[0.265, 0.071, 0.372, 0.944] higher than {the bar for Poorest 10% under Rights Protection}[0.679, 0.142, 0.786, 0.944]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 193 }, { "id": "4pCu3G6aytdoQM4I", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat color is the bar that is labeled immediately before the legend title located at [0.853, 0.083, 0.928, 0.106]?" }, { "from": "gpt", "value": "It's #69b3a2, which is a shade of light green." } ], "id_raw": 193 }, { "id": "CyClGc8iNZj5DaQ5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for the Poorest 10% in the Income Inequality category}[0.571, 0.935, 0.679, 0.944] have a higher value than {the bar for the Wealthiest 10% in the Rights Protection category}[0.372, 0.864, 0.479, 0.944]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 193 }, { "id": "tyTOKBSc8WzmViav", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the coordinates [0.804, 0.074, 0.978, 0.185] area." }, { "from": "gpt", "value": "This area contains the chart legend. Two category labels are represented: \"Income Inequality\" with its respective color tag {located at [0.809, 0.120, 0.837, 0.136]}, and \"Rights Protection\", with its color tag {located at [0.809, 0.155, 0.837, 0.171]}." } ], "id_raw": 193 }, { "id": "Btv6JXHZctDkBneb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.043, 0.406, 0.061, 0.429]?" }, { "from": "gpt", "value": "It's a label on the y-axis representing the number 60." } ], "id_raw": 193 }, { "id": "yXS70zQr7IguWVxR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be found in the area spanning [0.043, 0.582, 0.061, 0.605]." }, { "from": "gpt", "value": "The label \"40\" for the y-axis is located here." } ], "id_raw": 193 }, { "id": "djLsIrHI2Q8a5Fsl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label in the chart with coordinates [0.015, 0.398, 0.029, 0.608]?" }, { "from": "gpt", "value": "The y-axis label is 'Percentage Usage'." } ], "id_raw": 194 }, { "id": "vn2YZTUsyLKqoldZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text representing 'Coal' in the x-axis?" }, { "from": "gpt", "value": "The coordinates are [0.663, 0.952, 0.694, 0.975]." } ], "id_raw": 194 }, { "id": "CVM89YEkIp3Psraj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat value does {the text label}[0.170, 0.728, 0.201, 0.752] represent in the graph?" }, { "from": "gpt", "value": "It represents the Nonrenewable Energy percentage for Solar, which is 25.2%." } ], "id_raw": 194 }, { "id": "SrWsFTPEQMQGSwuK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat specific color does {the legend item}[0.780, 0.120, 0.808, 0.136] symbolize in the chart?" }, { "from": "gpt", "value": "It symbolizes the color '#69b3a2', which stands for Renewable Energy." } ], "id_raw": 194 }, { "id": "NTXEZzmqhS3dtCTx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the Renewable Energy bar for Solar}[0.132, 0.389, 0.168, 0.944] have a higher percentage usage than {the Nonrenewable Energy bar for Solar}[0.168, 0.758, 0.204, 0.944]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 194 }, { "id": "fezulfrdyEBNghu4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Renewable Energy bar for Coal}[0.643, 0.648, 0.679, 0.944] taller or shorter than {the Nonrenewable Energy bar for Coal}[0.679, 0.266, 0.714, 0.944]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 194 }, { "id": "MGoe9zGhZdxPZYyd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nBetween {the Renewable Energy bar for Wind}[0.234, 0.442, 0.270, 0.944] and {the Nonrenewable Energy bar for Wind}[0.270, 0.708, 0.306, 0.944], which one has higher energy utilization?" }, { "from": "gpt", "value": "Renewable Energy." } ], "id_raw": 194 }, { "id": "MF1ENR45hBPncfD7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Renewable Energy bar}[0.780, 0.120, 0.808, 0.136] color the same as the {Renewable Energy legend color}[0.819, 0.118, 0.948, 0.141]" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 194 }, { "id": "rcuWzN53JrWmiE7D", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content inside [0.775, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "Inside this area, there is a legend showing two categories of energy types: Renewable Energy labeled in color '#69b3a2' at the coordinates [0.780, 0.120, 0.808, 0.136], and Nonrenewable Energy labeled in color '#d98763' at the coordinates [0.780, 0.155, 0.808, 0.171]." } ], "id_raw": 194 }, { "id": "5Ks2SPu10agGJUeS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat object exists in the coordinate range [0.326, 0.952, 0.419, 0.975]?" }, { "from": "gpt", "value": "The category label for \"Hydroelectric\" is within this coordinate range." } ], "id_raw": 194 }, { "id": "4ki29PfFJPWLN4GN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within [0.168, 0.758, 0.204, 0.944]?" }, { "from": "gpt", "value": "There is 1 object:\n1. A bar showing the Nonrenewable Energy usage for Solar, corresponding to a value of 25.2% usage." } ], "id_raw": 194 }, { "id": "2xe0gZz3l5PpRU2p", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.663, 0.952, 0.694, 0.975]." }, { "from": "gpt", "value": "The area contains the 'Coal' text label, denoting the Coal category on the x-axis." } ], "id_raw": 194 }, { "id": "MGSvqgWyBqWvdqAs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the value annotated on the y-axis at coordinates [0.035, 0.814, 0.163, 0.831]?" }, { "from": "gpt", "value": "Tropical Rainforest." } ], "id_raw": 199 }, { "id": "NIjRPJdR46e9mFJ6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar group labeled as 'Time 60' in the legend?" }, { "from": "gpt", "value": "It's a light blue denoted by {the handle of 'Time 60'}[0.824, 0.142, 0.852, 0.155]." } ], "id_raw": 199 }, { "id": "OLWqD05UUlnCstgC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis coordinate at [0.492, 0.939, 0.514, 0.957] represent?" }, { "from": "gpt", "value": "It represents the sustainability score of 1.5." } ], "id_raw": 199 }, { "id": "oFf1MuQtdaymaibs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nHow many ecosystems are displayed in the chart?" }, { "from": "gpt", "value": "Five ecosystems, namely {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831], {Ocean}[0.119, 0.647, 0.163, 0.664], {Desert}[0.117, 0.480, 0.163, 0.498], {Arctic Tundra}[0.072, 0.313, 0.163, 0.331] and {Urban City}[0.089, 0.147, 0.163, 0.164] are displayed." } ], "id_raw": 199 }, { "id": "zn2Fbc1NiQqCVJPQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher sustainability score at Time 40, Arctic Tundra or Urban City?" }, { "from": "gpt", "value": "Arctic Tundra." } ], "id_raw": 199 }, { "id": "H7Fgj42Z0SWGThPf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the relative size of the 'Initial Condition' bar for the 'Desert' compared with its 'Time 60' bar?" }, { "from": "gpt", "value": "The {Desert's 'Initial Condition' bar}[0.173, 0.420, 0.283, 0.554] is larger than its {'Time 60' bar}[0.492, 0.420, 0.592, 0.554]." } ], "id_raw": 199 }, { "id": "7FpBocfrFVZlKK6G", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nWhich ecosystem has a lower 'Initial Condition' score, Tropical Rainforest or Ocean?" }, { "from": "gpt", "value": "Tropical Rainforest." } ], "id_raw": 199 }, { "id": "aKPypEVjDAyAMz0S", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nFor Ocean, which time period shows a higher sustainability score, Time 20 or Time 60?" }, { "from": "gpt", "value": "Time 20." } ], "id_raw": 199 }, { "id": "JgvPBuRmSBGpFMZF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.371, 0.754, 0.558, 0.887]." }, { "from": "gpt", "value": "It is a region containing the Time 20 bar for Tropical Rainforest, with its 'Sustainability Score' rating from 0.90 to 1.75." } ], "id_raw": 199 }, { "id": "7gqcd66vZfbYaDRk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the legend area [0.819, 0.055, 0.978, 0.165]." }, { "from": "gpt", "value": "The legend area contains four items. Each of the four bar groups are labeled: \"{Initial Condition}\"[0.863, 0.062, 0.972, 0.080] is dark blue, \"{Time 20}\"[0.863, 0.088, 0.919, 0.106] is blue, \"{Time 40}\"[0.863, 0.115, 0.919, 0.132] is medium blue and \"{Time 60}\"[0.863, 0.141, 0.919, 0.158] is light blue." } ], "id_raw": 199 }, { "id": "bRAg5BKW6zclTLga", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area between the x axis and the y ticks." }, { "from": "gpt", "value": "This area includes all of the sustainability scores for 5 ecosystems (Tropical Rainforest, Ocean, Desert, Arctic Tundra, Urban City) at four time points (Initial Condition, Time 20, Time 40, Time 60)." } ], "id_raw": 199 }, { "id": "nkMxGHfEMfMoibYH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.431, 0.019, 0.727, 0.041] contain?" }, { "from": "gpt", "value": "It contains the subtitle of the chart, which is 'Ecosystem Sustainability over Time'." } ], "id_raw": 199 }, { "id": "ngGCd8MBhWWwVzl7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the label with coordinates [0.114, 0.402, 0.136, 0.425] indicate?" }, { "from": "gpt", "value": "The label indicates a value of {6.7}[0.114, 0.402, 0.136, 0.425] for the Rural service category." } ], "id_raw": 211 }, { "id": "dI1UXCrYJhVBFFzl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar located at period 3 for urban development}[0.275, 0.283, 0.317, 0.889]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 211 }, { "id": "CCdWWHlhMLn5kIRx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the textual content of the x-axis label found at [0.474, 0.952, 0.573, 0.975]?" }, { "from": "gpt", "value": "Time (Periods)." } ], "id_raw": 211 }, { "id": "1JXI0VsApINfqwAQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for period 1 in urban development}[0.161, 0.228, 0.203, 0.889] taller than {the bar for period 1 in rural development}[0.161, 0.531, 0.203, 0.889]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 211 }, { "id": "jQvSbWvdLbqqOLUu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the green bar at period 12}[0.787, 0.482, 0.829, 0.889] represent a higher value than {the orange bar at the same period}[0.787, 0.565, 0.829, 0.889]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 211 }, { "id": "ktEyXlkaGMkUAh1B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nAre the values shown in {the green bars}[0.104, 0.179, 0.147, 0.889] generally higher than those in {the orange bars}[0.104, 0.427, 0.147, 0.889] across all periods?" }, { "from": "gpt", "value": "No." } ], "id_raw": 211 }, { "id": "poLw5YKeUZT9O2Jq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the region [0.886, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "This area contains the legend with two entries. The first, representing \"Urban\", has a green color marker at [0.891, 0.120, 0.919, 0.136] and text at [0.930, 0.118, 0.972, 0.141]; The second, representing \"Rural\", has an orange color marker at [0.891, 0.155, 0.919, 0.171] and text at [0.930, 0.153, 0.966, 0.176]." } ], "id_raw": 211 }, { "id": "1PHGlIiM3iiJ2ipg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region framed by [0.062, 0.062, 0.985, 0.889]?" }, { "from": "gpt", "value": "The main plot area where all the urban and rural bars are plotted across various periods, neatly aligned along a grid that assists in value assessments." } ], "id_raw": 211 }, { "id": "OBmOw90dzkOqU590", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the horizontal section from [0.043, 0.880, 0.052, 0.903]." }, { "from": "gpt", "value": "This area contains the y-axis label of '0' indicating the start or zero point for the bar values in the chart." } ], "id_raw": 211 }, { "id": "b5PgvBmt7yn4iwvB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the domain given in y-coordinate [0.015, 0.358, 0.029, 0.607]?" }, { "from": "gpt", "value": "The name of the domain is \"Trend Percentage [%]\"." } ], "id_raw": 213 }, { "id": "7ZnjFvWGUP8OxkPF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nCan you find what text is inside the coordinate [0.809, 0.565, 0.839, 0.588]?" }, { "from": "gpt", "value": "The text inside the coordinate is \"3.50\"." } ], "id_raw": 213 }, { "id": "1e2kdfl4C41S2oKT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {legend item}[0.837, 0.120, 0.864, 0.136]?" }, { "from": "gpt", "value": "The color of the legend item is grey-ish." } ], "id_raw": 213 }, { "id": "9dfEd8hImWMPcyaf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat company is represented at [0.132, 0.919, 0.234, 0.942] on the X-axis?" }, { "from": "gpt", "value": "The company represented is \"Biomedica Inc.\"." } ], "id_raw": 213 }, { "id": "lwsmmCERy9rMUhPs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nIn height, does the {bar}[0.410, 0.568, 0.489, 0.903] exceed {another bar}[0.557, 0.466, 0.637, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 213 }, { "id": "UImtP3opBALi9AJj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nIs {this company}[0.132, 0.919, 0.234, 0.942] situated to the left or right of {another company}[0.347, 0.919, 0.473, 0.942] on the X-axis?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 213 }, { "id": "QN7qe76l61dyDsJ5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nCompared to {this bar}[0.557, 0.466, 0.637, 0.903], is {another bar}[0.104, 0.693, 0.183, 0.903] higher or lower?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 213 }, { "id": "J9g1gpjR90LtTGxs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nIs the grey legend label {Stable growth}[0.831, 0.074, 0.978, 0.185] above or below the purple label {Sharp rise}[0.831, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 213 }, { "id": "0UwmrH3liml55UBn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.831, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "The area [0.831, 0.074, 0.978, 0.185] contains two items: The first is called \"Stable growth\", with a color label at [0.837, 0.120, 0.864, 0.136] and a text label at [0.875, 0.118, 0.972, 0.141], denoting a greyish bar; The second is called \"Sharp rise\", with a color label at [0.837, 0.155, 0.864, 0.171] and a text label at [0.875, 0.153, 0.946, 0.176], denoting a purple bar." } ], "id_raw": 213 }, { "id": "pmn1jAz73stXPEBJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the coordinates [0.557, 0.466, 0.637, 0.903]." }, { "from": "gpt", "value": "The coordinates [0.557, 0.466, 0.637, 0.903] compose a bar representing \"Biotec Corp\" having a value of 5.20 in the Stable growth trend." } ], "id_raw": 213 }, { "id": "dOQrqCxQsXb9lfQ7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.490, 0.952, 0.557, 0.975] entail?" }, { "from": "gpt", "value": "The region [0.490, 0.952, 0.557, 0.975] contains the label \"Company\" which is the title for the x-axis." } ], "id_raw": 213 }, { "id": "26earkLs77VpVjhi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat is highlighted in the area [0.807, 0.609, 0.864, 0.903]?" }, { "from": "gpt", "value": "The region [0.784, 0.609, 0.864, 0.903] includes a bar representing the company \"GeneWorks\" under the \"Stable growth\" trend with a recorded percentage of 3.50." } ], "id_raw": 213 }, { "id": "RqKIdH3a2xPYyWtG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar} at the coordinates [0.115, 0.322, 0.222, 0.506] in the chart?" }, { "from": "gpt", "value": "It is a Graduate bar of the group \"Young_Adults\"." } ], "id_raw": 216 }, { "id": "2rUdznUA642DQtT2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat text is located at the coordinates [0.879, 0.452, 0.901, 0.475]?" }, { "from": "gpt", "value": "The text is 1.1." } ], "id_raw": 216 }, { "id": "IFdTRljDobiJm3On", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the meaning of the {colored entity} at [0.835, 0.120, 0.863, 0.136]?" }, { "from": "gpt", "value": "The color represents Graduates." } ], "id_raw": 216 }, { "id": "ASoIEBUszZuUCfj9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Non-Graduate bar}[0.222, 0.506, 0.330, 0.547] taller than the {Graduate bar}[0.115, 0.322, 0.222, 0.506] in the Young_Adults group?" }, { "from": "gpt", "value": "No." } ], "id_raw": 216 }, { "id": "TFNjXXodOSpzVqJf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {the bar for Non-Graduates}[0.529, 0.396, 0.637, 0.506] and {the bar for Graduates}[0.422, 0.136, 0.529, 0.506] in the Middle_Aged group?" }, { "from": "gpt", "value": "The Non-Graduate bar is shorter than the Graduate bar." } ], "id_raw": 216 }, { "id": "qjaJasnX6shcCOCl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nComparing {bar for Seniors in the Graduate category}[0.729, 0.506, 0.836, 0.662] with {bar for Seniors in the Non-Graduate category}[0.836, 0.473, 0.944, 0.506], which is taller?" }, { "from": "gpt", "value": "The bar for Seniors in the Graduate category is taller." } ], "id_raw": 216 }, { "id": "7WVjtokryW4GSrcz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.830, 0.074, 0.978, 0.185]" }, { "from": "gpt", "value": "There are two entries in the legend in this region. The first entry named Graduate is associated with the color represented by the coordinate [0.835, 0.120, 0.863, 0.136] which is purple, and the text is located at [0.874, 0.118, 0.939, 0.141]. The second entry named Non-Graduate is associated with the color represented by the coordinate [0.835, 0.155, 0.863, 0.171] which is brown, and its text is located at [0.874, 0.153, 0.972, 0.176]." } ], "id_raw": 216 }, { "id": "Cl0P4tgowNX5dpK1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.729, 0.506, 0.836, 0.662]?" }, { "from": "gpt", "value": "This region contains a Graduate bar for the 'Seniors' age group. The bar is above the X-axis and has a value of -5.3 denoted by a text located at [0.769, 0.656, 0.796, 0.679]." } ], "id_raw": 216 }, { "id": "oExZzxg7XrpOqjP7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.185, 0.818, 0.260, 0.942]?" }, { "from": "gpt", "value": "The label 'Young_Adults' on the x-axis is found in this region." } ], "id_raw": 216 }, { "id": "CNj9YlN4HVbfXDUc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the main label at the position [0.015, 0.223, 0.029, 0.279] in the subplot?" }, { "from": "gpt", "value": "Y-axis label, called Values." } ], "id_raw": 227 }, { "id": "aYBQoAvc0ysXpaWx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the category \"Elevation\" in the legend?" }, { "from": "gpt", "value": "{the color label}[0.162, -0.007, 0.190, 0.006] shows that Elevation is represented by a light pink color." } ], "id_raw": 227 }, { "id": "vUC3WBQ3VsIA2upd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.764, -0.008, 0.838, 0.009] in the legend area?" }, { "from": "gpt", "value": "Population label." } ], "id_raw": 227 }, { "id": "vavUpSA6ciuJCCRn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nIs {the north region bar}[0.675, 0.436, 0.754, 0.437] taller than {the south region bar}[0.183, 0.437, 0.261, 0.437]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 227 }, { "id": "V8LjDWd66KaMGDBZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the label at}[0.549, -0.008, 0.698, 0.009] have a longer text than {the label at}[0.764, -0.008, 0.838, 0.009]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 227 }, { "id": "KFXoqBpL8AXcWtKv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nAre {the east region y-axis labels}>r>[0.324, 0.939, 0.354, 0.957] in the lower subplot positioned higher than {the west region y-axis labels}>r>[0.951, 0.939, 0.985, 0.957] in the upper subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 227 }, { "id": "0fPIGkXi1XeNME0P", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found within the rectangular area of [0.559, 0.556, 0.968, 0.927]." }, { "from": "gpt", "value": "The region contains another subplot with an x-axis labeled {Region}>r>[0.740, 0.964, 0.787, 0.981] and a y-axis labeled {Values}>r>[0.508, 0.713, 0.522, 0.770]. Also present are categorized bars indicating values for different geographic regions with respective x-tick labels such as {South}>r>[0.539, 0.939, 0.579, 0.957], {North}>r>[0.676, 0.939, 0.715, 0.957], {East}>r>[0.817, 0.939, 0.847, 0.957] and {West}>r>[0.951, 0.939, 0.985, 0.957]." } ], "id_raw": 227 }, { "id": "mMf5URCShY8nzn1Z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat are the primary elements within the coordinate space [0.066, 0.066, 0.475, 0.437]?" }, { "from": "gpt", "value": "This space primarily houses a subplot with axes labeled for a category of regions. There are x-axis labels for {South}>r>[0.104, 0.449, 0.144, 0.466], with corresponding values shown on a set of bars along the y-axis denoting different numerical data points." } ], "id_raw": 227 }, { "id": "YoxclCGEKrzNg1D5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area marked [0.559, 0.066, 0.968, 0.437] in the chart?" }, { "from": "gpt", "value": "This area includes a subplot highlighting various region data with x-axis labeled as {Region}>r>[0.740, 0.473, 0.787, 0.491] and y-axis as {Values}>r>[0.508, 0.223, 0.522, 0.279]. Each region such as {South}>r>[0.596, 0.449, 0.637, 0.466] has a colored bar representation associated with it." } ], "id_raw": 227 }, { "id": "shx4gbXbg3ynrFuL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the \"Direct Export\" in the legend of the chart?" }, { "from": "gpt", "value": "Pink. Details can be found in {the handle for \"Direct Export\"}[0.746, 0.093, 0.774, 0.106]" } ], "id_raw": 229 }, { "id": "o2jhbWi9KaXf7IPc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for the chart area showing data for Germany?" }, { "from": "gpt", "value": "Billion Dollars. It appears on {the y-axis label for Germany}[0.015, 0.673, 0.029, 0.792]" } ], "id_raw": 229 }, { "id": "5KsuKX9JNzDvCzDM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the text description for \"USA\" located at the bottom of the first subplot?" }, { "from": "gpt", "value": "USA. The specific coordinates are {the text for \"USA\"}[0.118, 0.449, 0.147, 0.466]" } ], "id_raw": 229 }, { "id": "p3UTjK01BEnpioKo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for USA in subplot_2}[0.092, 0.746, 0.173, 0.816] higher than {the bar for Japan in subplot_2}[0.393, 0.816, 0.473, 0.830]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 229 }, { "id": "m0vWCHH6qX1xO41W", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the text for \"China\" in subplot_0}[0.213, 0.449, 0.253, 0.466] appear to the left or right of {the text for \"Germany\" in subplot_0}[0.301, 0.449, 0.365, 0.466]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 229 }, { "id": "lK9AU4j1Upg8VEBD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nIs {the text for \"Japan\" in subplot_1}[0.906, 0.449, 0.945, 0.466] closer to {the text for \"Germany\" in subplot_1}[0.794, 0.449, 0.858, 0.466] or to {the border of the subplot_1}[0.985, 0.047, 0.985, 0.437]?" }, { "from": "gpt", "value": "Germany." } ], "id_raw": 229 }, { "id": "F2oCyq5NWiAZLWKo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the region including all texts in {the legend}[0.740, 0.059, 0.893, 0.195]." }, { "from": "gpt", "value": "The area contains descriptions for four trade variables:\n1. \"Direct Export\" text is present at [0.785, 0.092, 0.877, 0.109],\n2. \"Indirect Export\" text appears at [0.785, 0.118, 0.887, 0.135],\n3. The text for \"Import\" is found at [0.785, 0.144, 0.831, 0.162],\n4. Text indicating \"Net Export\" is located at [0.785, 0.170, 0.859, 0.188]." } ], "id_raw": 229 }, { "id": "24Apk23gtHk01wiU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents found within the region {the subplot for Japan in the third position}[0.566, 0.537, 0.985, 0.927]." }, { "from": "gpt", "value": "This area displays a bar chart comparing trade details (Direct export, Indirect export, Import, Net export) for four countries (USA, China, Germany, Japan). It also includes categorical labels on the x-axis and country names along with specific bars and their corresponding values." } ], "id_raw": 229 }, { "id": "WaCzlejI7ZolSo1d", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area encompassing {all x-axis labels for subplot_3}[0.611, 0.939, 0.945, 0.957] contain?" }, { "from": "gpt", "value": "This area contains labels for four countries:\n1. \"USA\" located at [0.611, 0.939, 0.639, 0.957],\n2. \"China\" situated at [0.705, 0.939, 0.745, 0.957],\n3. \"Germany\" at [0.794, 0.939, 0.858, 0.957],\n4. \"Japan\" found at [0.906, 0.939, 0.945, 0.957]." } ], "id_raw": 229 }, { "id": "8tgB0uYAAd0AA6Tx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nCan you specify {the subtitle} for the chart related to Africa?" }, { "from": "gpt", "value": "International Trade and Globalization Trends Africa[0.070, 0.019, 0.489, 0.041]." } ], "id_raw": 235 }, { "id": "QFX0z45i8TB8qOJG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.182, 0.029, 0.320] in the Asia chart indicate?" }, { "from": "gpt", "value": "GNP (in billions)." } ], "id_raw": 235 }, { "id": "IS3JRoI6zSz76dDW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nLocate {the legend icon} for \"GNP (2000)\" in the North-America chart." }, { "from": "gpt", "value": "[0.713, 0.581, 0.740, 0.593]." } ], "id_raw": 235 }, { "id": "FozyBdJPBFw6hezw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the {bar coordinate}[0.086, 0.326, 0.196, 0.437] for Africa's GNP in 2000 or the {bar coordinate}[0.717, 0.667, 0.827, 0.927] for North-America's GNP in 2010?" }, { "from": "gpt", "value": "North-America's GNP in 2010." } ], "id_raw": 235 }, { "id": "RNQeP2TISuv4YNgi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nIs the {Asia chart's y-axis tick labeled \"3.0\"}[0.527, 0.549, 0.549, 0.567] at a higher or lower position than {Europe chart's y-axis tick labeled \"3.0\"}[0.035, 0.549, 0.057, 0.567]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 235 }, { "id": "vElxFhCTL3doo65C", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar for Europe's GNP in 2020}[0.362, 0.722, 0.473, 0.927] have a greater value than the {bar for Asia's GNP in 2010}[0.717, 0.315, 0.827, 0.927]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 235 }, { "id": "Imx1ggFjFCN2dcFl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be deduced from the subplot in the region [0.559, 0.556, 0.985, 0.927]?" }, { "from": "gpt", "value": "This subplot includes the chart for North-America showcasing the GNP from 2000 to 2020. Bars indicating the values are shaded in light red, and the tallest {bar}[0.855, 0.632, 0.966, 0.927] corresponds to the year 2020." } ], "id_raw": 235 }, { "id": "oZnxcLgPDthwLe6m", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area with the coordinates [0.066, 0.066, 0.492, 0.437]." }, { "from": "gpt", "value": "This area details a subplot for Africa with a series of bars painted in light red, representing GNP values for years 2000, 2010, and 2020. Additionally, the y-axis labels indicate GNP values in billions with corresponding ticks." } ], "id_raw": 235 }, { "id": "XJQXWMg625eCAypi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements located in the bottom right region [0.559, 0.473, 0.985, 0.927] of the chart figure?" }, { "from": "gpt", "value": "The main components within this region include the x-axis labels of the years - \"GNP (2000)\", \"GNP (2010)\", and \"GNP (2020)\", the y-axis label indicating \"GNP (in billions)\" and a series of three light red bars representing the GNPs for the years listed, with the bar for \"GNP (2020)\" being the tallest." } ], "id_raw": 235 }, { "id": "9947dmfalGPnJkmu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the text}[0.035, 0.196, 0.043, 0.213] in the chart?" }, { "from": "gpt", "value": "The y-axis label representing the value 3 Trillion dollars." } ], "id_raw": 238 }, { "id": "512GtoH5nZevHIfc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the handle at}[0.532, -0.033, 0.560, -0.021] represent?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 238 }, { "id": "6kr7sRL3VdFgWUjS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat information is revealed by {the x-axis label}[0.751, 0.964, 0.780, 0.981]?" }, { "from": "gpt", "value": "It indicates the Year." } ], "id_raw": 238 }, { "id": "zO7HmCuPwu0BZ30s", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar in 2010 for Germany}[0.178, 0.809, 0.262, 0.927] higher than {the bar in 2005 for India}[0.566, 0.913, 0.650, 0.927]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 238 }, { "id": "tnF4AK9K7taRxUzA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for China in 2005}[0.566, 0.400, 0.650, 0.437] have a lower trade volume than {the bar for United States in 2020}[0.388, 0.265, 0.473, 0.437]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 238 }, { "id": "523ipOMrJMQiWO1b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nAre {the values on the y-axis for the subplot of India}[0.527, 0.920, 0.536, 0.548] the same range as {the values on the y-axis for the subplot of China}[0.527, 0.040, 0.536, 0.057]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 238 }, { "id": "JxDqGViuMGjMuVF8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nDescribe what is contained in the coordinates [0.546, 0.047, 0.985, 0.437]." }, { "from": "gpt", "value": "This area includes the entire plot for China's Global Trade Volumes Over the Years, showing bars from 2005 to 2020 with x-axis labels and y-axis labels indicating trade volumes." } ], "id_raw": 238 }, { "id": "hoDXDyd7Qpb2oyLf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat information is depicted within the area [0.053, 0.537, 0.493, 0.927]?" }, { "from": "gpt", "value": "This region delineates the subplot for Germany's Global Trade Volumes, including bars representing trade volumes from the years 2005 to 2020, x-axis labels denoting the years, and y-axis labels displaying the trade volumes in trillions." } ], "id_raw": 238 }, { "id": "ioJJh0RbsVcdQJQM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of [0.546, 0.537, 0.985, 0.927]." }, { "from": "gpt", "value": "This area consists of the subplot for India's Global Trade Volumes Over the Years, featuring bars from 2005 to 2020, along with corresponding x-axis year labels and y-axis labels detailing trade volume in trillions." } ], "id_raw": 238 }, { "id": "haNEtW48NEyVXsQw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat is shown by the coordinates [0.121, 0.447, 0.228, 0.686]?" }, { "from": "gpt", "value": "The bar representing the \"Software\" category in \"Healthcare\"." } ], "id_raw": 240 }, { "id": "oU5NdocqkHiJoMMn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {Software bar in Education}[0.240, 0.328, 0.347, 0.686]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 240 }, { "id": "GxnOdRINRX5Hwpdy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat's the category represented by {this bar}[0.359, 0.251, 0.465, 0.686]?" }, { "from": "gpt", "value": "Finance for \"Software\"." } ], "id_raw": 240 }, { "id": "wrpUnmj68iAODS9c", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nComparing {Software in Finance}[0.359, 0.251, 0.465, 0.686] and {Tech&Innovation in Finance}[0.359, 0.136, 0.465, 0.251], which one is higher?" }, { "from": "gpt", "value": "Software." } ], "id_raw": 240 }, { "id": "dneAM3CddIFokt4J", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat sector has a greater representation of {Tech&Innovation bar in Energy}[0.596, 0.136, 0.703, 0.261] or {Tech&Innovation bar in Transport}[0.715, 0.136, 0.822, 0.290]?" }, { "from": "gpt", "value": "Transport." } ], "id_raw": 240 }, { "id": "TZTGmS902opU1NfX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nDoes the {Software bar}[0.121, 0.447, 0.228, 0.686] related to Healthcare have a higher representation than the {Tech&Innovation bar}[0.121, 0.136, 0.228, 0.447] in the same field?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 240 }, { "id": "Wae943o5LcDyPNVI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the area [0.347, 0.070, 0.714, 0.133]?" }, { "from": "gpt", "value": "This region contains the legend of the chart, which includes two categories: Software with coordinates [0.403, 0.084, 0.480, 0.119] and its corresponding color represented by a green rectangle at [0.354, 0.088, 0.389, 0.112], and Tech&Innovation with coordinates [0.563, 0.084, 0.707, 0.119] and its corresponding color represented by a yellow rectangle at [0.515, 0.088, 0.549, 0.112]." } ], "id_raw": 240 }, { "id": "aprukj7NHR0jXiyQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nGive a summary of the chart region [0.359, 0.136, 0.465, 0.686]." }, { "from": "gpt", "value": "This part of the chart represents the Finance sector. It contains two bars: Software and Tech&Innovation. The green bar shows the representation of Software from coordinates [0.359, 0.251, 0.465, 0.686], and the yellow bar shows the representation of Tech&Innovation from coordinates [0.359, 0.136, 0.465, 0.251]." } ], "id_raw": 240 }, { "id": "9Sm99Ge6VgNKUwEm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat's found in region [0.596, 0.136, 0.703, 0.686]?" }, { "from": "gpt", "value": "This region contains the data bars for the Energy sector. There's a green Software bar from [0.596, 0.261, 0.703, 0.686], and a yellow Tech&Innovation bar from [0.596, 0.136, 0.703, 0.261]." } ], "id_raw": 240 }, { "id": "Bs3t0ejcUJ4Jbfr8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinate [0.667, 0.052, 0.881, 0.087]?" }, { "from": "gpt", "value": "It's the label for the \"Satellite Communication\" for the legend." } ], "id_raw": 241 }, { "id": "VrBkhe6ydBtITMOe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the legend label with coordinates [0.618, 0.055, 0.653, 0.079]?" }, { "from": "gpt", "value": "It's a shade of blue, indicating \"Satellite Communication\"." } ], "id_raw": 241 }, { "id": "WMOLrrmsP5Su8MGi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the value range of the bar located at [0.552, 0.366, 0.940, 0.903]?" }, { "from": "gpt", "value": "Its range is from 0.00 to 70.00, representing \"Mobile Internet\" under the category of \"Accelerated Decline\"." } ], "id_raw": 241 }, { "id": "Rj0Sgt1umlNqqo70", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing mobile internet trend for \"Slow Growth\"}[0.121, 0.635, 0.509, 0.903] taller than {the bar representing mobile internet for \"Accelerated Decline\"}[0.552, 0.366, 0.940, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 241 }, { "id": "ML6vOcLpQMbUK66C", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing \"Mobile Internet\"}[0.121, 0.635, 0.509, 0.903][0.552, 0.366, 0.940, 0.903] taller than {the bars representing \"Satellite Communication\"}[0.121, 0.136, 0.509, 0.443][0.552, 0.136, 0.940, 0.289]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 241 }, { "id": "0HPLxoVmpKRf9g3Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Slow Growth\"}([0.121, 0.635, 0.509, 0.903], [0.121, 0.443, 0.509, 0.635], [0.121, 0.136, 0.509, 0.443]) and {\"Accelerated Decline\"}([0.552, 0.366, 0.940, 0.903], [0.552, 0.289, 0.940, 0.366], [0.552, 0.136, 0.940, 0.289]), which category has a higher overall values?" }, { "from": "gpt", "value": "Slow Growth." } ], "id_raw": 241 }, { "id": "RVP9W5NYIXvfdTbg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the region [0.174, 0.038, 0.888, 0.101]." }, { "from": "gpt", "value": "This region contains three labels of the legend. They are {Mobile Internet}[0.181, 0.055, 0.216, 0.079], {Optical Network}[0.395, 0.055, 0.430, 0.079] and {Satellite Communication} [0.618, 0.055, 0.653, 0.079]. The respective labels for these colors are in the coordinates [0.229, 0.052, 0.361, 0.087], [0.444, 0.052, 0.584, 0.087], and [0.667, 0.052, 0.881, 0.087]." } ], "id_raw": 241 }, { "id": "5V1fBawpPlHg0pr6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.552, 0.289, 0.940, 0.366]." }, { "from": "gpt", "value": "Within these coordinates, there is a bar of the \"Optical Network\", category \"Accelerated Decline\". The value range of this bar is from 70.00 to 80.00 shown by the y-axis." } ], "id_raw": 241 }, { "id": "v7nR0RiYChcvt8qr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the region from [0.080, 0.136, 0.981, 0.903] contain?" }, { "from": "gpt", "value": "This region mainly contains the plotted chart, with all bars and axes. The bars represent the 3 categories: \"Mobile Internet\", \"Optical Network\", and \"Satellite Communication\" across the two time points: \"Slow Growth\" and \"Accelerated Decline\"" } ], "id_raw": 241 }, { "id": "BCw5QQZl7GlfHLVk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located in the region [0.552, 0.136, 0.940, 0.903]." }, { "from": "gpt", "value": "There are 3 bars in this region, each representing a different category of the \"Accelerated Decline\" class. These bars correspond to \"Mobile Internet\"[0.552, 0.366, 0.940, 0.903], \"Optical Network\"[0.552, 0.289, 0.940, 0.366] and \"Satellite Communication\"[0.552, 0.136, 0.940, 0.289]." } ], "id_raw": 241 }, { "id": "glhV8NNcUnFDx6xF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the {object with color #4e78bf}[0.121, 0.051, 0.228, 0.188] represent in the chart?" }, { "from": "gpt", "value": "It represents the project 'SecurityUpgrade' under the phase 'CashCow'." } ], "id_raw": 242 }, { "id": "ffxCNvCaUdblXQHN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat object is at the coordinate [0.817, 0.190, 0.851, 0.215]?" }, { "from": "gpt", "value": "It is the color represent 'CashCow' in {the legend}[0.810, 0.069, 0.973, 0.236]." } ], "id_raw": 242 }, { "id": "CBX1hWlGLttmP8xu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nWhich project does this object [0.240, 0.051, 0.347, 0.170] denote?" }, { "from": "gpt", "value": "It denotes the project 'BackendRebuild'." } ], "id_raw": 242 }, { "id": "feRxDhXPtXUJLKb2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nIn {the legend}[0.810, 0.069, 0.973, 0.236], which legend item is to the left of {the CashCow color-label}[0.817, 0.190, 0.851, 0.215]?" }, { "from": "gpt", "value": "The legend item 'ScalingUP' is to the left of the 'CashCow' color-label." } ], "id_raw": 242 }, { "id": "jnA1HU4TpdN9UWbn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nIs {the 'FrontendRevamp' bar for 'RampingUP'}[0.359, 0.366, 0.465, 0.475] higher or lower than {the 'FrontendRevamp' bar for 'ScalingUP'}[0.359, 0.158, 0.465, 0.366]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 242 }, { "id": "PXLmFpqmhxOM9iy8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'MarketExpansion' bar for 'CashCow'}[0.477, 0.051, 0.584, 0.147] and {the 'MarketExpansion' bar for 'RampingUP'}[0.477, 0.339, 0.584, 0.475], which one has a larger value?" }, { "from": "gpt", "value": "The 'MarketExpansion' bar for 'RampingUP'." } ], "id_raw": 242 }, { "id": "2fkoiu4AmYOKaiQf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.810, 0.069, 0.973, 0.236]." }, { "from": "gpt", "value": "The highlighted region represents the chart legend. Its components include the 'RampingUP' label located at [0.865, 0.083, 0.966, 0.118], with the corresponding color marker at [0.817, 0.086, 0.851, 0.110], the 'ScalingUP' label located at [0.865, 0.135, 0.951, 0.170] with the corresponding color marker at [0.817, 0.138, 0.851, 0.162], and the 'CashCow' label at [0.865, 0.187, 0.945, 0.222] with the corresponding color marker at [0.817, 0.190, 0.851, 0.215]." } ], "id_raw": 242 }, { "id": "kduP0t1FskIWtxYq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe about the region [0.121, 0.188, 0.228, 0.475]?" }, { "from": "gpt", "value": "The region contains two stacked bars for the project 'SecurityUpgrade'. The bottom bar [0.121, 0.188, 0.228, 0.318] represents the 'ScalingUP' phase while the top bar [0.121, 0.318, 0.228, 0.475] represents the 'RampingUP' phase." } ], "id_raw": 242 }, { "id": "6Z3R7tejju3a4ISF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat do you see in the region from [0.833, 0.305, 0.940, 0.475]?" }, { "from": "gpt", "value": "This region shows the 'RampingUP' phase of the 'CustomerFeedbackSystem' project, with its scale going from 0 to 40.11 in the y-axis." } ], "id_raw": 242 }, { "id": "FavkzgYv7FYq7hQ5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the bar chart?" }, { "from": "gpt", "value": "There is no title mentioned in the chart figure." } ], "id_raw": 243 }, { "id": "XDraR70xulPUpMI8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the orange bar at [0.790, 0.250, 0.940, 0.444] represent?" }, { "from": "gpt", "value": "It represents the value of the \"Education Aid\" for \"Volatile Growth\"." } ], "id_raw": 243 }, { "id": "7hhd5ZzcBHWZl2q0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart element present at the coordinates [0.454, 0.099, 0.574, 0.134]?" }, { "from": "gpt", "value": "It is the label for Education Aid in the legend." } ], "id_raw": 243 }, { "id": "UgbHOnzA1B299OuQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar with coordinates [0.623, 0.200, 0.606, 0.516] represent?" }, { "from": "gpt", "value": "It\u2019s the orange bar showing 15.40% to 85.60% of \"Education Aid\" for the \"Rapid Growth\" scenario." } ], "id_raw": 243 }, { "id": "U9z2u74oYoik2CTG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value in \"Rapid Growth\", {Education Aid}[0.456, 0.200, 0.606, 0.516] or {Medical Assistance}[0.456, 0.516, 0.606, 0.585]?" }, { "from": "gpt", "value": "Education Aid." } ], "id_raw": 243 }, { "id": "1HZ4Y74OSQI0HlQB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nIs the green rectangle representing \"Medical Assistance\" in \"Rapid Expansion\" taller than the orange rectangle representing \"Education Aid\" in the same scenario?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 243 }, { "id": "3pz0M0HESnUmQOkE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nIs {Education Aid of Rapid Growth}[0.456, 0.200, 0.606, 0.516] higher than {Medical Assistance of Marginal Decrease}[0.623, 0.472, 0.773, 0.585]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 243 }, { "id": "RlPnyK44MW8fEbVI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nAre the bars in \"Rapid Growth\" scenario<[0.456, 0.136, 0.606, 0.585]> higher than in \"Substantial Increase\" scenario<[0.288, 0.136, 0.439, 0.537]> overall?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 243 }, { "id": "Ov1Xa3AdvtznQfZX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat all components are in the region that spans [0.151, 0.086, 0.911, 0.148]?" }, { "from": "gpt", "value": "There are three handles representing three different categories in this region. The green handle at [0.158, 0.103, 0.192, 0.127] indicates \"Medical Assistance\", the orange handle at [0.405, 0.103, 0.440, 0.127] denotes \"Education Aid\", and the blue handle at [0.609, 0.103, 0.643, 0.127] stands for \"Environmental Conservation\". Respectively their corresponding text labels are at [0.206, 0.099, 0.371, 0.134], [0.454, 0.099, 0.574, 0.134], and [0.657, 0.099, 0.904, 0.134]." } ], "id_raw": 243 }, { "id": "8ZJmpJNJ3hXP4QQw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects represented in the region [0.121, 0.270, 0.940, 0.585]." }, { "from": "gpt", "value": "This region contains a group of five bars representing \"Medical Assistance\" category for each scenario: \"Rapid Expansion\" located in [0.121, 0.270, 0.272, 0.585]; \"Substantial Increase\" in [0.288, 0.537, 0.439, 0.585]; \"Rapid Growth\" in [0.456, 0.516, 0.606, 0.585]; \"Marginal Decrease\" in [0.623, 0.472, 0.773, 0.585]; \"Volatile Growth\" in [0.790, 0.444, 0.940, 0.585]." } ], "id_raw": 243 }, { "id": "LslmerRoxIYqolHM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in [0.623, 0.136, 0.773, 0.585]?" }, { "from": "gpt", "value": "It consists of 3 bars for the scenario \"Marginal Decrease\". The bars represent \"Medical Assistance\" at [0.623, 0.472, 0.773, 0.585], \"Education Aid\" at [0.623, 0.379, 0.773, 0.472], and \"Environmental Conservation\" at [0.623, 0.136, 0.773, 0.379]." } ], "id_raw": 243 }, { "id": "W6OZT4YLPU10CNU5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the detail for area [0.121, 0.270, 0.272, 0.585]?" }, { "from": "gpt", "value": "It contains the green bar representing the \"Medical Assistance\" of \"Rapid Expansion\" scenario." } ], "id_raw": 243 }, { "id": "3ttcrju0RGIhVffQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the sector with {the label 'Profit Sustainability'}[0.677, 0.065, 0.812, 0.093] in the legend?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 244 }, { "id": "PLp2qCb46JbQsI5L", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label 'Energy' on the x-axis?" }, { "from": "gpt", "value": "[0.359, 0.759, 0.403, 0.848]." } ], "id_raw": 244 }, { "id": "t2AUsmWANKNubVvV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nWhat category does the bar occupying the space [0.227, 0.497, 0.337, 0.740] represent?" }, { "from": "gpt", "value": "The category is 'E-commerce'." } ], "id_raw": 244 }, { "id": "sonnZAnEv9VjUA4W", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nWhat subcategory does the bar with the coordinates [0.591, 0.344, 0.701, 0.497] represent from the legend?" }, { "from": "gpt", "value": "This bar represents {Social Initiatives}[0.497, 0.065, 0.610, 0.093]." } ], "id_raw": 244 }, { "id": "p7k0yDwSEDMY6aYN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'E-commerce'}[0.227, 0.132, 0.337, 1.000] taller or shorter than {the bar representing 'Technology'}[0.106, 0.132, 0.215, 1.000]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 244 }, { "id": "qa2HRYEhyizAWwp5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar with the label 'Profit Sustainability'}[0.834, 0.132, 0.943, 0.192] higher than {the bar labeled 'Social Initiatives'}[0.834, 0.192, 0.943, 0.375] for the 'Healthcare' category?" }, { "from": "gpt", "value": "No." } ], "id_raw": 244 }, { "id": "pV4nIfRB7CdHAK3g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Energy'}[0.349, 0.132, 0.458, 1.000] wider than {the bar representing 'Healthcare'}[0.834, 0.132, 0.943, 1.000]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 244 }, { "id": "fKuTP7MFKEvOOJA5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nOf the three sections in the {Transportation bar}[0.470, 0.132, 0.579, 1.000], which color corresponds to {Environmental Impact}[0.276, 0.065, 0.430, 0.093]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 244 }, { "id": "uO0xD0RkYLCI0jw9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.232, 0.054, 0.817, 0.104]." }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes three labels: {Environmental Impact}[0.276, 0.065, 0.430, 0.093], {Social Initiatives}[0.497, 0.065, 0.610, 0.093], and {Profit Sustainability}[0.677, 0.065, 0.812, 0.093]. Associated colors with these labels are green, yellow, and blue respectively." } ], "id_raw": 244 }, { "id": "1xURXHPCnmjziHB0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.106, 0.132, 0.215, 1.000]?" }, { "from": "gpt", "value": "This area encompasses a bar from the 'Technology' category. The bar contains three segments representing 'Environmental Impact'[0.106, 0.527, 0.215, 0.740], 'Social Initiatives'[0.106, 0.253, 0.215, 0.527], and 'Profit Sustainability'[0.106, 0.132, 0.215, 0.253]." } ], "id_raw": 244 }, { "id": "xFNnBiO5OZQK9Wfd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what can be seen in the area [0.024, 0.364, 0.055, 0.392]?" }, { "from": "gpt", "value": "There is a '60%' label at the y-axis, indicating the percentage level in the chart." } ], "id_raw": 244 }, { "id": "dvCsEQNrRkaB5sx5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nWhat is discussed within the ranges [0.444, 0.759, 0.525, 0.921] and [0.591, 0.759, 0.646, 0.970]?" }, { "from": "gpt", "value": "These ranges include the x-axis labels for the 'Transportation' and 'Food and Beverage' categories respectively." } ], "id_raw": 244 }, { "id": "lWhIQDyPlKmYVOzV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object inside the coordinates [0.271, 0.064, 0.377, 0.099]?" }, { "from": "gpt", "value": "It is a text item in the legend that is labeled \"GDP Growth\"." } ], "id_raw": 245 }, { "id": "R5AxxaQiG6Rd0q43", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat color corresponds to {the legend term \"GDP Growth\"}[0.271, 0.064, 0.377, 0.099] in the chart?" }, { "from": "gpt", "value": "The corresponding color is green (#7e9671) marked in the coordinated box/range [0.223, 0.067, 0.257, 0.092]." } ], "id_raw": 245 }, { "id": "TG6zCtXPpAgb6CAQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat information is presented around the coordinate {tick}[0.903, 0.821] on the x-axis?" }, { "from": "gpt", "value": "It represents the \"Year10\" label." } ], "id_raw": 245 }, { "id": "EIT31esZbwEsM5Ur", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller: {the bar representing GDP Growth in Year2}[0.204, 0.794, 0.278, 0.821] or {the bar representing Unemployment Rate in Year2}[0.204, 0.752, 0.278, 0.794]?" }, { "from": "gpt", "value": "The bar representing GDP Growth in Year2 is taller." } ], "id_raw": 245 }, { "id": "xWO4UCbkeqOp6OmW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing Inflation Rate}[[0.121, 0.744, 0.196, 0.753], [0.204, 0.741, 0.278, 0.752], [0.287, 0.738, 0.361, 0.752], [0.369, 0.738, 0.444, 0.752], [0.452, 0.736, 0.527, 0.752], [0.535, 0.733, 0.609, 0.752], [0.618, 0.730, 0.692, 0.751], [0.700, 0.727, 0.775, 0.751], [0.783, 0.722, 0.858, 0.749], [0.866, 0.719, 0.940, 0.748]] overall shorter than {the bars representing GDP Growth}[[0.121, 0.791, 0.196, 0.821], [0.204, 0.794, 0.278, 0.821], [0.287, 0.795, 0.361, 0.821], [0.369, 0.799, 0.444, 0.821], [0.452, 0.802, 0.527, 0.821], [0.535, 0.805, 0.609, 0.821], [0.618, 0.808, 0.692, 0.821], [0.700, 0.810, 0.775, 0.821], [0.783, 0.813, 0.858, 0.821], [0.866, 0.816, 0.940, 0.821]] and {Unemployment Rate}[[0.121, 0.753, 0.196, 0.791], [0.204, 0.752, 0.278, 0.794], [0.287, 0.752, 0.361, 0.795], [0.369, 0.752, 0.444, 0.799], [0.452, 0.752, 0.527, 0.802], [0.535, 0.752, 0.609, 0.805], [0.618, 0.751, 0.692, 0.808], [0.700, 0.751, 0.775, 0.810], [0.783, 0.749, 0.858, 0.813], [0.866, 0.748, 0.940, 0.816]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 245 }, { "id": "unVwPo3lRzrHoSU9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nIs the value for {Unemployment Rate}[0.412, 0.067, 0.446, 0.092] in Year10 higher or lower than the value for {GDP Growth}[0.223, 0.067, 0.257, 0.092] in Year10?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 245 }, { "id": "11w3U6OtGNgBhcTH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the area in the coordinates [0.783, 0.722, 0.858, 0.749]." }, { "from": "gpt", "value": "This area represents the value of \"Inflation Rate\" in \"Year9\". It's a blue bar (color code: #4e78bf) that represents a range of '10.50 -> 14.40'." } ], "id_raw": 245 }, { "id": "x2hDORbQeacBUPFH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat elements lie within the coordinates [0.216, 0.050, 0.846, 0.113]?" }, { "from": "gpt", "value": "This is the legend area. Inside it, there are three legend entries representing \"GDP Growth\", \"Unemployment Rate\", and \"Inflation Rate\". Each entry consists of a square label colored respectively with '#7e9671', '#d3c475' and '#4e78bf' and a text." } ], "id_raw": 245 }, { "id": "pw42s3SeiVJnVLG5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's portrayed between [0.080, 0.136, 0.981, 0.821]?" }, { "from": "gpt", "value": "This area mainly contains data presented as stacked bars corresponding to GDP Growth, Unemployment Rate, and Inflation Rate for different years. Also, it includes x-axis labels representing various years (from \"Year1\" to \"Year10\") and y-axis labels giving percentage values (from '0%' to '100%'). The bars vary in length and correspond to the respective values of the different rates for each year." } ], "id_raw": 245 }, { "id": "59CPU5pF5iWIxu9L", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the coordinates [0.378, 0.108, 0.492, 0.143]?" }, { "from": "gpt", "value": "Text representing Local Culture in the legend." } ], "id_raw": 246 }, { "id": "AIFLNOx03kfKlrzq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the entity at coordinates [0.526, 0.111, 0.561, 0.135]?" }, { "from": "gpt", "value": "#d3c475, representing External Influence." } ], "id_raw": 246 }, { "id": "T21GyVoJXpojSJua", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.121, 0.238, 0.228, 0.531]?" }, { "from": "gpt", "value": "A bar representing Local Culture score for Asian Villages." } ], "id_raw": 246 }, { "id": "qPuFTGHCmNPccO7D", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nIs {the Local Culture bar of Asian Villages}[0.121, 0.238, 0.228, 0.531] higher or lower than {the External Influence bar of Asian Villages}[0.121, 0.136, 0.228, 0.238]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 246 }, { "id": "RbcVT6hrkkOlJIio", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nIs {World Heritage Sites in Local Culture}[0.833, 0.195, 0.940, 0.531] scoring higher than {European Architectures in External Influence}[0.240, 0.136, 0.347, 0.215]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 246 }, { "id": "GeNyWCjltHGEJFbG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nIs the category of {World Heritage Sites}[0.878, 0.555, 0.896, 0.912] has a higher percentage of {External Influence}[0.833, 0.136, 0.940, 0.195] than it does of {Local Culture}[0.833, 0.195, 0.940, 0.531]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 246 }, { "id": "cjWmUIeuSXjoyBBI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area with coordinates [0.322, 0.094, 0.739, 0.156]?" }, { "from": "gpt", "value": "This region contains the legend of the graph. It has two entries: Local Culture{Local Culture}[0.378, 0.108, 0.492, 0.143] is represented by a handle and text in color #7e9671, and External Influence{External Influence}[0.575, 0.108, 0.732, 0.143] is represented by a handle and text in color #d3c475." } ], "id_raw": 246 }, { "id": "kZEaZRZdhgFxh8hO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.121, 0.136, 0.228, 0.531]." }, { "from": "gpt", "value": "This region contains two bars corresponding to Asian Villages. The upper bar is {Local Culture bar}[0.121, 0.238, 0.228, 0.531] and the smaller, bottom one denotes {External Influence bar}[0.121, 0.136, 0.228, 0.238]." } ], "id_raw": 246 }, { "id": "FlUgd6LKalrVrtZP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat major elements does the box [0.80, 0.50, 1.00, 1.00] encompass?" }, { "from": "gpt", "value": "It encompasses the right half of the bars, which represent the categories of Indigenous Cultures, Global Expo, Street Festivals, and World Heritage Sites. It also includes the corresponding category labels and both the Local Culture and External Influence segments for each category." } ], "id_raw": 246 }, { "id": "40M1OZv74M7Kqeba", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.346, 0.083, 0.679, 0.113] in the chart?" }, { "from": "gpt", "value": "This is the chart's title, which reads \"Bar Chart Showing Food Security Trends\"." } ], "id_raw": 247 }, { "id": "6zAY12PXnyxg6Ury", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.797, 0.074, 0.731, 0.099] in the chart?" }, { "from": "gpt", "value": "It's the color label for {Social Initiatives}[0.797, 0.074, 0.731, 0.099], depicted in a specific blue." } ], "id_raw": 247 }, { "id": "Q9N3uP1Dd3j4rGos", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nWhat is denoted by the object at [0.121, 0.391, 0.246, 0.774]?" }, { "from": "gpt", "value": "It's a bar on the chart representing the percentage of Environment Protection initiative for {Apple}[0.121, 0.391, 0.246, 0.774], which is 60%." } ], "id_raw": 247 }, { "id": "zQn0VeghJhQG3NGR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Environment Protection bar for Apple}[0.121, 0.391, 0.246, 0.774] taller than {the Environment Protection bar for IBM}[0.399, 0.519, 0.524, 0.774]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 247 }, { "id": "BK5bykkgvV6Ze8fX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the Social Initiatives bar for Facebook}[0.677, 0.136, 0.801, 0.231] or {the Social Initiatives bar for Google}[0.538, 0.136, 0.663, 0.200]?" }, { "from": "gpt", "value": "The bar for Facebook." } ], "id_raw": 247 }, { "id": "K7lEcKRR57vfX3Pe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Charity Programs bar for Microsoft}[0.260, 0.295, 0.385, 0.455] inferior in height to {the Charity Programs bar for Amazon}[0.815, 0.263, 0.940, 0.583]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 247 }, { "id": "HPic71KeE56BvDVI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the region [0.168, 0.057, 0.894, 0.120]?" }, { "from": "gpt", "value": "This region contains the chart's legend. It consists of three labels with corresponding color codes: {Environment Protection}[0.223, 0.071, 0.427, 0.106] with a green color code, {Charity Programs}[0.511, 0.071, 0.662, 0.106] with a yellow color code, and {Social Initiatives}[0.745, 0.071, 0.887, 0.106] represented with a blue color code." } ], "id_raw": 247 }, { "id": "mEPo2Z0cjLOaP8TM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.399, 0.519, 0.524, 0.774]." }, { "from": "gpt", "value": "It's a green bar representing the Environmental Protection initiative for the company IBM, and it stretches from 0% to 40% in heights." } ], "id_raw": 247 }, { "id": "e3ivSxmCtHp6bbnr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the region [0.815, 0.136, 0.940, 0.774]?" }, { "from": "gpt", "value": "Within these coordinates, interestingly, there are three separate bars representing Amazon's initiatives in, from bottom to top, Social Initiatives, Charity Programs, and Environment Protection, with percentage heights of 20%, 50%, and 30%, respectively." } ], "id_raw": 247 }, { "id": "Wcijq9mHeQJTNo07", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is being represented in the data?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 248 }, { "id": "DAm7cQrVUvR6hJaQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the company_B bar}[0.121, 0.387, 0.509, 0.536]?" }, { "from": "gpt", "value": "#d3c475." } ], "id_raw": 248 }, { "id": "WJJW9czz6ddbR1Uy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the color #4e78bf represent in the legend?" }, { "from": "gpt", "value": "{Company_C}[0.689, 0.082, 0.790, 0.117]." } ], "id_raw": 248 }, { "id": "ZpU1TaFEX4trWrQd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis tick at coordinates [0.019, 0.122, 0.068, 0.157]?" }, { "from": "gpt", "value": "100%." } ], "id_raw": 248 }, { "id": "8LdBQLDNIHg9n4lG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nIs {Company_A}[0.121, 0.536, 0.509, 0.707] projected to have a smaller or larger market share compared to {Company_B}[0.121, 0.387, 0.509, 0.536]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 248 }, { "id": "uwGdtzsIOd936tvp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, is {Company_C}[0.121, 0.136, 0.509, 0.387] predicted to have the largest or smallest market share?" }, { "from": "gpt", "value": "Largest." } ], "id_raw": 248 }, { "id": "zXEg3g8h9wfWLZMB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nAre the bars of {Company_A} [0.121, 0.536, 0.509, 0.707] and [0.552, 0.541, 0.940, 0.707] taller than the bars of {Company_C}[0.121, 0.136, 0.509, 0.387] and [0.552, 0.136, 0.940, 0.370]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 248 }, { "id": "lyDS7n4xR7JrpcIB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nIs {Company_C's Projection bar}[0.552, 0.136, 0.940, 0.370] larger or smaller than {Company_C's Market Share bar}[0.121, 0.136, 0.509, 0.387]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 248 }, { "id": "8Aw4C9StCi0BbjB5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area in the region [0.265, 0.067, 0.797, 0.130]." }, { "from": "gpt", "value": "This region represents the legend of the graph. There are three labels, each with an associated color. The labels are 'Company_A', 'Company_B', and 'Company_C' with respective coordinates at [0.320, 0.082, 0.421, 0.117], [0.504, 0.082, 0.605, 0.117}, and [0.689, 0.082, 0.790, 0.117]. The colors associated with these labels in the legend are '#7e9671', '#d3c475', and '#4e78bf', respectively." } ], "id_raw": 248 }, { "id": "He0u4Vh8Cw4KsfLz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within coordinate area [0.552, 0.136, 0.940, 0.707]." }, { "from": "gpt", "value": "The region consists of three bar segments - one for each company (A, B, C), representing their respective projected market shares. {Company_A}[0.552, 0.541, 0.940, 0.707] has the lowest projected market share, followed by {Company_B}[0.552, 0.370, 0.940, 0.541] and finally, {Company_C}[0.552, 0.136, 0.940, 0.370] has the highest projected market share." } ], "id_raw": 248 }, { "id": "QP893kxejfVb3jSC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.121, 0.387, 0.509, 0.707]?" }, { "from": "gpt", "value": "This region contains the bars for 'Market Share' of the companies. The bars for {Company_A}[0.121, 0.536, 0.509, 0.707], {Company_B}[0.121, 0.387, 0.509, 0.536], and {Company_C}[0.121, 0.136, 0.509, 0.387] are included with 'Company_A' having the smallest market share and 'Company_C' having the largest market share." } ], "id_raw": 248 }, { "id": "3Auv9ZLjfxv1iBy8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the data located at [0.041, 0.693, 0.068, 0.728]." }, { "from": "gpt", "value": "This region represents a tick on the y-axis labeled '0%'." } ], "id_raw": 248 }, { "id": "gzeA5Co5eZ8Hkk0X", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the figure at the coordinates [0.318, 0.066, 0.345, 0.086]?" }, { "from": "gpt", "value": "It is the label color for Agriculture in the legend." } ], "id_raw": 249 }, { "id": "3n6fF1tqNjGsXRWS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the text inside the coordinate [0.357, 0.064, 0.433, 0.092]?" }, { "from": "gpt", "value": "It's a legend label, called Agriculture." } ], "id_raw": 249 }, { "id": "eTXHNVp4cQm61Eox", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the period marked between coordinates [0.527, 0.770, 0.596, 0.909]?" }, { "from": "gpt", "value": "Middle Ages." } ], "id_raw": 249 }, { "id": "WGcht5fkWXLlY0VN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat appears at [0.024, 0.492, 0.055, 0.520]?" }, { "from": "gpt", "value": "Y-axis percentage scale, labeled as 40%." } ], "id_raw": 249 }, { "id": "DsJ1mGz8vEorZLbh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nDoes the {Agriculture bar in Primitive Stage}[0.106, 0.626, 0.228, 0.750] have a higher value than the {Science bar in the same stage}[0.106, 0.595, 0.228, 0.626]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 249 }, { "id": "61HaNGtUxx9SCBbM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Industrialization bar in the Modern History stage}[0.678, -0.759, 0.800, -0.314] shorter than the {Science bar in the Modern History stage}[0.678, -0.314, 0.800, 0.200]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 249 }, { "id": "CtYTPweUcnxs6ju7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nDoes {Agriculture}[0.357, 0.064, 0.433, .092] have its highest value in the {Contemporary Era}[0.822, 0.193, 0.943, 0.750]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 249 }, { "id": "ohDZYtDgUlXlZbfp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Industrialization bar for the Classical Period}[0.392, 0.070, 0.514, 0.101] taller than the {Science Bar for the Classical Period}[0.392, 0.101, 0.514, 0.317]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 249 }, { "id": "MKcStE5a37odA0DS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the graphic elements between [0.312, 0.053, 0.737, 0.103]." }, { "from": "gpt", "value": "There are three color-text pairs representing each category; each group consists of a color label and a text label. They are: {Agriculture}[0.318, 0.066, 0.345, 0.086] paired with [0.357, 0.064, 0.433, 0.092], {Science}[0.461, 0.066, 0.489, 0.086] paired with [0.500, 0.064, 0.553, 0.092], and {Industrialization}[0.581, 0.066, 0.609, 0.086] paired with [0.620, 0.064, 0.732, 0.092]." } ], "id_raw": 249 }, { "id": "YcLxWr5a8YiZGQGs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area [0.067, 0.770, 0.882, 0.970]?" }, { "from": "gpt", "value": "This area contains the period labels along the x-axis, which includes: \"Primitive Stage\", \"Ancient Period\", \"Classical Period\", \"Middle Ages\", \"Modern History\", and \"Contemporary Era\"." } ], "id_raw": 249 }, { "id": "ZMIypPRZF8dZy2s3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat do we see in the region [0.822, 0.193, 0.943, 0.750]?" }, { "from": "gpt", "value": "There are three bars in this region. The first one represents the Agriculture value in the Contemporary Era [0.822, 0.193, 0.943, 0.750]; the second one represents the Science value in the same era [0.822, -0.400, 0.943, 0.193]; and the third one represents the Industrialization value [0.822, -0.975, 0.943, -0.400]." } ], "id_raw": 249 }, { "id": "PdqOkshv6m9Io8yV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area [0.024, 0.121, 0.055, 0.767]." }, { "from": "gpt", "value": "The area comprises the scaling for the Y-axis ranging from 0% to 100%, with intervals of 10% between marks." } ], "id_raw": 249 }, { "id": "MlJ3LnYNxi1Bi442", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the category of the bar in the coordinates [0.321, 0.310, 0.415, 0.572]?" }, { "from": "gpt", "value": "Yoga." } ], "id_raw": 250 }, { "id": "LH7c62LN6f5S0SIB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the \"Strength Training\" bar from \"Fitness Trends\" data set?" }, { "from": "gpt", "value": "[0.216, 0.327, 0.310, 0.572]." } ], "id_raw": 250 }, { "id": "QyJyUJxbSE5Zc0WC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the {Fitness Trends bar}[0.845, 0.275, 0.940, 0.572]?" }, { "from": "gpt", "value": "34." } ], "id_raw": 250 }, { "id": "JExR78Lx7QRxrKWf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the colour associated with {Fitness Trends} in the legend[0.301, 0.088, 0.749, 0.150]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 250 }, { "id": "mU5461rVKoZXn9SR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Treadmill Run\" bar of \"Fitness Trends\"}[0.845, 0.275, 0.940, 0.572] higher than {the \"Treadmill Run\" bar of \"Sports Health Trends\"}[0.845, -0.091, 0.940, 0.275]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 250 }, { "id": "nSIfEONA75YouqpU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat has a greater value in \"Sports Health Trends\", {Yoga}[0.321, 0.205, 0.415, 0.310] or {Dietary Supplements}[0.635, 0.066, 0.730, 0.327]?" }, { "from": "gpt", "value": "Dietary Supplements." } ], "id_raw": 250 }, { "id": "QFKf2ndE039oPaoG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nIs {the Physical Activity bar for Fitness Trends}[0.111, 0.354, 0.205, 0.572] taller than {the Strength Training bar for Fitness Trends} [0.216, 0.327, 0.310, 0.572]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 250 }, { "id": "Zob69pE56Xn53WNC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nComparing {\"Healthy Eating\" bar for Fitness Trends}[0.531, 0.240, 0.625, 0.572] and {\"Healthy Eating\" bar for Sports Health Trends}[0.531, 0.031, 0.625, 0.240], which trend shows a higher value?" }, { "from": "gpt", "value": "Sports Health Trends." } ], "id_raw": 250 }, { "id": "8O9iWBQGvhLzZs0C", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained in [0.301, 0.088, 0.749, 0.150]?" }, { "from": "gpt", "value": "This area contains the legend with two entries: {\"Fitness Trends\"} with coordinates [0.357, 0.101, 0.479, 0.136] represented by green colour [0.308, 0.105, 0.343, 0.129] and {\"Sports Health Trends\"} with coordinates [0.562, 0.101, 0.742, 0.136] represented by blue colour [0.514, 0.105, 0.548, 0.129]." } ], "id_raw": 250 }, { "id": "9nk88BDRPx5fQ3NM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat's in the rectangular area from [0.111, 0.227, 0.205, 0.572]?" }, { "from": "gpt", "value": "There is a green bar, representing \"Physical Activity\" from the \"Fitness Trends\" dataset." } ], "id_raw": 250 }, { "id": "RzHk98InMV9F2TeN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements within the region [0.531, 0.022, 0.625, 0.572]." }, { "from": "gpt", "value": "There are two bars within this region: one from the \"Fitness Trends\" dataset with coordinates [0.531, 0.240, 0.625, 0.572] and the other from the \"Sports Health Trends\" dataset with coordinates [0.531, 0.031, 0.625, 0.240]. Both represent the \"Healthy Eating\" category." } ], "id_raw": 250 }, { "id": "L6zTAoXYAh2uhRka", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the boundaries [0.569, 0.022, 0.663, 0.572]?" }, { "from": "gpt", "value": "There are two bar charts showing the \"Dietary Supplements\" information for both Fitness Trends [0.635, 0.327, 0.730, 0.572] and Sports Health Trends [0.635, 0.066, 0.730, 0.327]." } ], "id_raw": 250 }, { "id": "bVn1tDCdQZi6C8j4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar chart represent according to the title at [0.389, -0.003, 0.660, 0.027]?" }, { "from": "gpt", "value": "The chart represents \"Fitness and Sports Health Trends\"." } ], "id_raw": 251 }, { "id": "d5SDJBNDffPQsCLG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the Weight Lifting legend}[0.865, 0.090, 0.962, 0.113]?" }, { "from": "gpt", "value": "#d3c475 which corresponds to the color of weight lifting data bars." } ], "id_raw": 251 }, { "id": "Q6zJ2AZNsxbfMO49", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label for the bar located at [0.106, 0.800, 0.234, 0.935]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 251 }, { "id": "FPiiObmoKVxGco41", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Weight Lifting}[0.865, 0.090, 0.962, 0.113] in 2015 greater than {Cardio Exercise}[0.865, 0.055, 0.973, 0.078] in 2011?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 251 }, { "id": "vg4pzgSTmQUXsLT7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nIs the {Yoga trend}[0.865, 0.125, 0.898, 0.148] dropping over the years from 2010 to 2015?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 251 }, { "id": "n4OZEL0OBeQ6EM5n", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nAre the {Weight Lifting bars} generally taller than {Yoga bars}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 251 }, { "id": "ZWqe4enR9ScM02zV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.821, 0.046, 0.978, 0.157]." }, { "from": "gpt", "value": "The area contains the legend of the chart: The green bar represents Cardio Exercise ([0.865, 0.055, 0.973, 0.078]), the yellow bar stands for Weight Lifting ([0.865, 0.090, 0.962, 0.113]), and the blue bar is assigned to Yoga ([0.865, 0.125, 0.898, 0.148])." } ], "id_raw": 251 }, { "id": "FiSGxCz88IDRQkzJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.106, 0.034, 0.943, 0.935]?" }, { "from": "gpt", "value": "The region contains all the bars for the given years (2010-2015) representing three sports trends: Cardio Exercise, Weight Lifting, and Yoga." } ], "id_raw": 251 }, { "id": "JhuKyE02twxR0qGc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.024, 0.205, 0.055, 0.229]." }, { "from": "gpt", "value": "This region contains the y-axis label \"80%\" indicating the percentage for the sports fitness trends." } ], "id_raw": 251 }, { "id": "BSewR8RD6ADNEBuS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.541, 0.593, 0.730, 0.607] represent in the chart?" }, { "from": "gpt", "value": "It represents the value of the \"Education\" category for \"Social Awareness\", which is 3." } ], "id_raw": 252 }, { "id": "kttkofXI4Ukn2qdB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell what color the bar in the {subplot_0}[0.121, 0.296, 0.310, 0.404] is?" }, { "from": "gpt", "value": "The color of the bar is \"#d3c475\", which represents the category \"Sustainable Production.\"" } ], "id_raw": 252 }, { "id": "zUA9gPADeTXAWGGh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nWhat's the label for the percentage scale at {coordinates}[0.019, 0.122, 0.068, 0.157]?" }, { "from": "gpt", "value": "100%." } ], "id_raw": 252 }, { "id": "ON1EEXDlH0hCYhWS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nIs the \"Social Awareness\" bar for \"Environment\"[0.121, 0.404, 0.310, 0.607] taller than the corresponding \"Sustainable Production\" bar[0.121, 0.296, 0.310, 0.404]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 252 }, { "id": "svLss6OFkHnwOIWE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nBetween \"Environment\"[0.207, 0.632, 0.224, 0.853] and \"Work Policies\"[0.417, 0.632, 0.435, 0.859], which has a higher value for the \"Sustainable Production\" result?" }, { "from": "gpt", "value": "\"Work Policies\"." } ], "id_raw": 252 }, { "id": "f4LmU5lP4GolbMqv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nAre the \"Human Rights\" values \"[[0.121, 0.136, 0.310, 0.296], [0.331, 0.136, 0.520, 0.211], [0.541, 0.136, 0.730, 0.334], [0.751, 0.136, 0.940, 0.334]]\" generally higher than the \"Sustainable Production\" values [0.121, 0.296, 0.310, 0.404], [0.331, 0.211, 0.520, 0.362], [0.541, 0.334, 0.730, 0.593], [0.751, 0.334, 0.940, 0.428]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 252 }, { "id": "0LI50GJw5A0i9mPg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the composition of the area [0.180, 0.082, 0.882, 0.145]?" }, { "from": "gpt", "value": "It contains a legend of three items. The color labels are arranged from left to right as follows: \"Social Awareness\" has a color scale of [#7e9671] with the label at [0.236, 0.096, 0.386, 0.131], \"Sustainable Production\" has a color scale of [#d3c475] with the label at [0.469, 0.096, 0.669, 0.131], and \"Human Rights\" with a color scale of [#4e78bf] with the label at [0.752, 0.096, 0.875, 0.131]." } ], "id_raw": 252 }, { "id": "4rBUw86SRUWqgFRi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area labeled as {subplot_0}[0.080, 0.136, 0.981, 0.607]." }, { "from": "gpt", "value": "This area represents the main plot of the graph including the X and Y-axes, bars representing different categories of \"Social Awareness\", \"Sustainable Production\", and \"Human Rights\" for \"Environment\", \"Work Policies\", \"Education\", and \"Affordable Housing\". This area also includes the percentage scale on the Y-axis and category labels on the X-axis." } ], "id_raw": 252 }, { "id": "0wscCz92njr8Q6Zg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is in the area [0.121, 0.136, 0.940, 0.607]." }, { "from": "gpt", "value": "The area contains all the bars representing the values of \"Environment\", \"Work Policies\", \"Education\", and \"Affordable Housing\" categories for \"Social Awareness\", \"Sustainable Production\", and \"Human Rights.\"" } ], "id_raw": 252 }, { "id": "WMboX2RPerT4mvlV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.336, 0.072, 0.558, 0.107]?" }, { "from": "gpt", "value": "It is a legend text labeled as \"Community Development\"." } ], "id_raw": 253 }, { "id": "XBP5ZpKAX0UQXrG9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar that represents the value of \"Instance3\" for \"Urban Planning\"?" }, { "from": "gpt", "value": "[0.359, -0.105, 0.465, 0.402]" } ], "id_raw": 253 }, { "id": "d3dEjRSUGHaa4Kqv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the values shown in the {Urban Planning bar}[0.715, 0.009, 0.822, 0.395]?" }, { "from": "gpt", "value": "59 -> 120." } ], "id_raw": 253 }, { "id": "JRxbr7IHqnOiBj45", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nWhose color does the color label at [0.593, 0.075, 0.628, 0.099] represent?" }, { "from": "gpt", "value": "It represents the color of the Urban Planning bar." } ], "id_raw": 253 }, { "id": "qW3wrmfS6ostAAt2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nIs the height of the {Community Development bar at Instance1}[0.121, 0.452, 0.228, 0.769] greater than the {Urban Planning bar at Instance1}[0.121, 0.098, 0.228, 0.452]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 253 }, { "id": "MHAaVZFTNPXEeLSy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nDo the bars of Community Development overall contain higher values than those of Urban Planning?" }, { "from": "gpt", "value": "No." } ], "id_raw": 253 }, { "id": "j58HxN3ABtKQ6uV2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nAre {Instance1}<{Instance1 Community Development bar}[0.121, 0.452, 0.228, 0.769], {Instance1 Urban Planning bar}[0.121, 0.098, 0.228, 0.452]> and {Instance3}<{Instance3 Community Development bar}[0.359, 0.402, 0.465, 0.769], {Instance3 Urban Planning bar}[0.359, -0.105, 0.465, 0.402]> both having a higher value in Urban Planning?" }, { "from": "gpt", "value": "No." } ], "id_raw": 253 }, { "id": "riRFVetHoHeVHVJW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Urban Planning bar at Instance7}<{Urban Planning bar at Instance7}[0.833, -0.086, 0.940, 0.326]> taller than the {Urban Planning bar at Instance5}<{Urban Planning bar at Instance5}[0.596, -0.143, 0.703, 0.288]>?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 253 }, { "id": "OVze6YQBEin21Uhf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects located within [0.280, 0.058, 0.782, 0.121]?" }, { "from": "gpt", "value": "Inside this region are the legend title and two legend entries: For the first entry, the color label is at [0.287, 0.075, 0.322, 0.099], colored in green, corresponding to the Community Development text label located at [0.336, 0.072, 0.558, 0.107]; As for the second entry, the color label is at [0.593, 0.075, 0.628, 0.099], colored in golden yellow, corresponding to the Urban Planning text label located at [0.642, 0.072, 0.775, 0.107]." } ], "id_raw": 253 }, { "id": "HWRx8SLFFPaVwvhS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the items within [0.833, -0.086, 0.940, 0.769]." }, { "from": "gpt", "value": "There are two bars in this region. One bar ({Community Development}) located at [0.833, 0.326, 0.940, 0.769] represents a value for Community Development as 70.00, and another bar ({Urban Planning}) located at [0.833, -0.086, 0.940, 0.326] shows a value for Urban Planning as 135.00." } ], "id_raw": 253 }, { "id": "2eCGk4axUsusF8Kl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you identify in the region [0.240, -0.099, 0.347, 0.769]?" }, { "from": "gpt", "value": "In this region, there are a Community Development bar and an Urban Planning bar. The Community Development bar is located at [0.240, 0.345, 0.347, 0.769], with values ranging from 0 to 67, while the Urban Planning bar is at [0.240, -0.099, 0.347, 0.345], with values from 67 to 70." } ], "id_raw": 253 }, { "id": "eIFmBUWydR1P3emd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nGive a description of all objects within the region [0.041, 0.755, 0.068, 0.790]." }, { "from": "gpt", "value": "This area contains a horizontal axis \"yticks\" with label \"0%\" of the bar chart." } ], "id_raw": 253 }, { "id": "hn1Gyocbie4pj0xC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label name representing the pink bar {the pink bar}[0.541, 0.198, 0.730, 0.425] plotted for 'Campaign Promotions' in the chart?" }, { "from": "gpt", "value": "New Media" } ], "id_raw": 254 }, { "id": "IbBdEwELxbUvzJxS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nPlease identify the object placed at [0.703, 0.105, 0.818, 0.140]." }, { "from": "gpt", "value": "{the legend text 'Public Events'}[0.703, 0.105, 0.818, 0.140]" } ], "id_raw": 254 }, { "id": "BuyS6enJCA18384X", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the color in the legend at [0.654, 0.108, 0.689, 0.132]?" }, { "from": "gpt", "value": "Public Events." } ], "id_raw": 254 }, { "id": "x1dxh2vtxlH7Wbps", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.331, 0.177, 0.520, 0.466] represent?" }, { "from": "gpt", "value": "This bar represents the influence of {New Media}[0.331, 0.177, 0.520, 0.466] for 'Social Media Influence'." } ], "id_raw": 254 }, { "id": "y8UmEm6Gvy8Vdl5R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nIs {Traditional Media's bar for 'News Coverage'}[0.121, 0.322, 0.310, 0.549] higher or lower than {New Media's bar for 'News Coverage'}[0.121, 0.177, 0.310, 0.322]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 254 }, { "id": "yZyBRIUQQvdEsvgE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nDoes the bar at [0.751, 0.342, 0.940, 0.549] correspond to a higher or lower value compared to the bar at [0.751, 0.260, 0.940, 0.342] for 'Political debates'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 254 }, { "id": "mxuhEO0nAZJhZuWu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nIs {the influence of New Media}[0.331, 0.177, 0.520, 0.466] on 'Social Media Influence' greater than {the influence of Traditional Media}[0.331, 0.466, 0.520, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 254 }, { "id": "tCT73abJ4z9tFZdX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nBetween {New Media for 'Political Debates'}[0.751, 0.260, 0.940, 0.342] and {Public Events}[0.751, 0.136, 0.940, 0.260], which one has a higher stake?" }, { "from": "gpt", "value": "Public Events." } ], "id_raw": 254 }, { "id": "URfmwTeLmh3KSRRJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements in the region [0.121, 0.136, 0.940, 0.549]." }, { "from": "gpt", "value": "This region contains twelve bar plots grouped by four categories. The bars within each group correspond to the influence of Traditional Media, New Media, and Public Events respectively on the categories 'News Coverage', 'Social Media Influence', 'Campaign Promotions', and 'Political Debates'." } ], "id_raw": 254 }, { "id": "AUKPGMalo218NkKo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nCan you elaborate on the components found within [0.236, 0.091, 0.825, 0.154]?" }, { "from": "gpt", "value": "This region includes three color-text pairs used in the legend. The first pair represents 'Traditional Media' (green), the second pair represents 'New Media' (yellow), and the third pair signifies 'Public Events' (blue)." } ], "id_raw": 254 }, { "id": "nevT7oOC5CPNmoCb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements present within [0.541, 0.136, 0.730, 0.549]?" }, { "from": "gpt", "value": "The region contains three bars corresponding to 'Campaign Promotions' category. The bars represent the influence of 'Traditional Media'[0.541, 0.425, 0.730, 0.549], 'New Media'[0.541, 0.198, 0.730, 0.425], and 'Public Events'[0.541, 0.136, 0.730, 0.198] respectively." } ], "id_raw": 254 }, { "id": "WPRCHQEyNHsuOge0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the area [0.331, 0.136, 0.520, 0.549]." }, { "from": "gpt", "value": "This area accommodates three bars that indicate the impact of the 3 media types for 'Social Media Influence'. The bars represent 'Traditional Media'[0.331, 0.466, 0.520, 0.549], 'New Media'[0.331, 0.177, 0.520, 0.466], and 'Public Events'[0.331, 0.136, 0.520, 0.177] respectively." } ], "id_raw": 254 }, { "id": "BS4HM5uBEZJti4Fc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "The chart does not have a title text." } ], "id_raw": 256 }, { "id": "9yPxG9vP79FxhDse", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker that represents 2021 in the chart legend?" }, { "from": "gpt", "value": "The color of the marker that represents 2021 in {the legend}[0.885, 0.012, 0.991, 0.088] is #6e7a5f, which is a kind of green." } ], "id_raw": 256 }, { "id": "rnaUK8jxNCE0uLQv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label of this chart?" }, { "from": "gpt", "value": "The Y-axis label of this chart is \"{Improvement (%)}\"[0.019, 0.367, 0.036, 0.570]." } ], "id_raw": 256 }, { "id": "3OhtzCa3AU2y7Ttv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat process does the {first bar of group 1}[0.148, 0.034, 0.251, 0.182] represent?" }, { "from": "gpt", "value": "The bar represents the process \"Call Resolution\"." } ], "id_raw": 256 }, { "id": "WGsq8rUsZ2rbKbdC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nIs the 2022 bar for \"Call Resolution\" higher than the 2021 bar?" }, { "from": "gpt", "value": "Yes, {the 2022 bar for \"Call Resolution\"}[0.251, 0.034, 0.354, 0.220] is higher than {the 2021 bar for \"Call Resolution\"}[0.148, 0.034, 0.251, 0.182]." } ], "id_raw": 256 }, { "id": "1oaXDj6pqKEm3Ofz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nOn the Y-axis, are the values positive or negative?" }, { "from": "gpt", "value": "Negative, as the {Y axis}[0.043, 0.894, 0.096, 0.917], [0.043, 0.677, 0.096, 0.700], [0.043, 0.459, 0.096, 0.483], [0.054, 0.242, 0.096, 0.266], [0.069, 0.025, 0.096, 0.048] indicates negative values." } ], "id_raw": 256 }, { "id": "BFGlE7DzGWj56fui", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nBased on the bars in the chart, was there a general improvement or decline in processes from 2021 to 2022?" }, { "from": "gpt", "value": "There was a decline, as the {bars for 2022}[0.251, 0.034, 0.354, 0.220], [0.545, 0.034, 0.648, 0.385], [0.839, 0.034, 0.942, 0.706] are overall higher on the negative Y-axis than the {bars for 2021}[0.148, 0.034, 0.251, 0.182], [0.442, 0.034, 0.545, 0.278], [0.736, 0.034, 0.839, 0.550]." } ], "id_raw": 256 }, { "id": "dsGF7Q4EZWFOLBiM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nDoes the improvement rate for \"Budget Usage\" decrease or increase from 2021 to 2022?" }, { "from": "gpt", "value": "It decreases as {the bar for 2022}[0.839, 0.034, 0.942, 0.706] is higher (more negative) on the Y-axis than {the bar for 2021}[0.736, 0.034, 0.839, 0.550]." } ], "id_raw": 256 }, { "id": "B3VsFSYZSGdswTMN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.885, 0.012, 0.991, 0.088]." }, { "from": "gpt", "value": "The region [0.885, 0.012, 0.991, 0.088] is the {legend}[0.885, 0.012, 0.991, 0.088], which contains two entries. The first entry includes a green square color marker with coordinates [0.892, 0.023, 0.927, 0.039] denoting year 2021, with its label at the coordinates [0.940, 0.021, 0.984, 0.044]. The second entry includes a sort of beige square color marker with coordinates [0.892, 0.058, 0.927, 0.074] which represents year 2022, with its label at [0.940, 0.056, 0.984, 0.079]." } ], "id_raw": 256 }, { "id": "gLPW8vkm8MbDmKtO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be seen in the area [0.108, 0.034, 0.981, 0.903]." }, { "from": "gpt", "value": "In the rectangular region [0.108, 0.034, 0.981, 0.903], six bars are plotted comparing three different process improvement percentages for 2021 and 2022 - \"Call Resolution\", \"Meeting Efficiency\" and \"Budget Usage\". The y-axis to the left shows scale from 0% to -30%, and the bars stretch downwards from the x-axis as they represent negative percentage values. The color-coded legend to the right outside the rectangle associates green bars with 2021 and beige ones with 2022. X-axis labels for the processes are placed below the bars." } ], "id_raw": 256 }, { "id": "vocb96zAXauaikn0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.736, 0.034, 0.942, 0.550]?" }, { "from": "gpt", "value": "There are two objects within this region: \n1. The green bar that represents the \"Budget Usage\" process's improvement percentage for 2021, with the coordinate range of [0.736, 0.034, 0.839, 0.550].\n2. The text above the 2021 bar representing its numerical value of \"-17.80\", with the coordinate range of [0.765, 0.519, 0.810, 0.543]." } ], "id_raw": 256 }, { "id": "aY6F4lqFWOlWAa7C", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see within the region [0.148, 0.034, 0.354, 0.220]?" }, { "from": "gpt", "value": "Within the region [0.148, 0.034, 0.354, 0.220], there are two objects which are bars representing the \"Call Resolution\" process. The green bar with the coordinate range [0.148, 0.034, 0.251, 0.182] is for 2021 and its associated value \"-5.12\" with the coordinate range [0.177, 0.152, 0.222, 0.175]. The beige bar with the coordinate range [0.251, 0.034, 0.354, 0.220] is for 2022 and its associated value \"-6.43\" with the coordinate range [0.280, 0.190, 0.325, 0.213]." } ], "id_raw": 256 }, { "id": "b9sJlGNSpuBKBBFQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the third object in the legend?" }, { "from": "gpt", "value": "{The third object}[0.807, 0.856, 0.884, 0.879] in the legend is in the color #f4f1e0, which is off white." } ], "id_raw": 257 }, { "id": "63OxTfMdV4CwiBAG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nHow is the Y-axis labeled on the chart ?" }, { "from": "gpt", "value": "The Y-axis is labeled as {Investment Strategies}[0.019, 0.340, 0.036, 0.597]." } ], "id_raw": 257 }, { "id": "DaBwuu2ygLJ0oCLe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat text is located at the coordinates [0.807, 0.856, 0.884, 0.879]?" }, { "from": "gpt", "value": "The text located at these coordinates is {Dividend}[0.807, 0.856, 0.884, 0.879]." } ], "id_raw": 257 }, { "id": "ScRe70Kljeyxkl0m", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the second group of bars}[0.233, 0.495, 0.340, 0.903], [0.660, 0.295, 0.767, 0.903] referring to?" }, { "from": "gpt", "value": "The second group of bars is referring to the {RPI}[0.807, 0.821, 0.835, 0.844]." } ], "id_raw": 257 }, { "id": "LpERqmmmiUuS5IMH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nIs {the first bar of Dividend}[0.340, 0.469, 0.447, 0.903] taller than {the first bar of RPI}[0.233, 0.495, 0.340, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 257 }, { "id": "pvvLG8hXIp43VWA8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing \"Steep increase\"}[0.126, 0.642, 0.233, 0.903], [0.233, 0.495, 0.340, 0.903] generally shorter than {the bars representing \"Inexorable decline\"}[0.553, 0.121, 0.660, 0.903], [0.660, 0.295, 0.767, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 257 }, { "id": "4Je9qsaoXWhYThWD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the IPO label}[0.807, 0.786, 0.836, 0.809] correspond to {the bars in the color represented by the first item in the legend}[0.759, 0.788, 0.793, 0.804]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 257 }, { "id": "TU6X87OhxTJ3HwQt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nWhich shows a steeper increase, {Dividend}[0.768, 0.380, 0.875, 0.899] or {RPI}[0.660, 0.292, 0.767, 0.899]?" }, { "from": "gpt", "value": "{Dividend}[0.768, 0.380, 0.875, 0.899]." } ], "id_raw": 257 }, { "id": "gOvpyUwxXUZWMjKG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.088, 0.034, 0.912, 0.903]." }, { "from": "gpt", "value": "This area presents a grouped bar chart with two bars per group. It displays investment trends for IPO, RPI, and Dividend over two periods, Steep increase and Inexorable decline. X-axis labels for these periods are at [0.222, 0.919, 0.351, 0.942] and [0.634, 0.919, 0.793, 0.942] respectively. The heights of the bars reflect the value of the investment strategies in these periods with color codes for IPO,r>RPI, and Dividend located in the top right corner of the chart." } ], "id_raw": 257 }, { "id": "zOrI3RET0dhotrV0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.054, 0.459, 0.076, 0.483]." }, { "from": "gpt", "value": "The area [0.054, 0.459, 0.076, 0.483] contains a Y-axis label \"50\", specifying the midpoint value on the Y-axis scale." } ], "id_raw": 257 }, { "id": "vY5LtEsBouOaSTgr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.767, 0.382, 0.874, 0.903]?" }, { "from": "gpt", "value": "Within [0.767, 0.382, 0.874, 0.903] there is a single bar which represents the value for dividend during Inexorable decline. The bar has a text annotation with coordinates [0.596, 0.098, 0.618, 0.121] which specifies the data value this bar represents - 60.00." } ], "id_raw": 257 }, { "id": "gPpJxbSCuUvxr4kN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.752, 0.777, 0.891, 0.888]?" }, { "from": "gpt", "value": "In this region, there is a legend for the colored bars in the plot. The color label in the first pair is located at [0.759, 0.788, 0.793, 0.804] and its corresponding text label {'IPO'} is at [0.807, 0.786, 0.836, 0.809]; The second pair is located at [0.759, 0.823, 0.793, 0.839] with the label {'RPI'} at [0.807, 0.821, 0.835, 0.844]; The third pair is located at [0.759, 0.858, 0.793, 0.874] with its label {'Dividend'} at [0.807, 0.856, 0.884, 0.879]." } ], "id_raw": 257 }, { "id": "xmvuOTzJ0QBk6AXm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat is at the position [0.616, 0.221, 0.759, 0.244]?" }, { "from": "gpt", "value": "It's the label for the Home Amenities." } ], "id_raw": 258 }, { "id": "Wij425I6FwH1I6bD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat color is represented by the handle at [0.568, 0.258, 0.602, 0.274]?" }, { "from": "gpt", "value": "It's the color Orange, corresponding to the Mortgage Payment Percentage." } ], "id_raw": 258 }, { "id": "DoqkOiow2zC3FGBL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat value does [0.131, 0.512, 0.197, 1.772] represent?" }, { "from": "gpt", "value": "It's a bar representing 43.50% income for the \"SEQ\" family for Home Amenities." } ], "id_raw": 258 }, { "id": "JBVn00TTYNwDGhud", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nIs the value for Home Amenities for {the SEQ family}[0.131, 0.512, 0.197, 1.772] greater than the value for the {REPLAY family}[0.398, 0.231, 0.465, 1.772]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 258 }, { "id": "dxxEzzGAjMFpZamA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {MTL family}[0.665, 0.066, 0.732, 1.772] spend more on Mortgage Payment Percentage than {Home Amenities}[0.131, 0.512, 0.197, 1.772]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 258 }, { "id": "FnWyU0MCTwYhiaYQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nOf the Mortgage Payment Percentage and Food Shopping Average, which does the {MTL family}[0.732, 0.074, 0.799, 0.344] spend more on?" }, { "from": "gpt", "value": "Mortgage Payment Percentage" } ], "id_raw": 258 }, { "id": "HSBmy3XegXAhjv13", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.561, 0.212, 0.891, 0.323]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It includes three entries: {Home Amenities}[0.616, 0.221, 0.759, 0.244] with the color blue denoted by the handle [0.568, 0.223, 0.602, 0.239], {Mortgage Payment Percentage}[0.616, 0.256, 0.884, 0.279] with the color orange denoted by the handle [0.568, 0.258, 0.602, 0.274], and {Food Shopping Average}[0.616, 0.291, 0.822, 0.314] with the color red denoted by the handle [0.568, 0.293, 0.602, 0.309]." } ], "id_raw": 258 }, { "id": "qKxwtj2ZHbHbNWYE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the area [0.094, 0.034, 0.903, 0.903]." }, { "from": "gpt", "value": "The area [0.094, 0.034, 0.903, 0.903] is the main area of the chart. It includes the axes with labels (\"Family\" on the x-axis and \"Income (%)\" and \"Average Monthly Expenditure ($)\" on the y-axis), the grid, and three groups of bars. The three groups of bars represent Home Amenities, Mortgage Payment Percentage and Food Shopping Average for the SEQ, REPLAY and MTL families. Each bar is accompanied by a label with its exact value." } ], "id_raw": 258 }, { "id": "eKyDvykHYfR2odgG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the area from [0.398, 0.231, 0.465, 1.772]?" }, { "from": "gpt", "value": "There is one object, a bar, in this region. It represents 53.2% income for the \"REPLAY\" family spent on Home Amenities." } ], "id_raw": 258 }, { "id": "HsNAc2ktQiYd6FNF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you identify in the region [0.915, 0.025, 0.942, 0.048]?" }, { "from": "gpt", "value": "It is a y-axis label for the bargraph, called \"5.0\"." } ], "id_raw": 258 }, { "id": "Lwgh73w2maEacFJz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates [0.616, 0.221, 0.759, 0.244] represent in the chart?" }, { "from": "gpt", "value": "It represents the label of \"Home Amenities\"." } ], "id_raw": 259 }, { "id": "QOaVhaGCZ1fF368D", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {Home Amenities}[0.568, 0.223, 0.602, 0.239] in the legend?" }, { "from": "gpt", "value": "The color is blue." } ], "id_raw": 259 }, { "id": "94BChCvRrHSwIxa2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nThe {yticks}[0.915, 0.242, 0.942, 0.266] on the y-axis represents what reading?" }, { "from": "gpt", "value": "It represents a reading of 1.5." } ], "id_raw": 259 }, { "id": "DwiDaawe3kc8aJwU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Food Shopping Average Bar MTL}[0.799, 0.074, 0.866, 0.344] higher or lower than {Food Shopping Average Bar SEQ}[0.264, 0.344, 0.331, 0.793]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 259 }, { "id": "y2Pr1iJojp5IS4aD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nComparing {Home Amenities Bar MTL}[0.665, 0.066, 0.732, 1.772] and {Mortgage Payment Percentage Bar MTL}[0.732, 0.350, 0.799, 1.772], which one is taller?" }, { "from": "gpt", "value": "Home Amenities Bar." } ], "id_raw": 259 }, { "id": "XY0kAnGC6lL9T9f3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nBetween {Home Amenities}[0.568, 0.223, 0.602, 0.239] and {Mortgage Payment Percentage}[0.568, 0.258, 0.602, 0.274] which one is higher on the legend?" }, { "from": "gpt", "value": "Home Amenities." } ], "id_raw": 259 }, { "id": "lADjaByD0yXznkq6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.915, 0.919, 0.957, 0.942] region?" }, { "from": "gpt", "value": "It contains a y-axis tick label of \"-9.0\"." } ], "id_raw": 259 }, { "id": "Rnlq5Ef2Ihg6TRz9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nWhat information is there in the region [0.264, 0.344, 0.331, 0.793]?" }, { "from": "gpt", "value": "A bar of \"Food Shopping Average\" is present in this region with SEQ label. The value of this bar is \"-7.23\" at coordinate [0.275, 0.786, 0.320, 0.809]." } ], "id_raw": 259 }, { "id": "jXq2KHKvz7cmLJwF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.094, 0.034, 0.903, 0.903]." }, { "from": "gpt", "value": "This is the main plot area which contains the bars representing various expenditure values for the categories labeled \"SEQ\", \"REPLAY\", and \"MTL\". The x-axis and y-axis labels are out of this region." } ], "id_raw": 259 }, { "id": "r4ao0tIEX2vQEEJR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the text identified by the coordinates [0.721, 0.786, 0.873, 0.809]?" }, { "from": "gpt", "value": "The text is \"Economic Growth\"." } ], "id_raw": 260 }, { "id": "Ne89mz7qd8QVLJVP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar identified by the coordinates [0.551, 0.173, 0.653, 0.729]?" }, { "from": "gpt", "value": "The color of the bar is #6e7a5f, representing \"Economic Growth\"." } ], "id_raw": 260 }, { "id": "NTugHSDpWEf3L1R3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat is present at the location [0.721, 0.821, 0.884, 0.844] in the chart figure?" }, { "from": "gpt", "value": "The text \"Population Change\" is present." } ], "id_raw": 260 }, { "id": "0DkXIL8856MrXjml", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nIs the \"Population Change\" bar taller or shorter than the \"Economic Growth\" bar for the \"Country\" category?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 260 }, { "id": "bnMEqJCua5MLMgS1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nDoes the \"Economic Growth\" bar identified at [0.551, 0.173, 0.653, 0.729] represent the \"City\" or \"Country\" Category?" }, { "from": "gpt", "value": "Country." } ], "id_raw": 260 }, { "id": "1V4VEQe5qJoaFT9E", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nBetween \"Population Change\" and \"Economic Growth\", which has more significant bar for the \"City\" category?" }, { "from": "gpt", "value": "\"Economic Growth\"." } ], "id_raw": 260 }, { "id": "wVhk0uuXwRG0BP5b", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nDescribe what's represented in the area [0.144, 0.469, 0.246, 0.729]." }, { "from": "gpt", "value": "This area contains a single bar for the \"City\" category under the label \"Economic Growth\". The bar indicates a value of 1.5, which is shown by the text located at [0.181, 0.462, 0.209, 0.485]." } ], "id_raw": 260 }, { "id": "ScXXuqr2imOrrA50", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.665, 0.777, 0.891, 0.888]?" }, { "from": "gpt", "value": "The region includes a legend with three items. The first item is \"Economic Growth\" in color #6e7a5f with coordinates [0.672, 0.788, 0.707, 0.804] for the color label and [0.721, 0.786, 0.873, 0.809] for the text label. The second item is 'Population Change' in color #b8b7a5 with coordinates [0.672, 0.823, 0.707, 0.839] for the color label and [0.721, 0.821, 0.884, 0.844] for the text label. The third item is 'Land Use Change' in color #f4f1e0 with coordinates [0.672, 0.858, 0.707, 0.874] for the color label and [0.721, 0.856, 0.873, 0.879] for the text label." } ], "id_raw": 260 }, { "id": "GhfdzuEGM6xIFSFn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.108, 0.034, 0.892, 0.903] encompass in the chart figure?" }, { "from": "gpt", "value": "This region represents the graph area where all bars are plotted. This area includes both the \"City\" and \"Country\" categories, each containing three bars indicating \"Economic Growth\", \"Population Change\", and \"Land Use Change\". The x and y-axes labels, \"Categories\" and \"Accuracy (%)\", respectively, and their scales are also included in this area." } ], "id_raw": 260 }, { "id": "0rx81yeIIOaqaFMx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the entity represented by the color #b8b7a5 in the chart's legend?" }, { "from": "gpt", "value": "Observed Growth Rate. The legend can be seen at [0.687, 0.821, 0.884, 0.844]." } ], "id_raw": 261 }, { "id": "SrefI9s1fSVVT5yS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart label at the location [0.043, 0.894, 0.080, 0.917]?" }, { "from": "gpt", "value": "The label is -20." } ], "id_raw": 261 }, { "id": "8mbo9uz75A7U0bhB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nWhich category is at the coordinate range [0.429, 0.919, 0.555, 0.942] on the x-axis?" }, { "from": "gpt", "value": "This would be the category labeled \"Art Exhibitions\"." } ], "id_raw": 261 }, { "id": "RAvB8HCSTQ5FMQe4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nDoes the \"Probing Growth Rate\" bar of \"Opera Performances\" at [0.657, 0.555, 0.723, 0.592] have a higher or lower value than the \"Observed Growth Rate\" bar of \"Opera Performances\" at [0.723, 0.555, 0.789, 0.828]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 261 }, { "id": "7yYWWhA2Giy0qsNR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nIn the bars representing \"Book Publishing\", is {the \u201cObserved Growth Rate\u201d bar}[0.194, 0.342, 0.260, 0.555] placed to the left or right of {the \u201cProbing Growth Rate\u201d bar}[0.128, 0.534, 0.194, 0.555]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 261 }, { "id": "1deIjVyNEEdW8nWE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nComparing the bars for \"Art Exhibitions\", is {the \u201cExpanding Rate\u201d bar}[0.525, 0.121, 0.591, 0.655] taller than {the \u201cObserved Growth Rate\u201d bar}[0.459, 0.105, 0.525, 0.555]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 261 }, { "id": "ATpN3I9yJCj16BIX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe what can be found within the area [0.092, 0.034, 0.892, 0.903]?" }, { "from": "gpt", "value": "This area contains the main chart with x-axis labels at the area [0.159, 0.919, 0.845, 0.942] and y-axis labels at the area [0.043, 0.894, 0.080, 0.048]. There are bars representing various growth rates for three different categories namely \"Book Publishing\", \"Art Exhibitions\" and \"Opera Performances\"." } ], "id_raw": 261 }, { "id": "6P4jc4dBlQ8qdsF8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the coordinates [0.128, 0.534, 0.260, 0.555]?" }, { "from": "gpt", "value": "This area includes two bars representing \"Book Publishing\". Specifically, these are the \"Probing Growth Rate\" bar at [0.128, 0.534, 0.194, 0.555] and the \"Observed Growth Rate\" bar at [0.194, 0.342, 0.260, 0.555]." } ], "id_raw": 261 }, { "id": "L4a5YUVUMOXZH5hR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the area of [0.631, 0.777, 0.891, 0.888] include generally?" }, { "from": "gpt", "value": "This region represents the chart's legend. It lists three attributes namely \"Probing Growth Rate\", \"Observed Growth Rate\" and \"Expanding Rate\", which are located at [0.687, 0.786, 0.867, 0.809], [0.687, 0.821, 0.884, 0.844], and [0.687, 0.856, 0.825, 0.879] respectively. Each attribute is associated with a unique color to represent it in the bar chart." } ], "id_raw": 261 }, { "id": "f62lixYI3SUtDMAB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the primary entity located at coordinates [0.019, 0.401, 0.036, 0.536] represent?" }, { "from": "gpt", "value": "This is the label for the Y-axis, which is titled \"Index Value\"." } ], "id_raw": 262 }, { "id": "3b9TaD6HFCNgAGCn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the second legend text's coordinates?" }, { "from": "gpt", "value": "The coordinates are [0.653, 0.821, 0.884, 0.844]." } ], "id_raw": 262 }, { "id": "wMpet7gYFJx8lAmH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Y-axis scale} indicated at [0.346, 0.083, 0.679, 0.113]?" }, { "from": "gpt", "value": "Index Value." } ], "id_raw": 262 }, { "id": "73rG7RJPBYKbiKqJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat categories are represented in [0.597, 0.777, 0.891, 0.888]?" }, { "from": "gpt", "value": "The legend indicates that there are three categories: {Intangible Asset Turnover}[0.653, 0.786, 0.873, 0.809], {Human Capital Index 2022}[0.653, 0.821, 0.884, 0.844], and {TechInvestmentIndex}[0.653, 0.856, 0.837, 0.879]." } ], "id_raw": 262 }, { "id": "y1TnicN9Gh15XPwE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Advanced Manufacturing}[0.145, 0.859, 0.211, 0.862] larger in value than {the bar representing IT Services}[0.673, 0.857, 0.740, 0.862] under the Intangible Asset Turnover metric?" }, { "from": "gpt", "value": "No." } ], "id_raw": 262 }, { "id": "dI8JhFQTXk1M7FbH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Advanced Manufacturing}[0.277, 0.179, 0.343, 0.773] under the category TechInvestmentIndex more positive or negative than {the bar for Biotechnology}[0.541, 0.179, 0.607, 0.838]?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 262 }, { "id": "SbykU64Q66hbZxg9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nIn comparing {Human Capital Index 2022 for AdvancedManufacturing}[0.211, 0.477, 0.277, 0.862] with {Human Capital Index 2022 for ITServices}[0.740, 0.605, 0.806, 0.862], which is larger?" }, { "from": "gpt", "value": "Biotechnology." } ], "id_raw": 262 }, { "id": "58GGG2j6QLKOwzjX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nAmongst {TechInvestmentIndex for AdvancedManufacturing}[0.277, 0.179, 0.343, 0.773], {TechInvestmentIndex for Biotechnology}[0.541, 0.179, 0.607, 0.838] and {TechInvestmentIndex for ITServices}[0.806, 0.155, 0.872, 0.179], which has the highest value?" }, { "from": "gpt", "value": "ITServices." } ], "id_raw": 262 }, { "id": "UGHysAijNfCY6eVY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item at [0.709, 0.856, 0.820, 0.879] represent?" }, { "from": "gpt", "value": "It represents the Green Space." } ], "id_raw": 263 }, { "id": "12HSl7cQhDWv0y2V", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {Urban Development} mapped on the chart according to the legend[0.661, 0.823, 0.695, 0.839]?" }, { "from": "gpt", "value": "#b8b7a5." } ], "id_raw": 263 }, { "id": "JJGRK8ctGmSgQYvW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the y-axis on the chart at [0.019, 0.360, 0.036, 0.577]?" }, { "from": "gpt", "value": "This is representing the Annual Change(%)." } ], "id_raw": 263 }, { "id": "IPZ0XTftnjek8UIa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Population Growth in CITY_A}[0.134, 0.269, 0.202, 0.469] taller than the bar of {Green Space in CITY_A}[0.270, 0.555, 0.338, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 263 }, { "id": "e7skZrE3cQBD8lje", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nDoes City B or City C has a larger {Urban Development}[0.406, 0.469, 0.473, 0.738] [0.744, 0.460, 0.812, 0.469] bar?" }, { "from": "gpt", "value": "CITY_B." } ], "id_raw": 263 }, { "id": "N2XDZrfWkDX2Kpcy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat label at [0.207, 0.919, 0.266, 0.942] has the broader {Population Growth Bar} in terms of two cities libraries at [0.134, 0.269, 0.202, 0.469] and [0.406, 0.469, 0.473, 0.738]?" }, { "from": "gpt", "value": "CITY_A." } ], "id_raw": 263 }, { "id": "ZvgiliLU8jLVjWNS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area at [0.654, 0.777, 0.891, 0.888]." }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes three labels: \n1. {Population Growth}[0.709, 0.786, 0.869, 0.809] with a color of #6e7a5f,\n2. {Urban Development}[0.709, 0.821, 0.884, 0.844] with a color of #b8b7a5, and\n3. {Green Space}[0.709, 0.856, 0.820, 0.879] with a color of #f4f1e0." } ], "id_raw": 263 }, { "id": "D8eFiSyBCtmbWhK7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nDescribe what lies in the region [0.134, 0.269, 0.202, 0.903]." }, { "from": "gpt", "value": "Within these coordinates, we have the Population Growth bar for CITY_A. It ranges in height from 0.00 to 2.30 as indicated by the text at [0.155, 0.238, 0.182, 0.262]." } ], "id_raw": 263 }, { "id": "UEP7BpuNuSY3iURn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nCan you detail the features within the region [0.406, 0.469, 0.473, 0.903]?" }, { "from": "gpt", "value": "This region includes the bar representing Urban Development for CITY_B, with the value of -3.10 as shown by the text at coordinates [0.423, 0.708, 0.456, 0.731]." } ], "id_raw": 263 }, { "id": "sN8gg1GWsQqgj9yU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.684, 0.171, 0.884, 0.194] represent?" }, { "from": "gpt", "value": "The label represents \"Market Share 2018 (%)\"." } ], "id_raw": 264 }, { "id": "nEkbjL3OCg0Z4ZeO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the industry {the corresponding bar} at coordinates [0.155, 0.034, 0.209, 0.384]?" }, { "from": "gpt", "value": "The industry is Electronics." } ], "id_raw": 264 }, { "id": "BVLiFJ7uhpavK6z3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.458, 0.919, 0.557, 0.942] specify in the chart?" }, { "from": "gpt", "value": "It specifies the category name \"Automobile\"." } ], "id_raw": 264 }, { "id": "rgr1Q2C8lHFTho9i", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend} at [0.635, 0.243, 0.670, 0.259]?" }, { "from": "gpt", "value": "The color is #f4f1e0." } ], "id_raw": 264 }, { "id": "JcvjNkTsM5EswIA5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Electronics in 2018}[0.155, 0.034, 0.209, 0.384] higher or lower than {the bar for Electronics in 2019}[0.209, 0.034, 0.263, 0.555]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 264 }, { "id": "Fo1k7ZBVzAWUU0bi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nIs {the Automobile bar of 2019}[0.480, 0.034, 0.534, 0.382] to the right or left of {the Textile bar of 2019}[0.751, 0.033, 0.806, 0.034]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 264 }, { "id": "gYSXyLJzkoPbbgsh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nComparing the two bars {Electronics in 2018}[0.155, 0.034, 0.209, 0.384] and {Automobile in 2019}[0.480, 0.034, 0.534, 0.382], which one is taller?" }, { "from": "gpt", "value": "{Automobile in 2019}[0.480, 0.034, 0.534, 0.382]." } ], "id_raw": 264 }, { "id": "g9br173qLpn6GC72", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nConsidering {the yellow bar in 2019} at [0.480, 0.034, 0.534, 0.382] and {the white bar in 2020} at [0.534, 0.469, 0.589, 0.621], which Industry has a decrease in Market Share from 2019 to 2020?" }, { "from": "gpt", "value": "Automobile." } ], "id_raw": 264 }, { "id": "qaoUVQ0LQYRpbJF0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the region from [0.628, 0.162, 0.891, 0.273]?" }, { "from": "gpt", "value": "It is the legend of the chart, including three entries: {Market Share 2018 (%)}[0.684, 0.171, 0.884, 0.194] represented by a color marked at [0.635, 0.173, 0.670, 0.189], {Market Share 2019 (%)}[0.684, 0.206, 0.884, 0.229] represented by a color marked at [0.635, 0.208, 0.670, 0.224], and {Market Share 2020 (%)}[0.684, 0.241, 0.884, 0.264] represented by a color marked at [0.635, 0.243, 0.670, 0.259]." } ], "id_raw": 264 }, { "id": "BuFSLI1vh8zdEZbV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the boundary [0.119, 0.034, 0.895, 0.903]?" }, { "from": "gpt", "value": "It is the main graph area of the bar chart, including x-axis and y-axis, horizontal and vertical gridlines, bar representations of the industry categories Electronics, Automobile, and Textile for three years (2018, 2019, and 2020) with labels." } ], "id_raw": 264 }, { "id": "yaXW2OBwrZ27BICM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is in the region spanning [0.426, 0.034, 0.590, 0.729]." }, { "from": "gpt", "value": "There is one bar related to the Automobile industry for each year (2018, 2019, 2020). The bars represent the Market Share of the Automobile industry for those years." } ], "id_raw": 264 }, { "id": "N2hTDJxF3sF3QaZc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the region [0.119, 0.034, 0.317, 0.903]?" }, { "from": "gpt", "value": "The region contains all the bars and labels associated with the Electronics industry from 2018 to 2020." } ], "id_raw": 264 }, { "id": "D4izPeya1kdnkDCV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at position [0.678, 0.173, 0.871, 0.194]?" }, { "from": "gpt", "value": "That's labeled as 'Market Share 2018 (%)'." } ], "id_raw": 265 }, { "id": "369htUYhBhpoMSJL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar corresponding to 'Market Share 2019 (%)'?" }, { "from": "gpt", "value": "The color of the bar is '#b8b7a5' as described by the handle coordinate [0.635, 0.208, 0.670, 0.224]." } ], "id_raw": 265 }, { "id": "kZqskwl6lzvtrdJQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by [0.155, 0.034, 0.209, 0.384]?" }, { "from": "gpt", "value": "It's a bar representing the market share in 2018 for the Electronics industry." } ], "id_raw": 265 }, { "id": "xwPsYaUfblneHUr4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nWhich value is greater, 'Market Share 2018 (%)' for Electronics or 'Market Share 2019 (%)' for the same?" }, { "from": "gpt", "value": "The 'Market Share 2018 (%)' is greater as it extends to '[0.155, 0.034, 0.209, 0.384]' whereas 'Market Share 2019 (%)' only extends to '[0.209, 0.034, 0.263, 0.555]'." } ], "id_raw": 265 }, { "id": "HBBEUOnCyKdBqSl3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nIs the market share in 2018 for the Electronics industry higher than Automobile industry?" }, { "from": "gpt", "value": "No, as can be seen by the comparison of coordinates {Electronics 2018 bar}[0.155, 0.034, 0.209, 0.384] and {Automobile 2018 bar}[0.426, 0.034, 0.480, 0.729]." } ], "id_raw": 265 }, { "id": "k1IC7SxCOz69pmyX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nIs the \u2018Market Share 2019 (%)\u2019 higher for the Automobile industry than for the Electronics industry?" }, { "from": "gpt", "value": "Yes, this is indicated by comparing the coordinates of the Automobile {2019 bar}[0.480, 0.034, 0.534, 0.382] with the Electronics {2019 bar}[0.209, 0.034, 0.263, 0.555]." } ], "id_raw": 265 }, { "id": "AM7FktV8JDxM1w75", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the bounds [0.628, 0.162, 0.891, 0.273]?" }, { "from": "gpt", "value": "This area holds the chart legend, with three entries: 'Market Share 2018 (%)', 'Market Share 2019 (%)', and 'Market Share 2020 (%)'. Each entry consists of a colored box, with labels for respective color located at [0.684, 0.171, 0.884, 0.194], [0.684, 0.206, 0.884, 0.229] and [0.684, 0.241, 0.884, 0.264] respectively." } ], "id_raw": 265 }, { "id": "6mUwh1XYeqszhbBF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the bounds [0.458, 0.919, 0.557, 0.942]?" }, { "from": "gpt", "value": "These coordinates denote the x-axis label, labeling it as the 'Automobile' industry." } ], "id_raw": 265 }, { "id": "w1XXiVcLPM3e9qEE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the coordinates [0.155, 0.034, 0.209, 0.384]?" }, { "from": "gpt", "value": "It includes the bar representing the 'Market Share 2018 (%)' for the Electronics industry." } ], "id_raw": 265 }, { "id": "WeJV0xDUOzPbfkUk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar under the label \"Innovation\" in the chart's legend?" }, { "from": "gpt", "value": "light green.{Innovation's handle}[0.644, 0.823, 0.679, 0.839]." } ], "id_raw": 266 }, { "id": "sc5OYtsaYw8369ef", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text label of \"Gradual recovery\"?" }, { "from": "gpt", "value": "[0.718, 0.919, 0.868, 0.942]." } ], "id_raw": 266 }, { "id": "d2M6dmWKxmNFSIGr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the value represented by {the Customer Satisfaction bar of Gradual recovery}[0.814, 0.056, 0.855, 0.618]?" }, { "from": "gpt", "value": "5.92." } ], "id_raw": 266 }, { "id": "kH7XXGZRmKdnuD1P", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart title or main object at [0.346, 0.083, 0.679, 0.113]?" }, { "from": "gpt", "value": "There is no exact content showing at these coordinates in the chart. It seems like it's part of the chart title, but the complete values are missing." } ], "id_raw": 266 }, { "id": "QowR8IydemI8Itkr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nIs {the Productivity bar for Steady increase}[0.114, 0.248, 0.156, 0.903] higher or lower than {the Innovation bar for Steady increase}[0.156, 0.330, 0.197, 0.903]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 266 }, { "id": "TdvZseu9CLXX4ezh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the Customer Satisfaction bar for Moderate increase}[0.608, 0.266, 0.649, 0.618] represent a lower or higher value than {the Innovation bar for the same category}[0.567, 0.148, 0.608, 0.903]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 266 }, { "id": "0XdJpEm52Okcz0e8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Productivity\"}[0.693, 0.786, 0.796, 0.809] and {\"Innovation\"}[0.693, 0.821, 0.784, 0.844], which one has higher values in general in the chart?" }, { "from": "gpt", "value": "Innovation." } ], "id_raw": 266 }, { "id": "7IuI2KzWktjr54CU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Customer Satisfaction\"}[0.814, 0.056, 0.855, 0.618] and {\"Innovation\"}[0.773, 0.120, 0.814, 0.903] of the category \"Gradual recovery\", which one has higher values?" }, { "from": "gpt", "value": "Customer Satisfaction." } ], "id_raw": 266 }, { "id": "JNr03VIYgO7zPEJM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located in the area of [0.637, 0.777, 0.891, 0.888]." }, { "from": "gpt", "value": "The area contains the chart legend. It includes three pairs of color-text labels for the following components of the chart:\n1. \"Productivity\" signal as a dark green color located at [0.644, 0.788, 0.679, 0.804] with its matching text at [0.693, 0.786, 0.796, 0.809].\n2. \"Innovation\" indicator represented as a light green color located at [0.644, 0.823, 0.679, 0.839] with its corresponding text at [0.693, 0.821, 0.784, 0.844].\n3. \"Customer Satisfaction\" depicted as a beige color located at [0.644, 0.858, 0.679, 0.874] and its related text at [0.693, 0.856, 0.884, 0.879]." } ], "id_raw": 266 }, { "id": "dJOhPA3p3rQdU6UF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe content within the area [0.077, 0.048, 0.892, 0.903]." }, { "from": "gpt", "value": "The region within these coordinates includes the primary data representation of the chart. It consists of all the bars representing the categories of \"Productivity\", \"Innovation\", and \"Customer Satisfaction\" across different growth stages: \"Steady increase\", \"Moderate growth\", \"Moderate increase\", and \"Gradual recovery\". The area also includes the x and y-axes labels and gridlines." } ], "id_raw": 266 }, { "id": "mnTGjb5wWMRa6vXW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.156, 0.330, 0.197, 0.903]?" }, { "from": "gpt", "value": "There is a single object within these coordinates: a light green colored bar representing the \"Innovation\" of \"Steady increase\" category, and the y range value for this bar is 0.00 -> 13.42." } ], "id_raw": 266 }, { "id": "g2W5fJ2Im34rJPnR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nWhat is described in the area from [0.043, 0.039, 0.065, 0.063]?" }, { "from": "gpt", "value": "It contains the y-axis label, denoting the value '20'." } ], "id_raw": 266 }, { "id": "RlZIPtRKd0qBKTim", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nWhat color does the legend indicate at [0.116, 0.123, 0.150, 0.139]?" }, { "from": "gpt", "value": "This color is indicated as Brown in the legend." } ], "id_raw": 268 }, { "id": "3DiHcIpdEXoHgPWs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates of [0.164, 0.121, 0.445, 0.144]?" }, { "from": "gpt", "value": "The text is \"Budget Allocation in million USD\"." } ], "id_raw": 268 }, { "id": "ZtCAvSaRHfcysfSW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the coordinates [0.338, 0.919, 0.662, 0.942]?" }, { "from": "gpt", "value": "The term represented here is \"Infrastructure and Municipal Services\"." } ], "id_raw": 268 }, { "id": "xTmnFZ4qdzCV36pQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nWhich category does the bar set [0.126, 0.686, 0.212, 0.903] represent?" }, { "from": "gpt", "value": "This bar set represents the 'Planning and Land Use' category." } ], "id_raw": 268 }, { "id": "F9GtrMWFEfrcTPbm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nIs the legend square [0.116, 0.123, 0.150, 0.139] the same color as the bar [0.126, 0.686, 0.212, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 268 }, { "id": "uHah1kt7wtrwtHMR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nIs the label [0.164, 0.121, 0.445, 0.144] associated with the category [0.113, 0.919, 0.311, 0.942]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 268 }, { "id": "PtBjsqUzrI3O8xTt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nIs the satisfaction percentage column [0.212, 0.425, 0.298, 0.903] higher than budget allocation column [0.126, 0.686, 0.212, 0.903] for 'Planning and Land Use'?" }, { "from": "gpt", "value": "No." } ], "id_raw": 268 }, { "id": "JdlHYWJ55N1WKkkF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nBased on their height, is the community more satisfied with 'Infrastructure and Municipal Services' [0.500, 0.164, 0.586, 0.903] than 'Social and Recreational Services' [0.788, 0.251, 0.874, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 268 }, { "id": "nZDuKFMGwgdFU14V", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe the figure elements within the coordinates [0.088, 0.034, 0.912, 0.903]?" }, { "from": "gpt", "value": "This region contains the main graph, including both x and y axis." } ], "id_raw": 268 }, { "id": "2LiyQLC5swMRF0ik", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nCould you tell me about the region [0.924, 0.242, 0.946, 0.266]?" }, { "from": "gpt", "value": "It includes an element of the y-axis label series of the second graph, which is '75'." } ], "id_raw": 268 }, { "id": "m6zP0XWNHfqyVMhd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nDescribe all the bars within the coordinate segment [0.414, 0.179, 0.500, 0.903]." }, { "from": "gpt", "value": "This segment contains two sets of bars representing the category 'Infrastructure and Municipal Services':\n 1. A brown bar [0.414, 0.179, 0.500, 0.903] representing the budget allocation amount in USD millions.\n 2. A light yellow bar falling within same x-coordinate range in the second subplot, indicating the community satisfaction % for the same category." } ], "id_raw": 268 }, { "id": "2xr81wckpZbBBmtU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.338, 0.919, 0.662, 0.942]?" }, { "from": "gpt", "value": "It accommodates one category label 'Infrastructure and Municipal Services' for x-axis." } ], "id_raw": 268 }, { "id": "85wZdxb68P4IBHNL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat describes the object found in the coordinates [0.129, 0.396, 0.235, 0.758]?" }, { "from": "gpt", "value": "It's a bar representing Power Consumption in the Residential category at 125 consumption percentage." } ], "id_raw": 269 }, { "id": "8FqEirZAmiBKJ7no", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted at the coordinates [0.712, 0.821, 0.883, 0.844]?" }, { "from": "gpt", "value": "It's a text label reading \"Water Consumption\"." } ], "id_raw": 269 }, { "id": "HVFbxAHf0V2PhFK2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nThe content inside the area [0.712, 0.786, 0.884, 0.809] can be described as?" }, { "from": "gpt", "value": "It is a text label indicating 'Power Consumption'." } ], "id_raw": 269 }, { "id": "X5iY8S2RrEuwZaWd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object with coordinates [0.552, 0.179, 0.658, 0.758]?" }, { "from": "gpt", "value": "Yes, it is a bar representing Power Consumption in the Industrial category at 200 consumption percentage." } ], "id_raw": 269 }, { "id": "uEiBjofjWJizF6Mt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nHow does the Power Consumption data for {Residential}[0.129, 0.396, 0.235, 0.758] compare with that of {Industrial}[0.552, 0.179, 0.658, 0.758]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 269 }, { "id": "OCL9k0GoQoxvCSR9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nIs the Water Consumption in {Residential}[0.235, 0.700, 0.340, 0.758] greater or less than in {Industrial}[0.658, 0.758, 0.763, 0.787]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 269 }, { "id": "xrCuEieqgod9Qb2A", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nDoes the Power Consumption in {Residential}[0.129, 0.396, 0.235, 0.758] or {Industrial}[0.552, 0.179, 0.658, 0.758] have a higher C02 Emission as per {C02 Emission Residential}[0.340, 0.469, 0.446, 0.621] and {C02 Emission Industrial}[0.763, 0.432, 0.869, 0.469]?" }, { "from": "gpt", "value": "Industrial." } ], "id_raw": 269 }, { "id": "jWeB5gcdYRlXgkB6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nCould you explain the content within the area [0.656, 0.777, 0.891, 0.888]?" }, { "from": "gpt", "value": "This area contains the legend for the chart. The legend consists of 3 entries; Each entry includes a color code shown with its corresponding label. The first entry represents Power Consumption with color code at coordinates [0.663, 0.788, 0.698, 0.804] and label at coordinates [0.712, 0.786, 0.884, 0.809]. The second entry stands for Water Consumption, color code at [0.663, 0.823, 0.698, 0.839], and label at [0.712, 0.821, 0.883, 0.844]. The third entry denotes C02 Emission, color at [0.663, 0.858, 0.698, 0.874] and label at [0.712, 0.856, 0.828, 0.879]." } ], "id_raw": 269 }, { "id": "hDa1jOHP7qUfuQSv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.092, 0.034, 0.906, 0.903]?" }, { "from": "gpt", "value": "This region contains the entire bar chart area, with bars representing Power Consumption and Water Consumption for two categories - Residential and Industrial. The region also includes the x-axis and y-axis, with their labels and scale information, and the gridlines of the chart are also displayed within this area." } ], "id_raw": 269 }, { "id": "WBz2oLGJRj9DWCiV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the coordinates [0.240, 0.919, 0.335, 0.942]?" }, { "from": "gpt", "value": "It contains the label \"Residential\" which is a category label on the x-axis." } ], "id_raw": 269 }, { "id": "hhcvkjFqNBLCbb8f", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.670, 0.919, 0.751, 0.942] contain?" }, { "from": "gpt", "value": "It contains the label 'Industrial', which is a category on the x-axis." } ], "id_raw": 269 }, { "id": "oWgvqPvdQD4rTM55", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.025, 0.399, 0.048, 0.546]?" }, { "from": "gpt", "value": "It refers to the Y-axis label, which reads \"Cost Change\"." } ], "id_raw": 270 }, { "id": "YfnYBRNsApftUbhF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label for the supplier \"Zhang Inc\"?" }, { "from": "gpt", "value": "[0.493, 0.919, 0.608, 0.942]" } ], "id_raw": 270 }, { "id": "l6NZbschbNUme2ke", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at coordinates [0.799, 0.174, 0.965, 0.560] represent?" }, { "from": "gpt", "value": "It represents the Cost Change for the supplier \"Chen Industries\"." } ], "id_raw": 270 }, { "id": "zw8UscAkBEGhVdPL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nCan you identify the xlabel in coordinates [0.503, 0.952, 0.598, 0.975]?" }, { "from": "gpt", "value": "The xlabel is \"Supplier\"." } ], "id_raw": 270 }, { "id": "eM5LctiiPjSPqGKV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nIs the cost change bar of {Liu Corp}[0.302, 0.560, 0.468, 0.736] above or below the cost change bar of {Wang Enterprises} [0.136, 0.245, 0.302, 0.560]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 270 }, { "id": "fPn5qpzIhtlfv1Qn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Zhang Inc}[0.468, 0.490, 0.633, 0.560] larger than the bar of {Li LLC}[0.633, 0.560, 0.799, 0.841]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 270 }, { "id": "rzIGKE0iyyQmw1LM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nDoes {Wang Enterprises}[0.136, 0.245, 0.302, 0.560] have a higher or lower cost change than {Chen Industries}[0.799, 0.174, 0.965, 0.560]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 270 }, { "id": "2pypo5t3oPhmDERP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nWhich supplier between {Liu Corp}[0.302, 0.560, 0.468, 0.736] and {Li LLC}[0.633, 0.560, 0.799, 0.841] has more cost change?" }, { "from": "gpt", "value": "Li LLC." } ], "id_raw": 270 }, { "id": "QkbYnXF9kByR0I84", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.302, 0.174, 0.799, 0.841]." }, { "from": "gpt", "value": "There are 3 bars in this region. The first bar represents the cost change of Liu Corp at coordinates [0.302, 0.560, 0.468, 0.736]. The second bar represents the cost change of Zhang Inc at coordinates [0.468, 0.490, 0.633, 0.560]. The third bar at coordinates [0.633, 0.560, 0.799, 0.841] represents the cost change for Li LLC." } ], "id_raw": 270 }, { "id": "9OAmqp3u0FsESX7u", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.117, 0.919, 0.972, 0.942]." }, { "from": "gpt", "value": "This area contains the supplier labels on the horizontal axis, which are:\n1. \"Wang Enterprises\": [0.117, 0.919, 0.321, 0.942]\n2. \"Liu Corp\": [0.337, 0.919, 0.433, 0.942]\n3. \"Zhang Inc\": [0.493, 0.919, 0.608, 0.942]\n4. \"Li LLC\": [0.682, 0.919, 0.750, 0.942]\n5. \"Chen Industries\": [0.792, 0.919, 0.972, 0.942]." } ], "id_raw": 270 }, { "id": "2ZRhiLhhCgM6dHdS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nDiscuss the objects within the region [0.136, 0.174, 0.302, 0.841]." }, { "from": "gpt", "value": "There is only one bar within this region, it demonstrates the \"Cost Change\" of the \"Wang Enterprises\" at coordinates [0.136, 0.245, 0.302, 0.560]." } ], "id_raw": 270 }, { "id": "Yjsa4VNQ6w6T2asU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.136, 0.200, 0.965, 0.911]?" }, { "from": "gpt", "value": "The area contains the plot of Supplier vs Cost Change including the bars that represent each supplier's cost change, the x-axis, and y-axis." } ], "id_raw": 270 }, { "id": "0OtQTQGeehHJHWTx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is defined by this JSON data?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 272 }, { "id": "D2Oa7Sqnxcal8SLF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.516, 0.964, 0.556, 0.981] on the chart?" }, { "from": "gpt", "value": "Field." } ], "id_raw": 272 }, { "id": "OalqsaU8O9Rr7PAl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.019, 0.449, 0.036, 0.510]?" }, { "from": "gpt", "value": "Impact." } ], "id_raw": 272 }, { "id": "btzKLEPXKN72HhJb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the bar at [0.685, 0.116, 0.833, 0.593]?" }, { "from": "gpt", "value": "Mathematics." } ], "id_raw": 272 }, { "id": "kfwsm4ng8tMPLcYl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of Computer Science}[0.091, 0.139, 0.240, 0.593] greater or less than {the bar of Mathematics}[0.685, 0.116, 0.833, 0.593]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 272 }, { "id": "98eMDLeIC3vWA0i1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the Physics bar}[0.388, 0.298, 0.536, 0.593] represent a positive or negative impact?" }, { "from": "gpt", "value": "Positive." } ], "id_raw": 272 }, { "id": "zrN0EasNfV3jZ5hM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing Physics and Chemistry}[0.388, 0.298, 0.536, 0.593], [0.536, 0.593, 0.685, 0.774] of approximately the same height?" }, { "from": "gpt", "value": "No." } ], "id_raw": 272 }, { "id": "k42mqfUvpc9Zznng", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar of Philosophy}[0.833, 0.479, 0.981, 0.593] and {the bar of Biology}[0.240, 0.593, 0.388, 0.865], which has a higher impact?" }, { "from": "gpt", "value": "Philosophy." } ], "id_raw": 272 }, { "id": "xHqMy6yJjoXUF0J4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the area [0.091, 0.139, 0.981, 0.593]?" }, { "from": "gpt", "value": "This area contains all the bars of the chart, each representing a different field of study, such as Computer Science, Biology, Physics, Chemistry, Mathematics, and Philosophy." } ], "id_raw": 272 }, { "id": "wXVBvCODcAElIbx2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the area [0.058, 0.019, 0.085, 0.036]?" }, { "from": "gpt", "value": "This area contains the value \"2.5\" on the y-axis scale, indicating the maximum level of impact portrayed in the chart." } ], "id_raw": 272 }, { "id": "BoNIF1oPLKhDBrPL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.058, 0.132, 0.085, 0.150]." }, { "from": "gpt", "value": "The content in this region is the value \"2.0\" on the y-axis, which represents the second highest level of impact in the context of this chart." } ], "id_raw": 272 }, { "id": "AoJrk7oaxmnki2X3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat features are within [0.043, 0.699, 0.085, 0.717]?" }, { "from": "gpt", "value": "This region contains the y-axis value \"-0.5\". It is part of the y-axis scale, which illustrates the range of impact levels for different fields of study." } ], "id_raw": 272 }, { "id": "4O9QEEGNM7e8GRjL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nConsidering the coordinates [0.178, 0.144, 0.260, 0.473], what is the label of the bar in this location?" }, { "from": "gpt", "value": "Television." } ], "id_raw": 274 }, { "id": "AFK67xUsaZCUxpKt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the label 'OnlineShopping'?" }, { "from": "gpt", "value": "[0.789, 0.919, 0.972, 0.942]." } ], "id_raw": 274 }, { "id": "NyAUaFZDl0XSurzU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent given its location at [0.025, 0.413, 0.048, 0.532]?" }, { "from": "gpt", "value": "Relevance." } ], "id_raw": 274 }, { "id": "apLeA8qEJeqS1Rzq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nWhat object is located in the coordinate box [0.839, 0.473, 0.922, 0.670] and what does it represent?" }, { "from": "gpt", "value": "The blue colored bar represents 'OnlineShopping' and its relevance score is negative, between -0.45 and 0.0." } ], "id_raw": 274 }, { "id": "N8YSLNelIZ9Zb82t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar for 'Movies'[0.343, 0.473, 0.426, 0.867] taller or shorter than 'Television'[0.178, 0.144, 0.260, 0.473]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 274 }, { "id": "X6ntb7trup7TUH00", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nGiven the bar for 'Television'[0.178, 0.144, 0.260, 0.473] and 'Music'[0.674, 0.473, 0.757, 0.758], which one shows a stronger relevance?" }, { "from": "gpt", "value": "Television." } ], "id_raw": 274 }, { "id": "oGfGr36dAPsOqnRT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nComparing the position of 'Music'[0.674, 0.473, 0.757, 0.758] and 'OnlineShopping'[0.839, 0.473, 0.922, 0.670], which one is on the left?" }, { "from": "gpt", "value": "Music." } ], "id_raw": 274 }, { "id": "eijnz5THtqf3OMmB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nComparing the relevance of 'MobileGames'[0.509, 0.253, 0.591, 0.473] and 'Movies'[0.343, 0.473, 0.426, 0.867], which one has a higher positive relevance score?" }, { "from": "gpt", "value": "MobileGames." } ], "id_raw": 274 }, { "id": "Rge2WOYIkQxAmskd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nWhat features can be found in the region [0.509, 0.253, 0.591, 0.473]?" }, { "from": "gpt", "value": "There is a bar for the category 'MobileGames'. The bar is blue and its height indicates a positive relevance score between 0.00 and 0.50." } ], "id_raw": 274 }, { "id": "RmvNCvEsjqOETb8F", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the rectangular area [0.839, 0.473, 0.922, 0.670]." }, { "from": "gpt", "value": "The rectangular area encompasses a bar representing the 'OnlineShopping' category with a negative relevance score between -0.45 and 0.0. The bar is blue-colored." } ], "id_raw": 274 }, { "id": "PLlzRDGVBZSj7WVT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nDescribe what is shown in the area [0.682, 0.919, 0.748, 0.942]." }, { "from": "gpt", "value": "The area includes the label for 'Music' along the x-axis." } ], "id_raw": 274 }, { "id": "pNWbxpUqlGr55Keq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the content within the region [0.178, 0.144, 0.260, 0.473]?" }, { "from": "gpt", "value": "It contains a bar for the category 'Television'. The height of the bar indicates a positive relevance score within the 'Television' category, ranging between 0.00 and 0.75. The bar is colored blue." } ], "id_raw": 274 }, { "id": "BVm2NrpzOhUhr9mE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart title in the area [0.346, 0.083, 0.679, 0.113]?" }, { "from": "gpt", "value": "Bar Chart Showing Food Security Trends." } ], "id_raw": 275 }, { "id": "n4Whco2mhQDplnZV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.466, 0.823, 0.563, 0.911] in the chart?" }, { "from": "gpt", "value": "A bar representing the category \"Lack of Electricity\" with a value of 10%." } ], "id_raw": 275 }, { "id": "TOVZlDUzMspB9UZi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} at [0.025, 0.421, 0.048, 0.525]?" }, { "from": "gpt", "value": "%Family." } ], "id_raw": 275 }, { "id": "o2ar6s4IxqKJj1yN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Inadequate Sanitation\"}[0.304, 0.429, 0.401, 0.911] higher than {the bar representing \"Chronic Food Insecurity\"}[0.790, 0.806, 0.887, 0.911]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 275 }, { "id": "8eN7trF5BtMNJ6Yp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Lack of Electricity\"}[0.466, 0.823, 0.563, 0.911] and {\"No Access to Clean Drinking Water\"}[0.628, 0.885, 0.725, 0.911], which condition is more prevalent?" }, { "from": "gpt", "value": "Lack of Electricity." } ], "id_raw": 275 }, { "id": "B1tjzahJoduwHKXF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Minimal Basic Amenities\"}[0.142, 0.297, 0.239, 0.911] taller than 50%{yticks}[0.072, 0.463, 0.102, 0.487]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 275 }, { "id": "ZlqvSmtXPWRATuTt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.180, 0.906, 0.868, 0.930]?" }, { "from": "gpt", "value": "It contains x-axis labels including \"Minimal Basic Amenities\", \"Inadequate Sanitation\", \"Lack of Electricity\", \"No Access to Clean Drinking Water\", and \"Chronic Food Insecurity\"." } ], "id_raw": 275 }, { "id": "j2v3Fe6aomX81fZg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.050, 0.919, 0.878, 0.942]." }, { "from": "gpt", "value": "It includes the five x-axis labels: {The label \"Minimal Basic Amenities\"} is at [0.050, 0.919, 0.332, 0.942]. The second label for \"Inadequate Sanitation\" - [0.224, 0.919, 0.481, 0.942]. The third label \"Lack of Electricity\" - [0.411, 0.919, 0.617, 0.942]. The fourth label \"No Access to Clean Drinking Water\" - [0.475, 0.919, 0.878, 0.942]. The fifth label \"Chronic Food Insecurity\" - [0.702, 0.919, 0.974, 0.942]." } ], "id_raw": 275 }, { "id": "cpPLMxoZpZ76LGpO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.790, 0.806, 0.887, 0.911]?" }, { "from": "gpt", "value": "It contains {the bar representing \"Chronic Food Insecurity\"} and the value is 12%." } ], "id_raw": 275 }, { "id": "XESR5tnYQ8tIWrsK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat scientific field is located at [0.082, 0.919, 0.224, 0.942] on the x axis?" }, { "from": "gpt", "value": "The scientific field is Artificial Intelligence." } ], "id_raw": 276 }, { "id": "hOgWhSJBJuP540Gv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the y coordinate of the bar corresponding to the Robotics field in the first subplot?" }, { "from": "gpt", "value": "[0.292, -69.327, 0.361, 0.903]." } ], "id_raw": 276 }, { "id": "V78j5wKErlqyd9Sl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis located at [0.015, 0.314, 0.029, 0.614] in the first subplot?" }, { "from": "gpt", "value": "The Amount of New Discovery." } ], "id_raw": 276 }, { "id": "jCkQvvYmymQDLnu5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented within coordinates [0.639, -64.937, 0.708, 0.903] in the subplot?" }, { "from": "gpt", "value": "The bar representing \"Quantum Computing\" in the first subplot." } ], "id_raw": 276 }, { "id": "DgIfa7Sh0LMLgRv0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nIs the bar corresponding to {Space Technology}[0.465, -104.442, 0.535, 0.903] taller than the bar corresponding to {Artificial Intelligence}[0.118, -86.884, 0.188, 0.903] in the first subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 276 }, { "id": "iWTt8XpbRHGUFTIb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nIs {Robotics}[0.292, -69.327, 0.361, 0.903] left or right to {Quantum Computing}[0.639, -64.937, 0.708, 0.903] on the x-axis in the first subplot?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 276 }, { "id": "PiiUUKeQaOGNCdcY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nBetween {Robotics}[0.292, -69.327, 0.361, 0.903] and {Quantum Computing}[0.639, -64.937, 0.708, 0.903] in the first subplot, which has a higher \"Pace of Innovation\" in the second subplot?" }, { "from": "gpt", "value": "Quantum Computing." } ], "id_raw": 276 }, { "id": "iCFqrKns8y2JsInV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Artificial Intelligence\"}[0.118, -86.884, 0.188, 0.903] and {\"Space Technology\"}[0.465, -104.442, 0.535, 0.903], which one has a higher value in the first subplot's bar chart?" }, { "from": "gpt", "value": "Space Technology." } ], "id_raw": 276 }, { "id": "MyoC9oenYeB7r2KT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in [0.790, 0.919, 0.904, 0.942]." }, { "from": "gpt", "value": "The area contains the label for the last x-tick in the bar chart, which is the \"Nanotechnology\" scientific field." } ], "id_raw": 276 }, { "id": "sY6uG9odowOeMeGH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat information does the area [0.971, 0.357, 0.985, 0.571] in the second subplot hold?" }, { "from": "gpt", "value": "It contains the y-axis label \"Pace of Innovation\"." } ], "id_raw": 276 }, { "id": "BIrYOfbG9vGhWPNf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.292, -69.327, 0.361, 0.903]?" }, { "from": "gpt", "value": "The area features a bar from the \"bars_of_group_0\" set in subplot_0. It represents the \"Robotics\" scientific field with a \"New Discovery\" value of 80." } ], "id_raw": 276 }, { "id": "NMbShlSdA7oFEHOA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the area [0.118, -86.884, 0.188, 0.903]?" }, { "from": "gpt", "value": "A bar in subplot_0 representing \"Artificial Intelligence\". The bar depicts that the \"Amount of New Discovery\" value for \"Artificial Intelligence\" is 100." } ], "id_raw": 276 }, { "id": "J6EojmeaqXnfZDFP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.044, 0.159, 0.076, 0.183]?" }, { "from": "gpt", "value": "It is the y-axis label: 200." } ], "id_raw": 277 }, { "id": "rMjhEEdFU0FHD4Au", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat element is represented by the coordinates [0.837, 0.061, 0.945, 0.706]?" }, { "from": "gpt", "value": "It is the {bar for the category \"Telecommunications\"}[0.837, 0.061, 0.945, 0.706]." } ], "id_raw": 277 }, { "id": "4TAQqSq8vMzrwxVq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.323, 0.714, 0.381, 0.792] represent?" }, { "from": "gpt", "value": "It represents the {X-axis label for the \"Biotech\" category}[0.323, 0.714, 0.381, 0.792]." } ], "id_raw": 277 }, { "id": "ow4HqxpdUu1zbSr2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the one representing \"Space Technology\"}[0.658, 0.346, 0.766, 0.706] or {the one representing \"Renewable Energy\"}[0.478, 0.198, 0.586, 0.706]?" }, { "from": "gpt", "value": "The bar representing \"Renewable Energy\"." } ], "id_raw": 277 }, { "id": "T3piyEND6mI3CQQI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for \"Biotech\"}[0.298, 0.120, 0.406, 0.706] to the left or right of the {bar for \"AI and Machine Learning\"}[0.118, 0.346, 0.226, 0.706]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 277 }, { "id": "VfTIC4c05Gp3Kiv1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nBetween the {\"AI and Machine Learning\"}[0.118, 0.346, 0.226, 0.706] and {\"Space Technology\"}[0.658, 0.346, 0.766, 0.706] bars, which one has a greater number of patents?" }, { "from": "gpt", "value": "Both have the same number of patents." } ], "id_raw": 277 }, { "id": "jjonRF31h28OyDwn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the items in the coordinates [0.082, 0.034, 0.981, 0.706]?" }, { "from": "gpt", "value": "This region contains the entire bar chart plot, including both the x-axis and y-axis and all their labels, as well as all the bars representing the different technology sectors and their respective numbers of patents." } ], "id_raw": 277 }, { "id": "3MvXLDkNk7e6iyKI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region denoted by [0.118, 0.346, 0.946, 0.706]?" }, { "from": "gpt", "value": "It contains the bars for all five categories from \"AI and Machine Learning\" to \"Telecommunications\"." } ], "id_raw": 277 }, { "id": "2rvHMhCBJcGpILBu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nDescribe what's found at coordinates [0.298, 0.120, 0.406, 0.706]." }, { "from": "gpt", "value": "There is one bar present within these coordinates. This {bar represents the \"Biotech\" category}[0.298, 0.120, 0.406, 0.706] in the bar chart, indicating the number of patents in this sector." } ], "id_raw": 277 }, { "id": "ogohIWWfTIS1vQfD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the data represent at [0.113, 0.414, 0.204, 0.933]?" }, { "from": "gpt", "value": "That represents a bar for a Food Bank with a donation amount of 12500." } ], "id_raw": 278 }, { "id": "ikMuUE1Vnge74UNp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label displayed at the chart's coordinates [0.015, 0.400, 0.029, 0.552]?" }, { "from": "gpt", "value": "The label is \"Donation Amount\"." } ], "id_raw": 278 }, { "id": "XBhYexlumlEqClWV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the information at [0.860, 0.939, 0.960, 0.957] signify in the x-axis?" }, { "from": "gpt", "value": "It refers to the charity type \"Disaster Relief\"." } ], "id_raw": 278 }, { "id": "BIwWXoQM432bd9eg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the content with coordinates [0.414, 0.089, 0.504, 0.933]?" }, { "from": "gpt", "value": "It represents a bar for Environmental Conservation with a donation amount of 20300." } ], "id_raw": 278 }, { "id": "JWHNWYk7BwfOe1to", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nAre {the donation to Animal Welfare}[0.264, 0.601, 0.354, 0.933] more or less than {the donation to Food Bank}[0.113, 0.414, 0.204, 0.933]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 278 }, { "id": "etxQ7TwC1kXICCOF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nAmong {Food Bank}[0.113, 0.414, 0.204, 0.933] and {Disaster Relief}[0.865, 0.164, 0.955, 0.933], which one received more donations?" }, { "from": "gpt", "value": "Disaster Relief." } ], "id_raw": 278 }, { "id": "aplZIInNXuxDCNUy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nWhich receives the highest amount of donations between {Educational Support}[0.564, 0.285, 0.654, 0.933] and {Environmental Conservation}[0.414, 0.089, 0.504, 0.933]?" }, { "from": "gpt", "value": "Environmental Conservation." } ], "id_raw": 278 }, { "id": "tKTKiyRMeLRpuTuR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nIs the amount of donation to {Healthcare Services}[0.714, 0.538, 0.805, 0.933] more or less than {Educational Support}[0.564, 0.285, 0.654, 0.933]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 278 }, { "id": "SzLeS8EYCUZZqswU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the features on the y-axis label within the bounding box [0.015, 0.400, 0.029, 0.552]?" }, { "from": "gpt", "value": "It is the label for the y-axis. The label is \"Donation Amount\", denoting the amount of donation received by different charity types." } ], "id_raw": 278 }, { "id": "cGgB310yZReqBQsY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the content located at [0.256, 0.939, 0.361, 0.957] on the chart?" }, { "from": "gpt", "value": "It's a label on the x-axis representing the \"Animal Welfare\" charity type." } ], "id_raw": 278 }, { "id": "Uskg89uOard2eP2f", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the information at [0.564, 0.285, 0.654, 0.933] tells us?" }, { "from": "gpt", "value": "The box contains a bar from the bar chart. The bar represents the donation amount (15600) given to the charity type \"Educational Support\"." } ], "id_raw": 278 }, { "id": "oW4TLJIjWoaupjlX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the figure layout at coordinates [0.043, 0.719, 0.078, 0.736]?" }, { "from": "gpt", "value": "It's a label on the y-axis, representing a monetary value of 5000 which is one of the markers for donation amount." } ], "id_raw": 278 }, { "id": "VxLJD8KEhGRa3WNh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the coordinates [0.043, 0.926, 0.085, 0.944] in the chart?" }, { "from": "gpt", "value": "Y-axis label, representing the value -0.6." } ], "id_raw": 279 }, { "id": "cQuwc4HUf3LTq9yg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nWhat is at the location [0.896, 0.939, 0.940, 0.957]?" }, { "from": "gpt", "value": "The x-axis label \"2020\"." } ], "id_raw": 279 }, { "id": "sluWCbmKV7kHQoxF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.133, 0.939, 0.177, 0.957] represent?" }, { "from": "gpt", "value": "The x-axis label \"1960\"." } ], "id_raw": 279 }, { "id": "7fyWpiwQ7BpKOvrR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the bar at [0.218, 0.438, 0.346, 0.735] stands for?" }, { "from": "gpt", "value": "This bar shows the Value Change for the year \"1970\". The height of the bar represents a value of -0.4." } ], "id_raw": 279 }, { "id": "F3yhEZ2Uh09IG6Dz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nComparing the bars at [0.473, 0.438, 0.600, 0.809] and at [0.600, 0.438, 0.727, 0.669], which one is taller?" }, { "from": "gpt", "value": "The bar at [0.473, 0.438, 0.600, 0.809]." } ], "id_raw": 279 }, { "id": "VYazuYdVt5EXwmnn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar at [0.727, 0.314, 0.854, 0.438] taller or shorter than the bar at [0.854, 0.067, 0.981, 0.438]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 279 }, { "id": "UZBebmMkG5sJLyTG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nLooking at the values of the bars at [0.218, 0.438, 0.346, 0.735] and [0.854, 0.067, 0.981, 0.438], which decade saw a greater change in value?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 279 }, { "id": "7Va52YZCk4KMWm0M", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nIs the Value Change from 1980 to 1990 ({the 1980 bar}[0.346, 0.438, 0.473, 0.892], {the 1990 bar}[0.473, 0.438, 0.600, 0.809]) increasing or decreasing?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 279 }, { "id": "dUboogLFJe4D8frh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area [0.091, 0.314, 0.981, 0.892]?" }, { "from": "gpt", "value": "This area contains all the bars in the chart. Each bar represents the Value Change for a certain decade, with bars corresponding to the years from 1960 to 2020." } ], "id_raw": 279 }, { "id": "Tyw2MlLBQTSNRNk4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the coordinates [0.091, 0.026, 0.981, 0.933]." }, { "from": "gpt", "value": "This region encases the entire chart, including all bars representing the Value Change from 1960 to 2020, the x-axis labeled as Decades, and the y-axis labeled as Value Change. The x-axis has labels ranging from 1960 to 2020, and the y-axis has labels from -0.6 to 0.5." } ], "id_raw": 279 }, { "id": "qSc1UbTMR2t9cMi2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nCan you give details on the region defined by [0.854, 0.067, 0.981, 0.438]?" }, { "from": "gpt", "value": "This contains a single bar representing the Value Change for the year 2020. The height of the bar indicates a positive change of 0.5, and it's the highest bar in the chart." } ], "id_raw": 279 }, { "id": "zXG65r0lIzHw3B0n", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nTell me about [0.091, 0.438, 0.218, 0.537]." }, { "from": "gpt", "value": "Within these coordinates, there lies the first bar of the chart which represents the Value Change for the year 1960. The bar is located below the x-axis, indicating a negative change of -0.6." } ], "id_raw": 279 }, { "id": "kEXE82METGOA1hFb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the bounding box for the label of the y-axis?" }, { "from": "gpt", "value": "[0.025, 0.441, 0.048, 0.504]" } ], "id_raw": 280 }, { "id": "jge6YYFE3uF7lKPB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label} at [0.539, 0.919, 0.572, 0.942]?" }, { "from": "gpt", "value": "Q3" } ], "id_raw": 280 }, { "id": "bsdX0hx0cZUk8lAo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar located at [0.136, 0.122, 0.304, 0.473] represent?" }, { "from": "gpt", "value": "It represents the value for Q1, which is 0.80." } ], "id_raw": 280 }, { "id": "SM8x9Cfe3XG6xS4f", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Q1}[0.136, 0.122, 0.304, 0.473] higher than the bar for {Q2}[0.304, 0.210, 0.472, 0.473]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 280 }, { "id": "GLKK4WOxmx8Yg8PD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nIs {the value} for Q3[0.472, 0.473, 0.640, 0.692] less than {the value} for Q4[0.640, 0.473, 0.807, 0.845]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 280 }, { "id": "0h6hgpEl7n9azeZf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value: {Q5}[0.807, 0.210, 0.975, 0.473] or {Q1}[0.136, 0.122, 0.304, 0.473]?" }, { "from": "gpt", "value": "They are the same." } ], "id_raw": 280 }, { "id": "335ezxPHVy6Rfuag", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.136, 0.122, 0.975, 0.473]?" }, { "from": "gpt", "value": "This area contains five bars, each representing a different quarter in a selected year. The quarters are represented in left-to-right order: from Q1[0.136, 0.122, 0.304, 0.473] to Q5[0.807, 0.210, 0.975, 0.473]. The height of each bar corresponds to a specific value, with negative values below the x-axis and positive values above it." } ], "id_raw": 280 }, { "id": "WEycuGQMvztBiI9r", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained in [0.025, 0.441, 0.048, 0.504]?" }, { "from": "gpt", "value": "This region contains the label for the axis Y[i.e.,'Value'] that indicates what the bars represent in terms of value." } ], "id_raw": 280 }, { "id": "0XKhZyZV0jJt2gsR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat are located in the area [0.077, 0.025, 0.128, 0.048]?" }, { "from": "gpt", "value": "The y-axis label indicating '1.00' is within this box." } ], "id_raw": 280 }, { "id": "5EaNJRamWYrXy2hm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.058, 0.473, 0.096, 0.490] represent?" }, { "from": "gpt", "value": "Y-axis label, captioned as \"0.25\"." } ], "id_raw": 281 }, { "id": "IXZNJWiSewwbAbJF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.102, 0.147, 0.247, 0.631]?" }, { "from": "gpt", "value": "It is the bar representing the popularity growth of {Italian Cuisine}[0.102, 0.147, 0.247, 0.631]." } ], "id_raw": 281 }, { "id": "6LJlAwQgYT4noO4S", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the location [0.464, 0.964, 0.612, 0.981]?" }, { "from": "gpt", "value": "That's the X-axis label, {Cuisine Category}[0.464, 0.964, 0.612, 0.981]." } ], "id_raw": 281 }, { "id": "WUflhGVPtK7UJtQD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nDoes the {Italian Cuisine bar}[0.102, 0.147, 0.247, 0.631] stretch higher or lower than the {Chinese Cuisine bar}[0.247, 0.268, 0.393, 0.631]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 281 }, { "id": "7puezcCdGbauDjKd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nWhich is bigger, the {Mexican Cuisine bar}[0.393, 0.449, 0.538, 0.631] or the {Indian Cuisine bar}[0.538, 0.570, 0.683, 0.631]?" }, { "from": "gpt", "value": "Indian Cuisine bar." } ], "id_raw": 281 }, { "id": "QEkmbFozFOhwyBDm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nIs {Japanese Cuisine}[0.828, 0.939, 0.975, 0.957] more popular or less popular than {Thai Cuisine}[0.703, 0.939, 0.809, 0.957] according to the bar chart?" }, { "from": "gpt", "value": "Less popular." } ], "id_raw": 281 }, { "id": "nUDbbkdSKvh5sEXu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the coordinate region [0.395, 0.449, 0.538, 0.631]." }, { "from": "gpt", "value": "Within this region, there is a blue bar representing Mexico's popularity growth in the food industry. The bar signifies a positive growth of about 0.25." } ], "id_raw": 281 }, { "id": "itmkm22K3sbTVHyZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.829, 0.631, 0.974, 0.782]?" }, { "from": "gpt", "value": "In this area, there is a bar for {Japanese Cuisine}[0.828, 0.939, 0.975, 0.957] that clearly depicts a negative growth trend of about -0.25." } ], "id_raw": 281 }, { "id": "ZF2XyrKYS2C3unwm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the data located inside the region [0.551, 0.569, 0.673, 0.631]?" }, { "from": "gpt", "value": "Sure. The region contains a single bar pertaining to the popularity growth of {Indian Cuisine}[0.550, 0.939, 0.672, 0.957]. The bar indicates a small positive growth of about 0.05." } ], "id_raw": 281 }, { "id": "6rCNAeO2JE7uiUDw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart label at these coordinates [0.019, 0.376, 0.036, 0.569] ?" }, { "from": "gpt", "value": "The label is \"Innovation Index\"" } ], "id_raw": 282 }, { "id": "QlYzQRXPGrK5nf1b", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nWhat category appears at coordinates [0.837, 0.919, 0.974, 0.942]?" }, { "from": "gpt", "value": "The category is \"AI_in_Education\"" } ], "id_raw": 282 }, { "id": "IzdfCaFI5T7MqhIs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the value found at coordinates [0.861, 0.174, 0.950, 0.560]?" }, { "from": "gpt", "value": "It's the value for \"AI_in_Education\" and it is 2.2" } ], "id_raw": 282 }, { "id": "VM2RJRr9v5bdMNow", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 'AI_in_Education' bar}[0.861, 0.174, 0.950, 0.560] higher than {the 'Adaptive_Learning' bar}[0.713, 0.560, 0.802, 0.665]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 282 }, { "id": "PNyujkyLnTLnTXgt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nWhich bar is higher, {the 'Gamified_Education' bar}[0.417, 0.402, 0.506, 0.560] or {the 'Cloud_Based_Learning' bar}[0.121, 0.358, 0.210, 0.560]?" }, { "from": "gpt", "value": "'Gamified_Education' bar" } ], "id_raw": 282 }, { "id": "DUuHwpdC4ViNtkq6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nWhich is more negative between {the 'Adaptive_Learning' bar}[0.713, 0.560, 0.802, 0.665] and {the 'MOOCs_Adoption' bar}[0.269, 0.560, 0.358, 0.867]?" }, { "from": "gpt", "value": "'MOOCs_Adoption' bar" } ], "id_raw": 282 }, { "id": "CMkd7A4S6dXnjYHg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nDescribe what you see at coordinates [0.713, 0.560, 0.802, 0.665]." }, { "from": "gpt", "value": "This region includes a section of the 'Adaptive_Learning' bar, which extends below the zero line, indicating a negative value for the 'Innovation Index'." } ], "id_raw": 282 }, { "id": "egFzGuNrrnVy2nxw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe what can be seen at coordinates [0.269, 0.560, 0.358, 0.867]." }, { "from": "gpt", "value": "Within these coordinates, the bar for the 'MOOCs_Adoption' category is located. Its length extends above and below the zero line, meaning it represents a negative value for the 'Innovation Index'." } ], "id_raw": 282 }, { "id": "ASOT2AJFVLwtrq5m", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nDescribe what is within the region bounded by coordinates [0.121, 0.358, 0.210, 0.560]." }, { "from": "gpt", "value": "This region houses a bar corresponding to the 'Cloud_Based_Learning' category. The bar is entirely above the x-axis, indicating a positive 'Innovation Index' value for 'Cloud_Based_Learning'." } ], "id_raw": 282 }, { "id": "hHDKpNCJcScL8RZe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object located at [0.147, 0.143, 0.285, 0.903]?" }, { "from": "gpt", "value": "It is a bar representing earnings of age group 20-29." } ], "id_raw": 283 }, { "id": "wNDbFuJRSaFmmuAL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the content located at [0.025, 0.418, 0.048, 0.519]?" }, { "from": "gpt", "value": "The Y-axis label {Earnings}[0.025, 0.418, 0.048, 0.519]." } ], "id_raw": 283 }, { "id": "VEVyFh2cEUVXJ8pw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects found at [0.321, 0.919, 0.387, 0.942] and [0.354, 0.903]?" }, { "from": "gpt", "value": "Age group {30-39}[0.321, 0.919, 0.387, 0.942] and its corresponding X-tick [0.354, 0.903]." } ], "id_raw": 283 }, { "id": "bLhOL8uwjtmoADjx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred from the object at [0.699, 0.469, 0.837, 0.903]?" }, { "from": "gpt", "value": "It is a bar graph indicating that the earnings for the age group 60-69 are 50000." } ], "id_raw": 283 }, { "id": "wPtxxap5z9jc58wX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing the Age Group 20-29 {20-29}[0.147, 0.143, 0.285, 0.903], higher than the bar representing age group 30-39 {30-39}[0.285, 0.197, 0.423, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 283 }, { "id": "GqUc8SuPdHn9vGvU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nAre the earnings for age group 40-49 {40-49}[0.423, 0.251, 0.561, 0.903] higher than those for age group 70+ {70+}[0.837, 0.577, 0.975, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 283 }, { "id": "24SkvAa5JqinbkdG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nDoes the bar at [0.423, 0.251, 0.561, 0.903] have higher earnings than the bar at [0.837, 0.577, 0.975, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 283 }, { "id": "m46LMPkZeYHpowt5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nIs the earning for age group 50-59 {50-59}[0.561, 0.360, 0.699, 0.903] more than that for age group 60-69 {60-69}[0.699, 0.469, 0.837, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 283 }, { "id": "7WEnIkiYfCFVYI8O", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat is seen in the area bounded by [0.147, 0.143, 0.975, 0.903]?" }, { "from": "gpt", "value": "There are six bars representing the earnings of age groups 20-29, 30-39, 40-49, 50-59, 60-69, and 70+." } ], "id_raw": 283 }, { "id": "OJNDmUpJ13TfQcJy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.058, 0.025, 0.131, 0.808]." }, { "from": "gpt", "value": "This region contains labels marking the Y-axis. The labels range from 80000 at the bottom to 10000 at the top, representing the earnings amount." } ], "id_raw": 283 }, { "id": "n4aopTUSciNjPQR1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area [0.147, 0.034, 0.975, 0.903]?" }, { "from": "gpt", "value": "This area contains the main graph information, consisting of six bars. These bars represent the earnings of various age groups ranging from 20-29 through to 70+." } ], "id_raw": 283 }, { "id": "yqWluVDXnE4WZlKs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.500, 0.952, 0.622, 0.975]?" }, { "from": "gpt", "value": "The X-axis label {Age Group}[0.500, 0.952, 0.622, 0.975] is located in this area." } ], "id_raw": 283 }, { "id": "jLIn3QZmbaeuPCPy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart?" }, { "from": "gpt", "value": "The label of the y-axis is \"Frequency of Mentions\" {ylabel}[0.015, 0.333, 0.029, 0.595]." } ], "id_raw": 285 }, { "id": "rFc6fxsvzlSAZfbX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis in the chart?" }, { "from": "gpt", "value": "The label of the x-axis is \"Healthcare and Medical Technology Aspects\" {xlabel}[0.380, 0.952, 0.685, 0.975]." } ], "id_raw": 285 }, { "id": "YQTYMej1sN8YKQqb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the second element in x-axis?" }, { "from": "gpt", "value": "The second element of the x-axis is \"Treatment\" on {xticks: label}[0.233, 0.919, 0.304, 0.942]." } ], "id_raw": 285 }, { "id": "rwtEaigTF6DIj2F5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat color does legend refer to Negative aspect?" }, { "from": "gpt", "value": "The color for the Negative aspect as per the legend is red {sublegend: items: handle: color}[0.092, 0.048, 0.120, 0.064]." } ], "id_raw": 285 }, { "id": "FZaA4SX1uqN0C6V7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhich is mentioned more frequently, \"Technology\" or \"R&D\"?" }, { "from": "gpt", "value": "\"Technology\" is mentioned more frequently than \"R&D\"." } ], "id_raw": 285 }, { "id": "jjAKduAypKn1EaZl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"AI\" application}[0.754, 0.798, 0.838, 0.903] taller than {the bar for \"Medicine\"}[0.121, 0.767, 0.205, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 285 }, { "id": "ZimaOGrZZWufPrGJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhether the frequency of \"Medicine\" is equal to \"Treatment\" in Positive category?" }, { "from": "gpt", "value": "No, {the positive bar for \"Medicine\"}[0.121, 0.767, 0.205, 0.767] is lower than {the positive bar for \"Treatment\"}[0.226, 0.752, 0.311, 0.794]." } ], "id_raw": 285 }, { "id": "XnRj1Xel7UhCr5fu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nDoes the \"Medicine\" subtype have more Positive or Negative mentions?" }, { "from": "gpt", "value": "{Medicine}[0.121, 0.767, 0.205, 0.903] has more Negative mentions." } ], "id_raw": 285 }, { "id": "J6KogNs7q7Hf1vM2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.086, 0.037, 0.199, 0.183]." }, { "from": "gpt", "value": "This area refers to the chart legend, which includes labels for Negative, Positive, None and Mixed responses denoted by the colours red, green, gray and orange respectively. The Negative label is given at [0.131, 0.046, 0.193, 0.069], Positive label at [0.131, 0.081, 0.184, 0.104], None label at [0.131, 0.116, 0.167, 0.139], and Mixed label at [0.131, 0.151, 0.172, 0.174]." } ], "id_raw": 285 }, { "id": "AkVtC9Q2dss3POqS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nPlease explain what is shown in [0.079, 0.025, 0.985, 0.903]." }, { "from": "gpt", "value": "This region encompasses the chart's data, including the bars each representing different aspects of healthcare and medical technology along with their frequency in negative, positive, none and mixed categories. It spans from aspects like Medicine, Treatment, Diagnosis to AI applications in Medical technology." } ], "id_raw": 285 }, { "id": "oAXJiQKtNyuOIWus", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the section [0.437, 0.698, 0.522, 0.903] denote?" }, { "from": "gpt", "value": "This section houses the four bars that quantify the frequency of the \"Technology\" aspect in Negative, Positive, None and Mixed categories." } ], "id_raw": 285 }, { "id": "cEeqspoxL8ckVnGp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat is special about the data contained within [0.543, 0.088, 0.733, 0.903]?" }, { "from": "gpt", "value": "It contains the frequencies of mentions about \"Telemedicine\" in different contexts. Among them, there is a particularly large bar - {Telemedicine frequency in None category}[0.648, 0.088, 0.733, 0.903], indicating a significant amount of neutral mentions about telemedicine." } ], "id_raw": 285 }, { "id": "AwVqVJZFCK7dZnej", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nWhat is specified at the position [0.025, 0.363, 0.048, 0.542]?" }, { "from": "gpt", "value": "This is the label for the y-axis, which is the {Frequency}[0.025, 0.363, 0.048, 0.542]." } ], "id_raw": 286 }, { "id": "3kkLZwSWbGd1Hlyr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is located at [0.171, 0.453, 0.249, 0.854]?" }, { "from": "gpt", "value": "This is a green bar that represents {Growth} in the {Experimentation} field with a range of 0.00 -> 2000.00." } ], "id_raw": 286 }, { "id": "RGgfXJjN8tgn9eqn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the item at location [0.153, 0.086, 0.199, 0.110]?" }, { "from": "gpt", "value": "This is the color handle for the legend item {Growth}[0.153, 0.086, 0.199, 0.110], and it's green." } ], "id_raw": 286 }, { "id": "Xb1apf9E3qkuPOpb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nIn the legend, which label {Decline} or {Growth} is on top?" }, { "from": "gpt", "value": "{Growth}." } ], "id_raw": 286 }, { "id": "fHGF9o739WN5f7Pn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nComparing the bar lengths in [0.269, 0.523, 0.347, 0.854] and [0.269, 0.483, 0.347, 0.523], which is longer for {Analysis}?" }, { "from": "gpt", "value": "The bar {Growth}." } ], "id_raw": 286 }, { "id": "kHIbj3WRk2uUxzsJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nWhat field in {Fields}[0.521, 0.927, 0.586, 0.963] is farther right, {Implementation} or {Communication}?" }, { "from": "gpt", "value": "{Communication}." } ], "id_raw": 286 }, { "id": "7waupZneRiaRRh1g", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.144, 0.069, 0.311, 0.184]?" }, { "from": "gpt", "value": "This region contains a legend for the bar chart. It contains color-text label pairs for two categories - {Growth} and {Decline}. The {Growth} color handle is located at [0.153, 0.086, 0.199, 0.110], which is green, and the accompanying text label is located at [0.218, 0.083, 0.299, 0.118]. Similarly, the {Decline} color handle is at [0.153, 0.138, 0.199, 0.162], which is red, and the text label is at [0.218, 0.135, 0.301, 0.170]." } ], "id_raw": 286 }, { "id": "FZh4278mSHG1Tm5G", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.171, 0.453, 0.249, 0.854]." }, { "from": "gpt", "value": "This area contains a green bar associated with {Growth} in the {Experimentation} field. This bar spans a y-range from 0.00 to 2000.00." } ], "id_raw": 286 }, { "id": "8ecTG1JSBXxLcxhC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.269, 0.483, 0.347, 0.523]?" }, { "from": "gpt", "value": "In this area, there is a red bar indicating {Decline} in the {Analysis} field. This bar ranges from 1650.00 to 1850.00 on the y-axis." } ], "id_raw": 286 }, { "id": "qQ228wZ8IFtxMENi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the xlabel is derived from {xlabel}[0.508, 0.952, 0.572, 0.975]?" }, { "from": "gpt", "value": "The label is 'Sectors'." } ], "id_raw": 287 }, { "id": "T0Xllc7ZuU0cANPf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the handle}[0.115, 0.083, 0.150, 0.099] in the legend?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 287 }, { "id": "9GeTngOgQfz53BeZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nIn the yticks, what is represented at {yticks}[0.076, 0.894, 0.087, 0.917]?" }, { "from": "gpt", "value": "The label is '0'." } ], "id_raw": 287 }, { "id": "XAk1DGFVOASNhd3l", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nWhich sector is allocated more resources, {Healthcare}[0.139, 0.823, 0.308, 0.903] or {Education}[0.350, 0.504, 0.519, 0.903]?" }, { "from": "gpt", "value": "Education." } ], "id_raw": 287 }, { "id": "jFfT8clYoCPYPsF6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nWhich organization allocates more resources to {Infrastructure}[0.561, 0.105, 0.730, 0.903], {Social Services} or {Public Services}?" }, { "from": "gpt", "value": "Public Services." } ], "id_raw": 287 }, { "id": "x1tT43a38DV9PJfg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nWhere is the area dedicated to {Public Services}[0.115, 0.083, 0.150, 0.099] in relation to {Social Services}[0.115, 0.048, 0.150, 0.064] in the chart legend?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 287 }, { "id": "6MctHvblJbybi7eV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nPlease explain the objects found in this coordinate range [0.561, -0.294, 0.730, 0.903]." }, { "from": "gpt", "value": "There are 2 bars:\n1. The bar for the infrastructure sector by Public Services. Its allocated resources start from a y-value of 0 and reach to a maximum of 1000 with the coordinate range [0.561, 0.105, 0.730, 0.903].\n2. The placeholder for overspending by Social Services in the infrastructure sector which starts at a y value of 1000 and stretches to 1500 in the negative direction with the coordinate range [0.561, -0.294, 0.730, 0.105]" } ], "id_raw": 287 }, { "id": "uX9AFp4ftHKSp668", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nCan you specify the content within the region [0.108, 0.037, 0.300, 0.183]?" }, { "from": "gpt", "value": "This is the chart legend area containing the labels and corresponding colors for Public Services and Social Services. Specifically, it contains 4 items: \n1. Social Services is represented in Red, located at \n{handle}[0.115, 0.048, 0.150, 0.064]\n2. Public Services is represented in Green, located at {handle}[0.115, 0.083, 0.150, 0.099]" } ], "id_raw": 287 }, { "id": "9ZbnURyZWFEaM97j", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nWhat is visible within the coordinates [0.139, 0.504, 0.519, 0.903]?" }, { "from": "gpt", "value": "There are 2 bars representing resource distribution by Social Services and Public Services for Education. \n1. Social Services has allocated 500 units in the region [0.350, 0.504, 0.519, 0.903]\n2. Public Services hasn't allocated any resources as it's on the same level as Social Services' allocation." } ], "id_raw": 287 }, { "id": "7mhX8UExOxa3pLrE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.171, 0.754, 0.511, 0.854] in the chart?" }, { "from": "gpt", "value": "It's a blue bar representing the Surging Growth trend in the Innovation Ecosystem." } ], "id_raw": 288 }, { "id": "pdBpEWmjQ1EsYOto", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first entry in the legend?" }, { "from": "gpt", "value": "[0.153, 0.086, 0.199, 0.110]." } ], "id_raw": 288 }, { "id": "r7ql7OpLQm7mMQVV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat value does the {green bar}[0.596, 0.372, 0.937,0.513] represent?" }, { "from": "gpt", "value": "The value of Stable Market trend in the Technology Entrepreneurship category." } ], "id_raw": 288 }, { "id": "sb9VDMLyfsm8jGns", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat's in [0.144, 0.069, 0.433, 0.288]?" }, { "from": "gpt", "value": "It is the legend of the chart, showing colors for Surging Growth, Descending Crisis, Stable Market, and Emerging Trend." } ], "id_raw": 288 }, { "id": "inPO7okb11PWT2Kz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nIs the {Innovation Ecosystem bar for Emerging Trend}[0.171, 0.172, 0.511, 0.192] higher than the {Innovation Ecosystem bar for Stable Market}[0.171, 0.192, 0.511, 0.593]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 288 }, { "id": "eSiDnuEnlkR5b0jL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nIs the {Surging Growth trend in the Innovation Ecosystem}[0.171, 0.754, 0.511, 0.854] greater than the {Descending Crisis trend in the Technology Entrepreneurship}[0.596, 0.513, 0.937, 0.814]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 288 }, { "id": "k7C8dC7woyDywvME", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nAre the bars representing {Innovation Ecosystem}[0.171, 0.754, 0.511, 0.854],[0.171, 0.593, 0.511, 0.754],[0.171, 0.192, 0.511, 0.593],[0.171, 0.172, 0.511, 0.192] generally taller than the bars representing {Technology Entrepreneurship}[0.596, 0.814, 0.937, 0.854],[0.596, 0.513, 0.937, 0.814],[0.596, 0.372, 0.937, 0.513],[0.596, 0.282, 0.937, 0.372]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 288 }, { "id": "6RBlwyG2pFvavRel", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Surging Growth and Descending Crisis for Innovation Ecosystem} [0.171, 0.754, 0.511, 0.854],[0.171, 0.593, 0.511, 0.754] and {Stable Market and Emerging Trend for Innovation Ecosystem} [0.171, 0.192, 0.511, 0.593],[0.171, 0.172, 0.511, 0.192], which group has a higher overall value?" }, { "from": "gpt", "value": "Surging Growth and Descending Crisis for Innovation Ecosystem." } ], "id_raw": 288 }, { "id": "hynssY6zW0B2Yhij", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.144, 0.069, 0.433, 0.288]." }, { "from": "gpt", "value": "The region contains the chart's legend, with corresponding color-text labels for Surging Growth (Blue - [0.153, 0.086, 0.199, 0.110]), Descending Crisis (Red - [0.153, 0.138, 0.199, 0.162]), Stable Market (Green - [0.153, 0.190, 0.199, 0.215]), and Emerging Trend (Orange - [0.153, 0.243, 0.199, 0.267])." } ], "id_raw": 288 }, { "id": "icCYeybuC8Aepa3y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.132, 0.051, 0.975, 0.854]." }, { "from": "gpt", "value": "The area corresponds to the whole chart, including the bars representing the different values of each trend for two categories (Innovation Ecosystem and Technology Entrepreneurship), the x-axis label (Ecosystems), the y-axis labels representing the values, and the chart's legend." } ], "id_raw": 288 }, { "id": "8vC82ZnKS5EwLPWw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.596,0.282,0.937,0.372]?" }, { "from": "gpt", "value": "The object is an orange bar representing the Emerging Trend value for the Technology Entrepreneurship category." } ], "id_raw": 288 }, { "id": "6ez2ELrBy1pZXVXZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.485, 0.927, 0.622, 0.963]?" }, { "from": "gpt", "value": "It is the x-axis label, which is \"Ecosystems\"." } ], "id_raw": 288 }, { "id": "yWNvAAkekNbYP0fK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing {the falling rate of return}<[0.157, 0.349, 0.264, 0.646]> in 'Bond Investments'?" }, { "from": "gpt", "value": "Red" } ], "id_raw": 289 }, { "id": "pNxSzlUseFaT2xri", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the investment type at the coordinate [0.799, 0.671, 0.965, 0.835]?" }, { "from": "gpt", "value": "Cryptocurrency" } ], "id_raw": 289 }, { "id": "PVDaBeDeKigOTWfi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the 'Return Rate Same' measured by the bar at [0.291, 0.408, 0.399, 0.527]?" }, { "from": "gpt", "value": "20 percent" } ], "id_raw": 289 }, { "id": "hDonB9qNakyc9s0f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label at the coordinate [0.025, 0.210, 0.048, 0.487]?" }, { "from": "gpt", "value": "Return Rate (%)" } ], "id_raw": 289 }, { "id": "8IyFXAY4kPWfKN4E", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nIs {the rate of return for real estate investments}[0.425, 0.587, 0.533, 0.646] increasing or decreasing as represented in the bar chart?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 289 }, { "id": "eOE38vFXxez71Q3Z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhich type of investment has a higher 'Return Rate Falling', {Stock Investments}[0.291, 0.527, 0.399, 0.646] or {Mutual Funds}[0.560, 0.438, 0.667, 0.646]?" }, { "from": "gpt", "value": "Bond Investments." } ], "id_raw": 289 }, { "id": "AU3DJFjc7udOA7DA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nDoes {Commodity Investments}[0.694, 0.617, 0.802, 0.646] have a higher or lower 'Abrupt Drop' than {Cryptocurrency}[0.829, 0.051, 0.936, 0.141]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 289 }, { "id": "dyiUOb8B8ecunPzN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nBetween 'Bond Investments' and 'Cryptocurrency', which has a larger 'Return Rate Same' as represented by {Bond Investments}[0.157, 0.111, 0.264, 0.349] and {Cryptocurrency}[0.829, 0.408, 0.936, 0.468]?" }, { "from": "gpt", "value": "Bond Investments." } ], "id_raw": 289 }, { "id": "bGaJU67TSQHdxhJB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is shown in the region [0.129, 0.069, 0.432, 0.288]." }, { "from": "gpt", "value": "This region contains color labels and text representing various Return Rates. 'Return Rate Falling' is represented by red at [0.139, 0.086, 0.185, 0.110], 'Return Rate Same' is shown as green at [0.139, 0.138, 0.185, 0.162], 'Return Rate Rising' is grey at [0.139, 0.190, 0.185, 0.215], and 'Abrupt Drop' is orange at [0.139, 0.243, 0.185, 0.267]." } ], "id_raw": 289 }, { "id": "MQtESse9ljIFOkO3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area in the coordinates: [0.425, 0.587, 0.533, 0.646]?" }, { "from": "gpt", "value": "This is a red bar representative of a decreasing Return Rate for 'Real Estate Investments'." } ], "id_raw": 289 }, { "id": "XvP0YkqEpr3UMbMH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.619, 0.671, 0.877, 0.914] describe?" }, { "from": "gpt", "value": "This region includes the label 'Commodity Investments' for the X-axis category." } ], "id_raw": 289 }, { "id": "Q9QHJ3tI6mD9PyHJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.799, 0.051, 0.936, 0.141]?" }, { "from": "gpt", "value": "An orange bar in this region indicates the 'Abrupt Drop' rate for 'Cryptocurrency'." } ], "id_raw": 289 }, { "id": "SN4r0nw59WLlQvnP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nWhich entity is represented at the coordinates [0.025, 0.267, 0.048, 0.639]?" }, { "from": "gpt", "value": "The y-axis label, named \"Number of Resources\"." } ], "id_raw": 290 }, { "id": "b2YvnhEyg5LW5Mtq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the label for \"School Bags\" in the legend?" }, { "from": "gpt", "value": "[0.218, 0.187, 0.359, 0.222]" } ], "id_raw": 290 }, { "id": "ABqchi62e0aPK4ep", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nWhat's the object at coordinates [0.171, -0.501, 0.324, -0.320]?" }, { "from": "gpt", "value": "It's the bar for \"Laptops\" in \"Urban Schools\"." } ], "id_raw": 290 }, { "id": "O8vQAzabk4G5a0ZO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nWhat category does the label at [0.164, 0.879, 0.330, 0.914] represent?" }, { "from": "gpt", "value": "It's the label for \"Urban Schools\"." } ], "id_raw": 290 }, { "id": "5IWWGZijvyTHVcZ9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Textbooks in Urban Schools\"} [0.171, 0.578, 0.324, 0.854] taller than {the bar representing \"Textbooks in Rural Schools\"} [0.783, 0.714, 0.937, 0.854]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 290 }, { "id": "bg7iGszZFD6RPwb0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Laptops in Urban Schools\" bar}[0.171, -0.501, 0.324, -0.320] to the left or right of {the \"Laptops in Rural Schools\" bar}[0.783, 0.301, 0.937, 0.371]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 290 }, { "id": "nTyCD2DxPrrYiHCQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nDo the rural schools {textbook bar}[0.783, 0.714, 0.937, 0.854] and {pencil bar}[0.783, 0.458, 0.937, 0.714] have the same starting point?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 290 }, { "id": "rrOZi9YIULDE56fY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nIn the urban school category, which is larger: {The bar representing \"Textbooks\"} [0.171, 0.578, 0.324, 0.854] or {The bar representing \"Pencils\"} [0.171, 0.192, 0.324, 0.578]?" }, { "from": "gpt", "value": "Pencils." } ], "id_raw": 290 }, { "id": "tkCpDMt8yfgdSQlI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area given by the coordinates [0.144, 0.069, 0.368, 0.288]." }, { "from": "gpt", "value": "This area contains the legend of the chart, it includes text labels and colored rectangles signifying different types of resources like textbooks, pencils, school bags, and laptops. The colored rectangles correspond to the bars in the chart." } ], "id_raw": 290 }, { "id": "y47snYpqtWgneQa5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the section [0.782, 0.879, 0.938, 0.914]?" }, { "from": "gpt", "value": "This section represents the x-axis label for \"Rural Schools\"." } ], "id_raw": 290 }, { "id": "uKm9ljDgmPhD6uPN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nBriefly describe the elements located in the space [0.132, 0.051, 0.975, 0.854]." }, { "from": "gpt", "value": "This area is the main plot of the bar chart. It includes all the bars for both \"Urban Schools\" and \"Rural Schools\" which represent different types of school resources including textbooks, pencils, school bags, and laptops. It also includes the x-axis and y-axis labels, and scales." } ], "id_raw": 290 }, { "id": "ebueESl2MI8bJ7YG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of the contents within the region at [0.783, 0.714, 0.937, 0.854]?" }, { "from": "gpt", "value": "There is a single red bar within this region. This bar represents the quantity of \"Textbooks\" in \"Rural Schools\"." } ], "id_raw": 290 }, { "id": "a93xNWlkFuRUCNi0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.025, 0.378, 0.048, 0.528] represent?" }, { "from": "gpt", "value": "This is the Y-axis label, called Quantity." } ], "id_raw": 291 }, { "id": "xAHJL7ssn2aIojzh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first text in the legend?" }, { "from": "gpt", "value": "[0.218, 0.083, 0.345, 0.118]." } ], "id_raw": 291 }, { "id": "EQVDylH2SkRNjfim", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label}[0.529, 0.927, 0.578, 0.963] display?" }, { "from": "gpt", "value": "The label displays 'Year'." } ], "id_raw": 291 }, { "id": "Q4EFnMAkoGQyKGJA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the labels in [0.144, 0.069, 0.354, 0.236]?" }, { "from": "gpt", "value": "These are {'Pessimistic'}[0.218, 0.083, 0.345, 0.118], {'Normal'}[0.218, 0.135, 0.302, 0.170], and {'Optimistic'}[0.218, 0.187, 0.336, 0.222] labels for different scenarios being represented." } ], "id_raw": 291 }, { "id": "Ok04YOj5TifijoWN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Optimistic' rectangle for year 2022}[0.171, -0.772, 0.276, -0.330] positioned above or below {the 'Pessimistic' rectangle for the same year that is}[0.171, 0.232, 0.276, 0.854]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 291 }, { "id": "nZLr6kClW2qoXqku", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nComparatively, Does {the 'Pessimistic' bar of 2023}[0.303, 0.292, 0.408, 0.854] have more or less quantity than {the 'Pessimistic' bar of 2022}[0.171, 0.232, 0.276, 0.854]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 291 }, { "id": "jRhSGmGF1gQaRFhc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nAre {the 'Pessimistic' bars}[[0.171, 0.232, 0.276, 0.854], [0.303, 0.292, 0.408, 0.854], [0.435, 0.332, 0.540, 0.854], [0.567, 0.383, 0.673, 0.854], [0.699, 0.433, 0.805, 0.854], [0.831, 0.493, 0.937, 0.854]] generally taller than {the 'Optimistic'}[[0.171, -0.772, 0.276, -0.330], [0.303, -0.702, 0.408, -0.250], [0.435, -0.651, 0.540, -0.190], [0.567, -0.611, 0.673, -0.129], [0.699, -0.571, 0.805, -0.069], [0.831, -0.521, 0.937, 0.001]] bars?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 291 }, { "id": "Cjgty5gDkKRDGuFN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nBetween {\"2022 Pessimistic\"}[0.171, 0.232, 0.276, 0.854] and {\"2023 Normal\"}[0.303, -0.250, 0.408, 0.292], which one has a higher value?" }, { "from": "gpt", "value": "2022 Pessimistic." } ], "id_raw": 291 }, { "id": "XjqzMAHUnfST1GNk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the region [0.144, 0.069, 0.354, 0.236]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It has three entries which are as follows:\n1. Pessimistic, colored in red {handle color}[0.153, 0.086, 0.199, 0.110]\n2. Normal, displayed in green {handle color}[0.153, 0.138, 0.199, 0.162]\n3. Optimistic, displayed in grey {handle color}[0.153, 0.190, 0.199, 0.215]" } ], "id_raw": 291 }, { "id": "qFR4OZW3rK4baZXV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the area [0.132, 0.051, 0.975, 0.854]." }, { "from": "gpt", "value": "This region encapsulates the whole chart plotting area. It includes three types of bars: Pessimistic, Normal, and Optimistic for each year from 2022 to 2027 represented on the x-axis. The Y-axis is a scale marking the quantity." } ], "id_raw": 291 }, { "id": "WB0U82S4eblk9bcE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region [0.171, -0.772, 0.276, -0.330]?" }, { "from": "gpt", "value": "This region encompasses the 'Optimistic' bar for the year 2022, indicating the quantity considered as an optimistic prediction for that year." } ], "id_raw": 291 }, { "id": "gKWAxSwB2zlLrfMs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.132, 0.051, 0.975, 0.854] display?" }, { "from": "gpt", "value": "Essentially, it displays a bar chart showcasing three different scenarios: 'Pessimistic', 'Normal', and 'Optimistic' for prediction quantity across the years 2022 to 2027." } ], "id_raw": 291 }, { "id": "TKyBCRPie4acZYml", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text label for \"public finance\" on the x-axis?" }, { "from": "gpt", "value": "[0.866, 0.690, 0.938, 0.823]" } ], "id_raw": 292 }, { "id": "Axcl5bWfqTpFHf3j", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.531, 0.354, 0.625, 0.642] in the chart figure?" }, { "from": "gpt", "value": "The green bar represents the frequency of the positive sentiment of the Health and Welfare policy area." } ], "id_raw": 292 }, { "id": "mqnI34tP0zB3Imso", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the color at [0.084, 0.162, 0.109, 0.178] in the chart legend represent?" }, { "from": "gpt", "value": "The color represents mixed sentiment." } ], "id_raw": 292 }, { "id": "YQ8K6puAQ9uMrrmJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {this grey bar}[0.220, 0.525, 0.306, 0.533]?" }, { "from": "gpt", "value": "It represents the no sentiment frequency of the Environmental Protection policy area." } ], "id_raw": 292 }, { "id": "BoQTejIkKfAm54qS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the {green bar for Environmental Protection}[0.220, 0.533, 0.306, 0.538] or the {grey bar for Environmental Protection}[0.220, 0.514, 0.306, 0.525]?" }, { "from": "gpt", "value": "The green bar." } ], "id_raw": 292 }, { "id": "2qpBOh9sTjGXChhw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nIs the {orange bar for Public Safety}[0.433, 0.514, 0.519, 0.514] taller than the {grey bar for Public Safety}[0.433, 0.514, 0.519, 0.514]?" }, { "from": "gpt", "value": "No, they are the same height." } ], "id_raw": 292 }, { "id": "EA1e55ONNAGdJOYX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nIs the {negative bar for Children and families}[0.114, 0.610, 0.199, 0.674] taller or shorter than the {positive bar for Children and families}[0.114, 0.608, 0.199, 0.610]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 292 }, { "id": "i7ezshC6WqmlhS0t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nBetween the {positive bar for Education and Skills}[0.646, 0.578, 0.732, 0.594] and {the grey bar for Education and Skills}[0.646, 0.354, 0.732, 0.578], which one is taller?" }, { "from": "gpt", "value": "The grey bar." } ], "id_raw": 292 }, { "id": "bAJg5U9ytvENkkhf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat information does the region [0.072, 0.034, 0.986, 0.674] represent?" }, { "from": "gpt", "value": "This region represents the whole bar chart area, including the bars and x and y axes." } ], "id_raw": 292 }, { "id": "1AiTjPCWTsvKv1k7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in The area [0.078, 0.046, 0.179, 0.192]?" }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes four labels: \"negative\" with red color located at [0.084, 0.057, 0.109, 0.073], \"positive\" with green color located at [0.084, 0.092, 0.109, 0.108], \"none\" with grey color located at [0.084, 0.127, 0.109, 0.143], and \"mixed\" with orange color located at [0.084, 0.162, 0.109, 0.178]." } ], "id_raw": 292 }, { "id": "IBhfQGJnovyV1uqP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.535, 0.690, 0.630, 0.864]?" }, { "from": "gpt", "value": "The label \"Health and Welfare\" for the X-axis category is in this region." } ], "id_raw": 292 }, { "id": "eiu7LPyacn0vFdwz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content of area [0.433, 0.514, 0.519, 0.514]." }, { "from": "gpt", "value": "It contains one orange bar which represents the frequency of mixed sentiment for the 'Public Safety' category." } ], "id_raw": 292 }, { "id": "bUbUR32fHRhRk29v", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the object located at coordinates [0.472, 0.942, 0.600, 0.970]?" }, { "from": "gpt", "value": "It is the x-axis label named \"Migration Trends\"." } ], "id_raw": 293 }, { "id": "mdGbSWEkMp3MyLmH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat color does the legend item at [0.102, 0.152, 0.133, 0.172] represent?" }, { "from": "gpt", "value": "The color at these coordinates corresponds to the \"Rapid Decline\" category which is gray." } ], "id_raw": 293 }, { "id": "jJsTT0C0uVMuhkLc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat range of data does the bar located at [0.129, -0.823, 0.317, -0.380] represent?" }, { "from": "gpt", "value": "The bar corresponds to the \"Roller-Coaster Pattern\" for the \"Human\" trend and represents the range from 12000 to 16200." } ], "id_raw": 293 }, { "id": "6T6QplHNWdvxBDki", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, the bar representing \"Erratic Decline\" in the 'Human' category at coordinates [0.129, 0.515, 0.317, 0.883], or the \"Volatile Growth\" bar in the 'Industrial' category at coordinates [0.755, -0.085, 0.943, 0.652]?" }, { "from": "gpt", "value": "The 'Volatile Growth' bar in the 'Industrial' category." } ], "id_raw": 293 }, { "id": "nfVvg4ZUJEeo6Bv7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Sluggish Progress' bar in the 'Human' category}[0.129, -0.380, 0.317, -0.275] taller than {the 'Roller-Coaster Pattern' bar in the 'Human' category}[0.129, -0.823, 0.317, -0.380]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 293 }, { "id": "rMJ28JCASQgKPijg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nBetween the {\"Erratic Decline\"} bar in the 'Human' category<[0.129, 0.515, 0.317, 0.883]> and the {\"Volatile Growth\"} bar in the 'Industrial' category<[0.755, -0.085, 0.943, 0.652]>, which represents a greater range of count?" }, { "from": "gpt", "value": "The 'Volatile Growth' bar in the 'Industrial' category." } ], "id_raw": 293 }, { "id": "cmYK9fON3gaX32Hi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the legend with coordinates [0.096, 0.055, 0.320, 0.356]." }, { "from": "gpt", "value": "The legend contains color-coded categorical information about seven distinct types of migration trends that are each paired with a color. \"Erratic Decline\" is red ([0.102, 0.069, 0.133, 0.088]), \"Volatile Growth\" is green ([0.102, 0.110, 0.133, 0.130]), \"Rapid Decline\" is gray ([0.102, 0.152, 0.133, 0.172]), \"Sustained Growth\" is orange ([0.102, 0.194, 0.133, 0.214]), \"Sluggish Progress\" is purple ([0.102, 0.236, 0.133, 0.256]), \"Roller-Coaster Pattern\" is blue([0.102, 0.278, 0.133, 0.297]), and \"Moderate Decline\" is pink ([0.102, 0.320, 0.133, 0.339])." } ], "id_raw": 293 }, { "id": "9gTp4JENigECABtR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat do you see in the region [0.129, -1.033, 0.317, -0.823]?" }, { "from": "gpt", "value": "The region contains a pink bar labeled as \"Moderate Decline\", which represents count information for the \"Human\" category, with a range from 16200 to 18200." } ], "id_raw": 293 }, { "id": "4MvJSqAfcXJ0XRVh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region with coordinates [0.755, -0.907, 0.943, -0.486]?" }, { "from": "gpt", "value": "Within these coordinates there is a colored bar representing 'Sustained Growth' in the 'Industrial' category. This bar represents the count range from 13000 to 17000." } ], "id_raw": 293 }, { "id": "1GcQksfIbHbx8qpM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is represented in this structure?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 294 }, { "id": "0wDGnvl1ve4cPcgl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for \"India's Growth\" in this bar chart?" }, { "from": "gpt", "value": "[0.255, 0.915, 0.293, 0.917]" } ], "id_raw": 294 }, { "id": "WYEzbtXKa8B2WXgF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the colour label at [0.868, 0.079, 0.891, 0.093]} represent?" }, { "from": "gpt", "value": "Growth." } ], "id_raw": 294 }, { "id": "RHHD5M9T9dkexjX8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the colour label at [0.868, 0.049, 0.891, 0.063]} represent?" }, { "from": "gpt", "value": "Consumption." } ], "id_raw": 294 }, { "id": "eQ2gqQKkaYZLBKNH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nIs {India's Consumption bar}[0.217, 0.349, 0.255, 0.917] higher or lower than {China's Consumption bar}[0.108, 0.207, 0.146, 0.917]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 294 }, { "id": "Ad0STJQKn1Rwh7na", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nIs {Russia's Growth bar}[0.581, 0.917, 0.619, 0.918] higher or lower than {USA's Growth bar}[0.364, 0.916, 0.402, 0.917]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 294 }, { "id": "YOaNTEzHE1gRpP7x", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nDoes {Nigeria's Consumption bar}[0.761, 0.739, 0.799, 0.917] represent a larger or smaller value than {South Africa's Consumption bar}[0.652, 0.775, 0.690, 0.917]?" }, { "from": "gpt", "value": "Larger." } ], "id_raw": 294 }, { "id": "YLxS8daelE1QVleZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nCompare the sizes of {Brazil's Consumption bar}[0.434, 0.633, 0.472, 0.917] and {Brazil's Growth bar}[0.472, 0.915, 0.511, 0.917]." }, { "from": "gpt", "value": "The Consumption bar is larger." } ], "id_raw": 294 }, { "id": "iKRMOBizUdII8IT7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.863, 0.039, 0.982, 0.105]." }, { "from": "gpt", "value": "This region contains two colour-text labels. The first one represents 'Consumption' with a blue label and the coordinates are {[0.868, 0.049, 0.891, 0.063]: 'Blue', [0.901, 0.047, 0.977, 0.067]: 'Consumption'}. The second one represents 'Growth' with a red label and the coordinates are {[0.868, 0.079, 0.891, 0.093]: 'Red', [0.901, 0.077, 0.943, 0.097]: 'Growth'}." } ], "id_raw": 294 }, { "id": "4c8OjRK2c96hq64S", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nGive a description of the content in coordinates [0.029, 0.021, 0.058, 0.041]." }, { "from": "gpt", "value": "It is the Y-axis label that indicates a value of 5000." } ], "id_raw": 294 }, { "id": "JS5ib942Mk1trvOY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.108, 0.207, 0.146, 0.917]?" }, { "from": "gpt", "value": "The area contains a bar representing 'China's Consumption', denoting a value range of '0.00 -> 4000.00'." } ], "id_raw": 294 }, { "id": "ktoajoYZrp8UTH6S", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the region [0.880, 0.931, 0.935, 0.951]?" }, { "from": "gpt", "value": "There is a X-axis label \"Indonesia\" located within this region." } ], "id_raw": 294 }, { "id": "QF7oE8otRIxuTBvz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar with coordinates [0.350, 0.680, 0.382, 0.883] represent in the chart?" }, { "from": "gpt", "value": "It represents the frequency of \"Stable Growth\" for the 3rd time frame, and that the frequency value is 500." } ], "id_raw": 295 }, { "id": "9NLFEPL9Az5V7Oxx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label {Stable Growth} in the legend?" }, { "from": "gpt", "value": "[0.842, 0.055, 0.966, 0.083]" } ], "id_raw": 295 }, { "id": "c53e9rl3PnlllaBJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nWhat is time frame value at [0.377, 0.903, 0.387, 0.931] on the x-axis scale?" }, { "from": "gpt", "value": "It is 3." } ], "id_raw": 295 }, { "id": "GZdo8GD6OlViEo2q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the frequencies shown in [0.043, 0.669, 0.087, 0.697] and [0.043, 0.263, 0.087, 0.291] on the y-axis scale?" }, { "from": "gpt", "value": "The frequencies value are 500 and 1500 respectively." } ], "id_raw": 295 }, { "id": "nLbekxozyBZVRJzL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Stable Growth\" for 1st timeframe}[0.139, 0.680, 0.171, 0.883] higher than {the bar representing \"Rapid Decline\" for 1st timeframe}[0.171, 0.071, 0.203, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 295 }, { "id": "RVQVdOL5VXcpvHiv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Rapid Decline\" for 3rd timeframe}[0.382, 0.274, 0.414, 0.883] higher than {the bar representing \"Stable Growth\" for 8th timeframe}[0.878, 0.680, 0.910, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 295 }, { "id": "9ih6lZVacIQgKjgc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing \"Stable Growth\"}[[0.139, 0.680, 0.171, 0.883], [0.245, 0.721, 0.276, 0.883], [0.350, 0.680, 0.382, 0.883], [0.456, 0.701, 0.488, 0.883], [0.561, 0.680, 0.593, 0.883], [0.667, 0.741, 0.699, 0.883], [0.772, 0.721, 0.804, 0.883], [0.878, 0.680, 0.910, 0.883]] generally shorter than {the bars representing \"Rapid Decline\"}[[0.171, 0.071, 0.203, 0.883], [0.276, 0.172, 0.308, 0.883], [0.382, 0.274, 0.414, 0.883], [0.488, 0.375, 0.519, 0.883], [0.593, 0.477, 0.625, 0.883], [0.699, 0.558, 0.730, 0.883], [0.804, 0.640, 0.836, 0.883], [0.910, 0.721, 0.941, 0.883]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 295 }, { "id": "o81cS5rK7bvaUlTl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Stable Growth\"}[[0.139, 0.680, 0.171, 0.883], [0.245, 0.721, 0.276, 0.883], [0.350, 0.680, 0.382, 0.883], [0.456, 0.701, 0.488, 0.883], [0.561, 0.680, 0.593, 0.883], [0.667, 0.741, 0.699, 0.883], [0.772, 0.721, 0.804, 0.883], [0.878, 0.680, 0.910, 0.883]] and {\"Rapid Decline\"}[[0.171, 0.071, 0.203, 0.883], [0.276, 0.172, 0.308, 0.883], [0.382, 0.274, 0.414, 0.883], [0.488, 0.375, 0.519, 0.883], [0.593, 0.477, 0.625, 0.883], [0.699, 0.558, 0.730, 0.883], [0.804, 0.640, 0.836, 0.883], [0.910, 0.721, 0.941, 0.883]], which category has a higher frequency for 5th timeframe?" }, { "from": "gpt", "value": "Stable Growth." } ], "id_raw": 295 }, { "id": "oaqj5Ioo35uvPXhd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.786, 0.044, 0.973, 0.136]." }, { "from": "gpt", "value": "This is the legend of the chart. It contains two labels \"Stable Growth\" at [0.842, 0.055, 0.966, 0.083] colored in green and \"Rapid Decline\" at [0.842, 0.097, 0.962, 0.125] colored in red." } ], "id_raw": 295 }, { "id": "IamEkPqs2B0IhfWA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.099, 0.030, 0.981, 0.883]." }, { "from": "gpt", "value": "This area contains all the bars in the chart and their respective labels. For instance, the bar at [0.139, 0.680, 0.171, 0.883] represents the frequency of \"Stable Growth\" for the 1st timeframe while the bar at [0.171, 0.071, 0.203, 0.883] represents \"Rapid Decline\" for the same timeframe." } ], "id_raw": 295 }, { "id": "kfd34ABs3Uc0s88x", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart contains within [0.482, 0.375, 0.519, 0.883]?" }, { "from": "gpt", "value": "There are two bars in the area. The taller green bar denotes the frequency of \"Stable Growth\" for the 4th timeframe with a frequency of 450. The shorter red bar represents \"Rapid Decline\" in the same timeframe with a frequency of 1250." } ], "id_raw": 295 }, { "id": "CpVkSI3dGGS3ThKS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.776, 0.890, 0.910, 0.883]?" }, { "from": "gpt", "value": "This area contains the 8th timeframe on the x-axis scale and also the last bar for both the \"Stable Growth\" and \"Rapid Decline\" series. The blue bar at [0.878, 0.680, 0.910, 0.883] represents the \"Stable Growth\" series and the red bar at [0.910, 0.721, 0.941, 0.883] represents that \"Rapid Decline\" series." } ], "id_raw": 295 }, { "id": "0ehFG9wTtDHQkF9G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the item with coordinates [0.029, 0.598, 0.049, 0.640] on the Y-axis represent?" }, { "from": "gpt", "value": "It represents the number 1000 on the cost axis." } ], "id_raw": 296 }, { "id": "6cki6mwHdtYA1jUj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred from the subtitle located at [0.527, 0.015, 0.527, 0.015]?" }, { "from": "gpt", "value": "There is no subtitle for the chart, as its value is an empty string." } ], "id_raw": 296 }, { "id": "GO2mKb8XGptXmb4k", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the object located at [0.109, 0.041, 0.138, 0.059] in the sublegend represent?" }, { "from": "gpt", "value": "It is the label \"2010\" associated with the color \"#87ceeb\", representing the data for the year 2010." } ], "id_raw": 296 }, { "id": "JgXOHOhaQTcBCSYb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nIs {the 2010 bar for Market Research}[0.301, 0.093, 0.368, 0.299] higher or lower than {the 2020 bar for Market Research}[0.301, 0.246, 0.368, 0.44]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 296 }, { "id": "mAMnXfft0kkEa2kd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nIs {the 2010 bar for Expense}[0.108, 0.066, 0.175, 0.364] on the left or right of {the 2020 bar for Expense}[0.108, -0.083, 0.175, 0.066]?" }, { "from": "gpt", "value": "Neither, they share the same horizontal space." } ], "id_raw": 296 }, { "id": "ry2Pb4mQNXaAn1XQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nComparing the bars representing the two different years, do {the 2010 bars} overall represent smaller or larger values than {the 2020 bars}?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 296 }, { "id": "v9UhUFr9KiQyfoI0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is happening in the area [0.206, 0.158, 0.270, 0.439]." }, { "from": "gpt", "value": "This area includes both the 2010 and 2020 bars for the \"Customer_Meeting\" process. The 2010 bar is represented in blue (coordinates [0.204, 0.348, 0.272, 0.530]) and the 2020 bar is represented in orange (coordinates [0.204, 0.158, 0.272, 0.348]), both indicating different expenses for the process in the respective years." } ], "id_raw": 296 }, { "id": "JajI8hH0kdKBq9W2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the x-axis label coordinates [0.498, 0.964, 0.555, 0.981]?" }, { "from": "gpt", "value": "The object present within these coordinates is the x-axis title labelled \"Processes\"." } ], "id_raw": 296 }, { "id": "hurBqs5vaeGM4XTK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.788, 0.032, 0.941, 0.743]." }, { "from": "gpt", "value": "This region contains the majority of the bar chart. It includes the bars for the processes \"Order_Processing\", \"Warehouse_Management\", and \"Delivery\" for the years 2010 and 2020." } ], "id_raw": 296 }, { "id": "34y9V1By4NkBuMLS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the object with coordinates [0.393, 0.060, 0.612, 0.089] in the chart?" }, { "from": "gpt", "value": "The Y-axis label, called Measurements." } ], "id_raw": 297 }, { "id": "KHjVguN7WKfqAp4k", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first text label in the legend?" }, { "from": "gpt", "value": "[0.922, 0.055, 0.966, 0.078]" } ], "id_raw": 297 }, { "id": "7Qst9QCwjJrjsMEl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at the {y-axis scale}[0.076, 0.894, 0.087, 0.917]?" }, { "from": "gpt", "value": "0." } ], "id_raw": 297 }, { "id": "NvkMEw3QqhSktrGp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the indicators shown in the subplot at [0.866, 0.046, 0.973, 0.123]?" }, { "from": "gpt", "value": "{2010}[0.922, 0.055, 0.966, 0.078] and {2020}[0.922, 0.090, 0.965, 0.113]." } ], "id_raw": 297 }, { "id": "nxfgYvOsVs7kGq18", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 2010 bar for green practices}[0.139, 0.360, 0.206, 0.903] taller than {the 2020 bar for green practices}[0.206, 0.251, 0.273, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 297 }, { "id": "2Z0FJyeRpA8JtnlO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 2010 trend in Energy Consumption}[0.473, 0.360, 0.540, 0.903] taller than {the bar representing 2020 trend in Energy Consumption}[0.540, 0.577, 0.607, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 297 }, { "id": "d63wjDUoPnBPZDHh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nAre {the bars for 2010}[[0.139, 0.360, 0.206, 0.903], [0.306, 0.251, 0.373, 0.903], [0.473, 0.360, 0.540, 0.903], [0.640, 0.143, 0.707, 0.903], [0.808, 0.686, 0.874, 0.903]] generally taller than {the bars for 2020}[[0.206, 0.251, 0.273, 0.903], [0.373, 0.469, 0.440, 0.903], [0.540, 0.577, 0.607, 0.903], [0.707, 0.469, 0.774, 0.903], [0.874, 0.794, 0.941, 0.903]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 297 }, { "id": "GDsGNy6iDEQMAFAE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nBetween {Green Practises for 2010}[0.139, 0.360, 0.206, 0.903] and {Green Practises for 2020}[0.206, 0.251, 0.273, 0.903], which year's bar extends further upwards?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 297 }, { "id": "yerx1ULDVTfPZtla", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.866, 0.046, 0.973, 0.123]." }, { "from": "gpt", "value": "There are two color-text labels in the legend; the first color label is located at [0.873, 0.057, 0.908, 0.073], which is red, the text label is located at [0.922, 0.055, 0.966, 0.078], and is known as 2010 which indicates 2010's bars are red; the second color label is located at [0.873, 0.092, 0.908, 0.108], which is blue, and the text label is located at [0.922, 0.090, 0.965, 0.113], and is known as 2020 to indicate that 2020's bars are blue." } ], "id_raw": 297 }, { "id": "KqBEAyBZIgM4Eky5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nDiscuss the elements present within the area [0.076, 0.894, 0.087, 0.917]." }, { "from": "gpt", "value": "The area [0.076, 0.894, 0.087, 0.917] corresponds to the 0 point mark on the Y-axis." } ], "id_raw": 297 }, { "id": "bxXi2pEjqiS6ZNiZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the coordinates [0.206, 0.251, 0.273, 0.903]?" }, { "from": "gpt", "value": "There is one single object: The blue bar representing the value of Green Practises in 2020 with the value of 3000." } ], "id_raw": 297 }, { "id": "Hm3TrckyaFmFHhWO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat dominates the area [0.640, 0.143, 0.707, 0.903]?" }, { "from": "gpt", "value": "The area is dominated by the bar representing the value of the CO2 Emissions category in 2010, and it is red with a value of 3500." } ], "id_raw": 297 }, { "id": "R0CFhz8xCVWL7gRs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information can I get from the object at coordinates [0.015, 0.437, 0.029, 0.500]?" }, { "from": "gpt", "value": "It's the Y-axis label, which represents \"Value\"." } ], "id_raw": 298 }, { "id": "GLhvjevwMpfv9ujA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the the object located at [0.036, 0.134, 0.070, 0.157]?" }, { "from": "gpt", "value": "It is the y-axis ticks that represents the information \"7000\"." } ], "id_raw": 298 }, { "id": "2GZBYcBPwqpNsR8c", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred from {the bar}[0.121, 0.469, 0.217, 0.903]?" }, { "from": "gpt", "value": "This bar represents the \"Self-confidence\" characteristic for the \"Steady decline\" trend with a value approximately around 4000." } ], "id_raw": 298 }, { "id": "ONM1iNUsGVOiTEnH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the bar of 'self-confidence'}[0.121, 0.469, 0.217, 0.903] in the \"Steady decline\" category or {the bar of 'discipline'}[0.605, 0.555, 0.702, 0.903]?" }, { "from": "gpt", "value": "The bar of 'self-confidence' is higher." } ], "id_raw": 298 }, { "id": "sF2b9qukpMDDiFHl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nIs {the 'creativity' bar in the 'Inexorable decline' trend}[0.847, 0.360, 0.944, 0.490] shorter than {the 'creativity' bar in the 'Upward trend'}[0.847, 0.175, 0.944, 0.360]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 298 }, { "id": "cNyU6ppSNP8TPbpJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhich has a bigger value in the 'Minimal change' trend: {the bar of 'optimism'}[0.242, 0.382, 0.338, 0.490] or {the bar of 'discipline'}[0.605, 0.447, 0.702, 0.555]?" }, { "from": "gpt", "value": "They have the same value." } ], "id_raw": 298 }, { "id": "CuXfaVZKUWcwjlia", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.086, 0.046, 0.264, 0.192]?" }, { "from": "gpt", "value": "It's the legend of the chart. It contains four entries:\n1. \"Steady decline\" with color red located at [0.092, 0.057, 0.120, 0.073]\n2. \"Minimal change\" with color green located at [0.092, 0.092, 0.120, 0.108]\n3. \"Inexorable decline\" with color gray located at [0.092, 0.127, 0.120, 0.143]\n4. \"Upward trend\" with color yellow located at [0.092, 0.162, 0.120, 0.178]" } ], "id_raw": 298 }, { "id": "5ipsYT24hdVsFB4z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat do I see in the area [0.601, 0.447, 0.702, 0.447]?" }, { "from": "gpt", "value": "It is a bar representing the \"Discipline\" attribute for the \"Minimal change\" series. Its value is approximately 1000." } ], "id_raw": 298 }, { "id": "XFl59p69faN1gw9o", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nPlease provide details for the area [0.258, 0.125, 0.120, 0.143]." }, { "from": "gpt", "value": "This area contains the label \"Inexorable decline\" in the legend with its corresponding color square in gray." } ], "id_raw": 298 }, { "id": "ufAtXGBZps1GBDJg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.035, 0.677, 0.070, 0.700] contain?" }, { "from": "gpt", "value": "It contains the y-axis tick label \"2000.\"" } ], "id_raw": 298 }, { "id": "9c3GMQncvgaAhNzD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar with coordinates [0.757, -0.135, 0.840, 0.105] in the chart?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 299 }, { "id": "W1smnquHVV7Og2vn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text in the legend associated with color '#808080'?" }, { "from": "gpt", "value": "[0.155, 0.116, 0.196, 0.139]." } ], "id_raw": 299 }, { "id": "mDtztJZc9MEiyITz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.017, 0.349, 0.032, 0.579]?" }, { "from": "gpt", "value": "Units Sold/Returned." } ], "id_raw": 299 }, { "id": "lPPoj1KIWAKEhZuz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat categories are shown within the coordinates [0.138, 0.408, 0.221, 0.424]?" }, { "from": "gpt", "value": "{Clothing}[0.147, 0.919, 0.212, 0.942]." } ], "id_raw": 299 }, { "id": "220xTmmdY27NRr3C", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nIs {the offline bar for books}[0.757, 0.743, 0.840, 0.903] taller or shorter than {the online bar for books}[0.757, 0.504, 0.840, 0.743]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 299 }, { "id": "i4FjqDQQr5kNXlO3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the returns bar for furniture}[0.861, -0.733, 0.943, -0.693] have a higher or lower start point than {the sales bar for furniture}[0.861, -0.693, 0.943, 0.105]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 299 }, { "id": "itCOwaLYMRYqpL7B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nIn the Products, are {the offline bars}[[0.138, 0.823, 0.221, 0.903],[0.241, 0.663, 0.324, 0.903],[0.345, 0.863, 0.427, 0.903],[0.448, 0.847, 0.530, 0.903],[0.551, 0.839, 0.634, 0.903],[0.654, 0.879, 0.737, 0.903],[0.757, 0.743, 0.840, 0.903],[0.861, 0.663, 0.943, 0.903]] generally higher than {the online bars}[[0.138, 0.663, 0.221, 0.823],[0.241, 0.264, 0.324, 0.663],[0.345, 0.743, 0.427, 0.863],[0.448, 0.743, 0.530, 0.847],[0.551, 0.600, 0.634, 0.839],[0.654, 0.759, 0.737, 0.879],[0.757, 0.504, 0.840, 0.743],[0.861, 0.105, 0.943, 0.663]]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 299 }, { "id": "UEFnmrIuiVSwIdvc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Online\"}[0.155, 0.081, 0.205, 0.104] and {\"Offline\"}[0.155, 0.046, 0.206, 0.069], which one has more {units sold/returned}<{sales bars for products}>?" }, { "from": "gpt", "value": "Online." } ], "id_raw": 299 }, { "id": "RImyhwAJlow2bQrg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.106, 0.037, 0.220, 0.183]." }, { "from": "gpt", "value": "There are four color-text labels in the subplot_0 legend. The text label in the first pair is located at [0.155, 0.046, 0.206, 0.069], which is called Offline, and the color label is red; the text label in the second pair is located at [0.155, 0.081, 0.205, 0.104], and it's called Online with a green color label; text label of the third pair is [0.155, 0.116, 0.196, 0.139], referred to as Sales, and its color label is gray; the last pair's text label is at [0.155, 0.151, 0.214, 0.174], it's referred to Returns with an orange color label." } ], "id_raw": 299 }, { "id": "s2YTrfii9uNPnYnU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.734, 0.917, 0.939, 0.943]." }, { "from": "gpt", "value": "At [0.867, 0.919, 0.936, 0.942] lies the label for the last product category on the x-axis. It is labeled as \"Furniture\"." } ], "id_raw": 299 }, { "id": "mlKxp8nfrW3Kno1C", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.757, -0.135, 0.840, 0.105]?" }, { "from": "gpt", "value": "There lies a bar representing \"Returns\" for the \"Books\" category." } ], "id_raw": 299 }, { "id": "UwZZiq96jNmZcZDi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.138, 0.823, 0.221, 0.903]?" }, { "from": "gpt", "value": "This area contains a bar representing \"Offline\" sales for the \"Clothing\" product category, with units ranging from \"0.00 -> 1000.00\"." } ], "id_raw": 299 }, { "id": "xRrEzQaa4l1Y1aac", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the {subtitle}[0.146, 0.025, 0.645, 0.055] of the chart?" }, { "from": "gpt", "value": "Digital Content Creation on New Media Platforms Over Time." } ], "id_raw": 305 }, { "id": "fRkvGtt9wxVVIBRI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first x-tick label?" }, { "from": "gpt", "value": "[0.105, 0.919, 0.114, 0.942]" } ], "id_raw": 305 }, { "id": "hdnOfXMyTWTM3FGE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.369, 0.029, 0.596] in the chart?" }, { "from": "gpt", "value": "% Content Creation." } ], "id_raw": 305 }, { "id": "vRPnPrjYe8EmmHqm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat platform is represented by the color located at [0.826, 0.083, 0.854, 0.101]?" }, { "from": "gpt", "value": "YouTube." } ], "id_raw": 305 }, { "id": "oT7a6dxwnga4lGWy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for Instagram at 30 hours}[0.386, 0.484, 0.405, 0.624] higher than the {bar for YouTube at the same time}[0.386, 0.624, 0.405, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 305 }, { "id": "SNjwlxCMoo0aE7S4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nIs the {podcast bar at 60 hours}[0.672, 0.242, 0.691, 0.440] taller than the {TikTok bar at the same time}[0.672, 0.587, 0.691, 0.683]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 305 }, { "id": "wK5sUFkkdQrqkkgc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Blogs at 20 hours}[0.291, 0.286, 0.310, 0.433] taller than the {bar representing Instagram at the same time}[0.291, 0.521, 0.310, 0.668]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 305 }, { "id": "jHqkSvZzPGlJYUw3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nBetween {YouTube}[0.865, 0.083, 0.921, 0.106] and {Instagram}[0.865, 0.118, 0.936, 0.141], which platform has more content creation at 30 hours {YouTube bar at 30 hours}[0.386, 0.624, 0.405, 0.903], {Instagram bar at 30 hours}[0.386, 0.484, 0.405, 0.624]?" }, { "from": "gpt", "value": "YouTube." } ], "id_raw": 305 }, { "id": "ExwVHNZ4QAR0U7Z5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.821, 0.074, 0.941, 0.255]." }, { "from": "gpt", "value": "It's a legend consisting of five entries. Each legend entry consists of a color handle and a label text. The platforms are YouTube, represented by red at [0.826, 0.085, 0.854, 0.101] with the label at [0.865, 0.083, 0.921, 0.106]; Instagram, orange at [0.826, 0.120, 0.854, 0.136] with the label at [0.865, 0.118, 0.936, 0.141]; TikTok, light blue at [0.826, 0.155, 0.854, 0.171] with the label at [0.865, 0.153, 0.908, 0.176]; Blogs, yellow at [0.826, 0.190, 0.854, 0.206] with the label at [0.865, 0.188, 0.903, 0.211]; and Podcasts, green at [0.826, 0.225, 0.854, 0.241] with the label at [0.865, 0.223, 0.926, 0.246]." } ], "id_raw": 305 }, { "id": "cHVHmjJzEpHZr8YT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.100, 0.756, 0.119, 0.903]." }, { "from": "gpt", "value": "This region contains a red-colored bar representing the percentage of content creation by YouTube at time 0 hours. The value is 20% which is in the range of 0.00 -> 20.00" } ], "id_raw": 305 }, { "id": "7vOUQknkZ8SVmi3y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.196, 0.572, 0.215, 0.683]?" }, { "from": "gpt", "value": "One object is located in this area. It is a bar representing the Instagram platform at the time point of 10 hours. The bar indicates the percentage of content creation, and it ranges from 30.00% to 45.00%." } ], "id_raw": 305 }, { "id": "fZjSlNxPZhtC8KFj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.386, 0.102, 0.405, 0.234]?" }, { "from": "gpt", "value": "In this area, there is a yellow-colored bar which represents content creation of Blogs at the 30-hour point. The value is in the range of 70.00% to 91.00%." } ], "id_raw": 305 }, { "id": "AVVAGgNFunEMqtai", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y-axis in the chart?" }, { "from": "gpt", "value": "% Distribution, located at [0.025, 0.301, 0.048, 0.629]." } ], "id_raw": 306 }, { "id": "rSFN43Owt0XiZZ0E", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the data point at position [0.199, 0.462, 0.212, 0.644] represent in terms of category and time?" }, { "from": "gpt", "value": "It represents the category \"Workbooks\" at 10 seconds." } ], "id_raw": 306 }, { "id": "KGxtu0GAB5WqQexH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat color is used for the \"Live Seminars\" category in the legend?" }, { "from": "gpt", "value": "Grey, corresponding to the color indicator located at [0.639, 0.450, 0.685, 0.482]." } ], "id_raw": 306 }, { "id": "UyPtIb7R6ygwjCrh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nIs {the text label for EBooks}[0.703, 0.166, 0.788, 0.213] located above or below {the bar representing EBooks at 10s}[0.199, 0.644, 0.212, 0.806]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 306 }, { "id": "5laWqUDHuhMnNdbo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Video Materials' bar at 60s}[0.513, 0.423, 0.525, 0.598] reach higher on the chart than {the 'Workbooks' bar at 60s}[0.513, 0.598, 0.525, 0.708]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 306 }, { "id": "IyTlybJRdoTmInwm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nIs the starting point of {the bar for 'Webinars' at 20s}[0.262, 0.267, 0.274, 0.397] higher than {the bar for 'Live Seminars' at 20s}[0.262, 0.157, 0.274, 0.267]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 306 }, { "id": "cYLIpJaOH9NhVzDS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the area marked by coordinates [0.629, 0.148, 0.891, 0.511]." }, { "from": "gpt", "value": "This region contains the legend for the chart. It includes color labels and text labels for five categories: EBooks, Workbooks, Video Materials, Webinars, and Live Seminars. Color labels such as green for EBooks, red for Workbooks, purple for Video Materials, brown for Webinars, and grey for Live Seminars are respectively situated at [0.639, 0.170, 0.685, 0.203], [0.639, 0.240, 0.685, 0.273], [0.639, 0.310, 0.685, 0.342], [0.639, 0.380, 0.685, 0.412], and [0.639, 0.450, 0.685, 0.482]. Text labels for each category are also placed within this region." } ], "id_raw": 306 }, { "id": "UdvnEcJtdXzFuMeU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.110, 0.806, 0.677, 0.903]?" }, { "from": "gpt", "value": "This region holds the X-axis and its corresponding labels marking time intervals at 0s, 10s, 20s, and so forth, up to 80s. Time labels are strategically positioned within this region, each precisely denoting different time intervals essential for interpreting the chart's time-based data distributions." } ], "id_raw": 306 }, { "id": "eaoQd1RKu8wnIKlt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the zone bounded by [0.025, 0.301, 0.048, 0.629]." }, { "from": "gpt", "value": "This segment encloses the Y-axis of the chart, prominently displaying the percentage distribution ranging from 0.0 to 1.2. The numerical labels indicating these percentages are vertically aligned within this area, playing a crucial role in understanding the proportional distributions of various educational resources over time." } ], "id_raw": 306 }, { "id": "vIGivIccfhRypRnA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the thing in {position}[0.025, 0.212, 0.048, 0.718]?" }, { "from": "gpt", "value": "It's the y-axis label, named 'Global Changes Index'." } ], "id_raw": 310 }, { "id": "ipTa52Wy8b0P0yuc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.115, 0.654, 0.141, 0.695]?" }, { "from": "gpt", "value": "It's {a bar}[0.115, 0.654, 0.141, 0.695] under the category of 'Politics' from the year 2005." } ], "id_raw": 310 }, { "id": "TySsbawqGGKwO6Qc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nWhat is at the {position}[0.243, 0.530, 0.268, 0.599] on the chart?" }, { "from": "gpt", "value": "It's {a bar}[0.243, 0.530, 0.268, 0.599] representing the category 'Politics' for the year 2015." } ], "id_raw": 310 }, { "id": "KS2vVWdqGUH5deHv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the item at position [0.743, 0.445, 0.824, 0.492]?" }, { "from": "gpt", "value": "It is {a legend label}[0.743, 0.445, 0.824, 0.492] indicating the color code for the 'Politics' trend." } ], "id_raw": 310 }, { "id": "MTfqkoy7XCmnKvU6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 'GDP' bar for 2025}[0.370, 0.681, 0.395, 0.723] higher than {the 'GDP' bar for 2015}[0.243, 0.723, 0.268, 0.751]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 310 }, { "id": "XgDXF4HMfrAqICYh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nAre {the bars for 'Technology'}[0.115, 0.723, 0.141, 0.764],[0.243, 0.640, 0.268, 0.723],[0.370, 0.557, 0.395, 0.681],[0.497, 0.474, 0.523, 0.640],[0.624, 0.392, 0.650, 0.599] generally higher than {the bars for 'Environment'}[0.115, 0.695, 0.141, 0.723],[0.243, 0.599, 0.268, 0.640],[0.370, 0.502, 0.395, 0.557],[0.497, 0.405, 0.523, 0.474],[0.624, 0.309, 0.650, 0.392]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 310 }, { "id": "IvDAvS2p45fSQ4Du", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Population' trend}[0.115, 0.778, 0.141, 0.806],[0.243, 0.751, 0.268, 0.806],[0.370, 0.723, 0.395, 0.806],[0.497, 0.695, 0.523, 0.806],[0.624, 0.668, 0.650, 0.806] show an increasing trend over the years?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 310 }, { "id": "IfuJdrtr1XG4BEHK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value for 2045, {\"Population\"}[0.624, 0.668, 0.650, 0.806] or {\"GDP\"}[0.624, 0.599, 0.650, 0.668]?" }, { "from": "gpt", "value": "Population." } ], "id_raw": 310 }, { "id": "CrqPPSNDPNkpJty1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is inside [0.669, 0.148, 0.900, 0.511]?" }, { "from": "gpt", "value": "It is a legend containing five items. They are:\n1. Population with a color label at [0.678, 0.170, 0.725, 0.203],\n2. GDP with a color label at [0.678, 0.240, 0.725, 0.273],\n3. Technology with a color label at [0.678, 0.310, 0.725, 0.342],\n4. Environment with a color label at [0.678, 0.380, 0.725, 0.412],\n5. Politics with a color label at [0.678, 0.450, 0.725, 0.482]." } ], "id_raw": 310 }, { "id": "RSuVYehTGdw28gee", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nCan you tell me about the information in this area [0.353, 0.838, 0.412, 0.885]?" }, { "from": "gpt", "value": "This area contains the x-label \"2025\"." } ], "id_raw": 310 }, { "id": "4csoN7YS0rmUSVne", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.370, 0.723, 0.395, 0.806] and [0.370, 0.681, 0.395, 0.723]?" }, { "from": "gpt", "value": "There are two bars in these regions. The first is for {the 'Population'}[0.370, 0.723, 0.395, 0.806] category in 2025 and the second one is for {the 'GDP'}[0.370, 0.681, 0.395, 0.723] category in the same year." } ], "id_raw": 310 }, { "id": "VShI9VJRNqyx2QdC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the data within the region [0.243, 0.599, 0.268, 0.751]." }, { "from": "gpt", "value": "Within this region, there are two bars. One represents the {GDP}[0.243, 0.599, 0.268, 0.640] value for 2015, and the other represents {Population}[0.243, 0.640, 0.268, 0.751] for the same year." } ], "id_raw": 310 }, { "id": "sK5R3dWgn8NU2OmB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend item for \"Single\"}[0.671, 0.380, 0.717, 0.412] as depicted in the chart?" }, { "from": "gpt", "value": "Brown." } ], "id_raw": 311 }, { "id": "6o5JQSZlG3lWobKQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.365, 0.903, 0.422, 0.950] in the chart?" }, { "from": "gpt", "value": "Time." } ], "id_raw": 311 }, { "id": "jjhZuv3QGpqnRwDn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nWhat do {the bars at the \"0\" time mark}[0.136, 0.157, 0.149, 0.806] indicate?" }, { "from": "gpt", "value": "Different social demographics at time 0." } ], "id_raw": 311 }, { "id": "CshfpSMEGiOE1PrE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nIs the {color handle for \"Married\"}[0.671, 0.310, 0.717, 0.342] lighter or darker than the {color handle for \"Divorced\"}[0.671, 0.450, 0.717, 0.482]?" }, { "from": "gpt", "value": "Lighter." } ], "id_raw": 311 }, { "id": "XilX05KbDahwccwO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nAre the bars representing {the \"Employed\"}[0.136, 0.644, 0.149, 0.806] taller than the ones representing {the \"Unemployed\"}[0.136, 0.546, 0.149, 0.644] at the starting point of the x-axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 311 }, { "id": "ifWxv1I8JSKLnfxQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Divorced\" label}[0.736, 0.445, 0.839, 0.492] appear above or below {the \"Single\" label}[0.736, 0.376, 0.807, 0.422] in the chart's legend?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 311 }, { "id": "5uKnOimJ2vpUIKNm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nWhat specifics are shown in the subplot area enclosed by [0.110, 0.124, 0.677, 0.806]?" }, { "from": "gpt", "value": "This area contains the plotting grid with background color #d3d3d3 and the chart bars for different social demographics over time." } ], "id_raw": 311 }, { "id": "hzl9BD8pk4jr4hws", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the visible characteristics within the chart legend section [0.662, 0.148, 0.891, 0.511]." }, { "from": "gpt", "value": "The legend section contains five items each with a color handle and corresponding label describing the social demographics such as \"Employed\", \"Unemployed\", and others." } ], "id_raw": 311 }, { "id": "LGURN7v9v5RBAHeu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nWhat elements are contained in the domain from the bottom left to almost mid-right of the chart [0.025, 0.223, 0.645, 0.950]?" }, { "from": "gpt", "value": "This region includes the vertical axis with percentage scale labels, horizontal axis with time labels, associated grid lines, and the bars representing demographics over time." } ], "id_raw": 311 }, { "id": "822eqEGAxFPCZnWO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.012, 0.407, 0.024, 0.567]?" }, { "from": "gpt", "value": "Y-axis label, named Trend Percentages." } ], "id_raw": 313 }, { "id": "7d3KMDywwjQv1zgl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend indicate at [0.905, 0.064, 0.928, 0.076]?" }, { "from": "gpt", "value": "It indicates that {Time 1}[0.905, 0.064, 0.928, 0.076] is represented in the color blue." } ], "id_raw": 313 }, { "id": "1n9lidPOrUsyJFiS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat technology is denoted at [0.356, 0.089, 0.400, 0.927]?" }, { "from": "gpt", "value": "The technology is 'multimedia learning'." } ], "id_raw": 313 }, { "id": "zlkafbyTsMPPqHo0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the items represented at [0.938, 0.062, 0.977, 0.080] and [0.938, 0.088, 0.977, 0.106] in the chart?" }, { "from": "gpt", "value": "They represent two trend points {Time 1}[0.938, 0.062, 0.977, 0.080] and {Time 2}[0.938, 0.088, 0.977, 0.106]." } ], "id_raw": 313 }, { "id": "G1rv4HWY9IT2zXFe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nAre the bars in {Time 1}[0.105, 0.568, 0.148, 0.927] higher or lower than the bars in {Time 2}[0.148, 0.658, 0.192, 0.927] of the chart?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 313 }, { "id": "LlgO7Ee4nbuFxPJ1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Time 2 bar for technology 6}[0.902, 0.388, 0.945, 0.927] taller or shorter than the {Time 1 bar for technology 6}[0.858, 0.658, 0.902, 0.927]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 313 }, { "id": "vFP9M4ia1yj5n03b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nIs {Technology 0}[0.105, 0.568, 0.148, 0.927] more prominent in Time 1 or Time 2?" }, { "from": "gpt", "value": "Time 1." } ], "id_raw": 313 }, { "id": "S1GtrxTaRf9X3MM5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nIs {Technology 2 in Time 1}[0.356, 0.089, 0.400, 0.927] greater or less than {Technology 2 in Time 2}[0.400, 0.478, 0.443, 0.927]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 313 }, { "id": "LAQPoo9FKthu9Vvk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the coordinates [0.901, 0.055, 0.982, 0.113]." }, { "from": "gpt", "value": "It includes a legend with two key items. The first is titled 'Time 1' with coordinates [0.905, 0.064, 0.928, 0.076] in blue, and the second is titled 'Time 2' with coordinates [0.905, 0.090, 0.928, 0.102] in orange." } ], "id_raw": 313 }, { "id": "BKbxux1pMfk49sNV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat exists in the region [0.105, 0.568, 0.192, 0.927]?" }, { "from": "gpt", "value": "There are two bars, one for {Time 1}[0.105, 0.568, 0.148, 0.927] and the other for {Time 2}[0.148, 0.658, 0.192, 0.927]. Both bars represent the value for Technology 0." } ], "id_raw": 313 }, { "id": "LpgxsRMu0HYRxkqp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.333, 0.019, 0.717, 0.041]." }, { "from": "gpt", "value": "It contains a subtitle that reads \"Trends in Education and Training Technology Innovation\"." } ], "id_raw": 313 }, { "id": "JIInhiLUtiuBtEeF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.145, 0.939, 0.905, 0.957]?" }, { "from": "gpt", "value": "It includes all x-axis labels, representing different technology numbers, ranging from 0 to 6, each in an approximately equivalent interval." } ], "id_raw": 313 }, { "id": "PBPmmOgiO1QvVQSp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat is represented at the location with coordinates [0.171, 0.350, 0.215, 0.806]?" }, { "from": "gpt", "value": "It is a bar representing CO2 emission in the year 2000." } ], "id_raw": 314 }, { "id": "gyIN7Q06lhFIWczk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label} at [0.809, 0.166, 0.858, 0.213]?" }, { "from": "gpt", "value": "CO2." } ], "id_raw": 314 }, { "id": "M1oF40mfNBVgnsJH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the item} for the handles at coordinates [0.744, 0.170, 0.790, 0.203]?" }, { "from": "gpt", "value": "It is the color label for CO2 in the legend which is green." } ], "id_raw": 314 }, { "id": "cCVaPRln8S1Jjh57", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.171, 0.332, 0.215, 0.333] represent on the chart?" }, { "from": "gpt", "value": "This is the bar which represents CF4 greenhouse gas emission in the year 2000." } ], "id_raw": 314 }, { "id": "mqPpwMJumU6VNOiA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nAre the bars representing {N2O}[0.171, 0.333, 0.215, 0.337], [0.280, 0.299, 0.324, 0.299] taller than the bars for {CH4}[0.171, 0.337, 0.215, 0.350], [0.280, 0.304, 0.324, 0.317]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 314 }, { "id": "dRl4SWVLyH9kfKTQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nIs {the CF4 bar for year 2015}[0.499, 0.216, 0.543, 0.216] of the same height as {the C2F6 bar for year 2015}[0.499, 0.215, 0.543, 0.216]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 314 }, { "id": "7iZ303AWt201bbIs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nBetween {CF4}[0.171, 0.332, 0.215, 0.333] and {C2F6}[0.171, 0.332, 0.215, 0.332] bars for year 2000, which has higher emissions?" }, { "from": "gpt", "value": "CF4." } ], "id_raw": 314 }, { "id": "kfyfhR1WluUB8rrz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, the {CO2 bar for year 2005}[0.280, 0.317, 0.324, 0.806] or the {CO2 bar for year 2010}[0.390, 0.289, 0.434, 0.806]?" }, { "from": "gpt", "value": "The CO2 bar for year 2005." } ], "id_raw": 314 }, { "id": "yFXeHv8zS7CkJnsW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nPlease tell what's in [0.735, 0.148, 0.877, 0.511] region." }, { "from": "gpt", "value": "It's a region containing the legend, which includes 5 labels: {CO2}[0.744, 0.170, 0.790, 0.203], {CH4}[0.744, 0.240, 0.790, 0.273], {N2O}[0.744, 0.310, 0.790, 0.342], {CF4}[0.744, 0.380, 0.790, 0.412], and {C2F6}[0.744, 0.450, 0.790, 0.482]." } ], "id_raw": 314 }, { "id": "RhlfNhoFXEoffXgb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the region [0.171, 0.272, 0.215, 0.806]" }, { "from": "gpt", "value": "This region contains the bars for year 2000: The tallest bar, representing CO2, at an emissions of 16433.42 metric tons {CO2 bar}[0.171, 0.350, 0.215, 0.806]; A shorter bar in red representing CH4 at 454.036 metric tons {CH4 bar}[0.171, 0.338, 0.215, 0.350]; An even shorter bar in purple representing N2O at 156.307 metric tons {N2O bar}[0.171, 0.336, 0.215, 0.338]; A slightly shorter bar in brown corresponding to CF4 at 23 metric tons {CF4 bar}[0.171, 0.335, 0.215, 0.336]; and the shortest bar in grey representing C2F6 at 4.92 metric tons {C2F6 bar}[0.171, 0.335, 0.215, 0.335]." } ], "id_raw": 314 }, { "id": "CMAU8z7SIIykOOyu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.609, 0.157, 0.652, 0.806]?" }, { "from": "gpt", "value": "This area represents the year 2020 and contains five bars, each representing a different greenhouse gas: {CO2}[0.609, 0.179, 0.652, 0.806], {CH4}[0.609, 0.162, 0.652, 0.179], {N2O}[0.609, 0.158, 0.652, 0.162], {CF4}[0.609, 0.157, 0.652, 0.158], and {C2F6}[0.609, 0.157, 0.652, 0.157]." } ], "id_raw": 314 }, { "id": "kngUtDjyF9XKx5Wd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description for the area [0.147, 0.124, 0.677, 0.806]?" }, { "from": "gpt", "value": "This is the main plot area. It contains a grey grid backdrop with black x and y axis labels. The y-axis ranges from 0 to 25000 measuring \"Emissions (M Metric Tons)\" and x-axis represents \"Year\" ranging from 2000 to 2020. There are 5 overlapped bar charts for each year representing CO2, CH4, N2O, CF4 and C2F6 greenhouse gas emissions." } ], "id_raw": 314 }, { "id": "AeGfsy9rQ6O2cOuE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.886, 0.062, 0.982, 0.080] in the chart?" }, { "from": "gpt", "value": "It is the label \"Accelerating Decline\" for a legend entry." } ], "id_raw": 327 }, { "id": "O0S0KvddWEGOCXDP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the color handle associated with \"Inconsistent Growth\" in the legend?" }, { "from": "gpt", "value": "[0.860, 0.116, 0.879, 0.128]" } ], "id_raw": 327 }, { "id": "Iln0QwOTGSJzn38K", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar color} at [0.053, 0.544, 0.410, 0.560]?" }, { "from": "gpt", "value": "#66c4d5" } ], "id_raw": 327 }, { "id": "ctaUXwqW4I7NmC4Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar labeled \"2011\"}[0.053, 0.871, 0.945, 0.887] higher than {the bar labeled \"2020\"}[0.053, 0.136, 0.062, 0.152]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 327 }, { "id": "6dLeV5k30K4pEmf2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"2012\" for \"Slow Decline\"}[0.053, 0.773, 0.847, 0.789] extend farther right compared to {the bar representing \"2015\" for \"Inconsistent Growth\"}[0.053, 0.511, 0.874, 0.528]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 327 }, { "id": "5QbWS5eljyzwzlDF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nAre {the y-axis labels}[[0.023, 0.848, 0.047, 0.865], [0.023, 0.766, 0.047, 0.783], [0.023, 0.684, 0.047, 0.702], [0.023, 0.603, 0.047, 0.620], [0.023, 0.521, 0.047, 0.538], [0.023, 0.439, 0.047, 0.457], [0.023, 0.358, 0.047, 0.375], [0.023, 0.276, 0.047, 0.293], [0.023, 0.194, 0.047, 0.212], [0.023, 0.112, 0.047, 0.130]] listed in ascending order from bottom to top of the chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 327 }, { "id": "InUzLYCmkK19HNXH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.857, 0.055, 0.985, 0.165]." }, { "from": "gpt", "value": "This area contains the legend of the chart, comprising four elements each displaying a colored rectangle followed by a label:\n1. \"Accelerating Decline\" with a color rectangle at [0.860, 0.064, 0.879, 0.076] (color #66c4d5),\n2. \"Slow Decline\" at [0.860, 0.090, 0.879, 0.102] (color #eca198),\n3. \"Inconsistent Growth\" at [0.860, 0.116, 0.879, 0.128] (color #5886c7),\n4. \"Fluctuating Decline\" at [0.860, 0.142, 0.879, 0.155] (color #a791e8)." } ], "id_raw": 327 }, { "id": "OMfhD2gM2F3Mwznb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.053, 0.446, 0.812, 0.462]." }, { "from": "gpt", "value": "Within this area, there is a series bar representing \"Slow Decline\" in 2016, colored in [#eca198] and displaying a numerical value of 85.0. It extends horizontally from the left y-axis until approximately 81.2% of the width towards the right." } ], "id_raw": 327 }, { "id": "ZtDWU7kXzEzP3RpY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.053, 0.430, 0.928, 0.446]?" }, { "from": "gpt", "value": "The rectangular region contains a bar representing \"Inconsistent Growth\" for the year 2016. The color of the bar is[#5886c7], and it extends nearly to 92.8% of the area from the left, displaying a value of \"98.0\" at its right edge." } ], "id_raw": 327 }, { "id": "CHcOZMmrPdkivSeL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at coordinates [0.160, 0.505, 0.203, 0.890] in the chart?" }, { "from": "gpt", "value": "This is the bar representing the Income Growth for EN." } ], "id_raw": 330 }, { "id": "inHwO2YW9hgjFlT8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the third text, DE, on the x-axis?" }, { "from": "gpt", "value": "[0.297, 0.906, 0.357, 0.930]" } ], "id_raw": 330 }, { "id": "I31eB49uwuBLJJ6X", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nWhat provides the information at [0.413, 0.906, 0.489, 0.930] on the x-axis?" }, { "from": "gpt", "value": "Risk Control in FR." } ], "id_raw": 330 }, { "id": "eoCMgX3cQbvIL6mz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.203, 0.377, 0.247, 0.890] represent?" }, { "from": "gpt", "value": "This bar represents the Risk Control in EN." } ], "id_raw": 330 }, { "id": "uSFbtT5yfKD71TZl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Income Growth bar for EN}[0.160, 0.505, 0.203, 0.890] higher or lower than the {Risk Control bar for EN}[0.203, 0.377, 0.247, 0.890]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 330 }, { "id": "1SirYyD4GccI1dvQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nIs the bar at coordinates [0.451, 0.312, 0.494, 0.890] taller than the bar at coordinates [0.655, 0.110, 0.043, 0.513]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 330 }, { "id": "xbsNIgksKWYK9kCh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Income Growth bar for ES}[0.822, 0.110, 0.043, 0.642] taller than the {Risk Control bar for ES}[0.822, 0.248, 0.865, 0.890]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 330 }, { "id": "70154RLDp2HYO2pD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nComparing the bars representing {DE}<{Income Growth DE bar}[0.203, 0.110, 0.043, 0.513], {Risk Control DE bar}[0.327, 0.110, 0.043, 0.449]> and those representing {FR}<{Income Growth FR bar}[0.451, 0.110, 0.043, 0.578], {Risk Control FR bar}[0.574, 0.110, 0.043, 0.385]>, in which language are both bars generally higher?" }, { "from": "gpt", "value": "DE." } ], "id_raw": 330 }, { "id": "TQxBPvU3zhmTQZku", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.180, 0.906, 0.868, 0.930]?" }, { "from": "gpt", "value": "The region contains the labels for the languages on the x-axis, including EN, DE, FR, IT, and ES." } ], "id_raw": 330 }, { "id": "4rlcG2t0b0GPPWXr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.160, 0.377, 0.247, 0.890]?" }, { "from": "gpt", "value": "This area contains two bars, one representing the Income Growth for EN at coordinates [0.160, 0.505, 0.203, 0.890] and the other representing the Risk Control for EN at coordinates [0.203, 0.377, 0.247, 0.890]." } ], "id_raw": 330 }, { "id": "wFcNvgFmHSNDgJdO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the area [0.4, 0.05, 0.62, 0.9]?" }, { "from": "gpt", "value": "This area contains the bars representing Risk Control for FR and IT, along with their respective text annotations." } ], "id_raw": 330 }, { "id": "UKgeo9wpSSUghGgv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found in the area [0.792, 0.132, 0.893, 0.208]." }, { "from": "gpt", "value": "In this area, we find the legend of the chart, including two pairs of color-text labels. The first pair represents EN with the color label at [0.797, 0.143, 0.825, 0.159] and the text label at [0.836, 0.141, 0.888, 0.164]. The second pair represents ES with the color label at [0.797, 0.178, 0.825, 0.194] and the text label at [0.836, 0.176, 0.888, 0.199]." } ], "id_raw": 330 }, { "id": "UZegR2iqgpK4bNYG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinate [0.360, 0.025, 0.649, 0.055]?" }, { "from": "gpt", "value": "This is a subtitle of the chart, which is \"Comparing Electric Vehicle Ranges\"." } ], "id_raw": 335 }, { "id": "j84EscgZyF7PwIIX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the object at the coordinates [0.824, 0.120, 0.851, 0.136]?" }, { "from": "gpt", "value": "It is blue." } ], "id_raw": 335 }, { "id": "dLFTe57TmeHQecMw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at [0.560, 0.072, 0.893, 0.551]?" }, { "from": "gpt", "value": "This is an orange bar representing the 100KWh battery size under the \"Sustainable City Living\" condition with a range of 490 miles." } ], "id_raw": 335 }, { "id": "Uhar6J6S4Guy1MaQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nIs the {bar}[0.560, 0.072, 0.893, 0.551] higher than the {bar}[0.115, 0.238, 0.449, 0.551]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 335 }, { "id": "e4s9FzEvOXGcgfo1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the {blue bar }(48 KWh)[0.115, 0.238, 0.449, 0.551] and the {orange bar}(100 KWh)[0.560, 0.072, 0.893, 0.551], which one represents a longer range under Sustainable City Living condition?" }, { "from": "gpt", "value": "The orange bar." } ], "id_raw": 335 }, { "id": "anDstUyYaNUYyqAK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nIs the position of the {y-label '200'}[0.040, 0.346, 0.066, 0.370] higher than the {y-label '250'}[0.040, 0.298, 0.066, 0.321]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 335 }, { "id": "UF6QNZbMZr7kJvoP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.115, 0.238, 0.893, 0.551]." }, { "from": "gpt", "value": "This region contains two bars. The first bar, a blue bar, starts from 0.115 and ends at 0.449 on the x-axis, representing the 48KWh battery size under the \"Sustainable City Living\" condition with a range of 320 miles. The second bar, an orange bar, starts from 0.560 and ends at 0.893 on the x-axis, representing the 100KWh battery size under the \"Sustainable City Living\" condition with a range of 490 miles." } ], "id_raw": 335 }, { "id": "EFFUcbWoZX53e9q0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region [0.818, 0.074, 0.925, 0.185]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It has two items. The first item has a blue handle with coordinates [0.824, 0.120, 0.851, 0.136] and a label \"48KWh\" with coordinates [0.862, 0.118, 0.911, 0.141]. The second item has an orange handle with coordinates [0.824, 0.155, 0.851, 0.171] and a label \"100KWh\" with coordinates [0.862, 0.153, 0.920, 0.176]." } ], "id_raw": 335 }, { "id": "Ix1n1zSofjnmh4PW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nCan you give a simple description of what is in the region [0.076, 0.429, 0.932, 0.723]?" }, { "from": "gpt", "value": "This region is a subplot of the chart. It contains an x-y grid with labels on both axes. There are two ticks on the x-axis representing \"Sustainable City Living\" and \"Long Interstate Trip\". There are multiple ticks on the y-axis ranging from 200 to 500. This subplot also includes bars representing the ranges of 48KWh and 100KWh battery sizes for each condition." } ], "id_raw": 335 }, { "id": "VsItukasIc0Tkuof", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the graph in the upper part of the chart?" }, { "from": "gpt", "value": "Sales Trend Analysis of Products." } ], "id_raw": 337 }, { "id": "wqIO3pU8cSw2xJbm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the product Toaster_A in the chart?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 337 }, { "id": "CYlnnX3fUzQWgSFp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat's {the y-axis label} at [0.035, 0.206, 0.061, 0.230]?" }, { "from": "gpt", "value": "250." } ], "id_raw": 337 }, { "id": "XMtZvyXFEKobvzYu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat category is indicated at [0.588, 0.919, 0.612, 0.942]?" }, { "from": "gpt", "value": "April." } ], "id_raw": 337 }, { "id": "sKuXsAdFc8efNHuZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nIs {the February bar for Toaster_B}[0.257, 0.820, 0.365, 1.031] higher than {the February bar for Toaster_A}[0.257, 0.262, 0.365, 0.599]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 337 }, { "id": "Mjc5amaZhieaYmEu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the May bar of Toaster_B}[0.690, 0.666, 0.799, 1.031] exceed {the y-axis label at 300}[0.035, 0.130, 0.061, 0.153]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 337 }, { "id": "HAv7twULAePaB2A2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nIs {the April bar of Toaster_B}[0.546, 0.749, 0.654, 1.031] taller than {the June bar of the same brand}[0.835, 0.551, 0.943, 1.031]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 337 }, { "id": "Fsz5jMEj7eZ3YKyD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nAre {the bars of Toaster_A}[[0.112, 0.292, 0.221, 0.599], [0.257, 0.262, 0.365, 0.599], [0.401, 0.231, 0.510, 0.599], [0.546, 0.200, 0.654, 0.599], [0.690, 0.170, 0.799, 0.599], [0.835, 0.139, 0.943, 0.599]] generally shorter than {the bars of Toaster_B}[[0.112, 0.839, 0.221, 1.031], [0.257, 0.820, 0.365, 1.031], [0.401, 0.801, 0.510, 1.031], [0.546, 0.749, 0.654, 1.031], [0.690, 0.666, 0.799, 1.031], [0.835, 0.551, 0.943, 1.031]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 337 }, { "id": "g8WakTNeyHSHsZpl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in [0.071, 0.062, 0.985, 0.446]?" }, { "from": "gpt", "value": "This region mainly includes the bottom half graph of the sales trends of three products. The x-axis labels represent numerical values from -1 to 6; the y-axis labels range from 100 to 350 in increments of 50. The purple bars at various heights represent the sales amounts of Toaster_A during different months." } ], "id_raw": 337 }, { "id": "tJwyVQcWuvsoXdtU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the region at [0.861, 0.074, 0.978, 0.151]." }, { "from": "gpt", "value": "This region contains a legend indicating that a purple bar represents the product 'Toaster_A', with the color patch at [0.867, 0.120, 0.895, 0.136] and the text at [0.906, 0.118, 0.972, 0.142]." } ], "id_raw": 337 }, { "id": "lqhnRXw43ckEotEy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat's in the space [0.071, 0.519, 0.985, 0.903]?" }, { "from": "gpt", "value": "This region contains the top half graph, which shows the sales trend analysis for the product 'Toaster_B'. The x-axis has month labels from January to June, and the y-axis has numerical labels from 100 to 400. The purple bars represent the sales amounts of Toaster_B in respective months." } ], "id_raw": 337 }, { "id": "i2rcSEqOnXFyPzuJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe area [0.156, 0.919, 0.177, 0.942]." }, { "from": "gpt", "value": "The area contains the 'Jan' label of the x-axis in the upper chart." } ], "id_raw": 337 }, { "id": "XAMqcFB3dIvDY6gV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the rectangle used for {Morning}[0.843, 0.120, 0.871, 0.136] in the legend?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 338 }, { "id": "kidOpDsz6Yr2zCLd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the exact time label for {the top right bar in the first subplot}[0.748, 0.343, 0.910, 0.381]?" }, { "from": "gpt", "value": "Night." } ], "id_raw": 338 }, { "id": "oYGNEMRqjFYbR7Zg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the x-tick label at}[0.926, 0.397, 0.948, 0.420]?" }, { "from": "gpt", "value": "3.5." } ], "id_raw": 338 }, { "id": "W8pFPlH0iwf6atMn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nIs {the height of the \"Morning\" bar}[0.102, 0.077, 0.264, 0.381] greater than {the \"Noon\" bar}[0.318, 0.153, 0.479, 0.381] for basketball on Week 1?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 338 }, { "id": "FjrJkh34JDDHG2wu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Noon\" time segment}[0.843, 0.155, 0.871, 0.171] have a more vibrant color than {the \"Evening\" segment}[0.843, 0.190, 0.871, 0.206]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 338 }, { "id": "BgvHp1Ey6GFidWU0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nIs {the height of the first \"Night\" bar}[0.748, 0.267, 0.910, 0.381] greater than {the \"Noon\" bar}[0.318, 0.320, 0.479, 0.381]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 338 }, { "id": "S1O8pa0roTg3OOCo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area between {Week1 Basketball and Football bars}[0.066, 0.780, 0.350, 0.922]?" }, { "from": "gpt", "value": "This region contains the bars representing Week1 players in both basketball and football with their respective heights indicating the number of participants at different times of the day: morning, noon, evening, and night." } ], "id_raw": 338 }, { "id": "S4fRyvQdmjMCdXA8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the region adorned with [0.837, 0.074, 0.943, 0.255] imply?" }, { "from": "gpt", "value": "This region is the legend for the chart displaying time segments: Morning, Noon, Evening, and Night with colored handles from purple to gold, specifying the color representation of each time segment on the bars in the graph." } ], "id_raw": 338 }, { "id": "rbrXfFJPDXVUgWzU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the visualization features within [0.062, 0.062, 0.950, 0.381]?" }, { "from": "gpt", "value": "This area encapsulates the first subplot graph showing different colored bars for time segments across sports, plotted along a y-axis indicating participants and an x-axis with sporting events categorized by weeks." } ], "id_raw": 338 }, { "id": "E5ghZlIkIZz4DS3Q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.027, 0.406, 0.029, 0.571]?" }, { "from": "gpt", "value": "It is the y-axis label, named \"Interactions\"." } ], "id_raw": 345 }, { "id": "fDENfV6DfPR9EYXR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nWhere is the x-axis label situated in terms of coordinates?" }, { "from": "gpt", "value": "[0.480, 0.942, 0.576, 0.970]" } ], "id_raw": 345 }, { "id": "xDr8TobIp53Ct7ep", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the entity at [0.112, 0.587, 0.181, 0.883]?" }, { "from": "gpt", "value": "It is a bar representing the Facebook Interaction count of 45 for Quarter 1." } ], "id_raw": 345 }, { "id": "4K2yrqHh2oP9KKaG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at the coordinates [0.366, 0.039, 0.511, 0.067]?" }, { "from": "gpt", "value": "{The text label for Facebook Interaction in the legend}[0.366, 0.039, 0.511, 0.067]" } ], "id_raw": 345 }, { "id": "M786INnuJPqXWXKs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Facebook Interaction in Quarter 1}[0.112, 0.587, 0.181, 0.883] taller than {the bar representing Instagram Interaction in Quarter 1}[0.181, 0.620, 0.251, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 345 }, { "id": "BWfCNf3w9p0Oxbqv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nWhich bar is situated to the right, {the bar representing Facebook Interaction in Quarter 2}[0.285, 0.455, 0.355, 0.883] or {the bar representing Instagram Interaction in Quarter 2}[0.355, 0.554, 0.424, 0.883]?" }, { "from": "gpt", "value": "The bar representing Instagram Interaction in Quarter 2." } ], "id_raw": 345 }, { "id": "0Ys0HOmBnQ5WonAc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nCompared to {the bar representing Instagram Interaction in Quarter 5}[0.874, 0.238, 0.943, 0.883], is {the bar representing Facebook Interaction in Quarter 5}[0.805, 0.231, 0.874, 0.883] higher?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 345 }, { "id": "AQju42KCzdzogKJv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nBased on this chart figure, does {Facebook}[0.366, 0.039, 0.511, 0.067] or {Instagram}[0.578, 0.039, 0.728, 0.067] has higher interactions in the fourth fiscal quarter?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 345 }, { "id": "oCLbZGBjI5f98Q0V", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area inside [0.322, 0.028, 0.734, 0.078]?" }, { "from": "gpt", "value": "This area contains the bar chart legend, with two entries. The first entry, with color label at [0.327, 0.042, 0.355, 0.061] in blue, and text label at [0.366, 0.039, 0.511, 0.067], indicates that the blue bars represent the \"Facebook Interaction\". The second entry, with color label at [0.539, 0.042, 0.567, 0.061] in pink, and the text label at [0.578, 0.039, 0.728, 0.067], indicates that the pink bars represent \"Instagram Interaction\"." } ], "id_raw": 345 }, { "id": "kJy3q8zffJtLUalm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in [0.035, 0.214, 0.061, 0.242]?" }, { "from": "gpt", "value": "There is a label showing \"100\" on the y-axis, which denotes the maximum interaction value in the scale." } ], "id_raw": 345 }, { "id": "hEJO2iRF6XgvmRTA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.459, 0.291, 0.528, 0.883] contain?" }, { "from": "gpt", "value": "There are two bars in this region. The blue bar, located at [0.459, 0.291, 0.528, 0.883], represents that the Facebook Interaction count is 90 in Quarter 3." } ], "id_raw": 345 }, { "id": "1sVS9clv3rkHK9zr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are inside the area [0.632, 0.309, 0.675, 0.337]?" }, { "from": "gpt", "value": "There is an annotation \"83\" for the Facebook Interaction bar for Quarter 4. It is located at [0.658, 0.309, 0.675, 0.337], and it indicates that the count of Facebook Interactions for Quarter 4 is 83." } ], "id_raw": 345 }, { "id": "nRDJusaSq0nJC6ZK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the text represented at the coordinates [0.361, 0.039, 0.472, 0.067]?" }, { "from": "gpt", "value": "Social Networks" } ], "id_raw": 346 }, { "id": "hM09hKo1o8pKGSBm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the point at the coordinates [0.351, 0.883] represent?" }, { "from": "gpt", "value": "A tick along the x-axis." } ], "id_raw": 346 }, { "id": "dsO1ncOjDjmmD6NJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is at the coordinates [0.106, 0.330, 0.176, 0.488]?" }, { "from": "gpt", "value": "It is a bar representing the \"Social Networks\" with a value of 2.0 for the first fiscal quarter." } ], "id_raw": 346 }, { "id": "QVFy2wRAXNLBkigh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the object at these coordinates [0.015, 0.452, 0.029, 0.525]?" }, { "from": "gpt", "value": "It is the label for the y-axis, namely \"Level\"." } ], "id_raw": 346 }, { "id": "F8I7uXQEW3bLQRN2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nIs there a higher value recorded for {Social Networks}[0.361, 0.039, 0.472, 0.067] or {Interpersonal Relationships}[0.538, 0.039, 0.728, 0.067] during the fourth fiscal quarter?" }, { "from": "gpt", "value": "Social Networks." } ], "id_raw": 346 }, { "id": "6Detcuigslk61QoP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nIs the bar at {y_range: '0.00 -> 2.00'}[0.130, 0.302, 0.152, 0.330] taller than the one at {y_range: '0.00 -> 4.20'}[0.723, 0.128, 0.745, 0.156]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 346 }, { "id": "k39Fvh5GA8AYtoKj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nComparing the bars of {Social Networks}[0.361, 0.039, 0.472, 0.067] and {Interpersonal Relationships}[0.538, 0.039, 0.728, 0.067], which one has a higher value in the third fiscal quarter?" }, { "from": "gpt", "value": "Social Networks." } ], "id_raw": 346 }, { "id": "iiWjMaO47qxaJPzp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nAre there more x-ticks or y-ticks present in the chart?" }, { "from": "gpt", "value": "X-ticks." } ], "id_raw": 346 }, { "id": "RVJn3EdKLZXUDKxC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between [0.281, 0.172, 0.351, 0.488] and [0.525, 0.291, 0.595, 0.488]?" }, { "from": "gpt", "value": "These two regions represent two fiscal quarters. For the second quarter, located at [0.281, 0.172, 0.351, 0.488], there are two bars: one for Social Networks with a height of 4.0 and one for Interpersonal Relationships with a height of 2.0. The third quarter, located at [0.525, 0.291, 0.595, 0.488], also features two bars: one for Social Networks with a height of 3.5 and one for Interpersonal Relationships with a height of 2.5." } ], "id_raw": 346 }, { "id": "YjvaUojjxxmaR9Zm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.176, 0.172, 0.246, 0.488]?" }, { "from": "gpt", "value": "Within this region is a bar pertaining to Interpersonal Relationships with a value of 4.0 for the first fiscal quarter." } ], "id_raw": 346 }, { "id": "o9RglpQuMCW4gQXA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within the region [0.316, 0.028, 0.733, 0.078]?" }, { "from": "gpt", "value": "This region contains the labels for legend entries, namely \"Social Networks\", represented with color '#78a083' and \"Interpersonal Relationships\", represented with color '#d27a41'." } ], "id_raw": 346 }, { "id": "HqfAx8B6cCy4GvWl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within [0.629, 0.275, 0.699, 0.488]." }, { "from": "gpt", "value": "The area represents the fourth fiscal quarter with two recorded values: a 2.7 for Social Networks and a 4.2 for Interpersonal Relationships." } ], "id_raw": 346 }, { "id": "5rMQxdYCaeoeDGhX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.119, 0.463, 0.188, 0.565]?" }, { "from": "gpt", "value": "It's a bar representing the Risk Assessment in the first fiscal quarter." } ], "id_raw": 347 }, { "id": "f1hbcLIx4vpxxQny", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label present at the coordinates [0.397, 0.039, 0.513, 0.067]?" }, { "from": "gpt", "value": "The label says \"Risk Assessment\"." } ], "id_raw": 347 }, { "id": "Eu3UuYoQKdP7XArS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the coordinates [0.119, 0.463, 0.188, 0.565] referring to?" }, { "from": "gpt", "value": "It refers to {the Risk Assessment bar for the first fiscal quarter}[0.119, 0.463, 0.188, 0.565]." } ], "id_raw": 347 }, { "id": "uTQVzsajEg64Hs0e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the data point}[0.806, 0.361, 0.875, 0.565]?" }, { "from": "gpt", "value": "The data point represents the Risk Assessment value in the fifth fiscal quarter." } ], "id_raw": 347 }, { "id": "6FiclNoE7SVsX2zE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nCompared to {the Risk Management bar for the first quarter} [0.188, 0.246, 0.257, 0.565], is {the Risk Management bar for the third quarter}[0.531, 0.565, 0.600, 0.679] taller or shorter?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 347 }, { "id": "0n4bsiWgQuTCdKjR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Risk Assessment bar for the second quarter}[0.291, 0.565, 0.360, 0.718] higher or lower than {the Risk Management bar for the second quarter}[0.360, 0.399, 0.428, 0.565]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 347 }, { "id": "tyXF7YU2eJtqKzwb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nBased on {the Risk Assessment bars} [0.119, 0.463, 0.188, 0.565],[0.291, 0.565, 0.360, 0.718],[0.463, 0.259, 0.531, 0.565],[0.635, 0.565, 0.703, 0.692],[0.806, 0.361, 0.875, 0.565], is there a general trend in Risk Assessment for the five quarters?" }, { "from": "gpt", "value": "No, the values fluctuate." } ], "id_raw": 347 }, { "id": "Lf1vz72DTDQuKE2g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nIs {the fourth quarter}'s Risk Management bar[0.703, 0.093, 0.772, 0.565] taller than {the first quarter}'s Risk Management bar[0.188, 0.246, 0.257, 0.565]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 347 }, { "id": "5viCxUqwFAYldrvg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nDescribe what's within the area [0.119, 0.093, 0.944, 0.565]." }, { "from": "gpt", "value": "In this region, there are two sets of bars representing Risk Assessment and Risk Management over five fiscal quarters. Their respective colors are represented in the legend labeled \"Risk Assessment\"[0.397, 0.039, 0.513, 0.067] and \"Risk Management\"[0.580, 0.039, 0.705, 0.067]." } ], "id_raw": 347 }, { "id": "i6xQ7YzuZaLccdsp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements within [0.353, 0.028, 0.710, 0.078]?" }, { "from": "gpt", "value": "There are two labels housed within these coordinates. The words \"Risk Assessment\" at coordinates [0.397, 0.039, 0.513, 0.067] with a green box at [0.358, 0.042, 0.386, 0.061] representing its color and the words \"Risk Management\" at coordinates [0.580, 0.039, 0.705, 0.067], with an orange box at [0.541, 0.042, 0.569, 0.061] representing its color." } ], "id_raw": 347 }, { "id": "P8LQpZKQNNbBFPun", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the coordinates [0.119, 0.093, 0.188, 0.565]?" }, { "from": "gpt", "value": "It contains two bars. {The higher green bar}[0.119, 0.463, 0.188, 0.565] represents the Risk Assessment in the first quarter, and {the lower orange bar}[0.188, 0.246, 0.257, 0.565] represents the Risk Management in the first quarter." } ], "id_raw": 347 }, { "id": "ruk2UiqqYCTWNkin", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the area [0.806, 0.093, 0.275, 0.565]?" }, { "from": "gpt", "value": "In this area, {the green bar}[0.806, 0.361, 0.875, 0.565] represents Risk Assessment in the fifth quarter and {the orange bar}[0.875, 0.565, 0.944, 0.820] represents Risk Management in the same period." } ], "id_raw": 347 }, { "id": "uCBfB7VoH6Cgnusn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nCan you locate the label of the Y-axis in the chart?" }, { "from": "gpt", "value": "{Y-axis label}[0.015, 0.323, 0.029, 0.653] and it is named \"Increase in revenue (%)\"." } ], "id_raw": 349 }, { "id": "KAYFeU063qk6y5em", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label present at the coordinates [0.434, 0.039, 0.534, 0.067]?" }, { "from": "gpt", "value": "The label {Entertainment}[0.434, 0.039, 0.534, 0.067] is present at the given coordinates." } ], "id_raw": 349 }, { "id": "bBXwcagitgjDwFyY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat's the x-axis label referring to as per the chart's coordinates?" }, { "from": "gpt", "value": "The label referred as {X-axis}[0.477, 0.942, 0.573, 0.970] is \"Fiscal Quarter\"." } ], "id_raw": 349 }, { "id": "hdUHoCj566z8JPtP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nWhich categories are represented by the coordinates in the range of [0.389, 0.028, 0.660, 0.078]?" }, { "from": "gpt", "value": "There are two categories represented: {Entertainment}[0.434, 0.039, 0.534, 0.067] and {Gaming}[0.601, 0.039, 0.655, 0.067]." } ], "id_raw": 349 }, { "id": "Csb5kZgw26vWmM1J", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {Entertainment bar in quarter 1}[0.106, 0.521, 0.176, 0.686] or {Gaming bar in quarter 1}[0.176, 0.686, 0.246, 0.785]?" }, { "from": "gpt", "value": "Entertainment bar." } ], "id_raw": 349 }, { "id": "Q5BSgBn2vymv8O7P", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat's the relation between {5th bar of Entertainment group}[0.804, 0.159, 0.873, 0.686] and {5th bar of Gaming group}[0.873, 0.686, 0.943, 0.844] as far as their heights are concerned?" }, { "from": "gpt", "value": "The 5th bar of Entertainment group is taller." } ], "id_raw": 349 }, { "id": "Y4iDQRGoQ8sH7FqV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nIs the {4th bar of Entertainment Group}[0.629, 0.231, 0.699, 0.686] lower or higher than the {4th bar of Gaming Group}[0.699, 0.686, 0.769, 0.811]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 349 }, { "id": "vnYtmclkqrlu0zyx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nAs per the comparison of bars, do the revenue numbers increase or decrease for Entertainment and Gaming from quarter 1 to quarter 5? ({1st and 5th bar of Entertainment Group}<[0.106, 0.521, 0.176, 0.686],[0.804, 0.159, 0.873, 0.686]>; {1st and 5th bar of Gaming Group}<[0.176, 0.686, 0.246, 0.785],[0.873, 0.686, 0.943, 0.844]>)" }, { "from": "gpt", "value": "It increases for Entertainment but decreases for Gaming." } ], "id_raw": 349 }, { "id": "lVJznmhSj4XfkAlY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the key elements within the region defined by coordinates [0.065, 0.093, 0.985, 0.883]?" }, { "from": "gpt", "value": "This region contains all chart data including the bars, axes labels, ticks, grid, and legend. It notably includes several bars for two categories \"Entertainment\" and \"Gaming\" across five fiscal quarters." } ], "id_raw": 349 }, { "id": "UnXjy3oMIrU22D0b", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.389, 0.028, 0.660, 0.078]?" }, { "from": "gpt", "value": "The area [0.389, 0.028, 0.660, 0.078] contains the legend of the chart. It has two items: the {Entertainment label}[0.434, 0.039, 0.534, 0.067] with a corresponding color handle at [0.395, 0.042, 0.423, 0.061], and the {Gaming label}[0.601, 0.039, 0.655, 0.067] with a corresponding color handle at [0.562, 0.042, 0.589, 0.061]." } ], "id_raw": 349 }, { "id": "QtqTHpuJbbdAQFL4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the consolidated area [0.106, 0.159, 0.873, 0.944]?" }, { "from": "gpt", "value": "The selected area contains all the bars representing the two categories: \"Entertainment\" and \"Gaming\" over five fiscal quarters, along with their respective data value labels." } ], "id_raw": 349 }, { "id": "gLcjPGewvd5YccsN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat data can be found in the area [0.281, 0.408, 0.351, 0.686]?" }, { "from": "gpt", "value": "This region contains the second bar for the Entertainment category, representing revenue change in the second quarter. The green color bar is assigned the value of 3.8, which implies a 3.8% increase in revenue." } ], "id_raw": 349 }, { "id": "rOswyPkebl4Zd9Oo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the object between the coordinates [0.872, 0.097, 0.941, 0.125] represent?" }, { "from": "gpt", "value": "It's {the legend label} that is called \"Marketing\"[0.872, 0.097, 0.941, 0.125]." } ], "id_raw": 351 }, { "id": "xZ5wjmpjxcV8XodF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the bar contained in the coordinates [0.771, 0.387, 0.943, 0.744]?" }, { "from": "gpt", "value": "It's {the bar} referring to the \"Marketing\" operations at the \"Volatile Movement\" stage[0.771, 0.387, 0.943, 0.744]." } ], "id_raw": 351 }, { "id": "ZDqBsEg7jyPEYTmF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nWhat is denoted by [0.046, 0.376, 0.055, 0.404] in the y-axis markings?" }, { "from": "gpt", "value": "It is {the y-axis label} that represents \"0\" value[0.046, 0.376, 0.055, 0.404]." } ], "id_raw": 351 }, { "id": "VepfvHk27vETuUUM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Marketing\" at \"Stable Growth\"}[0.279, 0.280, 0.451, 0.387] taller than {the bar for \"Manufacturing\" at \"Stable Growth\"}[0.107, 0.244, 0.279, 0.387]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 351 }, { "id": "nu5XWLP4Nhshzp2S", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nBetween {the \"Manufacturing\" bar at \"Stable Growth\"}[0.107, 0.244, 0.279, 0.387] and {the \"Manufacturing\" bar at \"Volatile Movement\"}[0.599, 0.119, 0.771, 0.387], which one is taller?" }, { "from": "gpt", "value": "The \"Manufacturing\" bar at \"Volatile Movement\"." } ], "id_raw": 351 }, { "id": "YsDzFNJGpitRdAJa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Marketing\" bar at \"Stable Growth\"}[0.279, 0.280, 0.451, 0.387] located to the left or right of {the \"Manufacturing\" bar at \"Stable Growth\"}[0.107, 0.244, 0.279, 0.387]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 351 }, { "id": "XBJHswemv4K8EHPJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area [0.827, 0.044, 0.978, 0.136]?" }, { "from": "gpt", "value": "It's {the legend} of the chart. It contains two items; blue color tagged as \"Manufacturing\"[0.872, 0.055, 0.972, 0.083][0.833, 0.058, 0.861, 0.077], and orange color tagged as \"Marketing\"[0.872, 0.097, 0.941, 0.125][0.833, 0.099, 0.861, 0.119]." } ], "id_raw": 351 }, { "id": "jazkDvx17OLYVy9x", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the coordinate [0.229, 0.942, 0.328, 0.970]?" }, { "from": "gpt", "value": "It is the label for the x-axis tick mark \"Stable Growth\"[0.229, 0.942, 0.328, 0.970]." } ], "id_raw": 351 }, { "id": "9ICHMgrhQu8H31iU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within coordinates [0.599, 0.119, 0.771, 0.387]." }, { "from": "gpt", "value": "These coordinates contain two bars, one is the \"Manufacturing\" bar[0.771, 0.387, 0.943, 0.744] and the other is \"Marketing\" bar[0.771, 0.387, 0.943, 0.744] for the \"Volatile Movement\" phase. There is also a text label indicating the value \"1.5\"[0.674, 0.091, 0.696, 0.119]." } ], "id_raw": 351 }, { "id": "PAt1UR1kNUB42LW2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.055, 0.477, 0.064, 0.505]?" }, { "from": "gpt", "value": "It's the Y-axis label '0'." } ], "id_raw": 352 }, { "id": "9GDv4V0xZ728SC0p", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.574, 0.042, 0.602, 0.061] in the chart?" }, { "from": "gpt", "value": "It's the orange color handle in the legend indicating 'Conservative' strategy." } ], "id_raw": 352 }, { "id": "kpZDO79GmKeZOjkd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.481, 0.942, 0.577, 0.970] represent?" }, { "from": "gpt", "value": "It's the X-axis label, 'Fiscal Quarter'." } ], "id_raw": 352 }, { "id": "io3xMD0w6rRuX2wD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 2nd Quarter High-Risk High-Return Strategy}[0.288, 0.291, 0.357, 0.488] higher than the {the bar for 2nd Quarter Conservative Strategy}[0.357, 0.370, 0.426, 0.488]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 352 }, { "id": "YLbMpk85Q4cQafRV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nAre the bars indicating {High-Risk High-Return Strategy}[[0.115, 0.488, 0.184, 0.528], [0.288, 0.291, 0.357, 0.488], [0.460, 0.370, 0.529, 0.488], [0.633, 0.488, 0.702, 0.607], [0.805, 0.212, 0.875, 0.488]] generally higher than the bars for {Conservative Strategy}[[0.184, 0.409, 0.253, 0.488], [0.357, 0.370, 0.426, 0.488], [0.529, 0.370, 0.598, 0.488], [0.702, 0.409, 0.771, 0.488], [0.875, 0.409, 0.944, 0.488]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 352 }, { "id": "jvY5vVTxeJTZz8Ss", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nIs the color of {High-Risk High-Return Strategy handle}[0.354, 0.042, 0.382, 0.061] the same as the color of the {High-Risk High-Return Strategy bar for 3rd Quarter}[0.460, 0.370, 0.529, 0.488]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 352 }, { "id": "WTl3KCessLskurKc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.349, 0.028, 0.710, 0.078]." }, { "from": "gpt", "value": "There are two items in this area - One legend remark for High-Risk High-Return strategy and one for Conservative Strategy. The High-Risk High-Return strategy is represented with a blue color that is shown at the coordinates [0.354, 0.042, 0.382, 0.061] and the text is placed at [0.393, 0.039, 0.546, 0.067]. The Conservative strategy is represented with an orange color that is shown at the coordinates [0.574, 0.042, 0.602, 0.061] and the text is placed at [0.613, 0.039, 0.704, 0.067]." } ], "id_raw": 352 }, { "id": "kjvNSKrX2OHk5vnU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.184, 0.212, 0.253, 0.488]." }, { "from": "gpt", "value": "There are two bars in the area. The High-Risk High-Return bar for the 5th fiscal quarter is at the coordinates [0.805, 0.212, 0.875, 0.488] and the Conservative strategy bar for the 5th fiscal quarter is at the coordinates [0.875, 0.409, 0.944, 0.488]." } ], "id_raw": 352 }, { "id": "zrGkMheI8YUrxuBP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the region [0.702, 0.342, 0.906, 0.409]?" }, { "from": "gpt", "value": "It seems to be an orange bar that corresponds to the conservative strategy in the 4th quarter. The coordinates are [0.702, 0.409, 0.771, 0.488]." } ], "id_raw": 352 }, { "id": "PlxBfP8Mu8l6Uums", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\n{The fourth y-axis label} at [0.035, 0.636, 0.055, 0.664] shows what numeric value?" }, { "from": "gpt", "value": "The number is -2." } ], "id_raw": 353 }, { "id": "2K0tqabbJ2mZof07", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color used to represent {Employee Job Satisfaction} at [[0.526, 0.045, 0.554, 0.065]] in the legend?" }, { "from": "gpt", "value": "The color is a shade of orange, specifically \"#d27a41\"." } ], "id_raw": 353 }, { "id": "uAomh39KkiAt4OcY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nWhat quarter was tagged along the x-axis at [[0.869, 0.903, 0.878, 0.931]]?" }, { "from": "gpt", "value": "It is tagged as the 5th Fiscal Quarter." } ], "id_raw": 353 }, { "id": "d4VjSm7iT3QCQz6C", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nComparing {the Employee Productivity bar} [0.107, 0.089, 0.176, 0.490] and {the Employee Job Satisfaction bar} [0.176, 0.128, 0.246, 0.490] for Q1, which has a higher score?" }, { "from": "gpt", "value": "Employee Productivity." } ], "id_raw": 353 }, { "id": "tLIMLsASwDOrkGdm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nIs the score for {Employee Productivity} [0.455, 0.112, 0.525, 0.490] in Q3 higher or lower than that in {Q4} [0.629, 0.057, 0.699, 0.490]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 353 }, { "id": "X3YNJJ7TvKTWSFe9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nFor Q5, is the score for {Employee Job Satisfaction} [0.873, 0.191, 0.943, 0.490] larger than the score for {Employee Productivity} [0.804, 0.081, 0.873, 0.490]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 353 }, { "id": "UkgoCKyHP5aft7AI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content presented in the region [[0.455, 0.029, 0.769, 0.490]]." }, { "from": "gpt", "value": "This region depicts two bars for Q3 and Q4 of the fiscal year. For each quarter, two bars represent the scores of Employee Productivity and Employee Job Satisfaction. Specifically, for Q3, the Employee Productivity score is 4.8 [[0.455, 0.112, 0.525, 0.490]], and Employee Job Satisfaction score is 3.9 [[0.525, 0.183, 0.595, 0.490]]. Similarly, for Q4, the Employee Productivity score is 5.5 [[0.629, 0.057, 0.699, 0.490]], and Employee Job Satisfaction score is 4.1 [[0.699, 0.167, 0.769, 0.490]]." } ], "id_raw": 353 }, { "id": "dT3CfE6BqnCBa1ke", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nGive a description of the data mentioned in the region [[0.889, 0.435, 0.883, 0.883]]." }, { "from": "gpt", "value": "There's no data mentioned in this region since it's an area outside the chart." } ], "id_raw": 353 }, { "id": "fp9lyYfaGPgvifrN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the area [[0.35, 0.032, 0.746, 0.082]]." }, { "from": "gpt", "value": "This area contains the legend elements of the bar chart. It showcases two categories: Employee Productivity with associated color green [[0.343, 0.043, 0.499, 0.071], [0.304, 0.045, 0.332, 0.065]] and Employee Job Satisfaction with associated color orange [[0.565, 0.043, 0.746, 0.071], [0.526, 0.045, 0.554, 0.065]]." } ], "id_raw": 353 }, { "id": "6CY7uyNARyu2DzEf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart does the information represent?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 354 }, { "id": "6VbUuFBbMIyiYpo4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label located at [0.502, 0.942, 0.545, 0.970]?" }, { "from": "gpt", "value": "Epoch." } ], "id_raw": 354 }, { "id": "g434y1qfO2RJ1meG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.104, 0.725, 0.162, 0.883] represent?" }, { "from": "gpt", "value": "A bar representing the number of Innovations during the Stone Age." } ], "id_raw": 354 }, { "id": "AfOwZcPccgVqITYi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nBetween the bars representing {Innovations}[0.104, 0.725, 0.162, 0.883] and {Conflicts}[0.162, 0.852, 0.220, 0.883] during the Stone Age, which one is higher?" }, { "from": "gpt", "value": "Innovations." } ], "id_raw": 354 }, { "id": "EOG8PZ4gEusAEIUT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nRegarding the bar representing {Innovations} during the {Industrial Age}[0.683, 0.409, 0.740, 0.883], is it higher or lower than the bar representing {Innovations} during the {Middle Ages}[0.538, 0.567, 0.596, 0.883]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 354 }, { "id": "Pugxkn4RRpftgSWk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Conflicts bar} during the {Digital Age}[0.885, 0.567, 0.943, 0.883] positioned to the right or left of the {Innovations bar} during the {Digital Age}[0.827, 0.251, 0.885, 0.883]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 354 }, { "id": "Bzjg5AjJFG28YUR2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.395, 0.028, 0.652, 0.078]." }, { "from": "gpt", "value": "The area contains a legend with two entries representing Innovations and Conflicts. {Innovations} has a green color code at [0.400, 0.042, 0.428, 0.061], and {Conflicts} has an orange color code at [0.548, 0.042, 0.575, 0.061]." } ], "id_raw": 354 }, { "id": "EY1QN40lsrnSl8fx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the area defined by the coordinates [0.249, 0.662, 0.364, 0.883]?" }, { "from": "gpt", "value": "The objects here are the bars representing the epochs Bronze Age for both Innovations and Conflicts. The {Innovations bar} is in green and has coordinates [0.249, 0.662, 0.306, 0.883] and the {Conflicts bar} is in orange with coordinates [0.306, 0.820, 0.364, 0.883]." } ], "id_raw": 354 }, { "id": "dhxm5ceyM7xqPiyB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area located within [0.692, 0.409, 0.798, 0.883]?" }, { "from": "gpt", "value": "This area contains 2 objects representing Innovations and Conflicts during the Industrial Age. The {Innovations bar} is positioned at [0.683, 0.409, 0.740, 0.883] and the {Conflicts bar} is located at [0.740, 0.662, 0.798, 0.883]." } ], "id_raw": 354 }, { "id": "WV01jUM3XjB4CxqE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the text positioned at coordinates [0.015, 0.339, 0.029, 0.638]?" }, { "from": "gpt", "value": "Y-axis label, called GDP Growth Rate (%)." } ], "id_raw": 355 }, { "id": "igNbcCBetLUbaXRP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label of Russia in the legend?" }, { "from": "gpt", "value": "[0.570, 0.039, 0.614, 0.067]" } ], "id_raw": 355 }, { "id": "frQYVQ0uLR3Fzxdg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.510, 0.942, 0.540, 0.970]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 355 }, { "id": "XUZJis6GR4n7Jet7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the second bar of USA} at [0.308, 0.420, 0.324, 0.676]?" }, { "from": "gpt", "value": "The bar represents USA's GDP growth rate in 1990, which is 1.5." } ], "id_raw": 355 }, { "id": "uZzsecvOJy6QeF5n", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nIs {the USA growth in 1995} at [0.509, 0.300, 0.525, 0.676] higher or lower than {the Russia growth in 1995} at [0.525, 0.676, 0.541, 0.779]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 355 }, { "id": "3YXrPDeoBVBGcSp6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nIs {the Russia GDP growth in 2000} at [0.726, 0.676, 0.742, 0.745] negative or positive?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 355 }, { "id": "xleTUde0yIIMMXdU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nAre {the USA growth bars} generally higher than {the Russia growth bars} considering all years?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 355 }, { "id": "I0HXaANSrw9x7KJn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nBetween {\"USA\"} at [0.474, 0.039, 0.503, 0.067] and {\"Russia\"} at [0.570, 0.039, 0.614, 0.067], which country's GDP growth rate increased in {2005} at [0.911, 0.129, 0.927, 0.676],[0.927, 0.676, 0.943, 0.711]?" }, { "from": "gpt", "value": "USA." } ], "id_raw": 355 }, { "id": "HeUatG0wgN4g2BBM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.430, 0.028, 0.620, 0.078]." }, { "from": "gpt", "value": "The {legend}[0.430, 0.028, 0.620, 0.078] contains the labels for each country identified in the chart with their corresponding colors. {USA}[0.474, 0.039, 0.503, 0.067] is represented by the color located at [0.436, 0.042, 0.463, 0.061], which is [#78a083], and {Russia}[0.570, 0.039, 0.614, 0.067] is represented by the color located at [0.531, 0.042, 0.559, 0.061], which is [#d27a41]." } ], "id_raw": 355 }, { "id": "ERwe43fWUIGUXKVl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.065, 0.093, 0.985, 0.883]." }, { "from": "gpt", "value": "The area [0.065, 0.093, 0.985, 0.883] houses the main graph that represents the GDP growth rate for the USA and Russia from 1985 to 2005. These are evidenced by the bars with coordinates of the format [X.XXX, Y.YYY, Z.ZZZ, A.AAA] which vary for each year and country." } ], "id_raw": 355 }, { "id": "dkq419YRMtvs9Rzd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.123, 0.676, 0.139, 0.848]?" }, { "from": "gpt", "value": "There is a single value represented within these coordinates:\n1. The bar represents Russia's GDP growth rate in 1985, which is -1.0, and the coordinate range is [0.123, 0.676, 0.139, 0.848]." } ], "id_raw": 355 }, { "id": "tWgM08U1aUNmh5vq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat labeling text pieces can you see in the region [0.105, 0.903, 0.945, 0.931]?" }, { "from": "gpt", "value": "There are multiple labels representing the years 1985, 1990, 1995, 2000, and 2005, respective coordinates being [0.105, 0.903, 0.140, 0.931], [0.306, 0.903, 0.341, 0.931], [0.507, 0.903, 0.543, 0.931], [0.708, 0.903, 0.743, 0.931], and [0.910, 0.903, 0.945, 0.931]." } ], "id_raw": 355 }, { "id": "1y60bOqNWpuQpcZe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat's the label for the y-axis in the chart?" }, { "from": "gpt", "value": "Units, located at [0.015, 0.453, 0.029, 0.524]." } ], "id_raw": 356 }, { "id": "n56qRrZ7KbuLUhVk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat's located at the coordinates [0.181, 0.838, 0.251, 0.883]?" }, { "from": "gpt", "value": "A bar element representing Ozone Depletion Potential for the first fiscal quarter." } ], "id_raw": 356 }, { "id": "4n9NMnvbrXnF2v5O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.112, 0.658, 0.181, 0.883]?" }, { "from": "gpt", "value": "A bar element representing Greenhouse Gas Emissions for the first fiscal quarter." } ], "id_raw": 356 }, { "id": "TvIlqzMhxa0ezRoY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar showing Greenhouse Gas Emissions in first quarter}[0.112, 0.658, 0.181, 0.883] higher than {the bar reflecting Ozone Depletion Potential in first quarter}[0.181, 0.838, 0.251, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 356 }, { "id": "JjTNzLXjcEZD8Yvj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar showing Greenhouse Gas Emissions in second quarter}[0.285, 0.545, 0.355, 0.883] tall than {the bar representing Ozone Depletion Potential in second quarter}[0.355, 0.861, 0.424, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 356 }, { "id": "jA9frbyy38kZ7Szs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar representing Greenhouse Gas Emissions in first quarter}[0.112, 0.658, 0.181, 0.883] and {the bar representing Greenhouse Gas Emissions in the fifth quarter}[0.805, 0.206, 0.874, 0.883], which is taller?" }, { "from": "gpt", "value": "The one in the fifth quarter." } ], "id_raw": 356 }, { "id": "ITq2WvZgHy4InOyx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area in the coordinates [0.071, 0.093, 0.985, 0.883]." }, { "from": "gpt", "value": "This area covers the entire plot, including the x-axis and y-axis with their respective labels, the bars indicating the Greenhouse Gas Emissions and the Ozone Depletion Potential for each quarter, and the grid of the plot." } ], "id_raw": 356 }, { "id": "KK2UGxbBxRSK1g7q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat is located within [0.523, 0.903, 0.532, 0.931]?" }, { "from": "gpt", "value": "It is the label of the third quarter located on the x-axis." } ], "id_raw": 356 }, { "id": "hM7OibsTA1goucCQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.528, 0.404, 0.513, 0.432]?" }, { "from": "gpt", "value": "There's numbers 200.0, which represents the value of the bar element for Greenhouse Gas Emissions in the third quarter." } ], "id_raw": 356 }, { "id": "WraI4LDdmvElk0od", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.015, 0.443, 0.029, 0.534] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Values." } ], "id_raw": 357 }, { "id": "rX32zQ5glFYO4ENr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text in the second legend item?" }, { "from": "gpt", "value": "[0.546, 0.039, 0.825, 0.067]" } ], "id_raw": 357 }, { "id": "De6GanA6yXNkhEDM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.509, 0.942, 0.538, 0.970]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 357 }, { "id": "rwsKQX0998FI7P2T", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nWhat indicators or categories are shown in [0.216, 0.028, 0.830, 0.078]?" }, { "from": "gpt", "value": "{Number of People in Household}[0.261, 0.039, 0.480, 0.067] and {Percentage of Single-parent Households}[0.546, 0.039, 0.825, 0.067]." } ], "id_raw": 357 }, { "id": "ixXLrUx3FWjoRVFQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar}[0.290, 0.156, 0.477, 0.883] to the left or right of {the orange bar}[0.757, 0.125, 0.943, 0.883]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 357 }, { "id": "j4JYbnAcDjkBw0g8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Number of People in Household\"}[0.104, 0.808, 0.290, 0.883] taller than {the bar representing \"Percentage of Single-parent Households\"}[0.290, 0.156, 0.477, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 357 }, { "id": "CDucuO8RNSVbH8C2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Year 1\"}[0.104, 0.808, 0.290, 0.883] and {\"Year 2\"}[0.570, 0.801, 0.757, 0.883], which year has a higher \"Number of People in Household\"?" }, { "from": "gpt", "value": "Year 2." } ], "id_raw": 357 }, { "id": "9I8I3qA9uhv3Speh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Number of People in Household\"}[0.104, 0.808, 0.290, 0.883],[0.570, 0.801, 0.757, 0.883] and {\"Percentage of Single-parent Households\"}[0.290, 0.156, 0.477, 0.883],[0.757, 0.125, 0.943, 0.883], which category has higher values in general?" }, { "from": "gpt", "value": "Percentage of Single-parent Households." } ], "id_raw": 357 }, { "id": "vyxgEope2YmeFdLZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.216, 0.028, 0.830, 0.078]." }, { "from": "gpt", "value": "The area includes the legend of the chart. The first item in the legend is \"Number of People in Household\" with a color square at [0.222, 0.042, 0.250, 0.061] and its label is at [0.261, 0.039, 0.480, 0.067]. The second item is \"Percentage of Single-parent Households\" with a color square at [0.507, 0.042, 0.535, 0.061] and its label is at [0.546, 0.039, 0.825, 0.067]." } ], "id_raw": 357 }, { "id": "xCjXWBr6ZVXh27cM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.286, 0.903, 0.761, 0.931]." }, { "from": "gpt", "value": "This area contains the labels for the X-axis. The label \"1\" is at [0.286, 0.903, 0.295, 0.931] and the label \"2\" is at [0.752, 0.903, 0.761, 0.931]." } ], "id_raw": 357 }, { "id": "iTWt9Xg6GkWiP6mJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.104, 0.808, 0.290, 0.883]?" }, { "from": "gpt", "value": "Inside the given coordinates, there is a green bar representing the \"Number of People in Household\" for the \"Year 1\"." } ], "id_raw": 357 }, { "id": "4xi0493LDT1R7yX0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.570, 0.801, 0.757, 0.883]?" }, { "from": "gpt", "value": "Within these coordinates, there is a green bar that represents the \"Number of People in Household\" for the \"Year 2\"." } ], "id_raw": 357 }, { "id": "bj5pbnabRVXDTMUl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis?" }, { "from": "gpt", "value": "The label of the y-axis is 'Market Share (%)'." } ], "id_raw": 358 }, { "id": "N86ApxD01Q372cwi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {Music}[0.104, 0.381, 0.277, 0.903] bar?" }, { "from": "gpt", "value": "The color of the Music bar is '#78a083'." } ], "id_raw": 358 }, { "id": "wVQNPUov3GLQuh7w", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the year marked at the coordinate [0.753, 0.919, 0.788, 0.942] on the x-axis?" }, { "from": "gpt", "value": "The year marked at these coordinates is 2019." } ], "id_raw": 358 }, { "id": "8KaNz0SsvIrybycB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the {Books} bar in 2019[0.770, 0.146, 0.943, 0.903]?" }, { "from": "gpt", "value": "The value of the Books bar in 2019 is 46.56%." } ], "id_raw": 358 }, { "id": "Q5FroRbFLtsmOOyI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Music} bar in 2018[0.104, 0.381, 0.277, 0.903] taller than the {Books} bar[0.277, 0.184, 0.449, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 358 }, { "id": "Ghmt4UqvHGPD7mtn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Books} bar in 2019[0.770, 0.146, 0.943, 0.903] located to the right of the {Music} bar[0.597, 0.337, 0.770, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 358 }, { "id": "i34SjaeGGxalnEKb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nComparing the two years, did the market shares of {Books}<[0.277, 0.184, 0.449, 0.903],[0.770, 0.146, 0.943, 0.903]> increase or decrease?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 358 }, { "id": "9yUEeVaVnpYUEhJN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nBetween {Music} and {Books}, which one had a bigger market share in 2019[0.597, 0.337, 0.770, 0.903], [0.770, 0.146, 0.943, 0.903]?" }, { "from": "gpt", "value": "Books." } ], "id_raw": 358 }, { "id": "sRYEQK2MDNKgPkah", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the layout and contents of the coordinates [0.062, 0.090, 0.985, 0.903]?" }, { "from": "gpt", "value": "The area [0.062, 0.090, 0.985, 0.903] encompasses the entire chart. It includes the x-axis labels (2018 and 2019), the y-axis with labels from 0 to 50 representing market shares in percentages, and both the Music and Books bars for 2018 and 2019." } ], "id_raw": 358 }, { "id": "D9yZtAX2WKtWLL83", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is within the coordinates [0.424, 0.021, 0.622, 0.063]." }, { "from": "gpt", "value": "The area between [0.424, 0.021, 0.622, 0.063] contains the legend for the chart, which includes two entries. The first one is {Music}[0.469, 0.030, 0.508, 0.053], represented by a color code '#78a083', and the second one is {Books}[0.575, 0.030, 0.617, 0.053], represented by the color code '#d27a41'." } ], "id_raw": 358 }, { "id": "5H7NDA2mxinkP9N8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.597, 0.337, 0.770, 0.903] contain?" }, { "from": "gpt", "value": "The area [0.597, 0.337, 0.770, 0.903] contains the {Music} bar for the year 2019, which indicates a market share of 34.85%." } ], "id_raw": 358 }, { "id": "5JhgHJ4anL0hi08y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content and arrangement within [0.277, 0.184, 0.449, 0.903] and [0.770, 0.146, 0.943, 0.903]." }, { "from": "gpt", "value": "The area [0.277, 0.184, 0.449, 0.903] contains the {Books} bar for the year 2018, which shows a market share of 44.23%. The area [0.770, 0.146, 0.943, 0.903] contains the {Books} bar for the year 2019, which shows a market share of 46.56%." } ], "id_raw": 358 }, { "id": "lpGB4X5MMNzjMRJs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the value displayed at the coordinate [0.194, 0.630, 0.268, 0.658]?" }, { "from": "gpt", "value": "20000.000" } ], "id_raw": 359 }, { "id": "ZtCqybCHHJI1ecZy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label shown at [0.369, 0.039, 0.554, 0.067] in the legend?" }, { "from": "gpt", "value": "New Educational Institutes" } ], "id_raw": 359 }, { "id": "Mz6etgjP1M6MlCWo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the category located at this coordinate [0.689, 0.903, 0.724, 0.931] on the x-axis?" }, { "from": "gpt", "value": "2025" } ], "id_raw": 359 }, { "id": "eKIk52eHzMrmtLeO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color for {New Educational Institutes}[0.330, 0.042, 0.358, 0.061] in the legend?" }, { "from": "gpt", "value": "#78a083" } ], "id_raw": 359 }, { "id": "l9JaONImhOFDkDMY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {2025 New Educational Institutes}[0.638, 0.883, 0.706, 0.883] taller or shorter than the bar of {2022 Students Enrolled}[0.197, 0.658, 0.265, 0.883]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 359 }, { "id": "EZkrG6JuUHLvRZ4u", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nIs the color of {2025 New Educational Institutes bar}[0.638, 0.883, 0.706, 0.883] the same as {2023 Students Enrolled bar}[0.367, 0.545, 0.435, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 359 }, { "id": "dhL14qHVW81FNFHn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nDoes {2022 New Educational Institutes bar}[0.129, 0.883, 0.197, 0.883] have a higher or lower position than {2026 Students Enrolled bar}[0.876, 0.206, 0.944, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 359 }, { "id": "SyxlpSxyxZWH6IDF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nWhich color is used more frequently, {New Educational Institutes color}[0.330, 0.042, 0.358, 0.061] or {Students Enrolled color}[0.582, 0.042, 0.610, 0.061]?" }, { "from": "gpt", "value": "Both are used equally." } ], "id_raw": 359 }, { "id": "aFKOqnxNmkDDNP0L", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the area [0.324, 0.028, 0.749, 0.078]." }, { "from": "gpt", "value": "The area contains the legend of the chart. It has two entries: \"New Educational Institutes\" with a green color label at [0.330, 0.042, 0.358, 0.061], and \"Students Enrolled\" with an orange color label at [0.582, 0.042, 0.610, 0.061]." } ], "id_raw": 359 }, { "id": "nRkug3cJ3wVjKOky", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found in the region [0.367, 0.883, 0.435, 0.883]?" }, { "from": "gpt", "value": "The orange bar indicating the number of students enrolled in 2023 is located in this region." } ], "id_raw": 359 }, { "id": "EBNpGp7ZUvt118t3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents inside [0.597, 0.855, 0.778, 0.883]?" }, { "from": "gpt", "value": "This region contains the upper half of the bar for the year 2025. This bar represents the New Educational Institutes started in that year." } ], "id_raw": 359 }, { "id": "YjsNngZFANMcdzTu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat information is included in the region [0.035, 0.534, 0.078, 0.562]?" }, { "from": "gpt", "value": "The y-axis label which is 30000 is situated at this region." } ], "id_raw": 359 }, { "id": "3B1vY1Di2DUimMdH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object labeled {Website Redesign}[0.122, 0.831, 0.203, 0.952] in the chart?" }, { "from": "gpt", "value": "It's a bar indicating the evaluation score of the Website Redesign project." } ], "id_raw": 361 }, { "id": "mRheFHFvXly61aXO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the evaluation score of the {Data Analytics System}[0.102, 0.271, 0.203, 0.422], [0.211, 0.246, 0.897, 0.310]?" }, { "from": "gpt", "value": "The evaluation score is 90." } ], "id_raw": 361 }, { "id": "e6wdl4mU6hTJwPRX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the {Cloud Migration}[0.130, 0.751, 0.203, 0.860] score?" }, { "from": "gpt", "value": "[0.211, 0.726, 0.821, 0.790]" } ], "id_raw": 361 }, { "id": "EK4njIjpPlHWjitY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Online Marketing Campaign}[0.080, 0.591, 0.203, 0.775], [0.211, 0.566, 0.783, 0.630] evaluation score higher or lower than that of {E-commerce Platform}[0.106, 0.351, 0.203, 0.496], [0.211, 0.326, 0.844, 0.390]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 361 }, { "id": "ZDe4gVoGY6f2MRKF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher evaluation score - the {A.I. Customer Service Chatbots}[0.067, 0.511, 0.203, 0.715], [0.211, 0.486, 0.951, 0.550] or {Virtual Reality Product Showroom}[0.058, 0.111, 0.203, 0.329], [0.211, 0.086, 0.928, 0.150]?" }, { "from": "gpt", "value": "A.I. Customer Service Chatbots." } ], "id_raw": 361 }, { "id": "wUQYtwrRbbS9TsH8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nAre the evaluation scores of {Website Redesign}[0.122, 0.831, 0.203, 0.952], [0.211, 0.806, 0.859, 0.870] generally higher than the {Cybersecurity Improvement}[0.080, 0.191, 0.203, 0.375], [0.211, 0.166, 0.867, 0.230] scores?" }, { "from": "gpt", "value": "Nope." } ], "id_raw": 361 }, { "id": "gxGpWnq0QGXQbOJ1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.211, 0.047, 0.988, 0.909]." }, { "from": "gpt", "value": "This region contains a bar chart. Each bar represents an evaluation score of an innovation project. The x-axis indicates the evaluation score, and the y-axis indicates the name of each innovation project." } ], "id_raw": 361 }, { "id": "kkrqe9y7YjmzNXxa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.211, 0.246, 0.867, 0.230]." }, { "from": "gpt", "value": "This area contains the bar and score for the project named Cybersecurity Improvement. The bar starts at 0.211 on the x-axis and extends to 0.867, indicating an evaluation score of 86." } ], "id_raw": 361 }, { "id": "NmkdgbvsfSPlhZzR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.211, 0.406, 0.882, 0.470]?" }, { "from": "gpt", "value": "This area contains a single bar representing the Company Mobile App project with an evaluation score of 88, which is indicated by the bar extending to 0.882 on the x-axis." } ], "id_raw": 361 }, { "id": "xQJWPNOxjULQFRoy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.211, 0.806, 0.859, 0.870]?" }, { "from": "gpt", "value": "This area contains a single bar representing the Website Redesign project with an evaluation score of 85, indicated by the bar extending to 0.859 on the x-axis." } ], "id_raw": 361 }, { "id": "b5s368E0qtTC9rWo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the main object located at [0.260, 0.019, 0.847, 0.041] refer to?" }, { "from": "gpt", "value": "It refers to the subtitle: 'Instances of Sustainable Business Practices & Social Responsibility Activities in 2021'" } ], "id_raw": 362 }, { "id": "SysJYHiBRaZCsTWd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat does {the bar} at [-0.612, 0.822, 0.880, 0.887] indicate from the chart?" }, { "from": "gpt", "value": "It indicates that there were 8000 instances of 'CSR Activities' in 2021." } ], "id_raw": 362 }, { "id": "uRrZ2rImlxVU7phd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the x-axis label at [0.493, 0.964, 0.613, 0.981]?" }, { "from": "gpt", "value": "It represents 'Number of Instances'." } ], "id_raw": 362 }, { "id": "LRoBpPXkdqNVhOd2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nWhich is larger, {the 'Waste Reduction' bar}[[-0.612, 0.413, 0.469, 0.479]] or {the 'Renewable Energy' bar}[[-0.612, 0.495, 0.525, 0.560]]?" }, { "from": "gpt", "value": "The 'Renewable Energy' bar is larger." } ], "id_raw": 362 }, { "id": "nYIw5yf3IJ44vtOK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar for 'Animal Welfare'}[-0.612, 0.087, 0.171, 0.152] on the left indicate a lower or higher number of instances than the {bar for 'CSR Activities'}[-0.612, 0.822, 0.880, 0.887] on the right?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 362 }, { "id": "4Wc5qc4O4iaxB9tD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nAre the numbers indicated by {the bars for the practices/activities}[-0.612, 0.822, 0.880, 0.887], [-0.612, 0.740, 0.768, 0.805], [-0.612, 0.658, 0.675, 0.724] etc., decreasing or increasing as we move from top to bottom?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 362 }, { "id": "lhtt6aBd4661anz3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements present in the region spanning from [0.134, 0.047, 0.973, 0.927]?" }, { "from": "gpt", "value": "The region hosts the main chart area, with bars representing different 'Sustainable Business Practices & Social Responsibility Activities' plotted horizontally along the y-axis, and their respective numbers of instances (in the year 2021) plotted along the x-axis. The labels for these activities are found to the left of each bar, and the number of instances is annotated at the end of each bar. For instance, we have 'CSR Activities' {bar and label}[-0.612, 0.822, 0.918, 0.887] at the top with 8000 instances and 'Animal Welfare' {bar and label}[-0.612, 0.087, 0.210, 0.152] at the bottom with 4200 instances." } ], "id_raw": 362 }, { "id": "iBc0nYU9wASUBGRU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content of the region [0.119, 0.939, 0.988, 0.957]." }, { "from": "gpt", "value": "This region contains the x-axis labels ranging from 4000 {label and tick}[0.119, 0.939, 0.148, 0.957], [0.134, 0.927] to 8500 {label and tick}[0.958, 0.939, 0.988, 0.957], [0.973, 0.927], indicating the possible range for the number of instances of each activity." } ], "id_raw": 362 }, { "id": "sxzcn4B63XIDREDf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat is within the vertical range of [0.061, 0.848, 0.126, 0.945]?" }, { "from": "gpt", "value": "The vertical range hosts the y-axis label for the 'CSR Activities', indicating that the top-most bar of the chart represents the instances of 'CSR Activities'." } ], "id_raw": 362 }, { "id": "c331qMrdmWVMnikl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the entire chart represented at coordinates [0.440, 0.015, 0.676, 0.033]?" }, { "from": "gpt", "value": "The title is \"Number of Research Papers by AI Areas\"." } ], "id_raw": 363 }, { "id": "ttiqHgScHjVhNbAS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label with coordinates [0.489, 0.971, 0.627, 0.985]?" }, { "from": "gpt", "value": "The x-axis label is \"Number of Research Papers\"." } ], "id_raw": 363 }, { "id": "ivG9oMqSIjolD4Iq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat research area is represented by the coordinates [0.062, 0.862, 0.120, 0.943]?" }, { "from": "gpt", "value": "The research area is \"Deep Learning\"." } ], "id_raw": 363 }, { "id": "NqqO7dnBB67CHFtT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the value represented by the bar with coordinates [0.127, 0.833, 0.948, 0.901]?" }, { "from": "gpt", "value": "According to the annotation at [0.959, 0.860, 0.984, 0.874], the value is \"3950\"." } ], "id_raw": 363 }, { "id": "QKUsGzseuKrC2S3U", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nDoes \"Knowledge Representation\" at coordinates [0.019, 0.442, 0.120, 0.584] have more or less research papers than \"Planning and Navigation\" at coordinates [0.027, 0.358, 0.120, 0.488] referring to their bars at coordinates [0.127, 0.414, 0.268, 0.481] and [0.127, 0.330, 0.504, 0.397]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 363 }, { "id": "aHAxXPFLOUcSvb87", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nAmong the \"Deep Learning\" area at [0.062, 0.862, 0.120, 0.943] and \"Natural Language Processing\" area at [0.011, 0.526, 0.120, 0.679], which field has more research papers looking at their bars at coordinates [0.127, 0.833, 0.948, 0.901] and [0.127, 0.498, 0.896, 0.565]?" }, { "from": "gpt", "value": "\"Natural Language Processing\"." } ], "id_raw": 363 }, { "id": "D6OsDuSiuIgCvxXh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nIs the number of papers in \"Evolutionary Computation\" [0.021, 0.106, 0.120, 0.245] greater or lesser than that in \"Swarm Intelligence\" [0.046, 0.190, 0.120, 0.294] based on their bars at coordinates [0.127, 0.078, 0.321, 0.146] and [0.127, 0.162, 0.299, 0.229], respectively?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 363 }, { "id": "Tsm280l9ANNiDOei", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing \"Automated Reasoning\" [0.127, 0.666, 0.286, 0.733] have higher or lower height than the bar representing \"Multi-agent Systems\" [0.127, 0.246, 0.403, 0.313]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 363 }, { "id": "qSve3ry0LVEoxS7L", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region between coordinates [0.999, 0.951, 1.075, 0.965]?" }, { "from": "gpt", "value": "It does not contain any part of the chart. It's outside of the chart boundary." } ], "id_raw": 363 }, { "id": "3ll5e2IUb3GQVIV7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nCan you explain the sect that is bounded by coordinates [0.127, 0.414, 0.268, 0.481]?" }, { "from": "gpt", "value": "This region corresponds to the bar representing the number of research papers in the field of \"Knowledge Representation\". According to the text annotation at [0.278, 0.441, 0.297, 0.455], there are \"675\" papers in this field." } ], "id_raw": 363 }, { "id": "oQhw5uE2Za0vsmu9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.738, 0.951, 0.763, 0.965]." }, { "from": "gpt", "value": "This region corresponds to an x-axis tick label, which is the numerical value \"3000\"." } ], "id_raw": 363 }, { "id": "tmREhu5dprSiFgAy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the information within the region [0.124, 0.951, 0.130, 0.965]." }, { "from": "gpt", "value": "It contains the tick label '0' representing the starting point on the x-axis in terms of number of research papers." } ], "id_raw": 363 }, { "id": "ejYwHyiIjnsv0anx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is being described in this JSON structure?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 364 }, { "id": "3IVQOJqwQFEfkHqx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the {x-axis label}[0.496, 0.948, 0.617, 0.966] in the chart?" }, { "from": "gpt", "value": "Number of Initiatives." } ], "id_raw": 364 }, { "id": "RWtFZZqybngfgnnD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat activity is represented with coordinates [0.127, 0.808, 0.415, 0.872]?" }, { "from": "gpt", "value": "Franchise Ownership." } ], "id_raw": 364 }, { "id": "S4m1trS9oG97nmR3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Startup Founding}[0.127, 0.728, 0.754, 0.792] longer than the bar for {Product Innovation}[0.127, 0.648, 0.602, 0.712]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 364 }, { "id": "D8jMTSksCU4VvDns", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Social Entrepreneurship}[0.127, 0.166, 0.945, 0.230] shorter than the bar for {Startup Founding}[0.127, 0.728, 0.754, 0.792]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 364 }, { "id": "LUYfou3aR6xhTQ9H", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nBetween {Freelance Contracting}[0.127, 0.568, 0.490, 0.632] and {Business Consulting}[0.127, 0.487, 0.551, 0.551], which activity has a higher number of initiatives?" }, { "from": "gpt", "value": "Business Consulting." } ], "id_raw": 364 }, { "id": "jLl96k6aFm3w6KU0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.046, 0.601, 0.118, 0.740]." }, { "from": "gpt", "value": "In this region, there is a y-axis label denoting the activity type as 'Freelance Contracting'." } ], "id_raw": 364 }, { "id": "LwYWWVQPwIDKb10s", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat information is present in the area [0.127, 0.487, 0.551, 0.551]?" }, { "from": "gpt", "value": "This area contains a bar representing the number of initiatives for the 'Business Consulting' activity type, which is a total of 2162 initiatives." } ], "id_raw": 364 }, { "id": "3Ti3GCutq88gQw7W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe what appears in the coordinates [0.127, 0.086, 0.323, 0.150]." }, { "from": "gpt", "value": "In these coordinates, there is a bar representing 'Angel Investing' with a total of 1000 initiatives and the associated number." } ], "id_raw": 364 }, { "id": "wwu50zstmeuUNkpJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat is given by the coordinates [0.413, 0.019, 0.722, 0.041] in the chart's structure?" }, { "from": "gpt", "value": "The chart's subtitle: \"Estimated Financial Losses by Risk Category\"." } ], "id_raw": 365 }, { "id": "BYNfNHZGqqpG0WRb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information is at the place indicated by these coordinates [0.149, 0.858, 0.731, 0.896]?" }, { "from": "gpt", "value": "It represents the category \"Security Breach\" bar on the chart, with the value being 3200." } ], "id_raw": 365 }, { "id": "RibfEw7mTmAnJT5a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the text within this area [0.484, 0.964, 0.652, 0.981]?" }, { "from": "gpt", "value": "That is the X-axis label, and it reads \"Estimated Losses ($000,000)\"." } ], "id_raw": 365 }, { "id": "VfAKG6TEE7kALRap", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the object represented by [0.149, 0.822, 0.721, 0.887] coordinates?" }, { "from": "gpt", "value": "It is a green bar representing the \"Security Breach\" category, and its value is 3200." } ], "id_raw": 365 }, { "id": "elYUwpm6JmY7hPLx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Compliance Violations}[0.149, 0.332, 0.663, 0.397] larger or smaller than the bar for {Human Errors}[0.149, 0.168, 0.846, 0.234]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 365 }, { "id": "5tsZEtDBt3T1MtYq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nWhich one is larger, {the bar representing Security Breach}[0.149, 0.822, 0.721, 0.887] or {the bar representing Reputation Damage}[0.149, 0.495, 0.947, 0.560]?" }, { "from": "gpt", "value": "The bar representing Reputation Damage." } ], "id_raw": 365 }, { "id": "rj11DWukfG0VJY3e", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nWhich is positioned higher on the Y-Axis, {Market Shifts}[0.149, 0.577, 0.566, 0.642] or {Technology Failures}[0.149, 0.087, 0.690, 0.152]?" }, { "from": "gpt", "value": "Market Shifts." } ], "id_raw": 365 }, { "id": "IwNPTGJJKktEo0ot", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nCompare the length of {Financial Risks}[0.149, 0.250, 0.822, 0.315] bar and {Operational Risks}[0.149, 0.658, 0.640, 0.724] bar. Which one is longer?" }, { "from": "gpt", "value": "Financial Risks." } ], "id_raw": 365 }, { "id": "r4GhtNmjwyZmZPvO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat is represented within the bounding box [0.149, 0.087, 0.846, 0.152]?" }, { "from": "gpt", "value": "In this region, there is a green bar which represents the \"Technology Failures\" category, with the value being 3030." } ], "id_raw": 365 }, { "id": "e1PM02tcsqmBd7Ua", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the area with coordinates [0.149, 0.168, 0.846, 0.234] illustrate?" }, { "from": "gpt", "value": "The area contains a green bar that represents the \"Human Errors\" category. The bar's value is 3900." } ], "id_raw": 365 }, { "id": "OmV1DS7gsA2f1WWz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area [0.149, 0.527, 0.947, 0.567]." }, { "from": "gpt", "value": "The area includes a single green bar mapped to the \"Reputation Damage\" category, which has a value of 4467." } ], "id_raw": 365 }, { "id": "8TxohnSDyYO43clj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's inside the region [0.149, 0.740, 0.645, 0.805]?" }, { "from": "gpt", "value": "The region represents a green bar which stands for the category \"Natural Disasters\", having a value of 1950." } ], "id_raw": 365 }, { "id": "Huy7DU3Hog0WUCpJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the Chart?" }, { "from": "gpt", "value": "Stock Value Trend for Tech Companies at {the title}[0.396, 0.019, 0.659, 0.041]." } ], "id_raw": 366 }, { "id": "vyNHhah7k76rhVMe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat stock's data is in the coordinate [0.067, 0.822, 0.637, 0.887]?" }, { "from": "gpt", "value": "It's the data for {Amazon}[0.067, 0.822, 0.637, 0.887]." } ], "id_raw": 366 }, { "id": "sJsvgmhnXgKZdZjd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinates [0.018, 0.848, 0.059, 0.910]?" }, { "from": "gpt", "value": "It is the label for {Amazon}[0.018, 0.848, 0.059, 0.910]." } ], "id_raw": 366 }, { "id": "5VAcOcSASiYP2vos", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Tesla stock bar}[0.067, 0.413, 0.944, 0.479] longer than {the IBM stock bar}[0.067, 0.250, 0.441, 0.315]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 366 }, { "id": "7VFD3PTR27yGFMT4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nIs the value of {the Apple stock}[0.067, 0.740, 0.626, 0.805] lower than {the Amazon stock}[0.067, 0.822, 0.637, 0.887]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 366 }, { "id": "Uu3Dqj2gB6WRnNgW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nIs {the Oracle stock bar}[0.067, 0.087, 0.360, 0.152] closer to the X-axis than {the Microsoft stock bar}[0.067, 0.332, 0.705, 0.397]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 366 }, { "id": "Wd3o61jVmCOZVMrb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.067, 0.495, 0.506, 0.560]?" }, { "from": "gpt", "value": "Yes, this area represents {the Facebook stock bar}[0.067, 0.495, 0.506, 0.560] with its corresponding text stating its value as 4200." } ], "id_raw": 366 }, { "id": "DKzmHa5pySMtFyI6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nPlease explain the content in [0.262, 0.939, 0.291, 0.957] and [0.993, 0.939, 1.022, 0.957]." }, { "from": "gpt", "value": "These are X-axis labels. The first coordinate represents the label {2000}[0.262, 0.939, 0.291, 0.957], and the second coordinate represents the label {9000}[0.993, 0.939, 1.022, 0.957]." } ], "id_raw": 366 }, { "id": "imfhBsIhYf30fUfI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within [0.067, 0.740, 0.626, 0.805]." }, { "from": "gpt", "value": "There is a bar for {the Apple stock}[0.067, 0.740, 0.626, 0.805]. The value text of 5350 is also displayed near the end of the bar." } ], "id_raw": 366 }, { "id": "Z255M9RU7qHqjHqq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at the coordinates [0.112, 0.822, 0.246, 0.887] in the vertical bar chart?" }, { "from": "gpt", "value": "It is a vertical bar representing the return rate for {Mutual Fund}[0.112, 0.822, 0.246, 0.887]." } ], "id_raw": 367 }, { "id": "xJ94YpMEeXFQc3fN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinates [0.254, 0.846, 0.272, 0.863] adjacent to one of the bars?" }, { "from": "gpt", "value": "The label is {3.2}[0.254, 0.846, 0.272, 0.863], indicating the return rate for Mutual Fund." } ], "id_raw": 367 }, { "id": "1c22InQubHKFHq1h", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the y-axis label indicates at the coordinates [0.028, 0.112, 0.104, 0.226]?" }, { "from": "gpt", "value": "The y-axis label {Savings Account}[0.028, 0.112, 0.104, 0.226] indicates a type of investment." } ], "id_raw": 367 }, { "id": "L5JihmWAWwE10UnC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {ETFs}[0.112, 0.250, 0.433, 0.315] higher than the bar representing {Gold}[0.112, 0.168, 0.179, 0.234]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 367 }, { "id": "MUXfqLZ5J9VBK2wf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nIn relation to the bar for {Real Estate}[0.112, 0.658, 0.367, 0.724], where is the bar for {Bonds}[0.112, 0.577, 0.292, 0.642] located?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 367 }, { "id": "ikRjqHcOw0CakL4A", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nComparing the two bars {Startup Investments}[0.112, 0.495, 0.946, 0.560] and {Cryptocurrency}[0.112, 0.413, 0.550, 0.479], which one is longer?" }, { "from": "gpt", "value": "Startup Investments." } ], "id_raw": 367 }, { "id": "wpaX35B8lLUHZknK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the information contained in the region [0.112, 0.495, 0.946, 0.560]?" }, { "from": "gpt", "value": "The region represents the performance of {Startup Investments}[0.112, 0.495, 0.946, 0.560]. There is a long vertical bar indicating a return rate of 20.0." } ], "id_raw": 367 }, { "id": "NMdut10kHZMpOpJp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred by looking at the area [0.112, 0.250, 0.433, 0.315]?" }, { "from": "gpt", "value": "The area describes the return rate of {ETFs}[0.112, 0.250, 0.433, 0.315] as denoted by a vertical bar and it is 7.7." } ], "id_raw": 367 }, { "id": "0Woat7iKdCRxVRM1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nCan you outline the contents of the y-axis labels in the region [0.027, 0.766, 0.104, 0.882]?" }, { "from": "gpt", "value": "The region includes the y-axis label for {Blue-Chip Stocks}[0.027, 0.766, 0.104, 0.882], one of the types of investments." } ], "id_raw": 367 }, { "id": "2Bj1FrTcH0ceMBFx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by the object within the coordinates [0.346, 0.083, 0.679, 0.113]?" }, { "from": "gpt", "value": "It represents a title of the chart; \"Number of Internet Users by Industry\"." } ], "id_raw": 368 }, { "id": "EeYG0lXFofmQwZtw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is designated at the coordinates [0.130, 0.577, 0.431, 0.642]?" }, { "from": "gpt", "value": "It's a bar representing the Internet users count in \"Data Analysis\" industry." } ], "id_raw": 368 }, { "id": "XOI5KCGKrx1btMiC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at [0.130, 0.413, 0.583, 0.479] denote?" }, { "from": "gpt", "value": "It's the industry label for \"Telecommunications\"." } ], "id_raw": 368 }, { "id": "zJvekeKEHlITwsHv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Web Development\" at [0.130, 0.250, 0.748, 0.315] taller than the bar for \"Cloud Computing\" at [0.130, 0.822, 0.308, 0.887]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 368 }, { "id": "TOxifC4BYfUDTPdk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nDoes the \"AI and Machine Learning\" job category at [0.130, 0.495, 0.522, 0.560] have higher user counts than \"Data Analysis\" at [0.130, 0.577, 0.431, 0.642]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 368 }, { "id": "qr6FfxLH8zTOgtrd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nWhat's the relation between the size of the \"Cybersecurity\" bar at [0.130, 0.740, 0.343, 0.805] and the \"Social Media Marketing\" bar at [0.130, 0.332, 0.682, 0.397]?" }, { "from": "gpt", "value": "The \"Social Media Marketing\" bar is taller than the \"Cybersecurity\" bar." } ], "id_raw": 368 }, { "id": "PRkCuoJiI5Hb9tPJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents in the range [0.130, 0.495, 0.522, 0.560]?" }, { "from": "gpt", "value": "This area has a blue bar representing the \"AI and Machine Learning\" industry with a count of 52750 Internet users." } ], "id_raw": 368 }, { "id": "R8AOBuVLJOgT2RTe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content covered in the area [0.130, 0.822, 0.308, 0.887]." }, { "from": "gpt", "value": "It contains a blue column chart piece that stands for 24000 Internet users in the \"Cloud Computing\" field." } ], "id_raw": 368 }, { "id": "E4jvxMjdPWL92FhE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.130, 0.250, 0.748, 0.315]?" }, { "from": "gpt", "value": "There is a blue column in this region portraying the number of Internet users in the \"Web Development\" industry as 83200." } ], "id_raw": 368 }, { "id": "EYsl4DoD5E7EueBb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the tool}<{the tool name:\"Python\"}>[0.041, 0.848, 0.078, 0.902]?" }, { "from": "gpt", "value": "It's a big data tool." } ], "id_raw": 369 }, { "id": "j20HhhT2vfzP0tMN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the top right end of the bar showing the number of employees using {Apache Spark}<{coordinates of Apache Spark bar label:[0.820, 0.437, 0.849, 0.455]}>?" }, { "from": "gpt", "value": "8800." } ], "id_raw": 369 }, { "id": "Nd1xbDAllswJkqwp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the tool name} at [0.036, 0.112, 0.078, 0.174]?" }, { "from": "gpt", "value": "MATLAB." } ], "id_raw": 369 }, { "id": "YVNtEUtbAp9trn6f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the {x-axis label} at [0.472, 0.964, 0.601, 0.981] in the chart?" }, { "from": "gpt", "value": "Number of Employees." } ], "id_raw": 369 }, { "id": "Bi9ppjPRDCz3L3U3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nCompared to {Python}<{Python bar coordinates:[0.086, 0.822, 0.910, 0.887]}>, is the usage of {Java}<{Java bar coordinates:[0.086, 0.740, 0.780, 0.805]}> more or less?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 369 }, { "id": "yOuA3q45q4iNu0HG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nWhich one has more users, {Excel}<{Excel bar coordinates:[0.086, 0.168, 0.945, 0.234]}> or {SQL}<{SQL bar coordinates:[0.086, 0.577, 0.884, 0.642]}>?" }, { "from": "gpt", "value": "Excel." } ], "id_raw": 369 }, { "id": "7ty59rVUzRhG7zCb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nIs {MATLAB}<{MATLAB bar coordinates:[0.086, 0.087, 0.485, 0.152]}> used by more employees than {Apache Spark}<{Apache Spark bar coordinates:[0.086, 0.332, 0.884, 0.697]}?" }, { "from": "gpt", "value": "No." } ], "id_raw": 369 }, { "id": "fUxz6qngjgGzvCuV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nAmong {Python}<{Python bar coordinates:[0.086, 0.822, 0.910, 0.887]}> and {R}<{R bar coordinates:[0.086, 0.658, 0.745, 0.724]}>, which role has more users?" }, { "from": "gpt", "value": "Python." } ], "id_raw": 369 }, { "id": "BHFPk6LG7AwamiSY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.780, 0.493, 0.944, 0.566]." }, { "from": "gpt", "value": "There is one blue horizontal bar representing the number of employees using Hadoop, which is 8200." } ], "id_raw": 369 }, { "id": "AWcF6z6BgYvwuL5V", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is in the region [0.038, 0.413, 0.849, 0.479]?" }, { "from": "gpt", "value": "In this region, there is a blue bar that represents the tool \"Tableau\", and according to the length of the bar, it indicates that the usage count is 8800." } ], "id_raw": 369 }, { "id": "G8CEVfEWzK2wzF9F", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat elements are within [0.038, 0.358, 0.484, 0.455]?" }, { "from": "gpt", "value": "Within this area, a blue bar for the big data tool \"Apache Spark\" is present. According to the length of the bar, it shows that the tool is used by 9200 employees." } ], "id_raw": 369 }, { "id": "gv29NoceiP7jxVX8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are highlighted in the area [0.086, 0.201, 0.945, 0.234]?" }, { "from": "gpt", "value": "This region highlights a blue bar that represents the \"Excel\" tool. The length of the bar indicates that the usage count is 9900." } ], "id_raw": 369 }, { "id": "goUhg7AX3RjDqni8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.046, 0.358, 0.116, 0.464] in the chart?" }, { "from": "gpt", "value": "It's the y-axis label, named as Digital Banking." } ], "id_raw": 370 }, { "id": "db0IhFpqANuTb6v8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the second bar in the chart?" }, { "from": "gpt", "value": "[0.124, 0.740, 0.809, 0.805]" } ], "id_raw": 370 }, { "id": "ZnG0fXFtEzyj1zH2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis scale} at [0.391, 0.939, 0.405, 0.957]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 370 }, { "id": "RcP7YR9y6Udw81uz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"E-utilities\"}[0.124, 0.413, 0.453, 0.479] higher than {the bar representing \"Food Delivery\"}[0.124, 0.577, 0.726, 0.642]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 370 }, { "id": "1VKpJunKorsjtNM9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nAre {the bar representing Online Retail} [0.124, 0.822, 0.617, 0.887] higher than {the bar representing Online Education} [0.124, 0.250, 0.836, 0.315]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 370 }, { "id": "KeibCNNh7etmQQmM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Online Retail\"}[0.124, 0.822, 0.617, 0.887] and {\"Digital Banking\"}[0.124, 0.332, 0.946, 0.397], which has a higher Annual Revenue Growth?" }, { "from": "gpt", "value": "Digital Banking." } ], "id_raw": 370 }, { "id": "LlUUtTokilYzPHfR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.048, 0.112, 0.116, 0.214]." }, { "from": "gpt", "value": "There is one bar representation and one text label in this area. The bar is {Online Fashion's Revenue Growth bar}[0.124, 0.087, 0.863, 0.152], and the text is the label called {Online Fashion}[0.048, 0.112, 0.116, 0.214]." } ], "id_raw": 370 }, { "id": "cP1zHtv341041Inx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.391, 0.939, 0.405, 0.957]." }, { "from": "gpt", "value": "The area [0.391, 0.939, 0.405, 0.957] contains the x-axis scale label which is the number \"10\"." } ], "id_raw": 370 }, { "id": "liamutOwQM3Lsd1Z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.533, 0.005, 0.827, 0.983]?" }, { "from": "gpt", "value": "In the region, all bar charts along the y-axis from {Travel & Transportation}[0.124, 0.658, 0.535, 0.724] to {Online Fashion}[0.124, 0.087, 0.863, 0.152] are included. Each bar is followed on the right by a numerical label representing annual growth data, ranging from \"15%\" to \"27%\"." } ], "id_raw": 370 }, { "id": "nBnbEvoTXLiqc0Ut", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary component with coordinates [0.419, 0.021, 0.715, 0.047] in the chart?" }, { "from": "gpt", "value": "It's the subtitle of the chart: \"Number of Shares Traded By Sector\"." } ], "id_raw": 371 }, { "id": "ovwXSrwWmYBOvaYE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label for the sector 'Energy'?" }, { "from": "gpt", "value": "[0.102, 0.437, 0.147, 0.500]" } ], "id_raw": 371 }, { "id": "5mtyQam64p1ivvYk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.505, 0.959, 0.630, 0.979]?" }, { "from": "gpt", "value": "Number of Shares." } ], "id_raw": 371 }, { "id": "BOYmqmHB47trCQG0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat sectors are portrayed in [0.156, 0.173, 0.939, 0.237]?" }, { "from": "gpt", "value": "{Materials}[0.156, 0.173, 0.939, 0.237]." } ], "id_raw": 371 }, { "id": "0GuDBqUI43xTtik4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"High Tech\"}[0.156, 0.813, 0.592, 0.877] longer than {the bar representing \"Real Estate\"}[0.156, 0.493, 0.354, 0.557]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 371 }, { "id": "k8rIh4M5qMA8fGyc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Consumer Discretionary\"}[0.156, 0.573, 0.332, 0.637] shorter than {the bar representing \"Industrials\"}[0.156, 0.093, 0.774, 0.157]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 371 }, { "id": "IZoFWRp00abcEssz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Materials\"}[0.156, 0.173, 0.939, 0.237] and {\"Communication Services\"}[0.156, 0.253, 0.466, 0.317], which sector has more shares traded?" }, { "from": "gpt", "value": "Materials." } ], "id_raw": 371 }, { "id": "pRupvwuNTSZPfTCl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Materials\"}[0.156, 0.173, 0.939, 0.237] the longest bar in the chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 371 }, { "id": "WWB9emXUAtmsJJRN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nDepict the content within [0.156, 0.813, 0.592, 0.877]." }, { "from": "gpt", "value": "There is only one orange bar representing the 'High Tech' sector with the number of shares traded shown on the right. The value is '2308' [0.601, 0.835, 0.636, 0.855]." } ], "id_raw": 371 }, { "id": "Wu5JwcqamAg9qPZU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.156, 0.493, 0.354, 0.557]." }, { "from": "gpt", "value": "The area contains one orange bar which represents the 'Real Estate' sector with the number of shares traded. The numerical text is '1048' located at [0.363, 0.515, 0.399, 0.535]." } ], "id_raw": 371 }, { "id": "hxA0t0Efl7TswkmU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects exist within [0.156, 0.253, 0.466, 0.317]?" }, { "from": "gpt", "value": "There is 1 orange bar which represents that the value of the \"Communication Services\" sector shares traded is 1643, and the coordinate range is [0.156, 0.253, 0.466, 0.317]." } ], "id_raw": 371 }, { "id": "fpiQ1tUAUxFFTWOs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the area [0.415, 0.931, 0.457, 0.951]?" }, { "from": "gpt", "value": "It is the x-axis tick label '1500' [0.422, 0.931, 0.457, 0.951], which represents the number of shares traded." } ], "id_raw": 371 }, { "id": "nJbp7CKAYR9EEHZH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is [0.105, 0.047, 0.986, 0.927]?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 372 }, { "id": "8WKXUk60XRyGdP70", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar} at the position [0.105, 0.793, 0.355, 0.887] representing?" }, { "from": "gpt", "value": "{Sourcing} value in the Value Chain Visualization and Analysis, with volume 1250." } ], "id_raw": 372 }, { "id": "HVIMY7vIWfeLubDb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat's annotated at the coordinate [0.365, 0.831, 0.394, 0.849]?" }, { "from": "gpt", "value": "The value 1250 which corresponds to {the sourcing bar}[0.105, 0.793, 0.355, 0.887]." } ], "id_raw": 372 }, { "id": "g1fv90m3vGVilLx8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nIs {the Retail bar}[0.105, 0.204, 0.944, 0.299] longer than {the Distribution bar}[0.105, 0.322, 0.755, 0.416]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 372 }, { "id": "qCjpDScDl2gz3CoM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nIs the {Production bar}'s[0.105, 0.675, 0.445, 0.769] value higher than that of the {Packaging bar}[0.105, 0.558, 0.575, 0.652]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 372 }, { "id": "CXHq2QXM2B10HTFR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nCompared to {the bar of Transportation}[0.105, 0.440, 0.665, 0.534], is {the bar of After-Sales Service}[0.105, 0.087, 0.875, 0.181] shorter or longer?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 372 }, { "id": "wQGWRv4HcwxIljb0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.105, 0.047, 0.986, 0.927]." }, { "from": "gpt", "value": "This region represents the main body of the bar chart. The x-axis represents the Transaction Volume ranging from 0 to 4500, and the y-axis represents different stages in the Value Chain: Sourcing, Production, Packaging, Transportation, Distribution, Retail, and After-Sales Service. Each stage is depicted by a bar with the length proportional to the transaction volume of that stage." } ], "id_raw": 372 }, { "id": "BIQa6rImL6SaRjjz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat features are contained within the region [0.353, 0.204, 0.944, 0.299]?" }, { "from": "gpt", "value": "This region contains the {Retail bar}[0.105, 0.204, 0.944, 0.299] in the bar chart, showing a transaction volume of 4200." } ], "id_raw": 372 }, { "id": "8gcj2bldhkswFL8e", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the area ranging from [0.105, 0.087, 0.875, 0.181]." }, { "from": "gpt", "value": "This area features the {After-Sales Service bar}[0.105, 0.087, 0.875, 0.181] in the bar chart, indicating a transaction volume of 3850 in value chain analysis." } ], "id_raw": 372 }, { "id": "1xZvyQBcdGODUqgk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the axis label}[0.008, 0.487, 0.008, 0.487] on the chart?" }, { "from": "gpt", "value": "Y-axis label, it is not given on the chart." } ], "id_raw": 373 }, { "id": "Ognj2aSPYimT0oAw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the information about the bar with coordinates [0.099, 0.178, 0.196, 0.250]?" }, { "from": "gpt", "value": "This bar represents the 'Trainee_Manager' position, with the value of 800 employees." } ], "id_raw": 373 }, { "id": "CxDSZYtnl67Its3P", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the text within the coordinates [0.347, 0.478, 0.376, 0.496] indicate?" }, { "from": "gpt", "value": "The text indicates the number '2000' which is the number of employees under 'Manager' role." } ], "id_raw": 373 }, { "id": "50k4e4kGC10wiLMV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'CEO' bar}[0.099, 0.814, 0.945, 0.887] represent a greater number of employees than {the 'President' bar}[0.099, 0.723, 0.703, 0.796]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 373 }, { "id": "EQmFYn3dTtzPg12m", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Junior_Manager' bar}[0.099, 0.360, 0.280, 0.432] shorter than {the 'Team_Lead' bar}[0.099, 0.269, 0.220, 0.341]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 373 }, { "id": "XyNRUleNlHlGCxCN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nIs the number of employees for {the 'Intern' role}[0.099, 0.087, 0.111, 0.159] smaller than that for {the 'Trainee_Manager' role}[0.099, 0.178, 0.196, 0.250]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 373 }, { "id": "c0JaZIL22M20lifE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the x-axis using the coordinates [0.479, 0.964, 0.607, 0.981]." }, { "from": "gpt", "value": "The x-axis represents the 'Number of Employees' ranging from 0 to 8000." } ], "id_raw": 373 }, { "id": "5H7dQJLgKsdbjFk3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents between the coordinates [0.099, 0.814, 0.945, 0.887] and [0.099, 0.360, 0.280, 0.432]?" }, { "from": "gpt", "value": "They are bars representing the number of employees under various positions, from 'CEO' to 'Junior_Manager' respectively." } ], "id_raw": 373 }, { "id": "aDU7OH0nTKr90r41", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat information is present within the coordinates [0.368, 0.019, 0.718, 0.041]?" }, { "from": "gpt", "value": "This area contains the subtitle 'Number of Employees under each managerial role'." } ], "id_raw": 373 }, { "id": "IhDJ2AYx4XLfcmSA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.827, 0.427, 0.846, 0.444] in the chart?" }, { "from": "gpt", "value": "It is the number 4800, which represents the value of entrepreneurship activity for that particular activity." } ], "id_raw": 374 }, { "id": "cyKfWz3nzG2cZoF5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the seventh bar in the chart?" }, { "from": "gpt", "value": "[0.157, 0.332, 0.713, 0.397]" } ], "id_raw": 374 }, { "id": "7F9xuFtBU0qEwRsy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.068, 0.684, 0.149, 0.805]?" }, { "from": "gpt", "value": "Angel Investment." } ], "id_raw": 374 }, { "id": "pBePhY3RJv6iCfbg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.157, 0.740, 0.376, 0.805] represent?" }, { "from": "gpt", "value": "It represents the value for Venture Capital, approximately 1500." } ], "id_raw": 374 }, { "id": "7iFxh1WmV5RzOdYy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Angel Investment\"}[0.157, 0.658, 0.450, 0.724] longer or shorter than {the bar representing \"Crowdfunding\"}[0.157, 0.577, 0.464, 0.642]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 374 }, { "id": "rZtSsY5hWFj7c8uQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Partnership\" bar}[0.157, 0.413, 0.626, 0.479] represent a higher or lower number of entrepreneurs compared to {the \"Franchising\" bar}[0.157, 0.495, 0.479, 0.560]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 374 }, { "id": "55cz2YWJEZIHtAZt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Government Programs\"}[0.157, 0.087, 0.948, 0.152] taller or shorter than {the bar for \"Bootstrapping\"}[0.157, 0.822, 0.230, 0.887]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 374 }, { "id": "jLA4UzOIlxiPDlkB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Bootstrapping\"}<[0.157, 0.822, 0.230, 0.887]> and {\"Venture Capital\"}<[0.157, 0.740, 0.376, 0.805]>, which entrepreneurial activity has a higher recorded number?" }, { "from": "gpt", "value": "Venture Capital." } ], "id_raw": 374 }, { "id": "arn0gMOdjBY948aX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.082, 0.848, 0.149, 0.947]." }, { "from": "gpt", "value": "This region contains the label \"Bootstrapping\", which is one of the entrepreneurial activities noted on the y-axis." } ], "id_raw": 374 }, { "id": "KaxqstarBn0Dlv8o", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.157, 0.822, 0.594, 0.887]." }, { "from": "gpt", "value": "This area contains one horizontal bar representing \"Bootstrapping\" entrepreneurial activity. The length of the bar indicates the number of entrepreneurs." } ], "id_raw": 374 }, { "id": "qLeZAy2NgHCsNk1k", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.157, 0.740, 0.376, 0.805]?" }, { "from": "gpt", "value": "There are 2 objects:\n1. A horizontal bar graph, whose length represents the number of \"Venture Capital\" entrepreneurs. Its coordinate range is [0.157, 0.740, 0.376, 0.805].\n2. A text label \"1500\" indicating the number of entrepreneurs. Its coordinate range is [0.384, 0.764, 0.413, 0.782]." } ], "id_raw": 374 }, { "id": "IO3tyIuQCSJYUdse", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nCan you explain what is in the region [0.155, 0.939, 0.164, 0.957]?" }, { "from": "gpt", "value": "Yes, it contains the label \"0\" on the x-axis of the chart, indicating the starting point of the numbered entrepreneurs." } ], "id_raw": 374 }, { "id": "q8mp3RUZ8ofbmNyz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.187, 0.909, 0.212, 0.931] in the chart?" }, { "from": "gpt", "value": "It is the x-axis label for \"Noon\"." } ], "id_raw": 375 }, { "id": "8bfdt3EtoZ6ljC1x", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first category label in the y-axis on the lower subplot?" }, { "from": "gpt", "value": "[0.243, 0.946, 0.283, 0.969]" } ], "id_raw": 375 }, { "id": "sryqib6GitrNuwKx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the bar label}[0.328, 0.897, 0.358, 0.919]?" }, { "from": "gpt", "value": "Afternoon." } ], "id_raw": 375 }, { "id": "9SkBFZzZwmnD0pcs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Morning\" in 'Friend_Loss_Ours'}[0.037, 0.848, 0.105, 0.903] taller than {the bar for \"Morning\" in 'Friend_Loss_Saliency'}[0.525, 0.794, 0.593, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 375 }, { "id": "J00cgZ863RMiSdf3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nBetween {the 'Friend_Made_Ours' bar during the Evening}[0.546, 0.098, 0.692, 0.415] and {the 'Friend_Loss_Ours' bar during the Evening}[0.292, 0.631, 0.360, 0.903], which is taller?" }, { "from": "gpt", "value": "Friend_Made_Ours." } ], "id_raw": 375 }, { "id": "zaeWnMKRzz4KqHgG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for \"Night\" representing 'Friends_Made_Ours'}[0.729, 0.336, 0.875, 0.415] have more counts than {the bar for \"Night\" in 'Friend_Loss_Ours'}[0.378, 0.816, 0.446, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 375 }, { "id": "qZadf0MzSM9iUL1p", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.071, 0.409, 0.887, 0.500]." }, { "from": "gpt", "value": "This region predominantly represents information for x-axis labels and their corresponding tick positions. Specific entries include \"Morning\", \"Noon\", \"Afternoon\", \"Evening\", and \"Night\" each labeled respectively beneath the tick marks, indicating various periods of a day." } ], "id_raw": 375 }, { "id": "EPHbbDojFUeNr27H", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nPlease detail the contents within [0.100, 0.152, 0.356, 0.498]." }, { "from": "gpt", "value": "This area includes both the y-axis of the upper subplot with labels such as \"60\" and \"80\" and part of the bars representing 'Friends Made Ours' and 'Friends Made Saliency' for \"Morning\" and \"Noon\", demonstrating a visual comparative analysis of social interaction metrics over these time periods." } ], "id_raw": 375 }, { "id": "5oUNf5XDySq42ERi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the area [0.443, 0.011, 0.640, 0.070]?" }, { "from": "gpt", "value": "This zone showcases the legend of the chart, which indicates colors and categories. It features two color-text labels: 'Ours' in a red tone and 'Saliency' in a blue tone, clarifying the grouping of the bar charts." } ], "id_raw": 375 }, { "id": "EruhYE6fiig4b4hx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the blue bar}<{Consumer Electronics bar}[0.112, 0.397, 0.139, 0.638]> in the chart representing?" }, { "from": "gpt", "value": "It represents the sales of Consumer Electronics in region 1." } ], "id_raw": 376 }, { "id": "cFaWQKVQqBYRSItM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {Apparel bar} at [0.202, 0.909, 0.228, 1.045]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 376 }, { "id": "ZmCqLa54ijVtL5BG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nWhich label corresponds to {the region bar} at [0.827, 0.563, 0.854, 1.045]?" }, { "from": "gpt", "value": "The ninth region in which apparel is sold." } ], "id_raw": 376 }, { "id": "v3GtSJeOHHGp7WqP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nWhich had more sales in region 5, {Consumer Electronics}[0.470, 0.102, 0.497, 0.638] or {Apparel}[0.470, 0.724, 0.497, 1.045]?" }, { "from": "gpt", "value": "Apparel." } ], "id_raw": 376 }, { "id": "rIntUSAB6JJRA9p3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nIn region 10, did {Consumers Electronics}[0.917, 0.182, 0.943, 0.638] sales surpass {Apparel}[0.917, 0.563, 0.943, 1.045] sales?" }, { "from": "gpt", "value": "No." } ], "id_raw": 376 }, { "id": "DdArlT2FTtMyuLAL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nIs {Consumer Electronics sales in region 3}[0.291, 0.316, 0.318, 0.638] better than {in region 2}[0.202, 0.410, 0.228, 0.638]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 376 }, { "id": "TfxvqLnpp6XjvVDR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the region [0.559, 0.692, 0.586, 1.045] on the y-axis?" }, { "from": "gpt", "value": "It's a blue bar representing the sales of Apparel in region 6. The value of sales is 220." } ], "id_raw": 376 }, { "id": "4GR5yTsoUfXkaoLc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart display in the region [0.202, 0.909, 0.228, 1.045]?" }, { "from": "gpt", "value": "This region shows a blue bar for the sales of Apparel in region 2. The sales value is 85." } ], "id_raw": 376 }, { "id": "oNsJSQhH14j4uLnW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region [0.917, 0.563, 0.943, 1.045]?" }, { "from": "gpt", "value": "In this region, there is a bar representing the sales of Apparel in the region 10. The bar reaches a sales value of 300." } ], "id_raw": 376 }, { "id": "RtcilIim2cZ5K7qe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the x-axis label in the left subplot?" }, { "from": "gpt", "value": "Cultural Heritage." } ], "id_raw": 377 }, { "id": "sLljQ4OovZUnopIE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nWhat does {the tick mark label}[0.165, 0.919, 0.168, 0.942] on the x-axis of the right subplot represent?" }, { "from": "gpt", "value": "0." } ], "id_raw": 377 }, { "id": "0OFTZAx7xtA4HGa7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}[0.721, 0.508, 0.755, 0.567] aligned with on the y-axis?" }, { "from": "gpt", "value": "India." } ], "id_raw": 377 }, { "id": "07kqwdoEThwTbZQr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for India}[0.165, 0.508, 0.288, 0.567] in Cultural Heritage have a greater value than {the bar for India}[0.721, 0.508, 0.755, 0.567] in Cultural Integration?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 377 }, { "id": "VKVDFc0wKhWND1Rs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nAre {the bar for Greece}[0.165, 0.730, 0.339, 0.789] in the left subplot and {the bar for China}[0.721, 0.582, 0.776, 0.641] in the right subplot representing positive values?" }, { "from": "gpt", "value": "No." } ], "id_raw": 377 }, { "id": "VLlrHGhSwEpC8WLg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nWhich bar has a higher value, {the Russia bar in Cultural Heritage}[0.146, 0.139, 0.165, 0.198] or {the USA bar in Cultural Integration}[0.721, 0.065, 0.732, 0.124]?" }, { "from": "gpt", "value": "Russia." } ], "id_raw": 377 }, { "id": "HGMXgmNVCw8062QI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within {the bottom right corner of the left subplot}[0.395, 0.864, 0.495, 0.903]." }, { "from": "gpt", "value": "Part of the y-axis labels for 'United Kingdom' and 'USA', and a segment of the gray color of the subplot background, which has the coordinates of [0.482, 0.864, 0.495, 0.903]." } ], "id_raw": 377 }, { "id": "JZMSoliRwIjUxTn6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained within the region designed for the x-axis labels in both subplots?" }, { "from": "gpt", "value": "The region contains the x-axis labels for both Cultural Heritage and Cultural Integration. In Cultural Heritage, it shows labels such as '\u221220', '0', '20', '40', '60', '80', '100', '120'. In Cultural Integration, labels include '\u221260', '\u221240', '\u221220', '0', '20', '40', '60', '80', '100', '120'." } ], "id_raw": 377 }, { "id": "G1bkUk4tnf50Xp48", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nWhat details are present in the area near the y-axis of the right subplot?" }, { "from": "gpt", "value": "The area contains the y-axis label 'Country' and a list of country names used as y-tick labels, such as 'Rome', 'Greece', 'Egypt', 'China', 'India', 'France', 'United Kingdom', 'Spain', 'Turkey', 'Russia', and 'USA'; as well as the corresponding y-tick lines intersecting the axis." } ], "id_raw": 377 }, { "id": "LF5iBrgMRqqOWHNB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the label associated with \"Ours\" in the legend?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 379 }, { "id": "QcCcpGdwPmx06JVE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label in the subplot titled \"University Programs\"?" }, { "from": "gpt", "value": "{Rate of Participation Change [%]}[0.024, 0.066, 0.038, 0.445]." } ], "id_raw": 379 }, { "id": "LAUFD0OJGqNKzwkm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the tick at the coordinate [0.187, 0.448] on the x-axis represent in the \"University Programs\" subplot?" }, { "from": "gpt", "value": "{Ours}[0.171, 0.464, 0.203, 0.487]." } ], "id_raw": 379 }, { "id": "RprRsX4a3CiSByBn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Ours\"}[0.105, 0.088, 0.269, 0.319] higher than {the bar for \"Trend-based Sampling\"}[0.310, 0.101, 0.474, 0.319] in the \"University Programs\" subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 379 }, { "id": "bOrBMCn9RrdwmNAY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nWhich has a more negative participation change rate, {the bar for \"Ours\"}[0.597, 0.319, 0.762, 0.371] or {the bar for \"Trend-based Sampling\"}[0.803, 0.319, 0.967, 0.397] in \"Adult Education\"?" }, { "from": "gpt", "value": "{Trend-based Sampling}[0.803, 0.319, 0.967, 0.397]." } ], "id_raw": 379 }, { "id": "nRBfpTUvcoOFxyf8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nDo {the bars representing \"Ours\"}[0.597, 0.704, 0.762, 0.807] have a lower maximum value compared to {the bars representing \"Trend-based Sampling\"}[0.803, 0.717, 0.967, 0.807] in the \"Workplace Trainings\" subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 379 }, { "id": "IfdSf6jOo11x8VW8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the chart area [0.087, 0.062, 0.492, 0.448]?" }, { "from": "gpt", "value": "The subplot \"University Programs\" showing two bars for \"Ours\" and \"Trend-based Sampling\" with their respective participation rates charted from 0.00 to 1.00 and the Y-axis labeled as \"Rate of Participation Change [%]\"." } ], "id_raw": 379 }, { "id": "iUrrsr0M6J9oykT6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.579, 0.062, 0.985, 0.448]." }, { "from": "gpt", "value": "The region contains the \"Adult Education\" subplot including bars representing \"Ours\" and \"Trend-based Sampling\", their corresponding values indicated on the y-axis ranging from -0.6 to 0.0, and X-axis labels accurately describing each category." } ], "id_raw": 379 }, { "id": "8WlYN9HXvfeqZnOq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nExplain the objects and attributes found within the bounding box [0.087, 0.550, 0.492, 0.935]." }, { "from": "gpt", "value": "This segment holds the \"Online Certificate Programs\" subplot, featuring negative to positive participation change rates on the Y-axis, and two bars representing \"Ours\" and \"Trend-based Sampling\" spanning notable numeric ranges on the X-axis." } ], "id_raw": 379 }, { "id": "3OlPzh59fZdxN9aa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_6.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, the {Startup bar for VFX}[0.805, 0.544, 0.944, 0.624] or the {Funded startup bar for VFX}[0.805, 0.544, 0.944, 0.783]?" }, { "from": "gpt", "value": "Funded Startup bar." } ], "id_raw": 381 }, { "id": "ZFN7behd5ZcAhy23", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Startup bar for Technology}[0.115, 0.304, 0.253, 0.544] of a greater magnitude than the {Funded Startup bar for 3D Modelling}[0.460, 0.544, 0.598, 0.863]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 381 }, { "id": "s7TgFYAKPAO2DAPz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_6.png", "conversations": [ { "from": "human", "value": "\nHow is the growth of {Funded Startups in the domain of Social Media}[0.633, 0.384, 0.771, 0.544] compared to the growth of {Startups in the domain of Social Media}[0.633, 0.225, 0.771, 0.544]?" }, { "from": "gpt", "value": "They have grown the same." } ], "id_raw": 381 }, { "id": "lDQYpQTCRZCIUisk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the text content in the bounding box [0.522, 0.952, 0.551, 0.975]?" }, { "from": "gpt", "value": "The content is Year." } ], "id_raw": 384 }, { "id": "gzlrpr1FtobYEty7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nIn the area defined by [0.043, 0.184, 0.078, 0.207], what information does it contain?" }, { "from": "gpt", "value": "It has a y-axis label showing a value of 6000." } ], "id_raw": 384 }, { "id": "xa9INYhhVZw0g9V8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}[0.129, 0.209, 0.180, 0.415] showing?" }, { "from": "gpt", "value": "It indicates the trade volume of 1990." } ], "id_raw": 384 }, { "id": "t2Bp8ylOHW6ZgB8B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nIs {2015 bar}[0.766, 0.544, 0.817, 0.903] higher than {2005 bar}[0.511, 0.586, 0.562, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 384 }, { "id": "S0Of469Pd6bGYy6E", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nAmong these bars, {1990 bar}[0.129, 0.649, 0.180, 0.903] and {2020 bar}[0.893, 0.531, 0.944, 0.903], which one is taller?" }, { "from": "gpt", "value": "The 2020 bar." } ], "id_raw": 384 }, { "id": "YLk8iLAjPxahQy3P", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nIs the Globalization Index for {2000}[0.384, 0.607, 0.435, 0.903] higher than the index for {1995}[0.256, 0.628, 0.307, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 384 }, { "id": "LxECHMzcFiz1Wxod", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.088, 0.512, 0.985, 0.903]?" }, { "from": "gpt", "value": "This region represents a bar chart showing the Globalization Index from 1990 to 2020. It displays individual bars per year with different heights to represent the index values. The y-axis contains incremental labels to assist in the visualization of index changes." } ], "id_raw": 384 }, { "id": "OCqOqyv1rTJbOAjk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.088, 0.025, 0.985, 0.415] encompass?" }, { "from": "gpt", "value": "This area contains another bar chart that illustrates the Trade Volume in billions USD from 1990 to 2020. Each year has a corresponding bar and the heights of these bars indicate the volume of trade." } ], "id_raw": 384 }, { "id": "u1TVWxSQoEHTBZzT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in the area defined by coordinates [0.522, 0.464, 0.551, 0.488]?" }, { "from": "gpt", "value": "The label \"Year\" is placed in this region that marks the x-axis for the lower subplot representing the Trade Volume." } ], "id_raw": 384 }, { "id": "zsCDQqeLqyRRsjTi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary color indicated for the \"Accelerated_gradient\" group in the legend?" }, { "from": "gpt", "value": "Blue, identified within {the legend handle}[0.514, 0.028, 0.542, 0.044]." } ], "id_raw": 385 }, { "id": "L2Yq3lo3wxG2sw4Q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color noted for \"Random_systematic\" in the chart's legend?" }, { "from": "gpt", "value": "Red, visible in {the legend handle}[0.299, -0.007, 0.327, 0.009]." } ], "id_raw": 385 }, { "id": "zw5FaE4zqMPcaBOB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nWhere is the x-axis label located?" }, { "from": "gpt", "value": "The x-axis label \"Health & Medical Parameters\" is located at {the x-axis label area}[0.425, 0.464, 0.628, 0.488]." } ], "id_raw": 385 }, { "id": "6echJ1DtZVCQHnI4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"health_factors\" under \"Random_systematic\"}[0.022, 0.117, 0.074, 0.125] higher than {its counterpart under \"Accelerated_gradient\"}[0.074, 0.050, 0.125, 0.117]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 385 }, { "id": "WzTyOilVIu6Fg5YB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the bar for \"patient_noshow_rate\" under \"Random_systematic\"}[0.669, 0.117, 0.720, 0.130] or {under \"Accelerated_gradient\"}[0.720, 0.043, 0.772, 0.117]?" }, { "from": "gpt", "value": "Accelerated_gradient." } ], "id_raw": 385 }, { "id": "MgN1QkJeyHFQnumO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for \"lab_test_intervals\" under \"Random_systematic\"}[0.410, 0.117, 0.462, 0.173] represent a positive or negative trend compared to {the same factor under \"Accelerated_gradient\"}[0.462, 0.097, 0.513, 0.117]?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 385 }, { "id": "Z0VHwLgO5ecFu2me", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the coordinates [0.074, 0.026, 0.224, 0.226]." }, { "from": "gpt", "value": "This region includes the y-axis with tick labels ranging from \u221230 to 20 and corresponding bars for \"health_factors\" from both groups. Specifically, the \"Random_systematic\" bar is from [0.022, 0.117, 0.074, 0.125] and the \"Accelerated_gradient\" bar is from [0.074, 0.050, 0.125, 0.117]." } ], "id_raw": 385 }, { "id": "J8Qa25sD9Yn2TCuw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area spanning [0.420, 0.090, 0.635, 0.230]?" }, { "from": "gpt", "value": "It encompasses the positive and negative trend bars for \"medication_quantity\" and \"appointment_frequency\" for both groups. {Bars in the area}[0.539, 0.117, 0.591, 0.135], [0.281, 0.087, 0.332, 0.117], [0.591, 0.077, 0.643, 0.117], [0.332, 0.117, 0.384, 0.154] reflect these trends." } ], "id_raw": 385 }, { "id": "ExChXw0mEVRGzsaX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the boundary [0.160, 0.100, 0.350, 0.245]?" }, { "from": "gpt", "value": "This area marks the beginning of the x-axis with labels and bars for \"health_factors\" and \"medical_bill\" from both trend groups. Notable elements include {bar for \"health_factors\" under \"Random_systematic\"}[0.022, 0.117, 0.074, 0.125] and {bar for \"medical_bill\" under \"Accelerated_gradient\"}[0.203, 0.082, 0.255, 0.117]." } ], "id_raw": 385 }, { "id": "pmWJsDPnJt10RRqB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the item described at coordinate range [0.503, 0.001, 0.536, 0.024]?" }, { "from": "gpt", "value": "It is the label for the color representing \"Ours\" in the legend." } ], "id_raw": 386 }, { "id": "fQ3BH63fUhwPdy8N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nWhat type of data label can you find at [0.297, 0.269, 0.357, 0.292]?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 386 }, { "id": "uEQAQ5KZzItJKdAY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nWhich axis label is located in the area [0.489, 0.302, 0.578, 0.325]?" }, { "from": "gpt", "value": "X-axis label, called Engagement." } ], "id_raw": 386 }, { "id": "wQFJ2J9cIbRNuIGl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"YouTube\"}[0.485, 0.214, 0.582, 0.229] higher or lower than {the bar representing \"LinkedIn\"}[0.606, 0.229, 0.703, 0.243]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 386 }, { "id": "2HCtFx5IbYzsf8LL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nAre {ytick at '0'}[0.064, 0.219, 0.073, 0.243] higher or lower on the chart than {ytick at '700'}[0.046, 0.049, 0.073, 0.073]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 386 }, { "id": "0jj6XLG3gq2icmw5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nWhich color represents {the bar for \"Snapchat\"}[0.727, 0.432, 0.823, 0.554] from the legend?" }, { "from": "gpt", "value": "#ee8882." } ], "id_raw": 386 }, { "id": "8Zl0NrSkuS2sM6E7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.082, 0.034, 0.985, 0.253]." }, { "from": "gpt", "value": "This region holds the bar chart for \"Engagement\", including bars for social media platforms like Facebook, Instagram, and Twitter. Y-axis is labeled as \"Interaction rate [millions/month]\", and specific bars include {Facebook engagement bar}[0.123, 0.229, 0.220, 0.233], and {Instagram engagement bar}[0.244, 0.204, 0.341, 0.229]." } ], "id_raw": 386 }, { "id": "I4xRMbLk70vELRIh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nDescribe what exists within the coordinates [0.015, 0.280, 0.029, 0.657]." }, { "from": "gpt", "value": "Within this area, you can find the Y-axis for the subplot showing our data with labels spread from '\u2212100' to '800'. Each label depicts the interaction rate per million per month." } ], "id_raw": 386 }, { "id": "5bSpGLp8yBMGdJkj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the space [0.082, 0.684, 0.985, 0.903]?" }, { "from": "gpt", "value": "This space contains a bar chart showing \"Saliency-based Sampling\" for different social media platforms. It includes bars and y-ticks that show values ranging from '\u2212100' to '800'. Notable are the {Facebook saliency bar}[0.123, 0.794, 0.220, 0.879] and the {LinkedIn saliency bar}[0.606, 0.842, 0.703, 0.879]." } ], "id_raw": 386 }, { "id": "akMRLLYf7GGUELok", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat's {the category name} near the coordinate [0.053, 0.843, 0.150, 0.866]?" }, { "from": "gpt", "value": "Slow recovery." } ], "id_raw": 390 }, { "id": "wVCYNuAM4u8QOoc6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {the color orange} represent in the chart according to the legend at [0.547, 0.029, 0.649, 0.052]?" }, { "from": "gpt", "value": "French Cuisine." } ], "id_raw": 390 }, { "id": "HSTGWmcDE7bQuoV1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat value in percentage is represented by the bar at [0.415, 0.092, 0.568, 0.178]?" }, { "from": "gpt", "value": "36.0%." } ], "id_raw": 390 }, { "id": "umAUb9rZvG002FBF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat type of trend does the label at these coordinates [0.371, 0.029, 0.481, 0.052] refer to?" }, { "from": "gpt", "value": "Chinese Cuisine." } ], "id_raw": 390 }, { "id": "q4SlpUSMnBy5dDWM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nIn terms of values, is the bar of {Chinese Cuisine}[0.457, 0.236, 0.649, 0.322] higher or lower than that of {French Cuisine}[0.457, 0.809, 0.712, 0.895]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 390 }, { "id": "wEgrZa8L6YnH1ymU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nIs the category bar of {Inexorable decline}(for French Cuisine) at [0.564, 0.666, 0.946, 0.752] wider than {Slow recovery}(for Chinese Cuisine) at [0.160, 0.809, 0.457, 0.895]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 390 }, { "id": "uKz8jYoLKsPMDoy3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nAmong the bars of {Chinese Cuisine} and {French Cuisine}, which one reaches the 100 mark on the x-axis?" }, { "from": "gpt", "value": "Chinese Cuisine." } ], "id_raw": 390 }, { "id": "Dsn1xhx02kzl2YuM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nIs the {French Cuisine} bar higher for the {Slow recovery} category[0.457, 0.809, 0.712, 0.895] or the {Inexorable decline} category[0.564, 0.666, 0.946, 0.752]?" }, { "from": "gpt", "value": "Inexorable decline." } ], "id_raw": 390 }, { "id": "gRnfsTeUnke6MMMh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is inside the region from [0.160, 0.092, 0.569, 0.178]?" }, { "from": "gpt", "value": "Inside the region, there are two bars. The bar on the left from [0.160, 0.092, 0.415, 0.178] is colored blue, representing the percentage of \"Chinese Cuisine\" that have \"Accelerated growth\", which is 60%. The bar on the right from [0.415, 0.092, 0.568, 0.178] is colored orange, representing the percentage of \"French Cuisine\" that have \"Accelerated growth\", which is 36%." } ], "id_raw": 390 }, { "id": "LCcdZKU4OGamDyE6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nCould you describe the elements within the region from [0.326, 0.019, 0.654, 0.061]?" }, { "from": "gpt", "value": "The region contains a legend that contrasts two categories: \"Chinese Cuisine\" at [0.371, 0.029, 0.481, 0.052] and \"French Cuisine\" at [0.547, 0.029, 0.649, 0.052]. Their respective colors are depicted with a square at [0.332, 0.031, 0.359, 0.047] for \"Chinese Cuisine\" and [0.509, 0.031, 0.536, 0.047] for \"French Cuisine\"." } ], "id_raw": 390 }, { "id": "zwrLp573yOSLwDGL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat details can be specified about the region [0.160, 0.522, 0.861, 0.608]?" }, { "from": "gpt", "value": "Within the region, there are two bars signifying the \"Sustained decline\" trend, with the first bar at [0.160, 0.522, 0.521, 0.608] representing the \"Chinese Cuisine\" trend, denoting a value of 85%, and the second bar at [0.521, 0.522, 0.861, 0.608] denoting the \"French Cuisine\" trend, with a value of 80%." } ], "id_raw": 390 }, { "id": "O7utwVbBaozjaZsX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the area within [0.236, 0.952, 0.254, 0.975]?" }, { "from": "gpt", "value": "In that specified region, there is a label for the tick mark on the x-axis. It is labeled as '20', serving as a scale for the bars representing the trends in Chinese and French Cuisines." } ], "id_raw": 390 }, { "id": "w7115KdmjHxmKG4C", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat object can be found at the coordinates [0.397, 0.809, 0.705, 0.895]?" }, { "from": "gpt", "value": "That object is bar of the first group representing a Win for \"Road Construction vs. Waste Management\"." } ], "id_raw": 391 }, { "id": "s1s4ziBsRCvO6lbv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the text within the coordinates [0.529, 0.841, 0.573, 0.864]?" }, { "from": "gpt", "value": "The label says \"55.0%\" throughput." } ], "id_raw": 391 }, { "id": "RqTskvEA0rFkAOm7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat content can be seen in {the subplot_0 axes}[0.397, 0.052, 0.985, 0.935]?" }, { "from": "gpt", "value": "This is the main chart space that contains horizontal and vertical axes and bars representing the data." } ], "id_raw": 391 }, { "id": "6SSPIBCwYLKShNLQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Win' bar for 'Education Infrastructure vs. Healthcare Infrastructure'}[0.397, 0.092, 0.509, 0.178] longer than {the 'Loss' bar for the same category}[0.845, 0.092, 0.957, 0.178]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 391 }, { "id": "j7DTM2QGUpoDZwDU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat color represents 'Tie' according to the legend {the legend color label of 'Tie'}[0.601, 0.031, 0.628, 0.047]?" }, { "from": "gpt", "value": "Greenish hue, hex code #b7da91." } ], "id_raw": 391 }, { "id": "IlTBgRMgqXlpzrnI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nAre {the 'Win' bars}[0.397, 0.809, 0.705, 0.895]; [0.397, 0.666, 0.565, 0.752]; [0.397, 0.522, 0.649, 0.608]; [0.397, 0.379, 0.593, 0.465]; [0.397, 0.236, 0.705, 0.322]; [0.397, 0.092, 0.509, 0.178] generally longer than {the 'Loss' bars}[0.929, 0.809, 0.957, 0.895]; [0.929, 0.666, 0.957, 0.752]; [0.929, 0.522, 0.957, 0.608]; [0.901, 0.379, 0.957, 0.465]; [0.929, 0.236, 0.957, 0.322]; [0.845, 0.092, 0.957, 0.178]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 391 }, { "id": "uOUxK0wP4u3C5H9N", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents inside the region [0.502, 0.019, 0.763, 0.061]." }, { "from": "gpt", "value": "This area covers the chart legend. There are 3 legend entries which represent 3 different colors for \"Win\", \"Tie\", and \"Loss\". The color and text labels for \"Win\" are [0.508, 0.031, 0.536, 0.047] and [0.547, 0.029, 0.573, 0.052] respectively. The color and text labels for \"Tie\" are [0.601, 0.031, 0.628, 0.047] and [0.640, 0.029, 0.660, 0.052] respectively. The color and text labels for \"Loss\" are [0.688, 0.031, 0.716, 0.047] and [0.727, 0.029, 0.757, 0.052] respectively." } ], "id_raw": 391 }, { "id": "pWrWQ5rZ3E41XfzG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.397, 0.522, 0.649, 0.608]?" }, { "from": "gpt", "value": "In this area, there is a blue bar representing a \"Win\" for \"Healthcare Infrastructure vs. Road Construction\". The label \"45.0%\" can be found within [0.501, 0.554, 0.545, 0.577]." } ], "id_raw": 391 }, { "id": "mYWEj1lScdzAW5Im", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the sector [0.397, 0.236, 0.705, 0.322]?" }, { "from": "gpt", "value": "There is a blue bar corresponding to the \"Win\" category for \"Education Infrastructure vs. Road Construction\". The associated label \"55.0%\" can be found at coordinates [0.529, 0.267, 0.573, 0.290]. There is also a green bar representing a \"Tie\" that extends to [0.705, 0.236, 0.929, 0.322] with its associated label \"40.0%\" at [0.795, 0.267, 0.839, 0.290]." } ], "id_raw": 391 }, { "id": "Y82C2Kxz3lnVnn41", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.168, 0.064, 0.323, 0.135] in the chart?" }, { "from": "gpt", "value": "It's a blue bar representing the percentage of wins during the Stone Age." } ], "id_raw": 393 }, { "id": "6IVHDVZmK1oHR8AL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text label located at [0.302, 0.797, 0.345, 0.820]?" }, { "from": "gpt", "value": "It's the text \"40.0%\" associated with the blue bar for the Industrial Revolution." } ], "id_raw": 393 }, { "id": "HWHww8EesKbCrord", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the data represented by [0.632, 0.949, 0.660, 0.965]?" }, { "from": "gpt", "value": "It is the color label (red) in the chart legend, corresponding to the \"Loss\" category." } ], "id_raw": 393 }, { "id": "cM9lsA0zRtZCRc0W", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nCompare the heights of {the blue bar for the Renaissance era}[0.168, 0.655, 0.401, 0.726] and {the blue bar for the Stone Age}[0.168, 0.064, 0.323, 0.135], which one is taller?" }, { "from": "gpt", "value": "The Renaissance era bar." } ], "id_raw": 393 }, { "id": "FCoofuemeANFwb4U", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar for the Industrial Revolution}[0.168, 0.773, 0.479, 0.844] shorter than {the green bar for the same era}[0.479, 0.773, 0.790, 0.844]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 393 }, { "id": "Q33mRLYfzFjNkwAT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nAmong these two bars {the one for Stone Age wins}[0.168, 0.064, 0.323, 0.135] and {the one for Stone Age ties}[0.323, 0.064, 0.713, 0.135], which one is wider?" }, { "from": "gpt", "value": "The one for Stone Age ties." } ], "id_raw": 393 }, { "id": "TVUp5hTQEFSXYfPz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within the area [0.446, 0.938, 0.706, 0.979]?" }, { "from": "gpt", "value": "In this area, there is a legend with three elements: a blue square labeled \"Win\" located at {Win label}[0.491, 0.947, 0.517, 0.970], a green square labeled \"Tie\" located at {Tie label}[0.583, 0.947, 0.604, 0.970], and a red square labeled \"Loss\" located at {Loss label}[0.671, 0.947, 0.701, 0.970]." } ], "id_raw": 393 }, { "id": "bXL0iS2upV9TrMn7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nTell me about the region [0.168, 0.773, 0.479, 0.844]." }, { "from": "gpt", "value": "This region harbors the blue bar representing the percentage of wins during the Industrial Revolution, with the associated text \"40.0%\" located at [0.302, 0.797, 0.345, 0.820]." } ], "id_raw": 393 }, { "id": "XDlw8kboEBhQrEtu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the data within the boundaries [0.168, 0.064, 0.946, 0.135]." }, { "from": "gpt", "value": "Within these boundaries are three bars representing the outcomes of the Stone Age, namely, a blue bar for wins, a green bar for ties, and a red bar for losses. Associated percentages can be found on top of each bar." } ], "id_raw": 393 }, { "id": "6uxIhlfYZYiEBi9L", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinates [0.285, 0.025, 0.863, 0.058] indicate in this chart?" }, { "from": "gpt", "value": "The text at this position is the subtitle that reads \"Efficiency Improvement Potential Assessment\"." } ], "id_raw": 394 }, { "id": "YNX5lx1ik0lux55x", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the bar}[0.511, 0.277, 0.762, 0.380] representing Resource Utilisation's Actual Improvement rating?" }, { "from": "gpt", "value": "The bar is colored '#b7da91'." } ], "id_raw": 394 }, { "id": "LHvj6Owxc9QcjdWV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nWhich label does the ytick at [0.032, 0.835, 0.154, 0.859] refer to?" }, { "from": "gpt", "value": "The ytick at this coordinate refers to the label 'Overall Efficiency'." } ], "id_raw": 394 }, { "id": "ILjtQzhGIXMcQcAS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the category of the bar located at [0.556, 0.105, 0.834, 0.208]?" }, { "from": "gpt", "value": "The category of this bar is 'Energy Efficiency'." } ], "id_raw": 394 }, { "id": "qX0KFm2X3LIAhqJy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Overall Efficiency\"}[0.598, 0.793, 0.921, 0.896] longer or shorter than {the bar representing \"Waste Management\"}[0.640, 0.449, 0.946, 0.552]?" }, { "from": "gpt", "value": "Longer." } ], "id_raw": 394 }, { "id": "I4aSyKYiJ17qTuzL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Potential Improvement\"}[0.163, 0.105, 0.556, 0.208] for the category 'Energy Efficiency' wider than {the bar representing \"Actual Improvement\"}[0.556, 0.105, 0.834, 0.208]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 394 }, { "id": "iygQHeuEpXdN2Sgm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nLooking at {the bar for Process Automation Actual Improvement}[0.609, 0.621, 0.943, 0.724] and {the bar for Energy Efficiency Actual Improvement}[0.556, 0.105, 0.834, 0.208], which one has a greater value?" }, { "from": "gpt", "value": "Process Automation." } ], "id_raw": 394 }, { "id": "EeBuHHbUtovGp3nl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Potential Improvement for Energy Efficiency}[0.163, 0.105, 0.556, 0.208] better than {Potential Improvement for Resource Utilisation}[0.163, 0.277, 0.511, 0.380]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 394 }, { "id": "nxjjdMVf9aJwcT3J", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the rectangle [0.016, 0.663, 0.154, 0.687]." }, { "from": "gpt", "value": "This area contains the label for 'Process Automation' on the y-axis." } ], "id_raw": 394 }, { "id": "WbVYSAznfgopt4kD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the box [0.366, 0.034, 0.783, 0.075]." }, { "from": "gpt", "value": "This area contains the chart legend. It has two items: {Potential Improvement}[0.410, 0.043, 0.569, 0.066] with color '#9cc8e4' at [0.371, 0.045, 0.399, 0.061] and {Actual Improvement}[0.636, 0.043, 0.777, 0.066] with color '#b7da91' at [0.597, 0.045, 0.625, 0.061]." } ], "id_raw": 394 }, { "id": "OWoNz7FcVRZb3x2a", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the [0.707, 0.952, 0.733, 0.975] coordinates?" }, { "from": "gpt", "value": "This area contains the \"100\" x-axis label." } ], "id_raw": 394 }, { "id": "OPeqJHCiXLZcHq7s", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the region between [0.163, 0.793, 0.598, 0.896]." }, { "from": "gpt", "value": "This region contains a bar representing the Potential Improvement score for Overall Efficiency, which is 78%." } ], "id_raw": 394 }, { "id": "YuLsHi61MYhakSyz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nWhich industry is represented by the x-label at [0.044, 0.703, 0.099, 0.726]?" }, { "from": "gpt", "value": "Education." } ], "id_raw": 395 }, { "id": "407YgLcYHcfpKnp4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage shown at [0.537, 0.841, 0.581, 0.864]?" }, { "from": "gpt", "value": "35.5%." } ], "id_raw": 395 }, { "id": "uXeLnlRsNsTzHWot", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat color is indicated by the legend item at [0.484, 0.031, 0.512, 0.047]?" }, { "from": "gpt", "value": "Green as per {legend item}[0.484, 0.031, 0.512, 0.047]." } ], "id_raw": 395 }, { "id": "HZutiYHeD41Ob5n2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nIs {the green bar for Technology}[0.642, 0.236, 0.943, 0.322] longer than {the green bar for Healthcare}[0.604, 0.092, 0.930, 0.178]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 395 }, { "id": "KBO84SQa3OirhCFR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar for Finance}[0.102, 0.379, 0.385, 0.465] taller than {the blue bar for Retail}[0.102, 0.522, 0.479, 0.608]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 395 }, { "id": "4oUd102jCdrskR6L", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nBetween {Non-Profit}[0.024, 0.843, 0.093, 0.866] and {Education}[0.024, 0.700, 0.093, 0.723], which industry has a higher value for Entrepreneurship<{Entrepreneurship Non-Profit bar}[0.447, 0.809, 0.670, 0.895],{Entrepreneurship Education bar}[0.542, 0.666, 0.701, 0.752]>?" }, { "from": "gpt", "value": "Non-Profit." } ], "id_raw": 395 }, { "id": "tN8lXDFv9o69mBsc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.102, 0.052, 0.985, 0.935]." }, { "from": "gpt", "value": "It's a bar chart showing two sets of data per industry: Entrepreneurial Spirit represented by {blue bars}[0.102, 0.809, 0.447, 0.895], [0.102, 0.666, 0.542, 0.752], [0.102, 0.522, 0.479, 0.608], [0.102, 0.379, 0.385, 0.465], [0.102, 0.236, 0.642, 0.322], [0.102, 0.092, 0.604, 0.178] and Entrepreneurship represented by {green bars}[0.447, 0.809, 0.670, 0.895], [0.542, 0.666, 0.701, 0.752], [0.479, 0.522, 0.582, 0.608], [0.385, 0.379, 0.501, 0.465], [0.642, 0.236, 0.943, 0.322], [0.604, 0.092, 0.930, 0.178]. Each bar pair represents a specific industry which is enumerated on the y-axis [0.024, 0.843, 0.093, 0.866], [0.024, 0.700, 0.093, 0.723], [0.053, 0.556, 0.093, 0.580], [0.040, 0.413, 0.093, 0.436], [0.015, 0.269, 0.093, 0.293], [0.017, 0.126, 0.093, 0.149]]." } ], "id_raw": 395 }, { "id": "h0Qsg6w3MP5Ta3Vh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.263, 0.019, 0.648, 0.061]." }, { "from": "gpt", "value": "This area features the legend of the bar chart with two items. Labels indicate that the color blue at [0.268, 0.031, 0.296, 0.047] represents Entrepreneurial Spirit while the color green at [0.484, 0.031, 0.512, 0.047] represents Entrepreneurship." } ], "id_raw": 395 }, { "id": "Sf7DiXMWKeqfQr4x", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.447, 0.809, 0.670, 0.895]?" }, { "from": "gpt", "value": "There is 1 object: The green column representing the value of the \"Entrepreneurship\" category in Non-Profit, which is 35.5%, has a coordinate range of [0.447, 0.809, 0.670, 0.895]." } ], "id_raw": 395 }, { "id": "gpc3XxcIUm8wJW9a", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.102, 0.052, 0.604, 0.178]?" }, { "from": "gpt", "value": "It contains the bar data for Healthcare. There are two bars - a blue bar with a value of 80.0% for Entrepreneurial Spirit and a green bar with a value of 52.0% for Entrepreneurship. Their respective coordinates are {blue bar}[0.102, 0.092, 0.604, 0.178] and {green bar}[0.604, 0.092, 0.930, 0.178]." } ], "id_raw": 395 }, { "id": "2I5AELiknSNvLCVz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nWhat color does the label at [0.597, 0.031, 0.625, 0.047] represent?" }, { "from": "gpt", "value": "The color red." } ], "id_raw": 396 }, { "id": "KvOJXIGZdEh7nQ5O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the object found at [0.395, 0.379, 0.683, 0.465]?" }, { "from": "gpt", "value": "A green bar representing Environment's Volunteer Hours." } ], "id_raw": 396 }, { "id": "elmw11KMOjFYVjyO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nWhich grant has its value represented in [0.203, 0.809, 0.299, 0.895]?" }, { "from": "gpt", "value": "Grants provided for Religion." } ], "id_raw": 396 }, { "id": "p8k9whddPP8aGFiS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the meaning of the text at [0.732, 0.841, 0.763, 0.864]?" }, { "from": "gpt", "value": "It indicates that the Communities served in the field of Religion is 30." } ], "id_raw": 396 }, { "id": "sW6eOXF3tvEiFlvc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nIs {the green bar}<{Health's Volunteer Hours bar}[0.331, 0.236, 0.555, 0.322]> larger than {the red bar}<{Communities Served in Health field bar}[0.555, 0.236, 0.844, 0.322]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 396 }, { "id": "A1EUw0LksJmP6CWs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nAre {Volunteer Hours}<{Volunteer Hours bars}[[0.299, 0.809, 0.651, 0.895],[0.382, 0.666, 0.709, 0.752],[0.472, 0.522, 0.780, 0.608],[0.395, 0.379, 0.683, 0.465],[0.331, 0.236, 0.555, 0.322],[0.363, 0.092, 0.716, 0.178]]> generally greater than {Grants Provided}<{Grants Provided bars}[[0.203, 0.809, 0.299, 0.895],[0.203, 0.666, 0.382, 0.752],[0.203, 0.522, 0.472, 0.608],[0.203, 0.379, 0.395, 0.465],[0.203, 0.236, 0.331, 0.322],[0.203, 0.092, 0.363, 0.178]]>?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 396 }, { "id": "DxSBHMIIPR2DIGsf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nBetween {Religion}<{Grants for Religion bar}[0.203, 0.809, 0.299, 0.895]> and {Health}<{Grants for Health bar}[0.203, 0.236, 0.331, 0.322]>, which sector receives more grants?" }, { "from": "gpt", "value": "Religion." } ], "id_raw": 396 }, { "id": "7UOSdtaBjYqMliov", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Communities Served in Arts bar}<[0.709, 0.666, 0.844, 0.752]> greater than the {Grants provided for Arts bar}<[0.203, 0.666, 0.382, 0.752]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 396 }, { "id": "HjBAWNAooiJk8yGd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the area [0.203, 0.092, 0.844, 0.178]?" }, { "from": "gpt", "value": "The area contains three bars: a blue bar representing Grants Provided in Education at [0.203, 0.092, 0.363, 0.178], a green bar representing Volunteer Hours in Education at [0.363, 0.092, 0.716, 0.178], and a red bar representing Communities Served in Education at [0.716, 0.092, 0.844, 0.178]." } ], "id_raw": 396 }, { "id": "tPfrQlEZG42VIt1w", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.015, 0.556, 0.193, 0.580] contain?" }, { "from": "gpt", "value": "The label 'Community Development' for y-axis." } ], "id_raw": 396 }, { "id": "lpultOK8u5rVAYt4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.395, 0.379, 0.683, 0.465]?" }, { "from": "gpt", "value": "It contains a green bar representing the Volunteer Hours for Environment, which is 45." } ], "id_raw": 396 }, { "id": "AiBaGnaaJa4hKH9W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements in the region [0.235, 0.019, 0.786, 0.061]." }, { "from": "gpt", "value": "It shows the legend of the chart. Blue at [0.241, 0.031, 0.268, 0.047] represents 'Grants Provided'; green at [0.419, 0.031, 0.447, 0.047] stands for 'Volunteer Hours'; and red at [0.597, 0.031, 0.625, 0.047] denotes 'Communities Served'." } ], "id_raw": 396 }, { "id": "37xGAF4pmfkPamjv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the object located at coordinates [0.015, 0.700, 0.182, 0.723]?" }, { "from": "gpt", "value": "The object is the y-axis label \"HealthTech vs. PropTech\"." } ], "id_raw": 397 }, { "id": "7lLPuICmUOi9heun", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color indicated by the coordinate located at [0.565, 0.031, 0.592, 0.047] in the subplot legend?" }, { "from": "gpt", "value": "The color is #ef8b88, which refers to a shade of red." } ], "id_raw": 397 }, { "id": "ZZSQfN7TSPvCaOnk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the x-axis coordinates [0.787, 0.952, 0.805, 0.975]?" }, { "from": "gpt", "value": "It's a label for the value \"80\" on the x-axis." } ], "id_raw": 397 }, { "id": "GnjQLY8esavbHnAV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the rectangle at [0.503, 0.843, 0.752, 0.895] refer to in the chart data?" }, { "from": "gpt", "value": "It is {the percentage of Tie result for the comparison between \"PropTech vs. AdTech\"}[0.483, 0.809, 0.752, 0.895], which is 35.6%." } ], "id_raw": 397 }, { "id": "kGnXlaGpQ2ah0eRz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nCompare the bar for \"Win\" and \"Loss\" results in the \"HealthTech vs. PropTech\" match. Which one is longer?" }, { "from": "gpt", "value": "The \"Win\" bar is longer than the \"Loss\" bar." } ], "id_raw": 397 }, { "id": "2X1lCbdksP624YXx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nIn the \"Blockchain vs. AI\" category, which has the highest percentage - \"Win\", \"Tie\", or \"Loss\"?" }, { "from": "gpt", "value": "The \"Tie\" category has the highest percentage." } ], "id_raw": 397 }, { "id": "O8VC4dZfLxBamQwv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nIn the comparison of {the \"AI vs Fintech\" Win bar}[0.192, 0.236, 0.442, 0.322] and {the \"Edtech vs HealthTech\" Win bar}[0.192, 0.522, 0.498, 0.608], which bar is longer?" }, { "from": "gpt", "value": "The bar for \"Edtech vs HealthTech\" is longer." } ], "id_raw": 397 }, { "id": "nBlJX2SXYDJ4hYcB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nIs the {\"Fintech vs. Edtech\" Tie bar}[0.381, 0.379, 0.758, 0.465] shorter than the {\"Fintech vs. Edtech\" Loss bar}[0.758, 0.379, 0.947, 0.465]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 397 }, { "id": "lgbnAYPw8YL2VXby", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the area [0.431, 0.009, 0.567, 0.042]?" }, { "from": "gpt", "value": "This area contains part of the legend of the chart, indicating two categories: \"Tie\" located at [0.516, 0.029, 0.537, 0.052] with a color indication at [0.477, 0.031, 0.505, 0.047], and \"Loss\" located at [0.603, 0.029, 0.634, 0.052] with a color indication at [0.565, 0.031, 0.592, 0.047]." } ], "id_raw": 397 }, { "id": "OfRIgvNkbuEyRh8n", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the region [0.192, 0.379, 0.947, 0.465]." }, { "from": "gpt", "value": "This region includes three category bars of the match \"Fintech vs. Edtech\", including the \"Win\" bar at [0.192, 0.379, 0.381, 0.465], \"Tie\" bar at [0.381, 0.379, 0.758, 0.465], and \"Loss\" bar at [0.758, 0.379, 0.947, 0.465]. Also, the text coordinates of the values on these bars are [0.264, 0.410, 0.308, 0.434], [0.548, 0.410, 0.591, 0.434], and [0.831, 0.410, 0.875, 0.434] respectively." } ], "id_raw": 397 }, { "id": "o6lRFfGbiK3yNC7g", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the area [0.341, 0.019, 0.678, 0.052]?" }, { "from": "gpt", "value": "This region covers part of the chart's legend with identifiers for two results - \"Win\" located at [0.423, 0.029, 0.450, 0.052], and \"Tie\" located at [0.516, 0.029, 0.537, 0.052]. The corresponding color identifiers are found at [0.385, 0.031, 0.412, 0.047] for \"Win\" and [0.477, 0.031, 0.505, 0.047] for \"Tie\"." } ], "id_raw": 397 }, { "id": "3YUOAJndWL3Qh4th", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is in the region [0.485, 0.952, 0.503, 0.975]?" }, { "from": "gpt", "value": "It is an x-axis label that represents the data point \"40\"." } ], "id_raw": 397 }, { "id": "qFhwT13GCoEnhTOd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {the bar}[0.733, 0.401, 0.943, 0.586] represent in the dataset?" }, { "from": "gpt", "value": "The bar represents the loss percentage for Americas." } ], "id_raw": 398 }, { "id": "cVIAqQw47Evn6hF7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate provides the \"Win\" label in the legend?" }, { "from": "gpt", "value": "[0.370, 0.029, 0.396, 0.052]." } ], "id_raw": 398 }, { "id": "RgMKurO56TClLBwI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.176, 0.093, 0.199] in the chart?" }, { "from": "gpt", "value": "Asia Pacific." } ], "id_raw": 398 }, { "id": "F8gKSL5k4IdEkTPH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nWhat data is shown in [0.691, 0.710, 0.943, 0.895]?" }, { "from": "gpt", "value": "{The percentage of Loss for EMEA}[0.795, 0.791, 0.839, 0.814]." } ], "id_raw": 398 }, { "id": "t4sLTiBcCEEMKA78", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nIs {the tie bar for EMEA}[0.271, 0.710, 0.691, 0.895] taller than {the tie bar for Americas}[0.481, 0.401, 0.733, 0.586]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 398 }, { "id": "yMRN41fyQwR7YY8n", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nDo all regions have the same {Loss}<{Loss for EMEA}[0.691, 0.710, 0.943, 0.895], {Loss for Americas}[0.733, 0.401, 0.943, 0.586], {Loss for Asia Pacific}[0.733, 0.092, 0.943, 0.277]> percentage?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 398 }, { "id": "2iWKuJtfEBdLEG6N", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nIs {the Win bar for EMEA}[0.103, 0.710, 0.271, 0.895] shorter than {the Win bar for Americas}[0.103, 0.401, 0.481, 0.586]?" }, { "from": "gpt", "value": "Yes.." } ], "id_raw": 398 }, { "id": "LEqMawGAyL7udJP8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nIs the {Tie for Asia Pacific}[0.397, 0.092, 0.733, 0.277] taller than the {Tie for EMEA}[0.271, 0.710, 0.691, 0.895]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 398 }, { "id": "2sKbl0K0UyVynPh6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region in the chart specified by [0.325, 0.019, 0.586, 0.061]." }, { "from": "gpt", "value": "The area contains the legend, which has three labels indicating: {Win}[0.331, 0.031, 0.359, 0.047] in blue, {Tie}[0.424, 0.031, 0.452, 0.047] in light green, and {Loss}[0.511, 0.031, 0.539, 0.047] in red." } ], "id_raw": 398 }, { "id": "M3fzw2mPM73GTRRM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.103, 0.710, 0.943, 0.895]?" }, { "from": "gpt", "value": "There are 3 bars: the {Win bar}[0.103, 0.710, 0.271, 0.895] in blue, the {Tie bar}[0.271, 0.710, 0.691, 0.895] in light green, and the {Loss bar}[0.691, 0.710, 0.943, 0.895] in red for the EMEA region." } ], "id_raw": 398 }, { "id": "YWL2As9fna97gsOd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's in the region defined by [0.103, 0.401, 0.943, 0.586]?" }, { "from": "gpt", "value": "The {Win bar}[0.103, 0.401, 0.481, 0.586], {Tie bar}[0.481, 0.401, 0.733, 0.586] and the {Loss bar}[0.733, 0.401, 0.943, 0.586] for the Americas region are all within this area." } ], "id_raw": 398 }, { "id": "glDWjpCIoxbCC9ZT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.103, 0.092, 0.943, 0.277]?" }, { "from": "gpt", "value": "There are 3 objects: \n1. The {Win bar for Asia Pacific}[0.103, 0.092, 0.397, 0.277] which illustrates the winning percentage for this region. \n2. The {Tie bar for Asia Pacific}[0.397, 0.092, 0.733, 0.277] showing the tied matches percentage for Asia Pacific.\n3. The {Loss bar for Asia Pacific}[0.733, 0.092, 0.943, 0.277] which marks the losing percentage for the Asia Pacific region." } ], "id_raw": 398 }, { "id": "HUSAsB4GctLtY4dh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the coordinate with a bounding box of [0.432, 0.791, 0.788, 0.895]?" }, { "from": "gpt", "value": "It is the bar representing the \"Win\" percentage of the \"Slow increase\" category." } ], "id_raw": 399 }, { "id": "FR0C34Eimk6CafJT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what category the coordinate at [0.156, 0.092, 0.393, 0.197] refers to?" }, { "from": "gpt", "value": "This coordinate represents the \"Win\" percentage of the \"Stable growth\" category." } ], "id_raw": 399 }, { "id": "z3ctl0hqo6RU6CSZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the text marked by the coordinates [0.582, 0.029, 0.612, 0.052]?" }, { "from": "gpt", "value": "The word is \"Loss\"." } ], "id_raw": 399 }, { "id": "72fxPSjFBysjBadf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nIn terms of \"Tie\", is the \"Slow increase\" category{coordinates of the Slow increase category under Tie}[0.432, 0.791, 0.788, 0.895] higher or lower than the \"Fluctuating growth\" category{coordinates of the Fluctuating growth category under Tie}[0.353, 0.616, 0.748, 0.721]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 399 }, { "id": "ohO0YwTFs65v3GQY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nIs the \"Loss\" score of the \"Steady recovery\" category{coordinates of the Steady recovery category under Loss}[0.827, 0.441, 0.946, 0.546] greater than that of the \"Stable growth\" category{coordinates of the Stable growth category under Loss}[0.788, 0.092, 0.946, 0.197]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 399 }, { "id": "mFQmeJ9EGRym28hM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nIs the \"Win\" score of the \"Stable growth\" category{coordinates of the Stable growth category under Win}[0.156, 0.092, 0.393, 0.197] greater than that of the \"Steady recovery\" category{coordinates of the Steady recovery category under Win}[0.156, 0.441, 0.472, 0.546]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 399 }, { "id": "eLyIsWVdboOAAkwI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is the information of objects found in the bounding box [0.156, 0.267, 0.669, 0.371]?" }, { "from": "gpt", "value": "The objects found in the given bounding box include: \n1. The blue bar that represents the \"Win\" score for the \"Gradual recovery\" category and its value of 20% at location [0.156, 0.267, 0.314, 0.371].\n2. The green bar stands for the category \"Tie\" of \"Gradual recovery\", 45% score, and its coordinates are [0.314, 0.267, 0.669, 0.371]." } ], "id_raw": 399 }, { "id": "JE8j4BOYSjerGQIw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the information in the region [0.432, 0.791, 0.788, 0.895]." }, { "from": "gpt", "value": "In the region [0.432, 0.791, 0.788, 0.895], the green bar represents the \"Tie\" score of \"Slow increase\" category as 45%." } ], "id_raw": 399 }, { "id": "IZkGR9c2RNO8qtYM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nCan you detail the contents in the region [0.353, 0.616, 0.748, 0.721]?" }, { "from": "gpt", "value": "In this range, the green bar depicts a \"Tie\" in the \"Fluctuating growth\" category, recorded as a 50% score." } ], "id_raw": 399 }, { "id": "XjP9fvOGFY9N8rOD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.397, 0.029, 0.423, 0.052]?" }, { "from": "gpt", "value": "It's a legend text, \"Win\"." } ], "id_raw": 400 }, { "id": "Tuad8hgM9gCXUaxB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar represented by the coordinates [0.148, 0.809, 0.507, 0.895]?" }, { "from": "gpt", "value": "The color of the bar is blue, as indicated by its equivalent label {Win}[0.397, 0.029, 0.423, 0.052]." } ], "id_raw": 400 }, { "id": "O3ZsWBMAmYyCdMLe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the description for the graph element located at [0.044, 0.700, 0.139, 0.723]?" }, { "from": "gpt", "value": "It's a label on the y-axis denoting \"Wind vs. Hydro\"." } ], "id_raw": 400 }, { "id": "RProj0gQdnwWoJlt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object in the coordinates [0.148, 0.666, 0.865, 0.752]?" }, { "from": "gpt", "value": "Yes, it encompasses the win and tie bars for \"Wind vs. Hydro\" in the chart." } ], "id_raw": 400 }, { "id": "S8FezllgkShoGOP6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nIn the comparison \"Solar vs. Wind\", does the {win percentage}[0.148, 0.092, 0.347, 0.178] exceed the {loss percentage}[0.746, 0.092, 0.945, 0.178]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 400 }, { "id": "S1zcyoCAMsLHQtm0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nDoes the {win bar}[0.865, 0.522, 0.945, 0.608] of the category \"Wind vs. Nuclear\" taller than its {loss bar}[0.148, 0.522, 0.427, 0.608]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 400 }, { "id": "TSHhoxtT3aFjJr5P", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nIs the {tie bar for the category Solar vs. Wind}[0.347, 0.092, 0.746, 0.178] taller than the {tie bar for the category Wind vs. Hydro}[0.467, 0.666, 0.865, 0.752]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 400 }, { "id": "01pVo2K4NDlsctJG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nBetween {win and tie bar for \"Solar vs. Hydro\"}[0.148, 0.379, 0.706, 0.465] and {loss bar for \"Solar vs. Nuclear\"}[0.786, 0.236, 0.945, 0.322], which has a higher percentage?" }, { "from": "gpt", "value": "Win and tie bar for \"Solar vs. Hydro\"." } ], "id_raw": 400 }, { "id": "caYWkriZKF4rGgdf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the coordinate [0.148, 0.809, 0.945, 0.895]." }, { "from": "gpt", "value": "The coordinate encompasses the win, tie, and loss bars for the category \"Nuclear vs. Hydro.\" The win bar is located at [0.148, 0.809, 0.507, 0.895], the tie bar is at [0.507, 0.809, 0.865, 0.895], and the loss bar is at [0.865, 0.809, 0.945, 0.895]." } ], "id_raw": 400 }, { "id": "45FoiZH105ZmCoj0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat information can be extracted from the area demarcated by [0.148, 0.236, 0.945, 0.322]?" }, { "from": "gpt", "value": "This area contains three bars representing the win, tie, and loss percentages for the category \"Solar vs. Nuclear.\" The coordinates for these bars are [0.148, 0.236, 0.308, 0.322] for the win, [0.308, 0.236, 0.786, 0.322] for the tie, and [0.786, 0.236, 0.945, 0.322] for the loss." } ], "id_raw": 400 }, { "id": "TbumMMRTL6knxAne", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat's inside the coordinates [0.358, 0.031, 0.566, 0.047]?" }, { "from": "gpt", "value": "It represents the color boxes of the legend, corresponding to \"Win\", \"Tie\", and \"Loss\"." } ], "id_raw": 400 }, { "id": "aHUBNjnFikH3Kyfu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the items found within the coordinates [0.353, 0.019, 0.613, 0.061]." }, { "from": "gpt", "value": "This area contains the legend of the chart. It has three entries: \"Win\" with the color blue at coordinates [0.397, 0.029, 0.423, 0.052], \"Tie\" with the color green at [0.490, 0.029, 0.511, 0.052], and \"Loss\" with color red at [0.577, 0.029, 0.607, 0.052]." } ], "id_raw": 400 }, { "id": "pEtWkM28GrApvpOP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the rectangle labeled \"Win\" in the sub-legend?" }, { "from": "gpt", "value": "The color is {blue}[0.389, 0.031, 0.417, 0.047]." } ], "id_raw": 401 }, { "id": "tyPnaW4RHzyULuUs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text label corresponding to the \"Loss\" category in the sub-legend?" }, { "from": "gpt", "value": "The coordinate of the text label of \"Loss\" is [0.608, 0.029, 0.638, 0.052]." } ], "id_raw": 401 }, { "id": "gnzxRkktJH6l9xNk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented at [0.199, 0.092, 0.360, 0.178]?" }, { "from": "gpt", "value": "It is a blue bar which represents the category \"Win\" for the y-label \"Combines vs. Harvesters\", with a value range of \"0.00 -> 21.50\"." } ], "id_raw": 401 }, { "id": "Tmi2abEfICpxt5wZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat is described under the xticks within the coordinates [0.639, 0.952, 0.657, 0.975]?" }, { "from": "gpt", "value": "It is an x-axis tick labelled as \"60\"." } ], "id_raw": 401 }, { "id": "DkuoW8r6IQnTjiV1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Loss\" for Dairy vs Poultry}[0.891, 0.666, 0.948, 0.752] to the left of {the bar representing \"Loss\" for Fruits vs Grains}[0.873, 0.809, 0.948, 0.895]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 401 }, { "id": "OwlSsX2dQcOrylRY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nIs the {\"Win\"}[0.199, 0.236, 0.371, 0.322] column taller than the {\"Loss\"}[0.850, 0.236, 0.948, 0.322] column for \"Tractors vs. Ploughs\"?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 401 }, { "id": "s8M1lgHxwuQuSXso", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nAre the shopping behavior categories located on the left or right compared to the {legend}[0.383, 0.019, 0.643, 0.061]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 401 }, { "id": "gZ2arTRsGhQegfqr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nAmongst {the bars representing the \"Win\" category}[[0.199, 0.809, 0.334, 0.895], [0.199, 0.666, 0.427, 0.752], [0.199, 0.522, 0.453, 0.608], [0.199, 0.379, 0.412, 0.465], [0.199, 0.236, 0.371, 0.322], [0.199, 0.092, 0.360, 0.178]], which one is the tallest?" }, { "from": "gpt", "value": "The one representing \"Organic vs. Conventional." } ], "id_raw": 401 }, { "id": "qGrCdWCsqIzDZHW2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nDescribe everything within the area [0.199, 0.092, 0.948, 0.895]." }, { "from": "gpt", "value": "There are lots of bars representing different outcome patterns (Win, Tie, Loss), each for different pairs of categories (Combines vs. Harvesters, Tractors vs. Ploughs, Irrigation vs. Fertilizer, Organic vs. Conventional, Dairy vs. Poultry, Fruits vs. Grains). These bars are portrayed in different colors according to their outcome pattern: blue for \"Win\", green for \"Tie\", and red for \"Loss\"." } ], "id_raw": 401 }, { "id": "HswIcQn7wrWkRrGV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the information shown in the area [0.383, 0.019, 0.643, 0.061]." }, { "from": "gpt", "value": "This is the chart legend. It contains three rectangles: blue labeled \"Win\" [0.389, 0.031, 0.417, 0.047], green labeled \"Tie\" [0.482, 0.031, 0.509, 0.047], and red labeled \"Loss\" [0.569, 0.031, 0.597, 0.047]." } ], "id_raw": 401 }, { "id": "gnZ02ANaLuRbzrcA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the area [0.334, 0.809, 0.873, 0.895]?" }, { "from": "gpt", "value": "The area contains two bars. The blue bar represents the outcome \"Win\" for the category pair \"Fruits vs. Grains\". It has a value range of 0.00 -> 18.00 and is located at [0.199, 0.809, 0.334, 0.895]. The other green bar represents the outcome \"Tie\" for the same category pair. It has a value range of 18.00 -> 90.00 and is located at [0.334, 0.809, 0.873, 0.895]." } ], "id_raw": 401 }, { "id": "WfCKxE8WZ8dVgrdv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region from [0.334, 0.666, 0.891, 0.752]?" }, { "from": "gpt", "value": "In this region, two bars represent the results for the \"Dairy vs. Poultry\" category. The blue bar, which represents a \"Win\", is at [0.199, 0.666, 0.427, 0.752]. The bar labeled \"Tie\" is green and located at [0.427, 0.666, 0.891, 0.752]." } ], "id_raw": 401 }, { "id": "3Q9UpHZptnmwCnyy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the text content in the coordinate [0.261, 0.844, 0.304, 0.868]?" }, { "from": "gpt", "value": "It's the value of \"In-store purchase\" for \"Customer Preferences\", which is 25.7%." } ], "id_raw": 402 }, { "id": "SCvTejy2Vvr7vw1L", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at [0.304, 0.540, 0.755, 0.623]?" }, { "from": "gpt", "value": "The color of the bar is denoted by '#b7da91', represented by the json value: {'color': '#b7da91'}, which is a shade of green." } ], "id_raw": 402 }, { "id": "ZTWfRXTNxCyucutl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nTo what does the label in coordinates [0.358, 0.027, 0.479, 0.050] correspond in the legend?" }, { "from": "gpt", "value": "The label corresponds to the \"In-store purchase\" represented by the color{'color': '#9cc8e4'}." } ], "id_raw": 402 }, { "id": "m5nW2zstYS6hKaAS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nIs the value of \"In-store purchase\" for \"Customer Preferences\" at [0.184, 0.815, 0.380, 0.897] less than \"Online purchase\" for \"Customer Preferences\" at [0.380, 0.815, 0.703, 0.897]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 402 }, { "id": "quRKPZpNOOvjm61M", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher, \"In-store purchase\" for \"Return to Normalcy\" at [0.184, 0.678, 0.417, 0.760] or \"In-store purchase\" for \"Advent of Markets\" at [0.184, 0.266, 0.413, 0.348]?" }, { "from": "gpt", "value": "\"Return to Normalcy\"." } ], "id_raw": 402 }, { "id": "7oQV6tpusSZ30G9p", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nDoes the \"Rise of E-commerce\" bar for \"Online purchase\" at [0.337, 0.129, 0.718, 0.211] have a higher or lower value than the \"Rise of E-commerce\" bar for \"In-store purchase\" at [0.184, 0.129, 0.337, 0.211]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 402 }, { "id": "mdF2bpC7S9fx1Pz2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.184, 0.129, 0.947, 0.211]?" }, { "from": "gpt", "value": "Within the area [0.184, 0.129, 0.947, 0.211], there are 3 bars, they represent the \"Rise of E-commerce\" category for the three types of purchases: In-store, Online, and E-commerce Apps. The first bar representing \"In-store purchase\" is located at [0.184, 0.129, 0.337, 0.211], the second bar representing \"Online purchase\" is located at [0.337, 0.129, 0.718, 0.211] and the final bar, representing \"E-commerce Apps\", is at [0.718, 0.129, 0.947, 0.211]." } ], "id_raw": 402 }, { "id": "v4RbwPimSfHml0IA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell me about the bars within [0.184, 0.403, 0.947, 0.486]?" }, { "from": "gpt", "value": "The region contains the \"Innovations in Delivery\" information. There are three bars, each representing a different purchasing method: \"In-store purchase\" is denoted by the bar at [0.184, 0.403, 0.379, 0.486], \"Online purchase\" by the bar at [0.379, 0.403, 0.646, 0.486], and \"E-commerce Apps\" by the bar at [0.646, 0.403, 0.947, 0.486]." } ], "id_raw": 402 }, { "id": "nwWcpNj46jUyFkNG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the items within coordinates [0.313, 0.017, 0.856, 0.059]." }, { "from": "gpt", "value": "This region describes the three purchasing methods symbolized in the legend, which are: \"In-store purchase\" with a color block at [0.319, 0.029, 0.347, 0.045] and a label at [0.358, 0.027, 0.479, 0.050], \"Online purchase\" with a color block at [0.507, 0.029, 0.534, 0.045] and a label at [0.545, 0.027, 0.658, 0.050], and E-commerce Apps with a color block at [0.686, 0.029, 0.714, 0.045] and a label at [0.725, 0.027, 0.850, 0.050]." } ], "id_raw": 402 }, { "id": "svmxObTZEObOlfKg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the label is for the object located at [0.072, 0.126, 0.119, 0.149] on the y-axis?" }, { "from": "gpt", "value": "It's \"Twitter\"." } ], "id_raw": 403 }, { "id": "m4YINL6bxvcL2vU3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nWhat color is the first handle element in the legend? Its coordinates are [0.302, 0.031, 0.330, 0.047]." }, { "from": "gpt", "value": "The color is blue." } ], "id_raw": 403 }, { "id": "ZwoByYrxSTKIR4iz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage value displayed at the coordinates [0.217, 0.123, 0.261, 0.147]?" }, { "from": "gpt", "value": "The value is 27.3%." } ], "id_raw": 403 }, { "id": "7xkOQnodUoYYfd9T", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nWhat text is displayed in the figure at [0.471, 0.029, 0.641, 0.052]?" }, { "from": "gpt", "value": "The text is \"Public Opinion Guidance\"." } ], "id_raw": 403 }, { "id": "S1qlX1GWZE3uUKln", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nIn the bar chart, is {the bar}[0.359, 0.666, 0.938, 0.752] taller or shorter than {the bar}[0.533, 0.236, 0.938, 0.322]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 403 }, { "id": "s21DTVd9j5UFZm4a", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nIs the {group_0 bar for Facebook}[0.129, 0.236, 0.533, 0.322] less or more than {group_0 bar for Radio}[0.129, 0.666, 0.359, 0.752]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 403 }, { "id": "vQ4Rn4VgQbsSoHe3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nWhich is wider, {the bar for Print Media}[0.296, 0.809, 0.938, 0.895] or {the bar for News Websites}[0.463, 0.379, 0.938, 0.465]?" }, { "from": "gpt", "value": "They are the same width." } ], "id_raw": 403 }, { "id": "1nxkzac1NaQcmqxY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value for Public Opinion Guidance, {Twitter}[0.350, 0.092, 0.944, 0.178] or {Facebook}[0.533, 0.236, 0.938, 0.322]?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 403 }, { "id": "EAcZHMhanX4qgZtz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in [0.297, 0.019, 0.646, 0.061]." }, { "from": "gpt", "value": "This is the legend of the chart, consisting of two labels. The first label's color is blue and is named \"Influence\" with coordinates [0.341, 0.029, 0.405, 0.052], and the second label's color is green and is named \"Public Opinion Guidance\" with coordinates [0.471, 0.029, 0.641, 0.052]." } ], "id_raw": 403 }, { "id": "9DwFHTTpsYfPvKmC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is enclosed within the region [0.446, 0.522, 0.938, 0.608]?" }, { "from": "gpt", "value": "This region encloses a blue bar and a green bar, indicating the Influence and Public Opinion Guidance rates for Television." } ], "id_raw": 403 }, { "id": "7QVMJccx5e38luJi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.129, 0.892, 0.950, 0.957]." }, { "from": "gpt", "value": "This area represents the horizontal axis (x-axis) of the bar chart, where the values of Influence and Public Opinion Guidance are plotted from 0 to 100%." } ], "id_raw": 403 }, { "id": "PjZti4zmYTFXXbk8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe what's inside [0.129, 0.092, 0.350, 0.178]?" }, { "from": "gpt", "value": "There is a bar in blue that signifies the Influence percentage for Twitter, with a value of 27.3%." } ], "id_raw": 403 }, { "id": "C09PKwBN1ZG5pX6G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located at the coordinates [0.170, 0.791, 0.370, 0.895]?" }, { "from": "gpt", "value": "It is the info about the {Upward Trend} bar[0.170, 0.791, 0.370, 0.895] in the year \"2021\" which has a value percentage of 25%." } ], "id_raw": 404 }, { "id": "2jBHbZiaAphClz5x", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage value shown at the coordinates [0.589, 0.657, 0.633, 0.680]?" }, { "from": "gpt", "value": "The value is 55%." } ], "id_raw": 404 }, { "id": "9JqFtT4Ga5JTqZlb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat are the entry labels in the legend located at [0.346, 0.019, 0.635, 0.096]?" }, { "from": "gpt", "value": "The labels include {2017}[0.390, 0.029, 0.426, 0.052], {2018}[0.390, 0.063, 0.426, 0.087], {2019}[0.492, 0.029, 0.528, 0.052], {2020}[0.492, 0.063, 0.527, 0.087], and {2021}[0.594, 0.029, 0.629, 0.052]." } ], "id_raw": 404 }, { "id": "rFFhgV9sybdQb6iL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend of the bar in 2019 compared to the bar in 2017 for {Pronounced Increase}[0.170, 0.616, 0.290, 0.721] and [0.170, 0.616, 0.450, 0.721] respectively?" }, { "from": "gpt", "value": "Increased." } ], "id_raw": 404 }, { "id": "X96lsDbxFTglhBFe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for {Stable}[0.170, 0.267, 0.571, 0.371] in 2020 have a higher or lower percentage than it did in 2018 for {Stable}[0.170, 0.267, 0.579, 0.371]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 404 }, { "id": "ocOZmUi9YlVUWHFS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nIs the percentage value for {Increase}[0.170, 0.092, 0.290, 0.197] in 2019 higher or lower than that in 2021[0.170, 0.092, 0.338, 0.197]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 404 }, { "id": "H6h1yyeBERCBzTp7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's within the coordinates [0.170, 0.267, 0.579, 0.371]?" }, { "from": "gpt", "value": "It consists of a group of bars representing the category {Stable}. It shows the yearly trends from 2017 to 2021 with their respective percentages." } ], "id_raw": 404 }, { "id": "NPci8VAbbmSYokj4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.346, 0.019, 0.635, 0.096]?" }, { "from": "gpt", "value": "It represents the legend of the chart that includes color-coded labels for the years 2017 to 2021." } ], "id_raw": 404 }, { "id": "h4s8pdC7Z3elKnBy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe what's within the coordinates [0.170, 0.441, 0.811, 0.546]." }, { "from": "gpt", "value": "It includes a group of bars representing the category {Abrupt drop}. It provides the percentage values for the years 2017 to 2021." } ], "id_raw": 404 }, { "id": "2IsF42nWi9gxwmaz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.152, 0.564, 0.231, 0.903] in the chart?" }, { "from": "gpt", "value": "This is a bar representing the Q1 sale of a \"New Improved Product\"." } ], "id_raw": 406 }, { "id": "BpFQCkt24Ka5EIBp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.975, 0.334, 0.975, 0.743] refer to?" }, { "from": "gpt", "value": "It's the Y-axis label of the second subplot, named \"Declining Product Sales\"." } ], "id_raw": 406 }, { "id": "WGmZIEBOnV8WwPsm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat text does the coordinate [0.561, 0.776, 0.619, 0.811] refer to in the chart?" }, { "from": "gpt", "value": "The text \"735K\" above the Q3 sales bar of the \"Declining Product Sales\" chart." } ], "id_raw": 406 }, { "id": "OqFSzn4wnepPh2gP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat object is at [0.879, 0.322, 0.942, 0.357]?" }, { "from": "gpt", "value": "The label \"-200\" on the Y-axis of the second subplot." } ], "id_raw": 406 }, { "id": "k6NZMi2mzipGrfpo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Q1 New_Product Sale}[0.152, 0.564, 0.231, 0.903] higher than the {Q1 Decline_Product Sale}[0.152, 0.174, 0.231, 0.868]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 406 }, { "id": "RdCFfUth5sdrevvi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nIs {Q4 sale for New_Product}[0.749, 0.209, 0.829, 0.903] more than the {Q2 sale for New_Product}[0.351, 0.437, 0.431, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 406 }, { "id": "CU4I2VmKJmNhLRBe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nAre values in {ytick labels of subplot_0}[0.025, 0.290, 0.048, 0.788] greater than values in {ytick labels of subplot_1}[0.952, 0.334, 0.975, 0.743]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 406 }, { "id": "z6sDXbYXF0aSkc7N", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {New_Product Q4 sales}[0.749, 0.209, 0.829, 0.903] taller than the bar representing {New_Product Q1 sales}[0.152, 0.564, 0.231, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 406 }, { "id": "ZyxOuWaaiH0OFmTe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.874, 0.902, 0.957, 1.003]." }, { "from": "gpt", "value": "This region contains the y-tick label \"-1000\" from the second subplot, which defines the top bound value of \"Declining Product Sales\"." } ], "id_raw": 406 }, { "id": "33JZbFwBwe5xztOD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed within the region [0.152, 0.174, 0.829, 0.868]?" }, { "from": "gpt", "value": "These are bars for \"Declining Product Sales\" for Q1[0.152, 0.174, 0.231, 0.868], Q2[0.351, 0.174, 0.431, 0.836], Q3[0.550, 0.174, 0.630, 0.768], and Q4[0.749, 0.174, 0.829, 0.715]." } ], "id_raw": 406 }, { "id": "zV3MshrGath4MZvR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.118, 0.174, 0.863, 0.903]?" }, { "from": "gpt", "value": "This region includes two parts of the graph: New Product Sales and Declining Product Sales. It also includes their corresponding y-axis labels and bars indicating their sales for Q1 to Q4." } ], "id_raw": 406 }, { "id": "sRhoSsALUPJaEdUg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.5, 0.046, 0.851, 0.108] include?" }, { "from": "gpt", "value": "It contains a legend for the \"Declining Product Sales\" line on the chart, with the corresponding color and text location at [0.570, 0.060, 0.842, 0.095]." } ], "id_raw": 406 }, { "id": "8iTVLxDY2H1TJrvV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label at this coordinate [0.025, 0.458, 0.048, 0.658] indicate?" }, { "from": "gpt", "value": "It refers to the \"AI Adoption\" level." } ], "id_raw": 407 }, { "id": "URz1w69ponNUTggU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the text at the coordinates [0.157, 0.482, 0.186, 0.517] says?" }, { "from": "gpt", "value": "Yes, it reads \"50\"." } ], "id_raw": 407 }, { "id": "S0L0Ek8Z4Ix5oPrE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label name for {the legend}[0.189, 0.052, 0.322, 0.087]?" }, { "from": "gpt", "value": "It's labeled as \"AI Adoption\"." } ], "id_raw": 407 }, { "id": "11WHyU1rqm9BIlis", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nCompared to {the bar for AI Adoption at point [0.139, 0.462, 0.204, 0.941]}, is {the bar for Automation Trend at point [0.139, 0.558, 0.204, 0.941]} higher or lower?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 407 }, { "id": "sztz7xNcmxqIp8I8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Automation Trend at point[0.788, 0.558, 0.853, 0.941]} taller than {the bar for AI Adoption at point[0.788, 0.270, 0.853, 0.941]}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 407 }, { "id": "jFaIkUPl8mnndEtw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nRegarding the size of the bars, in general, are {the bars for AI Adoption} larger than {the bars for Automation Trend}?" }, { "from": "gpt", "value": "No." } ], "id_raw": 407 }, { "id": "ByjoCZWKEeif8uW0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in area [0.115, 0.038, 0.331, 0.101]." }, { "from": "gpt", "value": "This area includes a legend. The legend color is #404346, located at [0.124, 0.055, 0.170, 0.079], corresponding to the label \"AI Adoption\" located at [0.189, 0.052, 0.322, 0.087]." } ], "id_raw": 407 }, { "id": "B01HA9XU3lAe0qY0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.103, 0.174, 0.889, 0.941]?" }, { "from": "gpt", "value": "This region contains bar graphs representing AI Adoption and Automation Trend across multiple categories. The bars show the comparative values of each category for AI Adoption and Automation Trend." } ], "id_raw": 407 }, { "id": "5H8vV163spwJxWnc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nTell me what's in the area [0.139, 0.558, 0.204, 0.941]." }, { "from": "gpt", "value": "It is a bar from the Automation Trend, with a height indicating a value of \"40\"." } ], "id_raw": 407 }, { "id": "vjQ0ZLQtH9lIRvhJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat object lies within the coordinates [0.153, 0.030, 0.290, 0.053]?" }, { "from": "gpt", "value": "{The text label in the legend}[0.153, 0.030, 0.290, 0.053] displaying \"Monetary Policy\"." } ], "id_raw": 408 }, { "id": "KiwXy6Qm6809lGNv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.592, 0.345, 0.647, 0.368]?" }, { "from": "gpt", "value": "{The text on a bar}[0.592, 0.345, 0.647, 0.368] which reads \"55.6%\"." } ], "id_raw": 408 }, { "id": "PqhProFtFf8rMbwV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the coordinate [0.275, 0.500, 0.285, 0.530] show on the chart?" }, { "from": "gpt", "value": "{The axis label}[0.275, 0.500, 0.285, 0.530] isn't visible on the chart." } ], "id_raw": 408 }, { "id": "3VJSYENDaFtU1alA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar with 82.6%}[0.205, 0.236, 0.237, 0.564] larger than {the bar with 79.3%}[0.205, 0.564, 0.237, 0.879]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 408 }, { "id": "i3CD34Ri7uctdXr2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nAt {the y-axis}[0.019, 0.472, 0.036, 0.656], does the label \"60\" appear above the label \"40\"?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 408 }, { "id": "vDviVh3mp5X02uDs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher percentage, {the bar labeled \"55.6%\"}[0.592, 0.345, 0.647, 0.368] or {the bar labeled \"54.3%\"}[0.672, 0.350, 0.726, 0.374]?" }, { "from": "gpt", "value": "55.6%." } ], "id_raw": 408 }, { "id": "XXi671SPOLxNsZYn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.097, 0.020, 0.297, 0.062]." }, { "from": "gpt", "value": "This region contains {the legend box}[0.097, 0.020, 0.297, 0.062], which includes a label and handle indicating \"Monetary Policy\". The label's text is positioned at [0.153, 0.030, 0.290, 0.053], and the handle is colored gray at [0.104, 0.032, 0.139, 0.048]." } ], "id_raw": 408 }, { "id": "tJnIvjfUVMo6zAff", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the area [0.088, 0.167, 0.912, 0.961]?" }, { "from": "gpt", "value": "This large region contains all visual elements of the subplot, including {the axes}[0.088, 0.167, 0.912, 0.961], with labels for Monetary Policy, a series of bars representing different percentages, and a y-axis with labeled ticks ranging from 0 to 100." } ], "id_raw": 408 }, { "id": "5E609BZ8AORV4p5v", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.842, 0.564, 0.874, 0.805] encapsulate?" }, { "from": "gpt", "value": "Within this area, there is a {Fiscal Policy bar}[0.842, 0.564, 0.874, 0.805] illustrating a value of 60.7%, as evidenced by the bar itself and an accompanying data label at [0.831, 0.806, 0.886, 0.830]." } ], "id_raw": 408 }, { "id": "NqoKQo260C57RVYZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by the coordinates [0.025, 0.377, 0.048, 0.700] in the chart?" }, { "from": "gpt", "value": "Traditional Method, it's the Y-axis label of the subplot_0." } ], "id_raw": 409 }, { "id": "crEe43geVYj7VLOe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first text in subplot_1 sublegend?" }, { "from": "gpt", "value": "[0.680, 0.060, 0.861, 0.095]" } ], "id_raw": 409 }, { "id": "1cEnHcHEKiAsfKDW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.025, 0.377, 0.048, 0.700]?" }, { "from": "gpt", "value": "Traditional Method." } ], "id_raw": 409 }, { "id": "39kxoxNGws4klh8n", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat indicators or categories are shown in [0.130, 0.046, 0.428, 0.108]?" }, { "from": "gpt", "value": "Text label for Traditional Method in the subplot_0 legend [0.204, 0.060, 0.419, 0.095]." } ], "id_raw": 409 }, { "id": "CXTeBLEY26FRLfxc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Literature'}[0.335, 0.806, 0.409, 1.632] shorter than {the bar for 'History'}[0.701, 0.478, 0.774, 1.632]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 409 }, { "id": "wAehttixUEjYl3r9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Math\"}[0.591, 0.441, 0.665, 1.632] taller than {the bars representing \"Science\"}[0.226, 0.539, 0.299, 1.632]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 409 }, { "id": "8X7wZ22BPZGUbaDM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nAre {the bars for Traditional Method}[[0.153, 0.721, 0.226, 1.632], [0.335, 0.806, 0.409, 1.632], [0.518, 0.636, 0.591, 1.632], [0.701, 0.478, 0.774, 1.632]] generally shorter than {the bars for Modern Method}[[0.226, 0.539, 0.299, 1.632], [0.409, 0.660, 0.482, 1.632], [0.591, 0.441, 0.665, 1.632], [0.774, 0.296, 0.847, 1.632]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 409 }, { "id": "0FtTxh1sqHbRRU5S", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Science\"}[{Science bar for Traditional Method}[0.153, 0.721, 0.226, 1.632], {Science bar for Modern Method}[0.226, 0.539, 0.299, 1.632]] and {\"Math\"}[{Math bar for Traditional Method}[0.518, 0.636, 0.591, 1.632], {Math bar for Modern Method}[0.591, 0.441, 0.665, 1.632]], which study field scored higher for Modern Method?" }, { "from": "gpt", "value": "Math." } ], "id_raw": 409 }, { "id": "gxLYeiwk07kWjaXN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.606, 0.046, 0.870, 0.108]." }, { "from": "gpt", "value": "The region contains one pair of color-text labels in the legend of subplot_1. The color label is located at [0.615, 0.063, 0.662, 0.087], which is pink, and the text label is located at [0.680, 0.060, 0.861, 0.095], referred to as Modern Method, indicating that the Modern Method's bar is pink." } ], "id_raw": 409 }, { "id": "lOXnI4lL9fDVG0gJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.118, 0.174, 0.882, 0.903]." }, { "from": "gpt", "value": "This is the main area of the chart where both Traditional Method and Modern Method bars are plotted. It also contains all the category labels (Science, Literature, Mathematics, and History) on the horizontal axis, and the y-axis label, 'Traditional Method' as well. It also consists of tick marks and labels for the y-axis." } ], "id_raw": 409 }, { "id": "29MSLrU8Zoo7fuNG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.153, 0.721, 0.409, 1.632]?" }, { "from": "gpt", "value": "There are 2 objects:\n1. The black column, representing the Science category of the Traditional Method, with a value of 75, and its coordinates are [0.153, 0.721, 0.226, 1.632].\n2. The pink column, representing the Science category of the Modern Method, with a value of 90, and its coordinates are [0.226, 0.539, 0.299, 1.632]." } ], "id_raw": 409 }, { "id": "0gM554Yr1yjE4bZr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.118, 0.174, 0.882, 0.903]?" }, { "from": "gpt", "value": "In this region, you can see the main chart content. This includes all the bars catered by both Traditional and Modern Methods, the category labels on the x-axis (Science, Literature, Math, and History), the grid lines on the y-axis, as well as the y-axis labels for both Traditional and Modern Methods." } ], "id_raw": 409 }, { "id": "wK63vBdU3Qoq8tNN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the role of the object with coordinates [0.189, 0.175, 0.309, 0.210] in the chart?" }, { "from": "gpt", "value": "It is a text label, indicating the data is about \"Mountains\"." } ], "id_raw": 410 }, { "id": "I7RgL6HNMoRmcsFW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat object resides at coordinates [0.206, 0.153, 0.279, 0.188]?" }, { "from": "gpt", "value": "It is a text label representing the percentage data value of the mountain, specifically 55.3%." } ], "id_raw": 410 }, { "id": "7NBMUmvTAQ8o8OYa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the significance of {the highlighted region} at [0.728, 0.162, 0.885, 0.224]?" }, { "from": "gpt", "value": "It is the legend specifying that the pink color corresponds to data about \"Ocean\"." } ], "id_raw": 410 }, { "id": "Y2FYTWADyBvBbHuO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nDoes the percentage of {the rectangle representing \"Mountains\"}[0.139, 0.186, 0.345, 0.324] exceed the percentage of {the rectangle representing \"Ocean\"}[0.139, 0.324, 0.345, 0.435] in the \"Rapid recovery\" category?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 410 }, { "id": "Nj2wt6ZH4Gx7Fc1g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nComparing the two elements, {the \"Rapid recovery\" bar for \"Mountains\"}[0.139, 0.186, 0.345, 0.324] and {the \"Abrupt Drop\" bar for \"Mountains\"}[0.655, 0.244, 0.861, 0.324], which has a higher percentage?" }, { "from": "gpt", "value": "The \"Rapid recovery\" bar." } ], "id_raw": 410 }, { "id": "JOZD6UdfzZsOp5gq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the bar representing \"Mountains\" in the \"Abrupt Drop\" category}[0.655, 0.244, 0.861, 0.324] or {the bar representing \"Ocean\" in the \"Abrupt Drop\" category}[0.655, 0.324, 0.861, 0.441]?" }, { "from": "gpt", "value": "The bar representing \"Ocean\"." } ], "id_raw": 410 }, { "id": "HFYrIXy2mhuNHLEf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.139, 0.186, 0.345, 0.324]?" }, { "from": "gpt", "value": "There is a bar representing the percentage data of \"Mountains\", specified as 55.3%, during the \"Rapid recovery\" period." } ], "id_raw": 410 }, { "id": "ugKLRvFfFJKdvW4F", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.103, 0.174, 0.897, 0.324]." }, { "from": "gpt", "value": "This region contains four bars, two for \"Mountains\" data and two for \"Ocean\" data in two different categories: \"Rapid recovery\" and \"Abrupt Drop\". Labels for these bars with their respective percentages are also present within this region." } ], "id_raw": 410 }, { "id": "3e4oCVQpfLgatyHi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nDescribe what is within the region [0.115, 0.162, 0.318, 0.224]." }, { "from": "gpt", "value": "This region contains the legend for the \"Mountains\" category data, which is represented by a gray color in the chart. The color symbol is located at [0.124, 0.179, 0.170, 0.203]." } ], "id_raw": 410 }, { "id": "kIGozgpRCmaYFExC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.139, 0.272, 0.204, 0.492] in the chart?" }, { "from": "gpt", "value": "It's a bar from the 'Project Management' group." } ], "id_raw": 411 }, { "id": "LM7BSvptTskXEInf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is located at [0.132, 0.118, 0.860, 0.163]?" }, { "from": "gpt", "value": "The subtitle of the chart: 'Project Management: Project Innovation Comparison'." } ], "id_raw": 411 }, { "id": "jwlbnzcxKk75fgWR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at the [0.189, 0.093, 0.428, 0.128] coordinate refer to?" }, { "from": "gpt", "value": "It refers to the term 'Project Management' in the legend." } ], "id_raw": 411 }, { "id": "t5yTxNxFxn5ZBItK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nAre the bars representing {Project Management}[0.139, 0.272, 0.204, 0.492] taller than the bars representing {Project Innovation}[0.139, 0.549, 0.204, 0.769]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 411 }, { "id": "6sgjMQ02470641oS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nComparing the objects at [0.139, 0.272, 0.204, 0.492] and [0.301, 0.212, 0.366, 0.492], which has a higher position?" }, { "from": "gpt", "value": "The object at [0.301, 0.212, 0.366, 0.492]." } ], "id_raw": 411 }, { "id": "yIwcLr2MmxDeDQJj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nIn the legend, are the texts representing {Project Management}[0.189, 0.093, 0.428, 0.128] on the left of {Project Innovation}[0.658, 0.093, 0.868, 0.128]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 411 }, { "id": "FNX9Nk8rO19fm9kp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area marked by coordinates [0.115, 0.079, 0.438, 0.142]." }, { "from": "gpt", "value": "This area hosts the legend of the chart. Specifically, there is a rectangular color label at [0.124, 0.096, 0.170, 0.120] which is in 'Project Management' color, followed by the corresponding text label 'Project Management' at [0.189, 0.093, 0.428, 0.128]." } ], "id_raw": 411 }, { "id": "qtKbU4MGcX5i5aAy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nWhat do we see in the region [0.351, 0.067, 0.865, 0.153]?" }, { "from": "gpt", "value": "The chart title \"Project Management: Project Innovation Comparison\" is located in this region, with coordinates of [0.132, 0.118, 0.860, 0.163]." } ], "id_raw": 411 }, { "id": "yi9MKmwHfxRXvdFv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the chart area [0.139, 0.212, 0.853, 0.769]?" }, { "from": "gpt", "value": "This area contains all the bars representing the percentages of 'Project Management' at [0.139, 0.272, 0.204, 0.492], [0.301, 0.212, 0.366, 0.492], [0.463, 0.332, 0.528, 0.492], [0.626, 0.232, 0.691, 0.492], [0.788, 0.432, 0.853, 0.492] and 'Project Innovation' at [0.139, 0.549, 0.204, 0.769], [0.301, 0.549, 0.366, 0.720], [0.463, 0.549, 0.528, 0.865], [0.626, 0.549, 0.691, 0.834], [0.788, 0.549, 0.853, 0.679]." } ], "id_raw": 411 }, { "id": "KLAY5wL3nJaKv0rf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the subplot title with coordinates [0.302, 0.130, 0.694, 0.160]?" }, { "from": "gpt", "value": "Manufacturing Production Efficiency Evaluation." } ], "id_raw": 412 }, { "id": "b7IUUz9Xn5V3VZYp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the text in the legend at [0.782, 0.035, 0.921, 0.058] refer to?" }, { "from": "gpt", "value": "It refers to the {Product B Efficiency} bar." } ], "id_raw": 412 }, { "id": "N7mb2qNfHHxBQdZY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nWhat value is indicated by the text located at [0.116, 0.826, 0.159, 0.850]?" }, { "from": "gpt", "value": "It indicates the value of 40.8%." } ], "id_raw": 412 }, { "id": "eYmK2S1idjw8hDRD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nIs the {Product B Efficiency} bar of 2016 taller than the 2017 one?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 412 }, { "id": "JPDVeynbaQQNsXhn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Product A Efficiency\" bar of 2020}[0.822, 0.214, 0.894, 0.551] taller than {the \"Product A Efficiency\" bar of 2019}[0.642, 0.238, 0.714, 0.551]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 412 }, { "id": "KACy9QJrJnjFsa9k", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Product B Efficiency\" bar of 2018}[0.462, 0.551, 0.534, 0.842] have a higher value than {the \"Product B Efficiency\" bar of 2017}[0.282, 0.551, 0.354, 0.820]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 412 }, { "id": "DpFzrYyi6ki1os3j", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.643, 0.551, 0.894, 0.773]." }, { "from": "gpt", "value": "Within this region, there are two objects: \n1. {The \"Product A Efficiency\" bar of 2019}[0.642, 0.238, 0.714, 0.551], representing a value of 65.4%. \n2. {The \"Product B Efficiency\" bar of 2019}[0.642, 0.551, 0.714, 0.773], representing a negative value shown as 34.6%." } ], "id_raw": 412 }, { "id": "1GGaT7Zv7XIXA8Aw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.282, 0.551, 0.714, 0.820]?" }, { "from": "gpt", "value": "In this area, there are two Efficiency bars for the year 2017:\n1. {The \"Product A Efficiency\" bar}[0.282, 0.273, 0.354, 0.551], which shows a value of 58.1%.\n2. {The \"Product B Efficiency\" bar}[0.282, 0.551, 0.354, 0.820], displaying a negative value of 41.9%." } ], "id_raw": 412 }, { "id": "WHNybEP3ilC9AKk7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.743, 0.037, 0.771, 0.053]." }, { "from": "gpt", "value": "This region contains a color handle in pink that corresponds to {Product B Efficiency}[0.782, 0.035, 0.921, 0.058] in the legend." } ], "id_raw": 412 }, { "id": "Z1je2vDR9IZDofWr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart title located at the coordinates [0.109, 0.118, 0.883, 0.163]?" }, { "from": "gpt", "value": "The title is \"Comparison of Growth Rates in Digital Content and SEO\"." } ], "id_raw": 413 }, { "id": "u75Qix14LZrudnT0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at the coordinates [0.025, 0.339, 0.048, 0.738] stand for?" }, { "from": "gpt", "value": "It is labeled as \"Digital Content Growth\"." } ], "id_raw": 413 }, { "id": "JhRa0ds4jwvTvVPm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the figure represent with the coordinates [0.952, 0.435, 0.975, 0.643]?" }, { "from": "gpt", "value": "This represents the y-axis label for subplot 1, called \"SEO Growth\"." } ], "id_raw": 413 }, { "id": "1KfUCkTrBUWnPyA9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object situated at the coordinates [0.189, 0.060, 0.455, 0.095]?" }, { "from": "gpt", "value": "This is the text in the legend for subplot 0, named \"Digital Content Growth\"." } ], "id_raw": 413 }, { "id": "l1caNgw05WymHm0E", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar represents \"Video Production\"}[0.139, 0.295, 0.223, 0.791] higher or lower than {the bar represents \"Podcast\"}[0.349, 0.224, 0.433, 0.820]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 413 }, { "id": "4hjQzjPpuYq2bDcq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar represents \"Video Production\"}[0.139, 0.295, 0.223, 0.791] depicts lower growth rate than {the bar represents \"Graphic Design\"}[0.769, 0.572, 0.853, 0.696]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 413 }, { "id": "pya73bYXvh6L5bo0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar represents \"Video Prod.\"}[0.139, 0.572, 0.223, 0.791] depict a higher SEO Growth rate than {the bar represents \"Podcast\"}[0.349, 0.572, 0.433, 0.820]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 413 }, { "id": "ofECXag9ERZ4UuaO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nAmong the bars representing \"Video Production\" and \"Graphic Design\" which has a higher {growth rate}<{the bar represents \"Video Production\"}[0.139, 0.295, 0.223, 0.791], {the bar represents \"Graphic Design\"}[0.769, 0.572, 0.853, 0.696]>?" }, { "from": "gpt", "value": "Video Production." } ], "id_raw": 413 }, { "id": "wdnhwZAr7fPVOTTo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nPlease provide a description of the content in the region [0.346, 0.927, 0.436, 0.962]." }, { "from": "gpt", "value": "This region contains a category label on the x-axis for subplot 0 and subplot 1, specifically, the label \"Podcast\"." } ], "id_raw": 413 }, { "id": "cLdJd2HYvxAn48fO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the key elements within the area [0.115, 0.046, 0.464, 0.108]." }, { "from": "gpt", "value": "This area includes the rectangle representing the color of Digital Content Growth located at [0.124, 0.063, 0.170, 0.087], and the corresponding label inscription \"Digital Content Growth\" at [0.189, 0.060, 0.455, 0.095]." } ], "id_raw": 413 }, { "id": "mtLSgeRERNVCURer", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region [0.524, 0.927, 0.678, 0.962]?" }, { "from": "gpt", "value": "Here, you'd find an x-axis label associated with both subplots, which reads \"Content Writ.\"." } ], "id_raw": 413 }, { "id": "9uOJSxRbFojGjFaT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe what the region [0.905, 0.160, 0.934, 0.195] includes?" }, { "from": "gpt", "value": "1. Label \"60\" representing an y-axis tick for subplot 1 at [0.905, 0.160, 0.934, 0.195]. This label indicates a 60-point value in SEO growth." } ], "id_raw": 413 }, { "id": "9tyMmNiIbM18kjBA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.025, 0.311, 0.048, 0.767]?" }, { "from": "gpt", "value": "It is the Y-axis label which is \"Urban Development Index\"." } ], "id_raw": 414 }, { "id": "KB1brkNiWUZhOyFP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first text in the sublegend in the second subplot?" }, { "from": "gpt", "value": "[0.582, 0.060, 0.876, 0.095]" } ], "id_raw": 414 }, { "id": "sM20vUyLvijtnl1z", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the label say at [0.366, 0.118, 0.634, 0.163]?" }, { "from": "gpt", "value": "It says \"Urban : Rural = 2:0\"." } ], "id_raw": 414 }, { "id": "sa8UTt7Nzfm685Vz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects found at [0.139, 0.539, 0.345, 0.703] and [0.655, 0.539, 0.861, 0.757]?" }, { "from": "gpt", "value": "They are two bars representing the Rural Development Index for the years 2018 and 2020 respectively." } ], "id_raw": 414 }, { "id": "eHNvTKl9gae2rkPr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nComparing the 2 categories, which one has the higher index in 2020: {Urban Development Index}[0.655, 0.265, 0.861, 0.539] or {Rural Development Index}[0.655, 0.539, 0.861, 0.757]?" }, { "from": "gpt", "value": "Rural Development Index." } ], "id_raw": 414 }, { "id": "1bGexvHKQOoe0AS2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Urban Development Index of 2018}[0.139, 0.338, 0.345, 0.539] higher than the {Rural Development Index of 2018}[0.139, 0.539, 0.345, 0.703]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 414 }, { "id": "0Cy3IyYsmHXNS9tt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nComparing the indices, which category improved more from 2018 to 2020: {Urban Development Index} [0.139, 0.338, 0.345, 0.539] [0.655, 0.265, 0.861, 0.539] or {Rural Development Index} [0.139, 0.539, 0.345, 0.703] [0.655, 0.539, 0.861, 0.757]?" }, { "from": "gpt", "value": "Urban Development Index." } ], "id_raw": 414 }, { "id": "3GzICuradNFannhJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what is inside the coordinates [0.508, 0.046, 0.885, 0.108]." }, { "from": "gpt", "value": "There is an item in the sublegend; it has a label \"Rural Development Index\" located at [0.582, 0.060, 0.876, 0.095], and an associated handle, which is pink, at [0.517, 0.063, 0.564, 0.087]. It signifies that the Rural metric is depicted in pink." } ], "id_raw": 414 }, { "id": "bvO59FoBVRgN88rg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat area does the coordinates [0.213, 0.927, 0.272, 0.962] and [0.729, 0.927, 0.787, 0.962] encompass?" }, { "from": "gpt", "value": "They represent the X-axis labels, which show the years 2018 and 2020, respectively." } ], "id_raw": 414 }, { "id": "Q14mGmtG1p8IyWtb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the area [0.103, 0.174, 0.897, 0.903]?" }, { "from": "gpt", "value": "The specified area includes the overall axes frame of the chart. This also includes all the bars representing the Urban and Rural Development Indices for the years 2018 and 2020, the grid lines, Y-ticks, and X-ticks." } ], "id_raw": 414 }, { "id": "YG3j9AdwoDDlZwCF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the plotted data on the chart with coordinates [0.103, 0.174, 0.874, 0.903]?" }, { "from": "gpt", "value": "It is the Health Membership and Outdoor Sports data for the years 2017, 2018, 2019 and 2020." } ], "id_raw": 415 }, { "id": "kWQUHVEuLjYFoxc5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nWhere is the label of the first bar that represents the Outdoor Sports category located?" }, { "from": "gpt", "value": "[0.154, 0.876, 0.205, 0.911]" } ], "id_raw": 415 }, { "id": "9D58g0ggXo1qiBob", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nWhat group title is indicated by the text in the coordinate [0.189, 0.060, 0.415, 0.095]?" }, { "from": "gpt", "value": "The term is Health Membership." } ], "id_raw": 415 }, { "id": "2iIhDPcUDnt2v2wK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at {the coordinate} [0.058, 0.369, 0.087, 0.404] along the y-axis of the charts?" }, { "from": "gpt", "value": "It is marked as 85." } ], "id_raw": 415 }, { "id": "llb15iU0PLSqo2yt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nIs the \"{Health Membership bar in 2017}\"[0.138, 0.591, 0.221, 2.153] higher or lower than the \"{Outdoor Sports bar in 2017}\"[0.138, 0.558, 0.221, 0.884]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 415 }, { "id": "Xcaa4fLPaCRoxWyB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nIn 2020, did the \"{Outdoor Sports Membership}\"[0.757, 0.558, 0.839, 0.872] increase or decrease compared to the \"{Health Membership}\"[0.757, 0.570, 0.839, 2.153]?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 415 }, { "id": "c0lIBFl8ZsjWknRv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nBetween \"{Health Membership in 2018}\"[0.344, 0.549, 0.427, 2.153] and \"{Outdoor Sports in 2019}\"[0.550, 0.558, 0.633, 0.899], which bar has a higher value?" }, { "from": "gpt", "value": "Outdoor Sports in 2019." } ], "id_raw": 415 }, { "id": "qdQ4YpdhMT87oPlJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nLooking at the bars for 2019, is the \"{Health Membership}\"[0.550, 0.466, 0.633, 2.153] higher or lower than the \"{Outdoor Sports}\"[0.550, 0.558, 0.633, 0.899]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 415 }, { "id": "i3MN5LOwJOG3Mism", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe the details featured in the region [0.115, 0.046, 0.424, 0.108]?" }, { "from": "gpt", "value": "The region contains the legend for the Health Membership category. The blue color label is placed at [0.124, 0.063, 0.170, 0.087] and the corresponding text label \"Health Membership\" is located at [0.189, 0.060, 0.415, 0.095]." } ], "id_raw": 415 }, { "id": "Ugt1tvYOz38nUi8U", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nWhat is within the span of [0.138, 0.558, 0.633, 0.899]?" }, { "from": "gpt", "value": "It contains two bar charts for the years 2017 and 2019. The 2017 bar charts depict Outdoor Sports with its information located at [0.154, 0.876, 0.205, 0.911]. The 2019 bar charts depict the Outdoor Sports with its information located at [0.566, 0.891, 0.617, 0.926]." } ], "id_raw": 415 }, { "id": "izM5b3xmN9gnoauh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the configured region {at} [0.103, 0.174, 0.874, 0.903]?" }, { "from": "gpt", "value": "It covers bar charts for the Health Membership and Outdoor Sports, with respective categories and data values for the years 2017, 2018, 2019 and 2020." } ], "id_raw": 415 }, { "id": "4RTm7cqIX0dWzoUF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe what's being shown in the coordinates [0.952, 0.407, 0.975, 0.671]." }, { "from": "gpt", "value": "It includes the ylabel for the second aka bottom subplot, which refers to the \"Outdoor Sports\" category." } ], "id_raw": 415 }, { "id": "oOxS8IvtGq7J71V0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nWhat's depicted by the object at [0.087, 0.889, 0.102, 0.924]?" }, { "from": "gpt", "value": "The object is the y-axis label for the 'Transportation' metric, which is at 0." } ], "id_raw": 416 }, { "id": "jYoxOZNQkUYab83K", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred from the object at [0.153, 0.174, 0.235, 0.748]?" }, { "from": "gpt", "value": "The object is a bar representing the metric 'Logistics' for 'Vehicles'. The transportation percentage in logistics industry for 'Vehicles' is 55.1%." } ], "id_raw": 416 }, { "id": "19Z4Wi4ohaeFiHMU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.768, 0.060, 0.868, 0.095] denote?" }, { "from": "gpt", "value": "The label denotes {the chart's 'Logistics'[0.768, 0.060, 0.868, 0.095]} measurement." } ], "id_raw": 416 }, { "id": "tJJ2VV17YVbPF3It", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing logistics in 'Vehicles'}[0.153, 0.174, 0.235, 0.748] longer than {the bar representing logistics in 'Ships'}[0.359, 0.174, 0.441, 0.825]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 416 }, { "id": "XVVOqdXOVNZtzzbw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nIn comparison to {the bar representing logistics in 'Trains'}[0.565, 0.174, 0.648, 0.488], what is the length of {the bar representing logistics in 'Aircraft'}[0.771, 0.174, 0.854, 0.591]?" }, { "from": "gpt", "value": "Longer." } ], "id_raw": 416 }, { "id": "ceVB1BMbd970FY49", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the bar representing transportation in 'Vehicles'}[0.153, 0.406, 0.235, 0.903] or {the bar representing logistics in 'Vehicles'}[0.153, 0.174, 0.235, 0.748]?" }, { "from": "gpt", "value": "The bar representing transportation in 'Vehicles' is taller." } ], "id_raw": 416 }, { "id": "PlnSRWtIhKv9L935", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nCan you detail what is contained in the region [0.694, 0.046, 0.877, 0.108]?" }, { "from": "gpt", "value": "In the region specified, there is the color-label pair for 'Logistics' in the legend. The color box indicating 'Logistics' color is at [0.703, 0.063, 0.749, 0.087], and the text for 'Logistics' in the legend is at [0.768, 0.060, 0.868, 0.095]." } ], "id_raw": 416 }, { "id": "iZodv7pGuHphnApI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be identified inside the region [0.139, 0.063, 0.185, 0.087]?" }, { "from": "gpt", "value": "The region includes a color box from the legend, which is black in color and denotes 'Transportation'." } ], "id_raw": 416 }, { "id": "kuEShxf2e5zzXxEh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content inside the region [0.118, 0.174, 0.889, 0.903]." }, { "from": "gpt", "value": "This area includes the whole chart area with all bars and labels for both 'Transportation' and 'Logistics' metrics. The bars representing 'Transportation' in the 'Transportation' and 'Logistics industry' are black while those representing 'Logistics' are pink. The y-axis has two parts: the left part, with black labels, represents 'Transportation', and the right part, with pink labels, represents 'Logistics'. The x-axis contains labels for 'Vehicles', 'Ships', 'Trains', and 'Aircraft'." } ], "id_raw": 416 }, { "id": "bHtrAcjsKb3T8Wpj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinates [0.366, 0.130, 0.645, 0.160] say?" }, { "from": "gpt", "value": "It says \"Social Media Data Analysis\"." } ], "id_raw": 417 }, { "id": "jMN2NVEF23bJ85cJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the object at coordinates [0.629, 0.037, 0.664, 0.053]?" }, { "from": "gpt", "value": "The color is '#dc9dae', which resembles a pink shade." } ], "id_raw": 417 }, { "id": "SYxNSs32tpRkvjex", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.796, 0.689, 0.885, 0.935]?" }, { "from": "gpt", "value": "It is the bar for \"Snapchat\" showing the average usage time as 80.2 minutes." } ], "id_raw": 417 }, { "id": "YPEIK3brCM6Xe3p5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.104, 0.037, 0.139, 0.053] in the chart?" }, { "from": "gpt", "value": "A color label in the legend for 'Average Usage Time', which is color '#404346', a dark grey shade." } ], "id_raw": 417 }, { "id": "G46dax1AB1Oy4uni", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Instagram} [0.349, 0.345, 0.439, 0.935] taller than the bar for {Twitter} [0.126, 0.467, 0.215, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 417 }, { "id": "PkwdkTWzeDibTkZr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for {Twitter} [0.126, 0.167, 0.215, 0.522] have a higher y-end coordinate than the bar for {Snapchat} [0.796, 0.167, 0.885, 0.338] in terms of post engagement?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 417 }, { "id": "vRXXIHDWbllXinJQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nAre the bars for {Instagram} [0.349, 0.345, 0.439, 0.935] and {Facebook} [0.572, 0.240, 0.662, 0.935] of the same height?" }, { "from": "gpt", "value": "No." } ], "id_raw": 417 }, { "id": "tgoy5jPXL6dUdYZz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, the bar representing {Instagram}[0.349, 0.345, 0.439, 0.935] or the bar representing {Snapchat}[0.796, 0.689, 0.885, 0.935]?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 417 }, { "id": "mr2vM67dRw9ENShD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the area [0.622, 0.025, 0.914, 0.067]?" }, { "from": "gpt", "value": "In the area [0.622, 0.025, 0.914, 0.067], there are two objects:\n1. A text label for \"Average Post Engagement\" located at [0.678, 0.035, 0.907, 0.058].\n2. A color label (in pink '#dc9dae') indicating the color of bars for \"Average Post Engagement\", with its coordinates at [0.629, 0.037, 0.664, 0.053]." } ], "id_raw": 417 }, { "id": "aPlELGXkcgGHpZHC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.126, 0.467, 0.215, 0.935]." }, { "from": "gpt", "value": "The area [0.126, 0.467, 0.215, 0.935] contains a dark grey bar that represents the average usage time (152.6 minutes) of Twitter. The corresponding text label is situated at [0.123, 0.494, 0.219, 0.518]." } ], "id_raw": 417 }, { "id": "zA8AzZNKLC9uDntQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.796, 0.167, 0.885, 0.935]?" }, { "from": "gpt", "value": "There are two bars within the region [0.796, 0.167, 0.885, 0.935]. One bar with coordinates [0.796, 0.689, 0.885, 0.935] corresponds to the \"Average usage time\" for Snapchat (80.2 minutes). The other bar at [0.796, 0.167, 0.885, 0.338] is for \"Average Post Engagement\" (11.1%)." } ], "id_raw": 417 }, { "id": "hIEbE8WvEKxOYHLy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat do the objects in [0.349, 0.345, 0.439, 0.716] represent?" }, { "from": "gpt", "value": "The objects are two vertical bars. The first one [0.349, 0.345, 0.439, 0.935] is a dark grey bar that represents average usage time of Instagram (192.3 minutes). The second is a pink bar [0.349, 0.167, 0.439, 0.716] representing the Instagram's \"Average Post Engagement\" (35.7%)." } ], "id_raw": 417 }, { "id": "gX06Q0YPieHR8CQN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text label}[0.114, 0.584, 0.153, 0.608] associated with in the chart?" }, { "from": "gpt", "value": "The value 15.5 for the year 2022." } ], "id_raw": 418 }, { "id": "p77gcYA4ScIZ0j0g", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the handle inside {the legend}[0.086, 0.020, 0.258, 0.061]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 418 }, { "id": "JF9v0IiE94t2nWEa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.019, 0.459, 0.036, 0.605] in the chart?" }, { "from": "gpt", "value": "Slow Growth." } ], "id_raw": 418 }, { "id": "zW42DJZRInWh65nR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nIs {the y-value \"-25\"}<[0.929, 0.696, 0.957, 0.719] greater than {the y-value \"-30\"}<[0.929, 0.811, 0.957, 0.834]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 418 }, { "id": "1tzbtmiaQWnZYLeM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 2025}[0.388, 0.129, 0.424, 0.818] longer than {the bar for 2028}[0.660, 0.129, 0.697, 0.590]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 418 }, { "id": "jT62IV1mKIbb5Aoj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the first bar}[0.116, 0.623, 0.152, 0.935] have a smaller value than {the last bar}[0.842, 0.270, 0.878, 0.935]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 418 }, { "id": "FS3xzdqUNtEED7K9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.077, 0.129, 0.917, 0.935]." }, { "from": "gpt", "value": "It contains the complete chart area with axes, bars for \"Slow Growth\" and \"Unstable Behavior,\" y-ticks from labels \"-35\" to \"-5\" and x-labels from 2022 to 2030." } ], "id_raw": 418 }, { "id": "calz3Nb4a3xkEcN0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nDetail the elements found in [0.077, 0.020, 0.258, 0.061]." }, { "from": "gpt", "value": "It includes a legend item labeled \"Slow Growth\" with a color handle, coordinates for text are [0.142, 0.029, 0.251, 0.052], and for the handle are [0.093, 0.031, 0.128, 0.047]." } ], "id_raw": 418 }, { "id": "u1tGw7bqZXCgYWTT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat features are presented in [0.077, 0.929, 0.957, 0.950]?" }, { "from": "gpt", "value": "There are y-tick labels indicating negative values, specifically, -30, -25, -20, -15, -10, and -5, along with their corresponding tick marks." } ], "id_raw": 418 }, { "id": "Q5BXruoW0M09akiB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.025, 0.430, 0.048, 0.648]?" }, { "from": "gpt", "value": "This is the y-axis label, called \"Global Trade\"." } ], "id_raw": 419 }, { "id": "T9KqVvHH6oS7c16O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell what the coordinates of the item in the sublegend of subplot_1 is?" }, { "from": "gpt", "value": "The coordinate of the item in the sublegend of subplot_1 is [0.570, 0.060, 0.883, 0.095]." } ], "id_raw": 419 }, { "id": "3wiVMU87X3met0CT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label attached to the coordinates [0.144, 0.539, 0.351, 0.728]?" }, { "from": "gpt", "value": "It's a bar label, reading -2.6% for \"Global Trade\" in 2020." } ], "id_raw": 419 }, { "id": "u8w7v0mPGJftDZtO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color assigned to the handle at the coordinates [0.129, 0.063, 0.175, 0.087]?" }, { "from": "gpt", "value": "The color of {the handle}[0.129, 0.063, 0.175, 0.087] is '#404346' or dark grey which represents \"Global Trade\"." } ], "id_raw": 419 }, { "id": "njJxvZ179Ye2ej9m", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nIn the 2020 Global Trade trend, is the growth value positive or negative, as indicated by {the bar}[0.144, 0.539, 0.351, 0.728]?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 419 }, { "id": "ZN3uTBpqABEwZfSe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nComparing the 2020 and 2021 data for {Supply Chain Management}[0.144, 0.539, 0.351, 0.641] and [0.661, 0.480, 0.867, 0.539], which year saw a decrease?" }, { "from": "gpt", "value": "2021." } ], "id_raw": 419 }, { "id": "D3cColrmiFKKAopa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nIn subplot_0, does the bar for 2021 Global Trade {bar}[0.661, 0.269, 0.867, 0.539] show an increase or decrease compared to the 2020 bar {bar}[0.144, 0.539, 0.351, 0.728]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 419 }, { "id": "Cq7RJBEPrxpKj0Bq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nBetween {Global Trade} in 2020[0.144, 0.539, 0.351, 0.728] and {Supply Chain Management} in 2020[0.144, 0.539, 0.351, 0.641], which one showed a higher growth rate?" }, { "from": "gpt", "value": "Supply Chain Management." } ], "id_raw": 419 }, { "id": "kkwJ2IfnaxWaDVtp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.661, 0.269, 0.867, 0.539]?" }, { "from": "gpt", "value": "The area contains a bar that represents the Global Trade trend value for 2021 of 3.7%." } ], "id_raw": 419 }, { "id": "kiQcPekTUdA5XXId", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.108, 0.174, 0.903, 0.903]?" }, { "from": "gpt", "value": "The region contains the main plot area for subplot_0, showing bars for Global Trade trends in 2020 and 2021 with their corresponding growth rates. There's also vertical gridlines to help assessment of values." } ], "id_raw": 419 }, { "id": "WJSsWmI7nXULYysb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nTell me about the objects found in the coordinates [0.108, 0.174, 0.903, 0.903] in subplot_1." }, { "from": "gpt", "value": "This area encompasses all the plot elements for Supply Chain Management trends, including bars for two years with their heights representing growth rates respectively and y axis with labels. The horizontal gridlines aid in value comparison." } ], "id_raw": 419 }, { "id": "XX9FdUKYDMV2LoNv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the region outlined by [0.119, 0.046, 0.348, 0.108] contain?" }, { "from": "gpt", "value": "The outlined region contains a sub-legend, which provides a colored handle and a label titled \"Global Trade\". This legend's color_decoder is grey, corresponding to the color of the \"Global Trade\" bars." } ], "id_raw": 419 }, { "id": "wFkfZ5l6JaFrONkj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label at the y-axis}[0.008, 0.084, 0.016, 0.217] indicate?" }, { "from": "gpt", "value": "Performance Score (%)." } ], "id_raw": 420 }, { "id": "4Pqlgd1nUryGb3qP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nWhat value is displayed above {the bar for Astra-778E in the Creative Destruction plot}[0.929, 0.099, 0.951, 0.270]?" }, { "from": "gpt", "value": "71.6." } ], "id_raw": 420 }, { "id": "L4GOmeExkJItKTCn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nWhat type of innovation does {the subtitle in the region}[0.130, 0.342, 0.237, 0.357] refer to?" }, { "from": "gpt", "value": "Incremental Innovation." } ], "id_raw": 420 }, { "id": "OSHMXIRg3uD56svd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for MatrixQ-999R in Incremental Innovation}[0.295, 0.399, 0.317, 0.599] taller than {the bar for AIBot-983X in the same chart}[0.050, 0.397, 0.072, 0.599]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 420 }, { "id": "gQlkO6IJeFL8aN67", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the score for technological leap by SiliconM-112L}[0.875, 0.382, 0.896, 0.599] exceed {the maximum y-tick label in the same subplot}[0.680, 0.356, 0.695, 0.367]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 420 }, { "id": "v0cZqntRHuhRLH7e", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nAre {the values for Fradulent Patents by NeuraVista-6Z and MindfulB-66X}[0.241, 0.928, 0.263, 0.928], [0.132, 0.927, 0.154, 0.928] identical?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 420 }, { "id": "TiwIXFryghFOMuLk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the information within {the region marked from top left to bottom right for the Incremental Innovation subplot}[0.037, 0.360, 0.331, 0.599]." }, { "from": "gpt", "value": "This region contains bars for different models where each bar's height represents the corresponding performance score in percent for Incremental Innovation. Specific labels and performance centages of models like AIBot-983X, SiliconM-112L, and NeuraVista-6Z are visualized here." } ], "id_raw": 420 }, { "id": "5k6fiAvAsG0tjTEb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be identified in {the specified subsection of the subplot for Technological Discontinuity}[0.367, 0.360, 0.661, 0.599]?" }, { "from": "gpt", "value": "This area displays multiple vertical bars, each representing a different model's performance in Technological Discontinuity. The bars are colored similarly, placed along an x-axis with model names and y-axis indicating the performance score percentage." } ], "id_raw": 420 }, { "id": "gwMx3k61p5LNPSij", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nOutline the visible elements in {the top right subplot area themed Creative Destruction}[0.698, 0.031, 0.992, 0.270]." }, { "from": "gpt", "value": "In this section, various bars represent the scores obtained by models in Creative Destruction. Each bar has a score displayed on top, situated against labels for the models and scaled by performance percentage indicators on y-axis. The backdrop is neutral, assisting in visually emphasizing the bar graphs." } ], "id_raw": 420 }, { "id": "fwo3IqfeGOWZkBXz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the subplot labeled \"Oil\"?" }, { "from": "gpt", "value": "Energy Capacity (TWh), located at {the label}[0.015, 0.397, 0.029, 0.598]." } ], "id_raw": 421 }, { "id": "c1mcTbFcLcCVGLBK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the fifth x-axis tick label for the \"Hydro\" subplot?" }, { "from": "gpt", "value": "2019, displayed at {the tick label}[0.897, 0.628, 0.918, 0.654]." } ], "id_raw": 421 }, { "id": "TOiHSrJp5BIpuRq5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nWhere can I find the label for bars representing the year 2020 in the \"Geothermal\" energy subplot?" }, { "from": "gpt", "value": "The label is found at {the label}[0.952, 0.936, 0.963, 0.947]." } ], "id_raw": 421 }, { "id": "djRsa9IjA4ZeX9Hh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nIs {the first bar of Solar energy}[0.086, 0.292, 0.118, 0.294] shorter than {the first bar of Nuclear energy}[0.743, 0.245, 0.775, 0.294]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 421 }, { "id": "HrekwxXhIjatJlwR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing Bio Mass in 2018}[0.205, 0.942, 0.237, 0.949] have a lower value than {the bar representing Coal in the same year}[0.534, 0.937, 0.566, 0.949]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 421 }, { "id": "69xmFTgU31lIZ9tg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nAre the energy values for {Hydro in 2015}[0.743, 0.558, 0.775, 0.621] greater than those for {Gas in 2020}[0.613, 0.376, 0.645, 0.621]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 421 }, { "id": "daojQhLIvok29DGs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within {the y-axis region of the Coal subplot}[0.343, 0.724, 0.357, 0.925]." }, { "from": "gpt", "value": "This region contains various energy capacity labels denoted in TWh from 0 to 9600 in increments, specifically distinguished by their top-right coordinates like {the label for '4800'}[0.363, 0.832, 0.398, 0.849]." } ], "id_raw": 421 }, { "id": "Y3riZchx09LjZgq9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nWhat details are seen in {the x-axis region for the Wind energy subplot}[0.403, 0.628, 0.657, 0.654]?" }, { "from": "gpt", "value": "This region encompasses x-axis tick labels for years from 2015 to 2020, detailed at positions such as {the label for '2017'}[0.489, 0.628, 0.510, 0.654]." } ], "id_raw": 421 }, { "id": "XhcRluRY6rjq87JT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nExplain the elements inside {the range labeled Solar bar chart}[0.075, 0.047, 0.328, 0.294]." }, { "from": "gpt", "value": "This area contains a collection of energy production bars for Solar from 2015 to 2020, with individual bars pointed out like {the bar for 2018}[0.205, 0.290, 0.237, 0.294] which indicates the energy capacity." } ], "id_raw": 421 }, { "id": "cAKDKVNqqZHOH9rs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nWhich platform's data is represented by the bar with coordinates [0.145, 0.170, 0.200, 0.293]?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 422 }, { "id": "fSov9UCC2P9O10XH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nCould you tell me the number associated with {LinkedIn's bar}[0.418, 0.240, 0.473, 0.293] from Q1?" }, { "from": "gpt", "value": "15." } ], "id_raw": 422 }, { "id": "mLl4a4dxINEfw4ED", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.015, 0.075, 0.029, 0.264] in the chart?" }, { "from": "gpt", "value": "Active users (Millions)." } ], "id_raw": 422 }, { "id": "90pOg64dDo1x5WXO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Instagram}[0.213, 0.187, 0.268, 0.293] in Q1 higher than {the bar for Reddit}[0.350, 0.257, 0.404, 0.293]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 422 }, { "id": "QCpXitPebszDOp6r", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nWhich has a larger value in Q2, {Twitter}[0.774, 0.152, 0.829, 0.293] or {Reddit}[0.842, 0.222, 0.897, 0.293]?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 422 }, { "id": "hH1lMSzDegsBlJXm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nFor Q4, does {LinkedIn}[0.911, 0.560, 0.965, 0.637] have a greater value than {Reddit}[0.842, 0.588, 0.897, 0.637]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 422 }, { "id": "j8zn4J55gy420YTV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart elements in the region [0.272, 0.299, 0.520, 0.342]." }, { "from": "gpt", "value": "This region contains {Facebook's label}[0.138, 0.299, 0.172, 0.342] and its corresponding data bar for Q1 with a value of 35 at {bar coordinate}[0.145, 0.170, 0.200, 0.293]." } ], "id_raw": 422 }, { "id": "Xz1HtuWnFkkq3Fet", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are depicted within the region [0.050, 0.200, 0.250, 0.350] on the chart?" }, { "from": "gpt", "value": "The region includes {the ylabel}[0.015, 0.075, 0.029, 0.264] denoting \"Active users (Millions),\" and a section of the y-axis with {ticks and labels}[0.057, 0.082, 0.052, 0.303] indicating values from 0 to 70 million." } ], "id_raw": 422 }, { "id": "sCab7Aw0XRVnYuRR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.550, 0.180, 0.780, 0.330]?" }, { "from": "gpt", "value": "The region encompasses {Instagram's label}[0.697, 0.299, 0.733, 0.344] for Q2 and {Twitter's label}[0.775, 0.299, 0.801, 0.331] with associated data bars for their respective active users at {Instagram's bar coordinate}[0.706, 0.205, 0.760, 0.293] and {Twitter's bar coordinate}[0.774, 0.152, 0.829, 0.293]." } ], "id_raw": 422 }, { "id": "LaGaYqRYdwZqvxyp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at {the coordinates}[0.843, 0.683, 0.864, 0.749] in the chart?" }, { "from": "gpt", "value": "It is {the ylabel}[0.672, 0.771, 0.686, 0.878] for the subplot 'Digital Transformation'." } ], "id_raw": 423 }, { "id": "MII4YHTLUHVjYr4o", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the text at {the coordinates}[0.421, 0.503, 0.431, 0.514]?" }, { "from": "gpt", "value": "This represents the {2016 value}[0.421, 0.503, 0.431, 0.514] for the metric 'Work-Life Balance' matching with {Work-Life Balance\u2019s bar labeled 2016}[0.406, 0.518, 0.446, 0.787]." } ], "id_raw": 423 }, { "id": "nl7O6JPQ2Y0xihyF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar located at {the coordinates}[0.177, 0.792, 0.217, 1.114] signify?" }, { "from": "gpt", "value": "That bar represents {the value for 2018}[0.177, 0.792, 0.217, 1.114] in the 'Diversity & Inclusion' subplot." } ], "id_raw": 423 }, { "id": "uQHtg30s81jdePDe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing '2017'}[0.456, 0.845, 0.496, 1.114] taller than {the bar representing '2018'}[0.506, 0.833, 0.545, 1.114] in the subplot 'Digital Transformation'?" }, { "from": "gpt", "value": "No." } ], "id_raw": 423 }, { "id": "0uh8AOGtkq9uiD7F", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nAre {the 90 value labels}[[0.092, 0.400, 0.103, 0.411], [0.291, 0.400, 0.302, 0.411]] all located in {the upper half of their respective subplots}[[0.066, 0.701, 0.328, 0.825], [0.394, 0.701, 0.657, 0.825]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 423 }, { "id": "dTLwejMO0S3mQg7X", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nIs the text color for {the y-axis labels}[0.700, 0.942, 0.718, 0.959] the same as that for {the bar values}[0.749, 0.462, 0.760, 0.473] across subplots?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 423 }, { "id": "Pc0cf4v2Mj96MdUC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within {the plotted region}[0.394, 0.701, 0.657, 0.949]." }, { "from": "gpt", "value": "This region includes: \n1. The subplot label 'Digital Transformation'.\n2. Y-axis marked with values ranging from 40 to 100.\n3. X-axis representing years 2016 to 2020.\n4. Blue colored bars showing incremental trends for the years within 'Digital Transformation'." } ], "id_raw": 423 }, { "id": "K2h76MgHX8r3HI1X", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the region spanning from {coordinates}[0.066, 0.374, 0.328, 0.621] contain?" }, { "from": "gpt", "value": "This contains: \n1. The subplot label 'Leadership Development'.\n2. Y-axis with values labeled from 40 to 100.\n3. X-axis denoting the years between 2016 and 2020.\n4. Bars representing values for these years concerning 'Leadership Development'." } ], "id_raw": 423 }, { "id": "mfUCHWuOik47Yxya", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the area between {coordinates}[0.066, 0.701, 0.328, 0.949]?" }, { "from": "gpt", "value": "The area showcases:\n1. The subplot title 'Diversity & Inclusion'.\n2. Y-axis values from 40 to 100.\n3. X-axis year markers from 2016 to 2020.\n4. Blue bars corresponding to values from 2016 to 2020 for 'Diversity & Inclusion'." } ], "id_raw": 423 }, { "id": "X3ElIbfQB1DHwtTK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.015, 0.145, 0.029, 0.307] in the first subplot?" }, { "from": "gpt", "value": "Number of Patents." } ], "id_raw": 424 }, { "id": "qDauVfRCGTmUvePF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar at the bottom left}[0.094, 0.064, 0.193, 0.405] in the subplot labeled 'Artificial Intelligence'?" }, { "from": "gpt", "value": "Brown." } ], "id_raw": 424 }, { "id": "cJMX31llFxuJuI4a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nWhat numerical value is associated with {the highest bar in the '5G Technology' subplot}[0.094, 0.554, 0.193, 0.896]?" }, { "from": "gpt", "value": "55220." } ], "id_raw": 424 }, { "id": "CEd4LbyS1wyu7yjW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Patent Applications' in 'IoT'}[0.423, 0.554, 0.522, 0.896] taller than {the bar for 'Patent Applications' in '5G Technology'}[0.094, 0.554, 0.193, 0.896]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 424 }, { "id": "pMV5Y96DG1hynJWa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Big Data' subplot show a greater number for {Patents Granted}[0.875, 0.712, 0.974, 0.896] compared to {Patents Granted in the 'IoT' subplot}[0.547, 0.713, 0.646, 0.896]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 424 }, { "id": "pXJyEKOEvtDsEcRs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nIs {the label corresponding to the lower tick mark on the y-axis}[0.691, 0.580, 0.735, 0.598] higher in value than {the second lowest tick mark on the y-axis in the 'IoT' subplot}[0.363, 0.640, 0.407, 0.658]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 424 }, { "id": "uIpoKSSGlEkkeMZV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the significant elements and their arrangement in the subplot area labeled 'Blockchain' [0.412, 0.047, 0.657, 0.405]." }, { "from": "gpt", "value": "The 'Blockchain' subplot contains a gray background, two brown bars representing 'Patent Applications' and 'Patents Granted', respectively. The bars are labeled with numbers positioned just below the top edge of each bar. Additionally, the y-axis shows multiple tick marks with values, and the x-axis has labels positioned at an angle." } ], "id_raw": 424 }, { "id": "qJ4m8Crfb3pFGqKf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nCan you provide a detailed description of what is contained in the bounding box from [0.083, 0.537, 0.328, 0.896] that includes the '5G Technology' subplot?" }, { "from": "gpt", "value": "This region includes a pair of bars - one for 'Patent Applications' and one for 'Patents Granted'. It has labeled axes where the y-axis indicates the 'Number of Patents' and tick marks with corresponding numerical values. The bars also have numerical annotations directly above them signifying the exact values they represent." } ], "id_raw": 424 }, { "id": "CklCcoGZxbgEkaJW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nExplain the contents across the area [0.740, 0.537, 0.985, 0.896] for the 'Big Data' subplot." }, { "from": "gpt", "value": "This region highlights the 'Big Data' category, featuring a panel with numeric labels on the y-axis corresponding to patent counts and two bars indicating 'Patent Applications' and 'Patents Granted', each annotated with their respective numerical values at the top. The x-axis has labeled ticks for each bar, angled for better readability." } ], "id_raw": 424 }, { "id": "ZK6yua5cMg2AUYOA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is located at [0.080, 0.218, 0.100, 0.256]?" }, { "from": "gpt", "value": "It is a bar that denotes the return rate {GreenOak in 2010's bar}[0.080, 0.218, 0.100, 0.256]." } ], "id_raw": 425 }, { "id": "7QAi2yY5Nwm6otwu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the element at [0.855, 0.334, 0.855, 0.334] represent?" }, { "from": "gpt", "value": "It represents {the x-axis label}[0.855, 0.334, 0.855, 0.334] which is not labeled with any text." } ], "id_raw": 425 }, { "id": "eg3pKXIoxy7xWrhy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nCould you identify the text label shown at coordinates [0.161, 0.673, 0.236, 0.695]?" }, { "from": "gpt", "value": "Yes, this is the subtitle {Cerberus}[0.161, 0.673, 0.236, 0.695]." } ], "id_raw": 425 }, { "id": "gaGvClaOTHelEMPg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of 2013 from Fortress}[0.481, 0.134, 0.500, 0.256] higher than {the bar of 2013 from GGP}[0.809, 0.199, 0.829, 0.256]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 425 }, { "id": "P2lPz3wWuprx389D", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nAre the data presentations of {Fortress}[0.397, 0.047, 0.657, 0.294] and {GGP}[0.725, 0.047, 0.985, 0.294] utilizing similar colors?" }, { "from": "gpt", "value": "No." } ], "id_raw": 425 }, { "id": "aptUgBjv4RuKfrAb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for 2017}[0.577, 0.161, 0.597, 0.256] in Fortress's panel have a larger value than {the bar for 2017}[0.906, 0.142, 0.925, 0.256] in GGP's panel?" }, { "from": "gpt", "value": "No." } ], "id_raw": 425 }, { "id": "IqYftq9mBIC4S7Ya", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat contains the coordinates [0.397, 0.374, 0.657, 0.621]?" }, { "from": "gpt", "value": "The subplot area for {Carlyle}[0.397, 0.374, 0.657, 0.621] which includes a performnance chart of annual return rates ranging from 2010 to 2019, displayed using bars. Each year's performance is denoted by a vertical bar and the y-axis is labeled with percentage values in increments of 10." } ], "id_raw": 425 }, { "id": "nxPu168YFgxKaCkg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.069, 0.047, 0.328, 0.294]." }, { "from": "gpt", "value": "This region {GreenOak's subplot}[0.069, 0.047, 0.328, 0.294] includes a small chart displaying annual return rates from 2010 to 2019 with a grey grid background, and values scaling from -10 to 30 in increments of 10, as observed on the y-axis." } ], "id_raw": 425 }, { "id": "3km216BbZBtNyfmq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the features in the coordinates [0.069, 0.374, 0.328, 0.621]?" }, { "from": "gpt", "value": "This section of {Blackstone's subplot}[0.069, 0.374, 0.328, 0.621] contains a bar chart spanning years 2010 to 2019, illustrating return rates with the y-axis scaling from -10 to 50 in increments of 10. Each year's return is represented by vertical bars, all set against a light grey pane." } ], "id_raw": 425 }, { "id": "rWrFnr98cM0i8yiP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the text located within {the bounding box}[0.299, 0.213, 0.325, 0.224]?" }, { "from": "gpt", "value": "The percentage value 60.5%." } ], "id_raw": 427 }, { "id": "3bqxbFA4boMMNYBJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing \"Hydrogen\" in the lower part of the chart?" }, { "from": "gpt", "value": "{Blue}[0.534, 0.301, 0.602, 0.508]." } ], "id_raw": 427 }, { "id": "DWjlx3kXqCBVqncw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nWhat category does the label at {this position}[0.870, 0.913, 0.909, 0.961] refer to?" }, { "from": "gpt", "value": "Smart Grid." } ], "id_raw": 427 }, { "id": "LocqTmY3CXDicrz3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar labeled \"Hydro Power\" higher in the {upper}[0.278, 0.606, 0.346, 0.999] or {lower}[0.278, 0.229, 0.346, 0.508] subplot?" }, { "from": "gpt", "value": "Upper." } ], "id_raw": 427 }, { "id": "T5pyMDnL6RVMJZec", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the bar for \"Solar Power\" or the bar for \"Geothermal\" in the {Consistent expansion}[0.108, 0.673, 0.176, 0.999], [0.363, 0.635, 0.432, 0.999] subplot?" }, { "from": "gpt", "value": "Solar Power." } ], "id_raw": 427 }, { "id": "n3xJAoipt9D150Vh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nDo the \"Electric Vehicles\" bars in the {Slow recovery}[0.619, 0.367, 0.687, 0.508] and {Consistent expansion}[0.619, 0.577, 0.687, 0.999] subplots have the same color?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 427 }, { "id": "90rfIt2FcHm18dTJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area {spanning}[0.066, 0.537, 0.985, 0.907]?" }, { "from": "gpt", "value": "The area contains the subplot background for the Consistent expansion scenario, including performance score labels and the bars representing different energy technologies." } ], "id_raw": 427 }, { "id": "80Ee6yZzrbhiYtVs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements found within the {bounding box}[0.100, 0.422, 0.909, 0.470]?" }, { "from": "gpt", "value": "This region includes the x-axis category labels for technologies such as \"Solar Power\", \"Wind Power\", up to \"Smart Grid\", with an angle of 45 degrees." } ], "id_raw": 427 }, { "id": "M5J50995kpDbLU7s", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the key features in the {specified region}[0.035, 0.040, 0.061, 0.548]?" }, { "from": "gpt", "value": "This area contains the y-axis labels for percentage values from 20% to 100% placed alongside the corresponding tick marks." } ], "id_raw": 427 }, { "id": "8fFaDGI43ybPIIc1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the first bar in the subplot for \"Emails processed\"}[0.094, 0.218, 0.119, 0.445]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 428 }, { "id": "uUtYfnEm3nWwcePR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label text positioned at [0.198, 0.509, 0.369, 0.532] in the chart?" }, { "from": "gpt", "value": "Customer feedbacks." } ], "id_raw": 428 }, { "id": "3PFzoQtIFn5HHGXS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nWhat is shown by {the ylabel}[0.508, 0.662, 0.522, 0.808] in the subplot for \"New contracts\"?" }, { "from": "gpt", "value": "Process Quantity." } ], "id_raw": 428 }, { "id": "QxrZWrL04Y1Ju2xh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar corresponding to \"Month 1\" in \"Emails processed\"}[0.094, 0.218, 0.119, 0.445] taller than {the bar corresponding to \"Month 1\" in \"Help tickets solved\"}[0.586, 0.275, 0.612, 0.445]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 428 }, { "id": "Vaaf6Nr76K8lhV1O", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for \"Month 6\" in the subplot of \"Customer feedbacks\"}[0.255, 0.918, 0.280, 0.936] represent a higher value than {the bar for \"Month 6\" in \"New contracts\"}[0.747, 0.929, 0.773, 0.936]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 428 }, { "id": "3IIDLQR92A3iO4MU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nIs {the final bar in the \"New contracts\" subplot}[0.940, 0.926, 0.966, 0.936] positioned to the right of {the final bar in \"Customer feedbacks\"}[0.448, 0.910, 0.474, 0.936]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 428 }, { "id": "GAifJn1DUk4czFwN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the organization of elements within the region [0.567, 0.047, 0.985, 0.442]." }, { "from": "gpt", "value": "The region contains a subplot for \"Help tickets solved\", including a range of blue bars representing monthly data points from \"Month 1\" to \"Month 12\", labels for each month positioned below the bars, and quantification labels for y-axis along the left side." } ], "id_raw": 428 }, { "id": "l7Oqkj1eeWnA5K1U", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the bounded area [0.075, 0.537, 0.493, 0.932]?" }, { "from": "gpt", "value": "This section shows the subplot for \"Customer feedbacks\" which includes vertical blue bars representing data across twelve months, month labels at the bottom, process quantity labels on the left side, and a subtitle at the top identifying the chart's focus." } ], "id_raw": 428 }, { "id": "9X3ddQiFqFJLwMux", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe what information is contained within [0.567, 0.537, 0.985, 0.932]?" }, { "from": "gpt", "value": "This area represents the \"New contracts\" subplot. It includes a series of blue bars for each month showing contracted quantities, monthly labels beneath the bars, numerical labels of quantities to the left, and a heading at the top describing the data." } ], "id_raw": 428 }, { "id": "wDT6YXXMtOlEjbti", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nWhat information is displayed at {the Y-axis label}[0.015, 0.131, 0.029, 0.331]?" }, { "from": "gpt", "value": "Performance Score (%)." } ], "id_raw": 429 }, { "id": "sZJegVy3OU4Q4Te4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the bar label}[0.198, 0.136, 0.217, 0.147]?" }, { "from": "gpt", "value": "80.0." } ], "id_raw": 429 }, { "id": "LRJaFdWrBAPGNTve", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the representation of {the x-tick}[0.423, 0.416]?" }, { "from": "gpt", "value": "IPT-3." } ], "id_raw": 429 }, { "id": "W9DKKxi3HYhVv1Oc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Electra-29b\"}[0.301, 0.124, 0.329, 0.574] taller than {the bar for \"Yan-PaLA\"}[0.829, 0.159, 0.858, 0.574]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 429 }, { "id": "a2K71HwRH53KPPZ4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nIs {the value label above \"Wi-300b\"}[0.270, 0.125, 0.288, 0.136] higher in numeric value than {the value label above \"Electra-29b\"}[0.306, 0.107, 0.324, 0.118]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 429 }, { "id": "EST5et6GWL0ritVh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Wi-300b\"}[0.265, 0.141, 0.294, 0.574] shorter than {the bar for \"IPT-3\"}[0.901, 0.122, 0.930, 0.574]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 429 }, { "id": "VOKh5uQv7diDH9co", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.558, 0.047, 0.985, 0.416]." }, { "from": "gpt", "value": "This section includes a chart with bars visualizing \"Firewall Breach\" performance scores. Each bar is associated with a tech tool, and their labels range from {Cyberlock-5d}[0.546, 0.422, 0.592, 0.479] to {Gemini Shield}[0.903, 0.422, 0.951, 0.482], with performance scores displayed above each bar at locations like {for Cyberlock-5d}[0.583, 0.369, 0.601, 0.380] and {for Gemini Shield}[0.942, 0.080, 0.960, 0.091]." } ], "id_raw": 429 }, { "id": "bidUSS1POuDLoXHY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nWhat details are present in the region [0.066, 0.537, 0.493, 0.906]?" }, { "from": "gpt", "value": "The area covers a chart detailing \"Encrypted Data Leak\" with various bars such as {Cyberlock-5d}[0.085, 0.590, 0.114, 1.065] through {Gemini Shield}[0.444, 0.772, 0.473, 1.065]. Performance scores are indicated with details like the score for {Yan-PaLA}[0.342, 0.714, 0.360, 0.725] displayed above each corresponding bar." } ], "id_raw": 429 }, { "id": "vhUiBixyJIOJ4WXi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nProvide an overview of the elements in the space [0.558, 0.537, 0.985, 0.906]." }, { "from": "gpt", "value": "This designated chart area illustrates the \"Darkweb Activity\" data. It includes related technology names from {Cyberlock-5d}[0.546, 0.912, 0.592, 0.970] to {Gemini Shield}[0.903, 0.912, 0.951, 0.972]. Their associated performance labels, for instance, the label for {Yan-PaLA}[0.834, 0.596, 0.853, 0.607] are presented above each bar, reflecting operational effectiveness." } ], "id_raw": 429 }, { "id": "nDOWM6eRHntynwLt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.015, 0.131, 0.029, 0.331] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Performance Score (%)." } ], "id_raw": 430 }, { "id": "dANZSNFq3ATk1Adm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second text in the legend?" }, { "from": "gpt", "value": "Legend data is not visibly present within the provided chart, thus no coordinates are available for any legend text." } ], "id_raw": 430 }, { "id": "ab7bseSuZ5QqqpXq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.234, 0.151, 0.253, 0.163]?" }, { "from": "gpt", "value": "A label displaying the score of 77.0 for 'Mirage-1b-alpha'." } ], "id_raw": 430 }, { "id": "NpI5lyPMD1qFMY8I", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Cyberlock-5d\"}[0.085, 0.337, 0.114, 0.574] taller or shorter than {the bar representing \"IPT-3\"}[0.901, 0.122, 0.930, 0.574]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 430 }, { "id": "oHt6FxPY99mlssQn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar labeled \"30%\"}[0.043, 0.409, 0.061, 0.426] higher or lower on the axis than {the bar labeled \"80%\"}[0.043, 0.145, 0.061, 0.163]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 430 }, { "id": "LKT0LOwXX8ZkvqOk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nAre {the subsidiary titles}{\"Encrypted Data Leak\"}[0.192, 0.509, 0.366, 0.532] and {\"Firewall Breach\"}[0.709, 0.019, 0.835, 0.041] on the same side {left or right} of the central axis dividing the chart?" }, { "from": "gpt", "value": "No." } ], "id_raw": 430 }, { "id": "Hzjz5K5N3N0aWlX0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.558, 0.537, 0.985, 0.906]." }, { "from": "gpt", "value": "This area contains bars and labels for 'Darkweb Activity'. The labels along the bottom are 'Cyberlock-5d' to 'Gemini Shield', marked at specific intervals, indicating different cybersecurity models. Performance scores are indicated by vertical bars that indicate the height relative to the score percentage on the left axis." } ], "id_raw": 430 }, { "id": "fpv9MjbXPZuQlSez", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nPlease provide details about the area [0.066, 0.537, 0.493, 0.906]." }, { "from": "gpt", "value": "This section displays the 'Encrypted Data Leak' part of the chart, containing diverse bars each representing different cybersecurity models like 'Cyberlock-5d', 'Fort-7m', etc., plotted against their performance scores. The percentage scores range from 30 to 100 as marked on the left vertical y-axis." } ], "id_raw": 430 }, { "id": "JvWAAFMcZg2HN2S4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.558, 0.047, 0.985, 0.416]?" }, { "from": "gpt", "value": "The region encompasses bars depicting 'Firewall Breach' performances of different cybersecurity models such as 'Cyberlock-5d', 'Fort-7m', and others. It also includes x-axis labels for each model and y-axis labels for score percentages." } ], "id_raw": 430 }, { "id": "tGP1QH6sDpCK7sRI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary color of the bar representing \"Commercial Buildings\" in the \"Business District\" subplot?" }, { "from": "gpt", "value": "{the primary color of the bar representing \"Commercial Buildings\"}[0.857, 0.726, 0.881, 0.955] is blue." } ], "id_raw": 431 }, { "id": "Gm9RGnzm0mKgIX50", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for the \"Countryside\" subplot?" }, { "from": "gpt", "value": "The y-axis label for the \"Countryside\" subplot is {Project Volume}[0.015, 0.398, 0.029, 0.528]." } ], "id_raw": 431 }, { "id": "Oxv6k7rZwi96jYcN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nWhat value is displayed on the bar for \"Educational Institutions\" in the \"Scenic Area\" subplot?" }, { "from": "gpt", "value": "The value displayed is {70}[0.505, 0.789, 0.515, 0.800]." } ], "id_raw": 431 }, { "id": "3ZVTxl1bPyCbSw1X", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Healthcare Institutions\" in the \"Economic Development Zone\"}[0.292, 0.790, 0.316, 0.955] shorter than {the bar for \"Community Facilities\" in the same zone}[0.261, 0.752, 0.286, 0.955]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 431 }, { "id": "frXzOw3rwtgY8zYk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for \"Commercial Buildings\" in the \"Countryside\"}[0.200, 0.463, 0.225, 0.628] reach higher than {the bar for \"Healthcare Institutions\" in the \"Revitalization Zone\"}[0.620, 0.803, 0.645, 0.955]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 431 }, { "id": "jS0bx1qEW2PdKn7b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nAre there more {labels on the y-axis in the \"Suburbs\"}[0.700, 0.218, 0.718, 0.235] than in {the \"Historic\" area}[0.372, 0.545, 0.389, 0.562]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 431 }, { "id": "7sY7chb3HpIp39IM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {the y-label}[0.015, 0.018, 0.029, 0.313]?" }, { "from": "gpt", "value": "Engagement Score (in thousands)." } ], "id_raw": 433 }, { "id": "0r8C8O64jOCaR3XS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nWhat type of grid lines are visible along {the y-axis}[0.015, 0.018, 0.029, 0.313]?" }, { "from": "gpt", "value": "Dashed lines." } ], "id_raw": 433 }, { "id": "4OdIZ4sfSKmXeShy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nWhat category is indicated by {x-axis label under the first subplot}[0.061, 0.290, 0.105, 0.345]?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 433 }, { "id": "S9Ab4F7a9nOBxWQY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar representing Facebook in the third subplot}[0.743, 0.189, 0.781, 0.487] higher than {the bar for Facebook in the fourth subplot}[0.434, 0.565, 0.453, 0.836]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 433 }, { "id": "MBH4nhdcIJHEYMZP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for Twitter in the first plot}[0.134, 0.196, 0.173, 0.487] represent a higher value than {the bar for Instagram in the same plot}[0.182, 0.236, 0.221, 0.487]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 433 }, { "id": "vQ1dpLBolN5GOZgU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar for YouTube in the first plot}[0.278, 0.121, 0.317, 0.487] wider than {the same YouTube bar in the fifth plot}[0.935, 0.416, 0.973, 0.836]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 433 }, { "id": "EFOcUGRvZ53jO9vF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within {the region covering engagement scores on y-axis}[0.035, 0.018, 0.070, 0.313]." }, { "from": "gpt", "value": "It displays the engagement scores ranging from 3000 to 6500, each label at intervals of 500, visible respectively at [0.035, 0.277, 0.070, 0.294], [0.035, 0.243, 0.070, 0.260], down to [0.035, 0.074, 0.070, 0.091] for each corresponding score." } ], "id_raw": 433 }, { "id": "cvKq5D1oKgqr6Ba6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you detail about the area including the y-axis labeling and grid pattern in {the first subplot}[0.015, 0.018, 0.029, 0.313]?" }, { "from": "gpt", "value": "This area contains y-axis labels that denote the engagement score in thousands from 3000 up to 6500, paired with visible dashed grid lines that help in reading values across the plot." } ], "id_raw": 433 }, { "id": "xLpz2yQ81G0refoO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nCould you provide information regarding the objects placed in {the tick region on the x-axis}[0.105, 0.290, 0.298, 0.345] of the first subplot?" }, { "from": "gpt", "value": "The x-axis ticks here include category labels such as 'Facebook', 'Twitter', 'Instagram', 'LinkedIn', and 'YouTube', with precise alignment for defining exact points for data representation along x-axis." } ], "id_raw": 433 }, { "id": "6YGvnKFlrfj4QBnK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nCan you identify {the x-axis label} for the first subplot?" }, { "from": "gpt", "value": "No x-axis label present." } ], "id_raw": 434 }, { "id": "AJoWkSxntSjTESE6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {the subtitle}[0.014, 0.019, 0.249, 0.041]?" }, { "from": "gpt", "value": "Theater Performance Rating." } ], "id_raw": 434 }, { "id": "qWXG7QJftgPZI8Zu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.152, 0.029, 0.352] read?" }, { "from": "gpt", "value": "Performance Score (%)." } ], "id_raw": 434 }, { "id": "QrINREAUpRCTPyqg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar labeled \"Stand-up Comedy\"}[0.120, 0.140, 0.130, 0.869] higher than {the blue bar labeled \"Ballet\"}[0.108, 0.284, 0.118, 0.869]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 434 }, { "id": "zuWVAyjy0e7ADMyq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the blue bar for \"Opera\"}[0.133, 0.089, 0.142, 0.869] have a higher performance score than {the blue bar for \"Musical\"}[0.084, 0.208, 0.094, 0.869]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 434 }, { "id": "6Njr9gBRMCQp7W84", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nIs {the y-axis tick labeled \"90\"}[0.043, 0.122, 0.061, 0.140] positioned below {the y-axis tick labeled \"80\"}[0.043, 0.204, 0.061, 0.222]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 434 }, { "id": "nsBaFDBhQ4xAiqNV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in {the first subplot's x-axis labels}[0.023, 0.464, 0.186, 0.558]." }, { "from": "gpt", "value": "The labels on the x-axis include a list of genres from \"Regional Drama\" to \"Storytelling\" with their corresponding x-tick marks." } ], "id_raw": 434 }, { "id": "DHg503SRHUvzpTxw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted within {the second subplot's y-axis tick labels region}[0.232, 0.040, 0.258, 0.468]?" }, { "from": "gpt", "value": "Performance score percentage labels ranging from \"50\" to \"100\" with y-axis ticks for each label." } ], "id_raw": 434 }, { "id": "Pvf9lzMzTYQ7x1et", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nCan you explain what is within {the second subplot's bars and text annotations region}[0.269, 0.138, 0.392, 0.869]?" }, { "from": "gpt", "value": "A series of blue bars showing the art exhibition ratings for different genres, labeled with their corresponding performance score percentages." } ], "id_raw": 434 }, { "id": "s01bH249s0gRXw8l", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nWhich color is {the Career Progression bar for Year 3}[0.259, 0.435, 0.290, 0.923]?" }, { "from": "gpt", "value": "#d87769." } ], "id_raw": 435 }, { "id": "JOtunFeompoKGu64", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the {tick label on the x-axis for the first value}[0.111, 0.942, 0.154, 0.970]?" }, { "from": "gpt", "value": "Year 1." } ], "id_raw": 435 }, { "id": "cYsG5x2XRykkfL7H", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the text read at [0.775, 0.870, 0.901, 0.898] in the legend?" }, { "from": "gpt", "value": "Obstacle Removal." } ], "id_raw": 435 }, { "id": "Ix8ujKgdR3WZ2oUO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Career Progression bar for Year 6}[0.496, 0.414, 0.527, 0.923] higher than the {Obstacle Removal bar for Year 3}[0.290, 0.075, 0.322, 0.414]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 435 }, { "id": "C2rKH0vdjRgtwD8e", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nDoes the color of {the Career Progression label in the legend}[0.074, 0.872, 0.102, 0.892] match the {color of the bars representing Career Progression}[0.101, 0.711, 0.132, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 435 }, { "id": "d3ptXqu9HmfSCKwp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nWhich is wider, the span of {years labeled on the x-axis}[0.111, 0.942, 0.869, 0.970] or the span of {values labeled on the y-axis}[0.043, 0.064, 0.052, 0.940]?" }, { "from": "gpt", "value": "X-axis." } ], "id_raw": 435 }, { "id": "wUxLJZ5xgN9v3nrA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.101, 0.711, 0.843, 0.923]." }, { "from": "gpt", "value": "This region encompasses the bars representing Career Progression for all ten years. Notably, the {Year 1 bar}[0.101, 0.711, 0.132, 0.923] starts from the baseline and extends upwards to indicate a value of 10, and the {Year 10 bar}[0.811, 0.181, 0.843, 0.923] starts from the baseline and extends upwards to indicate the highest value of 35." } ], "id_raw": 435 }, { "id": "tHGdUThOyFULM0Eg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the content within the area [0.843, 0.075, 0.875, 0.923] signify?" }, { "from": "gpt", "value": "This region contains the bar representing the Obstacle Removal value for {Year 10}[0.843, 0.075, 0.875, 0.499], which indicates a negative trend with a value of -10, compared to the baseline at {0.0}[0.043, 0.064, 0.052, 0.092]." } ], "id_raw": 435 }, { "id": "OZrTokWj9t1L3CQo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found in [0.035, 0.170, 0.052, 0.516] on the chart." }, { "from": "gpt", "value": "This vertical range captures the y-axis tick labels for the values 35, 30, 25, 20, and 15, suggesting increments of 5 units along the axis marking Career Progression. For example, the label for {25}[0.035, 0.382, 0.052, 0.410] is halfway within this range." } ], "id_raw": 435 }, { "id": "pfy2ITp9t9Les9qN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.768, 0.102, 0.796, 0.122]?" }, { "from": "gpt", "value": "It is the color label for {2019 Popularity}[0.807, 0.100, 0.917, 0.128] in the legend." } ], "id_raw": 436 }, { "id": "dF5KsxSlvd4FUbL9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the value at [0.939, 0.276, 0.965, 0.304] on the y-axis represents?" }, { "from": "gpt", "value": "The value at those coordinates is 500." } ], "id_raw": 436 }, { "id": "C73BaCX3Tx6AgMXr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nWhat label's color is located at [0.083, 0.102, 0.111, 0.122] on the chart?" }, { "from": "gpt", "value": "The label color at those coordinates is for {2018 Popularity}[0.122, 0.100, 0.232, 0.128]." } ], "id_raw": 436 }, { "id": "0b39neIC9hbRg3Qh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nIs the {2019 Popularity bar for Vintage}[0.500, 0.541, 0.565, 1.347] higher or lower than the {2018 Popularity bar for Vintage}[0.435, 0.499, 0.500, 1.347]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 436 }, { "id": "VKOjTQumDyHTVhNR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nWhich one is higher, {2019 Popularity bar for Streetwear}[0.337, 0.393, 0.402, 1.347] or {2018 Popularity bar for Streetwear}[0.272, 0.626, 0.337, 1.347]?" }, { "from": "gpt", "value": "2019 Popularity bar for Streetwear." } ], "id_raw": 436 }, { "id": "ojbaX8FTjBH65D6i", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nIs {2019 Popularity bar for Gothic}[0.175, 0.297, 0.240, 1.347] shorter or taller than the {2019 Popularity bar for Skater}[0.825, 0.636, 0.890, 1.347]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 436 }, { "id": "uZSclJsLeOwitpff", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the features within the region [0.435, 0.499, 0.565, 1.347]?" }, { "from": "gpt", "value": "Inside this region, there are 2 bars: {2018 Popularity bar for Vintage}[0.435, 0.499, 0.500, 1.347] and {2019 Popularity bar for Vintage}[0.500, 0.541, 0.565, 1.347]. Both represent the popularity of the Vintage subculture for the years 2018 and 2019, respectively." } ], "id_raw": 436 }, { "id": "1nkfrYtpedbhm2UE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is in the region [0.272, 0.626, 0.402, 1.347]?" }, { "from": "gpt", "value": "It contains 2 bars: {2018 Popularity bar for Streetwear}[0.272, 0.626, 0.337, 1.347] and {2019 Popularity bar for Streetwear}[0.337, 0.393, 0.402, 1.347], which both represent the popularity of Streetwear style for the years 2018 and 2019, respectively." } ], "id_raw": 436 }, { "id": "xOq87sbsw7ue7PxM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the coordinates [0.175, 0.297, 0.240, 1.347]." }, { "from": "gpt", "value": "This area contains 2 bars: {2018 Popularity bar for Gothic}[0.110, 0.287, 0.175, 1.347] and {2019 Popularity bar for Gothic}[0.175, 0.297, 0.240, 1.347]. Both bars represent the popularity of the Gothic subculture for the years 2018 and 2019, respectively." } ], "id_raw": 436 }, { "id": "ga1Q7PtF92z0uOXI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.971, 0.422, 0.985, 0.575] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called 2019 Sales." } ], "id_raw": 437 }, { "id": "jKzE1WGjjCWEjEG3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first text in the sublegend?" }, { "from": "gpt", "value": "[0.829, 0.870, 0.905, 0.898]" } ], "id_raw": 437 }, { "id": "o3jt8Nk2jGW1mfJ0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.184, 0.942, 0.217, 0.970]?" }, { "from": "gpt", "value": "Food." } ], "id_raw": 437 }, { "id": "zveLBxhdT2ChrCNb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat indicators or categories are shown in [0.784, 0.858, 0.911, 0.909]?" }, { "from": "gpt", "value": "{2019 Sales}[0.829, 0.870, 0.905, 0.898]." } ], "id_raw": 437 }, { "id": "aluxIiCwdcqgXnp5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar}[0.360, 0.075, 0.400, 0.801] to the left or right of {the orange bar}[0.600, 0.075, 0.680, 0.438]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 437 }, { "id": "4MO9dGZvgja4GkCu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Food\"}[0.200, 0.075, 0.280, 0.438] shorter than {the bar representing \"Clothing\"}[0.800, 0.075, 0.880, 0.862]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 437 }, { "id": "B8HY6USdH4SzGVHN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nAre {the 2018 Sales bars}[[0.120, 0.075, 0.200, 0.680], [0.320, 0.075, 0.400, 0.801], [0.520, 0.075, 0.600, 0.377], [0.720, 0.075, 0.800, 0.680]] generally taller than {the 2019 Sales bars}[[0.200, 0.075, 0.280, 0.438], [0.400, 0.075, 0.480, 0.620], [0.600, 0.075, 0.680, 0.438], [0.800, 0.075, 0.880, 0.862]]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 437 }, { "id": "TlC3RYzgBHYPtN4G", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Food\"}[[0.120, 0.075, 0.200, 0.680],[0.200, 0.075, 0.280, 0.438]] and {\"Clothing\"}[[0.720, 0.075, 0.800, 0.680],[0.800, 0.075, 0.880, 0.862]], which category has a higher value for {2019 Sales}<{2019 Sales Food bar}[0.200, 0.075, 0.280, 0.438], {2019 Sales Clothing bar}[0.800, 0.075, 0.880, 0.862]>?" }, { "from": "gpt", "value": "Clothing." } ], "id_raw": 437 }, { "id": "k2f8ZTqEOf9UfVv5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.784, 0.858, 0.911, 0.909]." }, { "from": "gpt", "value": "There is a color-text label in the sublegend. The color label is located at [0.790, 0.872, 0.818, 0.892], which is blue, and the text label is located at [0.829, 0.870, 0.905, 0.898], which is called 2019 Sales, and they denote that 2019 Sales's bar is blue." } ], "id_raw": 437 }, { "id": "Kd3Juq3uKFQICYHo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.184, 0.942, 0.829, 0.970]." }, { "from": "gpt", "value": "The area [0.184, 0.942, 0.829, 0.970] contains all label texts on the horizontal axis, which are:\n1. \"Food\": [0.184, 0.942, 0.217, 0.970],\n2. \"Electronics\": [0.362, 0.942, 0.438, 0.970],\n3. \"Books\": [0.579, 0.942, 0.621, 0.970],\n4. \"Clothing\": [0.771, 0.942, 0.829, 0.970]." } ], "id_raw": 437 }, { "id": "mKSKJbVDeeAjn98n", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.400, 0.075, 0.480, 0.620]?" }, { "from": "gpt", "value": "There is 1 object:\n1. The blue column represents that the value of the \"Electronics\" category in 2019 Sales is -90, and the coordinate range is [0.400, 0.075, 0.480, 0.620]." } ], "id_raw": 437 }, { "id": "tF6Wm7aOxvPBzlZ4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.600, 0.075, 0.800, 0.862]?" }, { "from": "gpt", "value": "1. Two bars representing Books and Clothing in 2019 Sales. \n2. The bar representing Books is [0.600, 0.075, 0.680, 0.438] and its value is -60.\n3. The bar representing Clothing is [0.800, 0.075, 0.880, 0.862] and its value is -130." } ], "id_raw": 437 }, { "id": "KE8zLjxLz5BT34ee", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinates [0.354, 0.015, 0.646, 0.033] represent?" }, { "from": "gpt", "value": "It represents the subtitle of the chart, \"Annual Academic Research Papers in Different Fields\"." } ], "id_raw": 438 }, { "id": "VAulynAOOyybTId6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the bar that represents the academic papers for 'Biology' in the year 2019?" }, { "from": "gpt", "value": "The coordinates are [0.580, 0.373, 0.623, 0.875]." } ], "id_raw": 438 }, { "id": "nR4RmHqiFtJwt546", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the color identifier for '2020 Papers' in the chart?" }, { "from": "gpt", "value": "The coordinates of the color label for '2020 Papers' are [0.861, 0.051, 0.880, 0.061]." } ], "id_raw": 438 }, { "id": "PNQcjcVRQJ0T0Hz5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nIs the {2019 Paper bar for Economics}[0.703, 0.135, 0.746, 0.875] higher than the {2020 Paper bar for Economics}[0.746, 0.079, 0.789, 0.875]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 438 }, { "id": "PeXPvO3SYL068Nss", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for Psychology papers in 2019}[0.826, 0.526, 0.869, 0.875] located to the right or left of the {bar for Psychology papers in 2020}[0.869, 0.568, 0.912, 0.875]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 438 }, { "id": "klcQBjFEI1txSOhJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Mathematics\"}[[0.211, 0.428, 0.254, 0.875], [0.254, 0.401, 0.297, 0.875]] and {\"Biology\"}[[0.580, 0.373, 0.623, 0.875], [0.623, 0.331, 0.666, 0.875]], which field had a larger increase of academic papers from 2019 to 2020?" }, { "from": "gpt", "value": "Biology." } ], "id_raw": 438 }, { "id": "OKz4t43a2Ips0PeO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.047, 0.037, 0.953, 0.875]." }, { "from": "gpt", "value": "The content within these coordinates is a bar chart representing the annual academic research papers in different fields. There are two groups of bar charts in different colors representing the years 2019 and 2020. The x-axis is labeled with 7 different academic fields: Computer Science, Mathematics, Physics, Chemistry, Biology, Economics, and Psychology. The y-axis of this bar chart is representing the number of papers published in the respective years, ranging from 0 to 600." } ], "id_raw": 438 }, { "id": "8E1HYAiyOvzToOrC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nTell me about the space in [0.088, 0.107, 0.174, 0.875]." }, { "from": "gpt", "value": "This region includes two vertical bars representing the annual academic papers in the field of Computer Science for the years 2019 and 2020. The green bar on the left with coordinates [0.088, 0.247, 0.131, 0.875] represents 450 papers in 2019. The red bar on the right with coordinates [0.131, 0.107, 0.174, 0.875] represents 550 papers in 2020." } ], "id_raw": 438 }, { "id": "whGQvUhllSy9VcZa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements or objects within [0.023, 0.032, 0.041, 0.046]." }, { "from": "gpt", "value": "This very small region contains only the y-axis label '600', and represents the maximum values of papers published in each field for the years 2019 and 2020." } ], "id_raw": 438 }, { "id": "1foMeHTmqDbyaOGY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.015, 0.263, 0.029, 0.735] in the chart?" }, { "from": "gpt", "value": "It is the ylabel for the first subplot indicating \"Reduced Forest Area (in hectares)\"." } ], "id_raw": 439 }, { "id": "FyyXDFu9eTEa2EBR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found at the coordinates [0.636, 0.089, 0.922, 0.139]?" }, { "from": "gpt", "value": "That is the legend for the first subplot, explicitly for \"Reduced Forest Area (in hectares)\"." } ], "id_raw": 439 }, { "id": "NsT3T9m7NmdFCZmu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the set of coordinates [0.681, 0.100, 0.917, 0.128] represent in the chart?" }, { "from": "gpt", "value": "It represents the text label for the legend entry of \"Reduced Forest Area (in hectares)\"." } ], "id_raw": 439 }, { "id": "BgAKECL8pMKIFvd4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nIs the 2021 bar for \"Reduced Forest Area (in hectares)\" higher or lower compared to the 2020 bar at {[0.126, 0.357, 0.296, 0.923], [0.551, 0.470, 0.721, 0.923]}?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 439 }, { "id": "vjRUsM0j8QOuFQuR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nComparing the \"Reduced Forest Area (in hectares)\" bar at [0.126, 0.357, 0.296, 0.923] with the \"Reduced Carbon Emissions (in million tonnes)\" bar at [0.296, 0.357, 0.466, 0.923], which is higher?" }, { "from": "gpt", "value": "The bar for \"Reduced Forest Area (in hectares)\"." } ], "id_raw": 439 }, { "id": "1EPZTouSDfDmDLSh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nComparing the two bars for 2021 at the coordinates {[0.551, 0.470, 0.721, 0.923], [0.721, 0.216, 0.891, 0.923]}, which one is higher?" }, { "from": "gpt", "value": "The bar for \"Reduced Carbon Emissions (in million tonnes)\" has a higher value." } ], "id_raw": 439 }, { "id": "lOw3UZGCEd0yXMuo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the area spanned by these coordinates [0.095, 0.089, 0.463, 0.139]." }, { "from": "gpt", "value": "This region contains the legend for the second subplot of the bar chart. The legend specifies that the \"#588b8b\" colored bar represents \"Reduced Carbon Emissions (in million tonnes)\"." } ], "id_raw": 439 }, { "id": "46vgp8z0EEAs5qrl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the chart elements within the area [0.088, 0.075, 0.929, 0.923]?" }, { "from": "gpt", "value": "This area consists of two overlapping subplots. Each subplot displays two bars, one for the year 2020 and the other for 2021. The first subplot quantifies \"Reduced Forest Area (in hectares)\" and the second subplot represents \"Reduced Carbon Emissions (in million tonnes)\"." } ], "id_raw": 439 }, { "id": "KxblEWPqe7gxsnpq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.636, 0.089, 0.922, 0.139] contain?" }, { "from": "gpt", "value": "This area hosts the legend for the first subplot indicating that the \"#83a95c\" colored bars show \"Reduced Forest Area (in hectares)\"." } ], "id_raw": 439 }, { "id": "7M11uGILEWgUcYi0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at this coordinate: [0.235, 0.942, 0.323, 0.970]?" }, { "from": "gpt", "value": "It is an x-axis label, which is {the label 'Urban Living'}[0.235, 0.942, 0.323, 0.970]." } ], "id_raw": 440 }, { "id": "M5YvuOppVhlWbHdD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is at these coordinates: [0.971, 0.340, 0.985, 0.657]?" }, { "from": "gpt", "value": "It's the y-axis label, {the label 'Population (in millions)'}[0.971, 0.340, 0.985, 0.657]." } ], "id_raw": 440 }, { "id": "ik7Gws8gRa8S1gXB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color and name of {the legend item}[0.681, 0.100, 0.917, 0.128]?" }, { "from": "gpt", "value": "The label name is \"Reduced Forest Area (in hectares)\" and the color associated with it is green." } ], "id_raw": 440 }, { "id": "K6QvxZ0a7yAulAcB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nWhich bar, {bar 1, year 2025, Urban Living}[0.126, -0.349, 0.279, 0.923] or {bar 2, year 2025, Rural Living}[0.509, -0.208, 0.662, 0.923], represents a larger population?" }, { "from": "gpt", "value": "Bar 1, the Urban Living bar for year 2025." } ], "id_raw": 440 }, { "id": "IQYWELAM8A46bptR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nIs the{Reduced Carbon Emissions bar for Urban Living}[0.356, 0.357, 0.509, 0.923] taller than the {Population bar in 2025 for Urban Living}[0.126, -0.349, 0.279, 0.923]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 440 }, { "id": "PX1dDNnEz8OWotZE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nIs the {Population bar for Rural Living in 2030}[0.662, -0.293, 0.815, 0.923] larger or smaller than the {Population bar for Urban Living in 2030}[0.279, -0.547, 0.432, 0.923]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 440 }, { "id": "lPPMU9XtPW7CjhjR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is located in the region [0.095, 0.089, 0.463, 0.223]?" }, { "from": "gpt", "value": "This region is the chart legend, consisting of three elements: a color denotation for Reduced Carbon Emissions located at [0.101, 0.102, 0.128, 0.122] colored as green, a year denotation for 2025 located at [0.101, 0.144, 0.128, 0.164] colored as blue, and a year denotation for 2030 located at [0.101, 0.186, 0.128, 0.205] colored as orange." } ], "id_raw": 440 }, { "id": "fKtufGmxkwQP8Vd6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.203, 0.357, 0.356, 0.923]?" }, { "from": "gpt", "value": "A bar that represents {Reduced Forest Area (in hectares)}[0.203, 0.357, 0.356, 0.923] for Urban Living." } ], "id_raw": 440 }, { "id": "eQhHtu764dDVAMtk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is illustrated in the region [0.126, -0.349, 0.432, 0.923]?" }, { "from": "gpt", "value": "Two bars are represented. The first is the {Population bar for Urban Living in 2025}[0.126, -0.349, 0.279, 0.923] and the second is the {Population bar for Urban Living in 2030}[0.279, -0.547, 0.432, 0.923]." } ], "id_raw": 440 }, { "id": "IVik3s0p9vVayToE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the purpose of the object mentioned at the location [0.971, 0.365, 0.985, 0.632] in the diagram?" }, { "from": "gpt", "value": "That's the Y-axis label for subplot_1, denoting '2021 Trade Volume'." } ], "id_raw": 441 }, { "id": "1Kh6gmavG7EOrBHc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color assigned to the '2021 Trade Volume' according to the chart's legend?" }, { "from": "gpt", "value": "The color is '#7da1c7' or a specific shade of blue. This color assignment is observed in the legend {sublegend}[0.712, 0.089, 0.896, 0.139]." } ], "id_raw": 441 }, { "id": "MCeZDYnPUsnZ72qS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinate [0.326, 0.030, 0.674, 0.066] in the chart structure?" }, { "from": "gpt", "value": "It's the subtitle of subplot_1, which states 'International Trade Volume: 2016 vs 2021'." } ], "id_raw": 441 }, { "id": "TVPfGUw3ddQgVnTg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nBetween {Country 1 '2021 Trade Volume'}[0.211, 0.393, 0.288, 1.770] and {Country 1 '2016 Trade Volume'}[0.134, 0.764, 0.211, 1.770], which one has shown a higher trade volume?" }, { "from": "gpt", "value": "2021 Trade Volume." } ], "id_raw": 441 }, { "id": "LT5GpB27BoRbHaHd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nIn terms of '2021 Trade Volume', is {Country 2}[0.404, 0.477, 0.481, 1.770] doing better than {Country 3}[0.596, 0.552, 0.674, 1.770]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 441 }, { "id": "DqK9ED6YThXrUeXC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nAre the '2016 Trade Volumes' for {Country 1}<{Country 1 '2016 Trade Volume'}[0.134, 0.764, 0.211, 1.770]> and {Country 2}<{Country 2 '2016 Trade Volume'}[0.326, 0.848, 0.404, 1.770]> roughly the same?" }, { "from": "gpt", "value": "No." } ], "id_raw": 441 }, { "id": "pEbHCOeLYE4HOrjU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe objects located within the coordinates [0.097, 0.075, 0.903, 0.923]." }, { "from": "gpt", "value": "This region defines the coordinate space for each subplot, encapsulating the bar graphs representing trade volumes for different countries in 2016 and 2021. In each subplot, there are axes, bars, labels, ticks for x and y-axes, legends, and subtitles." } ], "id_raw": 441 }, { "id": "UikyOy79aX4qU1rm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.134, 0.764, 0.288, 1.770]." }, { "from": "gpt", "value": "This area contains two bars. The first bar is the '2016 Trade Volume' for Country 1 at [0.134, 0.764, 0.211, 1.770], in a specific shade of red, '#d87769'. The second bar represents the '2021 Trade Volume' for Country 1, located at [0.211, 0.393, 0.288, 1.770], in a similar shade of blue, '#7da1c7'." } ], "id_raw": 441 }, { "id": "UBHmYDgKTs1aZKWP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinate box [0.326, 0.030, 0.674, 0.066]?" }, { "from": "gpt", "value": "This area includes the descriptive title for subplot_1, which reads 'International Trade Volume: 2016 vs 2021'." } ], "id_raw": 441 }, { "id": "85PKI3TAvyZ0iESE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.971, 0.377, 0.985, 0.620]?" }, { "from": "gpt", "value": "It's the y-axis label of the 2021 participants." } ], "id_raw": 442 }, { "id": "nriPUhAzoPJdzNn2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the content of the coordinates [0.364, 0.030, 0.636, 0.066]?" }, { "from": "gpt", "value": "It's the subtitle of the chart, which reads \"Fitness and Sports Health Trends\"." } ], "id_raw": 442 }, { "id": "tXRYCSDiRhcEodDM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by the bar at coordinates [0.603, 0.340, 0.685, 1.982]?" }, { "from": "gpt", "value": "It's a bar for Yoga participants of 2021." } ], "id_raw": 442 }, { "id": "ZtgLjYU2964lZXFo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Jogging\" for 2020 at [0.110, 0.393, 0.192, 1.982] higher than the bar representing \"Yoga\" for 2021 at [0.603, 0.340, 0.685, 1.982]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 442 }, { "id": "ykhs6VN8bEK4exUF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nIs the height of the \"Gym\" bar for 2021 at [0.397, 0.817, 0.479, 1.982] greater than that of the \"Jogging\" bar for 2020 at [0.110, 0.393, 0.192, 1.982]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 442 }, { "id": "xLTcT6cZ0xqLxdDa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Swimming\" for 2020 [0.726, 0.287, 0.808, 1.982] taller than the bar for 2021 at [0.808, 0.181, 0.890, 1.982]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 442 }, { "id": "DeZhWugYYoq1RLNt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information in the region [0.364, 0.030, 0.636, 0.066]?" }, { "from": "gpt", "value": "This region contains the chart's subtitle, stating \"Fitness and Sports Health Trends\"." } ], "id_raw": 442 }, { "id": "tR7Sv5wcqAj8kPz8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area [0.726, 0.287, 0.808, 1.982]?" }, { "from": "gpt", "value": "This region contains a bar representing the number of \"Swimming\" participants for the year 2020." } ], "id_raw": 442 }, { "id": "Avt6XaEqDo9F3HgH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe what you see in the area [0.971, 0.377, 0.985, 0.620]?" }, { "from": "gpt", "value": "In this region I see the label on the Y axis of the chart, reading \"2021 Participants\"." } ], "id_raw": 442 }, { "id": "AOcrMctjSdTCtxGO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the {Y-axis label}[0.012, 0.389, 0.024, 0.609] for subplot_0?" }, { "from": "gpt", "value": "2020 Market Share." } ], "id_raw": 443 }, { "id": "O4wbvj0y2xLEAVoU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {bar}[0.092, 0.436, 0.160, 0.935] for Company A in subplot_0?" }, { "from": "gpt", "value": "#d87769 (corresponding to some kind of red)." } ], "id_raw": 443 }, { "id": "vTP1UVcqS6vj5aWL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the {Y-axis label}[0.976, 0.389, 0.988, 0.609] for subplot_1?" }, { "from": "gpt", "value": "2021 Market Share." } ], "id_raw": 443 }, { "id": "dmHudgYIB1pUVskA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the bar}[0.330, 0.287, 0.398, 0.935] of subplot_1?" }, { "from": "gpt", "value": "Company B." } ], "id_raw": 443 }, { "id": "SCKQrJUMQtPuEYgo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 2020 Market Share of Company A}[0.092, 0.436, 0.160, 0.935] greater than {the 2021 Market Share of Company A}[0.160, 0.362, 0.228, 0.935]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 443 }, { "id": "k07G6QSOYhu4UPhj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 2020 Market Share of Company C}[0.432, 0.187, 0.500, 0.935] equal to {the 2021 Market Share of Company C}[0.500, 0.237, 0.568, 0.935]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 443 }, { "id": "0e420dOdHiWlfRMr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 2020 Market Share of Company E}[0.772, 0.686, 0.840, 0.935] smaller than {the 2021 Market Share of Company E}[0.840, 0.561, 0.908, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 443 }, { "id": "IRy68NVViBrcgAXJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nBetween {2020 Market Share of Company B}[0.262, 0.312, 0.330, 0.935] and {2021 Market Share of Company B}[0.330, 0.287, 0.398, 0.935], which year saw Company B having a higher market share?" }, { "from": "gpt", "value": "2021." } ], "id_raw": 443 }, { "id": "2pdf3hmb236zvYCI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.052, 0.062, 0.948, 0.935]." }, { "from": "gpt", "value": "It forms the main plotting area of the comparative bar chart of 2020 and 2021 Market Shares for Companies A, B, C, D, and E. It contains grey background colour. It also contains the x-ticks representing the labels of the 5 Companies and y-ticks representing the range values from 0 to 35. In the subplot_0, there are the bars indicating the market share in 2020. In the subplot_1, there are the bars indicating the market share in 2021." } ], "id_raw": 443 }, { "id": "mEdhQtWm3Y2IULfZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe what can be seen in the area [0.791, 0.882, 0.943, 0.924]." }, { "from": "gpt", "value": "This area contains the legend for the 2021 Market Share with a corresponding colour indicator of #7da1c7 (which is a shade of blue) and a text indicating \"2021 Market Share\"." } ], "id_raw": 443 }, { "id": "l0CZkjGu8uf3egSH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted within the region [0.262, 0.287, 0.398, 0.935]?" }, { "from": "gpt", "value": "The 2021 Market Share of Company B is depicted with a blue bar." } ], "id_raw": 443 }, { "id": "AuGmCax6n9aNdudW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects come into the region [0.330, 0.486, 0.738, 0.935]?" }, { "from": "gpt", "value": "A blue bar indicating the 2021 Market Share of Company D is depicted." } ], "id_raw": 443 }, { "id": "HM9vZROey1RZyPLM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label in the subplot_0?" }, { "from": "gpt", "value": "{The Y-axis label for subplot_0} is at [0.021, 0.389, 0.041, 0.608] and is named '2020 Popularity'." } ], "id_raw": 444 }, { "id": "nzMuHxntoNxQoMfT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label in the subplot_1?" }, { "from": "gpt", "value": "{The Y-axis label for subplot_1} is at [0.959, 0.389, 0.979, 0.608] and is named '2021 Popularity'." } ], "id_raw": 444 }, { "id": "oFAD0VQLCL8z56bx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bars representing '2020 Popularity'?" }, { "from": "gpt", "value": "{The color of the bars representing '2020 Popularity'}[0.137, 0.655, 0.298, 1.347] is '#d87769'." } ], "id_raw": 444 }, { "id": "6XLRSUg84krHcpLE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nIs the '2020 Popularity' higher for 'Gothic Fashion' or 'Streetwear Fashion'?" }, { "from": "gpt", "value": "'Streetwear Fashion'." } ], "id_raw": 444 }, { "id": "EtsaRdbQ4CDmHsLc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nIs the '2021 Popularity' higher for 'Gothic Fashion' or 'Streetwear Fashion'?" }, { "from": "gpt", "value": "'Streetwear Fashion'." } ], "id_raw": 444 }, { "id": "YqnkxC4DUGRU2Tbq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nCompare the popularity of 'Gothic Fashion' in 2020 and 2021." }, { "from": "gpt", "value": "The popularity of {Gothic Fashion}[0.226, 0.942, 0.371, 0.970] has decreased from 2020 to 2021." } ], "id_raw": 444 }, { "id": "w7zXuAXo8xLuWDab", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.111, 0.858, 0.338, 0.909]." }, { "from": "gpt", "value": "Within this region is one legend item for the subplot_0. The label is called '2020 Popularity', which is located at [0.174, 0.870, 0.330, 0.898], and the color handle is '#d87769', which is located at [0.119, 0.872, 0.158, 0.892]." } ], "id_raw": 444 }, { "id": "hZTCHuv0JxDDJoYH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects between [0.540, 0.456, 0.702, 1.347]." }, { "from": "gpt", "value": "There is a bar graph that represents the '2020 Popularity' of 'Streetwear Fashion', with a value of 210." } ], "id_raw": 444 }, { "id": "MXOaL7Ja7FnbahDP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the main components in the region [0.319, 0.030, 0.681, 0.066]?" }, { "from": "gpt", "value": "{The region at}[0.319, 0.030, 0.681, 0.066] is a subtitle of 'Subculture and Fashion Trends'." } ], "id_raw": 444 }, { "id": "5aH58L6mptfEBX9G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat description is assigned to the label with coordinates [0.971, 0.378, 0.985, 0.619] in the chart?" }, { "from": "gpt", "value": "2021 Innovations." } ], "id_raw": 445 }, { "id": "axhNRPE2qf0h8BdO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nWhere is the bar that represents Country 3 for the 2021 Innovations located in the chart?" }, { "from": "gpt", "value": "[0.608, 0.145, 0.673, 1.629]." } ], "id_raw": 445 }, { "id": "490zVcOgoTnVgVPi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the subtitle} with coordinates [0.354, 0.030, 0.646, 0.066] is?" }, { "from": "gpt", "value": "Technological Innovations Indicator." } ], "id_raw": 445 }, { "id": "l7vYgrM6BECwxaor", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the item described within the legend at [0.752, 0.089, 0.922, 0.139]?" }, { "from": "gpt", "value": "{2021 Innovations}[0.797, 0.100, 0.917, 0.128]." } ], "id_raw": 445 }, { "id": "LE6Nxbo0AoUIwh2N", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nBetween the bars representing {Country 1}[0.110, 0.569, 0.175, 1.629] and {Country 4}[0.760, 0.640, 0.825, 1.629] for the 2020 Innovations, which one is higher?" }, { "from": "gpt", "value": "Country 1." } ], "id_raw": 445 }, { "id": "RwZpDph3rG24JVrJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nAre the bars for {the 2020 Innovations}[0.110, 0.569, 0.175, 1.629] higher or lower than {the 2021 Innovations}[0.175, 0.456, 0.240, 1.629] in Country 1?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 445 }, { "id": "21nVDojDj2BhUm50", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 2020 Innovations bar for Country 1}[0.110, 0.569, 0.175, 1.629] to the left or right of {the 2021 Innovations bar for the same country}[0.175, 0.456, 0.240, 1.629]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 445 }, { "id": "Beft8rKTdu6REWrk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nBetween {the 2020 Innovations}[0.327, 0.287, 0.392, 1.629] and {the 2021 Innovations}[0.392, 0.456, 0.457, 1.629] in Country 2, which has the higher value?" }, { "from": "gpt", "value": "2021 Innovations." } ], "id_raw": 445 }, { "id": "7Fz0fv5scWRB0A2I", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.071, 0.075, 0.929, 0.923]." }, { "from": "gpt", "value": "This area mainly contains two bar charts that correspond to the technological innovation indicators for 2020 and 2021. For each year, we have different bars representing various countries. The bars clearly indicate that the quantity of technological innovations varied among the countries and between the two years." } ], "id_raw": 445 }, { "id": "cl0i0xtwUZW6v9oA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.971, 0.378, 0.985, 0.619]." }, { "from": "gpt", "value": "This area contains the Y-axis label for the 2021 innovations. The label is rotated vertically and mentions \"2021 Innovations\" indicating what the data on the Y-axis signifies." } ], "id_raw": 445 }, { "id": "z7KBDmYtl1QiR65k", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.043, 0.346, 0.061, 0.374]?" }, { "from": "gpt", "value": "There is only one object, which is the Y-axis tick label for the value of 90." } ], "id_raw": 445 }, { "id": "uJHT5jKjuwV7YRqr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.571, 0.942, 0.643, 0.970]?" }, { "from": "gpt", "value": "In this area, we can see the x-axis label for Country 3." } ], "id_raw": 445 }, { "id": "fWoPiJ9uVBcGNXE2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the item located at [0.910, 0.877, 0.982, 0.897] in the legend of the chart?" }, { "from": "gpt", "value": "It is the label for \"Creativity 2021\"." } ], "id_raw": 446 }, { "id": "o08cTEcwwPqGMSOM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the box with coordinates [0.661, 0.214, 0.697, 1.836] represent?" }, { "from": "gpt", "value": "It's a bar of the bar chart, representing the value of \"Creativity 2020\" for Product D." } ], "id_raw": 446 }, { "id": "yZLOx6YQnlFIZQHK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {x-label \"Product B\"} at [0.318, 0.959, 0.362, 0.979] refers to?" }, { "from": "gpt", "value": "It refers to the category \"Product B\" on the x-axis." } ], "id_raw": 446 }, { "id": "8zahVJKp93Gyyqfn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.010, 0.417, 0.019, 0.581]?" }, { "from": "gpt", "value": "Innovation Level." } ], "id_raw": 446 }, { "id": "E2rNpJspEWPNHibo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nWhich one is higher, {the bar for \"Product A\" in \"Creativity 2021\"}[0.161, 0.232, 0.197, 1.836] or {the bar for \"Product B\" in \"Design 2020\"}[0.269, 0.446, 0.304, 1.836]?" }, { "from": "gpt", "value": "The bar for \"Product A\" in \"Creativity 2021\"." } ], "id_raw": 446 }, { "id": "sJFfeqYAMz8SPcnT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for \"Design 2020\" for \"Product A\"}[0.090, 0.677, 0.126, 1.836] represent a higher value than {the bar for \"Creativity 2020\" for \"Product A\"}[0.126, 0.588, 0.161, 1.836]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 446 }, { "id": "NxLnAiBxF7MqpoRb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nDoes {Product C in \"Creativity 2021\"}[0.519, 0.142, 0.554, 1.836] have a higher value than {Product D in \"Design 2021\"}[0.733, 0.107, 0.769, 1.836]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 446 }, { "id": "klFjO8CN95nxFfhl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nBetween {Product E in \"Creativity 2021\"}[0.876, 0.053, 0.911, 1.836] and {\"Design 2021\" for Product D}[0.733, 0.107, 0.769, 1.836], which bar is taller?" }, { "from": "gpt", "value": "Product E in \"Creativity 2021\"." } ], "id_raw": 446 }, { "id": "GLg4ANKEmW4obYEi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.881, 0.809, 0.985, 0.935]." }, { "from": "gpt", "value": "The area [0.881, 0.809, 0.985, 0.935] includes a legend with four entries: \"Creativity 2020\" at [0.910, 0.817, 0.982, 0.837], \"Design 2020\" at [0.910,0.847,0.968,0.867], \"Creativity 2021\" at [0.910, 0.877, 0.982, 0.897], and \"Design 2021\" at [0.910, 0.907, 0.969, 0.927]. Each entry has a color square to its left denoting the corresponding bar's color." } ], "id_raw": 446 }, { "id": "rOxmqVAKp3v96RZL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area from [0.362, 0.959, 0.541, 0.979]." }, { "from": "gpt", "value": "The area [0.362, 0.959, 0.541, 0.979] contains two category labels on the x-axis which are: \n1. \"Product B\": [0.318, 0.959, 0.362, 0.979],\n2. \"Product C\": [0.496, 0.959, 0.541, 0.979]." } ], "id_raw": 446 }, { "id": "pRQAWVMvbnRix1bG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nTell me about the items within the area [0.447, 0.410, 0.483, 1.836]." }, { "from": "gpt", "value": "There are two items: \n1. The blue bar which represents the value of \"Design 2020\" for \"Product C\", \n2. The red bar which represents the value of \"Creativity 2020\" for \"Product C\"." } ], "id_raw": 446 }, { "id": "MUkDCaohFuI3q6TV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.476, 0.142, 0.590, 1.836]?" }, { "from": "gpt", "value": "In the area [0.476, 0.142, 0.590, 1.836], there are four bars representing the values for \"Product C\" in \"Creativity 2020\", \"Design 2020\", \"Creativity 2021\", and \"Design 2021\"." } ], "id_raw": 446 }, { "id": "l9M7L9nnRV5zmNI6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} at [0.015, 0.303, 0.029, 0.694]?" }, { "from": "gpt", "value": "Number of Papers Published." } ], "id_raw": 447 }, { "id": "UChJ3OUQrDxGg3OD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label placed at [0.605, 0.942, 0.728, 0.970]?" }, { "from": "gpt", "value": "Machine Learning." } ], "id_raw": 447 }, { "id": "fFpQch3axDnGlNDI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat color and information does the {legend at [0.101, 0.186, 0.128, 0.205]} represent?" }, { "from": "gpt", "value": "It represents 'Active Developers 2020' and its color is marked '#c490c0'." } ], "id_raw": 447 }, { "id": "7IQnkbB2Rf6JWSEw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar at [0.126, 0.393, 0.209, 0.923]?" }, { "from": "gpt", "value": "It represents the number of 'Deep Learning' papers published in 2019 which is 50000." } ], "id_raw": 447 }, { "id": "JHGfl8gaQYJ3UbwI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, the {bar depicting Deep Learning in 2020}[0.209, 0.128, 0.292, 0.923] or the {bar depicting Machine Learning in 2020}[0.625, 0.605, 0.708, 0.923]?" }, { "from": "gpt", "value": "Deep Learning." } ], "id_raw": 447 }, { "id": "I7sGVqfRHtyQ7OJ7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nAre the bars in {Deep Learning}[0.126, 0.393, 0.209, 0.923] [0.209, 0.128, 0.292, 0.923] generally taller than the bars in {Machine Learning}[0.542, 0.711, 0.625, 0.923] [0.625, 0.605, 0.708, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 447 }, { "id": "xfYmLbgH9zCDOq2g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nComparing year 2019 and 2020, which year has more {Active Developers in Machine Learning} [0.708, 0.390, 0.791, 0.923] [0.791, 0.341, 0.874, 0.923]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 447 }, { "id": "16WsAHXVR408EvA3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nWhich is greater, the {number of Active Developers} in {Deep Learning} [0.292, -0.531, 0.375, 0.923] [0.375, -0.652, 0.458, 0.923] or in {Machine Learning} [0.708, 0.390, 0.791, 0.923] [0.791, 0.341, 0.874, 0.923]?" }, { "from": "gpt", "value": "Deep Learning." } ], "id_raw": 447 }, { "id": "TXHOp7BJapwS6kpk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the legend at position [0.689, 0.089, 0.905, 0.223]?" }, { "from": "gpt", "value": "The legend at position [0.689, 0.089, 0.905, 0.223] contains two parts relating to 'Active Developers'. The first item is for 'Active Developers 2019', with a text label positioned at [0.734, 0.142, 0.899, 0.170] and color indicator at [0.695, 0.144, 0.723, 0.164]. The second item with text at [0.734, 0.183, 0.899, 0.211] and color indicator at [0.695, 0.186, 0.723, 0.205] is for 'Active Developers 2020'." } ], "id_raw": 447 }, { "id": "eqdZaoM1tNgoONzZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the region [0.605, 0.942, 0.728, 0.970]?" }, { "from": "gpt", "value": "The region [0.605, 0.942, 0.728, 0.970] contains one of the x-axis labels, 'Machine Learning'." } ], "id_raw": 447 }, { "id": "FFOO769wXI9Kh4my", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area under [0.971, 0.295, 0.985, 0.703] represent?" }, { "from": "gpt", "value": "The area under [0.971, 0.295, 0.985, 0.703] represents the ylabel 'Number of Active Developers' in #d26252 color." } ], "id_raw": 447 }, { "id": "IHbrvr2U5xLdGwDo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the visual elements in [0.200, 0.942, 0.301, 0.970]." }, { "from": "gpt", "value": "The region [0.200, 0.942, 0.301, 0.970] contains the label 'Deep Learning', corresponding to an x-axis tick." } ], "id_raw": 447 }, { "id": "hr76nrsDJnaf9BUg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat does label at [0.015, 0.427, 0.029, 0.571] represent?" }, { "from": "gpt", "value": "This is the y-axis label for the first subplot, representing the number of Inventions." } ], "id_raw": 448 }, { "id": "RPHjuNkN6B6dlmPT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at [0.170, 0.942, 0.243, 0.970] on the chart?" }, { "from": "gpt", "value": "This is the x-axis label for the first historical period, which is \"Prehistoric\"." } ], "id_raw": 448 }, { "id": "mY7ONnuYXKGsAhLy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend item} at [0.722, 0.858, 0.902, 0.909] in the second subplot?" }, { "from": "gpt", "value": "It indicates that Cultural Expansion is represented in blue in the second subplot." } ], "id_raw": 448 }, { "id": "2hQ9nNF9OyDuN4m0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Ancient epoch}[0.402, 0.075, 0.480, 0.094] in the second subplot taller than {the bar for Prehistoric epoch}[0.206, 0.075, 0.285, 0.075]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 448 }, { "id": "nemafRWhFBENYTr0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Middle Ages}[0.598, 0.075, 0.676, 0.383] in the second subplot taller than {the bar for the same era}[0.520, 0.075, 0.598, 0.460] in the first subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 448 }, { "id": "RadOWsqM6vHuhYFL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nHow do {the bar for the Modern era} [0.794, 0.075, 0.872, 0.807] in the second subplot and {the bar for the Modern era} [0.715, 0.075, 0.794, 0.845] in the first subplot compare?" }, { "from": "gpt", "value": "They are almost the same height." } ], "id_raw": 448 }, { "id": "8SS6oVkFVsHE2kck", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the area [0.706, 0.858, 0.892, 0.898]?" }, { "from": "gpt", "value": "It's the legend of the second subplot. It specifies that the bars in blue represents 'Cultural Expansion'." } ], "id_raw": 448 }, { "id": "c0SCipgKuI6aHaGX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell us about the region [0.324, 0.075, 0.402, 0.152]?" }, { "from": "gpt", "value": "This region includes one rectangle (bar) in the first subplot representing the number of Inventions during the Ancient period." } ], "id_raw": 448 }, { "id": "I6ipJyWI1F8nDzgn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.091, 0.075, 0.909, 0.923]." }, { "from": "gpt", "value": "It contains 2 subplots, both of which are bar charts representing two different aspects, 'Inventions' and 'Cultural Expansion' across different historical periods: Prehistoric, Ancient, Middle Ages, and Modern." } ], "id_raw": 448 }, { "id": "CC37BSynZL7ICQxi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart type that is represented in the subplot_0 part of the JSON structure?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 449 }, { "id": "SmrUOaDLpR8tlFMF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat company does {the bar}[0.197, 0.075, 0.257, 0.753] in subplot_1 represent?" }, { "from": "gpt", "value": "Company A." } ], "id_raw": 449 }, { "id": "04r9QuykcV4OjlRf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the caption of {the ylabel}[0.971, 0.422, 0.985, 0.575] label in subplot_1?" }, { "from": "gpt", "value": "2021 Sales." } ], "id_raw": 449 }, { "id": "wlmHR15ykLiB5JtB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bars represented by the data in {[0.136, 0.075, 0.197, 0.680],[0.288, 0.075, 0.348, 0.801],[0.439, 0.075, 0.500, 0.583],[0.591, 0.075, 0.652, 0.753],[0.743, 0.075, 0.803, 0.705]}?" }, { "from": "gpt", "value": "The color of the bars is #d87769." } ], "id_raw": 449 }, { "id": "NlMAZm2WFQvidvCX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nAre the 2021 Sales higher than 2020 Sales for {Company A}<{the 2020 sales bar for Company A}[0.136, 0.075, 0.197, 0.680],{the 2021 sales bar for Company A}[0.197, 0.075, 0.257, 0.753]>?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 449 }, { "id": "wCkpjPr2nmvPcBo5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nIs the sales of {Company B}<{2021 sales bar for Company B}[0.348, 0.075, 0.409, 0.705]> less than the sales of {Company D}<{2021 sales bar for Company D}[0.652, 0.075, 0.712, 0.801]> in 2021?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 449 }, { "id": "UHT5jZ9VAri7nt0Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nAmong {Company A}<{2021 sales bar for Company A}[0.197, 0.075, 0.257, 0.753]>, {Company B}<{2021 sales bar for Company B}[0.348, 0.075, 0.409, 0.705]>, {Company C}<{2021 sales bar for Company C}[0.500, 0.075, 0.561, 0.777]>, {Company D}<{2021 sales bar for Company D}[0.652, 0.075, 0.712, 0.801]>, and {Company E}<{2021 sales bar for Company E}[0.803, 0.075, 0.864, 0.608]>, which company had the lowest sales in 2021?" }, { "from": "gpt", "value": "Company E." } ], "id_raw": 449 }, { "id": "pM2BcYANzJBwNYMj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nWhich company had higher sales in 2020, {Company D}<{2020 sales bar for Company D}[0.591, 0.075, 0.652, 0.753]> or {Company E}<{2020 sales bar for Company E}[0.743, 0.075, 0.803, 0.705]>?" }, { "from": "gpt", "value": "Company D." } ], "id_raw": 449 }, { "id": "vJq52lS5NhpDXUg6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.136, 0.075, 0.803, 0.801]?" }, { "from": "gpt", "value": "In this region, we can see a bar chart representing the 2020 sales for 5 companies. Each company {Company A}[0.136, 0.075, 0.197, 0.680], {Company B}[0.288, 0.075, 0.348, 0.801], {Company C}[0.439, 0.075, 0.500, 0.583], {Company D}[0.591, 0.075, 0.652, 0.753], {Company E}[0.743, 0.075, 0.803, 0.705] is represented by a bar with height proportional to its sales volume in 2020." } ], "id_raw": 449 }, { "id": "Ut1r9SDMkcriPVKe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the region [0.100, 0.073, 0.900, 0.923] in subplot_0." }, { "from": "gpt", "value": "This region contains an axes area with grey background, one Y label that describes the \"2020 Sales\", one subtitle \"Market Competition Analysis and Comparison\", 5 bars representing \"2020 Sales\" for each company, tick labels indicating company's name on X axis and a legend for \"2020 Sales\"." } ], "id_raw": 449 }, { "id": "r4sdvNlM74CQYl4U", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region of [0.197, 0.075, 0.257, 0.753]?" }, { "from": "gpt", "value": "This region contains a bar in the subplot_1 of the bar chart, representing the 2021 sales for Company A, which has a color code '#7da1c7', indicating a sales volume for Company A of 28000 in 2021." } ], "id_raw": 449 }, { "id": "qEcuALpJTBKMCBDj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content present in the region [0.767, 0.858, 0.893, 0.909] in subplot_1." }, { "from": "gpt", "value": "This region contains a legend item for \"2021 Sales\". It has a color identifier[{color box for the legend}[0.772, 0.872, 0.800, 0.892]] and a textual label[{text for '2021 Sales'}[0.811, 0.870, 0.888, 0.898]], and the color has a code of '#7da1c7'." } ], "id_raw": 449 }, { "id": "Urt6jxQD8X5DT0Zy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the vertical axis in the chart positioned at [0.012, 0.420, 0.024, 0.554]?" }, { "from": "gpt", "value": "Percentage (%)." } ], "id_raw": 451 }, { "id": "B5BoA2WPkmv8CNjz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [0.181, 0.427, 0.250, 0.927]?" }, { "from": "gpt", "value": "The bar representing the percentage (39.8%) of research in Physics." } ], "id_raw": 451 }, { "id": "FdJMkNjaMRd4rueT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the bar at [0.355, 0.390, 0.424, 0.927] stands for?" }, { "from": "gpt", "value": "It stands for research output in Biology, which is 42.7%." } ], "id_raw": 451 }, { "id": "8TKdKoqfzku6ZcOC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat category is located at [0.617, 0.939, 0.682, 0.957]?" }, { "from": "gpt", "value": "The category is Psychology." } ], "id_raw": 451 }, { "id": "7BbF9MG1WHIzim2o", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nComparing the bars representing Biology {[0.355, 0.390, 0.424, 0.927]} and Mathematics {[0.441, 0.262, 0.511, 0.927]}, which field has a higher percentage?" }, { "from": "gpt", "value": "Mathematics." } ], "id_raw": 451 }, { "id": "saYnzOWHOnTUqXe1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Sociology}[0.702, 0.517, 0.771, 0.927] higher or lower than {the bar representing Musicology}[0.876, 0.698, 0.945, 0.927]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 451 }, { "id": "9OafQAVR7ELB1zlJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nAmong {the bars representing Biology, Mathematics, and Engineering} [0.355, 0.390, 0.424, 0.927],[0.441, 0.262, 0.511, 0.927],[0.528, 0.181, 0.598, 0.927], which one has the highest percentage?" }, { "from": "gpt", "value": "Engineering." } ], "id_raw": 451 }, { "id": "oVIirr5YMJwCpg0a", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nAmong {the bars representing Computer Science and Physics} [0.094, 0.359, 0.164, 0.927],[0.181, 0.427, 0.250, 0.927], which one holds a higher percentage?" }, { "from": "gpt", "value": "Computer Science." } ], "id_raw": 451 }, { "id": "mgSk80b4ZbRFUFIu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the area [0.355, 0.181, 0.598, 0.927]?" }, { "from": "gpt", "value": "It includes two bars representing Biology at [0.355, 0.390, 0.424, 0.927] with percentage 42.7% and Engineering at [0.528, 0.181, 0.598, 0.927] with percentage 59.3%." } ], "id_raw": 451 }, { "id": "eJVfUdxccseSuEgC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the information of the region [0.181, 0.396, 0.337, 0.927]?" }, { "from": "gpt", "value": "The region includes a bar signifying 'Physics', with a percentage of 39.8%. The coordinates of the bar are [0.181, 0.427, 0.250, 0.927]." } ], "id_raw": 451 }, { "id": "HPiiCOtgVg9qAbhq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is located in the region [0.026, 0.289, 0.578, 0.993]." }, { "from": "gpt", "value": "This region contains all the bars from Physics to Psychology, as well as their corresponding percentages. More specifically, it includes Physics at [0.181, 0.427, 0.250, 0.927] with percentage 39.8%, Chemistry at [0.268, 0.486, 0.337, 0.927] with percentage 35.1%, Biology at [0.355, 0.390, 0.424, 0.927] with percentage 42.7%, Mathematics at [0.441, 0.262, 0.511, 0.927] with percentage 52.9%, and Psychology at [0.615, 0.306, 0.684, 0.927] with percentage 49.4%." } ], "id_raw": 451 }, { "id": "bOoCMQIGtT3QB6Mh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat is identifiable in the region from [0.575, 0.149, 1.000, 0.966]?" }, { "from": "gpt", "value": "It contains bars for fields from Psychology to Musicology with their relative percentages. In particular, the bar for Psychology at [0.615, 0.306, 0.684, 0.927] with 49.4%, the bar for Sociology at [0.702, 0.517, 0.771, 0.927] with a percentage of 32.6%, the bar for Literature at 0.789, 0.601, 0.858, 0.927] with a percentage of 25.9%, and the bar for Musicology at [0.876, 0.698, 0.945, 0.927] with a percentage of 18.2%." } ], "id_raw": 451 }, { "id": "npigwNc3dAP5p17i", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the term located at [0.241, 0.025, 0.806, 0.055] signify in the chart?" }, { "from": "gpt", "value": "It is the subtitle of the chart, \"Usage of Materials in Scientific Research & Technological Innovation.\"" } ], "id_raw": 452 }, { "id": "o3P4uuULGmPMCpYt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat material is represented by the bar at [0.104, 0.146, 0.220, 0.903]?" }, { "from": "gpt", "value": "It represents \"Copper\"." } ], "id_raw": 452 }, { "id": "zwjUhQDYmsqfjg5B", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat proportion is situatedat [0.730, 0.829, 0.751, 0.852]?" }, { "from": "gpt", "value": "It has a percentage of \"3%\"." } ], "id_raw": 452 }, { "id": "DE0v1MYTSiCqcjPC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Iridium}[0.827, 0.869, 0.943, 0.903] taller than the bar representing {Gold}[0.538, 0.819, 0.654, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 452 }, { "id": "LSQTUnAoSFirY9Qd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nIn terms of their height, how does the {Silver}[0.393, 0.735, 0.509, 0.903] bar compare to the {Gold}[0.538, 0.819, 0.654, 0.903] bar ?" }, { "from": "gpt", "value": "Silver bar is higher." } ], "id_raw": 452 }, { "id": "MZ0IeqbkG6DfItC6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nDoes the {Copper}[0.104, 0.146, 0.220, 0.903] bar have a higher percentage compared to {Aluminium}[0.249, 0.314, 0.364, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 452 }, { "id": "PEvJWQMUKUO3xgEo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart elements present in [0.062, 0.062, 0.985, 0.903]." }, { "from": "gpt", "value": "The region contains the main chart area which includes the x and y axes, tick labels, and bars representing six different materials: Copper, Aluminium, Silver, Gold, Platinum, and Iridium." } ], "id_raw": 452 }, { "id": "TAad2l03z3O0v3he", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.249, 0.314, 0.364, 0.903]?" }, { "from": "gpt", "value": "There is a bar representing \"Aluminium\" with a usage percentage of 35% given in this region." } ], "id_raw": 452 }, { "id": "0NYs1nEK1RAxDoDJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you deduce from the region between [0.827, 0.869, 0.943, 0.903]?" }, { "from": "gpt", "value": "The region contains the shortest bar, which corresponds to the use of Iridium, with a usage percentage of 2%." } ], "id_raw": 452 }, { "id": "a2Cp95lKezV17BxV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nWhich data trend corresponds to the color handle located at [0.083, 0.120, 0.111, 0.136]?" }, { "from": "gpt", "value": "The color handle corresponds to the \"Upskilling\" trend." } ], "id_raw": 453 }, { "id": "vhoxaIZX36tmm7hB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at [0.589, 0.919, 0.624, 0.942] on the x-axis?" }, { "from": "gpt", "value": "The text at [0.589, 0.919, 0.624, 0.942] is \"2019\"." } ], "id_raw": 453 }, { "id": "8taF341AsIXz7zT3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of Y values for the bar at [0.896, 0.061, 0.943, 0.329]?" }, { "from": "gpt", "value": "The Y range for the bar at [0.896, 0.061, 0.943, 0.329] is from 116.00 to 170.20." } ], "id_raw": 453 }, { "id": "FnzGog9oL70YX47t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nIs the color handle for {Upskilling}[0.083, 0.120, 0.111, 0.136] above or below the one for {Employee Wellness}[0.083, 0.155, 0.111, 0.171]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 453 }, { "id": "cnznQHVPpJ8Tntnu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nComparing both bars representing the year 2020 at {bar for Upskilling}[0.740, 0.427, 0.787, 0.654] and {bar for Employee Wellness}[0.740, 0.233, 0.787, 0.427], which trend had a higher value?" }, { "from": "gpt", "value": "Employee Wellness." } ], "id_raw": 453 }, { "id": "tPAf1gh3oUIjrdhS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nIn the year 2018, were the values for {Remote Working}[0.426, 0.786, 0.473, 0.903] trend higher than that of {Upskilling}[0.426, 0.638, 0.473, 0.786]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 453 }, { "id": "wx8LhkbcgDP5omKn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the labels present within [0.078, 0.074, 0.262, 0.185]?" }, { "from": "gpt", "value": "This region contains labels for 3 data trends, each associated with a color code. These are {Remote Working}[0.122, 0.083, 0.237, 0.106] attached to a blue color code at [0.083, 0.085, 0.111, 0.101], {Upskilling}[0.122, 0.118, 0.190, 0.141] with an orange color code at [0.083, 0.120, 0.111, 0.136], and {Employee Wellness}[0.122, 0.153, 0.256, 0.176] joined with a green color code at [0.083, 0.155, 0.111, 0.171]." } ], "id_raw": 453 }, { "id": "nCPav51nGrtwK1fb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information is contained within the region [0.487, 0.638, 0.609, 0.903]?" }, { "from": "gpt", "value": "This region contains one blue bar representing {Remote Working}[0.583, 0.741, 0.630, 0.903] and two bars below it, i.e., an orange bar for {Upskilling}[0.583, 0.560, 0.630, 0.741] and a green bar for {Employee Wellness}[0.583, 0.418, 0.630, 0.560]." } ], "id_raw": 453 }, { "id": "gCTfB70FAaH0S6Mg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis?" }, { "from": "gpt", "value": "Percentage (%)." } ], "id_raw": 454 }, { "id": "kSYCZMBJX8Yxl6SP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar at the far right}[0.852, 0.886, 0.938, 0.903] representing?" }, { "from": "gpt", "value": "Automated Reasoning." } ], "id_raw": 454 }, { "id": "z6a6cRKtRIDhqUfj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar representing 'Neural Networks'}[0.318, 0.495, 0.403, 0.903]?" }, { "from": "gpt", "value": "Blue (#4169e1)." } ], "id_raw": 454 }, { "id": "VNyWLoRoOZRSJmY6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage associated with {the bar representing 'Machine Learning'}[0.424, 0.589, 0.510, 0.903]?" }, { "from": "gpt", "value": "22.4%" } ], "id_raw": 454 }, { "id": "G3Et2JcAkH6kcaSC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar labeled 'Neural Networks'}[0.318, 0.495, 0.403, 0.903] come before or after {the bar labeled 'Deep Learning'}[0.104, 0.073, 0.189, 0.903]?" }, { "from": "gpt", "value": "After." } ], "id_raw": 454 }, { "id": "VoFoGEgVpBqa2Uf2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Natural Language Processing'}[0.531, 0.738, 0.617, 0.903] taller or shorter than {the bar representing AI Ethics'}[0.638, 0.819, 0.724, 0.903]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 454 }, { "id": "kCIelyikKGGjLWEh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nAre the percentage represented by the {bar for 'Robotic Process Automation'}[0.211, 0.403, 0.296, 0.903] higher or lower than the {bar for 'Machine Learning'}[0.424, 0.589, 0.510, 0.903]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 454 }, { "id": "Omlp5ohKXI2YSk7j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the one for 'Cognitive Computing'}[0.745, 0.854, 0.831, 0.903] or {the one for 'Automated Reasoning'}[0.852, 0.886, 0.938, 0.903]?" }, { "from": "gpt", "value": "Cognitive Computing." } ], "id_raw": 454 }, { "id": "ozIBKrRl160r823O", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is happening in the box [0.745, 0.854, 0.938, 0.903]?" }, { "from": "gpt", "value": "This box identifies the bars for 'Cognitive Computing' and 'Automated Reasoning'. The 'Cognitive Computing' bar has a higher value of 3.5% while the Automated Reasoning bar had a lower value of 1.2%." } ], "id_raw": 454 }, { "id": "vnIi8driypfJsWYL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the scene within the area [0.637, 0.714, 0.699, 0.819]." }, { "from": "gpt", "value": "This is the scene of the 'AI Ethics' bar which has an estimated value of 6%." } ], "id_raw": 454 }, { "id": "gRtjAzsOUzpYTGIF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the region [0.715, 0.854, 0.861, 0.942]." }, { "from": "gpt", "value": "This area contains one blue bar representing 'Cognitive Computing' with its corresponding value marked as '3.5%' on its top side." } ], "id_raw": 454 }, { "id": "JYVKc1h0Ar58Nnki", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area [0.035, 0.193, 0.052, 0.216]?" }, { "from": "gpt", "value": "This area contains a y-axis label, which corresponds to a value of 50% on the graph." } ], "id_raw": 454 }, { "id": "H6mylo6WUgC1wYdC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart's y-axis represent with coordinates [0.015, 0.400, 0.029, 0.565]?" }, { "from": "gpt", "value": "The y-axis label represents the \"Donations (%)\"." } ], "id_raw": 455 }, { "id": "xq89pf9ARygpjgNC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what's at [0.103, 0.182, 0.201, 0.903]?" }, { "from": "gpt", "value": "It's a bar denoting the percentage of donations received by {Red Cross}[0.103, 0.182, 0.201, 0.903], which is 60%." } ], "id_raw": 455 }, { "id": "KHTUJhJrsjDU9hc1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the data under the x-axis label {UNICEF}[0.371, 0.919, 0.422, 0.942]?" }, { "from": "gpt", "value": "The bar representing {UNICEF}[0.348, 0.543, 0.446, 0.903] shows that UNICEF received 30.0% of donations." } ], "id_raw": 455 }, { "id": "PriuJTEJM3k0WzZj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat is specified by the points [0.415, 0.025, 0.623, 0.055]?" }, { "from": "gpt", "value": "That's the subtitle of the chart which reads \"Donations by Percentage\"." } ], "id_raw": 455 }, { "id": "r2xtyuMdK20xnfM7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nCompare the donations received by {Amnesty International}[0.715, 0.783, 0.813, 0.903] against the donations received by {Make-A-Wish Foundation}[0.837, 0.843, 0.935, 0.903]. Which organization received more?" }, { "from": "gpt", "value": "Amnesty International." } ], "id_raw": 455 }, { "id": "rjH1aUS0FFDj3MBP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Red Cross}[0.103, 0.182, 0.201, 0.903] taller than the one representing {Doctors Without Borders}[0.593, 0.362, 0.690, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 455 }, { "id": "lGtiOsClmltGruYJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nBetween {UNICEF}[0.348, 0.543, 0.446, 0.903] and {Save the Children}[0.470, 0.603, 0.568, 0.903], which organization received less donations?" }, { "from": "gpt", "value": "Save the Children." } ], "id_raw": 455 }, { "id": "yIYFnE4TOg4xIUpN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nIs the donation percentage for {Habitat for Humanity}[0.226, 0.302, 0.324, 0.903] higher than {Doctors Without Borders}[0.593, 0.362, 0.690, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 455 }, { "id": "cIl6me6bUpeYNy6t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nCan you explain what's occurring within the boundaries of [0.035, 0.053, 0.052, 0.917]?" }, { "from": "gpt", "value": "This area primarily contains the y-axis values or 'yticks', with each providing a donation percentage ranging from 0 to 70 at intervals of 10." } ], "id_raw": 455 }, { "id": "KPPEUDcj88t87kxT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.062, 0.182, 0.935, 0.903] encompass?" }, { "from": "gpt", "value": "This area is populated with bars representing the donation percentage for various charities: {Red Cross}[0.103, 0.182, 0.201, 0.903], {Habitat for Humanity}[0.226, 0.302, 0.324, 0.903], {UNICEF}[0.348, 0.543, 0.446, 0.903], {Save the Children}[0.470, 0.603, 0.568, 0.903], {Doctors Without Borders}[0.593, 0.362, 0.690, 0.903], {Amnesty International}[0.715, 0.783, 0.813, 0.903], and {Make-A-Wish Foundation}[0.837, 0.843, 0.935, 0.903]." } ], "id_raw": 455 }, { "id": "Zis7kw5eiizEmNwu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be seen in the area [0.118, 0.919, 0.971, 0.942]." }, { "from": "gpt", "value": "The area is where the names of the charities are located on the x-axis, the charities are: \n1. {Red Cross}[0.118, 0.919, 0.187, 0.942],\n2. {Habitat for Humanity}[0.201, 0.919, 0.348, 0.942],\n3. {UNICEF}[0.371, 0.919, 0.422, 0.942],\n4. {Save the Children}[0.457, 0.919, 0.581, 0.942],\n5. {Doctors Without Borders}[0.556, 0.919, 0.727, 0.942],\n6. {Amnesty International}[0.687, 0.919, 0.841, 0.942],\n7. {Make-A-Wish Foundation}[0.801, 0.919, 0.971, 0.942]." } ], "id_raw": 455 }, { "id": "7MdkFkso3lFgYB4C", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.415, 0.025, 0.623, 0.055] contain?" }, { "from": "gpt", "value": "This region contains the subtitle of the chart, \"Donations by Percentage\"." } ], "id_raw": 455 }, { "id": "ezWCvWu3sNIRpYGt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "Big Data Integration Overview at the position of {the title}[0.398, 0.025, 0.649, 0.055]." } ], "id_raw": 456 }, { "id": "u1Db3j7mLTzemnPK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the lable of Y-axis in the chart?" }, { "from": "gpt", "value": "{The Y-axis}[0.015, 0.377, 0.029, 0.588] is labeled as 'Adoption Rate (%)'." } ], "id_raw": 456 }, { "id": "ul7vm14Vzo0ndLZ1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat tool is represented at the position with coordinates [0.104, 0.405, 0.203, 0.903]?" }, { "from": "gpt", "value": "The tool is MuleSoft." } ], "id_raw": 456 }, { "id": "XDdMivF4YZkn6aKU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nAre the adoption rates of {MuleSoft}[0.104, 0.405, 0.203, 0.903] and {Dell Boomi}[0.351, 0.493, 0.449, 0.903] similar?" }, { "from": "gpt", "value": "No." } ], "id_raw": 456 }, { "id": "ILEXAHsw87IH3IqX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nBetween {IBM Infosphere}[0.474, 0.634, 0.573, 0.903] and {Oracle Data Integrator}[0.721, 0.138, 0.820, 0.903], which tool has a higher adoption rate?" }, { "from": "gpt", "value": "Oracle Data Integrator." } ], "id_raw": 456 }, { "id": "ooSdLLUG4yodky2r", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nIs the adoption rate of {Jitterbit}[0.844, 0.590, 0.943, 0.903] lower than that of {Informatica PowerCenter}[0.597, 0.365, 0.696, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 456 }, { "id": "LIsR1t5Ws5T9sisr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.062, 0.062, 0.985, 0.903]" }, { "from": "gpt", "value": "This region is the main charting area containing all bars, respective y-coordinates, and tool labels. The x-axis labels are seven different data integration tools, while the y-axis labels represent the adoption rates (%)." } ], "id_raw": 456 }, { "id": "8mACF3MpOSfA6htu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the area [0.035, 0.263, 0.052, 0.287] contains?" }, { "from": "gpt", "value": "The area contains a y-axis label \"30\"." } ], "id_raw": 456 }, { "id": "OiPDCsEbqGOsyie3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.597, 0.365, 0.696, 0.903]?" }, { "from": "gpt", "value": "The area contains a bar representing the adoption rate of the tool Informatica PowerCenter. The rate is 25.6%." } ], "id_raw": 456 }, { "id": "yLjhrwvgcRifdkGk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the labels located at [0.102, 0.919, 0.222, 0.942], [0.280, 0.919, 0.333, 0.942], [0.410, 0.919, 0.492, 0.942], [0.543, 0.919, 0.649, 0.942], [0.679, 0.919, 0.802, 0.942], [0.852, 0.919, 0.918, 0.942]?" }, { "from": "gpt", "value": "These are the province names on the x-axis of the bar chart, from left to right: New South Wales, Victoria, Queensland, South Australia, Western Australia, Tasmania." } ], "id_raw": 457 }, { "id": "7oC8nAo1xmSR5qRn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the characteristic of the rectangular object located at [0.249, 0.310, 0.364, 0.903]?" }, { "from": "gpt", "value": "It is a bar representing a GIS uptake of 63.5% in Victoria." } ], "id_raw": 457 }, { "id": "ZaAWJwtLyL6bXhRf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at [0.494, 0.952, 0.553, 0.975] in the chart?" }, { "from": "gpt", "value": "It's the label for the x-axis, named \"Province\"." } ], "id_raw": 457 }, { "id": "RvABFiM4HnPLC2dF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Victoria}[0.249, 0.310, 0.364, 0.903] taller than {the bar for South Australia}[0.538, 0.476, 0.654, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 457 }, { "id": "l3SkSFNDxuI8zBPy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nAre central marks of {Victoria} at [0.306, 0.903] and {South Australia} at [0.596, 0.903] the same in height?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 457 }, { "id": "WAMFq7HD8UoqMmjy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\n{Tasmania bar}[0.827, 0.760, 0.943, 0.903] is lying on which side of {New South Wales bar}[0.104, 0.146, 0.220, 0.903]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 457 }, { "id": "1Al2ceHeTrtZIyx5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.102, 0.919, 0.222, 0.903]." }, { "from": "gpt", "value": "This region contains a single bar and its x-axis label. The blue bar represents the GIS uptake value for the province New South Wales, while the x-axis label is \"New South Wales\"." } ], "id_raw": 457 }, { "id": "RKn3n1YqtJIvQJNQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects located in the region from [0.249, 0.310, 0.364, 0.903]?" }, { "from": "gpt", "value": "This area contains a bar and an x-label. The bar represents the GIS uptake in the province called Victoria, which stands at 63.5%, and the x-label is Victoria." } ], "id_raw": 457 }, { "id": "n4qDiu8ZwxjX8o5t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates [0.035, 0.333, 0.052, 0.357]?" }, { "from": "gpt", "value": "It contains a label on the y-axis representing the 60% mark." } ], "id_raw": 457 }, { "id": "G8z4jT9CbZWWoSYA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nWhat's on the x-axis based on the coordinates [0.504, 0.952, 0.543, 0.975]?" }, { "from": "gpt", "value": "The label \"Trend\" is on the x-axis." } ], "id_raw": 458 }, { "id": "BNURyEfqi9U24e6q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the blue bar represents at the coordinates [0.104, 0.623, 0.244, 0.903]?" }, { "from": "gpt", "value": "It represents a \"steep decline\" trend with a value of 30%." } ], "id_raw": 458 }, { "id": "cqnAAHrrpH0oNz07", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nWhat's represented by the coordinate [0.327, 0.132, 0.371, 0.156]?" }, { "from": "gpt", "value": "It's the value of the bar for the \"Dramatic Surge\" trend, which is 80%." } ], "id_raw": 458 }, { "id": "sI2CSx9cppoUBfjz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Dramatic Surge\"}[0.279, 0.156, 0.419, 0.903] higher than {the bar representing \"Steep Decline\"}[0.104, 0.623, 0.244, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 458 }, { "id": "A6z6QR9qqzDlSbuI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar representing \"Gradual Recovery\"}[0.454, 0.436, 0.593, 0.903] and {the bar representing \"Upward Trajectory\"}[0.628, 0.249, 0.768, 0.903], which is taller?" }, { "from": "gpt", "value": "{the bar representing \"Upward Trajectory\"}[0.628, 0.249, 0.768, 0.903]. It has a higher value." } ], "id_raw": 458 }, { "id": "9O2ckA1tLIVBqNkW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar at the coordinate [0.628, 0.249, 0.768, 0.903] taller than the bar at the coordinate [0.803, 0.389, 0.943, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 458 }, { "id": "XC0C4vPo75RwBX4d", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.279, 0.132, 0.419, 0.903]?" }, { "from": "gpt", "value": "It contains a blue bar, which represents the \"Dramatic Surge\" trend with a value of 80%." } ], "id_raw": 458 }, { "id": "0zqdg4XuDRBoXtN5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nCapture what's within the coordinates [0.628, 0.226, 0.768, 0.903]?" }, { "from": "gpt", "value": "Inside these coordinates, there's a blue bar representing \"Upward Trajectory\" with a value of 70%." } ], "id_raw": 458 }, { "id": "nuGnrFkxtKLw6x9U", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the area from [0.151, 0.599, 0.196, 0.623] to [0.851, 0.366, 0.895, 0.389]." }, { "from": "gpt", "value": "These two regions represent the percentages of the \"Steep Decline\" and \u201cSlight Decrease\u201d trends respectively. The value for \"Steep Decline\" is 30%, and for \"Slight Decrease\", it is 55%." } ], "id_raw": 458 }, { "id": "GwYCLWCWdRvpKINX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "Future Technological Development Prospects" } ], "id_raw": 459 }, { "id": "oKcJNbllxzNuIs8F", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at [0.013, 0.304, 0.024, 0.536] represent?" }, { "from": "gpt", "value": "Development Prospect (%)" } ], "id_raw": 459 }, { "id": "ULiUwNcugNTtU534", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the X-axis represent at the coordinates [0.491, 0.964, 0.556, 0.981]?" }, { "from": "gpt", "value": "Technology" } ], "id_raw": 459 }, { "id": "ymjVL3wYE9f89SrC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of 'Green Energy' as represented at [0.892, 0.093, 0.929, 0.110]?" }, { "from": "gpt", "value": "91.5%" } ], "id_raw": 459 }, { "id": "ru8QNaXjpFEN76jz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Nanotechnology'}[0.273, 0.439, 0.342, 0.793] higher or lower than {the bar representing 'Biotechnology'}[0.360, 0.245, 0.428, 0.793]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 459 }, { "id": "3Oms8tJoBoWi93GP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for '5G Technology'}[0.618, 0.351, 0.687, 0.793] show a higher or lower development prospect than {the bar for 'AI Technology'}[0.101, 0.196, 0.170, 0.793]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 459 }, { "id": "vIi2fkN2H0yQvO3K", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nBetween {Quantum Computing}[0.187, 0.307, 0.256, 0.793] and {Electric Cars}[0.704, 0.207, 0.773, 0.793], which technology has a higher development prospect?" }, { "from": "gpt", "value": "Electric Cars" } ], "id_raw": 459 }, { "id": "S80f1G2nCFKVR4PO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nBetween {VR/AR Technology}[0.790, 0.400, 0.859, 0.793] and {Green Energy}[0.876, 0.110, 0.945, 0.793], which technology has a higher development prospect?" }, { "from": "gpt", "value": "Green Energy" } ], "id_raw": 459 }, { "id": "cG9x6HzYdinddj9R", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the area from [0.101, 0.196, 0.170, 0.793]." }, { "from": "gpt", "value": "This area contains the bar representing AI Technology's development prospect, which is shown to be 80.0%." } ], "id_raw": 459 }, { "id": "NPBEELBGT1e2hPxg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhat details can you provide for the area [0.273, 0.439, 0.342, 0.793]?" }, { "from": "gpt", "value": "This area contains the bar representing the development prospect of Nanotechnology, which is indicated to be 47.5%." } ], "id_raw": 459 }, { "id": "nuAewWYIl9QgLJx5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.790, 0.400, 0.859, 0.793]?" }, { "from": "gpt", "value": "The area contains the bar representing VR/AR Technology's development prospect, which is marked as 52.7%." } ], "id_raw": 459 }, { "id": "5E2y62wo0ZyWrnQq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhat information is captured within the region [0.532, 0.159, 0.601, 0.793]?" }, { "from": "gpt", "value": "The region includes the bar reflecting the development prospect for Space Exploration, which is given as 85.0%." } ], "id_raw": 459 }, { "id": "MKWXXePFguHIw6MV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of chart used?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 460 }, { "id": "NykjGFNco98Jveeo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the chart title} at [0.334, 0.025, 0.721, 0.055]?" }, { "from": "gpt", "value": "Tourism and Hospitality Industry Data Analysis." } ], "id_raw": 460 }, { "id": "yGTv86hlUvYLc9h8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the information denoted by the bar at [0.846, 0.104, 0.943, 0.903]?" }, { "from": "gpt", "value": "The bar represents {Wellness Tourism}[0.846, 0.104, 0.943, 0.903] with a percentage of 95.0%." } ], "id_raw": 460 }, { "id": "DzpF8HOOdAwHAMC9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.035, 0.053, 0.061, 0.076] represent?" }, { "from": "gpt", "value": "They indicate the position of the Y-axis label for 100." } ], "id_raw": 460 }, { "id": "ERuUZzgsLCWcaRK5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Sustainable Tourism\"}[0.112, 0.857, 0.210, 0.903] shorter or taller than {the bar representing \"Dark Tourism\"}[0.479, 0.559, 0.577, 0.903]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 460 }, { "id": "wWgS8JvxQtCTPtnV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nIs the label {Dark Tourism}[0.482, 0.919, 0.573, 0.942] to the left or right of {Space Tourism}[0.722, 0.919, 0.822, 0.942]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 460 }, { "id": "Rap4EFDmA2goyaaE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nWhich is higher in percentage, {Eco-Tourism}[0.601, 0.387, 0.699, 0.903] or {Space Tourism}[0.723, 0.211, 0.821, 0.903]?" }, { "from": "gpt", "value": "Space Tourism." } ], "id_raw": 460 }, { "id": "w6ZbN6lIGXl6ATmD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Culinary Tourism\"}[0.234, 0.775, 0.332, 0.903] taller than {the \"60\" label on the Y-axis}[0.043, 0.389, 0.061, 0.413]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 460 }, { "id": "LdGa5Fn6KILT2Y1c", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.091, 0.919, 0.231, 0.942]." }, { "from": "gpt", "value": "It is the x-axis label for the category {Sustainable Tourism}[0.091, 0.919, 0.231, 0.942]." } ], "id_raw": 460 }, { "id": "OuGlNgvnUZl7gMUe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nWhat's within the vertical region from 0 to 100, [0.052, 0.894, 0.061, 0.076]?" }, { "from": "gpt", "value": "These are the Y-axis labels with the range of values for the bar chart in percentage (%), and the values are 0%, 20%, 40%, 60%, 80% and 100%." } ], "id_raw": 460 }, { "id": "DhtTPLKATOhxb2B9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects exist in the area [0.723, 0.211, 0.821, 0.903]?" }, { "from": "gpt", "value": "This area contains the bar representing {Space Tourism}[0.723, 0.211, 0.821, 0.903] which indicates its percentage is 82.3%." } ], "id_raw": 460 }, { "id": "MYq3iumVoeHd1oHI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's within the area [0.035, 0.053, 0.061, 0.076]?" }, { "from": "gpt", "value": "Within these coordinates, there is a Y-axis label indicating a value of 100% on the scale." } ], "id_raw": 460 }, { "id": "OES9K8HSuHuj5WIG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the figure?" }, { "from": "gpt", "value": "Profit change in Fintech and Digital Currency Companies at {the title}[0.325, 0.019, 0.716, 0.041]." } ], "id_raw": 461 }, { "id": "EViZCRYequ9qZy5N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of Y-axis?" }, { "from": "gpt", "value": "Profit change (%) at {the Y-axis label}[0.013, 0.412, 0.024, 0.562]." } ], "id_raw": 461 }, { "id": "kKjixmCJSgyb0Yji", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the bar} at [0.096, 0.157, 0.166, 0.707]?" }, { "from": "gpt", "value": "It represents a 5.0% profit change for Coinbase." } ], "id_raw": 461 }, { "id": "Bv3vOAfpu5UGf7JN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the X-axis label at the 8th tick?" }, { "from": "gpt", "value": "Monzo at {the 8th tick on the X-axis}[0.718, 0.939, 0.756, 0.957]." } ], "id_raw": 461 }, { "id": "wCie84zcsRKZSThg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nIs the profit change of {Robinhood}[0.443, 0.476, 0.512, 0.707] higher than that of {Stripe}[0.616, 0.256, 0.685, 0.707]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 461 }, { "id": "m8Y0O0F41kuAKWw3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nDid {Starling}[0.789, 0.707, 0.858, 0.762] experience a profit increase compared to {TransferWise}[0.876, 0.487, 0.945, 0.707]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 461 }, { "id": "NDVe0PiQgTIIZJMc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nIs the profit change of {Monzo}[0.703, 0.707, 0.772, 0.817] positive or negative?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 461 }, { "id": "Z83gtqz3mv8h4AiY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Coinbase}[0.096, 0.157, 0.166, 0.707] taller than {the bar for Revolut}[0.183, 0.355, 0.252, 0.707]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 461 }, { "id": "NnHPkgkPioV0EV3j", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area from [0.056, 0.600, 0.945, 0.800]?" }, { "from": "gpt", "value": "There are bars representing the companies Stripe, Monzo, Starling, and TransferWise along with their respective profit changes, including negative values for Monzo and Starling." } ], "id_raw": 461 }, { "id": "bo3qZtw8sAMmbhVe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region with coordinates: [0.049, 0.700, 0.945, 0.929]." }, { "from": "gpt", "value": "This region contains bars representing all ten companies: Coinbase, Revolut, N26, Square, Robinhood, Paypal, Stripe, Monzo, Starling, and TransferWise. It also includes text annotations showing the specific profit change percentages for each company. The profit change values range from -1.0% to 5.0%." } ], "id_raw": 461 }, { "id": "2IKb8p3bp7CF9saM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's found in the region [0.096, 0.157, 0.166, 0.707]?" }, { "from": "gpt", "value": "The region contains a blue bar representing the profit change of Coinbase, which shows a 5.0% increase as indicated by the text annotation." } ], "id_raw": 461 }, { "id": "2wtc6LOkyiMP0LUX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the region bounded by [0.700, 0.550, 0.949, 0.929]?" }, { "from": "gpt", "value": "The area contains the bars for Monzo, Starling, and TransferWise. Here, we see the profit changes for these three companies where Monzo has a negative 1.0% change, Starling with a -0.5% change, and TransferWise at a positive 2.0% change." } ], "id_raw": 461 }, { "id": "eq8psyuZ5YIQxJTt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat category does the bar with coordinates [0.219, 0.744, 0.319, 0.927] correspond to?" }, { "from": "gpt", "value": "Interest rate." } ], "id_raw": 462 }, { "id": "10mRYb8QvfIV9hX6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the bar located at [0.845, 0.193, 0.945, 0.927]?" }, { "from": "gpt", "value": "50.0%." } ], "id_raw": 462 }, { "id": "ttZ41NBBRxrEBthM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel} at [0.012, 0.402, 0.024, 0.572]?" }, { "from": "gpt", "value": "Percentage risk (%)." } ], "id_raw": 462 }, { "id": "DfUVq145aBdZWDNE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat is shown by the bar at [0.094, 0.780, 0.194, 0.927]?" }, { "from": "gpt", "value": "It represents that the Inflation risk factor is 10.0%." } ], "id_raw": 462 }, { "id": "GqajmIkfEFyJd30g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Inflation\"}[0.094, 0.780, 0.194, 0.927] higher than {the bar representing \"Interest rate\"}[0.219, 0.744, 0.319, 0.927]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 462 }, { "id": "E2maYZi4dWmDacWR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the market risk bar}[0.845, 0.193, 0.945, 0.927] or {the operational risk bar}[0.720, 0.414, 0.820, 0.927]?" }, { "from": "gpt", "value": "The market risk bar." } ], "id_raw": 462 }, { "id": "EmhH7Pu2eHNYaC1Z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Market risk\"}[0.845, 0.193, 0.945, 0.927] riskier than {the bar representing \"Credit risk\"}[0.344, 0.487, 0.444, 0.927]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 462 }, { "id": "qeXOgv0b5sM9pXoM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nBetween {Liquidity risk}[0.595, 0.663, 0.695, 0.927] and {Operational risk}[0.720, 0.414, 0.820, 0.927], which risk category has less risk?" }, { "from": "gpt", "value": "Liquidity risk." } ], "id_raw": 462 }, { "id": "kyHeFZnrvCyYKrV6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.036, 0.920, 0.043, 0.938]." }, { "from": "gpt", "value": "It is the scale value '0' on the y-axis." } ], "id_raw": 462 }, { "id": "F73yXyN94y3nAFu8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area in [0.595, 0.663, 0.695, 0.927]." }, { "from": "gpt", "value": "It comprises a blue bar that corresponds to the 'Liquidity risk' factor with a value of 18.0%." } ], "id_raw": 462 }, { "id": "QEeLXnL3hxsx5Aya", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within [0.720, 0.414, 0.820, 0.927]?" }, { "from": "gpt", "value": "It contains a bar representing 'Operational risk' at 35.0%." } ], "id_raw": 462 }, { "id": "K3B9CoouJokZ24hP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the coordinates [0.094, 0.780, 0.194, 0.927]?" }, { "from": "gpt", "value": "It comprises a bar that represents the risk factor 'Inflation' and has a risk percentage of 10.0%." } ], "id_raw": 462 }, { "id": "GZKXJu4Um1kCTSjf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis represent in the chart coordinates [0.448, 0.955, 0.596, 0.981]?" }, { "from": "gpt", "value": "Training Industry." } ], "id_raw": 463 }, { "id": "04uw0IUB6Gn9XV7Y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.013, 0.388, 0.029, 0.587]?" }, { "from": "gpt", "value": "Percentage (%)." } ], "id_raw": 463 }, { "id": "uL0GenxFbS4GZlEL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar} at [0.772, 0.634, 0.849, 0.918]?" }, { "from": "gpt", "value": "It represents the value of \"Blended_Learning\" in percentage, which is 29.7%." } ], "id_raw": 463 }, { "id": "4RtGGguLDukHw4M1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhich industry does the bar at coordinate [0.676, 0.148, 0.753, 0.918] refer to?" }, { "from": "gpt", "value": "E-learning Platforms." } ], "id_raw": 463 }, { "id": "8N5R9SJP35NzebJJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nCompared to {the bar representing \"In-person_Seminars\"}[0.195, 0.701, 0.272, 0.918], is {the bar representing \"Online_Courses\"}[0.291, 0.208, 0.368, 0.918] higher or lower?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 463 }, { "id": "F9hE9Nib5CiB4d7l", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Blended_Learning'}[0.772, 0.634, 0.849, 0.918] taller than {the bar representing 'Webinar'}[0.099, 0.607, 0.176, 0.918]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 463 }, { "id": "EuiF5M75aPaqAbr2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhich bar is higher, {the bar representing 'Certifications'}[0.580, 0.450, 0.657, 0.918] or {the bar representing 'Microlearning'}[0.868, 0.407, 0.945, 0.918]?" }, { "from": "gpt", "value": "Microlearning." } ], "id_raw": 463 }, { "id": "fcVZhcsKv5jSNKGB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nIs the percentage of {Webinar}[0.099, 0.607, 0.176, 0.918] greater than the percentage of {In-house_Training}[0.387, 0.740, 0.464, 0.918]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 463 }, { "id": "7yn9RGfBBLGCT3iu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.034, 0.050, 0.048, 0.067]." }, { "from": "gpt", "value": "It is a tick on the y-axis representing the value \"90%\"." } ], "id_raw": 463 }, { "id": "LLli9P74uMTHsFQO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the region [0.772, 0.634, 0.849, 0.918]?" }, { "from": "gpt", "value": "It is a bar representing the \"Blended_Learning\" category with a value of 29.7%." } ], "id_raw": 463 }, { "id": "69yeuX9ESOQRWhGN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's inside coordinates [0.676, 0.148, 0.753, 0.918]?" }, { "from": "gpt", "value": "It's a bar that represents the percentage of usage in the \"E-learning_Platforms\" category, which is 80.5%." } ], "id_raw": 463 }, { "id": "9jhGDMnkvEpYozey", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.291, 0.194, 0.348, 0.212]?" }, { "from": "gpt", "value": "It's the label \"74.2%\", which indicates the value of the bar for the \"Online_Courses\" category." } ], "id_raw": 463 }, { "id": "3DPKkP2y8L4M5m5V", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the context of the chart based on the title at [0.364, 0.025, 0.692, 0.055]?" }, { "from": "gpt", "value": "The context of the chart is \"Energy Use and Resource Consumption\"." } ], "id_raw": 464 }, { "id": "ysbbpKMfDfrzJUth", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis with coordinates [0.015, 0.394, 0.029, 0.571] represent?" }, { "from": "gpt", "value": "The Y-axis represents \"Percentage (%)\"." } ], "id_raw": 464 }, { "id": "TPL6E2PLuDlcHfTe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nWhich resource type is denoted by the X-axis label at [0.869, 0.919, 0.916, 0.942]?" }, { "from": "gpt", "value": "The resource type is \"Oil\"." } ], "id_raw": 464 }, { "id": "K2oGBCctP8be1CYC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nWhat percentage use does {Coal}<[0.773, 0.165, 0.833, 0.188]> reflect?" }, { "from": "gpt", "value": "Coal represents a usage percentage of 85.0%." } ], "id_raw": 464 }, { "id": "afV9LLKOCie0Z87j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nIs {the percentage of Geothermal energy usage}<[0.490, 0.442, 0.550, 0.466]> greater than {Natural Gas}<[0.695, 0.207, 0.739, 0.230]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 464 }, { "id": "mImoNseEzB8dWHUY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nWhich resource has a higher consumption, {Wind Energy}<[0.301, 0.578, 0.361, 0.601]> or {Nuclear Energy}<[0.585, 0.320, 0.644, 0.343]>?" }, { "from": "gpt", "value": "Nuclear Energy." } ], "id_raw": 464 }, { "id": "qZ3GnCb7CnbN6Yj6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nIs {Biofuel}<[0.396, 0.507, 0.455, 0.530]> consumption greater than 50%?" }, { "from": "gpt", "value": "No." } ], "id_raw": 464 }, { "id": "bBddlaz0qxdRwARR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nWhich resource is marked at the tick coordinate [0.433, 0.903] on the X-axis?" }, { "from": "gpt", "value": "Biofuel." } ], "id_raw": 464 }, { "id": "ONQWQoFncn05hPQy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.676, 0.165, 0.833, 0.230]." }, { "from": "gpt", "value": "This area represents two crucial energy resources: Natural Gas and Coal. The orange bar symbolizes Natural Gas with a usage percentage of 80.0%, coordinates [0.679, 0.207, 0.739, 0.230]. The following green bar symbolizes Coal with a higher usage percentage of 85.0%, coordinates [0.789, 0.165, 0.833, 0.188]." } ], "id_raw": 464 }, { "id": "fefPDR2xqml2IWKb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.112, 0.832, 0.282, 0.903] entail?" }, { "from": "gpt", "value": "This area includes two bars which represent the percentages of Solar Power and Hydropower. Solar Power [0.132, 0.832, 0.168, 0.856] is shown to have a use percentage of 5.6%. Hydropower [0.222, 0.747, 0.266, 0.770] has a higher percentage of 15.8%." } ], "id_raw": 464 }, { "id": "hIjsfUNm7TMhSJiO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nWhat resources are depicted in the region [0.301, 0.507, 0.455, 0.530]?" }, { "from": "gpt", "value": "This region includes the bars representing Wind Energy with a consumption percentage of 35.9%, coordinates [0.317, 0.578, 0.361, 0.601], and Biofuel with a consumption percentage of 44.3%, coordinates [0.411, 0.507, 0.455, 0.530]." } ], "id_raw": 464 }, { "id": "lng8I6n0l1U8WHmR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the entries located in the region [0.490, 0.366, 0.644, 0.442]?" }, { "from": "gpt", "value": "This spans two resources: Geothermal and Nuclear Energy. Geothermal's entry is the blue bar at [0.506, 0.442, 0.550, 0.466], with a usage percentage of 52.0%. Nuclear Energy follows, the green bar at [0.600, 0.366, 0.644, 0.394], with a noticeably higher usage of 66.6%." } ], "id_raw": 464 }, { "id": "0oHJ6G4KwIDZpCyJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.015, 0.518, 0.033, 0.596] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Values" } ], "id_raw": 465 }, { "id": "hyQkwoyfd7nzsqu4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate information of the metric called \"Sales Volume\"?" }, { "from": "gpt", "value": "[0.711, 0.925, 0.804, 0.945]" } ], "id_raw": 465 }, { "id": "nETUBO0JhvCvguaA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the chart title} found at [0.271, 0.168, 0.780, 0.197]?" }, { "from": "gpt", "value": "Market Competition Analysis and Comparison" } ], "id_raw": 465 }, { "id": "GJFRyOXOSF6gYbA5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nWhat is described by the labels at [0.294, 0.082, 0.414, 0.104] and [0.294, 0.115, 0.414, 0.136]?" }, { "from": "gpt", "value": "{Product A Sales}[0.294, 0.082, 0.414, 0.104] and {Product B Sales}[0.294, 0.115, 0.414, 0.136]" } ], "id_raw": 465 }, { "id": "t1aZC4afwOQ0RYsz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Product A Sales at Market Share}[0.108, 0.794, 0.479, 0.911] taller than {the bar representing Product C Sales at Sales Volume}[0.572, 0.668, 0.943, 0.758]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 465 }, { "id": "46FWBIgaVlMQs32W", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar of Product B Sales at Sales Volume}[0.572, 0.758, 0.943, 0.812] have a higher or lower value than {the bar of Product D Sales at Market Share}[0.108, 0.417, 0.479, 0.479]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 465 }, { "id": "mhY5qwiHfKNe2GqM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nWhich bar is wider: {Product E Sales at Market Share}[0.108, 0.309, 0.479, 0.417] or {Product A Sales at Sales Volume}[0.572, 0.812, 0.943, 0.911]?" }, { "from": "gpt", "value": "Product E Sales at Market Share" } ], "id_raw": 465 }, { "id": "FXHHABnzrfEtnTLC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nBased on the labels, do {Product B Sales}[0.294, 0.115, 0.414, 0.136] have a similar color as {Product D Sales}[0.487, 0.115, 0.608, 0.136]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 465 }, { "id": "HtdEuadoFBh5Bm72", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.245, 0.037, 0.806, 0.145]." }, { "from": "gpt", "value": "This area contains the legend for the chart, with labels and color handles for each product's sales. It includes six sets of color-text labels representing the sales of Products A to F. These products are color-coded, with each label and it's corresponding color handle located within the listed coordinates." } ], "id_raw": 465 }, { "id": "7yQ9RORSiRj6GL2v", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region between the coordinates [0.572, 0.282, 0.943, 0.399]?" }, { "from": "gpt", "value": "Within this region, there is a single bar that represents the sales volume of Product F, indicating a range between 57 and 70." } ], "id_raw": 465 }, { "id": "uT5F04IT5k3i3nnq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the area [0.047, 0.903, 0.056, 0.923] through [0.039, 0.184, 0.056, 0.204]?" }, { "from": "gpt", "value": "This region on the vertical axis displays the numeric scale for the bar chart, starting from 0 up to 80 in increments of 10. Within this region, there are 9 labels, indicating values at each 10 step interval." } ], "id_raw": 465 }, { "id": "tVe17AaA7QMyyafF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area from [0.108, 0.794, 0.479, 0.911] to [0.572, 0.812, 0.943, 0.911]?" }, { "from": "gpt", "value": "This area contains two bars, one representing 'Product A Sales' at 'Market Share' and the other representing 'Product A Sales' at 'Sales Volume.' Both bars have a different height, indicating they have different values." } ], "id_raw": 465 }, { "id": "OAOYED3iMucnDj6y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinates [0.202, 0.091, 0.849, 0.120] represent?" }, { "from": "gpt", "value": "It is the subtitle of the chart, which is titled \"Bar Chart of Media Influence and Public Opinion Guidance\"." } ], "id_raw": 467 }, { "id": "jQPqVJ3MlZ2QcVqY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat's located at the coordinates [0.474, 0.953, 0.577, 0.979]?" }, { "from": "gpt", "value": "The x-axis label, labeled as \"Media Types\"." } ], "id_raw": 467 }, { "id": "N8VnXZVdUmgLHxE6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat object is situated at [0.630, 0.273, 0.769, 0.911]?" }, { "from": "gpt", "value": "A bar representing \"Online\" in the dataset." } ], "id_raw": 467 }, { "id": "mSu6w7SITGPjs1pq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the term at coordinates [0.446, 0.043, 0.563, 0.059] refers to?" }, { "from": "gpt", "value": "The text label is missing in the given coordinates." } ], "id_raw": 467 }, { "id": "GZ3ha1DBxp1cgbIB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nIs the content at {the TV bar}[0.282, 0.565, 0.421, 0.911] taller or shorter than {the Radio bar}[0.456, 0.692, 0.595, 0.911]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 467 }, { "id": "WmaJQk5welzE2TsA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Newspaper}[0.108, 0.492, 0.247, 0.911] larger than or smaller than the size of {the bar for Social Media}[0.804, 0.163, 0.943, 0.911]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 467 }, { "id": "FQjOTUIsJiVfuFGy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nAre {the bars for TV, Radio, and Online}[0.282, 0.565, 0.421, 0.911], [0.456, 0.692, 0.595, 0.911], [0.630, 0.273, 0.769, 0.911] generally taller or shorter than {the bars for Newspaper and Social Media}[0.108, 0.492, 0.247, 0.911], [0.804, 0.163, 0.943, 0.911]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 467 }, { "id": "IyftWRL3heffbvpY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nBetween {TV}[0.282, 0.565, 0.421, 0.911] and {Online}[0.630, 0.273, 0.769, 0.911], which has a higher influence level?" }, { "from": "gpt", "value": "Online." } ], "id_raw": 467 }, { "id": "mYwuVqRI9H7v0cDh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nCan you explain what elements exist within the area [0.044, 0.08, 0.532, 0.920]?" }, { "from": "gpt", "value": "This region contains most of the y-axis including its label, the \"Level of Influence\", the y-axis scale, the bars representing influence levels for \"Newspaper\", \"TV\", and \"Radio\"." } ], "id_raw": 467 }, { "id": "CDDqrmQsO65tI8lZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nCould you detail the contents within [0.282, 0.11, 0.943, 0.92]?" }, { "from": "gpt", "value": "The specified area includes the bars representing influence levels for media channels \"TV\", \"Radio\", \"Online\", and \"Social Media\", and also part of the x-axis including its label \"Media Types\" and the x-axis scale." } ], "id_raw": 467 }, { "id": "xwuktIPC5f4nBfGW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.258, 0.019, 0.793, 0.095]." }, { "from": "gpt", "value": "The area covers the legend of the chart, which indicates \"Media Influence\" is denoted by an orange color." } ], "id_raw": 467 }, { "id": "INxkAcRV3Rulk9vM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found within the region defined by [0.108, 0.492, 0.421, 0.911]?" }, { "from": "gpt", "value": "This region includes the bars for two items on the x-axis: \"Newspaper\" and \"TV\"." } ], "id_raw": 467 }, { "id": "nOkjwHvSoI7vM1YV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.039, 0.707, 0.065, 0.727] on the Y-axis represent?" }, { "from": "gpt", "value": "100." } ], "id_raw": 468 }, { "id": "tz6ibFqtfVPrq7I4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the rectangular bar marked by the coordinates [0.713, 0.471, 0.809, 0.479]?" }, { "from": "gpt", "value": "It represents the Incremental Level of the HealthCare Awareness." } ], "id_raw": 468 }, { "id": "vz4GIBivIV19MrQX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nWhat title can be inferred from the label placed within these coordinates [0.239, 0.091, 0.805, 0.120]?" }, { "from": "gpt", "value": "'Performance Indicators in Health and Medical Field'." } ], "id_raw": 468 }, { "id": "MODQQJXwDHsuuueT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nIf I were to refer to an object located at [0.788, 0.925, 0.973, 0.945], what would it be called in actuality?" }, { "from": "gpt", "value": "It's the x-axis label for 'Medical Research Progress'." } ], "id_raw": 468 }, { "id": "4FlDL7EKKO8fBiPi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nIs the 'Achieved Level' higher than the 'Standard Level' for 'Medical Staff Availability' with the bars {standard level}[0.115, 0.519, 0.211, 0.911] and {achieved level}[0.115, 0.173, 0.211, 0.519]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 468 }, { "id": "7ovx3fH5TlVvmHo0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nLooking into the intensity of colors within the legend {Standard Level}[0.243, 0.066, 0.274, 0.082], {Achieved Level}[0.432, 0.066, 0.462, 0.082], and {Incremental Level}[0.621, 0.066, 0.651, 0.082], which color seems to be the darkest?" }, { "from": "gpt", "value": "Standard Level." } ], "id_raw": 468 }, { "id": "4HMu98xY8yKn6aIL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value, the standard level or achieved level for the 'Medical Research Progress' with {the standard level bar}[0.833, 0.793, 0.929, 0.911] and {the achieved level bar}[0.833, 0.652, 0.929, 0.793]?" }, { "from": "gpt", "value": "Achieved level." } ], "id_raw": 468 }, { "id": "6jzKMGpxj09ZKT5S", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nBetween the labels {Medical Staff Availability}[0.078, 0.925, 0.249, 0.945] and {Clinical Equipment Efficiency}[0.182, 0.925, 0.384, 0.945], which one on the x-axis is located at the farthest left position?" }, { "from": "gpt", "value": "Medical Staff Availability." } ], "id_raw": 468 }, { "id": "N1r5Zlmdrhl0w3TK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is in the region [0.788, 0.925, 0.973, 0.945]?" }, { "from": "gpt", "value": "It's an x-axis label for 'Medical Research Progress'." } ], "id_raw": 468 }, { "id": "tp7BhqTpMA6CidxT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.237, 0.019, 0.807, 0.095]." }, { "from": "gpt", "value": "It's the legend area of the chart. The legend title 'Legend' is at coordinates [0.489, 0.028, 0.555, 0.054]. With color indications 'Standard Level': [0.286, 0.065, 0.401, 0.086]; 'Achieved Level': [0.474, 0.065, 0.590, 0.086] and 'Incremental Level': [0.663, 0.065, 0.801, 0.086]." } ], "id_raw": 468 }, { "id": "t0eAbnMbkoxuAjZv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the axis labels and ticks within the section [0.182, 0.925, 0.384, 0.945]?" }, { "from": "gpt", "value": "This is part of the x-axis labels, having one label - the 'Clinical Equipment Efficiency' label positioned at [0.182, 0.925, 0.384, 0.945] with a tick at [0.283, 0.911]." } ], "id_raw": 468 }, { "id": "FpH2II7n1c3SLQ7X", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what is enclosed within the [0.075, 0.126, 0.969, 0.911]?" }, { "from": "gpt", "value": "It contains all the rectangular bars of the chart which represents the performance indicators in health and medical field including Medical Staff Availability, Clinical Equipment Efficiency, Patients' Satisfaction Score, Hospital Cleanliness Rating, Disease Recovery Rate, HealthCare Awareness and Medical Research Progress." } ], "id_raw": 468 }, { "id": "THJVXV18ozfgf61d", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.015, 0.440, 0.033, 0.597]?" }, { "from": "gpt", "value": "It is the y-axis label, titled as \"Market Share\"." } ], "id_raw": 469 }, { "id": "j2TLwWTpH9rkEJCP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat category does the x-axis point at [0.359, 0.925, 0.459, 0.945] represent?" }, { "from": "gpt", "value": "It refers to the \"Movie industry\"." } ], "id_raw": 469 }, { "id": "1TWtCwLJros3k5ed", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend entry located between the coordinates [0.588, 0.065, 0.644, 0.086] represent?" }, { "from": "gpt", "value": "That is the legend entry for \"Trend 2\"." } ], "id_raw": 469 }, { "id": "bn9PR8jLittiF0dD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat industry is the label at [0.853, 0.925, 0.937, 0.945] referring to?" }, { "from": "gpt", "value": "This label is referring to the \"Toy industry\"." } ], "id_raw": 469 }, { "id": "o60yqEcj8rZqD3He", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWho has a bigger market share in the \"TV industry\", {Trend 1}[0.116, 0.697, 0.213, 0.911] or {Trend 2}[0.116, 0.505, 0.213, 0.697]?" }, { "from": "gpt", "value": "Trend 1." } ], "id_raw": 469 }, { "id": "7HDBFj128aAxrZ7Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWhich industry shows a larger market share for {Trend 1}; {Movie industry}[0.359, 0.697, 0.457, 0.911] or {Book industry}[0.603, 0.697, 0.700, 0.911]?" }, { "from": "gpt", "value": "Movie industry." } ], "id_raw": 469 }, { "id": "xsPaUuK5UqXx9ykk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nIs there a gap in the market shares between {Trend 1} and {Trend 2} in the {Music industry}[0.481, 0.334, 0.579, 0.612][0.481, 0.612, 0.579, 0.911]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 469 }, { "id": "DL8xV8XZfsDFW8JU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWho owns the largest market share {Trend 1 Toy industry}[0.846, 0.569, 0.944, 0.911] or {Trend 2 Art industry}[0.725, 0.163, 0.822, 0.527]?" }, { "from": "gpt", "value": "Trend 2 Art industry." } ], "id_raw": 469 }, { "id": "BSEfTBzfttemquL6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area from [0.116, 0.697, 0.213, 0.911] reflect?" }, { "from": "gpt", "value": "This area shows the market share of the \"TV industry\" in \"Trend 1\". The height of the bar represents the numerical value of the share." } ], "id_raw": 469 }, { "id": "kD6BCXCFODUxNOXB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the area [0.359, 0.492, 0.457, 0.697]." }, { "from": "gpt", "value": "The area includes a bar for \"Trend 2\" in the \"Movie Industry\". The length of the bar indicates the size of the market share." } ], "id_raw": 469 }, { "id": "NJdSSDYZp8K6VJno", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the coordinates [0.725, 0.163, 0.822, 0.527]?" }, { "from": "gpt", "value": "There are 1 object: The green bar, which represents the market share of the \"Art industry\" in \"Trend 2\"." } ], "id_raw": 469 }, { "id": "yl8ccH37THGl4VSL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region from [0.846, 0.245, 0.944, 0.569]?" }, { "from": "gpt", "value": "A green bar indicates the \"Toy industry\" market share for \"Trend 2\". The bar's length is proportional to the value of the market share." } ], "id_raw": 469 }, { "id": "z6EeKDrXfE4yObeV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the label \"Big Data\"}[0.522, 0.065, 0.588, 0.086] on the chart?" }, { "from": "gpt", "value": "Orange, seen at [0.479, 0.066, 0.510, 0.082]." } ], "id_raw": 470 }, { "id": "XrO4W0i3afhmxyvW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed next to the {year marker \"2017\"}[0.565, 0.925, 0.600, 0.945] on the x-axis?" }, { "from": "gpt", "value": "Tick mark coordinates are [0.583, 0.911]." } ], "id_raw": 470 }, { "id": "lJaPEtG1J7Kffxj2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nHow is the growth value represented for {Cloud Computing in 2020}[0.859, 0.318, 0.944, 0.540]?" }, { "from": "gpt", "value": "The range is 151.00 to 241.00." } ], "id_raw": 470 }, { "id": "Io74xUisMHgOjR4C", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nIs the {2013 bar for Blockchain}[0.116, 0.810, 0.201, 0.813] shorter than the {2013 bar for VR/AR}[0.116, 0.805, 0.201, 0.810]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 470 }, { "id": "cCV1HgDT492462ZJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nIs the {color handle for Blockchain}[0.685, 0.066, 0.716, 0.082] similar to the {color handle for VR/AR}[0.685, 0.099, 0.716, 0.114]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 470 }, { "id": "VRxnKrXcZGWm3L8d", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nDoes {Virtual Reality in 2018}[0.647, 0.360, 0.731, 0.409] have a lower value compared to {Blockchain in 2018}[0.647, 0.409, 0.731, 0.449]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 470 }, { "id": "wmpHqP8vGdOyESi0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the yearly distribution in [0.116, 0.763, 0.944, 0.925]." }, { "from": "gpt", "value": "The years from 2013 to 2020 are evenly distributed along the x-axis with their respective tick marks and labels, providing clear markers for each year. Specifically, \"2013\" at [0.141, 0.925, 0.176, 0.945], \"2014\" at [0.247, 0.925, 0.282, 0.945], \"2015\" at [0.353, 0.925, 0.388, 0.945], \"2016\" at [0.459, 0.925, 0.494, 0.945], \"2017\" at [0.565, 0.925, 0.600, 0.945], \"2018\" at [0.671, 0.925, 0.707, 0.945], \"2019\" at [0.777, 0.925, 0.813, 0.945], and \"2020\" at [0.884, 0.925, 0.919, 0.945]." } ], "id_raw": 470 }, { "id": "Px93lbLNSWRKEgpI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nCan you provide an overview of the technology growth values between [0.116, 0.163, 0.944, 0.911]?" }, { "from": "gpt", "value": "This area represents the growth values for various technology sectors such as Artificial Intelligence, Internet of Things, Big Data, Cloud Computing, Blockchain, and VR/AR for the years 2013 to 2020. Each technology is represented by a specific color and the growth is illustrated through the height of bars corresponding to each year. It shows a general trend of increasing growth for all technologies." } ], "id_raw": 470 }, { "id": "Z3MoiXzoq4sb3Wei", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat is visible in the legend section [0.243, 0.019, 0.817, 0.127]?" }, { "from": "gpt", "value": "The legend section includes color handles alongside labels for six technological categories: Artificial Intelligence, the Internet of Things, Big Data, Cloud Computing, Blockchain, and VR/AR. Each category's color is represented in a small rectangle next to the category name, helping to visually link each color to its corresponding data in the bar chart." } ], "id_raw": 470 }, { "id": "1WycO8sfsiSqZDwY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing Deep Learning in the chart?" }, { "from": "gpt", "value": "The color is '#2e8b57' or a sort of greenish color: {Deep Learning's bar color}[0.353, 0.084, 0.383, 0.099]." } ], "id_raw": 471 }, { "id": "obh0PQJv9ud54oTL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinate of the y-axis label?" }, { "from": "gpt", "value": "Certainly, the y-axis label is located at these coordinates: [0.015, 0.526, 0.033, 0.588]." } ], "id_raw": 471 }, { "id": "nfhYqqDTtZEtDRyw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.508, 0.953, 0.552, 0.979]?" }, { "from": "gpt", "value": "The x-axis label at these coordinates is \"Years\"." } ], "id_raw": 471 }, { "id": "zwsk3Lp0eH2b4pvC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat technologies or categories are present in [0.324, 0.037, 0.736, 0.145]?" }, { "from": "gpt", "value": "This area includes {Deep Learning}[0.395, 0.082, 0.507, 0.104], {RPA}[0.395, 0.115, 0.425, 0.136], {NLP}[0.581, 0.082, 0.610, 0.104], and {Computer Vision}[0.581, 0.115, 0.707, 0.136]." } ], "id_raw": 471 }, { "id": "5VWvchq3plbau6nd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Deep Learning\"}[0.116, 0.845, 0.213, 0.911] higher than {the bar representing \"RPA\"}[0.116, 0.763, 0.213, 0.845]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 471 }, { "id": "nZvPip9KXHbnUVRn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nAmong {the bars representing \"NLP\"}[[0.359, 0.620, 0.457, 0.703], [0.481, 0.577, 0.579, 0.670], [0.603, 0.582, 0.700, 0.593], [0.725, 0.412, 0.822, 0.549], [0.846, 0.418, 0.944, 0.544]], which year showed the highest level?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 471 }, { "id": "RkikjFhUa1W4NVup", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nIs the level of {Deep Learning}[0.846, 0.681, 0.944, 0.911] higher than the level of {NLP}[0.846, 0.418, 0.944, 0.544] in 2021?" }, { "from": "gpt", "value": "No." } ], "id_raw": 471 }, { "id": "7J4TsRXc5CKm1ir7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nAmong the \"Computer Vision\" category {[0.116, 0.642, 0.213, 0.714],[0.238, 0.577, 0.335, 0.659],[0.359, 0.522, 0.457, 0.620],[0.481, 0.456, 0.579, 0.577],[0.603, 0.445, 0.700, 0.582],[0.725, 0.253, 0.822, 0.412],[0.846, 0.237, 0.944, 0.418]}, in which year was the lowest level recorded?" }, { "from": "gpt", "value": "2015" } ], "id_raw": 471 }, { "id": "Y0FYktnTrhGLPUeO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.324, 0.037, 0.736, 0.145]." }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes a legend title \"Artificial Intelligence and Automation Trends\", and four pairs of color-text labels each corresponding to a different category of technology: Deep Learning with a green color labelling, RPA with a blue color labelling, NLP with a purple color labelling, and Computer Vision with a brown color labelling." } ], "id_raw": 471 }, { "id": "fUzLsN7w5yqWHf5g", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.075, 0.203, 0.985, 0.911]." }, { "from": "gpt", "value": "This area corresponds to the main body of the chart where the data is represented. It includes the bars that correspond to different technologies (Deep Learning, RPA, NLP, and Computer Vision) and their changes over seven years from 2015 to 2021. The x-axis represents the years, while the y-axis represents the level of the corresponding technology." } ], "id_raw": 471 }, { "id": "zxWtptWiKKgAUYwj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.146, 0.912, 0.213, 0.947]?" }, { "from": "gpt", "value": "The object within these coordinates is the x-axis label for the year \"2015\"." } ], "id_raw": 471 }, { "id": "fAYRDdyYc6DmHZ2E", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.075, 0.203, 0.985, 0.911]?" }, { "from": "gpt", "value": "In this area, one can see the entire plot of the chart, which includes bars depicting the level of Deep Learning, RPA, NLP, and Computer Vision technologies over the years. Also, the x-axis and y-axis labels, their respective ticks, and the chart's grid lines are visible." } ], "id_raw": 471 }, { "id": "fbE7icxnzsvjlp0V", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart?" }, { "from": "gpt", "value": "Metrics[0.015, 0.396, 0.033, 0.482]." } ], "id_raw": 472 }, { "id": "xB02i99eDy1jydpG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing \"Product Launches\" in 2019?" }, { "from": "gpt", "value": "#ffffe0[0.687, 0.189, 0.801, 0.294]." } ], "id_raw": 472 }, { "id": "zpi5kYZ30KyKVW6Z", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the range indicated by the highest label on the y-axis scale in the chart?" }, { "from": "gpt", "value": "200[0.039, 0.008, 0.065, 0.028]." } ], "id_raw": 472 }, { "id": "iRXMPQexggQXXecW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nIs {the oldest data bar for \"Startup Incubation\"}[0.116, 0.769, 0.230, 0.821] taller than {the latest data bar for \"Patent Application\"}[0.829, 0.213, 0.944, 0.350]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 472 }, { "id": "8KxZXC7H2a4wh7eP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the data range reflected on the first y-axis label}[0.056, 0.813, 0.065, 0.833] cover the base of {the 2020 bar for \"Venture Capital Investment\"}[0.829, 0.350, 0.944, 0.503]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 472 }, { "id": "pgsiqW3IbPyz21Bj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bottom coordinate of Startup Incubation's 2015 bar}[0.769] above {the top coordinate of Startup Acceleration's 2015 bar}[0.725]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 472 }, { "id": "4xbPyfnE6zDYNPqb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.176, 0.870, 0.884, 0.978]?" }, { "from": "gpt", "value": "The chart's legend including 6 items:\n1. \"Startup Incubation\" with orange color handle at [0.182, 0.918, 0.213, 0.933],\n2. \"Startup Acceleration\" with salmon color handle at [0.182, 0.950, 0.213, 0.965],\n3. \"Angel Investment\" with dark orange color handle at [0.412, 0.918, 0.443, 0.933],\n4. \"Venture Capital Investment\" with gold color handle at [0.412, 0.950, 0.443, 0.965],\n5. \"Patent Application\" with yellow color handle at [0.695, 0.918, 0.726, 0.933],\n6. \"Product Launches\" with lemon chiffon color handle at [0.695, 0.950, 0.726, 0.965]." } ], "id_raw": 472 }, { "id": "ndAmhkeZSZ94uRx1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the space covering [0.116, 0.688, 0.944, 0.821]." }, { "from": "gpt", "value": "This region covers the base of the chart where all initial year bars (2015) for each category are displayed. Each category's bar ranges slightly vary within these coordinates." } ], "id_raw": 472 }, { "id": "yVU8fd8LjiDWDaPg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements found in [0.156, 0.835, 0.904, 0.855]." }, { "from": "gpt", "value": "This region contains the x-axis year labels from 2015 to 2020, with each label properly aligned above its respective set of data bars for each year." } ], "id_raw": 472 }, { "id": "56K2jtRdpk1zTxLp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.039, 0.202, 0.056, 0.222] in the chart?" }, { "from": "gpt", "value": "A label on the y-axis, which is 30." } ], "id_raw": 473 }, { "id": "jkwnMHVMUZr22nCW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.108, 0.677, 0.206, 0.911]?" }, { "from": "gpt", "value": "This is the bar representing the Series A investment in VC A, which is 10 millions dollars." } ], "id_raw": 473 }, { "id": "bUD12XbSqEDlJ5RO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {the legend}[0.243, 0.019, 0.808, 0.095] denote?" }, { "from": "gpt", "value": "The legend indicates the color configuration for the bars. It tells us that the bars in the color #ff4500 represent Fintech Series A investments, whereas the bars in the color #ff6347 represent Fintech Series B investments." } ], "id_raw": 473 }, { "id": "BColj0tJo4ci9ZKF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nIn terms of investment value, is {VC A for Series A}[0.108, 0.677, 0.206, 0.911] higher or lower than {VC A for Series B}[0.108, 0.374, 0.206, 0.911]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 473 }, { "id": "UNlhSy38RmV7FJJ1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nIs {VC D for Series A}[0.476, 0.327, 0.575, 0.911] to the left or right of {VC E for Series A}[0.599, 0.584, 0.697, 0.911]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 473 }, { "id": "09yPMCZ9Bpc7e9EG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nBased on their coordinates, which VC has a higher investment in {Series B}[0.108, 0.374, 0.206, 0.911],[0.231, 0.163, 0.329, 0.911],[0.353, 0.327, 0.452, 0.911],[0.476, 0.561, 0.575, 0.911],[0.599, 0.210, 0.697, 0.911],[0.722, 0.467, 0.820, 0.911],[0.845, 0.397, 0.943, 0.911]: {VC B}[0.231, 0.163, 0.329, 0.911] or {VC E}[0.599, 0.210, 0.697, 0.911]?" }, { "from": "gpt", "value": "VC B." } ], "id_raw": 473 }, { "id": "MOhbP7oSuJxGDP6L", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat is this {object}[0.015, 0.390, 0.033, 0.724] on the chart?" }, { "from": "gpt", "value": "This is the Y-axis title, called \"Number of Topics Discussed\"." } ], "id_raw": 475 }, { "id": "LV0gSPS2Khm636NA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object of coordinates [0.527, 0.925, 0.668, 0.945]?" }, { "from": "gpt", "value": "It's the x-axis label \"Arts and Humanities\"." } ], "id_raw": 475 }, { "id": "k9cnZGP1k1bwZr76", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat category does this {bar}[0.108, 0.671, 0.223, 0.806] represent?" }, { "from": "gpt", "value": "This bar represents the \"Computer Science\" category under \"Rapid Growth\"." } ], "id_raw": 475 }, { "id": "79NVZUtjPrLs9wqL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Social Science under Rapid Growth}[0.684, 0.357, 0.799, 0.791] higher than the {bar representing Economics under Volatile Movement}[0.828, 0.731, 0.943, 0.911]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 475 }, { "id": "zckrDYSv8PBnpSR0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar of Economics}[0.828, 0.237, 0.943, 0.731] under \"Rapid Growth\" show a higher value than that in \"Volatile Movement\"[0.828, 0.731, 0.943, 0.911]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 475 }, { "id": "T5uobpFCEEXCO4ep", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nIs {Social Science in Volatile Movement}[0.684, 0.791, 0.799, 0.911] less discussed than {Computer Science in Rapid Growth}[0.108, 0.671, 0.223, 0.806]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 475 }, { "id": "SWrZau9nooi8kHy8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region bounded by [0.307, 0.037, 0.744, 0.112]?" }, { "from": "gpt", "value": "This is the legend for the chart, with two entries. One is {Volatile Movement with its color indicator}[0.345, 0.084, 0.375, 0.099] in orange and text at [0.387, 0.082, 0.529, 0.104]. The other is {Rapid Growth with its color indicator}[0.560, 0.084, 0.591, 0.099] in light red and text at [0.603, 0.082, 0.706, 0.104]." } ], "id_raw": 475 }, { "id": "kZWo8uY7YH964hrD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within the box [0.396, 0.297, 0.511, 0.911]?" }, { "from": "gpt", "value": "This box contains two bars that correspond to the \"Physics\" activity in two scenarios. The bar {[0.396, 0.716, 0.511, 0.911]} in orange indicates the \"Volatile Movement\" of 13 topics while the other bar {[0.396, 0.297, 0.511, 0.716]} in light red under \"Rapid Growth\" presents a growth in 28 topics." } ], "id_raw": 475 }, { "id": "ePquOGLBWJlK9moz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.066, 0.203, 0.985, 0.911]?" }, { "from": "gpt", "value": "This region includes the entire chart plot area. It covers vertical and horizontal axes, all bars for \"Volatile Movement\" and \"Rapid Growth\" in each academic field, as well as all labels and tick marks." } ], "id_raw": 475 }, { "id": "CzAKdPeJutVNmrZq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the yellow bar}[0.125, 0.486, 0.153, 0.544] in the chart?" }, { "from": "gpt", "value": "It represents the 2018 trend value for AR/VR." } ], "id_raw": 476 }, { "id": "BVMBTKKHRYT4HWrg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart as per the coordinates received?" }, { "from": "gpt", "value": "Emerging Technologies and Trends Outlook." } ], "id_raw": 476 }, { "id": "RRPOBUjWCZUfClbS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.476, 0.450, 0.524, 0.472] in the legend area?" }, { "from": "gpt", "value": "The color label for the Cybersecurity trend line." } ], "id_raw": 476 }, { "id": "LUPJxYLHdaz2Gq5H", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label, located at [0.023, 0.376, 0.052, 0.576], represent?" }, { "from": "gpt", "value": "The label represents Trend Value." } ], "id_raw": 476 }, { "id": "QpQoB0U0GA6Diyjs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nDoes the 2022 bar for {5G}[0.264, 0.247, 0.292, 0.344] extend above the one for {AR/VR}[0.264, 0.344, 0.292, 0.429]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 476 }, { "id": "rqG45AQrmoyZie3m", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nIs the {Blockchain bar in 2018}[0.125, 0.544, 0.153, 0.561] shorter or taller than the {AI bar in 2018}[0.125, 0.753, 0.153, 0.870]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 476 }, { "id": "z2gTVue9NmO2TF0h", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nBetween {Cybersecurity}[0.125, 0.318, 0.153, 0.419] and {5G}[0.125, 0.419, 0.153, 0.486] in 2018, which technology trend has a higher value?" }, { "from": "gpt", "value": "5G." } ], "id_raw": 476 }, { "id": "SDUFleucGJrRoS4M", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Internet of Things\"}[0.181, 0.621, 0.223, 0.738] and {\"AR/VR\"}[0.229, 0.504, 0.257, 0.604] in 2021, which trend has a higher value?" }, { "from": "gpt", "value": "Quantum Computing." } ], "id_raw": 476 }, { "id": "99Mgstc5g5R82Sej", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat is presented in the region of [0.322, 0.098, 0.947, 0.491]?" }, { "from": "gpt", "value": "This is the legend area. It contains seven pairs of color-label indicating the seven technology trends: AI, Internet of Things, Blockchain, Quantum Computing, AR/VR, 5G, and Cybersecurity." } ], "id_raw": 476 }, { "id": "Dp27cQuFyEV5Uj76", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found within the area [0.111, 0.891, 0.306, 0.920]?" }, { "from": "gpt", "value": "This area features x-axis labels representing the years from 2018 to 2022." } ], "id_raw": 476 }, { "id": "drQ09XUaXbd3F4W8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of the region with the given coordinates [0.117, 0.082, 0.301, 0.870]?" }, { "from": "gpt", "value": "This is the graph area where the bar graph is plotted. It involves sets of bars showing trend values for different technologies in various years." } ], "id_raw": 476 }, { "id": "vKVz50EPtuGFhYKC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.195, 0.153, 0.257, 0.273]?" }, { "from": "gpt", "value": "This region includes a portion of the bar for Cybersecurity trends in 2021." } ], "id_raw": 476 }, { "id": "7ZB6kTGj6VvbF8DE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the label located at [0.377, 0.912, 0.450, 0.933] signify in the chart?" }, { "from": "gpt", "value": "The label indicates the platform \"Facebook\"." } ], "id_raw": 478 }, { "id": "o3M1txtycWgs6iWi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat color represents Instagram in the chart, according to the legend?" }, { "from": "gpt", "value": "#e1306c[0.335, 0.946, 0.365, 0.961]." } ], "id_raw": 478 }, { "id": "eGeTO72AnltVPtIX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at [0.903, 0.090, 0.929, 0.110]?" }, { "from": "gpt", "value": "The data value label \"245\" for December, representing Pinterest user activity." } ], "id_raw": 478 }, { "id": "pwYCSiojQHpxhiBB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Twitter in January}[0.124, 0.401, 0.180, 0.447] shorter than {the bar for Pinterest in January}[0.124, 0.202, 0.180, 0.216]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 478 }, { "id": "3NvbkyHa1WQVANta", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nWhich platform\u2019s bar is higher in July, {LinkedIn}[0.541, 0.167, 0.597, 0.372] or {Pinterest}[0.541, 0.150, 0.597, 0.167]?" }, { "from": "gpt", "value": "LinkedIn." } ], "id_raw": 478 }, { "id": "x0rBDUYnu5xdNAI2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the May bar for Facebook}[0.402, 0.678, 0.458, 0.783] have more users compared to {the May bar for Twitter}[0.402, 0.381, 0.458, 0.431]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 478 }, { "id": "Zp3DThAVOZKGBfJe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the section defined by the coordinates [0.328, 0.866, 0.740, 0.974]?" }, { "from": "gpt", "value": "This region contains the legend detailing six platform labels and colors. The specific elements include labels with text and corresponding color handles indicating the representation of each social media platform." } ], "id_raw": 478 }, { "id": "Le0oJbmJhZOmb4QB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart features within the area [0.083, 0.056, 0.985, 0.783]." }, { "from": "gpt", "value": "This region includes the entire bar chart showing monthly user activities for multiple social platforms throughout the year. It encompasses the vertical bars representing user counts, labels indicating each month, and different colors for each platform." } ], "id_raw": 478 }, { "id": "KcmtAfVbg6qP4TKk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat visual elements can be found within the bounds [0.660, 0.866, 1.000, 1.000]?" }, { "from": "gpt", "value": "There are no chart elements or data visualizations in this area; it's primarily empty or possibly contains whitespace." } ], "id_raw": 478 }, { "id": "d3vK3EqQaLGEgC3B", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.039, 0.253, 0.056, 0.273]?" }, { "from": "gpt", "value": "Y-axis label '20'." } ], "id_raw": 479 }, { "id": "1F5apDfrWeGsJjG0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the color represent at the coordinates [0.523, 0.066, 0.554, 0.082] in the legend?" }, { "from": "gpt", "value": "The color represents 'Incubators'." } ], "id_raw": 479 }, { "id": "xce49tGKhKzAZSYr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at [0.856, 0.925, 0.882, 0.945]?" }, { "from": "gpt", "value": "The x-axis label is 'Exit'." } ], "id_raw": 479 }, { "id": "Eh1amnJqhZg49lgI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat does {the bar}[0.671, 0.749, 0.784, 0.911] represent?" }, { "from": "gpt", "value": "The bar represents the number of 'Startups' at the 'Series C' investment stage." } ], "id_raw": 479 }, { "id": "uGHN6mIBWEwVY1nZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Startups bar}[0.389, 0.488, 0.502, 0.911] for 'Series A' bigger than the {Incubators bar}[0.389, 0.261, 0.502, 0.488] for the same stage?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 479 }, { "id": "6SzlWttil6S6uSjI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Startups bar}[0.813, 0.879, 0.926, 0.911] at the 'Exit' stage shorter than the {Startups bar}[0.671, 0.749, 0.784, 0.911] at the 'Series C' stage?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 479 }, { "id": "Y5ZZnKDtnybBSSYC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Incubators bar}[0.530, 0.326, 0.643, 0.586] for 'Series B' taller than the {Incubators bar}[0.671, 0.651, 0.784, 0.749] for 'Series C'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 479 }, { "id": "7s7id4gGiK3kevVH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nBetween the {Startups bar}[0.107, 0.651, 0.220, 0.911] and the {Incubators bar}[0.107, 0.586, 0.220, 0.651] at the 'Pre-seed' stage, which one is taller?" }, { "from": "gpt", "value": "The Startups bar." } ], "id_raw": 479 }, { "id": "15BxAyF9SzFUUabd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.110, 0.019, 0.922, 0.095]." }, { "from": "gpt", "value": "This area includes the legend for the chart. It contains two entries. The first one has a color symbol at [0.385, 0.066, 0.416, 0.082] representing 'Startups' with a label at [0.428, 0.065, 0.493, 0.086]. The second entry has a color symbol at [0.523, 0.066, 0.554, 0.082] representing 'Incubators' with its label at [0.566, 0.065, 0.647, 0.086]." } ], "id_raw": 479 }, { "id": "eDZAGLKS6ZGK8mB0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.530, 0.586, 0.643, 0.911]?" }, { "from": "gpt", "value": "The region [0.530, 0.586, 0.643, 0.911] contains two bars. One {Startups bar}[0.530, 0.586, 0.643, 0.911] representing 'Series B' stage and one {Incubators bar}[0.530, 0.326, 0.643, 0.586] also representing 'Series B' stage." } ], "id_raw": 479 }, { "id": "LrpKYSxCMt11PfI4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.107, 0.586, 0.220, 0.911]." }, { "from": "gpt", "value": "This region includes two bars. The {Startups bar}[0.107, 0.651, 0.220, 0.911] for the 'Pre-seed' investment stage, and the {Incubators bar}[0.107, 0.586, 0.220, 0.651] for the same 'Pre-seed' stage." } ], "id_raw": 479 }, { "id": "nwdVqJmtDOXTCm2W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the area [0.287, 0.925, 0.322, 0.945]?" }, { "from": "gpt", "value": "The area [0.287, 0.925, 0.322, 0.945] contains the x-axis label 'Seed'." } ], "id_raw": 479 }, { "id": "euIIXvIKm0ZUPk28", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the environmental indicator at [0.738, 0.648, 0.850, 0.871]?" }, { "from": "gpt", "value": "Pollution control." } ], "id_raw": 480 }, { "id": "g8cd9fkOXHMBAfoP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text just below the bar representing recycling rate?" }, { "from": "gpt", "value": "[0.874, 0.117, 0.923, 0.152]." } ], "id_raw": 480 }, { "id": "grqdGThnd4NHN9Xh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the score of {the bar}[0.438, 0.163, 0.522, 0.624] representing Habitat restoration?" }, { "from": "gpt", "value": "0.805." } ], "id_raw": 480 }, { "id": "qpmWfWEd48WgoQlq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates at [0.124, 0.278, 0.207, 0.624]?" }, { "from": "gpt", "value": "A bar representing the environmental indicator Air quality with a score of 0.605." } ], "id_raw": 480 }, { "id": "lSxTsk2IVQ6GJ0A9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nIs the score for {Habitat restoration}[0.438, 0.163, 0.522, 0.624] higher than the score for {Biodiversity}[0.647, 0.212, 0.731, 0.624]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 480 }, { "id": "fpVw6UchsX3UMsis", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nOn the x-axis, is {Water quality}[0.223, 0.648, 0.318, 0.838] located to the left or to the right of {Land use}[0.341, 0.648, 0.409, 0.784]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 480 }, { "id": "YSGELYFT2GWdf4bx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nDoes the {Recycling rate}[0.857, 0.152, 0.940, 0.624] have a higher score than {Air quality}[0.124, 0.278, 0.207, 0.624]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 480 }, { "id": "g6pGzsDHIfCizJpT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nIs the score for {Ecosystem services}[0.543, 0.255, 0.626, 0.624] less than {Pollution control}[0.752, 0.232, 0.836, 0.624]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 480 }, { "id": "V7bpsuvH92Z9m7wf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the coordinates [0.543, 0.255, 0.626, 0.624]." }, { "from": "gpt", "value": "The region contains one bar representing the environmental indicator Ecosystem services. The bar has a score of 0.645, with the text displaying the score located at [0.560, 0.220, 0.609, 0.255]." } ], "id_raw": 480 }, { "id": "sJpfA3EYiiIRqxh4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nCan you explain what the coordinates [0.043, 0.267, 0.071, 0.302] refer to?" }, { "from": "gpt", "value": "The coordinates correspond to a y-axis label. The label displays the value \"0.6\"." } ], "id_raw": 480 }, { "id": "3WZUxfb9RzVmZ8Ln", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region defined by [0.857, 0.152, 0.940, 0.624]?" }, { "from": "gpt", "value": "The area encompasses a bar representing the score for the environmental indicator Recycling rate, with the score being 0.825. The text showing the score is located at [0.874, 0.117, 0.923, 0.152]." } ], "id_raw": 480 }, { "id": "zciShkZFcXgPKvLI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.415, 0.648, 0.544, 0.906]?" }, { "from": "gpt", "value": "The region contains a label for the x-axis, specifically the environmental indicator Habitat restoration." } ], "id_raw": 480 }, { "id": "5WvkeAJMUYwkcaGs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis of this chart?" }, { "from": "gpt", "value": "Platforms" } ], "id_raw": 482 }, { "id": "TecEmG9ODok37BUT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at coordinates [0.108, 0.193, 0.184, 0.799] represent?" }, { "from": "gpt", "value": "The User Engagement level for {Facebook}[0.087, 0.818, 0.146, 0.924]." } ], "id_raw": 482 }, { "id": "6h1vVAfBHiRsHTcj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat indicates the User Engagement level in this plot at [0.661, 0.458, 0.738, 0.799]?" }, { "from": "gpt", "value": "The User Engagement level for {Pinterest}[0.659, 0.818, 0.715, 0.919]." } ], "id_raw": 482 }, { "id": "jPsfocXCdWN8H6Vc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher User Engagement level, {Facebook}[0.087, 0.818, 0.146, 0.924] or {Tumblr}[0.765, 0.818, 0.810, 0.900]?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 482 }, { "id": "GLvKMXIKma1u31GL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nIs the User Engagement level for {Twitter}[0.576, 0.818, 0.621, 0.900] greater than that for {Snapchat}[0.466, 0.818, 0.526, 0.924]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 482 }, { "id": "dP7x6tHSco09u97Z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nDoes {Reddit}[0.862, 0.818, 0.905, 0.897] have a lower User Engagement level compared to {WhatsApp}[0.272, 0.818, 0.336, 0.931]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 482 }, { "id": "gczxuN7InGyfFECr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements within the region [0.108, 0.193, 0.184, 0.799]." }, { "from": "gpt", "value": "This region contains the bar representing the User Engagement level for {Facebook}[0.087, 0.818, 0.146, 0.924]. The User Engagement level represented by this bar is 0.8, and the color of the bar is '#ff9999'." } ], "id_raw": 482 }, { "id": "duDfCHqpA2Z6Wn0e", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present in the region [0.035, 0.182, 0.057, 0.210]?" }, { "from": "gpt", "value": "This region contains the y-axis label {0.8}[0.035, 0.182, 0.057, 0.210], indicating the User Engagement level of 0.8." } ], "id_raw": 482 }, { "id": "RmQJUVdfZZ0Z6MPJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed from the region [0.377, 0.818, 0.431, 0.914]?" }, { "from": "gpt", "value": "This region contains the x-axis label for {LinkedIn}[0.377, 0.818, 0.431, 0.914]. To the left of this label is a bar denoting the User Engagement level for LinkedIn, which is relatively low compared to other platforms." } ], "id_raw": 482 }, { "id": "ys3RVwC1rNAFxn6l", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the area [0.035, 0.030, 0.057, 0.058]?" }, { "from": "gpt", "value": "This area contains the y-axis label {1.0}[0.035, 0.030, 0.057, 0.058], which denotes the maximum User Engagement level that can be represented on the chart." } ], "id_raw": 482 }, { "id": "AuZIg8dI0OfjjKVV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text in the upper left corner located at [0.015, 0.309, 0.029, 0.409]?" }, { "from": "gpt", "value": "Revenue." } ], "id_raw": 483 }, { "id": "srmPcK7x22rAh9dW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the category for the blue bar with the coordinates of [0.182, 0.476, 0.180, 0.683]?" }, { "from": "gpt", "value": "Video Games." } ], "id_raw": 483 }, { "id": "rOm5R4avSBuVuPac", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the value marked at the coordinates [0.896, 0.361, 0.923, 0.385]?" }, { "from": "gpt", "value": "115." } ], "id_raw": 483 }, { "id": "jXgG4qIeSXuR6jiJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the bar located at [0.876, 0.385, 0.943, 0.683]?" }, { "from": "gpt", "value": "Theme Parks." } ], "id_raw": 483 }, { "id": "hxFLDBc3k11uChwm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat industry has higher revenue, {Video Games}[0.112, 0.476, 0.180, 0.683] or {Music}[0.367, 0.566, 0.435, 0.683]?" }, { "from": "gpt", "value": "Video Games." } ], "id_raw": 483 }, { "id": "Mnohfv3jYB4GczXp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {Online Sports Betting bar}[0.706, 0.540, 0.774, 0.683] have a higher revenue than the {Podcasts bar}[0.621, 0.605, 0.689, 0.683]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 483 }, { "id": "gUA8RbpAnGek5VmW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nIs the {Film bar}[0.282, 0.164, 0.350, 0.683] taller than the {Theme Parks bar}[0.876, 0.385, 0.943, 0.683]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 483 }, { "id": "v6asEvmKXoRAfPPI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nDoes {Surveillance Entertainment}[0.791, 0.631, 0.859, 0.683] have a lower revenue than {Social Media Entertainment}[0.536, 0.527, 0.604, 0.683]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 483 }, { "id": "UMuHFBuc3eWC7zkv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area [0.112, 0.476, 0.180, 0.683]." }, { "from": "gpt", "value": "This area contains the green bar representing \"Video Games\" with the revenue marked as 80 at coordinates [0.137, 0.452, 0.155, 0.476]." } ], "id_raw": 483 }, { "id": "QUwyHDr0Oh9ikRzK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the group of objects in the region [0.446, 0.592, 0.519, 0.683]?" }, { "from": "gpt", "value": "This area consists of a single green bar representing the industry \"Virtual Reality\" with a revenue value of \"35\" observed at position [0.477, 0.569, 0.494, 0.592]." } ], "id_raw": 483 }, { "id": "Y8hCA04BtLgbBmXc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area between [0.035, 0.025, 0.061, 0.048]." }, { "from": "gpt", "value": "This contains the value \"250\" labeled at Y-axis which represents the maximum value of 'Revenue'." } ], "id_raw": 483 }, { "id": "LqtFHisicSFYxysP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nCan you detail the region [0.301, 0.699, 0.331, 0.749]?" }, { "from": "gpt", "value": "In this region, there is an axis label for one of the categories: 'Film'." } ], "id_raw": 483 }, { "id": "Sns2DaeuKRX8MaG5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is being described in this data?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 484 }, { "id": "oqNXkilz7kuQWR4M", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the Y-axis?" }, { "from": "gpt", "value": "Value Chain." } ], "id_raw": 484 }, { "id": "3OxTft5AHez1vreg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the stage name} at [0.323, 0.716, 0.401, 0.872]?" }, { "from": "gpt", "value": "Production." } ], "id_raw": 484 }, { "id": "En1CdeOScWZEVwmQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nBased on the coordinates, what is the data value of the bar {After Sales} at [0.853, 0.176, 0.891, 0.211]?" }, { "from": "gpt", "value": "0.75." } ], "id_raw": 484 }, { "id": "hnACevwoSozvagpt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Production}[0.294, 0.308, 0.430, 0.692] taller than {the bar for Marketing}[0.634, 0.372, 0.770, 0.692]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 484 }, { "id": "opaFVfkb9NJyzZwf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nAre there more stages to the left or right of {the Distribution bar}[0.464, 0.083, 0.600, 0.692]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 484 }, { "id": "O9K668ONRr0crWD3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nBetween {the Raw Materials bar}[0.124, 0.436, 0.260, 0.692] and the {After Sales bar}[0.804, 0.211, 0.940, 0.692], which bar is taller?" }, { "from": "gpt", "value": "After Sales." } ], "id_raw": 484 }, { "id": "Jd5n9qdLbceYAUP1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nWhich bar is located at [0.634, 0.372, 0.770, 0.692]?" }, { "from": "gpt", "value": "Marketing." } ], "id_raw": 484 }, { "id": "PVlWGwWpVNo53XR6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the bars contained within the area from [0.294, 0.308, 0.430, 0.692] to [0.634, 0.372, 0.770, 0.692]." }, { "from": "gpt", "value": "In the specified region, there are two bars: the bar for Production with a value of 0.6 located at [0.294, 0.308, 0.430, 0.692] and the bar for Marketing with a value of 0.5 located at [0.634, 0.372, 0.770, 0.692]." } ], "id_raw": 484 }, { "id": "PYbwBHkBrLUvxKdF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat part of the chart can be seen in the area [0.143, 0.696, 0.241, 0.914]?" }, { "from": "gpt", "value": "The x-axis label for the stage \"Raw Materials\" can be seen in this area." } ], "id_raw": 484 }, { "id": "1eMN0FksPPJqqwQH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the range [0.602, 0.083, 0.692, 0.692]." }, { "from": "gpt", "value": "Within this range, there is one bar representing the stage \"Distribution\" with a value of 0.95. The area also includes the x-ticks label for \"Distribution\"." } ], "id_raw": 484 }, { "id": "qR86bAoQnq2MrNq5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the content shown at [0.019, 0.309, 0.036, 0.537] on the chart?" }, { "from": "gpt", "value": "It is the Y-axis label called \"Consumer Behavior\"." } ], "id_raw": 485 }, { "id": "eVq9sn8vGexnJLjR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at coordinates [0.124, 0.306, 0.207, 0.811] in the chart?" }, { "from": "gpt", "value": "It is a bar representing the 'BRIC' category with a value of 0.65, according to {the corresponding chart text}[0.146, 0.283, 0.185, 0.306]." } ], "id_raw": 485 }, { "id": "QL5TmOs0OvXDE7xe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what is the data represented at [0.775, 0.151, 0.813, 0.174]?" }, { "from": "gpt", "value": "It is the value \"0.82\" for the \"EAGLEs\" category, according to {the corresponding bar}[0.752, 0.174, 0.836, 0.811]." } ], "id_raw": 485 }, { "id": "tEkTenX4vngX5W5G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the data indicated at [0.251, 0.205, 0.289, 0.228]?" }, { "from": "gpt", "value": "It shows a value of \"0.75\" for the \"MINT\" category, according to {its corresponding bar}[0.228, 0.228, 0.312, 0.811]." } ], "id_raw": 485 }, { "id": "ccs7gVbzbOgggaUk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"MINT\"}[0.228, 0.228, 0.312, 0.811] taller than {the bar representing \"VISTA\"}[0.333, 0.384, 0.417, 0.811]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 485 }, { "id": "eCOVUjSEvqbdrYqs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nIs {the 'FRANK' bar}[0.543, 0.244, 0.626, 0.811] taller or shorter than {the 'CIVETS' bar}[0.647, 0.283, 0.731, 0.811]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 485 }, { "id": "tdmbXqaIyr6mhjja", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Next Eleven\"}[0.438, 0.190, 0.522, 0.811] represent a higher or lower value than {the \"3G\" bar}[0.857, 0.267, 0.940, 0.811]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 485 }, { "id": "y5C7sXpLiveAJQ24", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nAmong the {bar for \"BRIC\"}[0.124, 0.306, 0.207, 0.811] and {bar for \"CIVETS\"}[0.647, 0.283, 0.731, 0.811], which one represents a greater value?" }, { "from": "gpt", "value": "The bar for \"CIVETS\"." } ], "id_raw": 485 }, { "id": "iLtUNYt2OhZ1l5Er", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.124, 0.306, 0.940, 0.811]?" }, { "from": "gpt", "value": "There are several bars representing different 'Emerging Markets' categories and their respective 'Consumer Behavior' values. The bars correspond to 'BRIC', 'MINT', 'VISTA', 'Next Eleven', 'FRANK', 'CIVETS', 'EAGLEs', and '3G' from left to right. Each bar is labeled with a respective value indication above it." } ], "id_raw": 485 }, { "id": "xCEpY6fwHmbv8GGZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.043, 0.802, 0.071, 0.826]." }, { "from": "gpt", "value": "It contains one vertical (Y-axis) scale label \"0.0\", where {the corresponding y-axis tick}[0.083, 0.811] is located." } ], "id_raw": 485 }, { "id": "iO0qSiY8CeqXLgwy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.043, 0.025, 0.071, 0.048] encompass?" }, { "from": "gpt", "value": "It includes a Y-axis scale label \"1.0\", with {the relevant tick}[0.083, 0.034] located on the Y-axis." } ], "id_raw": 485 }, { "id": "zb4452rravMHmLl4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is included in the area from [0.124, 0.828, 0.899, 0.867]?" }, { "from": "gpt", "value": "All x-axis labels are included in this area. The labels from left to right are 'BRIC', 'MINT', 'VISTA', 'Next Eleven', 'FRANK', 'CIVETS', 'EAGLEs', and '3G'. Each label has a corresponding tick on the X-axis." } ], "id_raw": 485 }, { "id": "kBcvghL6EWZSowhV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar in coordinates [0.543, 0.172, 0.626, 0.767] represent?" }, { "from": "gpt", "value": "The bar represents the Participation Rate of Cycling exercise." } ], "id_raw": 486 }, { "id": "TKtBGE3kCY5lHDoC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat is marked in coordinates [0.019, 0.281, 0.036, 0.528]?" }, { "from": "gpt", "value": "It's the Y-axis label, called Participation Rate." } ], "id_raw": 486 }, { "id": "81wXAzMP6EAEu61c", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat object is located in these coordinates [0.492, 0.942, 0.573, 0.970]?" }, { "from": "gpt", "value": "It's the X-axis label, which is named Exercises." } ], "id_raw": 486 }, { "id": "4puqN2n8XEjUUA5d", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at [0.843, 0.787, 0.899, 0.876]?" }, { "from": "gpt", "value": "It is the label of the X-axis tick for the category Zumba." } ], "id_raw": 486 }, { "id": "vH9AGfBtYFSYwK2u", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing running}[0.438, 0.085, 0.522, 0.767] taller than {the bar representing swimming}[0.752, 0.201, 0.836, 0.767]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 486 }, { "id": "s1tsu3nDeYN9kveU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nCompared to {the bar for Yoga}[0.124, 0.433, 0.207, 0.767], is {the bar for Pilates}[0.228, 0.520, 0.312, 0.767] taller or shorter?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 486 }, { "id": "Eq31dFk05Ddf3Apt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher participation rate between {Cycling}[0.543, 0.172, 0.626, 0.767] and {Weightlifting}[0.647, 0.244, 0.731, 0.767]?" }, { "from": "gpt", "value": "Cycling." } ], "id_raw": 486 }, { "id": "VAGFFb0WDKfkApks", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nBetween {Zumba}[0.857, 0.448, 0.940, 0.767] and {CrossFit}[0.333, 0.346, 0.417, 0.767], which exercise has a lower participation rate?" }, { "from": "gpt", "value": "CrossFit" } ], "id_raw": 486 }, { "id": "yuRwIYGz8opDy1vd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the display in the coordinates [0.124, 0.405, 0.207, 0.767]." }, { "from": "gpt", "value": "This region represents a bar for the category of Yoga, with a corresponding participation rate of 0.23. The blue bar is indicated by coordinates [0.124, 0.433, 0.207, 0.767] and the annotation text of 0.23 is located at [0.146, 0.405, 0.185, 0.433]." } ], "id_raw": 486 }, { "id": "5yjuep0C5d7CAQZj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat content is present in [0.599, 0.787, 0.689, 0.931]?" }, { "from": "gpt", "value": "Within this area lies the x-axis label of Weightlifting; its actual coordinates are [0.599, 0.787, 0.689, 0.931]." } ], "id_raw": 486 }, { "id": "2KDXS00vdl2NioyI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.847, 0.420, 0.940, 0.767]?" }, { "from": "gpt", "value": "This region contains an orange bar and its value. The bar represents the participation rate for Zumba and the value is 0.22. The coordinates for the bar are [0.857, 0.448, 0.940, 0.767] and for the value, they are [0.879, 0.420, 0.918, 0.448]." } ], "id_raw": 486 }, { "id": "wtRqLQt8bEmHY4hv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is shown in the area [0.043, 0.756, 0.071, 0.784]?" }, { "from": "gpt", "value": "This region contains the y-axis label of '0.0', which indicates zero participation rate in any exercises. The coordinates for this text are [0.043, 0.756, 0.071, 0.784]." } ], "id_raw": 486 }, { "id": "RqEikf6K6bzfA27U", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the specific object at location [0.043, 0.386, 0.071, 0.421] on the y-axis?" }, { "from": "gpt", "value": "It is the y-axis scale labeled \"0.4\"." } ], "id_raw": 487 }, { "id": "Bf15cg3khSsK86Gi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.228, 0.226, 0.312, 0.633] on the chart?" }, { "from": "gpt", "value": "It is a bar representing {\nthe healthcare services}[0.228, 0.226, 0.312, 0.633], with a funding level of 0.7." } ], "id_raw": 487 }, { "id": "dqXdpS98qkx0Xrsu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated at the coordinates [0.543, 0.435, 0.626, 0.633]?" }, { "from": "gpt", "value": "It's a bar representing {\nthe public transport services}[0.543, 0.435, 0.626, 0.633], with a funding level of 0.34." } ], "id_raw": 487 }, { "id": "nsSz1B0zazImkBC5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nIs the {healthcare bar}[0.228, 0.226, 0.312, 0.633] taller than the {educational services bar}[0.124, 0.284, 0.207, 0.633]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 487 }, { "id": "DBGUYZXId1KvvbnC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nWhich is longer, {the bar representing \"infrastructure\"}[0.752, 0.109, 0.836, 0.633] or {the bar representing \"social services\"}[0.857, 0.121, 0.940, 0.633]?" }, { "from": "gpt", "value": "Infrastructure." } ], "id_raw": 487 }, { "id": "78QwvSWyklg9DFPI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nFrom left to right, is the {police services bar}[0.333, 0.400, 0.417, 0.633] located before or after the {firefighting services bar}[0.438, 0.313, 0.522, 0.633]?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 487 }, { "id": "nabKnQSmSdsvDsdm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat is visible in the area [0.043, 0.386, 0.904, 0.633]?" }, { "from": "gpt", "value": "It displays the main plot of the chart, which consists of vertically arranged service sectors such as education, healthcare, police, firefighting, public transport, parks & recreation, infrastructure, and social services. The bars representing these sectors indicate the proportion of funding allocated." } ], "id_raw": 487 }, { "id": "hfra9Jj8NneXTHkp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within [0.043, 0.503, 0.071, 0.538]." }, { "from": "gpt", "value": "The section shows the 0.2 tick mark on the y-axis." } ], "id_raw": 487 }, { "id": "SFpYPX8td2ZCdfYl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of the objects within the coordinates [0.333, 0.400, 0.417, 0.633]?" }, { "from": "gpt", "value": "Within these coordinates is a bar that represents the funding level for the police service. The bar height indicates a funding level of 0.4." } ], "id_raw": 487 }, { "id": "bOvKtjUm9LXVIdMp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the rectangle at coordinates [0.119, 0.289, 0.204, 0.561]?" }, { "from": "gpt", "value": "Fashion & Apparel." } ], "id_raw": 488 }, { "id": "gfs0LciOjEBhbiqB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar}[0.119, 0.289, 0.204, 0.561]?" }, { "from": "gpt", "value": "#3f5e8a." } ], "id_raw": 488 }, { "id": "M9dQT9W4nOFyItpc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at {coordinates}[0.351, 0.624, 0.395, 0.652]?" }, { "from": "gpt", "value": "-0.174." } ], "id_raw": 488 }, { "id": "A2EeSc45o9e4HBDZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for 'Toys, Hobby & DIY'}[0.542, 0.324, 0.627, 0.561] higher or lower than the {bar for 'Furniture & Appliances'}[0.436, 0.361, 0.521, 0.561]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 488 }, { "id": "DXxwYKKrlAJRlYPa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nCompare the bars of the {Health & Wellness industry}[0.648, 0.241, 0.732, 0.561] and the {Automotive & Parts industry}[0.753, 0.561, 0.838, 0.609], which one is higher?" }, { "from": "gpt", "value": "Health & Wellness." } ], "id_raw": 488 }, { "id": "368sNTStTTDrZGxt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nWhich value is greater, the one at {coordinates}[0.142, 0.261, 0.181, 0.289] or at {coordinates}[0.882, 0.251, 0.921, 0.279]?" }, { "from": "gpt", "value": "The value at [0.882, 0.251, 0.921, 0.279]." } ], "id_raw": 488 }, { "id": "ilUwA5U0Hf16o4UO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.357, 0.685, 0.479, 0.927]." }, { "from": "gpt", "value": "It's a label for the X-axis called 'Furniture & Appliances'." } ], "id_raw": 488 }, { "id": "IOrDnQpEAjIJXi9D", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.330, 0.561, 0.415, 0.652]?" }, { "from": "gpt", "value": "I can see a bar representing negative growth in the 'Food & Personal Care' sector, the value is -0.174 as shown in the coordinates [0.351, 0.624, 0.395, 0.652]." } ], "id_raw": 488 }, { "id": "F6rWjIwgAk3jRuqm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.542, 0.324, 0.627, 0.561]?" }, { "from": "gpt", "value": "It's a bar that represents 'Toys, Hobby & DIY' with growth of 0.455 as shown in the coordinates [0.565, 0.296, 0.604, 0.324]." } ], "id_raw": 488 }, { "id": "hBwIIcOizNTdEyAi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the y-axis in the chart?" }, { "from": "gpt", "value": "Sales (in thousands)." } ], "id_raw": 491 }, { "id": "6ILRMjTtimFg0wpZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat is shown on the x-axis of the chart?" }, { "from": "gpt", "value": "Months." } ], "id_raw": 491 }, { "id": "HpA2V9iRLlNHUrRd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the color #69bd78 represent in the bar chart?" }, { "from": "gpt", "value": "It represents the sales for each month." } ], "id_raw": 491 }, { "id": "h1Cng9UUDJu8sxQb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the data at {the bar label}<{bar label coordinates}[0.688, 0.253, 0.732, 0.288]>?" }, { "from": "gpt", "value": "4400." } ], "id_raw": 491 }, { "id": "ra5jhntqctY5LJPH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for February}[0.207, 0.396, 0.262, 0.814] higher than {the bar for July}[0.547, 0.336, 0.601, 0.814]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 491 }, { "id": "iqrQW4R2YA23yJeL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nIs {the highest bar}[0.819, 0.097, 0.873, 0.814] for November or December?" }, { "from": "gpt", "value": "November." } ], "id_raw": 491 }, { "id": "mAmBksR57shIRpHG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nAre sales in {the first half of the year}[0.139, 0.276, 0.194, 0.814],[0.207, 0.396, 0.262, 0.814],[0.275, 0.348, 0.330, 0.814],[0.343, 0.318, 0.398, 0.814],[0.411, 0.264, 0.466, 0.814], [0.479, 0.240, 0.533, 0.814] generally higher than sales in {the second half of the year}[0.547, 0.336, 0.601, 0.814],[0.615, 0.431, 0.669, 0.814],[0.683, 0.288, 0.737, 0.814],[0.751, 0.205, 0.805, 0.814], [0.819, 0.097, 0.873, 0.814], [0.887, 0.240, 0.941, 0.814]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 491 }, { "id": "S1lFnP6JDKvTR4Ik", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher sales figure, {April}[0.343, 0.318, 0.398, 0.814] or {June}[0.479, 0.240, 0.533, 0.814]?" }, { "from": "gpt", "value": "June." } ], "id_raw": 491 }, { "id": "BYFp8R2gbMFoOPxz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the coordinates [0.099, 0.037, 0.981, 0.814]." }, { "from": "gpt", "value": "This is the main plot area of the bar chart. It includes x and y axis labels, x and y ticks labeled with months and sales respectively, and the sales bars colored in #69bd78, each representing the sales number for a month." } ], "id_raw": 491 }, { "id": "HsxG6axalVbREUc1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe all the objects in the chart from April [0.343, 0.318, 0.398, 0.814] to September [0.683, 0.288, 0.737, 0.814]." }, { "from": "gpt", "value": "From April to September, the chart displays the sales volume for each month with green bars. The height of each bar corresponds to the sales in that month. April, May, June, July, August, and September have corresponding sales of 4150, 4600, 4800, 4000, 3200, and 4400 respectively." } ], "id_raw": 491 }, { "id": "SFDOQ6qtSbes2eNO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the coordinate [0.076, 0.800, 0.087, 0.835]?" }, { "from": "gpt", "value": "It is the y-axis label for the value 0." } ], "id_raw": 491 }, { "id": "J9L9ScQmMNdXkel8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart contain in the region [0.819, 0.097, 0.873, 0.814]?" }, { "from": "gpt", "value": "There is a bar in the chart within this region, which represents the sales for the month of November. The height of the bar corresponds to a sales volume of 6000." } ], "id_raw": 491 }, { "id": "d5dA4DyGNY8vZ85O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label on the y-axis?" }, { "from": "gpt", "value": "{Number of Students}[0.015, 0.244, 0.029, 0.477]." } ], "id_raw": 492 }, { "id": "1sILh4Ct4A3ef4NO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat category is represented by the bar at coordinates [0.888, 0.312, 0.944, 0.687]?" }, { "from": "gpt", "value": "{Scientific Research}[0.813, 0.704, 0.916, 0.875]." } ], "id_raw": 492 }, { "id": "7EymAgcErpoc53eT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the bar representing \"Online Education Platforms\"?" }, { "from": "gpt", "value": "{3500}[0.689, 0.092, 0.724, 0.116]." } ], "id_raw": 492 }, { "id": "G98jyoPEA9Tsx4xC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat category is at coordinate [0.563, 0.704, 0.707, 0.942]?" }, { "from": "gpt", "value": "{Online Education Platforms}[0.563, 0.704, 0.707, 0.942]." } ], "id_raw": 492 }, { "id": "ccpMkOhOmBaecYw4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Software Engineering}[0.103, 0.704, 0.218, 0.896] shorter or taller than the bar representing {Mechanical Engineering}[0.231, 0.704, 0.358, 0.916]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 492 }, { "id": "6FfZoilQ0GsRiPdr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nIn comparing the bar for {Overseas Education}[0.818, 0.606, 0.874, 0.687] and {Environmental Sciences}[0.370, 0.704, 0.497, 0.916], which one has a lower value?" }, { "from": "gpt", "value": "Overseas Education." } ], "id_raw": 492 }, { "id": "bUBAPOVCrdAxdxgR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Electrical Engineering}[0.260, 0.527, 0.316, 0.687] to the right or left of the bar for {Environmental Sciences}[0.370, 0.704, 0.497, 0.916]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 492 }, { "id": "JYBNX6zfMQ1eZlqc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Active Learning}[0.539, 0.442, 0.595, 0.687] shorter or taller compared to the bar for {Language Learning}[0.609, 0.361, 0.665, 0.687]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 492 }, { "id": "Do8rb9bycQ9aPze8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the bars and their labels seen in the region [0.100, 0.300, 0.300, 0.690]." }, { "from": "gpt", "value": "In the specified region, there are two bars with their labels. The first bar represents {Software Engineering}[0.190, 0.516, 0.246, 0.687] with a value of 1050{1050}[0.201, 0.493, 0.236, 0.516]. The second bar stands for {Electrical Engineering}[0.260, 0.527, 0.316, 0.687] with a value of {980}[0.275, 0.504, 0.301, 0.527]." } ], "id_raw": 492 }, { "id": "IqtBrN7zQq4U3id9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.480, 0.400, 0.600, 0.690]." }, { "from": "gpt", "value": "There are two bars in the region, the first being {Active Learning}[0.539, 0.442, 0.595, 0.687] with a value of {1500}[0.550, 0.419, 0.585, 0.442] and the second is {Language Learning}[0.609, 0.361, 0.665, 0.687] with a value of {2000}[0.619, 0.337, 0.654, 0.361]." } ], "id_raw": 492 }, { "id": "s1feERqDfJY3VsEv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within region [0.370, 0.500, 0.530, 0.870]?" }, { "from": "gpt", "value": "There is one bar in the region which represents {Environmental Sciences}[0.469, 0.560, 0.525, 0.687]. The value of the bar is {780}[0.484, 0.537, 0.511, 0.560]." } ], "id_raw": 492 }, { "id": "BWjaDTPWCHP1mtK0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the data represented in the region [0.800, 0.200, 0.950, 0.750]?" }, { "from": "gpt", "value": "There are two bars within the region. The first is for {Practical Training}[0.748, 0.393, 0.804, 0.687] with a value {1800}[0.759, 0.370, 0.794, 0.393]. The second bar represents {Overseas Education}[0.818, 0.606, 0.874, 0.687] and has a value of {500}[0.833, 0.582, 0.859, 0.606]." } ], "id_raw": 492 }, { "id": "6jjxaiKMck8r1gAF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the Y-axis in this chart?" }, { "from": "gpt", "value": "The label for the Y-axis is \"GDP growth rate\", as seen in the coordinates {Y-axis label}[0.013, 0.403, 0.024, 0.568]." } ], "id_raw": 495 }, { "id": "df8iaIpwTwAc9TJL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the last recorded year in the X-axis?" }, { "from": "gpt", "value": "The last recorded year is \"2020\" and its coordinates are [0.896, 0.931, 0.925, 0.951]." } ], "id_raw": 495 }, { "id": "Tk0xnWWE9BlCZBVN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the GDP growth rate for the year {2018}[0.703, 0.168, 0.772, 0.629] represented in this chart?" }, { "from": "gpt", "value": "The GDP growth rate for the year 2018 is 3.2." } ], "id_raw": 495 }, { "id": "hBFCpCXTLuzSNEDo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value on the y-axis scale?" }, { "from": "gpt", "value": "The highest value on the y-axis scale is {4}[0.039, 0.045, 0.046, 0.065]." } ], "id_raw": 495 }, { "id": "ufZvOrGhIUdP6O7B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nIs the GDP growth rate for {2019}[0.789, 0.298, 0.858, 0.629] greater or less than the rate for {2016}[0.529, 0.499, 0.599, 0.629]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 495 }, { "id": "EyI0h8oG3XOfbgRM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nBetween {2014}[0.356, 0.629, 0.425, 0.701] and {2020}[0.876, 0.629, 0.945, 0.859], which year had a negative GDP growth rate?" }, { "from": "gpt", "value": "Both." } ], "id_raw": 495 }, { "id": "mca8myexRhVfBtjr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nDid the GDP growth rate increase or decrease from {2012}[0.183, 0.514, 0.252, 0.629] to {2013}[0.270, 0.384, 0.339, 0.629]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 495 }, { "id": "rsDIj97vUgjZmuYl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nBetween {2018}[0.703, 0.168, 0.772, 0.629] and {2019}[0.789, 0.298, 0.858, 0.629], which year had the higher GDP growth?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 495 }, { "id": "LG6PGymqTIVqTnQj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the bar area for the year {2016}[0.529, 0.499, 0.599, 0.629]." }, { "from": "gpt", "value": "The year of the bar is 2016, as stated by the x-axis label below the bar. The height and coordinates of the bar indicate that the GDP growth rate for 2016 was 0.9." } ], "id_raw": 495 }, { "id": "uP9SlbALqRb6E7QI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content situated within [0.180, 0.959, 0.925, 0.979]?" }, { "from": "gpt", "value": "The content within [0.180, 0.959, 0.925, 0.979] represents the X-axis label. The label is \"Year\"." } ], "id_raw": 495 }, { "id": "fJkialqlEBxd3tIw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed within the region [0.549, 0.429, 0.685, 0.629]?" }, { "from": "gpt", "value": "This area showcases three bars. The first bar represents the year 2016 with a GDP growth rate of 0.9. The second bar represents the year 2017 achieving a GDP growth rate of 1.5, and lastly, the third bar represents the year 2018 with a surged GDP growth rate of 3.2." } ], "id_raw": 495 }, { "id": "J6WurbU5WDHGCrHx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the information within the area [0.116, 0.931, 0.252, 0.951]." }, { "from": "gpt", "value": "Within the area [0.116, 0.931, 0.252, 0.951] lie the labels for the years 2011 and 2012." } ], "id_raw": 495 }, { "id": "Q6qlhKsrhVyYisP1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nWhat is being labeled with coordinates [0.015, 0.207, 0.029, 0.402] in the bar chart?" }, { "from": "gpt", "value": "Y-axis label, it's titled \"Performance (%)\"." } ], "id_raw": 496 }, { "id": "pDZOrl9TTyBqcsfF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.876, 0.197, 0.943, 0.582] in the diagram?" }, { "from": "gpt", "value": "It is a bar representing the project of \"Environmental Protection\"." } ], "id_raw": 496 }, { "id": "c6QVdebqU9QHnuGd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the project indicated at coordinates [0.112, 0.142, 0.180, 0.582]?" }, { "from": "gpt", "value": "The project is \"Housing Availability\"." } ], "id_raw": 496 }, { "id": "sjLZSoxhuUzKVaXw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nWhat is mentioned at coordinates [0.180, 0.599, 0.153, 0.827]?" }, { "from": "gpt", "value": "The label for a project called \"Housing Availability\"." } ], "id_raw": 496 }, { "id": "ninos0HUpaU308Fn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Housing Availability\"}[0.112, 0.142, 0.180, 0.582] perform better or worse than {the bar representing \"Pension Schemes\"}[0.706, 0.087, 0.774, 0.582]?" }, { "from": "gpt", "value": "Worse." } ], "id_raw": 496 }, { "id": "I2BCaTEmBSIv3dVE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nWhich project performs better, {the bar representing \"Youth Development Programs\"}[0.282, 0.115, 0.350, 0.582] or {the bar representing \"Healthcare Services\"}[0.621, 0.142, 0.689, 0.582]?" }, { "from": "gpt", "value": "The \"Youth Development Programs\" perform better." } ], "id_raw": 496 }, { "id": "ckTE7WsPg4Gqn2PT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Infrastructure Development\"}[0.197, 0.170, 0.265, 0.582] on the right or left of {the bar of \"Vocational Training Programs\"}[0.536, 0.181, 0.604, 0.582] in the chart?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 496 }, { "id": "0tgtzayV0wihYXfe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nComparing the {Employment Assistance bar}[0.451, 0.225, 0.519, 0.582] and the {Adult Literacy Programs bar}[0.367, 0.197, 0.435, 0.582], which one indicates a higher performance?" }, { "from": "gpt", "value": "The \"Adult Literacy Programs\" bar." } ], "id_raw": 496 }, { "id": "Pu9e2YKtvpxHiVwg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nDescribe each labelled project in the region [0.043, 0.353, 0.061, 0.376]." }, { "from": "gpt", "value": "The region [0.043, 0.353, 0.061, 0.376] contains labels for the 40th percentile of the performance scores being analyzed." } ], "id_raw": 496 }, { "id": "obdtOoy6QdZO8VXw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects can you see within the area [0.112, 0.087, 0.180, 0.582]?" }, { "from": "gpt", "value": "Within the specified region, there is a bar showing the performance score of \"Housing Availability\" on the chart. It seems to be showing a score of approximately 80." } ], "id_raw": 496 }, { "id": "ErpJuWIKpQ5HMQf6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nCould you tell what's in the area [0.067, 0.0, 0.978, 0.582]?" }, { "from": "gpt", "value": "This area mainly consists of the bar chart itself. It contains the bars representing performance scores for various community development projects, the y-axis labels indicating scores from 0 to 100, and the x-axis labels naming the different community development projects." } ], "id_raw": 496 }, { "id": "sq82krz5f9kJ04iH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content shown in [0.624, 0.599, 0.638, 0.827]." }, { "from": "gpt", "value": "The region corresponds to the project label \"Healthcare Services\" on the x-axis." } ], "id_raw": 496 }, { "id": "vCgARtj5NBPU41KD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the research field at the coordinate [0.193, 0.280, 0.261, 0.903]?" }, { "from": "gpt", "value": "The research field at this coordinate is Physics." } ], "id_raw": 497 }, { "id": "QfVpR0BawCqZNUVp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nWhat kind of object is found at [0.790, 0.248, 0.858, 0.903]?" }, { "from": "gpt", "value": "A bar representing the Innovation Score for Nanotechnology is found at the coordinates." } ], "id_raw": 497 }, { "id": "3mgJfumtP9kUAg9Y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the text written at [0.202, 0.919, 0.253, 0.942]?" }, { "from": "gpt", "value": "The text written at this coordinate is 'Physics'." } ], "id_raw": 497 }, { "id": "5nPVBxC0HtN0IlSV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nWhich is higher {the bar of Biotechnology}[0.364, 0.264, 0.432, 0.903] or {the bar of Astronomy}[0.449, 0.328, 0.517, 0.903]?" }, { "from": "gpt", "value": "Astronomy." } ], "id_raw": 497 }, { "id": "Fu0YSbk93g9HWHKl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nAre {the bars of AI and Nanotech}[0.108, 0.185, 0.176, 0.903, 0.790, 0.248, 0.858, 0.903] of equal height?" }, { "from": "gpt", "value": "No." } ], "id_raw": 497 }, { "id": "4xKrrq0MbAlpENzq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of Genetics}[0.875, 0.105, 0.943, 0.903] the tallest among all the research fields?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 497 }, { "id": "A4IG1rg70bB6IkYz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area from [0.108, 0.185, 0.176, 0.903] to [0.193, 0.280, 0.261, 0.903]?" }, { "from": "gpt", "value": "These areas consist of two bars. The bar from [0.108, 0.185, 0.176, 0.903] represents the Innovation Score of AI research field while the bar at [0.193, 0.280, 0.261, 0.903] signifies the Innovation Score of Physics research field. The bar representing AI is taller than the bar of Physics, indicating AI has a higher Innovation Score." } ], "id_raw": 497 }, { "id": "4LbfzM1boCOFdgtb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed from [0.718, 0.919, 0.760, 0.942] to [0.879, 0.919, 0.939, 0.942]?" }, { "from": "gpt", "value": "These areas contain two labels corresponding to two different research fields. The coordinates at [0.718, 0.919, 0.760, 0.942] label the field as 'EnvSc' and the coordinates at [0.879, 0.919, 0.939, 0.942] label the field as 'Genetics'." } ], "id_raw": 497 }, { "id": "SndqIuOhHYK4YYKW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region from [0.790, 0.248, 0.858, 0.903] to [0.875, 0.105, 0.943, 0.903]." }, { "from": "gpt", "value": "The area contains two bars representing the Innovation Scores for the 'Nanotech' and 'Genetics' research fields. The bar for 'Nanotech' is located at [0.790, 0.248, 0.858, 0.903] and is not as tall as the 'Genetics' bar, which is located at [0.875, 0.105, 0.943, 0.903]. This indicates that 'Genetics' has a higher Innovation Score than 'Nanotech'." } ], "id_raw": 497 }, { "id": "2wYQUv8dVdVCiRcL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.492, 0.952, 0.560, 0.975] in the chart?" }, { "from": "gpt", "value": "X-axis label, called Processes." } ], "id_raw": 499 }, { "id": "yVyNDgjnZpeaYdC5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth bar in the chart?" }, { "from": "gpt", "value": "[0.599, 0.085, 0.698, 0.684]." } ], "id_raw": 499 }, { "id": "IXwM9rYdDsBsRr2N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {y-axis}[0.015, 0.277, 0.029, 0.431]?" }, { "from": "gpt", "value": "Effectiveness." } ], "id_raw": 499 }, { "id": "9VaSa1j8O2eMZ1Ko", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.722, 0.474, 0.820, 0.684]?" }, { "from": "gpt", "value": "{Bar for \"Process of Concern\"}[0.722, 0.474, 0.820, 0.684]." } ], "id_raw": 499 }, { "id": "ch3UlZmaEwPCPYQS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Underperforming\"}[0.108, 0.414, 0.206, 0.684] taller than {the bar for \"Average\"}[0.231, 0.294, 0.329, 0.684]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 499 }, { "id": "MG04oLpKStZslmuB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for \"Good\"}[0.354, 0.235, 0.452, 0.684] have a higher value than {the bar for \"Exceptional\"}[0.477, 0.145, 0.575, 0.684]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 499 }, { "id": "Z7jVGruXoHcWLLjV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nAre the {bars for \"Good\", \"Exceptional\", and \"Outstanding\"}[[0.354, 0.235, 0.452, 0.684], [0.477, 0.145, 0.575, 0.684], [0.599, 0.085, 0.698, 0.684]] higher than the {bar for \"Process of Concern\"}[0.722, 0.474, 0.820, 0.684]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 499 }, { "id": "zMvPhMO7WA8Ni2pH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Process of Concern\"}[0.722, 0.474, 0.820, 0.684] to the left or right of {the bar for \"Another Process of Concern\"}[0.845, 0.534, 0.943, 0.684]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 499 }, { "id": "Gd85XV1OtDaCDPdT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.108, 0.414, 0.206, 0.684]." }, { "from": "gpt", "value": "There is a blue vertical bar labeled \"Underperforming\". The bar represents an effectiveness of 0.45." } ], "id_raw": 499 }, { "id": "DoAC5yUtdJZzFyTz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.035, 0.435, 0.057, 0.458]." }, { "from": "gpt", "value": "The label \"0.4\" is located on the y-axis at this coordinate, representing the effectiveness level." } ], "id_raw": 499 }, { "id": "biOMQDyrq4Z4L2iB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.477, 0.145, 0.575, 0.684]?" }, { "from": "gpt", "value": "The blue vertical bar representing the process \"Exceptional\" with an effectiveness of 0.90." } ], "id_raw": 499 }, { "id": "4sz9uCpVi6Zw5BLw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the area [0.354, 0.235, 0.452, 0.684]?" }, { "from": "gpt", "value": "There is a blue bar for the process \"Good\", which represents an effectiveness of 0.75." } ], "id_raw": 499 }, { "id": "C4wzpaQVfp1YyC5J", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis?" }, { "from": "gpt", "value": "{Frequency}[0.015, 0.327, 0.029, 0.447]" } ], "id_raw": 500 }, { "id": "fDHxyNKZzeIt5tPO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the eighth activity labeled on the chart?" }, { "from": "gpt", "value": "{Travel}[0.719, 0.765, 0.758, 0.830]" } ], "id_raw": 500 }, { "id": "qMubACDX22yCgilK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nCould I have the coordinates of the last bar - {Watching Movie}[0.875, 0.124, 0.943, 0.749]- please?" }, { "from": "gpt", "value": "The coordinates of {Watching Movie} bar are [0.875, 0.124, 0.943, 0.749]" } ], "id_raw": 500 }, { "id": "jlDPlYOKGVZg9l30", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat data is presented at the second x-tick mark on the x-axis?" }, { "from": "gpt", "value": "It's {Walking}[0.203, 0.765, 0.252, 0.846]." } ], "id_raw": 500 }, { "id": "IXRoDA3hbpbaIYPo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \u201cSwimming\u201d}[0.364, 0.170, 0.432, 0.749] higher than {the bar for \u201cDancing\u201d}[0.534, 0.354, 0.602, 0.749]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 500 }, { "id": "Ejm71p2IWAZuZ7mc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nIs {Gardening's bar}[0.108, 0.334, 0.176, 0.749] located at the left of {Travel's bar}[0.705, 0.157, 0.773, 0.749]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 500 }, { "id": "2ZPEVqVK2AEsmJ3H", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nCompared to the rest, does {Cooking's frequency}[0.790, 0.255, 0.858, 0.749] stand at the lower half or upper half?" }, { "from": "gpt", "value": "Lower half." } ], "id_raw": 500 }, { "id": "H3Y94njXvHe4UEh3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, the {Running}[0.278, 0.216, 0.347, 0.749] bar or the {Reading books}[0.449, 0.288, 0.517, 0.749] bar?" }, { "from": "gpt", "value": "{Running}[0.278, 0.216, 0.347, 0.749] bar." } ], "id_raw": 500 }, { "id": "fQBGuIGw1wtFRE0t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found within the coordinates [0.108, 0.124, 0.176, 0.749]?" }, { "from": "gpt", "value": "Within those coordinates, there is a bar for the activity {Gardening}[0.108, 0.334, 0.176, 0.749]." } ], "id_raw": 500 }, { "id": "uSQ2Hemh15EcQVIJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat is in the bounding box [0.862, 0.765, 0.957, 0.923]?" }, { "from": "gpt", "value": "That bounding box contains the x-axis label for the activity {Watching_Movies}[0.862, 0.765, 0.957, 0.923]." } ], "id_raw": 500 }, { "id": "v3poM5oliAU1lr9E", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the rectangle [0.364, 0.170, 0.432, 0.749] contain?" }, { "from": "gpt", "value": "This rectangle includes a bar that represents the frequency of {Swimming}[0.364, 0.170, 0.432, 0.749]." } ], "id_raw": 500 }, { "id": "1dM9Gbpf8H89srpj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nPlease give a brief description of the objects within the bounding box [0.034, 0.082, 0.058, 0.763]." }, { "from": "gpt", "value": "Included within these coordinates are various labels on the y-axis representing frequencies, including {0.0}[0.035, 0.740, 0.057, 0.763], {0.2}[0.035, 0.608, 0.057, 0.631], {0.4}[0.035, 0.476, 0.057, 0.500], {0.6}[0.035, 0.345, 0.057, 0.368], {0.8}[0.035, 0.213, 0.057, 0.237], and {1.0}[0.035, 0.082, 0.057, 0.105]." } ], "id_raw": 500 }, { "id": "er0Hp5KNwkisqVF6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label situated at [0.015, 0.233, 0.029, 0.370]?" }, { "from": "gpt", "value": "Return Rate." } ], "id_raw": 501 }, { "id": "Hg4XubGRa2XaPnci", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nIn which part of the chart is the label for \"Small_Cap_Investing\" present?" }, { "from": "gpt", "value": "[0.737, 0.586, 0.751, 0.825]." } ], "id_raw": 501 }, { "id": "42MrLUshK4CzTe7y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat investment strategy does the bar at [0.877, 0.351, 0.944, 0.521] represent?" }, { "from": "gpt", "value": "Inflation_Hedging." } ], "id_raw": 501 }, { "id": "ZcR9dRG5yj8ynCBH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat area corresponds to the x-axis label?" }, { "from": "gpt", "value": "[0.459, 0.952, 0.613, 0.975]." } ], "id_raw": 501 }, { "id": "2F5XDYRKep5dcxx5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nIs the {return rate for Quality_Investing}[0.461, 0.180, 0.527, 0.521] higher than {return rate for Growth_Investing}[0.294, 0.278, 0.361, 0.521]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 501 }, { "id": "Xdsh9RcAH2ASctHX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar visualizing the return for Inflation_Hedging}[0.877, 0.351, 0.944, 0.521] taller than {the bar illustrating the return for Value_Investing}[0.211, 0.326, 0.277, 0.521]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 501 }, { "id": "w6YO5bFf8Sm5fruZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nBetween {Socially_Responsible_Investing}[0.544, 0.521, 0.611, 0.545] and {Index_Investing}[0.627, 0.302, 0.694, 0.521], which one shows a better return rate?" }, { "from": "gpt", "value": "Index_Investing." } ], "id_raw": 501 }, { "id": "MPbYaCSltHF5MBpW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nAre the return rates for {Diversification}[0.127, 0.229, 0.194, 0.521] and {Momentum_Investing}[0.377, 0.253, 0.444, 0.521] different?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 501 }, { "id": "fPTdEm83GrXKLMOS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.127, 0.156, 0.944, 0.521]." }, { "from": "gpt", "value": "This area contains bars representing return rates for 10 different investment strategies - Diversification, Value_Investing, Growth_Investing, Momentum_Investing, Quality_Investing, Socially_Responsible_Investing, Index_Investing, Small_Cap_Investing, Balanced_Investment, and Inflation_Hedging." } ], "id_raw": 501 }, { "id": "rwnMX79qyraoiRU8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the region between [0.087, 0.034, 0.985, 0.570]?" }, { "from": "gpt", "value": "This area includes the entire chart, bars representing return rates for different investment strategies, labels for Y-axis and X-axis, and the ticks and labels for both axes." } ], "id_raw": 501 }, { "id": "6ceMC2JIssn998Ox", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the rectangle [0.087, 0.570, 0.918, 0.952]." }, { "from": "gpt", "value": "It contains the labels for all the investment strategies namely Diversification, Value_Investing, Growth_Investing, Momentum_Investing, Quality_Investing, Socially_Responsible_Investing, Index_Investing, Small_Cap_Investing, Balanced_Investment, and Inflation_Hedging." } ], "id_raw": 501 }, { "id": "vxcdyvbEEzlECWHM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat information is held within the region [0.035, 0.025, 0.077, 0.584]?" }, { "from": "gpt", "value": "It contains labels for y-axis, representing negative and positive return rates, from '-0.02' to '0.2'." } ], "id_raw": 501 }, { "id": "3bNhLQddZMBmKX7i", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.422, 0.019, 0.621, 0.041] refer to?" }, { "from": "gpt", "value": "This is the chart's subtitle, which reads \"AI Application Success Rates\"." } ], "id_raw": 502 }, { "id": "LK41AAsnM4BlMBb9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar at [0.616, 0.279, 0.686, 0.684]?" }, { "from": "gpt", "value": "The bar represents the success rate of \"Autonomous Vehicles\", which is approximately 0.70." } ], "id_raw": 502 }, { "id": "tcQTuz3E6gwGlWd5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-range of {the bar of Education}[0.789, 0.174, 0.859, 0.684]?" }, { "from": "gpt", "value": "The y-range for the \"Education\" bar is '0.00 -> 0.88'." } ], "id_raw": 502 }, { "id": "6JuH5SxCN5rdoLTS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Gaming bar}[0.876, 0.105, 0.945, 0.684] higher than {the Natural Language Processing bar}[0.184, 0.157, 0.253, 0.684]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 502 }, { "id": "xVIXZCd1KYQBhfeZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nIs {the HealthCare bar}[0.444, 0.134, 0.513, 0.684] left or right of {the Education bar}[0.789, 0.174, 0.859, 0.684]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 502 }, { "id": "VllBOft67ODVqvJU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the Cybersecurity bar}[0.703, 0.134, 0.772, 0.684] or {the Robotics bar}[0.271, 0.186, 0.340, 0.684]?" }, { "from": "gpt", "value": "The Cybersecurity bar." } ], "id_raw": 502 }, { "id": "oQsAip3Sa5kYN44E", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the region [0.098, 0.105, 0.945, 0.684]." }, { "from": "gpt", "value": "This region includes ten bars representing the success rates of different AI applications: {Machine Vision}[0.098, 0.209, 0.167, 0.684], {Natural Language Processing}[0.184, 0.157, 0.253, 0.684], {Robotics}[0.271, 0.186, 0.340, 0.684], {Forecasting}[0.357, 0.232, 0.426, 0.684], {HealthCare}[0.444, 0.134, 0.513, 0.684], {Speech Recognition}[0.530, 0.151, 0.599, 0.684], {Autonomous Vehicles}[0.616, 0.279, 0.686, 0.684], {Cybersecurity}[0.703, 0.134, 0.772, 0.684], {Education}[0.789, 0.174, 0.859, 0.684], and {Gaming}[0.876, 0.105, 0.945, 0.684]." } ], "id_raw": 502 }, { "id": "7B5Xh4yHjckSGGGg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is happening in [0.026, 0.096, 0.056, 0.696]?" }, { "from": "gpt", "value": "This region represents the y-axis of the chart with coordinates ranging from 0 to 1 and respective labels are: \"0.0\"[0.029, 0.677, 0.047, 0.695], \"0.2\"[0.029, 0.562, 0.047, 0.579], \"0.4\" [0.029, 0.446, 0.047, 0.463], \"0.6\" [0.029, 0.330, 0.047, 0.347], \"0.8\" [0.029, 0.214, 0.047, 0.231], \"1.0\" [0.029, 0.098, 0.047, 0.115]." } ], "id_raw": 502 }, { "id": "tcbEnP7kQ8Fq7VYX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.126, 0.696, 0.916, 0.830] represent?" }, { "from": "gpt", "value": "The region represents the application names in AI along the x-axis. It includes labels: {\"Machine_Vision\"}[0.126, 0.696, 0.138, 0.830], {\"Natural_Language_Processing\"}[0.213, 0.696, 0.225, 0.957], {\"Robotics\"}[0.299, 0.696, 0.311, 0.770], {\"Forecasting\"}[0.386, 0.696, 0.397, 0.797], {\"HealthCare\"}[0.472, 0.696, 0.484, 0.794], {\"Speech_Recognition\"}[0.559, 0.696, 0.570, 0.871], {\"Autonomous_Vehicles\"}[0.645, 0.696, 0.657, 0.885], {\"Cybersecurity\"}[0.732, 0.696, 0.743, 0.817], {\"Education\"}[0.818, 0.696, 0.830, 0.783], {\"Gaming\"}[0.905, 0.696, 0.916, 0.764]." } ], "id_raw": 502 }, { "id": "1YspNPu8hL1CJqU2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar corresponding to the coordinates [0.357, 0.134, 0.426, 0.626] in the graph?" }, { "from": "gpt", "value": "The bar represents Two-factor Authentication with an effectiveness score of 0.90." } ], "id_raw": 503 }, { "id": "nblYRtEKIlOu14PM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nWhere is {the bar for Data Encryption}[0.616, 0.134, 0.686, 0.626] located?" }, { "from": "gpt", "value": "The bar is located at [0.616, 0.134, 0.686, 0.626]." } ], "id_raw": 503 }, { "id": "2piVT1iluXMtpPAG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the x-axis label found at [0.400, 0.952, 0.642, 0.975]?" }, { "from": "gpt", "value": "It represents the label 'Cybersecurity and Data Privacy Measures'." } ], "id_raw": 503 }, { "id": "1jAMDlRls8BLY5bi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nIs the {Encryption Methods' bar}[0.098, 0.200, 0.167, 0.626] taller than the {Anonymising Services' bar}[0.703, 0.194, 0.772, 0.626]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 503 }, { "id": "MaXaAZll1qrVNq7o", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher effectiveness score, {Password Managers}[0.271, 0.145, 0.340, 0.626] or {Biometric Verification}[0.789, 0.167, 0.859, 0.626]?" }, { "from": "gpt", "value": "Password Managers." } ], "id_raw": 503 }, { "id": "7T05OmMoMD3Xc1oJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nIs the {Two-factor Authentication bar}[0.357, 0.134, 0.426, 0.626] taller than the {Data Encryption bar}[0.616, 0.134, 0.686, 0.626]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 503 }, { "id": "CttpvpJOjCA2gY4l", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.357, 0.112, 0.599, 0.626]." }, { "from": "gpt", "value": "There are two bars:\n1. The bar representing Two-factor Authentication is within these coordinates [0.357, 0.134, 0.426, 0.626].\n2. The bar representing VPN is within these coordinates [0.530, 0.112, 0.599, 0.626]." } ], "id_raw": 503 }, { "id": "6r1UffpzhDQ64NSy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat information is in the area [0.029, 0.071, 0.047, 0.094]?" }, { "from": "gpt", "value": "The y-axis tick label '1.0' is located at the coordinates [0.029, 0.071, 0.047, 0.094]." } ], "id_raw": 503 }, { "id": "oyXz9Fh0HCFT7zMt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.098, 0.112, 0.859, 0.626]." }, { "from": "gpt", "value": "This region includes a series of bars representing the effectiveness of different Cybersecurity and Data Privacy measures. It encompasses Encryption Methods [0.098, 0.200, 0.167, 0.626], Private Browsing [0.184, 0.178, 0.253, 0.626], Password Managers [0.271, 0.145, 0.340, 0.626], Two-factor Authentication [0.357, 0.134, 0.426, 0.626], Secure Wireless Networks [0.444, 0.271, 0.513, 0.626], VPN [0.530, 0.112, 0.599, 0.626], Data Encryption [0.616, 0.134, 0.686, 0.626], Anonymising Services [0.703, 0.194, 0.772, 0.626], Biometric Verification [0.789, 0.167, 0.859, 0.626], and part of the Privacy-Focused Browsers bar." } ], "id_raw": 503 }, { "id": "GFMX9KS7gLKOKWaM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart element at [0.098, 0.128, 0.167, 0.817]?" }, { "from": "gpt", "value": "Bar for 'Math'." } ], "id_raw": 504 }, { "id": "l2eHhvRvLNsV2Xgr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.565, 0.817] represent in the bar chart?" }, { "from": "gpt", "value": "X-axis tick for 'Computer Science'." } ], "id_raw": 504 }, { "id": "rmuMRi1H64ydbPaL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the significance of the area [0.098, 0.128, 0.167, 0.817]?" }, { "from": "gpt", "value": "Represents the utilization of 'Math' resources." } ], "id_raw": 504 }, { "id": "t6EU0c2ATrBnXRiq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the entity located at [0.461, 0.964, 0.581, 0.981] name?" }, { "from": "gpt", "value": "X-axis label, 'Education Resources'." } ], "id_raw": 504 }, { "id": "RlO6gmmz6jS2iAAE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing 'Math'[0.098, 0.128, 0.167, 0.817] higher than that of 'Art'[0.616, 0.324, 0.686, 0.817]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 504 }, { "id": "AXstn8OxyQWgNeXC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar of 'Physics'[0.444, 0.244, 0.513, 0.817] to the left or right of the bar of 'Chemistry'[0.357, 0.207, 0.426, 0.817]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 504 }, { "id": "muT9VWEk1QFlEPKk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing 'English'[0.184, 0.171, 0.253, 0.817] taller than the bar representing 'Physical Education'[0.789, 0.157, 0.859, 0.817]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 504 }, { "id": "o5DwadFHU7bqzedq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nComparing 'Biology'[0.271, 0.295, 0.340, 0.817] and 'History'[0.876, 0.135, 0.945, 0.817], which has greater resource utilization?" }, { "from": "gpt", "value": "History." } ], "id_raw": 504 }, { "id": "Agv6VvOMXdM0kh41", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what you can see between [0.098, 0.128, 0.167, 0.817] and [0.876, 0.135, 0.945, 0.817]." }, { "from": "gpt", "value": "It's a bar chart displaying the utilization of various education resources; from 'Math', 'English', 'Biology', 'Chemistry', 'Physics', 'Computer Science', 'Art', 'Music', to 'Physical Education', and 'History'. The height of each bar denotes the utilization score, with 'Computer Science' showing the highest and 'Art' the lowest." } ], "id_raw": 504 }, { "id": "lqVCK6RRb2rCb6Nq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nCharacterize the region [0.055, 0.019, 0.988, 0.817] in the chart?" }, { "from": "gpt", "value": "It spans the complete chart, covering ten vertical bars representing utilization of various education resources, labels of resources (Education Resources) and their utilization score, and also includes the x and y-axes with their labels." } ], "id_raw": 504 }, { "id": "oa8ljNujCNlZHuel", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat's noticeable in the area around [0.703, 0.309, 0.772, 0.817]?" }, { "from": "gpt", "value": "This area contains the bar representing 'Music' with its utilization score visually depicted by the height of the bar." } ], "id_raw": 504 }, { "id": "nJc331jHewNESFPd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the area [0.565, 0.099, 0.599, 0.817]?" }, { "from": "gpt", "value": "This region is containing the bar for 'Computer Science' which is the highest among all other resource bars indicating its highest utilization." } ], "id_raw": 504 }, { "id": "8yt8aCZLMGvLqpgH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with the y-axis of the chart having coordinates [0.015, 0.299, 0.029, 0.433]?" }, { "from": "gpt", "value": "The label is 'Trade Value'." } ], "id_raw": 505 }, { "id": "2KaLlLnjfWl7ijIN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nWhich industry is represented by the bar at coordinates [0.790, 0.161, 0.858, 0.707]?" }, { "from": "gpt", "value": "The industry represented at these coordinates is {Lumber_Industry}[0.778, 0.723, 0.870, 0.878]." } ], "id_raw": 505 }, { "id": "fajOeaUjvXFIcDTa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nWhat value does the bar at position [0.619, 0.143, 0.688, 0.707] represent?" }, { "from": "gpt", "value": "The bar represents a trade value of 0.91 for {Metal_Industry}[0.612, 0.723, 0.695, 0.861]." } ], "id_raw": 505 }, { "id": "hk6O6bPWTOkEvCu1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Metal_Industry}[0.619, 0.143, 0.688, 0.707] having a higher trade value than that of {the bar representing Energy_Sector}[0.534, 0.316, 0.602, 0.707]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 505 }, { "id": "cvonL8uWXCst0S4Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nWithin the bar at coordinates [0.193, 0.292, 0.261, 0.707], does {Automobile_Industry}[0.172, 0.723, 0.283, 0.909] have a smaller trade value than {Food_Industries}[0.449, 0.723, 0.526, 0.868] represented by the bar at coordinates [0.449, 0.199, 0.517, 0.707]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 505 }, { "id": "52IA9eHvV6ImFeT0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nIs the {Bar representing Electronics_Industry}[0.278, 0.236, 0.347, 0.707] closer to {bar representing Chemical_Industry}[0.705, 0.168, 0.773, 0.707] or {bar representing Automobile_Industry}[0.193, 0.292, 0.261, 0.707] in terms of trade value?" }, { "from": "gpt", "value": "Automobile Industry." } ], "id_raw": 505 }, { "id": "BZSrsL3ihkDz9Npy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is shown in the area [0.193, 0.292, 0.261, 0.707]?" }, { "from": "gpt", "value": "The area contains a single bar showing the trade value of the {Automobile_Industry}[0.172, 0.723, 0.283, 0.909]. The trade value is 0.67 as indicated by the height of the bar." } ], "id_raw": 505 }, { "id": "W6xx2WW0gGcuRKUx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nWhat is present within the area with the coordinate [0.332, 0.723, 0.464, 0.942]?" }, { "from": "gpt", "value": "In this area, we can see {Pharmaceutical Industry} label and the corresponding bar. The bar [0.364, 0.354, 0.432, 0.707] indicates a trade value of 0.57." } ], "id_raw": 505 }, { "id": "8UcSncGgD5pUvqMK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nCould you provide a description of the content within the coordinates [0.066, 0.025, 0.985, 0.707]?" }, { "from": "gpt", "value": "This area represents the entire plotting area of the bar chart. It includes ten bars, each representing the trade value of a different industry sector. The sector names are on the x-axis and have coordinates ranging from [0.094, 0.723, 0.190, 0.884] for {Clothing_Industry} to [0.877, 0.723, 0.941, 0.830] for {Agriculture}. The y-axis signifies the trade value ranging from 0 at [0.035, 0.698, 0.057, 0.721] up to 1 at [0.035, 0.078, 0.057, 0.101]." } ], "id_raw": 505 }, { "id": "nQtoTB5znTuA0aUI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.015, 0.211, 0.029, 0.455] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Annual Growth Score." } ], "id_raw": 506 }, { "id": "BjgAEpPQBAUY5bpu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the bar corresponding to Biotechnology?" }, { "from": "gpt", "value": "[0.189, 0.215, 0.258, 0.641]" } ], "id_raw": 506 }, { "id": "wR6ji83dOK1HjWDb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label} at [0.361, 0.952, 0.686, 0.975] represent?" }, { "from": "gpt", "value": "Knowledge Economy and Innovation Industries." } ], "id_raw": 506 }, { "id": "1RE0yU25WCGrMOiX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the labels shown in [0.043, 0.631, 0.052, 0.655] and [0.035, 0.072, 0.052, 0.095]?" }, { "from": "gpt", "value": "They are y-axis scale labels representing {0}[0.043, 0.631, 0.052, 0.655] and {10}[0.035, 0.072, 0.052, 0.095] respectively." } ], "id_raw": 506 }, { "id": "cw7lzIUQikddlNf0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for AI and Machine Learning}[0.104, 0.098, 0.172, 0.641] taller or shorter than {the bar for Biotechnology}[0.189, 0.215, 0.258, 0.641]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 506 }, { "id": "Gwdjb5AgWAE6UmhZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nBetween {IoT}[0.875, 0.154, 0.943, 0.641] and {Green Energy}[0.361, 0.109, 0.429, 0.641], which category scored higher in annual growth?" }, { "from": "gpt", "value": "Green Energy." } ], "id_raw": 506 }, { "id": "CSlUuT1oKg30f54B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing Nanotechnology}[0.618, 0.087, 0.686, 0.641] achieve a higher annual growth score than {the bar representing Internet Security}[0.703, 0.221, 0.772, 0.641]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 506 }, { "id": "oxPOeBuUX2s49OCZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nBetween {AI and Machine Learning}[0.104, 0.098, 0.172, 0.641] and {Data Science}[0.275, 0.165, 0.344, 0.641], which industry has a higher annual growth score?" }, { "from": "gpt", "value": "AI and Machine Learning." } ], "id_raw": 506 }, { "id": "QEJiAAY5oxBoi5Ps", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the area from [0.062, 0.025, 0.985, 0.641]." }, { "from": "gpt", "value": "This is the primary chart area containing the bar plots for different Knowledge Economy and Innovation Industries with their respective Annual Growth Score. The industry names are positioned above each bar on the x-axis and the growth scores are marked along the y-axis." } ], "id_raw": 506 }, { "id": "XvFFbfu2bvO0E9EZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the x-axis using the information found within [0.043, 0.631, 0.052, 0.095] and [0.361, 0.952, 0.686, 0.975]?" }, { "from": "gpt", "value": "The x-axis represents different categories in Knowledge Economy and Innovation Industries with the label placed at [0.361, 0.952, 0.686, 0.975]. The range of scale appears to be from 0 to 10, based on the scale labels at [0.043, 0.631, 0.052, 0.655] and [0.035, 0.072, 0.052, 0.095] respectively." } ], "id_raw": 506 }, { "id": "fUd6Eg5F3CTBnRIc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.043, 0.296, 0.052, 0.319]?" }, { "from": "gpt", "value": "There is a label indicating a y-axis scale of '4'." } ], "id_raw": 506 }, { "id": "sT0WBr4priNCNryS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is included in the region [0.723, 0.215, 0.858, 0.641]?" }, { "from": "gpt", "value": "There are two bars, representing the annual growth scores for {Internet Security}[0.703, 0.221, 0.772, 0.641] and {Quantum Computing}[0.789, 0.201, 0.857, 0.641], respectively." } ], "id_raw": 506 }, { "id": "coqYazBuz4zlyjGj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat could the labelled item at these coordinates [0.036, 0.368, 0.044, 0.385] be referring to?" }, { "from": "gpt", "value": "Y-axis value, which is 12." } ], "id_raw": 507 }, { "id": "0tVvsGMWaaD7T2WE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates [0.365, 0.879, 0.413, 0.951] represent?" }, { "from": "gpt", "value": "X-axis category, known as \"Soybeans\"." } ], "id_raw": 507 }, { "id": "hXwIHj3YeOQtvGAH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the title {subplot title}[0.386, 0.019, 0.653, 0.041] state?" }, { "from": "gpt", "value": "Food and Agriculture Industry Analysis." } ], "id_raw": 507 }, { "id": "dWgbLUnIcEMmmBDs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the element represented by [0.181, 0.313, 0.250, 0.867]?" }, { "from": "gpt", "value": "{The Corn bar}[0.181, 0.313, 0.250, 0.867]." } ], "id_raw": 507 }, { "id": "sHhUcbrvKSqaGUrv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Cotton}[0.528, 0.621, 0.598, 0.867] shorter or taller than the bar of {Coffee}[0.441, 0.760, 0.511, 0.867]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 507 }, { "id": "oc2kXcTVfjdt6d2t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nIs the {Fruits bar}[0.876, 0.207, 0.945, 0.867] located to the left or right of the {Grains bar}[0.789, 0.264, 0.858, 0.867]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 507 }, { "id": "eLjKk5I5OE4Ge1gt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Coffee\"}[0.441, 0.760, 0.511, 0.867] and {\"Sugarcane\"}[0.615, 0.067, 0.685, 0.867], which commodity has higher production level?" }, { "from": "gpt", "value": "Sugarcane." } ], "id_raw": 507 }, { "id": "IM6aX7pTYziPthoU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nIs the x-axis label {\"Wheat\"}[0.112, 0.879, 0.146, 0.931] located to the left or the right of {\"Rice\"}[0.290, 0.879, 0.315, 0.918]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 507 }, { "id": "37uwRdF2RT4KTydy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the area [0.094, 0.444, 0.164, 0.867]?" }, { "from": "gpt", "value": "It represents a green bar of the \"Wheat\" category on the chart, showing a production level of about 10.3 million metric tons." } ], "id_raw": 507 }, { "id": "2lFbCuUIMsdOQ9ml", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in [0.615, 0.067, 0.685, 0.867]?" }, { "from": "gpt", "value": "There is a green bar representing the \"Sugarcane\" category, with a high production level stretch upwards reaching nearly 20 million metric tons." } ], "id_raw": 507 }, { "id": "DGx4HgEWzgrZIoYX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat is presented in the region [0.181, 0.313, 0.250, 0.867]?" }, { "from": "gpt", "value": "It contains a green bar showing the production level of \"Corn\", which amount to around 13.5 million metric tons." } ], "id_raw": 507 }, { "id": "lqwpOHwP1oMJ9C2m", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content visually represented in [0.790, 0.264, 0.858, 0.867]?" }, { "from": "gpt", "value": "The area contains a green bar representing the data of \"Grains\". The height of the bar represents its production value, around 14.7 million metric tons." } ], "id_raw": 507 }, { "id": "rG9j6QO8gl0pjNdA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nWhat colour is the bar at the coordinates [0.330, 0.729, 0.393, 1.588]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 508 }, { "id": "qvSaaJTWy3llHlex", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nIn which year does the figure element at [0.882, 0.597, 0.945, 1.588] correspond to?" }, { "from": "gpt", "value": "Year 2020." } ], "id_raw": 508 }, { "id": "bElcSifyLzTa67Ah", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nCan you give the size or positional information for the y-axis label?" }, { "from": "gpt", "value": "The y-axis label, \"Art funds in Millions\", has a bounding box that spans from [0.012, 0.401, 0.024, 0.572]." } ], "id_raw": 508 }, { "id": "XV8rVUDCmyfIgfhd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what text is written in the bounding box from [0.384, 0.019, 0.656, 0.041]?" }, { "from": "gpt", "value": "The text \"Culture and Arts Industry Development\" is contained within the bounding box [0.384, 0.019, 0.656, 0.041]." } ], "id_raw": 508 }, { "id": "PKsEiFqVDapcWRGX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nDoes the bar with coordinates [0.330, 0.729, 0.393, 1.588] extend higher on the chart than the bar at [0.803, 0.333, 0.866, 1.588]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 508 }, { "id": "DKgcDzNzCa5n8JXl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nIs the axis label at [0.507, 0.964, 0.532, 0.981] positioned lower than the figure element at [0.029, 0.920, 0.043, 0.938]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 508 }, { "id": "IqNNXetrmUnNg9CH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nWithin the chart, are the bars quickly becoming taller from [0.094, 0.839, 0.157, 1.588] to [0.330, 0.729, 0.393, 1.588]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 508 }, { "id": "0SI73wKz58yG3vXR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nDoes the bar at [0.724, 0.355, 0.787, 1.588] represent a greater value than the label at [0.029, 0.480, 0.043, 0.498]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 508 }, { "id": "HUzL7zBkFI7wmlSU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements contained within the region from [0.057, 0.021, 0.708, 1.0]?" }, { "from": "gpt", "value": "This region contains the main plotting area of the chart showing year-wise allocation of Art funds from 2010 to 2020. The bars indicating funds allocation span from bottom to top, located at various points along the x-axis representing years." } ], "id_raw": 508 }, { "id": "NlkuQT23xIixCKOn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nWhat is within the plot area [0.052, 0.047, 0.988, 0.927]?" }, { "from": "gpt", "value": "The plotting area [0.052, 0.047, 0.988, 0.927] contains the bars that represent the Art funds for each year. The bottom edge of these bars aligns with the x-axis (year), and the height of these bars indicates the value of the Art funds in that year." } ], "id_raw": 508 }, { "id": "wGoVMr5AdKwVDVyX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the box with coordinates [0.252, 0.597, 0.315, 1.588]?" }, { "from": "gpt", "value": "Within this bounding box, you can see a bar that corresponds to the year 2012 and reflects the funding for Art in millions for that year." } ], "id_raw": 508 }, { "id": "ldbvCnWu5jenfXNH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements located inside the region [0.583, 0.920, 0.613, 0.957]." }, { "from": "gpt", "value": "The region [0.583, 0.920, 0.613, 0.957] contains the text label \"2016\" which represents one of the x-axis (years) labels." } ], "id_raw": 508 }, { "id": "TPqabp0DjvVAlE0s", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nCould you identify the object found at [0.010, 0.319, 0.019, 0.469] in the chart?" }, { "from": "gpt", "value": "It is the y-axis label, named Leadership Score." } ], "id_raw": 509 }, { "id": "sXYFG2GihEs6c4OH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinates of the third text in the x-axis scale?" }, { "from": "gpt", "value": "The coordinates are [0.295, 0.774, 0.305, 0.931]." } ], "id_raw": 509 }, { "id": "CKTOURJDfD95h22u", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar in the position [0.265, 0.210, 0.335, 1.867] represent?" }, { "from": "gpt", "value": "It represents the Leadership Score of the Human Resources department." } ], "id_raw": 509 }, { "id": "JvycWxTCatSJnv6a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the region [0.023, 0.019, 0.041, 0.036] of the chart?" }, { "from": "gpt", "value": "It is the value \"100\" on the y-axis scale." } ], "id_raw": 509 }, { "id": "4l6WoIRMUpwRGMLm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Sales\"}[0.090, 0.486, 0.160, 1.867] higher or lower than {the bar representing \"Marketing\"}[0.177, 0.394, 0.247, 1.867]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 509 }, { "id": "bh3sgYgWKuCPlqY2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Operations\" department's score bar}[0.527, 0.578, 0.597, 1.867] have a higher value than {the \"Finance\" department's score bar}[0.440, 0.118, 0.510, 1.867]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 509 }, { "id": "htLDRbfLeu6uM8Lk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nAre {the bars on the left side of the chart}[[0.090, 0.486, 0.160, 1.867],[0.177, 0.394, 0.247, 1.867],[0.265, 0.210, 0.335, 1.867], [0.352, 0.302, 0.422, 1.867]] generally lower than {the bars on the right side of the chart}[[0.527, 0.578, 0.597, 1.867],[0.615, 0.247, 0.685, 1.867],[0.702, 0.541, 0.772, 1.867],[0.790, 0.431, 0.860, 1.867],[0.877, 0.173, 0.947, 1.867]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 509 }, { "id": "KvMGpgsw4DwyHOLZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nWhich department has a higher leadership score, {Sales}[0.090, 0.486, 0.160, 1.867] or {IT}[0.352, 0.302, 0.422, 1.867]?" }, { "from": "gpt", "value": "IT." } ], "id_raw": 509 }, { "id": "s0FnmNNxo9DzyrGa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat details can you provide about the region [0.023, 0.019, 0.041, 0.128]?" }, { "from": "gpt", "value": "This region includes the lower y-axis values, specifically \"60\", \"65\", \"70\", \"75\", and \"80\" with coordinates:\n1. \"60\": [0.029, 0.755, 0.041, 0.773]\n2. \"65\": [0.029, 0.663, 0.041, 0.681]\n3. \"70\": [0.029, 0.571, 0.041, 0.589]\n4. \"75\": [0.029, 0.479, 0.041, 0.497]\n5. \"80\": [0.029, 0.387, 0.041, 0.405]" } ], "id_raw": 509 }, { "id": "vYLL181hgdZDu900", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within [0.702, 0.541, 0.772, 1.867]?" }, { "from": "gpt", "value": "This area contains a single bar that represents the Leadership Score of the Product department, which is 72." } ], "id_raw": 509 }, { "id": "VQhlBmIbaJW4VvAv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.090, 0.486, 0.160, 1.867] consist of in the chart?" }, { "from": "gpt", "value": "This region mainly contains one bar that represents the Leadership Score of the Sales department, which is 75." } ], "id_raw": 509 }, { "id": "H23WxJa07Skhrn4M", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat features are contained in the region [0.208, 0.774, 0.217, 0.861]?" }, { "from": "gpt", "value": "This region contains the label for the 'Marketing' category on the x-axis." } ], "id_raw": 509 }, { "id": "1KDL8o3a38wO0coU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the performance in Q1 according to the legend?" }, { "from": "gpt", "value": "{The color representing performance in Q1}[0.804, 0.214, 0.832, 0.226] is a shade of mint green." } ], "id_raw": 514 }, { "id": "hIqyqINQPQTyCny9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nWhich company at the position [0.219, 0.009, 0.441, 0.760] has the highest performance in the Q3?" }, { "from": "gpt", "value": "{The company name located at the position}[0.160, 0.729, 0.209, 0.747] is Regeneron Pharmaceuticals." } ], "id_raw": 514 }, { "id": "OsibO1CYCa9e6ako", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-range for Johnson & Johnson's bars in the first subplot?" }, { "from": "gpt", "value": "{The x-range for Johnson & Johnson}[0.219, 0.139, 0.881, 0.205] starts from 0.00 and goes up to 390.00." } ], "id_raw": 514 }, { "id": "YGH0UyEWo6ZuItgI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nIs {the Q3 performance bar for Pfizer}[0.638, 0.467, 0.881, 0.532] shorter than {the Q2 performance bar for Gilead Sciences}[0.408, 0.548, 0.611, 0.613]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 514 }, { "id": "H2APSsFwDKyi5bPo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nWhich is wider, {the Q1 bar for Novartis}[0.219, 0.892, 0.395, 0.940] or {the Q3 bar for Bristol-Myers Squibb}[0.633, 0.772, 0.871, 0.820]?" }, { "from": "gpt", "value": "{The Q3 bar for Bristol-Myers Squibb}[0.633, 0.772, 0.871, 0.820]." } ], "id_raw": 514 }, { "id": "gTBO3vZSm230dmgz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the subtitle for the third subplot}[0.324, 0.673, 0.880, 0.695] contain more companies than {the subtitle for the first subplot}[0.437, 0.019, 0.767, 0.041]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 514 }, { "id": "q2agQj91SDblfxUi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nWhat details are visible in the region [0.219, 0.701, 0.985, 0.952]?" }, { "from": "gpt", "value": "This region encompasses the subplot for companies Novartis, Sanofi, Bristol-Myers Squibb, and Regeneron Pharmaceuticals. Included are their Q1 to Q3 performance bars, x-ticks from 0 to 800, and labels for the y-ticks of each company." } ], "id_raw": 514 }, { "id": "CI6PKAHruLUhB6Uz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.219, 0.374, 0.985, 0.625]." }, { "from": "gpt", "value": "The area contains parts of the second subplot which holds Q1 to Q3 performance bar details, x-ticks for ranges between 0 to 600, and y-tick labels for Gilead Sciences, Pfizer, and Roche." } ], "id_raw": 514 }, { "id": "SHeohn1FD9xYizDx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.219, 0.047, 0.985, 0.297] consist of?" }, { "from": "gpt", "value": "This specific area includes a subplot featuring three companies: Amgen, Johnson & Johnson, and Merck & Co with their associated performance bars from Q1 to Q3, x-tick labels ranging from 0 to 500, and relevant legends depicting various performance stages color-coded." } ], "id_raw": 514 }, { "id": "QUz17A6WldCQ4WTZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.010, 0.416, 0.019, 0.582] in the first subplot?" }, { "from": "gpt", "value": "Risk Factors." } ], "id_raw": 518 }, { "id": "w6NnPliGu5SxusKr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color label of {the bar associated with \"Compliance Risk\"}[0.106, 0.113, 0.272, 0.192] in subplot 0?" }, { "from": "gpt", "value": "#9dcec9." } ], "id_raw": 518 }, { "id": "s0ahdKSx2CpyqGi8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nCan you identify {the value range indicated by the bar}[0.106, 0.410, 0.258, 0.489] for \"Reputation Risk\" in the first interval data?" }, { "from": "gpt", "value": "0.00 -> 0.55." } ], "id_raw": 518 }, { "id": "hdUfzVTuvGHCrDC6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nIs {the starting point of the \"Legal Risk\" bar}[0.106, 0.212, 0.203, 0.291] higher or lower than {the starting point of the \"Operational Risk\" bar}[0.106, 0.607, 0.189, 0.686]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 518 }, { "id": "4bv19gsyo3Yzu9up", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Market Risk\" bar}[0.106, 0.508, 0.285, 0.588] in the start interval reach further right than {the \"Credit Risk\" bar}[0.707, 0.706, 0.845, 0.785] in the second interval?" }, { "from": "gpt", "value": "No." } ], "id_raw": 518 }, { "id": "5LAeN4fqSEx0hBoa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater range, {the \"Strategic Risk\" bar}[0.106, 0.311, 0.327, 0.390] in the first interval or {the same \"Strategic Risk\" bar}[0.707, 0.311, 0.941, 0.390] in the third interval?" }, { "from": "gpt", "value": "Third interval." } ], "id_raw": 518 }, { "id": "fbH7OxlVLoiU4n5D", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within {the y-axis ticks region}[0.050, 0.142, 0.100, 0.861] in subplot 0." }, { "from": "gpt", "value": "This region contains the labels for the risk factors on the y-axis, which are listed from top to bottom: Compliance Risk, Legal Risk, Strategic Risk, Reputation Risk, Market Risk, Operational Risk, Credit Risk, and Cyber Risk." } ], "id_raw": 518 }, { "id": "tJq3b3nPBeBfkWG9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within {the region}[0.707, 0.075, 0.983, 0.923] corresponding to subplot 2?" }, { "from": "gpt", "value": "Subplot 2 shows a series of bars representing risk factors for Interval 2, with varying lengths indicating the risk measurement values. The background color for the subplot is #d3d3d3." } ], "id_raw": 518 }, { "id": "ObWzaJg60pRSO3Zo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present in {the xticks region}[0.399, 0.942, 0.690, 0.970] of subplot 1?" }, { "from": "gpt", "value": "The x-axis tick region contains numerical labels that correspond to the data points at regular intervals across the bottom of the chart. The numbers are 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0." } ], "id_raw": 518 }, { "id": "BJUeTnU2h1UZKybs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat information is displayed at the coordinates [0.840, 0.506, 0.944, 0.518]?" }, { "from": "gpt", "value": "Modern Age." } ], "id_raw": 523 }, { "id": "Nl4FqPq9pyFPEPug", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nWhich axis label is located at [0.528, 0.202, 0.528, 0.202]?" }, { "from": "gpt", "value": "There is no axis label at these coordinates." } ], "id_raw": 523 }, { "id": "ZpmPayQMi51NoRMF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the hue of {the subtitle for the \"Iron Age\"}[0.485, 0.407, 0.571, 0.422]?" }, { "from": "gpt", "value": "Black." } ], "id_raw": 523 }, { "id": "IiZWwbCEu7rTZKVC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nIs {the textual label for \"Bronze Age\"}[0.840, 0.059, 0.939, 0.071] to the left or right of {the corresponding color handle}[0.791, 0.060, 0.826, 0.068]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 523 }, { "id": "RywVQfsQ5hUl7wnn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value, {the bar for \"100\" in the \"Medieval Ages\"}[0.452, 0.145, 0.520, 0.178] or {the bar for \"100\" in the \"Modern Age\"}[0.452, 0.132, 0.520, 0.178]?" }, { "from": "gpt", "value": "Medieval Ages." } ], "id_raw": 523 }, { "id": "EynUgA2f8Yk5IpoH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the color of legends for \"Stone Age\"}[0.791, 0.043, 0.826, 0.051] match {the color of the bars for the \"Stone Age\" at \"20\"}[0.116, 0.038, 0.183, 0.178]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 523 }, { "id": "kKkjraXUA12jsESt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the region between [0.784, 0.432, 0.973, 0.523]." }, { "from": "gpt", "value": "The legend for the Iron Age is located in this region, and it includes category labels like \"Stone Age\", \"Bronze Age\", \"Iron Age\", \"Medieval Ages\", and \"Modern Age\". The handle for the Iron Age, specifically, is in color/r>[0.791, 0.473, 0.826, 0.481]." } ], "id_raw": 523 }, { "id": "Oz3nxIAS4c8usMrN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area from [0.075, 0.031, 0.981, 0.178]." }, { "from": "gpt", "value": "This section represents the \"Stone Age\" subplot including bars representing different values at intervals from 20 to 200. The color of the bar for \"100\" is/r>[0.791, 0.043, 0.826, 0.051] which matches the legend color indication for Stone Age." } ], "id_raw": 523 }, { "id": "CydiParoCmkQhMKE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within [0.075, 0.426, 0.981, 0.573]?" }, { "from": "gpt", "value": "This area contains the \"Iron Age\" subplot. It includes bars reflecting values for \"20\" to \"200\", x-ticks, y-ticks, and a subtitle in/r>[0.485, 0.407, 0.571, 0.422]. The corresponding legend for this age is also included." } ], "id_raw": 523 }, { "id": "jOWv53K3ua9clhUe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.127, 0.039, 0.824, 0.223] in the chart?" }, { "from": "gpt", "value": "It is a bar representing USA's Middle Tier." } ], "id_raw": 527 }, { "id": "XEUEXIC3yjoQvjq4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nWhat color does the legend associate with the label 'USA'?" }, { "from": "gpt", "value": "{Color for 'USA'}[0.361, -0.083, 0.396, -0.071] is '#ff6347' or 'tomato'." } ], "id_raw": 527 }, { "id": "vNf3eSfK2IvePzIS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of label 'Germany' in the upper tier {[0.127, 0.269, 0.708, 0.452]} according to the chart?" }, { "from": "gpt", "value": "50." } ], "id_raw": 527 }, { "id": "QTLmWobZIWlAiKyZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nIs the 'USA' bar for the 'Upper Tier'[0.127, 0.269, 0.592, 0.452] higher than the 'Germany' bar for the 'Upper Tier'[0.127, 0.748, 0.708, 0.931]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 527 }, { "id": "3ACKIZk3hrgriTCT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nIn the 'Middle Tier' are the bars for 'USA'[0.127, 0.518, 0.941, 0.702] and 'Germany'[0.127, 0.518, 0.941, 0.702] at the same height?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 527 }, { "id": "bSswf40OL7vOgLCW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nIs the 'Canada' bar in the 'Upper Tier'[0.127, 0.748, 0.650, 0.931] shorter than the 'UK' bar in the 'Upper Tier'[0.127, 0.748, 0.708, 0.931]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 527 }, { "id": "hVDW2wlpLsiAzrDT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.127, 0.518, 0.941, 0.702]." }, { "from": "gpt", "value": "It contains bars for 'USA', 'Canada', 'UK' and 'Germany' for 'Middle Tier'. The bars are respectively titled 'USA', 'Canada', 'UK' and 'Germany' and the bars have different lengths indicating different values for these countries." } ], "id_raw": 527 }, { "id": "hLA82rL5l0m39RYu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the area with coordinates [0.354, -0.091, 0.646, -0.008]?" }, { "from": "gpt", "value": "The plot legend is located in these coordinates. The legend includes five items which correspond to different labels: 'USA', 'Canada', 'UK', 'Germany' and 'France'. Each item in the legend includes a color symbol and a corresponding text label. The colors are '#ff6347' for USA, '#f5deb3' for Canada, '#81acce' for UK, '#8fbc8f' for Germany, and '#191970' for France." } ], "id_raw": 527 }, { "id": "96lcjnGSPR1IKCC9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region bounded by [0.127, 0.039, 0.824, 0.223]." }, { "from": "gpt", "value": "This region contains bars representing the Middle Tier for the countries USA, Canada, UK, Germany, and France. Each bar corresponds to a different value, with the width of the bar indicating the value for each country." } ], "id_raw": 527 }, { "id": "yztePHLUsFnkPmVS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the legend label of \"steady_increase\"}[0.349, -0.067, 0.461, -0.053] correspond to?" }, { "from": "gpt", "value": "#ff6347" } ], "id_raw": 528 }, { "id": "CXoitEVk8XeHKBuA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-range for the bar labeled \"Cinema\" in the category of {rapid expansion}[0.113, 0.036, 0.694, 0.125]?" }, { "from": "gpt", "value": "0.00 -> 280.00" } ], "id_raw": 528 }, { "id": "sw6l6qx5Kpn5Y118", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nWhich label is associated with {the yellow colored handle}[0.310, -0.045, 0.338, -0.035] in the legend?" }, { "from": "gpt", "value": "inexorable_rise" } ], "id_raw": 528 }, { "id": "sepH7FANGGm3gOF6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nAre {theatre bars in subplot 1}[0.113, 0.741, 0.611, 0.829] higher than {dance bars in subplot 1}[0.113, 0.852, 0.736, 0.940]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 528 }, { "id": "rqdqXQEapL6HtTI4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for \"Literature\" under \"steady_increase\"}[0.113, 0.147, 0.943, 0.236] longer than the {bar for \"Literature\" under \"inexorable_rise\"}[0.113, 0.147, 0.238, 0.236]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 528 }, { "id": "t324xytaPuOeJTgL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {green bar for \"Dance\"}[0.113, 0.852, 0.736, 0.940] reach higher levels than the {blue bar for \"Dance\"}[0.113, 0.852, 0.269, 0.940]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 528 }, { "id": "jbZUSqXvilM19q64", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside the section [0.113, 0.036, 0.653, 0.125]." }, { "from": "gpt", "value": "It contains a bar for \"Cinema\" under the label \"steady_increase\" with a range shown as \"0.00 -> 260.00\" for the red colored bar." } ], "id_raw": 528 }, { "id": "DyFm3X7JQ0kPXnIw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.113, 0.630, 0.736, 0.719] in the chart." }, { "from": "gpt", "value": "This area includes a bar for \"Photography\" reflecting data under the label \"steady_increase\" colored in red, indicating a range of \"0.00 -> 300.00\"." } ], "id_raw": 528 }, { "id": "h4piFkSqJY5PSwYg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the quadrant [0.113, 0.852, 0.591, 0.940]?" }, { "from": "gpt", "value": "It represents a bar graph segment for the category \"Dance,\" under \"steady_increase\" which is color-coded in red, with an x-range measuring from \"0.00 -> 230.00.\"" } ], "id_raw": 528 }, { "id": "WodwfR8bAM1dk8jO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nWhat behavior is the color '#6d98ba' associated with in the chart?" }, { "from": "gpt", "value": "{Slow Recovery}[0.582, -0.084, 0.709, -0.067]." } ], "id_raw": 529 }, { "id": "Abyt0iUGzxFxMUCO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nWhere is 'Canada' label located?" }, { "from": "gpt", "value": "{Canada}[0.032, 0.772, 0.099, 0.790]." } ], "id_raw": 529 }, { "id": "2RzA2bmezR2EMV7G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nWhat value, associated with 'Germany', has the color '#c1d37f'?" }, { "from": "gpt", "value": "{Unstable Behavior}[0.111, 0.627, 0.280, 0.714]." } ], "id_raw": 529 }, { "id": "har4ng2i52Xo7nW9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nWhich region is associated with the label 'USA'?" }, { "from": "gpt", "value": "{USA}[0.063, 0.881, 0.099, 0.898]" } ], "id_raw": 529 }, { "id": "J0AoFSFzqpjgxX7x", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nIs {the USA bar of Unstable Behavior}[0.111, 0.844, 0.781, 0.931] thicker than {the USA bar of Slow Recovery}[0.111, 0.844, 0.922, 0.931]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 529 }, { "id": "8mH2gVAwjAGvaCXR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nIs {Canada's Unstable Behavior}[0.111, 0.735, 0.126, 0.822] value less than {France's Unstable Behavior}[0.111, 0.518, 0.256, 0.605]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 529 }, { "id": "grVW4IQvQDeMxaYk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nIs the label 'Unstable Behavior' at the left or right of 'Slow Recovery'?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 529 }, { "id": "oYUshIvaVRjtu80S", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nCompared to Germany's {Unstable Behavior}[0.111, 0.627, 0.280, 0.714], is its {Slow Recovery}[0.111, 0.627, 0.280, 0.714] equal, lower or higher?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 529 }, { "id": "viPpivdwIC5mdX8V", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nWhat is showing in the coordinates [0.111, 0.257, 0.126, 0.344]?" }, { "from": "gpt", "value": "There is a single bar representing 'Canada' for the scenario 'Unstable Behavior' with a value of approximately 55." } ], "id_raw": 529 }, { "id": "KL3X1FEOfnusIAoj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area [0.105, 0.964, 0.415, 0.981]?" }, { "from": "gpt", "value": "There are three numerical labels located on the x axis, which correspond to the numerical values 0, 500 and 1000 respectively." } ], "id_raw": 529 }, { "id": "2MMSGh4XHveTi4Dj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the area [0.284, -0.091, 0.716, -0.060]?" }, { "from": "gpt", "value": "It includes the legend of the graph displaying two color-text pairs. First pair includes a color label with color='#c1d37f' representing 'Unstable Behavior' and another pair includes a color label with color='#6d98ba' corresponding to 'Slow Recovery'" } ], "id_raw": 529 }, { "id": "heBFJS5DS7RftJDW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the information displayed in coordinates [0.111, 0.844, 0.922, 0.931]." }, { "from": "gpt", "value": "There are 2 bars representing two different behaviors for the country 'USA'. The grey bar with a range of 0.00->2370.00 represents 'Unstable Behavior', while the blue bar with a range of 0.00->2870.00 represents 'Slow Recovery'." } ], "id_raw": 529 }, { "id": "VrBjX2DCauItqMnb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}[0.157, 0.353, 0.942, 0.430] in this chart?" }, { "from": "gpt", "value": "It is the Heart Disease cases in California." } ], "id_raw": 530 }, { "id": "gl0uQQT0WgW15d9a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value written at [0.495, 0.461, 0.643, 0.479]?" }, { "from": "gpt", "value": "It says \"Number of Cases\", which is the label for the x-axis." } ], "id_raw": 530 }, { "id": "xEx9sYR6vYKQc08N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the horizontal line} at [0.271, 0.448]?" }, { "from": "gpt", "value": "It is an x-axis tick representing the value of 500 cases." } ], "id_raw": 530 }, { "id": "Yf6bZfIPzBwHVNB6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the information about the health conditions found at [0.157, 0.65, 0.817, 0.75]?" }, { "from": "gpt", "value": "It signifies the Heart Disease cases in {Georgia}[0.076, 0.671, 0.145, 0.689] with a value of 2500 cases." } ], "id_raw": 530 }, { "id": "ldgPVHKEvo02LHzG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nIs {Heart Disease cases in Georgia}[0.157, 0.640, 0.277, 0.717] higher or lower than {Heart Disease cases in California}[0.157, 0.353, 0.942, 0.430]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 530 }, { "id": "orTMMB7UBBtphCVX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher, {the Cancer cases in Ohio}[0.157, 0.736, 0.259, 0.813] or {the Cancer cases in North Carolina}[0.157, 0.544, 0.314, 0.621]?" }, { "from": "gpt", "value": "North Carolina." } ], "id_raw": 530 }, { "id": "PDWBzDgE5pnihaiU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nIs {Heart Disease in New York}[0.157, 0.065, 0.817, 0.142] lesser than {Heart Disease in Texas}[0.157, 0.257, 0.783, 0.334]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 530 }, { "id": "JULalgWMPYUdwwaH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nCompared to {Cancer in Illinois}[0.157, 0.832, 0.282, 0.909], is the {Heart Disease in Texas}[0.157, 0.257, 0.783, 0.334] higher or lower?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 530 }, { "id": "mbbtZQP1ckP1zJdZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content seen in [0.157, 0.544, 0.317, 0.621]." }, { "from": "gpt", "value": "It is a bar representing the number of Cancer cases in North Carolina which is 690 cases." } ], "id_raw": 530 }, { "id": "24r689cMbR92YgnA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nKindly describe the features found at [0.065, 0.096, 0.145, 0.114]." }, { "from": "gpt", "value": "It is the text label for New York on the y-axis of the chart." } ], "id_raw": 530 }, { "id": "hI7st5pwLBH1oo44", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.157, 0.257, 0.783, 0.334] consist of?" }, { "from": "gpt", "value": "This region contains a bar that signifies the Heart Disease cases in Texas, which is 2750 cases." } ], "id_raw": 530 }, { "id": "iocZGSW8dWdY817S", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the items located at [0.850, 0.534, 0.973, 0.566]." }, { "from": "gpt", "value": "It is a legend label denoting the color '#f5deb3' represents Cancer." } ], "id_raw": 530 }, { "id": "37IMNtJaU7gVCsBQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {bar representing \"Cancer\"}[0.157, 0.544, 0.314, 0.621]?" }, { "from": "gpt", "value": "#f5deb3." } ], "id_raw": 531 }, { "id": "bn0pUBY9M3QQHq3G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the bar representing \"Cancer\"}[0.157, 0.544, 0.314, 0.621] for the state \"North Carolina\"?" }, { "from": "gpt", "value": "690.00 cases." } ], "id_raw": 531 }, { "id": "QJaZ2jYdwL0Pv65S", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the upper bar chart?" }, { "from": "gpt", "value": "It does not have a label." } ], "id_raw": 531 }, { "id": "65DikfM8Oum7Pqel", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher number of cases in {New York}, {Heart Disease}[0.157, 0.065, 0.817, 0.142] or {Cancer}[0.157, 0.544, 0.314, 0.621]?" }, { "from": "gpt", "value": "Heart Disease." } ], "id_raw": 531 }, { "id": "PhlWGVLH7jMFENJa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nWhich health condition, {Heart Disease}[0.157, 0.065, 0.817, 0.142] or {Cancer}[0.157, 0.544, 0.314, 0.621], has a larger case count in the {state of Illinois}[0.157, 0.832, 0.282, 0.909]?" }, { "from": "gpt", "value": "Cancer." } ], "id_raw": 531 }, { "id": "n6KYSLxrFPUuOSig", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nIs the {state of Florida's Heart Disease count}[0.157, 0.161, 0.680, 0.238] larger than {Illinois' Cancer number}[0.157, 0.832, 0.282, 0.909]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 531 }, { "id": "vyEC1yPESsyeajqd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the bar chart that's represented within the region [0.157, 0.526, 0.981, 0.927]." }, { "from": "gpt", "value": "It looks like a horizontal bar chart that is showing the number of cancer cases in various states. There are four bars in the chart, corresponding to the states North Carolina, Georgia, Ohio, and Illinois. The bar for Illinois has the highest value at 550.00 cases, with coordinates located at [0.157, 0.832, 0.282, 0.909]." } ], "id_raw": 531 }, { "id": "PEl5XrBjuDb8OmdU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nWhat is evident from the region [0.157, 0.047, 0.981, 0.448]?" }, { "from": "gpt", "value": "In this region, there is a horizontal dataset representing the incidence of Heart Disease in four states: New York, Florida, Texas, and California. The state with the highest number of Heart disease cases is California with 3450.00 cases, and its coordinates are [0.157, 0.353, 0.942, 0.430]." } ], "id_raw": 531 }, { "id": "ds4iVcPw5oKuGJQx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the items present within the region [0.849, 0.534, 0.973, 0.566]." }, { "from": "gpt", "value": "This region represents the legend of the bar chart. There is one item, represented as a rectangle of color #f5deb3 with its coordinates being [0.856, 0.543, 0.890, 0.555]. Next to it is the text \"Cancer\", which is the label for the data values presented in the form of the bar chart." } ], "id_raw": 531 }, { "id": "RZu3uYtN61OSyU0d", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the blue bar at coordinates [0.094, 0.039, 0.573, 0.223] represent?" }, { "from": "gpt", "value": "It represents the Adult Literacy Rate for females." } ], "id_raw": 533 }, { "id": "eH25QWxMvb9VcbAh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at coordinates [0.353, -0.134, 0.554, -0.117]?" }, { "from": "gpt", "value": "It is the label of the legend: Adult Literacy Rate (%)." } ], "id_raw": 533 }, { "id": "65psWb6T5Fix9jWw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the orange bar at [0.573, 0.518, 0.654, 0.702] in the chart?" }, { "from": "gpt", "value": "It represents the Youth Unemployment Rate for females." } ], "id_raw": 533 }, { "id": "zWn9t0wXcKS3CPUA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the green bar at [0.654, 0.518, 0.926, 0.702] refer to?" }, { "from": "gpt", "value": "It represents the Tertiary Education Completion Rate for females." } ], "id_raw": 533 }, { "id": "c2yxn0AQugjSdr95", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nIs the {bar representing the Tertiary Education Completion Rate for males}[0.654, 0.748, 0.899, 0.931] bigger or smaller than the {bar representing the Tertiary Education Completion Rate for females}[0.654, 0.518, 0.926, 0.702]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 533 }, { "id": "mSvwtmMKfj15HDRI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing the Youth Unemployment Rate for females}[0.573, 0.518, 0.654, 0.702] located at the left or right of {the bar representing the Adult Literacy Rate for females}[0.094, 0.039, 0.573, 0.223]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 533 }, { "id": "AxHP8yoeX1Vh1cWA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the bar indicating Adult Literacy Rate for males}[0.094, 0.269, 0.594, 0.452] or {the bar indicating Youth Unemployment Rate for males}[0.594, 0.748, 0.654, 0.931]?" }, { "from": "gpt", "value": "The bar indicating Adult Literacy Rate for males." } ], "id_raw": 533 }, { "id": "Jf9hxaMv97OYpoiJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar representing Tertiary Education Completion Rate for males}[0.654, 0.748, 0.899, 0.931] have the same, higher or lower value than {the bar representing Youth Unemployment Rate for males}[0.594, 0.748, 0.654, 0.931]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 533 }, { "id": "wozrbLnAQdm8i5EJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents in the coordinates [0.297, -0.141, 0.703, -0.058]." }, { "from": "gpt", "value": "This area contains the chart legend. The legend has three entries. The first entry with a blue color label located at [0.304, -0.133, 0.339, -0.121] represents the Adult Literacy Rate (%). The second entry with an orange color label located at [0.304, -0.107, 0.339, -0.094] represents the Youth Unemployment Rate (%). The third entry with a green color label located at [0.304, -0.080, 0.339, -0.068] represents the Tertiary Education Completion Rate (%)." } ], "id_raw": 533 }, { "id": "uEt0OhgEiKWnZyE2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.094, 0.498, 0.968, 0.952]." }, { "from": "gpt", "value": "This region contains six bars and two y-axis labels. The bars represent the Adult Literacy Rate, Youth Unemployment Rate, and Tertiary Education Completion Rate for both males and females. The y-axis labels correspond to the categories 'Male' and 'Female'." } ], "id_raw": 533 }, { "id": "x91jt8PUm1nAtILo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat is described in the region [0.094, 0.019, 0.964, 0.473]?" }, { "from": "gpt", "value": "This area contains the same elements as the area [0.094, 0.498, 0.968, 0.952], also including two y-axis Male and Female labels and six bars which represent the Adult Literacy Rate, Youth Unemployment Rate, and Tertiary Education Completion Rate for both genders." } ], "id_raw": 533 }, { "id": "0Ajxla8smibEmhza", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat occupy the area [0.094, 0.039, 0.573, 0.702]?" }, { "from": "gpt", "value": "These coordinates correspond to a cluster of three vertical bars in the chart. The blue bar represents the Female's Adult Literacy Rate, the orange bar represents the Female's Youth Unemployment Rate, and the green bar represents the Female's Tertiary Education Completion Rate." } ], "id_raw": 533 }, { "id": "BqH4APFDE6koADaE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information does the legend show from the coordinates [0.144, -0.091, 0.856, -0.060]?" }, { "from": "gpt", "value": "The legend indicates two types of business indicators: {High Tech Startups (in 000s)}[0.200, -0.084, 0.445, -0.067] and {New Business Registrations (in 000s)}[0.528, -0.084, 0.849, -0.067]." } ], "id_raw": 534 }, { "id": "Ryyp0VnVWesInBvO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the country that corresponds to the ytick at coordinates [0.061, 0.555, 0.099, 0.572]?" }, { "from": "gpt", "value": "The country is Italy." } ], "id_raw": 534 }, { "id": "kthfVSOItfzyz0GF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of High Tech Startups for Germany present at [0.111, 0.365, 0.118, 0.452]?" }, { "from": "gpt", "value": "The value is 51,000." } ], "id_raw": 534 }, { "id": "aj1mK1oKxBgkAWh3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.113, 0.844, 0.595, 0.931]?" }, { "from": "gpt", "value": "This is the bar representing New Business Registrations (in 000s) for the USA." } ], "id_raw": 534 }, { "id": "tbI5lLZLYiZJWbDN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nWhich is higher: the number of High Tech Startups for Canada at [0.111, 0.257, 0.117, 0.344] or France at [0.111, 0.627, 0.113, 0.714]?" }, { "from": "gpt", "value": "Canada." } ], "id_raw": 534 }, { "id": "PUeGfXWD5rkPPRGH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nIs the number of High Tech Startups greater than New Business Registrations for the USA?" }, { "from": "gpt", "value": "No. The former is located at {the USA's High Tech Startups bar}[0.111, 0.844, 0.116, 0.931], and the latter is at {the USA's New Business Registrations bar}[0.116, 0.844, 0.595, 0.931]." } ], "id_raw": 534 }, { "id": "qZgmZf8ESEBKBZbA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nCompare the bar at coordinates [0.113, 0.627, 0.683, 0.714] with the bar at [0.111, 0.735, 0.114, 0.822]. Which bar is wider?" }, { "from": "gpt", "value": "The bar at [0.113, 0.627, 0.683, 0.714] is wider, indicating France's New Business Registrations." } ], "id_raw": 534 }, { "id": "N88THcRqmNdPvfzn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what you see in the region [0.111, 0.498, 0.981, 0.952]." }, { "from": "gpt", "value": "This region contains bars that show the number of High Tech Startups and New Business registrations for countries such as Italy at {Italian bars}[0.111, 0.518, 0.638, 0.605], France at {French bars}[0.111, 0.627, 0.683, 0.714], the UK at {UK bars}[0.111, 0.735, 0.729, 0.822], and the USA at {USA bars}[0.111, 0.844, 0.595, 0.931]." } ], "id_raw": 534 }, { "id": "wVkUIebvWPbck523", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located in the region [0.144, -0.091, 0.856, -0.060]." }, { "from": "gpt", "value": "This region comprises the legend of the chart, which includes two categories, {High Tech Startups (in 000s)}[0.200, -0.084, 0.445, -0.067] colored in #ff6347 and {New Business Registrations (in 000s)}[0.528, -0.084, 0.849, -0.067] colored in #81acce." } ], "id_raw": 534 }, { "id": "QfPC1UXl9xgNrIoH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.111, 0.019, 0.981, 0.473]?" }, { "from": "gpt", "value": "This region contains a bar plot that depicts High Tech Startups and New Business Registrations for several countries including Japan at {Japanese bars}[0.111, 0.039, 0.940, 0.126], Australia at {Australian bars}[0.111, 0.148, 0.642, 0.235], Canada at {Canadian bars}[0.111, 0.257, 0.795, 0.344], and Germany at {German bars}[0.111, 0.365, 0.878, 0.452]." } ], "id_raw": 534 }, { "id": "OqSGra3UipcOPAku", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the {legend}[0.149, -0.091, 0.851, -0.034]?" }, { "from": "gpt", "value": "The legend contains three items: {Social Media Active Users (millions)}[0.205, -0.084, 0.513, -0.067], {Digital Advertising Spending (US$ billions)}[0.205, -0.058, 0.573, -0.041], and {Email Users (millions)}[0.656, -0.084, 0.844, -0.067]." } ], "id_raw": 535 }, { "id": "kEXFW02dTa6IyGUr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-coordinate range for the entry {Facebook}[0.026, 0.813, 0.107, 0.831] in the Y-axis scale?" }, { "from": "gpt", "value": "0.026 to 0.107." } ], "id_raw": 535 }, { "id": "JCEEw7xi1cx5JMgr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-value for {Email Users (millions)}[0.656, -0.084, 0.844, -0.067] for Facebook according to the chart?" }, { "from": "gpt", "value": "6700." } ], "id_raw": 535 }, { "id": "FNPQ1ZNjokCjtuX7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nIs the bar {Instagram's 'Email Users'}[0.349, 0.061, 0.640, 0.240] longer than the bar {Facebook's 'Email Users'}[0.383, 0.731, 0.749, 0.909]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 535 }, { "id": "kT0cmuRd3ByVV09y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nHow does the spending on {Digital Advertising}[0.205, -0.058, 0.573, -0.041] for {WhatsApp}[0.355, 0.284, 0.404, 0.463] compare with that for {Twitter}[0.303, 0.507, 0.345, 0.686]?" }, { "from": "gpt", "value": "WhatsApp's spending on digital advertising was higher than Twitter's." } ], "id_raw": 535 }, { "id": "kQg3tayx0MDFzo5r", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nAre the {active social media users}[0.205, -0.084, 0.513, -0.067] for {Twitter}[0.120, 0.507, 0.303, 0.686] more than those for {Instagram}[0.120, 0.061, 0.284, 0.240]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 535 }, { "id": "tjtAkWfW2cy4DRmc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is inside this region [0.120, 0.507, 0.428, 0.686]." }, { "from": "gpt", "value": "The region includes two bars. One for {Social Media Active Users}[0.120, 0.507, 0.303, 0.686] in Twitter, which is from 0 to 1950 millions. The other is for {Digital Advertising Spending}[0.303, 0.507, 0.345, 0.686] in Twitter, ranging from 1950 to 2400 billion US dollars." } ], "id_raw": 535 }, { "id": "qGKa4pl4cHijl9sN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is within the x-axis range [0.120, 0.019, 0.981, 0.952]?" }, { "from": "gpt", "value": "The x-axis range includes all the bars and labels related to the three different sets of data: Social Media Active Users, Digital Advertising Spending, and Email Users from 0 to 10000 for all four platforms: Instagram, WhatsApp, Twitter, and Facebook." } ], "id_raw": 535 }, { "id": "zTNcwKwwZzb44KyA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.345, 0.507, 0.428, 0.686]." }, { "from": "gpt", "value": "It contains a part of Twitter's data, the {bar}[0.345, 0.507, 0.428, 0.686] represents the 'Digital Advertising Spending' for Twitter, which ranges from 2400 to about 2450 billion US dollars." } ], "id_raw": 535 }, { "id": "ZHyetZ1psHwjRvfZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the rectangle representing 'WhatsApp' Social Media Active Users (millions) in the chart?" }, { "from": "gpt", "value": "Tomato color, denoted as '#ff6347'." } ], "id_raw": 536 }, { "id": "PQcw25TfjCH5lpD2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat legend does the color '#81acce' correspond to?" }, { "from": "gpt", "value": "\"Email Users (millions)\"." } ], "id_raw": 536 }, { "id": "DS6HZozsH42qj2rB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the {social media platform} at [0.019, 0.367, 0.107, 0.384] signify?" }, { "from": "gpt", "value": "{\"WhatsApp\"} [0.019, 0.367, 0.107, 0.384] " } ], "id_raw": 536 }, { "id": "3YeqINK8nEc4Y8vr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Email Users of Twitter}[0.345, 0.507, 0.428, 0.686] wider than {the bar representing Digital Advertising Spending of Twitter}[0.303, 0.507, 0.345, 0.686]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 536 }, { "id": "KqPWMr7Cr2ZMNMgs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nAmong {Instagram bars}<[0.120, 0.061, 0.284, 0.240], [0.284, 0.061, 0.349, 0.240], [0.349, 0.061, 0.640, 0.240]>, which one represents the 'Email Users (millions)'?" }, { "from": "gpt", "value": "{the last bar}[0.349, 0.061, 0.640, 0.240]." } ], "id_raw": 536 }, { "id": "vZwfCZBU6td6a9ps", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nWhich is larger, the {number of Social Media Active Users of WhatsApp}[0.120, 0.284, 0.355, 0.463] or {the number of Email Users of WhatsApp}[0.404, 0.284, 0.940, 0.463]?" }, { "from": "gpt", "value": "Email Users." } ], "id_raw": 536 }, { "id": "nRobn5nXD2rx3HLN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nOf {Email Users}[0.356, 0.507, 0.428, 0.686] and {Digital Advertising Spending}[0.303, 0.507, 0.345, 0.686] which Twitter bar is longer?" }, { "from": "gpt", "value": "Email Users." } ], "id_raw": 536 }, { "id": "Z4jAvjHaKljg8Sl5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.120, 0.061, 0.640, 0.240]." }, { "from": "gpt", "value": "The area represents the different statistics for Instagram, including \"Social Media Active Users (millions)\" represented by {a tomato colored bar}[0.120, 0.061, 0.284, 0.240], \"Digital Advertising Spending (US$ billions)\" represented by {a wheat colored bar}[0.284, 0.061, 0.349, 0.240], and \"Email Users (millions)\" represented by {a light steel blue bar}[0.349, 0.061, 0.640, 0.240]." } ], "id_raw": 536 }, { "id": "qiyhomhjj4BC7pF5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.355, 0.284, 0.940, 0.463] contain?" }, { "from": "gpt", "value": "This region features two bars representing the \"Digital Advertising Spending (US$ billions)\" and \"Email Users (millions)\" for the \"WhatsApp\" platform. The first bar is {a wheat-colored bar}[0.355, 0.284, 0.404, 0.463] representing \"Digital Advertising Spending (US$ billions)\", and the second bar is {a light steel blue colored bar}[0.404, 0.284, 0.940, 0.463] represents \"Email Users (million)\"." } ], "id_raw": 536 }, { "id": "ERIDJv0AEfDtwoZY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region from [0.284, 0.061, 0.640, 0.240]?" }, { "from": "gpt", "value": "This region belongs to Instagram and contains two bar segments representing two categories of data. The first bar segment is colored wheat [0.284, 0.061, 0.349, 0.240], representing \"Digital Advertising Spending (US$ billions)\". The second bar segment is light steel blue [0.349, 0.061, 0.640, 0.240], signifying \"Email Users (millions)\"." } ], "id_raw": 536 }, { "id": "4eBQxSOKHDeEykEE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color represented by the handle associated with the \"AR Trend\" label?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 537 }, { "id": "AbXzAZzz2OiHlwTR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the year mentioned at {the first tick mark on the y-axis}[0.019, 0.067, 0.063, 0.084]?" }, { "from": "gpt", "value": "2022." } ], "id_raw": 537 }, { "id": "HS9oRKrwMSxOj1xH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nWhich label is located at {coordinates}[0.297, 0.906, 0.357, 0.930] in the chart?" }, { "from": "gpt", "value": "Nutrition." } ], "id_raw": 537 }, { "id": "kd2yIdHVLNiBwCMh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 2024 in the VR Trend series}[0.075, 0.211, 0.328, 0.280] taller than {the bar for 2023 in the AR Trend series}[0.233, 0.125, 0.427, 0.194]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 537 }, { "id": "DkFIS06NtDn42IZH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nIs the value at {the base of the second y-axis tick mark}[0.075, 0.160] greater than the value at {the base of the fourth y-axis tick mark}[0.075, 0.332]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 537 }, { "id": "UfFVQrxD7eJYSqqy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nAre {the coordinates of the VR Trend color handle}[0.357, -0.083, 0.392, -0.071] on the left or right of {the AR Trend color handle}[0.517, -0.083, 0.552, -0.071]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 537 }, { "id": "HRzAiJoeVmTnHoL5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the visual elements found in the range [0.075, 0.019, 0.981, 0.473]." }, { "from": "gpt", "value": "The area includes the x-axis, y-axis, several vertical bars corresponding to VR Trends from years 2022 to 2026, and an accompanying grid. The colors used for the bars vary from shades of blue to green per different years." } ], "id_raw": 537 }, { "id": "dkCcLEXiEMzayeii", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region defined by [0.075, 0.518, 0.938, 0.931]?" }, { "from": "gpt", "value": "This region encompasses VR Trend and AR Trend bars for each year from 2022 to 2026, showing respective values ascending from left to right across the plot. It also includes y-axis labels for each year and corresponding bars extending to indicate the values for each trend." } ], "id_raw": 537 }, { "id": "nYwEcuubiKi5hz90", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.258, 0.498, 0.442, 0.952] contain?" }, { "from": "gpt", "value": "The region contains part of the x-axis with tick labels, a gridline, and vertical bars representing the values for \"500\" on the x-axis scale across different years for VR and AR Trends." } ], "id_raw": 537 }, { "id": "NYba2VEgUAHgy8er", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar labeled \"Population Growth (M)\" for India in {the upper chart}[0.137, 0.498, 0.981, 0.952]?" }, { "from": "gpt", "value": "Dark blue." } ], "id_raw": 538 }, { "id": "mfXVj30qLyILRt2q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nWhich country corresponds to {the largest GDP per Capita bar}[0.937, 0.452] in the lower chart?" }, { "from": "gpt", "value": "China." } ], "id_raw": 538 }, { "id": "ACreLWWRtSpUkvuf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for {the lower chart}[0.137, 0.498, 0.981, 0.952]?" }, { "from": "gpt", "value": "No label is prescribed for the y-axis." } ], "id_raw": 538 }, { "id": "bomt8pAEzb851VeF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Brazil}[0.137, 0.039, 0.271, 0.223] in the upper chart wider than {the bar for India}[0.137, 0.518, 0.922, 0.702] in the lower chart?" }, { "from": "gpt", "value": "No." } ], "id_raw": 538 }, { "id": "BwrK70vNYoJGvnEP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the blue bar for Unemployment Rate}[0.262, 0.039, 0.269, 0.223] have a higher value range than {the gray bar for Healthcare Expenditure}[0.269, 0.039, 0.271, 0.223] in Brazil?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 538 }, { "id": "bkhz44LWx7560OKQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nDoes {India's GDP bar in the upper chart}[0.137, 0.518, 0.918, 0.702] end before {India's GDP bar in the lower chart}[0.916, 0.518, 0.918, 0.702]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 538 }, { "id": "XZBPsvLMRn2H5jV8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the section in the upper chart that indicates Brazil's economic details at {in the range}[0.137, 0.039, 0.271, 0.223]." }, { "from": "gpt", "value": "This range includes multiple bars representative of Brazil's statistics, including Population Growth colored in dark blue, GDP per Capita in royal blue, Unemployment Rate in sky blue, and Healthcare Expenditure in light blue." } ], "id_raw": 538 }, { "id": "6GfDOeEdWlfnEXQY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the section in [0.137, 0.498, 0.981, 0.952] that relates to unemployment rates." }, { "from": "gpt", "value": "This section contains bars representing the unemployment rates for India and South Africa, each colored in sky blue. The bar for India spans from the GDP values to the unemployment rate at 1373.60." } ], "id_raw": 538 }, { "id": "q1Mh2dL6SI9vexBU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat information does the left half of {the complete chart}[0.137, 0.019, 0.981, 0.952] display?" }, { "from": "gpt", "value": "It primarily shows Population Growth data for all pictured countries (Brazil, China, India, South Africa) represented by dark blue bars, starting from no growth to each country's specific population increase." } ], "id_raw": 538 }, { "id": "KpwmmCrMp1vjIyeb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object with coordinates [0.214, -0.084, 0.534, -0.067]?" }, { "from": "gpt", "value": "AI Research Funding (in billions USD)." } ], "id_raw": 539 }, { "id": "0bJfJCI0Trqdh9XZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nWhich specific color does the legend element at [0.569, -0.083, 0.604, -0.071] represent?" }, { "from": "gpt", "value": "#00bfff." } ], "id_raw": 539 }, { "id": "6obEjHrhg5NggIsx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {ytick label} at [0.050, 0.076, 0.099, 0.093]?" }, { "from": "gpt", "value": "Japan." } ], "id_raw": 539 }, { "id": "97pO4267guuWx0tR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar representing in [0.111, 0.844, 0.112, 0.931]?" }, { "from": "gpt", "value": "{This bar}[0.111, 0.844, 0.112, 0.931] represents \"USA\" for \"AI Research Funding (in billions USD)\" in the first subplot." } ], "id_raw": 539 }, { "id": "VcWGVjwbkHLLcJTf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nIs {AI Research Funding for Germany}[0.111, 0.365, 0.111, 0.452] greater than {AI Research Funding for Canada}[0.111, 0.257, 0.111, 0.344]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 539 }, { "id": "ManjoGYj2Pj5CYVc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nAre {Number of AI Companies in China}[0.111, 0.735, 0.848, 0.822] higher than {Number of AI companies in France}[0.111, 0.627, 0.525, 0.714]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 539 }, { "id": "ELyioKeBXzdypsjs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nDoes {AI Research Funding for Italy}[0.111, 0.518, 0.111, 0.605] exceed {AI Research Funding for France}[0.111, 0.627, 0.111, 0.714]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 539 }, { "id": "xEG5i2Fo27An7sjD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nAmong {Number of AI Companies in the USA}[0.112, 0.844, 0.940, 0.931] and {Number of AI Companies in Italy}[0.111, 0.518, 0.341, 0.605], which one has more AI companies?" }, { "from": "gpt", "value": "USA." } ], "id_raw": 539 }, { "id": "XV232YPtj8dbdgMA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.159, -0.091, 0.841, -0.060]." }, { "from": "gpt", "value": "There are two components in the legend. First part is AI Research Funding (in billions USD) denoted by color #dda0dd, located at {legend color box}[0.166, -0.083, 0.200, -0.071] and {legend text}[0.214, -0.084, 0.534, -0.067]. The second part denotes the number of AI companies and is represented by color #00bfff, located at {legend color box}[0.569, -0.083, 0.604, -0.071] and {legend text}[0.618, -0.084, 0.834, -0.067]." } ], "id_raw": 539 }, { "id": "H2I45ClPaGNEAFhk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.111, 0.019, 0.981, 0.473]." }, { "from": "gpt", "value": "In the region [0.111, 0.019, 0.981, 0.473], there are two sets of bar graphs for four countries - Japan, Australia, Canada, and Germany. The first set represents AI Research funding, and the second set represents the number of AI Companies." } ], "id_raw": 539 }, { "id": "0qGQO62tHeC9kDqx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are there in the range from [0.111, 0.498, 0.981, 0.952]?" }, { "from": "gpt", "value": "This region encompasses the second subplot displays two sets of bar graphs for four countries - Italy, France, China, and USA. Two types of figures represented are: AI Research Funding and the Number of AI Companies." } ], "id_raw": 539 }, { "id": "EukGPSoI0AwUIrW9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the objects in the region [0.111, 0.039, 0.286, 0.126]?" }, { "from": "gpt", "value": "The coordinates correspond to the bars for 'Japan' in the first subplot: one pink bar and one blue bar. The pink bar represents the AI Research Funding, and the blue bar represents the Number of AI companies." } ], "id_raw": 539 }, { "id": "luHuvB05s7T2KnCk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object labeled {the \"Risk Factor\"}[0.015, 0.403, 0.029, 0.555]?" }, { "from": "gpt", "value": "It's the y-axis label." } ], "id_raw": 540 }, { "id": "aFTVA4A2vWbMSiAq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nTell me what {rectangle}[0.327, 0.111, 0.476, 0.171] signifies." }, { "from": "gpt", "value": "The rectangle represents the risk score of the risk factor \"Financial Risk\". The risk score is -30." } ], "id_raw": 540 }, { "id": "duh3Q56aYJwZL2FD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nCan you identify the subplot title marked at {coordinates}[0.430, 0.030, 0.721, 0.066]?" }, { "from": "gpt", "value": "The title is \"Risk Assessment and Management\"." } ], "id_raw": 540 }, { "id": "9D7M6NSxebqg3DVn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nIs {the risk score for \"Cyber-security\"}[0.377, 0.787, 0.476, 0.847] higher or lower than {the risk score for \"Security Breach\"}[0.476, 0.712, 0.699, 0.772]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 540 }, { "id": "jGMea02szlJP2drI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nWhich risk score is more negative, {the risk score for \"Regulatory Risk\"}[0.228, 0.412, 0.476, 0.472] or {the risk score for \"Reputation Risk\"}[0.278, 0.261, 0.476, 0.322]?" }, { "from": "gpt", "value": "Regulatory Risk." } ], "id_raw": 540 }, { "id": "T1nj3AIkPovQnO59", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nBased on risk assessment, is {the risk score for \"Environmental Risk\"}[0.476, 0.337, 0.823, 0.397] more or less risky than {the risk score for \"Market Volatility\"}[0.476, 0.487, 0.650, 0.547]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 540 }, { "id": "a8JwQeY42e3J0ZaL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the region starting at {coordinates}[0.228, 0.412, 0.650, 0.547]?" }, { "from": "gpt", "value": "The area contains the risk scores for three risk factors. From top to bottom they are: \"Regulatory Risk\"[-50.00], \"Environmental Risk\"[70.00], \"Market Volatility\"[35.00]." } ], "id_raw": 540 }, { "id": "TeFMmdN6MzedBPhy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region {with coordinates}[0.179, 0.075, 0.972, 0.883]." }, { "from": "gpt", "value": "The area includes the whole plot of a bar chart. The chart represents risk scores for different risk factors and the scale of risk scores from -60 to 100. Labels 'Risk Score (%)' on x-axis and 'Risk Factor' on y-axis define the context. The bars represent the risk score by their length and position along the x-axis for each risk factor." } ], "id_raw": 540 }, { "id": "yrVsO0y1cPFWWWP4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the area defined by these {coordinates}[0.377, 0.787, 0.922, 0.246] represent?" }, { "from": "gpt", "value": "In this area, there are four bars corresponding to the risk scores for \"Cyber-security\", \"Security Breach\", \"Geo-Political Risk\", and from highest to lowest on the y-axis." } ], "id_raw": 540 }, { "id": "kMR1tHPuSS2KulPW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the technology labelled at the coordinates [0.162, 0.125, 0.238, 0.153]?" }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 541 }, { "id": "Gia6Y7sqdSsvV7d7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the values of the bar at [0.493, 0.548, 0.567, 0.597]?" }, { "from": "gpt", "value": "0.00 to 15.00." } ], "id_raw": 541 }, { "id": "6hEa4BV1SqUOt99n", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the y-axis at [0.015, 0.401, 0.029, 0.557]?" }, { "from": "gpt", "value": "Technology." } ], "id_raw": 541 }, { "id": "1Ffn1EKmY3I9Z6pB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the tick label positioned at [0.927, 0.903, 0.945, 0.931] on the x-axis?" }, { "from": "gpt", "value": "90." } ], "id_raw": 541 }, { "id": "AgZEpMAVFRcLnxN5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Neural Networks\"}[0.419, 0.298, 0.493, 0.348] longer than {the bar for \"Natural Language Processing\"}[0.493, 0.548, 0.567, 0.597]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 541 }, { "id": "PO7N8hB8v4zYMHn1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nIs {the tick label -50}[0.233, 0.903, 0.262, 0.931] on the left or right side of {the tick label 30}[0.632, 0.903, 0.649, 0.931] on the x-axis?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 541 }, { "id": "UVX4SSQrAEXavAuT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nBetween {the technology \"Artificial Intelligence\"}[0.095, 0.811, 0.238, 0.839] and {the technology \"Machine Learning\"}[0.115, 0.686, 0.238, 0.714], which has a higher percentage change?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 541 }, { "id": "7JY7f9A9ZItrbmsb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Blockchain\"}[0.493, 0.111, 0.715, 0.161] longer than {the bar for \"Robotics\"}[0.493, 0.735, 0.887, 0.784]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 541 }, { "id": "mCCO88tHYa6A0unB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the varied bars in the region [0.493, 0.111, 0.936, 0.847]." }, { "from": "gpt", "value": "This region consists of 12 bars each correlating to a certain technology. The bars extend both to the left and right from the y-axis based on their respective change percentages. Each bar also has a corresponding label on the y-axis. Starting from the top, the labels are 'Artificial Intelligence', 'Robotics', 'Machine Learning', 'Cloud Computing', 'Natural Language Processing', 'Automation', 'Self Driving Cars', 'Biometrics', 'Neural Networks', 'Data Analytics', 'Intelligent Apps', and 'Blockchain'." } ], "id_raw": 541 }, { "id": "wmpyYW5OZy83hcMz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.346, 0.672, 0.493, 0.722] contain?" }, { "from": "gpt", "value": "This area contains the bar for \"Machine Learning\" that extends to the left from the y-axis indicating a negative percentage change." } ], "id_raw": 541 }, { "id": "iI60VyPVOM161neh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the coordinates range [0.247, 0.075, 0.985, 0.883]." }, { "from": "gpt", "value": "The specified coordinates highlight the main plotting area of the graph. The horizontal x-axis scale is a measure of the change in percentage ranging from -50 to 90 and the vertical y-axis scale represents different 'Technologies'. This coordinates area is filled with bars that express the growth or decline of the respective technology." } ], "id_raw": 541 }, { "id": "zE8vj4t75Oz9q8iy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area outlined by the coordinates [0.095, 0.811, 0.238, 0.839]." }, { "from": "gpt", "value": "This area contains the y-axis tick label \"Artificial Intelligence\", which corresponds to the first bar in the plot seen at [0.493, 0.797, 0.739, 0.847] indicating that Artificial Intelligence had a positive percentage change of 50." } ], "id_raw": 541 }, { "id": "Bl5HNBlm8gYRy2JX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the coordinate [0.549, 0.942, 0.649, 0.970] on the chart?" }, { "from": "gpt", "value": "It is the label for the x-axis, namely, \"Difference (%)\"." } ], "id_raw": 542 }, { "id": "SuzaEZLHskRK2PRK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate for the title of the chart?" }, { "from": "gpt", "value": "The coordinate is [0.281, 0.030, 0.917, 0.066]." } ], "id_raw": 542 }, { "id": "uIucJIeX7UvXjpNX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the object found at the coordinate [0.485, 0.787, 0.940, 0.847] stand for?" }, { "from": "gpt", "value": "It is a horizontal bar representing the 100% growth trend for the technology \"Blockchain\"." } ], "id_raw": 542 }, { "id": "sgJpIFGiv7sU8UYj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis, found at [0.015, 0.390, 0.029, 0.568], label represent?" }, { "from": "gpt", "value": "It represents the label \"Technologies\"." } ], "id_raw": 542 }, { "id": "a2QEJf7w8Vd0Sx2M", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Cybersecurity\"{[0.485, 0.261, 0.690, 0.322]} wider than the bar representing \"IoT\"{[0.485, 0.111, 0.576, 0.171]}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 542 }, { "id": "0dgN8HXW2fYtfuWE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nIs the growth trend for \"AI/ML\"{[0.258, 0.712, 0.485, 0.772]} positive or negative compared to \"Big Data\"{[0.349, 0.487, 0.485, 0.547]}?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 542 }, { "id": "cw4R4YUOVGlHtIUn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nIs \"Blockchain\"{[0.485, 0.787, 0.940, 0.847]} showing a higher or lower growth trend compared to \"Cryptocurrency\"{[0.485, 0.637, 0.826, 0.697]}?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 542 }, { "id": "6LoyuglZXh3jUJTP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nIs the trend bar for \"Data Protection\"{[0.485, 0.412, 0.758, 0.472]} longer or shorter than the bar for \"Digital Wallet\"{[0.485, 0.337, 0.622, 0.397]}?" }, { "from": "gpt", "value": "Longer." } ], "id_raw": 542 }, { "id": "oOFukhX4oOrJUNUg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nWhat details can you give about the region [0.213, 0.075, 0.985, 0.883]?" }, { "from": "gpt", "value": "This is the main plotting area of the chart. All horizontal bars representing the different technologies are found within this region, such as \"Blockchain\"{[0.485, 0.787, 0.940, 0.847]}, \"AI/ML\"{[0.258, 0.712, 0.485, 0.772]}, \"Cryptocurrency\"{[0.485, 0.637, 0.826, 0.697]}, among others." } ], "id_raw": 542 }, { "id": "yBA2TNQikdEnD9eC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the region [0.035, 0.581, 0.203, 0.609]?" }, { "from": "gpt", "value": "This region contains the label of one of the technology categories, specifically, \"Peer-to-Peer Networking\"." } ], "id_raw": 542 }, { "id": "NyY1NCtvNVRQzt9G", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements enclosed by the region [0.440, 0.186, 0.485, 0.246]." }, { "from": "gpt", "value": "The region contains a horizontal bar representing the negative growth trend of \"Cloud computing\"{[0.440, 0.186, 0.485, 0.246]}." } ], "id_raw": 542 }, { "id": "mJKY7je3lNmZ9QWK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nWhat information is provided within the coordinates [0.128, 0.806, 0.203, 0.834]?" }, { "from": "gpt", "value": "These coordinates contain the y-axis label \"Blockchain\"." } ], "id_raw": 542 }, { "id": "HryL00vc7MdK2FJ9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the category name located at [0.107, 0.824, 0.196, 0.848]?" }, { "from": "gpt", "value": "{Technologies}[0.107, 0.824, 0.196, 0.848]." } ], "id_raw": 543 }, { "id": "2r2qpvWq5j9nhjqF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the value marked at coordinate [0.493, 0.178, 0.780, 0.241]?" }, { "from": "gpt", "value": "{60}[0.493, 0.178, 0.780, 0.241]." } ], "id_raw": 543 }, { "id": "6dcmOC0ZESYaSUyc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-label located at [0.489, 0.919, 0.497, 0.942]?" }, { "from": "gpt", "value": "{0}[0.489, 0.919, 0.497, 0.942]." } ], "id_raw": 543 }, { "id": "HftQIx1Rohm39Heh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart at location [0.240, 0.025, 0.937, 0.055]?" }, { "from": "gpt", "value": "{Change in Relative Importance in Technological Innovation and Intellectual Property}[0.240, 0.025, 0.937, 0.055]." } ], "id_raw": 543 }, { "id": "BTCEHlwxRdvczW5t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Copyrights\"}[0.302, 0.646, 0.493, 0.709] shorter in length than {the bar representing \"Trademarks\"}[0.493, 0.568, 0.637, 0.631]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 543 }, { "id": "MOP4LvVHd7gkHQS3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nIs {the value of \"Industrial Design\"}[0.493, 0.256, 0.541, 0.319] less than {the value of Licensing Agreements}[0.493, 0.412, 0.876, 0.475]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 543 }, { "id": "7nVbAvjysyXhiE8j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Infringement Cases\"}[0.493, 0.100, 0.541, 0.163] have the same value as {the bar representing \"Industrial Design\"}[0.493, 0.256, 0.541, 0.319]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 543 }, { "id": "5BHcKVp9WBT8GVY9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Patents\"}[0.493, 0.724, 0.972, 0.787] longer than {the bar representing \"Technologies\"}[0.493, 0.802, 0.732, 0.865]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 543 }, { "id": "hkDxmLqYVGRVTiQ2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.206, 0.062, 0.972, 0.903]?" }, { "from": "gpt", "value": "It's about the main part of the chart. It includes all bar elements representing different types of intellectual property. Each bar shows the relative change of importance in technological innovation. Positive values are to the right of {0} axis[0.493, 0.903] indicates a rise, and negative values to the left of {0} axis[0.493, 0.903] indicates a drop." } ], "id_raw": 543 }, { "id": "dLHMdeaMD45NOVY8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents at location [0.493, 0.568, 0.780, 0.241]." }, { "from": "gpt", "value": "It contains three bars showing the relative change of importance in Trademarks, Industrial Design, and Research Development respectively in technological innovation. Their values are 30, 10 and 60." } ], "id_raw": 543 }, { "id": "DYy5ljei8Jjx9U0E", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the area of [0.206, 0.062, 0.972, 0.903]." }, { "from": "gpt", "value": "This area contains the majority of the chart, including all x and y axis labels, as well as the bars representing various categories of intellectual property. Negative values are shown in bars to the left of {the 0 on the x-axis}[0.493, 0.903], and positive values are shown in bars to the right side." } ], "id_raw": 543 }, { "id": "OI23fTjt4x11TO8c", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nKindly describe the region [0.493, 0.568, 0.780, 0.631]." }, { "from": "gpt", "value": "This section contains bars and labels which represent a portion of the data. Specifically, they pertain to the categories of Trademarks, Industrial Design, Research Development, and Infringement Cases. The relative change in importance for each item is displayed by the length and direction of its associated bar." } ], "id_raw": 543 }, { "id": "HrBWwxlcPO2ilkfG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar} at [0.488, 0.797, 0.681, 0.847]?" }, { "from": "gpt", "value": "It is the bar representing the \"Number of Followers\"." } ], "id_raw": 544 }, { "id": "rluqgKaP5vWJUIOH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "\"Changes in Aspects of Social Network Presence and Interpersonal Relationships\"" } ], "id_raw": 544 }, { "id": "nkaJqyygjY5lao1q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the value on x-axis for {the block} at [0.294, 0.423, 0.488, 0.473]?" }, { "from": "gpt", "value": "The value ranges from -100 to 0." } ], "id_raw": 544 }, { "id": "luf7krkIXrsf40rY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Number of Comments\"}[0.488, 0.610, 0.875, 0.660] longer than {the bar of \"Likes per Post\"}[0.488, 0.236, 0.730, 0.286]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 544 }, { "id": "mSraHOCUOCRe3T3v", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar of \"Number of Shares\"}[0.391, 0.548, 0.488, 0.597] have a negative value on the x-axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 544 }, { "id": "UGnVCEnr16A7ydd2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Number of Unfollows\"}[0.488, 0.485, 0.633, 0.535] located above {the bar of \"Number of Blocks\"}[0.294, 0.423, 0.488, 0.473]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 544 }, { "id": "klDUATMscRmB9Eya", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the bars found within the region [0.391, 0.236, 0.875, 0.785]?" }, { "from": "gpt", "value": "The region contains 4 bars. {The bar representing \"Number of Likes\"}[0.488, 0.672, 0.778, 0.722] that starts from 0 and ends at 150, {the bar representing \"Number of Comments\"}[0.488, 0.610, 0.875, 0.660] that starts from 0 and ends at 200, {The bar representing \"Number of Unfollows\"}[0.488, 0.485, 0.633, 0.535] that starts from 0 and ends at 75, and {The bar representing \"Likes per Post\"}[0.488, 0.236, 0.730, 0.286] that starts from 0 and ends at 125." } ], "id_raw": 544 }, { "id": "C4ecylXKrkZvU7pV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.045, 0.811, 0.188, 0.839]." }, { "from": "gpt", "value": "This region covers the y-axis label for \"Number of Followers\"." } ], "id_raw": 544 }, { "id": "m1OPjMX7jF2QwgUf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the region [0.275, 0.903, 0.313, 0.931]?" }, { "from": "gpt", "value": "It is the x-axis label for \"-100\"." } ], "id_raw": 544 }, { "id": "kIOTdXAwgZ9kRu1O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object encompassed by the coordinates [0.339, 0.030, 0.860, 0.066]?" }, { "from": "gpt", "value": "This is the subtitle of the chart, which is named \"Percent Change in Popularity of Cultural Items Post Integration\"." } ], "id_raw": 545 }, { "id": "I6knwPrXkyuKxWwV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.530, 0.942, 0.669, 0.970]?" }, { "from": "gpt", "value": "It is the label of the x-axis, named \"Percent Change (%)\"." } ], "id_raw": 545 }, { "id": "lfpnkDQOe9SI0rg9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at the coordinates [0.015, 0.381, 0.029, 0.577]?" }, { "from": "gpt", "value": "It is the label for the y-axis, named \"Cultural Items\"." } ], "id_raw": 545 }, { "id": "tYYWVJKeBiRSaaKu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented by the coordinates [0.387, 0.787, 0.546, 0.847]?" }, { "from": "gpt", "value": "It is a bar for the item \"Ethnic Cuisine\" showing a 30% change in its popularity." } ], "id_raw": 545 }, { "id": "7CHI5g27RQWT7SFs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nIs the height of {the bar representing \"Folk Dances\"}[0.387, 0.637, 0.493, 0.697] greater than the height of {the bar representing \"Traditional Clothing\"}[0.387, 0.562, 0.573, 0.622]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 545 }, { "id": "W4LNrGucnmgGl90g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nHow do {the bar indicating \"Marriage Customs\"}[0.387, 0.412, 0.759, 0.472] and {the bar showing \"Music and Poetry\"}[0.387, 0.261, 0.812, 0.322] compare in terms of position along the y-axis?" }, { "from": "gpt", "value": "The bar denoting \"Marriage Customs\" is above the \"Music and Poetry\" bar." } ], "id_raw": 545 }, { "id": "xLThZXfjlwlotlZy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nCompared to {the bar for \"Art and Craft\"}[0.307, 0.337, 0.387, 0.397], is {the bar for \"Architectural Styles\"}[0.280, 0.186, 0.387, 0.246] placed higher or lower along the y-axis?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 545 }, { "id": "m2ZTQuKyA4O6Hc2f", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nObserving {the bar representing \"Religious Practices\"}[0.333, 0.712, 0.387, 0.772] and {the bar representing \"Ethnic Cuisine\"}[0.387, 0.787, 0.546, 0.847], which one extends further along the x-axis?" }, { "from": "gpt", "value": "The bar representing \"Ethnic Cuisine\"." } ], "id_raw": 545 }, { "id": "CJKVNJzTmgmLkhX3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area calculated by the coordinates [0.045, 0.506, 0.217, 0.534]?" }, { "from": "gpt", "value": "The area comprises the y-axis label for \"Language and Literature\"." } ], "id_raw": 545 }, { "id": "Sx0jYthssbXx0EHs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nCould you provide a description of the region defined by [0.387, 0.412, 0.759, 0.472]?" }, { "from": "gpt", "value": "The area contains a bar denoting \"Marriage Customs\", and it displays a 70% change." } ], "id_raw": 545 }, { "id": "mGZiVUulbOQ4o11A", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nWhat is situated within the region confined by the coordinates [0.387, 0.261, 0.812, 0.322]?" }, { "from": "gpt", "value": "The zone includes a bar that represents \"Music and Poetry\", showing an 80% change." } ], "id_raw": 545 }, { "id": "l94DIxEyUz7O4wLl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nSummarize what objects can be found in the spatial area between [0.280, 0.186, 0.387, 0.246]." }, { "from": "gpt", "value": "Within these bounds, there is a bar signifying \"Architectural Styles\" showing a -20% change." } ], "id_raw": 545 }, { "id": "iYGEVY31VPI7S29A", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by the bar with coordinates [0.447, 0.842, 0.671, 0.878]?" }, { "from": "gpt", "value": "The bar stands for Color Theory, showing that its score is 50." } ], "id_raw": 546 }, { "id": "717hDeimiBHO2tRR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the bottom-left label {label} at [0.059, 0.102, 0.124, 0.122] refer to?" }, { "from": "gpt", "value": "The label is for the element \"Proximity\"." } ], "id_raw": 546 }, { "id": "2uwmFw5Qd3Yc4dHH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located in [0.504, 0.959, 0.615, 0.979]?" }, { "from": "gpt", "value": "It is the label for the x-axis, \"Creativity Score\"." } ], "id_raw": 546 }, { "id": "zNllg4vTmZxFbkl2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat label of Y-axis at [0.085, 0.543, 0.124, 0.563]?" }, { "from": "gpt", "value": "The label on the Y-axis at these coordinates is \"Value\"." } ], "id_raw": 546 }, { "id": "ZgnDideilrj6FWS1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nWhich is scored higher on creativity, {Texture}[0.447, 0.578, 0.806, 0.613] or {Variety}[0.447, 0.357, 0.716, 0.392]?" }, { "from": "gpt", "value": "Texture." } ], "id_raw": 546 }, { "id": "lhCLsvblbHzN34RB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Whitespace}[0.156, 0.754, 0.447, 0.789] scored positively or negatively?" }, { "from": "gpt", "value": "Negatively." } ], "id_raw": 546 }, { "id": "lGtUBW3kSpWUHINS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nDoes {Emphasis}[0.380, 0.181, 0.447, 0.216] have a higher or lower score than {Lines}<[0.447, 0.622, 0.649, 0.657]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 546 }, { "id": "1286dBFH8sbds8HA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher score - {Alignment}[0.447, 0.269, 0.806, 0.304] or {Hierarchy}[0.447, 0.225, 0.851, 0.260]?" }, { "from": "gpt", "value": "Hierarchy." } ], "id_raw": 546 }, { "id": "A5XD0g7SD1t8T1Wp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area spanning from [0.447, 0.269, 0.851, 0.304]?" }, { "from": "gpt", "value": "This area contains the bar for the design element \"Alignment\", and it is positively scored at 80." } ], "id_raw": 546 }, { "id": "i0wzhazQDiaQQHj1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content inside the bounding box [0.042, 0.764, 0.124, 0.784]." }, { "from": "gpt", "value": "It contains the label \"Whitespace\" for the Y-axis." } ], "id_raw": 546 }, { "id": "qzx3NOnoOSvzQoew", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in [0.447, 0.842, 0.671, 0.878]?" }, { "from": "gpt", "value": "This area includes the bar for \"Color Theory\", which is positively scored at 50." } ], "id_raw": 546 }, { "id": "mpG5KpcClVNSa1zo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.073, 0.587, 0.124, 0.607]?" }, { "from": "gpt", "value": "It includes a Y-axis label, specifically \"Texture\"." } ], "id_raw": 546 }, { "id": "03XRgQNruLtEbIS1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.347, 0.716, 0.614, 0.754]?" }, { "from": "gpt", "value": "It corresponds to the bar representing the growth rate in \"Online Banking\"." } ], "id_raw": 547 }, { "id": "ETQclhMKD5kH6sp8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nWhat part of the chart is at the coordinates [0.103, 0.817, 0.185, 0.845]?" }, { "from": "gpt", "value": "It is the {y-axis label \"Ecommerce\"}[0.103, 0.817, 0.185, 0.845]." } ], "id_raw": 547 }, { "id": "F2Qrnblx9rWWASm6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the information shown at coordinates [0.533, 0.942, 0.648, 0.970]?" }, { "from": "gpt", "value": "It is label of the x-axis, specifically the phrase \"Growth Rate (%)\"." } ], "id_raw": 547 }, { "id": "OxuFl4M1Pm0BhdYy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nWhat is categorized in [0.083, 0.491, 0.185, 0.519]?" }, { "from": "gpt", "value": "It is the y-axis label for \"Cloud Services\"." } ], "id_raw": 547 }, { "id": "dF9zwSdDsXkyiUMQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nIs the growth rate for \"VR/AR\" {VR/AR bar}[0.347, 0.344, 0.575, 0.381] higher than that for \"Web Development\" {Web Development bar}[0.347, 0.251, 0.545, 0.288]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 547 }, { "id": "epF8VLD1BAfiTIoD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nIs the growth rate for {Email Services bar}[0.317, 0.204, 0.347, 0.242] negative compared to {VoIP bar}[0.347, 0.111, 0.469, 0.149]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 547 }, { "id": "GiAnS6fMAI7AgJUO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {\"Artificial Intelligence\"}[0.347, 0.437, 0.803, 0.474] longer than the bar for {\"Cloud Services\"}[0.347, 0.484, 0.651, 0.521]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 547 }, { "id": "WSLqyeVD0IBfqRBu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nDoes the growth rate of {\"Online Banking\"}[0.347, 0.716, 0.614, 0.754] exceed that of {\"Social Media\"}[0.347, 0.763, 0.514, 0.800]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 547 }, { "id": "WRBq22JUd3p2Etdl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe what's outlined in this set of coordinates [0.259, 0.030, 0.921, 0.066]." }, { "from": "gpt", "value": "It is the subtitle of the chart, which reads \"Growth Rate (%) of Various Sectors in the Internet and Communication Industry\"." } ], "id_raw": 547 }, { "id": "ctqwYJa6bnb3FrOc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.347, 0.809, 0.955, 0.428]." }, { "from": "gpt", "value": "The area includes the bars representing the growth rate of various sectors in the internet and communication industry. The bars starting from top, represent 'Ecommerce', 'Social Media', 'Online Banking', 'Video Streaming', 'Online Gaming', 'Search Engines', 'Online Advertisement', 'Cloud Services', 'Artificial Intelligence', 'Internet of Things', 'VR/AR', 'Mobile Apps', 'Web Development', and 'Email Services'." } ], "id_raw": 547 }, { "id": "sQw5C3DqcW0xSI9b", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within [0.015, 0.223, 0.029, 0.735]?" }, { "from": "gpt", "value": "The set of coordinates corresponds to the y-axis label, which is \"Internet and Communication Sectors\"." } ], "id_raw": 547 }, { "id": "PlqFAhsCay6omY3M", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nWhat does region [0.083, 0.491, 0.185, 0.519] contain?" }, { "from": "gpt", "value": "The region contains the name of an internet sector labelled \"Cloud Services\"." } ], "id_raw": 547 }, { "id": "Mz9xC4rBiIxPYOLM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.416, 0.082, 0.916, 0.119] represent in the chart?" }, { "from": "gpt", "value": "The bar representing the issue \"Racial Equality\"." } ], "id_raw": 548 }, { "id": "7fCZyI1VTAJQFKwx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the \"Privacy Rights\" y-axis label?" }, { "from": "gpt", "value": "[0.114, 0.368, 0.212, 0.384]" } ], "id_raw": 548 }, { "id": "RfLdazID3n4FMY7B", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat range does {the bar representing \"Healthcare Rights\"} represent at [0.416, 0.630, 0.527, 0.666]?" }, { "from": "gpt", "value": "The range is 0.00 to 40.00." } ], "id_raw": 548 }, { "id": "6xClM3okWmaEGKOE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat label is at the y-axis coordinates [0.127, 0.414, 0.212, 0.429]?" }, { "from": "gpt", "value": "Free Speech." } ], "id_raw": 548 }, { "id": "FzzGObt0qKH4wfOx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Child Protection\"}[0.416, 0.767, 0.444, 0.803] longer than {the bar representing \"Harassment\"}[0.361, 0.812, 0.416, 0.849]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 548 }, { "id": "rruWRuPRkT5ZJdK9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Economic Inequality\"}[0.416, 0.493, 0.611, 0.529] show a higher value than {the bar representing \"Gender Equality\"}[0.416, 0.539, 0.583, 0.575]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 548 }, { "id": "TmiJdRSttVri56zd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nAmong {the bars representing issues}[[0.416, 0.402, 0.639, 0.438], [0.416, 0.356, 0.666, 0.393], [0.416, 0.310, 0.694, 0.347], [0.250, 0.265, 0.416, 0.301], [0.416, 0.219, 0.750, 0.256], [0.416, 0.173, 0.805, 0.210], [0.416, 0.128, 0.861, 0.164]], which issue has the highest prioritization?" }, { "from": "gpt", "value": "Racial Equality." } ], "id_raw": 548 }, { "id": "aFvAGFa3Kg8gPKCX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar}[0.333, 0.858, 0.416, 0.895] a positive or negative value?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 548 }, { "id": "mGq61lUtpnHayHsq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.416, 0.082, 0.916, 0.119]." }, { "from": "gpt", "value": "It is a bar representing the issue \"Racial Equality\" on the y-axis. It extends from 0.00 to 180.00 on the x-axis, indicating a prioritization value of 180. The coordinate range for the bar is [0.416, 0.082, 0.916, 0.119]." } ], "id_raw": 548 }, { "id": "JAJHT0d7bfOX7PKx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.333, 0.858, 0.416, 0.895]." }, { "from": "gpt", "value": "The area [0.333, 0.858, 0.416, 0.895] contains a single bar, which represents the issue of \"Discrimination\" on the y-axis. The bar extends from -30.00 to 0.00 on the x-axis, indicating a negative prioritization value." } ], "id_raw": 548 }, { "id": "1MrpwcWicwlu2ynq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.416, 0.402, 0.861, 0.164]?" }, { "from": "gpt", "value": "This range includes bars representing the issues of \"Free Speech\", \"Privacy Rights\", \"Environmental Protection\", \"Fair Trade\", \"Minorities Representation\", \"Immigration Rights\", and \"Racial Equality\". These bars indicate the respective prioritization values of these issues on the x-axis." } ], "id_raw": 548 }, { "id": "r1fVmP2ENZroNQr8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.049, 0.459, 0.212, 0.475]?" }, { "from": "gpt", "value": "The area [0.049, 0.459, 0.212, 0.475] contains the y-axis label \"Political Representation\"." } ], "id_raw": 548 }, { "id": "V3BQtLGDTQOA8R9g", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at coordinates [0.580, 0.835, 0.753, 0.877]?" }, { "from": "gpt", "value": "It is a bar representing the Local Growth Rate for {Community Gardens}[0.580, 0.835, 0.753, 0.877]." } ], "id_raw": 549 }, { "id": "UQu8MEpuHEGcX0Zt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis?" }, { "from": "gpt", "value": "The label of the y-axis is {City Projects}[0.015, 0.423, 0.029, 0.547]." } ], "id_raw": 549 }, { "id": "AMu5zreYEwNSYldj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the \"City Beautification\" project on the chart?" }, { "from": "gpt", "value": "The location of the \"City Beautification\" project is {the bar}[0.580, 0.623, 0.696, 0.665]." } ], "id_raw": 549 }, { "id": "P7LAeunRPIFZeHri", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the horizontal axis}[0.551, 0.959, 0.666, 0.979]?" }, { "from": "gpt", "value": "The label for the horizontal axis is Growth Rate (%)." } ], "id_raw": 549 }, { "id": "ocm3uXsh4MN7vRZK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Public Parks bar}[0.580, 0.782, 0.927, 0.824] containing higher value than the {Community Gardens bar}[0.580, 0.835, 0.753, 0.877]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 549 }, { "id": "qSfKkIVP0FhqIC5K", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the Internet Connectivity project}[0.580, 0.517, 0.811, 0.559] have a greater local growth rate than {the Renewable Energy Sources project}[0.580, 0.199, 0.927, 0.241]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 549 }, { "id": "22H2C5mYzOcwphAg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nAre the projects located above {Fire Stations bar}[0.522, 0.093, 0.580, 0.135] generally have positive growth rates?" }, { "from": "gpt", "value": "No." } ], "id_raw": 549 }, { "id": "wje79TNOY3Au4zLk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nIf we examine {Housing Projects bar}[0.464, 0.676, 0.580, 0.718] and {Public Schools bar}[0.290, 0.358, 0.580, 0.400], which one has a higher local growth rate?" }, { "from": "gpt", "value": "Housing Projects." } ], "id_raw": 549 }, { "id": "lItNNeb6lcbUir1c", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in region [0.464, 0.676, 0.696, 0.815]." }, { "from": "gpt", "value": "There are two bars in this region: {The Housing Projects bar}[0.464, 0.676, 0.580, 0.718] and {The City Beautification bar}[0.580, 0.623, 0.696, 0.665]. They represent the Local Growth rate of their respective city projects." } ], "id_raw": 549 }, { "id": "DwKimuo95aCZDMIr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.522, 0.093, 0.580, 0.400]." }, { "from": "gpt", "value": "This region includes the {Fire Stations bar}[0.522, 0.093, 0.580, 0.135] and a significant portion of the {Public Schools bar}[0.290, 0.358, 0.580, 0.400], which represent the Local Growth Rate of the Fire Stations and Public Schools city projects." } ], "id_raw": 549 }, { "id": "UzMDPulUtq2flhWT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area with coordinates [0.290, 0.199, 0.927, 0.400]?" }, { "from": "gpt", "value": "This area contains three City Projects bars: {Public Schools bar}[0.290, 0.358, 0.580, 0.400], {Bike Lanes bar}[0.580, 0.305, 0.580, 0.347], and part of the {Street Lighting bar}[0.580, 0.252, 0.661, 0.294]. All bars represent their respective Local Growth Rates." } ], "id_raw": 549 }, { "id": "Ti0su3lc7bSZ0nHJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects contained in the area [0.405, 0.749, 0.672, 0.870]?" }, { "from": "gpt", "value": "It includes part of the {Public Transport bar}[0.580, 0.729, 0.672, 0.771] and the whole {Public Parks bar}[0.580, 0.782, 0.927, 0.824]. Each bar represents the local growth rate of the respective City Project." } ], "id_raw": 549 }, { "id": "iWZkdQoNrC6TgPW9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object labelled at coordinates [0.015, 0.439, 0.029, 0.519]?" }, { "from": "gpt", "value": "It is the label for the Y-axis, which reads as 'Stage'." } ], "id_raw": 550 }, { "id": "eCH12zsJVTqfCjW7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label content at the coordinates [0.526, 0.942, 0.633, 0.970] of this chart?" }, { "from": "gpt", "value": "It's a X-axis label saying 'Differences (%)'." } ], "id_raw": 550 }, { "id": "NS8ioBgWFrh6vDcR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nWhat's the term at the coordinate [0.453, 0.030, 0.705, 0.066]?" }, { "from": "gpt", "value": "It's the subtitle of the chart, \"Big Data Integration Overview\"." } ], "id_raw": 550 }, { "id": "le8PREyi1fNuqB43", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.039, 0.280, 0.177, 0.308] represent?" }, { "from": "gpt", "value": "It's the label, \"Duplicate Detection\" for the Y-axis." } ], "id_raw": 550 }, { "id": "wn9zmL5lCvhVSCPC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar indicating \"Entity Resolution\"}[0.344, 0.337, 0.595, 0.397] longer than {the bar indicating \"Schema Matching\"}[0.344, 0.412, 0.407, 0.472]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 550 }, { "id": "jZlz4T7vgv5wf4g9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nIs the length of {the bar representing \"Data Integration\"}[0.344, 0.487, 0.532, 0.547] greater than the length of {the bar representing \"Data Wrangling\"}[0.344, 0.712, 0.501, 0.772]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 550 }, { "id": "pJzjbdWDbGBuLT9p", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar denoting \"Conflict Resolution\"}[0.344, 0.111, 0.815, 0.171] have a higher value than {the bar denoting \"Data Fusion\"}[0.344, 0.186, 0.752, 0.246]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 550 }, { "id": "rfeMkfxGka2ccBPz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Data Wrangling\"}[0.344, 0.712, 0.501, 0.772] further to the right than {the bar for \"Data Extraction\"}[0.312, 0.787, 0.344, 0.847]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 550 }, { "id": "p3olDEhw9vDv08T1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information is within the region [0.015, 0.439, 0.029, 0.519]?" }, { "from": "gpt", "value": "In this region, the Y-axis, labelled with 'Stage,' is located." } ], "id_raw": 550 }, { "id": "TbLlWEud4UcYf4Cy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nWhat is enclosed in the range [0.187, 0.075, 0.972, 0.883]?" }, { "from": "gpt", "value": "This area includes the entire chart plot with all the bars representing various stages of big data integration and the X and Y axes." } ], "id_raw": 550 }, { "id": "ZoDFHaKnDtlCYPoM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nWhat elements does the region [0.344, 0.111, 0.815, 0.171] contain?" }, { "from": "gpt", "value": "This span encompasses the bar chart for the stage \"Conflict Resolution,\" which indicates a positive difference of 150%." } ], "id_raw": 550 }, { "id": "JGy0Rwjqajh5oxR5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nWhat is shown within the area [0.344, 0.186, 0.752, 0.246]?" }, { "from": "gpt", "value": "The bar representing the \"Data Fusion\" stage with a positive difference of 130% is located within these coordinates." } ], "id_raw": 550 }, { "id": "sGdnxrOUDgoQGCha", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat's the coordinate for the label \"Child Care\" in y-axis ticks?" }, { "from": "gpt", "value": "The coordinates are [0.141, 0.182, 0.213, 0.205]." } ], "id_raw": 551 }, { "id": "tGjULOekDyLANRlN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat item is indicated by this rectangle [0.512, 0.230, 0.889, 0.282] in the bar groups?" }, { "from": "gpt", "value": "This rectangle indicates the \"Emergency Relief\" item." } ], "id_raw": 551 }, { "id": "fGGilJmD0yttF3SS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the numerical figure on the x-axis represent according to its label at [0.546, 0.952, 0.653, 0.975]?" }, { "from": "gpt", "value": "It represents the \"Difference (%)\" according to its label." } ], "id_raw": 551 }, { "id": "LPdpRCHZtqY5SuVa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nHow many ticks are there on the x-axis and what's the coordinate of the mid-point tick?" }, { "from": "gpt", "value": "There are 14 ticks on the x-axis, and the coordinate of the mid-point tick's label \"0\" is [0.508, 0.919, 0.517, 0.942]." } ], "id_raw": 551 }, { "id": "nlQY6JzIQopYLsPe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nIs the value for {Monetary Contribution}[0.454, 0.683, 0.512, 0.735] greater or smaller than {Medical Assistance}[0.512, 0.619, 0.541, 0.670]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 551 }, { "id": "3ZDrQ5JkkO3mv1RJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nDoes {Educational Project}[0.512, 0.554, 0.657, 0.606] have a higher or lower value than {Community Development}[0.512, 0.359, 0.710, 0.411]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 551 }, { "id": "LHIJvz6BOErNuIk0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nIs {Home Support}[0.483, 0.813, 0.512, 0.865] positioned higher or lower on the chart than {Child Care}[0.512, 0.165, 0.715, 0.217]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 551 }, { "id": "FhwpR6qBoFpa6y8O", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nBetween {Emergency Relief}[0.512, 0.230, 0.889, 0.282] and {Free Counselling}[0.512, 0.424, 0.744, 0.476], which one has a longer bar?" }, { "from": "gpt", "value": "Emergency Relief." } ], "id_raw": 551 }, { "id": "XISeOghiF1rCYU75", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents included in [0.223, 0.062, 0.976, 0.903]?" }, { "from": "gpt", "value": "This region contains all bars illustrating the relative difference in focus or value across various social welfare and charity activities. Also, it includes both the x and y-axis with their corresponding labels and ticks." } ], "id_raw": 551 }, { "id": "KWYggAC5hsAHlDQw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements found in the region [0.346, 0.583, 0.547, 0.653]?" }, { "from": "gpt", "value": "This particular region is in the center of activities like \"Medical Assistance\", \"Educational Project\" and \"Clothing Donation\". There are two bars associated with each of these activities." } ], "id_raw": 551 }, { "id": "GKjiJyLsj2u42qBe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat do you see in area [0.512, 0.774, 0.570, 0.800]?" }, { "from": "gpt", "value": "In this area, we see a portion of the bar associated with the \"Food Program\". Shows a value of '10' on the Differences (%) scale." } ], "id_raw": 551 }, { "id": "iBJVMuWyoONDWSt7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects inside [0.051, 0.430, 0.221, 0.535]." }, { "from": "gpt", "value": "The area mainly covers the y-axis label \"Activities\" lying horizontally in vertical direction." } ], "id_raw": 551 }, { "id": "jySQ8ZQGOAtlgO8u", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at the coordinate [0.167, 0.808, 0.180, 0.836]?" }, { "from": "gpt", "value": "It is a y-axis label for AI." } ], "id_raw": 552 }, { "id": "WqxqFAHsxzOdfzFn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat is at the position [0.362, 0.903, 0.371, 0.931]?" }, { "from": "gpt", "value": "It is the x axis label for value \"0\"." } ], "id_raw": 552 }, { "id": "ITxJDGIGtwXLFbTc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.366, 0.792, 0.570, 0.847] correspond to in the chart?" }, { "from": "gpt", "value": "It refers to the bar representing \"AI\" category on the chart." } ], "id_raw": 552 }, { "id": "1Me9Te0cHSYL5vqw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the coordinate [0.322, 0.384, 0.366, 0.438] display?" }, { "from": "gpt", "value": "It is the negative bar for \"Space Exploration\" showing -5%." } ], "id_raw": 552 }, { "id": "X7z7keobOm9YgUgl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nIs {the AI bar}[0.366, 0.792, 0.570, 0.847] shorter or taller than {the Quantum Computing bar}[0.366, 0.724, 0.941, 0.779]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 552 }, { "id": "azuSggxWWXsnPQaN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nWhich label is located above, {Blockchain}[0.105, 0.468, 0.180, 0.496] or {Space Exploration}[0.054, 0.400, 0.180, 0.428]?" }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 552 }, { "id": "gAi8yoJC6QW43VJF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nIs {Cybersecurity}[0.366, 0.248, 0.799, 0.302] showing a larger or smaller change percent compared to {Internet of Things}[0.366, 0.180, 0.676, 0.234]?" }, { "from": "gpt", "value": "Larger." } ], "id_raw": 552 }, { "id": "ASKbHIoK8ZP3Mk7m", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nIs {the Blockchain bar}[0.366, 0.452, 0.782, 0.506] located above or under {the Ai bar}[0.366, 0.792, 0.570, 0.847]?" }, { "from": "gpt", "value": "Under." } ], "id_raw": 552 }, { "id": "CozrReBo0FPwOAIG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe what can be seen in the region [0.366, 0.180, 0.799, 0.302]." }, { "from": "gpt", "value": "It is a bar showing the development prospects of Cybersecurity, whose change percentage is around 49%." } ], "id_raw": 552 }, { "id": "jRvrudmL5xWtv5RY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region [0.081, 0.672, 0.180, 0.700]?" }, { "from": "gpt", "value": "It is the y-axis label for Biotechnology." } ], "id_raw": 552 }, { "id": "NHOP4c963ZFu4F22", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.278, 0.111, 0.366, 0.166]." }, { "from": "gpt", "value": "This displays a bar for Smart Cities on the chart, and it shows a negative change of 10 percent." } ], "id_raw": 552 }, { "id": "9bA3GgpeWMlYyc9B", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat is portrayed in the region [0.366, 0.588, 0.632, 0.642]?" }, { "from": "gpt", "value": "In this region, a positive development bar for 5G Technology is shown, as the change percentage is around 30%." } ], "id_raw": 552 }, { "id": "X5e32h9YdCQbe4xX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the area [0.298, 0.903, 0.307, 0.931] in the graph?" }, { "from": "gpt", "value": "It is an xlabel indicating 20 years of protection." } ], "id_raw": 553 }, { "id": "8lkM1cBELJWGUrhO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the bar located at [0.161, 0.652, 0.298, 0.739]?" }, { "from": "gpt", "value": "This bar represents the Hardware Patent category, which has 20 years of protection." } ], "id_raw": 553 }, { "id": "MUJWgJ0nnZR4Ixdx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart title at [0.319, 0.030, 0.827, 0.066] say?" }, { "from": "gpt", "value": "The chart title is \"Years of Protection for Different Types of Intellectual Property.\"" } ], "id_raw": 553 }, { "id": "ZaLduVOoKFPfc4C0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nWhich one is longer, {the bar representing 'Software Patent'}[0.161, 0.760, 0.264, 0.847] or {the bar representing 'Trade Secret'}[0.161, 0.328, 0.848, 0.414]?" }, { "from": "gpt", "value": "The bar representing 'Trade Secret'." } ], "id_raw": 553 }, { "id": "cHyXkUkWs2dnZ3W6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nIs the protection period of {the 'Design Patent'}[0.161, 0.544, 0.257, 0.630] less than {the 'Hardware Patent'}[0.161, 0.652, 0.298, 0.739]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 553 }, { "id": "fZsFv09A024JEQB3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nBetween the {Utility Patent}[0.161, 0.436, 0.298, 0.522] and the {Copyright}[0.161, 0.111, 0.642, 0.198], which one has a longer protection period?" }, { "from": "gpt", "value": "Copyright." } ], "id_raw": 553 }, { "id": "ISTbb8RNqgwp0sON", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nPlease tell me what is contained in the region [0.161, 0.075, 0.985, 0.883]." }, { "from": "gpt", "value": "This region contains the body of the bar chart, showing the lines of protection for various types of intellectual property such as Software Patent, Hardware Patent, Design Patent, Utility Patent, Trade Secret, Trademark, and Copyright." } ], "id_raw": 553 }, { "id": "rwrucoygyNIlIKZr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the area [0.064, 0.360, 0.848, 0.414]?" }, { "from": "gpt", "value": "A bar which represents the Trade Secret category that has an unlimited (depicted as 100 years for visualization purposes) protection period is located in this area." } ], "id_raw": 553 }, { "id": "vGa22siopF04Ugcy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe in the region [0.040, 0.792, 0.151, 0.820]?" }, { "from": "gpt", "value": "The region [0.040, 0.792, 0.151, 0.820] contains the label for the 'Software Patent' category on the Y-Axis." } ], "id_raw": 553 }, { "id": "Ll3tOanh4JbwUx9D", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat elements are within the area [0.848, 0.903, 0.861, 0.931]?" }, { "from": "gpt", "value": "The label '100' on the x-axis, which indicates the highest possible value of protection years in the chart, is located in this area." } ], "id_raw": 553 }, { "id": "Lmp6QOnCQccs1wSP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at the coordinates [0.169, 0.111, 0.458, 0.213] in the chart?" }, { "from": "gpt", "value": "The color of {the bar}[0.169, 0.111, 0.458, 0.213] is red." } ], "id_raw": 554 }, { "id": "iE3qk7HBfFGPeHSd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third label in the legend ?" }, { "from": "gpt", "value": "The coordinate of the third label in the legend is [0.940, 0.183, 0.959, 0.211]." } ], "id_raw": 554 }, { "id": "St3C6ArpzUdtZagA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented at [0.015, 0.397, 0.029, 0.561] on the chart?" }, { "from": "gpt", "value": "It's {the y-axis label}[0.015, 0.397, 0.029, 0.561], which is \"Sales / Rent\"." } ], "id_raw": 554 }, { "id": "xWjQuCme0vNmYdD4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.046, 0.365, 0.159, 0.466] refer to?" }, { "from": "gpt", "value": "The text at [0.046, 0.365, 0.159, 0.466] refers to {Residential Rent}[0.046, 0.365, 0.159, 0.466] category on the y-axis." } ], "id_raw": 554 }, { "id": "VE6t7pbFaHsnk74K", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Q2 bar of Industrial Sales}[0.169, 0.492, 0.490, 0.593] taller than {the Q2 bar of Commercial Sales}[0.169, 0.619, 0.715, 0.720]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 554 }, { "id": "tuHLRIGJ5hOTbFaU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nDo {the bars representing Q1}[0.169, 0.745, 0.876, 0.847],[0.169, 0.619, 0.554, 0.720], [0.169, 0.492, 0.651, 0.593], [0.169, 0.365, 0.940, 0.466], [0.169, 0.238, 0.586, 0.340], [0.169, 0.111, 0.490, 0.213] generally occupy more chart area than {the bars representing Q2}[0.169, 0.745, 0.747, 0.847], [0.169, 0.619, 0.715, 0.720], [0.169, 0.492, 0.490, 0.593], [0.169, 0.365, 0.908, 0.466], [0.169, 0.238, 0.747, 0.340], [0.169, 0.111, 0.458, 0.213]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 554 }, { "id": "sEmrNLlnsHhr9v1Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Q4 bar representing Commercial Rent}[0.169, 0.238, 0.747, 0.340] shorter than {the Q4 bar representing Residential Rent}[0.169, 0.365, 0.940, 0.466]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 554 }, { "id": "hVb2aWWh1yGMeyhb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nBetween {Residential Sales}[0.041, 0.785, 0.159, 0.813] and {Commercial Sales}[0.035, 0.658, 0.159, 0.686], which category has a higher value for Q4<{Q4 Residential Sales bar}[0.169, 0.745, 0.811, 0.847], {Q4 Commercial Sales bar}[0.169, 0.619, 0.683, 0.720] >?" }, { "from": "gpt", "value": "Residential Sales." } ], "id_raw": 554 }, { "id": "jwMtoUnluk8eno7m", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.895, 0.089, 0.965, 0.264]." }, { "from": "gpt", "value": "The rectangle [0.895, 0.089, 0.965, 0.264] contains the legend of the chart. The legend includes:\n1." } ], "id_raw": 554 }, { "id": "QlSR8ozZof6T8W8q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nA blue colour label at [0.901, 0.102, 0.929, 0.122] and a text label at [0.940, 0.100, 0.959, 0.128];\n2. Q2: An orange colour label at [0.901, 0.144, 0.929, 0.164] and a text label at [0.940, 0.142, 0.959, 0.170];\n3. Q3: A green color label at [0.901, 0.186, 0.929, 0.205] and a text label at [0.940, 0.183, 0.959, 0.211];\n4. Q4: A red color label at [0.901, 0.228, 0.929, 0.247] and a text label at [0.940, 0.225, 0.959, 0.253].\n \nQ2: Please describe the area [0.031, 0.079, 0.965, 0.883]." }, { "from": "gpt", "value": "The area contains the entire chart excluding the title and components on the x and y axis. It includes the bars representing the sales and rents of different types of buildings in each quarter. Each bar type is color-coded, and the corresponding colors and quarters are represented in the legend located at the right-hand side of the chart at [0.895, 0.089, 0.965, 0.264]." } ], "id_raw": 554 }, { "id": "E15eI6fzrb7zPIAT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.169, 0.492, 0.490, 0.593]?" }, { "from": "gpt", "value": "The horizontal bars within [0.169, 0.492, 0.490, 0.593] are the representations of Q1, Q2, Q3, and Q4 for {Industrial Sales}[0.053, 0.531, 0.159, 0.559]." } ], "id_raw": 554 }, { "id": "TH6C4HR4FyD3SHfY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.169, 0.111, 0.972, 0.213]?" }, { "from": "gpt", "value": "The area [0.169, 0.111, 0.972, 0.213] contains the sales and rent data of the \"Industrial Rent\" category. There are four bars with different colors corresponding to different quarters with the color code explained in the legend to the right of the chart at [0.895, 0.089, 0.965, 0.264]." } ], "id_raw": 554 }, { "id": "8vXPgdCCpZzeK797", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.016, 0.274, 0.029, 0.608]?" }, { "from": "gpt", "value": "The y-axis label, called 'Technology Development Trend Values'." } ], "id_raw": 570 }, { "id": "kV3DjXO6aXQCH4M2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the 'Geothermal' label on the x-axis?" }, { "from": "gpt", "value": "[0.358, 0.863, 0.426, 0.947]" } ], "id_raw": 570 }, { "id": "RoYA3emVWcmTbUqp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the {technology} at coordinates [0.704, 0.863, 0.768, 0.942]?" }, { "from": "gpt", "value": "Smart Grid." } ], "id_raw": 570 }, { "id": "Id1MN3T49R7gVOxU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the technology category referred to within [0.606, 0.863, 0.694, 0.972]?" }, { "from": "gpt", "value": "Carbon Capture." } ], "id_raw": 570 }, { "id": "0BLfOLKnxEbC6l1O", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nIs the {bar representing Solar}[0.099, 0.337, 0.168, 0.857] taller than the {bar representing Hydro}[0.271, 0.389, 0.340, 0.857]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 570 }, { "id": "RTKciOC9tNpJmd6N", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Nuclear}[0.530, 0.129, 0.598, 0.857] higher than {the bar representing Wind}[0.185, 0.233, 0.254, 0.857]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 570 }, { "id": "pU905yVzC4QfmLF4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nBetween {Biomass}[0.444, 0.337, 0.512, 0.857] and {Energy Efficiency}[0.874, 0.285, 0.943, 0.857], which bar is taller?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 570 }, { "id": "ZCccriTLrMb9vvmO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nBetween {Geothermal}[0.357, 0.285, 0.426, 0.857] and {Smart Grid}[0.702, 0.285, 0.771, 0.857], which technology has a higher trend value?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 570 }, { "id": "LrSr2bsU276SVAOT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nPlease explain the content inside the area, [0.099, 0.337, 0.168, 0.857]." }, { "from": "gpt", "value": "Inside these coordinates is a blue bar representing the Solar technology with a y-value range of '0.00 -> 50.00'." } ], "id_raw": 570 }, { "id": "3pIep1qLd273ywqk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.615, 0.129, 0.685, 0.857]." }, { "from": "gpt", "value": "There is a single blue bar in this region. This bar corresponds to the Nuclear technology with a y-value range from '0.00 -> 70.00'." } ], "id_raw": 570 }, { "id": "mckS9NYRdopwckmW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.874, 0.285, 0.943, 0.857]?" }, { "from": "gpt", "value": "This region includes a blue bar that stands for the Energy Efficiency technology. The y-value range for this bar is '0.00 -> 55.00'." } ], "id_raw": 570 }, { "id": "3W557z9a04dX9Y38", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area defined by [0.202, 0.863, 0.237, 0.906]?" }, { "from": "gpt", "value": "The region contains an x-axis label with the text 'Wind'." } ], "id_raw": 570 }, { "id": "8vPEqg84VFKxsPtj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.019, 0.344, 0.036, 0.515]?" }, { "from": "gpt", "value": "This coordinate represents the Y-axis label, which is \"Preference (%)\"." } ], "id_raw": 571 }, { "id": "eH4FJlCv1AB8T0hj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.728, 0.113, 0.789, 0.825]?" }, { "from": "gpt", "value": "It is a bar for the category \"Online_Gaming\" it has a range of '0.00 -> 90.00'." } ], "id_raw": 571 }, { "id": "lrNmncHCca2hHKpe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the coordinates [0.123, 0.469, 0.184, 0.825]?" }, { "from": "gpt", "value": "It is a bar representing the preference for \"Reading\", with a value range of '0.00 -> 45.00'." } ], "id_raw": 571 }, { "id": "RivXOtRFAcF1sJJX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Travel\"}[0.274, 0.272, 0.335, 0.825] have a higher preference percentage than {the bar representing \"Eating_Out\"}[0.426, 0.232, 0.486, 0.825]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 571 }, { "id": "Bfv3KzxnVCLPiTB0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Online_Gaming\"}[0.728, 0.113, 0.789, 0.825] taller than {the bar representing \"Music\"}[0.501, 0.192, 0.562, 0.825]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 571 }, { "id": "TbrnLeW5zs4mBsTj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nAmong {the bar representing \"Reading\"}[0.123, 0.469, 0.184, 0.825] and {the bar representing \"Nightlife\"}[0.880, 0.430, 0.940, 0.825], which has a higher preference percentage?" }, { "from": "gpt", "value": "Nightlife." } ], "id_raw": 571 }, { "id": "y9Jkshi5Z0hlKdu7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nDescribe what you see in the range [0.122, 0.833, 0.185, 0.916]." }, { "from": "gpt", "value": "In the given range, there is a label for the X-axis category \"Reading\"." } ], "id_raw": 571 }, { "id": "kUgmiR8DAgnqac4X", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the coordinates [0.426, 0.232, 0.486, 0.825]?" }, { "from": "gpt", "value": "At these coordinates, you see a bar that represents the preference for \"Eating_Out\", with a recorded preference percentage from '0.00 -> 75.00'." } ], "id_raw": 571 }, { "id": "McYu7uhe8BG1YMDl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the coordinate range [0.019, 0.344, 0.036, 0.515]?" }, { "from": "gpt", "value": "This range covers the Y-axis label, which is named \"Preference (%)\"." } ], "id_raw": 571 }, { "id": "c82tlhyPcdApflEX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label on {the y-axis}[0.015, 0.329, 0.029, 0.482]?" }, { "from": "gpt", "value": "Accuracy (%)" } ], "id_raw": 572 }, { "id": "QVxEIRMfv18EpbvT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the first bar represent in the chart? Please provide the coordinates of the bar." }, { "from": "gpt", "value": "The first bar represents the health predictor \"Age\" with an accuracy of 70%. The coordinates are [0.108, 0.257, 0.176, 0.777]." } ], "id_raw": 572 }, { "id": "fMGW2dh0qyHWG2x4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the health predictor located at [0.271, 0.785, 0.354, 0.923] on the x-axis?" }, { "from": "gpt", "value": "Blood Pressure." } ], "id_raw": 572 }, { "id": "Bm2jBRXUvpH8OPjj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nDoes the {Sleep Quality bar}[0.704, 0.272, 0.773, 0.777] have a higher value than the {Diet bar}[0.534, 0.220, 0.602, 0.777]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 572 }, { "id": "MT4LkZntEB4l1adn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nAmong the {Exercise Frequency bar}[0.619, 0.168, 0.687, 0.777] and the {Heart Rate bar}[0.363, 0.183, 0.432, 0.777], which one has a higher value?" }, { "from": "gpt", "value": "Exercise Frequency." } ], "id_raw": 572 }, { "id": "ZH25QWAXfDqJPhwF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nIs the {Smoking bar}[0.790, 0.443, 0.858, 0.777] shorter than the {Air Quality bar}[0.449, 0.331, 0.517, 0.777]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 572 }, { "id": "F9FQvVPeKps7rZOG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the box [0.108, 0.257, 0.176, 0.777]." }, { "from": "gpt", "value": "Within this box, there is a blue bar that goes from 0.00 to 70.00 representing the health prediction accuracy for the \"Age\" category." } ], "id_raw": 572 }, { "id": "4Wk8ToHXwH7aoO2c", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area [0.043, 0.174, 0.061, 0.197]?" }, { "from": "gpt", "value": "That's where the y-axis label for 80 is positioned." } ], "id_raw": 572 }, { "id": "vNrLUTinFXEAhvZv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.043, 0.025, 0.061, 0.048]" }, { "from": "gpt", "value": "The label for 100 on the y-axis is within this regional area." } ], "id_raw": 572 }, { "id": "j2e8BSuOVZBYBeWg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {this object}[0.015, 0.395, 0.029, 0.530] in the chart?" }, { "from": "gpt", "value": "That is the ylabel titled 'Usage Rate (%).'" } ], "id_raw": 573 }, { "id": "3XTWCtIL1dQpR4CC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {the bar representing 'Telegram'}[0.788, 0.708, 0.857, 0.900]?" }, { "from": "gpt", "value": "It is located in [0.788, 0.708, 0.857, 0.900]." } ], "id_raw": 573 }, { "id": "HQk33gKINq9r9anY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nHow is the {bar} for {Telegram}[0.788, 0.708, 0.857, 0.900] titled?" }, { "from": "gpt", "value": "It does not have a specific title but represents the usage rate for the network Telegram." } ], "id_raw": 573 }, { "id": "8VXcf2HDycO4aaIt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing 'WeChat'}[0.874, 0.078, 0.943, 0.900] have a higher usage rate than {the bar representing 'Telegram'}[0.788, 0.708, 0.857, 0.900]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 573 }, { "id": "bd39OO8ahcRfO1Y8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Twitter'}[0.185, 0.638, 0.254, 0.900] longer than {the bar representing 'Facebook'}[0.099, 0.778, 0.168, 0.900]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 573 }, { "id": "XV6EksCMdX9tIw8s", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nAre there more users in {Snapchat}[0.357, 0.131, 0.426, 0.900] or {LinkedIn}[0.444, 0.410, 0.512, 0.900]?" }, { "from": "gpt", "value": "Snapchat." } ], "id_raw": 573 }, { "id": "ndEfzZavzcZCf8NT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nProvide a brief description of the area from [0.271, 0.323, 0.340, 0.900] to [0.426, 0.131, 0.512, 0.900]." }, { "from": "gpt", "value": "The area specified encompasses three bars representative of Instagram, Snapchat, and LinkedIn. Each of these represents the usage rate of the respective social network." } ], "id_raw": 573 }, { "id": "suxAArSR5j9rpKcU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.443, 0.194, 0.052, 0.211]?" }, { "from": "gpt", "value": "This area consists of the horizontal grid line on the y-axis representing the numerical value of 40%." } ], "id_raw": 573 }, { "id": "YTY2CDxfNgTDaeU9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nPlease explain what the bars in the range [0.185, 0.638, 0.254, 0.900] to [0.444, 0.410, 0.512, 0.900] represent." }, { "from": "gpt", "value": "These bars represent the usage rates of three different social networks. The first is Twitter, the second is Instagram, and the third one is LinkedIn. Each bar's height indicates the respective usage rate." } ], "id_raw": 573 }, { "id": "J4wNLVtWOsEm3c5N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat label is given to the last bar on the plot with coordinates [0.852, 0.206, 0.918, 0.749]?" }, { "from": "gpt", "value": "The label given to this bar is \"Successful Entrepreneurial Activity\"." } ], "id_raw": 574 }, { "id": "5CA4U0xnQCXjooL5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar located at [0.272, 0.206, 0.338, 0.749] represent?" }, { "from": "gpt", "value": "This bar represents the value for \"Government Support\"." } ], "id_raw": 574 }, { "id": "wDavzH1ev2PpkLBd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.043, 0.236, 0.061, 0.253]?" }, { "from": "gpt", "value": "It is the y-axis label for the value \"70\"." } ], "id_raw": 574 }, { "id": "zFtnT5WQUF7YzowC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Investment}[0.438, 0.134, 0.504, 0.749] larger than the one for {Cultural Support}[0.686, 0.351, 0.752, 0.749]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 574 }, { "id": "LZvZWXWQ27YkX5ki", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Business Angel Activity}[0.769, 0.170, 0.835, 0.749] higher than the one for {Government Support}[0.272, 0.206, 0.338, 0.749]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 574 }, { "id": "WupLilxa4DzbgNr2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Market Openness} bar[0.355, 0.423, 0.421, 0.749] shorter than the {Cultural Support} bar[0.686, 0.351, 0.752, 0.749]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 574 }, { "id": "ukN3G8ZHlxJ0ft10", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found in the [0.189, 0.315, 0.255, 0.749] range?" }, { "from": "gpt", "value": "The range contains a single bar which represents the contribution of \"Entrepreneurial Education\" to the ecosystem, which is 60." } ], "id_raw": 574 }, { "id": "yynjvd6Y7AO3inZg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within the range [0.035, 0.019, 0.061, 0.036]?" }, { "from": "gpt", "value": "It's the y-axis label for the maximum value \"100\"." } ], "id_raw": 574 }, { "id": "cZz1LHapXbW1VCkZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area between [0.672, 0.206, 0.918, 0.749]?" }, { "from": "gpt", "value": "There are two bars in this area. One for {Cultural Support}[0.686, 0.351, 0.752, 0.749] with a value of 55, and another for {Successful Entrepreneurial Activity}[0.852, 0.206, 0.918, 0.749] with a value of 75." } ], "id_raw": 574 }, { "id": "D5ZmIJn6bGT9Funb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the y-axis label is in the chart?" }, { "from": "gpt", "value": "The y-axis label is \"Success Percentage (%)\"[0.015, 0.324, 0.029, 0.532]." } ], "id_raw": 575 }, { "id": "hJjWS2OecgTNwzJN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat category does the third bar from the left represent?" }, { "from": "gpt", "value": "The category is \"Problem Solving\"[0.278, 0.388, 0.346, 0.831]." } ], "id_raw": 575 }, { "id": "UPEw5aMSZXnNXgO0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the seventh bar from the left?" }, { "from": "gpt", "value": "The coordinate is [0.619, 0.428, 0.687, 0.831]." } ], "id_raw": 575 }, { "id": "YMJI31qwlJvaG30T", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the variable \"Time Management\"?" }, { "from": "gpt", "value": "The value of \"Time Management\" is 80% as represented by the bar at coordinates [0.704, 0.187, 0.773, 0.831]." } ], "id_raw": 575 }, { "id": "lzwtH8QIQ1g79Qrm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Conflict Management\"[0.534, 0.509, 0.602, 0.831] taller than the bar representing \"Motivation\"[0.449, 0.308, 0.517, 0.831]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 575 }, { "id": "EORnRHkgtTtLuw2o", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nBetween \"Conflict Management\" and \"Team Building\", which one has a higher value?" }, { "from": "gpt", "value": "\"Team Building\"." } ], "id_raw": 575 }, { "id": "RJJidJ6v59ldsXoA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Problem Solving\"[0.278, 0.388, 0.346, 0.831] taller than the bar representing \"Leadership\"[0.363, 0.227, 0.432, 0.831]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 575 }, { "id": "4bu4sl8VajwWsLtS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nWhich one is taller between the \"Decision Making\"[0.193, 0.469, 0.261, 0.831] bar and the \"Delegation\"[0.619, 0.428, 0.687, 0.831] bar?" }, { "from": "gpt", "value": "\"Delegation\"." } ], "id_raw": 575 }, { "id": "SwlZpizGyZzj02OT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.278, 0.388, 0.346, 0.831] contain?" }, { "from": "gpt", "value": "The area contains a bar representing the category \"Problem Solving\" with a value of 55% represented by the height of the bar." } ], "id_raw": 575 }, { "id": "TDLyPqzXC6PuI1BX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.066, 0.670, 0.985, 0.831]." }, { "from": "gpt", "value": "The specified area encompasses the entire chart area, including all the bars representing different categories such as \"Communication\", \"Decision Making\", \"Problem Solving\", \"Leadership\", \"Motivation\", \"Conflict Management\", \"Delegation\", \"Time Management\", \"Team Building\", and \"Strategic Planning\". Each bar represents the level of success in percentage terms." } ], "id_raw": 575 }, { "id": "CJjUx081ida3N1kq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within [0.043, 0.824, 0.061, 0.842]?" }, { "from": "gpt", "value": "The area [0.043, 0.824, 0.061, 0.842] contains the '0' label on the Y-axis, indicating that this is the starting point of the percentage scale for 'Success'." } ], "id_raw": 575 }, { "id": "RClYAdqdNlMyj1yT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the area [0.108, 0.267, 0.176, 0.831]?" }, { "from": "gpt", "value": "The area contains a single bar representing the category \"Communication\"[0.108, 0.267, 0.176, 0.831]. The bar's height indicates a success percentage of 70." } ], "id_raw": 575 }, { "id": "8c80rqwexx2PL4qk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart type mentioned in the provided JSON structured information?" }, { "from": "gpt", "value": "The chart type mentioned in the supplied JSON structured information is bar chart." } ], "id_raw": 576 }, { "id": "ZhGXRGIKpdwbXf51", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.015, 0.467, 0.029, 0.516]?" }, { "from": "gpt", "value": "The object located at the coordinates [0.015, 0.467, 0.029, 0.516] is the y-axis label for the graph, called \"Score\"." } ], "id_raw": 576 }, { "id": "2licL43GhpPea5IV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the location {the x-axis label for the third bar}[0.276, 0.492, 0.343, 0.958]?" }, { "from": "gpt", "value": "The location represents the \"Budget Management\" bar on the x-axis." } ], "id_raw": 576 }, { "id": "o815Ub5PTaxktHuW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar labeled 'Time Management'}[0.192, 0.631, 0.259, 0.958] higher than {the bar labeled 'Risk Management'}[0.360, 0.603, 0.428, 0.958]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 576 }, { "id": "uanAU1gdhWKbZtbw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Quality Control' bar}<[0.445, 0.343, 0.512, 0.958]> have a higher score than {the 'Software Innovation' bar}<[0.782, 0.305, 0.850, 0.958]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 576 }, { "id": "zdIHkIfmAel6IGGX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Hardware Innovation' bar}[0.698, 0.520, 0.765, 0.958] placed to the left or the right of {the 'Communication' bar}[0.529, 0.548, 0.596, 0.958]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 576 }, { "id": "Bh1QXlwEis1Q2oF5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the coordinates [0.066, 0.026, 0.975, 0.958]?" }, { "from": "gpt", "value": "This region is the main chart area. It contains individual bars representing various innovation projects along with their associated scores. These projects include \"Idea Generation\", \"Time Management\", \"Budget Management\", \"Risk Management\", \"Quality Control\", \"Communication\", \"Project Closure\", \"Hardware Innovation\", \"Software Innovation\", and \"Marketing Innovation\". Each bar has its own unique coordinates; for example, the \"Idea Generation\" bar is located at [0.107, 0.380, 0.175, 0.958], and the \"Marketing Innovation\" bar is at [0.866, 0.650, 0.934, 0.958]." } ], "id_raw": 576 }, { "id": "ZjIZ2ep4dnydyeR7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the area [0.107, 0.380, 0.175, 0.958]?" }, { "from": "gpt", "value": "This region represents a single green bar in the bar chart; it corresponds to the \"Idea Generation\" project which had a score of 62." } ], "id_raw": 576 }, { "id": "T8V0qYgytNnIGjvW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be found in the region from [0.529, 0.548, 0.596, 0.958] to [0.782, 0.305, 0.850, 0.958]." }, { "from": "gpt", "value": "In these regions, two items, 'Communication' and 'Software Innovation', respectively, can be found. The 'Communication' is documented in green from [0.529, 0.548, 0.596, 0.958] with a score of 44. The 'Software Innovation' is chronicled in green from [0.782, 0.305, 0.850, 0.958] with a score of 70." } ], "id_raw": 576 }, { "id": "ioEO5iry8utNSiym", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.090, 0.196, 0.160, 0.818] refer to in the chart?" }, { "from": "gpt", "value": "It refers to the bar denoting the Funding Received for Food Bank." } ], "id_raw": 577 }, { "id": "d7b90lZUpC8sgEWA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.013, 0.292, 0.024, 0.552] represent?" }, { "from": "gpt", "value": "It represents the y-axis label, titled \"Funding Received (in millions)\"." } ], "id_raw": 577 }, { "id": "XAnBJEtsAqo0sNeU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the coordinates [0.100, 0.824, 0.151, 0.901]?" }, { "from": "gpt", "value": "The x-axis label stating \"Food Bank\" is designated by these coordinates." } ], "id_raw": 577 }, { "id": "cXWsLjOPfMcMxuI5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the element labeled as {Disabled Support category bar}[0.439, 0.309, 0.509, 0.818]?" }, { "from": "gpt", "value": "It's a vertical bar on the chart indicating the amount of funding received for Disabled Support, which is equivalent to 45 million." } ], "id_raw": 577 }, { "id": "BurqtjZODlLK0t3t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Elderly Care}[0.352, 0.229, 0.422, 0.818] taller than {the bar for Disaster Relief}[0.265, 0.139, 0.334, 0.818]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 577 }, { "id": "VBVSAkKgYCA7FZSP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Education category bar}[0.613, 0.331, 0.683, 0.818] to the left or right of {the Disabled Support category bar}[0.439, 0.309, 0.509, 0.818]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 577 }, { "id": "60eoXbvUMkiVtInj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for Environment Protection}[0.788, 0.252, 0.858, 0.818] receive more funding than {the bar for Food Bank}[0.090, 0.196, 0.160, 0.818]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 577 }, { "id": "UZkg8rEneaRz76ok", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat's the relative position of {the Child Welfare bar}[0.526, 0.388, 0.596, 0.818] with respect to {the Animal Welfare bar}[0.875, 0.275, 0.945, 0.818]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 577 }, { "id": "CK5nntDVGXbPP60Y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe what you see in the region marked by [0.775, 0.824, 0.870, 0.981]." }, { "from": "gpt", "value": "In this area, there's the last x-axis label which is \"Animal Welfare\", which is located at the extreme right." } ], "id_raw": 577 }, { "id": "YODXoChckQRAmVQZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is included in the region from [0.029, 0.019, 0.048, 0.716]?" }, { "from": "gpt", "value": "This region includes labels for the y-axis. The labels indicate value ranges with increments of 10, starting at '10' and ending at '70', which are the ticks of the y-axis." } ], "id_raw": 577 }, { "id": "bDODcefswMEa4gp1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.430, 0.310, 0.510, 0.820] on the chart?" }, { "from": "gpt", "value": "Within this region, there is a bar that represents funding received for the category \"Disabled Support\". The bar rises from a baseline number 0 up to 45 million in funding." } ], "id_raw": 577 }, { "id": "Ca7M2xdnC3m0nbKB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.090, 0.196, 0.945, 0.818]?" }, { "from": "gpt", "value": "All the bars representing the funding received for various categories are located within this region. For instance, the bars for {Food Bank}[0.090, 0.196, 0.160, 0.818], {Medical Aid}[0.177, 0.252, 0.247, 0.818], and so on up to the bar for {Animal Welfare}[0.875, 0.275, 0.945, 0.818]." } ], "id_raw": 577 }, { "id": "p79Z1HBThE5YyDlz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label positioned at [0.518, 0.988, 0.518, 0.988]?" }, { "from": "gpt", "value": "There's no label." } ], "id_raw": 578 }, { "id": "iLCmGA44qXKVH5mT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar chart's y-axis reported as at coordinates [0.012, 0.308, 0.024, 0.525]?" }, { "from": "gpt", "value": "It's known as \"Company Dedication (%)\"." } ], "id_raw": 578 }, { "id": "9CmWCuMlXxaL80vm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat element is featured at location [0.090, 0.416, 0.160, 0.807] on the chart?" }, { "from": "gpt", "value": "It's a bar from the first group of bars, representing the category \"Workplace Flexibility\" with a percentage of 40%." } ], "id_raw": 578 }, { "id": "PX2Mohe25dQkPxlP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the two elements located at [0.875, 0.074, 0.945, 0.807] and [0.854, 0.813, 0.966, 0.981] respectively?" }, { "from": "gpt", "value": "The first is a bar from the first group of bars, representing the category \"Mental Health Awareness\" with a percentage of 75%. The second is the corresponding x-axis label, \"Mental Health Awareness\"." } ], "id_raw": 578 }, { "id": "R80E3uXmbhICA3si", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Emotional Intelligence}[0.613, 0.319, 0.683, 0.807] longer than the bar for {Artificial Intelligence}[0.788, 0.270, 0.858, 0.807]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 578 }, { "id": "m0Lv7YNg44tPIZ9A", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nDoes the bar of {Workplace Flexibility}[0.090, 0.416, 0.160, 0.807] have a smaller height than the bar of {Continuous Learning}[0.439, 0.123, 0.509, 0.807]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 578 }, { "id": "F9GHQtPM7CFM24ax", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nBetween {Digital Literacy}[0.701, 0.465, 0.770, 0.807] and {Work-Life Balance}[0.526, 0.172, 0.596, 0.807], which bar is taller?" }, { "from": "gpt", "value": "Work-Life Balance." } ], "id_raw": 578 }, { "id": "2hpVYlIjCvJPkT0X", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nLooking at the bars, is {Leadership Development}[0.352, 0.221, 0.422, 0.807] valued higher than {Diversity & Inclusion}[0.265, 0.368, 0.334, 0.807]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 578 }, { "id": "6P8Cb9ofqhALE1n1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nCould you please go over the elements in the region [0.079, 0.813, 0.172, 0.953]?" }, { "from": "gpt", "value": "It contains the x-axis label \"Workplace Flexibility\"." } ], "id_raw": 578 }, { "id": "PNlJGvDkguC71K63", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the space [0.439, 0.123, 0.509, 0.807]." }, { "from": "gpt", "value": "It contains a blue bar that represents the value of \"Continuous Learning\" in the dataset." } ], "id_raw": 578 }, { "id": "ufLSezCDRMUqvckM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the visualization within [0.854, 0.813, 0.966, 0.981]." }, { "from": "gpt", "value": "It's the x-axis label for \"Mental Health Awareness\"." } ], "id_raw": 578 }, { "id": "DnAa9KrhOJEk2Mbc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat do we find in the area [0.090, 0.416, 0.160, 0.807]?" }, { "from": "gpt", "value": "It features a blue bar that represents the value for \"Workplace Flexibility\" which is 40%." } ], "id_raw": 578 }, { "id": "UGt1mE66FomBpRRw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.015, 0.329, 0.029, 0.537] on the Y-axis?" }, { "from": "gpt", "value": "Sustainability Score (%)." } ], "id_raw": 579 }, { "id": "aW8bkCJqm6JKauSW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nIn the bar chart, what category represents the bar at coordinates [0.108, 0.270, 0.176, 0.840]?" }, { "from": "gpt", "value": "Air Quality." } ], "id_raw": 579 }, { "id": "l3HgeBQGYo2yHVBM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the mark of the X-axis at [0.793, 0.846, 0.855, 0.923]?" }, { "from": "gpt", "value": "Population." } ], "id_raw": 579 }, { "id": "Pc6neguYFBYCqKSM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat category is represented by the bar at [0.790, 0.229, 0.858, 0.840]?" }, { "from": "gpt", "value": "Population." } ], "id_raw": 579 }, { "id": "W4L8qSs8bKliObSu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nWhich one is higher, the {bar of \"Population\"}[0.790, 0.229, 0.858, 0.840] or the {bar of \"Air Quality\"}[0.108, 0.270, 0.176, 0.840]?" }, { "from": "gpt", "value": "Air Quality." } ], "id_raw": 579 }, { "id": "IVddg3deu9neTa6Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nIs the {bar of \"Pollution\"}[0.704, 0.107, 0.773, 0.840] to the right of the {bar of \"Land Use\"}[0.619, 0.148, 0.687, 0.840]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 579 }, { "id": "moV3COTqalKIDxw1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar of \"Climate Change\"}[0.363, 0.148, 0.432, 0.840] and {the bar of \"Biodiversity\"}[0.193, 0.189, 0.261, 0.840], which one is taller?" }, { "from": "gpt", "value": "Climate Change." } ], "id_raw": 579 }, { "id": "b8Gr4BDXfA7AtdMO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Resources\"}[0.875, 0.270, 0.943, 0.840] to the left of {the bar of \"Population\"}[0.790, 0.229, 0.858, 0.840]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 579 }, { "id": "NuvaMBiKguDICltr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the information in the area with coordinates [0.066, 0.026, 0.985, 0.840]?" }, { "from": "gpt", "value": "This area contains the main chart space, with a gray background, and includes all the bars representing different categories with corresponding labels on the X-axis and values on the Y-axis." } ], "id_raw": 579 }, { "id": "wCR4L2sbgv947EuC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what you see in the region [0.043, 0.019, 0.061, 0.118]." }, { "from": "gpt", "value": "This is the bottommost segment of the Y-axis, showing labels for the range of y-values from 0 to 10, with their corresponding scale marks." } ], "id_raw": 579 }, { "id": "Zdx1x3HG47Qqj3LI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the layout in the range [0.066, 0.351, 0.985, 0.514]?" }, { "from": "gpt", "value": "This range mostly includes the middle area of the graph, which includes parts of the bars for all categories from Air Quality to Resources, reflecting the values associated with each category. The exact values are between 40 and 60 on the Y-axis." } ], "id_raw": 579 }, { "id": "UKRYCEJTnLK8Czp4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe any unique features found in the range [0.043, 0.100, 0.061, 0.362] on the y-axis?" }, { "from": "gpt", "value": "In this range, you can see the vertical labels for the values from 20 to 60 on the Y-axis, along with their corresponding scale marks on the axis." } ], "id_raw": 579 }, { "id": "Lu95esN36ufz0pyo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat label is present at [0.015, 0.364, 0.029, 0.537] in the chart?" }, { "from": "gpt", "value": "The Y-axis label, called Innovation Rate (%)." } ], "id_raw": 580 }, { "id": "ZJHjDpLumhy7eFFU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinate for the third label in the x-axis?" }, { "from": "gpt", "value": "[0.283, 0.881, 0.329, 0.939]" } ], "id_raw": 580 }, { "id": "AsEH8yHg5F530VYl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat object does the coordinates [0.271, 0.132, 0.340, 1.513] correlate to?" }, { "from": "gpt", "value": "The bar representing \"FinTech\" in the chart." } ], "id_raw": 580 }, { "id": "u4jk4b6yZXbqsuc4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nWhich industries are located at [0.794, 0.881, 0.851, 0.953] and [0.883, 0.881, 0.934, 0.946]?" }, { "from": "gpt", "value": "{NanoTech}[0.794, 0.881, 0.851, 0.953] and {GeoTech}[0.883, 0.881, 0.934, 0.946]." } ], "id_raw": 580 }, { "id": "zEiDdAU4p2tokjyc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nComparing {the GreenTech bar}[0.702, 0.387, 0.771, 1.513] and {the NanoTech bar}[0.788, 0.535, 0.857, 1.513], which one has a higher value?" }, { "from": "gpt", "value": "GreenTech." } ], "id_raw": 580 }, { "id": "q2PGQWtZpXrmLunK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nIs {the BioTech bar}[0.099, 0.344, 0.168, 1.513] taller than {the HealthTech bar}[0.616, 0.281, 0.685, 1.513]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 580 }, { "id": "hlPG2MiSYosu4UmH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nAre there more industries with innovation rate over 50% than those under 50%?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 580 }, { "id": "6qFdZGGk9XN1iSRC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nIn terms of innovation rate, is {GeoTech}[0.874, 0.196, 0.943, 1.513] performing better than {AgriTech}[0.357, 0.302, 0.426, 1.513]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 580 }, { "id": "DIO9bjcI0zYOd341", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the details within the area [0.035, 0.019, 0.052, 0.249]?" }, { "from": "gpt", "value": "This area has two Y-axis labels from top-bottom: \"50\" at [0.035, 0.444, 0.052, 0.461] and \"60\" at [0.035, 0.231, 0.052, 0.249]." } ], "id_raw": 580 }, { "id": "WGGo3usdb0FknLJA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's being shown in the coordinates [0.099, 0.344, 0.168, 1.513]?" }, { "from": "gpt", "value": "This is the bar representing the BioTech industry, with an innovation rate of 55%." } ], "id_raw": 580 }, { "id": "muan3a7rSryZWuzE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area [0.780, 0.881, 0.934, 0.946]?" }, { "from": "gpt", "value": "Near the right of the X-axis, it contains a label for the final category: 'GeoTech' with its corresponding tick." } ], "id_raw": 580 }, { "id": "0XRTUlJjL3rPVHEZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat indicators are within the regional coordinates [0.035, 0.019, 0.052, 0.886]?" }, { "from": "gpt", "value": "This contains the Y-axis labels from bottom-top: \"70\", \"60\", \"50\", \"40\", and \"30\"." } ], "id_raw": 580 }, { "id": "wMrkVHPWvbsFJH2a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.015, 0.384, 0.029, 0.476]?" }, { "from": "gpt", "value": "Values (%)." } ], "id_raw": 581 }, { "id": "qkt6SnjjFrkD9CYk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first text in the x-axis?" }, { "from": "gpt", "value": "[0.052, 0.846, 0.138, 0.953]" } ], "id_raw": 581 }, { "id": "kUG62ED0xzejTb8e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the category represented by the bar at [0.104, 0.278, 0.172, 0.834]?" }, { "from": "gpt", "value": "GradualDecline." } ], "id_raw": 581 }, { "id": "ak5wvdu5ZMc67yK2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the indicators or categories represented by the bars in [0.446, 0.177, 0.515, 0.834] and [0.532, 0.582, 0.601, 0.834]?" }, { "from": "gpt", "value": "{Outlier1}[0.446, 0.177, 0.515, 0.834] and {Outlier2}[0.532, 0.582, 0.601, 0.834]." } ], "id_raw": 581 }, { "id": "7xrYovhl39zrRtly", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"GradualDecline\"}[0.104, 0.278, 0.172, 0.834] higher or lower than {the bar representing \"SignificantIncrease\"}[0.875, 0.127, 0.943, 0.834]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 581 }, { "id": "vda6x6GTlCpQ1rhZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Outlier1\"}[0.446, 0.177, 0.515, 0.834] higher than {the bar representing \"Outlier2\"}[0.532, 0.582, 0.601, 0.834]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 581 }, { "id": "bStD6wzf5HPFffh0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing increases} [[0.618, 0.329, 0.686, 0.834], [0.703, 0.278, 0.772, 0.834], [0.789, 0.228, 0.857, 0.834], [0.875, 0.127, 0.943, 0.834]] generally higher than {the bars representing decreases} [[0.104, 0.278, 0.172, 0.834], [0.189, 0.329, 0.258, 0.834], [0.275, 0.430, 0.344, 0.834], [0.361, 0.531, 0.429, 0.834]]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 581 }, { "id": "EVgvcruGJfQWg6Xc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nWithin {\"GradualDecline\"}[0.104, 0.278, 0.172, 0.834], {\"GradualIncrease\"}[0.703, 0.278, 0.772, 0.834], and {\"SignificantIncrease\"}[0.875, 0.127, 0.943, 0.834], which category has the highest percentage?" }, { "from": "gpt", "value": "SignificantIncrease." } ], "id_raw": 581 }, { "id": "pQUYtCeAYef9TGRa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.043, 0.827, 0.052, 0.845] and [0.035, 0.019, 0.052, 0.036]." }, { "from": "gpt", "value": "There are two labels on the y-axis. The top label \"0\" is located at [0.043, 0.827, 0.052, 0.845], and the bottom label \"80\" is located at [0.035, 0.019, 0.052, 0.036];" } ], "id_raw": 581 }, { "id": "8O6xk3LB0qMALVE2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.052, 0.846, 0.909, 0.976]." }, { "from": "gpt", "value": "The area [0.052, 0.846, 0.909, 0.976] contains all category labels on the x-axis, which are: {GradualDecline}[0.052, 0.846, 0.138, 0.953], {SteadyDecline}[0.143, 0.846, 0.224, 0.947], {SuddenDecrease}[0.216, 0.846, 0.309, 0.963], {SignificantDecrease}[0.287, 0.846, 0.395, 0.981], {Outlier1}[0.431, 0.846, 0.481, 0.908], {Outlier2}[0.517, 0.846, 0.566, 0.908], {SteadyIncrease}[0.566, 0.846, 0.652, 0.953], {GradualIncrease}[0.647, 0.846, 0.738, 0.959], {SuddenIncrease}[0.735, 0.846, 0.823, 0.957], and {SignificantIncrease}[0.805, 0.846, 0.909, 0.976]." } ], "id_raw": 581 }, { "id": "MKyrhKGoDwAIKS68", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.275, 0.430, 0.429, 0.834]?" }, { "from": "gpt", "value": "There is 1 object which is the bar representing {SuddenDecrease}[0.275, 0.430, 0.344, 0.834] with a value between 0.00 and 40.00." } ], "id_raw": 581 }, { "id": "jhQGbNfXGwYi2vtz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.104, 0.278, 0.943, 0.834]?" }, { "from": "gpt", "value": "In this range, there are 10 bars that summarize the percentages of various categories, including GradualDecline, SteadyDecline, SuddenDecrease, SignificantDecrease, Outlier1, Outlier2, SteadyIncrease, GradualIncrease, SuddenIncrease, and SignificantIncrease." } ], "id_raw": 581 }, { "id": "aYQhtAQHzU96zGgN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label indicated by the coordinates [0.015, 0.374, 0.029, 0.557]?" }, { "from": "gpt", "value": "The Y-axis label is \"Revenue Change (%)\"." } ], "id_raw": 583 }, { "id": "3ARvW23hxv1xQjhi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the company name associated with the item at coordinates [0.099, 0.528, 0.168, 0.905]?" }, { "from": "gpt", "value": "The company name is Apple." } ], "id_raw": 583 }, { "id": "byyjUhPvnyCSXKtn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage number for the bar at the coordinates [0.185, 0.277, 0.254, 0.905]?" }, { "from": "gpt", "value": "The percentage number is 25%." } ], "id_raw": 583 }, { "id": "eZx1h94QY2oCBgM3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat color is the bar that is represented at [0.530, 0.302, 0.598, 0.905]?" }, { "from": "gpt", "value": "The color is Purple (hex code is #8e44ad)." } ], "id_raw": 583 }, { "id": "XDfWysiG0OP4muVW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nIs {Facebook's bar}[0.271, 0.453, 0.340, 0.905] higher than {Google's bar}[0.185, 0.277, 0.254, 0.905]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 583 }, { "id": "Q1HZnLGZzVmVulJF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Twitter}[0.788, 0.704, 0.857, 0.905] shorter than the bar for {Microsoft}[0.357, 0.352, 0.426, 0.905]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 583 }, { "id": "6BaNHsrTUuIRyUfx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing {SpaceX}[0.702, 0.201, 0.771, 0.905] have a value that is less than the bar representing {Netflix}[0.530, 0.302, 0.598, 0.905]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 583 }, { "id": "zF595wg2E5ipn1Mz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nBetween {Apple}[0.099, 0.528, 0.168, 0.905] and {Amazon}[0.444, 0.603, 0.512, 0.905], which company had a higher percentage revenue change?" }, { "from": "gpt", "value": "Amazon." } ], "id_raw": 583 }, { "id": "gP1yR5EwG6LLJoIp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects represented within the boundary [0.271, 0.151, 0.771, 0.905]." }, { "from": "gpt", "value": "There are 5 bars within the boundary which represent the revenue change of Facebook, Microsoft, Amazon, Netflix, and Tesla. Each bar's coordinates are:\n1. Facebook: [0.271, 0.453, 0.340, 0.905].\n2. Microsoft: [0.357, 0.352, 0.426, 0.905].\n3. Amazon: [0.444, 0.603, 0.512, 0.905].\n4. Netflix: [0.530, 0.302, 0.598, 0.905].\n5. Tesla: [0.616, 0.151, 0.685, 0.905]." } ], "id_raw": 583 }, { "id": "uBD6PGqnBdESxYK3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.883, 0.911, 0.934, 0.975]?" }, { "from": "gpt", "value": "The region [0.883, 0.911, 0.934, 0.975] contains the label \"LinkedIn\", which is a company name on the x-axis." } ], "id_raw": 583 }, { "id": "QMTZdDuhTmZdRYIj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the area [0.043, 0.898, 0.052, 0.916]?" }, { "from": "gpt", "value": "The area [0.043, 0.898, 0.052, 0.916] contains a label marker of 0 on the y-axis." } ], "id_raw": 583 }, { "id": "hMCiQXuvpcdy5fqz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the area from [0.357, 0.352, 0.426, 0.905] pertain to?" }, { "from": "gpt", "value": "This area is the region where the bar representing the company Microsoft is located, signifying a 22% revenue change." } ], "id_raw": 583 }, { "id": "y1IkkYr2viRlRDhQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label of the chart?" }, { "from": "gpt", "value": "Active Public Interest Level, found at {coordinates}[0.015, 0.328, 0.029, 0.562]." } ], "id_raw": 584 }, { "id": "ctMwWf9P8gUYVi3y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.322, 0.319, 0.408, 0.864]?" }, { "from": "gpt", "value": "The bar representing the topic \"Protest\"." } ], "id_raw": 584 }, { "id": "7yqNIXNdZGevBqFO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.543, 0.871, 0.615, 0.960]?" }, { "from": "gpt", "value": "\"Social Media\"." } ], "id_raw": 584 }, { "id": "rTlRww9QNfxzQsHL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar at coordinates [0.750, 0.571, 0.836, 0.864] representing?" }, { "from": "gpt", "value": "It represents the topic \"Unemployment\"." } ], "id_raw": 584 }, { "id": "gnbYkNsdCg7QhRzN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Climate Change\"}[0.108, 0.235, 0.193, 0.864] taller than {the bar representing \"Trade War\"}[0.429, 0.361, 0.515, 0.864]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 584 }, { "id": "N5THbMMCbD0i0r0K", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Healthcare\"}[0.858, 0.193, 0.943, 0.864] represent a higher interest level than {the bar representing \"Immigration\"}[0.643, 0.277, 0.729, 0.864]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 584 }, { "id": "msi1gx6C3fAEeXQ5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar representing \"Social Media\"}[0.536, 0.403, 0.622, 0.864] and {the bar representing \"Protest\"}[0.322, 0.319, 0.408, 0.864], which has a lower level of interest?" }, { "from": "gpt", "value": "Social Media." } ], "id_raw": 584 }, { "id": "m6O6QF9IseSIiAGm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.322, 0.319, 0.622, 0.864]." }, { "from": "gpt", "value": "This area includes three bars, each representing the topic and its respective Active Public Interest Level: \n1. \"Protest\" indicates a level of 65 located at {coordinates}[0.322, 0.319, 0.408, 0.864]. \n2. \"Trade War\" indicates a level of 60 located at {coordinates}[0.429, 0.361, 0.515, 0.864].\n3. \"Social Media\" indicates a level of 55 located at {coordinates}[0.536, 0.403, 0.622, 0.864]." } ], "id_raw": 584 }, { "id": "EJsWkzEnXcMlHiIa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content that is located within the coordinates [0.651, 0.277, 0.943, 0.864]?" }, { "from": "gpt", "value": "This region contains the bars for \"Immigration\" with a level of 70 located at {coordinates}[0.643, 0.277, 0.729, 0.864], \"Unemployment\" with a level of 35 located at {coordinates}[0.750, 0.571, 0.836, 0.864] and \"Healthcare\" with a level of 80 located at {coordinates}[0.858, 0.193, 0.943, 0.864]." } ], "id_raw": 584 }, { "id": "i7MoLMk1j93DnSdw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the labeling of the Y-axis of this chart?" }, { "from": "gpt", "value": "Virtual_Reality_Training, Artificial_Intelligence_Education, Innovative_Learning_Methods, and E-Learning_Platform are the categories, found at positions {Virtual_Reality_Training}[0.074, 0.172, 0.236, 0.195], {Artificial_Intelligence_Education}[0.015, 0.373, 0.236, 0.396], {Innovative_Learning_Methods}[0.030, 0.574, 0.236, 0.598], and {E-Learning_Platform}[0.097, 0.775, 0.236, 0.799]." } ], "id_raw": 585 }, { "id": "g3wPmHtY8AUvqi9t", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the X-axis of this chart represent?" }, { "from": "gpt", "value": "It represents the Risk Complication Score, which can be inferred from the label found at {x-axis label}[0.526, 0.952, 0.694, 0.975]." } ], "id_raw": 585 }, { "id": "dKnOAbNgXdtDJxsG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nWhere can I find the descriptor of the color corresponding to '5 Years'?" }, { "from": "gpt", "value": "You can find it in the legend with bar color at [0.259, 0.120, 0.286, 0.136] and label at {5 Years}[0.297, 0.118, 0.347, 0.141]." } ], "id_raw": 585 }, { "id": "PamjEUiznDwSNpOQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nIs the risk complication associated with Virtual_Reality_Training higher or lower in 5 years compared to 3 years?" }, { "from": "gpt", "value": "Lower, referencing the data points {3 years for Virtual_Reality_Training}[0.727, 0.100, 0.974, 0.141] and {5 years for Virtual_Reality_Training}[0.626, 0.141, 0.974, 0.181]." } ], "id_raw": 585 }, { "id": "jzObhUdXRyshCFmg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nIs the risk complication higher in E-Learning_Platform or in Innovative_Learning_Methods over 7 years?" }, { "from": "gpt", "value": "It is higher in the category {E-Learning_Platform}[0.391, 0.784, 0.974, 0.824] when compared to the configuration {Innovative_Learning_Methods}[0.552, 0.583, 0.974, 0.623]." } ], "id_raw": 585 }, { "id": "bRCvPNtWoSPEcHbJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nIs the risk complication of Artificial_Intelligence_Education higher in 10 years compared to 3 years?" }, { "from": "gpt", "value": "Yes, we can determine this from the comparison of {10 years for Artificial_Intelligence_Education}[0.351, 0.422, 0.974, 0.462] and {3 years for Artificial_Intelligence_Education}[0.512, 0.302, 0.974, 0.342]." } ], "id_raw": 585 }, { "id": "f23MarovhOeli4G3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe what you see in the subtitle area [[0.320, 0.025, 0.900, 0.055]]." }, { "from": "gpt", "value": "In the given area, there is a subtitle that reads \"Risk complications associated with EdTech Innovations over the years\"." } ], "id_raw": 585 }, { "id": "4qaYv5gcMFNOF4Bn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the figure show in the region [[0.574, 0.303, 0.728, 0.545]]?" }, { "from": "gpt", "value": "That region contains the \"Artificial_Intelligence_Education\" category with four bars representing the risk complication scores over 3, 5, 7, and 10 years." } ], "id_raw": 585 }, { "id": "mou55lS3ZACy0roB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat information does the [0.727, 0.100, 0.974, 0.141] region provide?" }, { "from": "gpt", "value": "This region displays a bar that represents a risk complication score for 3 Years for the Virtual_Reality_Training category. The X-axis range of -1.23 -> 0.00 indicates a negative risk complication score." } ], "id_raw": 585 }, { "id": "dVTCmr0xJaKq0BKY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label with coordinates [0.566, 0.952, 0.605, 0.975]?" }, { "from": "gpt", "value": "The label is \"Score\"." } ], "id_raw": 586 }, { "id": "eTmVUwAeJOFibBIV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the blue rectangle with coordinates [0.720, 0.775, 0.974, 0.820]?" }, { "from": "gpt", "value": "This is a bar representing the 2009 score for 5G Infrastructure." } ], "id_raw": 586 }, { "id": "wGzJLTFs8jm1DXZD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the orange bar at [0.741, 0.820, 0.974, 0.865]?" }, { "from": "gpt", "value": "This is a bar representing the 2019 score for 5G Infrastructure." } ], "id_raw": 586 }, { "id": "wzhgd6cwKZD7sbvA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nIs {the 2009 score for Optic-fiber provision}[0.276, 0.325, 0.974, 0.370] higher or lower than {the 2019 score for the same}[0.911, 0.370, 0.974, 0.415]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 586 }, { "id": "AgvXIrzvmICa2lb3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the 2009 bar for Ethernet-Cable provision}[0.234, 0.100, 0.974, 0.145] or {the 2019 bar for the same}[0.826, 0.145, 0.974, 0.190]?" }, { "from": "gpt", "value": "The 2009 bar for Ethernet-Cable provision" } ], "id_raw": 586 }, { "id": "zAoaDEGlc3gh3p0O", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nIs the {2009 Wi-Fi Hotspots bar}[0.847, 0.550, 0.974, 0.595] taller or shorter than {the 2009 5G Infrastructure bar}[0.720, 0.775, 0.974, 0.820]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 586 }, { "id": "4KtTrX9OamnpjEIZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.204, 0.815, 0.289, 0.891]." }, { "from": "gpt", "value": "This area contains two legend labels, one for 2009 represented with a handle color of pale red at coordinates [0.209, 0.826, 0.237, 0.842], and one for 2019 represented with a handle color of pale blue at coordinates [0.209, 0.861, 0.237, 0.877]." } ], "id_raw": 586 }, { "id": "1VazBGxASi0S1fTC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region [0.048, 0.586, 0.187, 0.609]?" }, { "from": "gpt", "value": "This region contains the y-axis label \"Wi-Fi Hotspots\"." } ], "id_raw": 586 }, { "id": "A1wZaFHfhp1xCven", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nDescribe what is included in the area from [0.009, 0.483, 0.009, 0.483]." }, { "from": "gpt", "value": "This area includes the y-axis label component of the chart." } ], "id_raw": 586 }, { "id": "wG6vXQAdLH8hed4a", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.490, 0.025, 0.698, 0.055] referring to?" }, { "from": "gpt", "value": "The text is a subtitle, \"Global Trends 2017-2020\"." } ], "id_raw": 587 }, { "id": "o1IxHCjUJAEOxpxK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object located at the coordinates [0.898, 0.190, 0.926, 0.206]?" }, { "from": "gpt", "value": "It represents the colour label of the legend for the year 2020." } ], "id_raw": 587 }, { "id": "4l7Uef0wxBpnvGLE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.082, 0.172, 0.193, 0.195] on the chart represent?" }, { "from": "gpt", "value": "It represents the category \"Global Warming\"." } ], "id_raw": 587 }, { "id": "5iFSEH9wCnalmi8M", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nIs the bar for \"2017\" at {coordinates of the 2017 bar}[0.203, 0.100, 0.513, 0.141] longer than the bar for \"2018\" at {coordinates of the 2018 bar}[0.203, 0.141, 0.573, 0.181]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 587 }, { "id": "ze57Gkvxjw9AbFnm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller between the \"Population Growth\" one located at {Population Growth bar 2017}[0.203, 0.503, 0.824, 0.543] and the \"Technology Advancement\" one located at {Technology Advancement bar 2017}[0.203, 0.704, 0.617, 0.744] for the year 2017?" }, { "from": "gpt", "value": "\"Population Growth\"." } ], "id_raw": 587 }, { "id": "1LH85oKwhUtNW3tJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nCompared to the \"Global Warming\" bar for 2018 located at {Global Warming bar 2018}[0.203, 0.141, 0.573, 0.181], is the \"Global Warming\" bar for 2020 located at {Global Warming bar 2020}[0.203, 0.221, 0.543, 0.261] taller or shorter?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 587 }, { "id": "69JsRMkUxvuMoNjB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat is shown within the coordinates [0.893, 0.074, 0.978, 0.220]?" }, { "from": "gpt", "value": "This is a legend for the bar chart. It contains color labels and ranked years - 2017 is represented by {color label}[0.898, 0.085, 0.926, 0.101], 2018 is represented by {color label}[0.898, 0.120, 0.926, 0.136], 2019 is represented by {color label}[0.898, 0.155, 0.926, 0.171], and 2020 is represented by {color \"label}[0.898, 0.190, 0.926, 0.206]." } ], "id_raw": 587 }, { "id": "xfKsrBp3ekf4StJX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is present in the region defined by [0.203, 0.100, 0.513, 0.141]" }, { "from": "gpt", "value": "In this region, there is a bar representing the score (30.0) for the category \"Global Warming\" in 2017." } ], "id_raw": 587 }, { "id": "YZ54EQiUaDJMT2Sl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat details are included in [0.065, 0.574, 0.193, 0.597]?" }, { "from": "gpt", "value": "This region encompasses the label for the category \"Population Growth\"." } ], "id_raw": 587 }, { "id": "w8hLs8SChlYVNRWv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the location [0.106, 0.574, 0.195, 0.597]?" }, { "from": "gpt", "value": "VPN Security" } ], "id_raw": 588 }, { "id": "Y9lczRUW0C8nBpsf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat color does the rectangle at [0.218, 0.525, 0.245, 0.542] represent?" }, { "from": "gpt", "value": "The rectangle color is #add8e6, representing for the year 2014." } ], "id_raw": 588 }, { "id": "ptnKTPH7oaFKix1e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the value represented by the bar at [0.457, 0.744, 0.974, 0.784]?" }, { "from": "gpt", "value": "-2.4" } ], "id_raw": 588 }, { "id": "VGO8ZDseyxBXYUw4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nWhich category does the coordinate [0.015, 0.775, 0.195, 0.798] refer to?" }, { "from": "gpt", "value": "Two-Factor Authentication" } ], "id_raw": 588 }, { "id": "eXzTCRcidtxhlEE0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nDoes the element {bar of 2011 for Firewall Protection}[0.306, 0.302, 0.974, 0.342] have a higher value than {bar of 2014 for Firewall Protection}[0.457, 0.422, 0.974, 0.462]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 588 }, { "id": "zAkuCEYaDBmkiwnT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nIs {cybersecurity category bar of 2013}[0.306, 0.583, 0.974, 0.623] shorter or taller than {cybersecurity category bar of 2014}[0.328, 0.623, 0.974, 0.664]?" }, { "from": "gpt", "value": "Taller" } ], "id_raw": 588 }, { "id": "2uxTqVCZesFuA8Xw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nAmong {the 2011 bars} grouped by each cybersecurity category and {the 2012 bars}, which group of bars are generally taller?" }, { "from": "gpt", "value": "The 2011 bars" } ], "id_raw": 588 }, { "id": "EcusNX65zHhBUFOh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nAmong the bars representing {\"Two-Factor Authentication\"} in different years, which year's bar is the shortest?" }, { "from": "gpt", "value": "2014" } ], "id_raw": 588 }, { "id": "4DCjKI13zX8pdATr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.212, 0.409, 0.297, 0.556]." }, { "from": "gpt", "value": "The region [0.212, 0.409, 0.297, 0.556] constitutes the legend of the chart which lists and describes all distinct items of the plot. It contains four small colored rectangles, each representing a year from 2011 to 2014, along with their associated labels. The color corresponding to each year is as follows: 2011 is #fa8072, 2012 is #87ceeb, 2013 is #ff7f50, and 2014 is #add8e6." } ], "id_raw": 588 }, { "id": "ZI5l5QVch6UNXsYr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nPlease discuss what you can see in the area [0.205, 0.062, 0.974, 0.903]." }, { "from": "gpt", "value": "In this region, one can see the main body of the chart including all bars representing the values of different cybersecurity categories (Encryption Standard, Firewall Protection, VPN Security, Two-Factor Authentication) across four years (2011-2014), the legend denoting the color-year relationship, and the axes of the chart." } ], "id_raw": 588 }, { "id": "P5YpTF2SgpKYBt3z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in this region [0.053, 0.172, 0.195, 0.195]?" }, { "from": "gpt", "value": "In the region [0.053, 0.172, 0.195, 0.195], there is a y-axis label representing the cybersecurity category \"Encryption Standard\"." } ], "id_raw": 588 }, { "id": "x2xmMDOg7mXG8rJf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be identified in the area [0.053, 0.172, 0.974, 0.221]?" }, { "from": "gpt", "value": "The area corresponds to the row representing the \"Encryption Standard\" in the bar chart. And the horizontal bars for all four years are within the region. Among them, the 2011 bar extends to a point corresponding to a score of -3.0, the 2012 bar extends to a point corresponding to a score of -2.8, the 2013 bar extends to a score of -2.8, and the 2014 bar extends the least, only to a point corresponding to a score of -2.6." } ], "id_raw": 588 }, { "id": "LHPsYFPwMedFbmYQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at [0.217, 0.100, 0.510, 0.141]?" }, { "from": "gpt", "value": "The main object is a bar representing the trend of Electric Vehicle Sales projected for Year 2020." } ], "id_raw": 589 }, { "id": "pG9MYmXLq4L9AcLX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinates of the legend that denotes Year 2021?" }, { "from": "gpt", "value": "[0.904, 0.118, 0.973, 0.141]." } ], "id_raw": 589 }, { "id": "zbdHulg4p6mCXyvu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted at coordinates [0.217, 0.824, 0.656, 0.865]?" }, { "from": "gpt", "value": "It is a bar representing Green Energy Consumption for Year 2023." } ], "id_raw": 589 }, { "id": "IwLd5ECyEgMOTmSI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nWhat section does the notation [0.904, 0.188, 0.972, 0.211] refer to?" }, { "from": "gpt", "value": "It refers to {the legend item for Year 2023}[0.904, 0.188, 0.972, 0.211]." } ], "id_raw": 589 }, { "id": "lHDQvzXo3MU3kgo4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Year 2022 of the Green Energy Consumption}[0.217, 0.784, 0.546, 0.824] longer than {the bar for Year 2021 of the same}[0.217, 0.744, 0.436, 0.784]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 589 }, { "id": "k5jc9lACQCcNo3yc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nIs {the Year 2021 bar of Electric Vehicle Sales}[0.217, 0.141, 0.583, 0.181] taller than {the Year 2020 bar of the same}[0.217, 0.100, 0.510, 0.141]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 589 }, { "id": "FfVSyJHT5KqLSsrO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nWhich is higher - {the bar for AI Technology Stocks in Year 2022}[0.217, 0.382, 0.846, 0.422] or {the same for Year 2023}[0.217, 0.422, 0.817, 0.462]?" }, { "from": "gpt", "value": "Year 2022." } ], "id_raw": 589 }, { "id": "w5Y5lrkoK5sKuUuq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing Green Energy Consumption}[0.217, 0.704, 0.363, 0.744], [0.217, 0.744, 0.436, 0.784], [0.217, 0.784, 0.546, 0.824], [0.217, 0.824, 0.656, 0.865] generally growing over the years?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 589 }, { "id": "Fx89o0E2yjlAErfu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.859, 0.074, 0.978, 0.220] contain?" }, { "from": "gpt", "value": "This region contains the legend of the chart. The legend presents four items, each consisting of a color-label and a corresponding text-label, representing four different years from 2020 to 2023." } ], "id_raw": 589 }, { "id": "TYvo4dDfP2QDzhqV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area of the plot at [0.217, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "This area represents the main plot of the graph. It contains bars of four different colors, each showing the trend predictions over four years for Electric Vehicle Sales, AI Technology Stocks, Cyber Security Investments, and Green Energy Consumption." } ], "id_raw": 589 }, { "id": "Aoo3md2TI4uNmJDI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the bars within [0.217, 0.100, 0.948, 0.342]." }, { "from": "gpt", "value": "This area contains the bars representing the Electric Vehicle Sales and AI Technology Stocks over four years (2020 - 2023). Each year's performance is represented by a rectangular bar, colored distinctly as per the legend." } ], "id_raw": 589 }, { "id": "l55Dt7GeOAsoZ0Ti", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what you see in the region [0.015, 0.574, 0.207, 0.597]?" }, { "from": "gpt", "value": "This region mentions the label for the 'Cyber Security Investments' indicator, which is part of the y-axis labels." } ], "id_raw": 589 }, { "id": "MwjwcTU1DwJQ4Bdn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the legend}[0.849, 0.074, 0.978, 0.220] assign to the \"Emo\" fashion trend?" }, { "from": "gpt", "value": "Purple represented by #ff00ff." } ], "id_raw": 590 }, { "id": "wukjQjrgFXPJJnhP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar length representing the \"Steampunk\" trend in the category \"Boho\", and where is it located?" }, { "from": "gpt", "value": "The bar length for \"Steampunk\" in the \"Boho\" category is 5.7, and the coordinates are [0.104, 0.621, 0.687, 0.652]." } ], "id_raw": 590 }, { "id": "GQ2vyzxvJvyJ3J6R", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nWhat does {the trend category}[0.893, 0.188, 0.929, 0.211] in the legend refer to?" }, { "from": "gpt", "value": "It refers to the \"Boho\" fashion trend." } ], "id_raw": 590 }, { "id": "rduazpuS81U5dttE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the orange bar labeled \"Boho\" in the chart?" }, { "from": "gpt", "value": "The value of the \"Boho\" bar, located at [0.104, 0.833, 0.769, 0.865], is 6.5." } ], "id_raw": 590 }, { "id": "KDuuITalVTqFNZkD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing \"Emo\" have a higher score in \"Grunge\" or in \"Boho\"?" }, { "from": "gpt", "value": "In \"Boho\"." } ], "id_raw": 590 }, { "id": "586NV9Jl7VyLwbdg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Steampunk in Emo}[0.104, 0.164, 0.319, 0.196] shorter or longer than {the bar for Steampunk in Grunge}[0.104, 0.376, 0.718, 0.408]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 590 }, { "id": "jKyJmhKT1KbYZEKH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nCompare the score of {the Grunge trend in the Boho category}[0.104, 0.769, 0.820, 0.801] to {the Grunge trend in the Steampunk category}[0.104, 0.557, 0.790, 0.589]. Which one is higher?" }, { "from": "gpt", "value": "The score in the Boho category is higher." } ], "id_raw": 590 }, { "id": "GRVQmkBX5c2pnsLS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nBetween {the Emo bar}[0.104, 0.100, 0.626, 0.132] and {the Grunge bar}[0.104, 0.313, 0.585, 0.345], which one is longer?" }, { "from": "gpt", "value": "The Emo bar." } ], "id_raw": 590 }, { "id": "tcENdJi6h129xSi5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nDescribe all elements in this area [0.104, 0.557, 0.902, 0.621]." }, { "from": "gpt", "value": "This area includes:\n 1. Blue bar representing the score of the Emo fashion trend in the Steampunk category, the coordinates are [0.104, 0.557, 0.708, 0.557].\n 2. Green bar representing the score of the Grunge fashion trend in the Steampunk category, the coordinates are [0.104, 0.557, 0.790, 0.557].\n 3. The \"Steampunk\" label on the y-axis, coordinates are [0.015, 0.580, 0.094, 0.603]." } ], "id_raw": 590 }, { "id": "vcdI2sxbMM0Fohus", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is present in the region defined by the coordinates [0.104, 0.769, 0.820, 0.801]?" }, { "from": "gpt", "value": "This region contains three bars, each representing the score of a distinct fashion trend (Emo, Grunge, and Steampunk) within the Boho category. Additionally, there is a y-axis label for \"Boho\"." } ], "id_raw": 590 }, { "id": "b9Q4LdZa1UReq0sB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.043, 0.367, 0.094, 0.391] contain?" }, { "from": "gpt", "value": "This region contain the Y-axis label referring to the \"Grunge\" fashion trend." } ], "id_raw": 590 }, { "id": "2zZWaPvME1QVzWOU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nWhat is described by the area [0.104, 0.100, 0.626, 0.132]?" }, { "from": "gpt", "value": "This area contains a single pink bar representing the score of the Emo trend within the Emo category." } ], "id_raw": 590 }, { "id": "w6GAP8LqFOH5oqbK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat text is indicated by the coordinates [0.361, 0.025, 0.752, 0.055]?" }, { "from": "gpt", "value": "The text indicated is the subtitle, which reads: \"Education Resource Distribution and Utilization\"." } ], "id_raw": 591 }, { "id": "zAyUeR4eAdjJ85BZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.797, 0.861, 0.910, 0.877]?" }, { "from": "gpt", "value": "It is the color label of Cycle 2 represented in the legend, which is orange." } ], "id_raw": 591 }, { "id": "zjXc8FNtK5D4Gyfn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label located at [0.009, 0.483, 0.009, 0.483]?" }, { "from": "gpt", "value": "The y-axis label is not specified as it is an empty string." } ], "id_raw": 591 }, { "id": "GqWmqcd20BXR8K3s", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located on the x-axis at the coordinates [0.344, 0.919, 0.362, 0.942]?" }, { "from": "gpt", "value": "The object is the x-axis tick label, which reads '40'." } ], "id_raw": 591 }, { "id": "SBWcA7VVeMTS1jZa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nAre there more bars in {Cycle 1}[0.127, 0.100, 0.888, 0.792] or {Cycle 2}[0.127, 0.173, 0.899, 0.865]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 591 }, { "id": "ws4KwWckYJBib3nH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nAmong the bars in {Cycle 1}[0.127, 0.100, 0.888, 0.792], is the {Vicuna-HAG bar}[0.127, 0.720, 0.730, 0.792] larger than the {LLAMA-Default bar}[0.127, 0.100, 0.888, 0.173]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 591 }, { "id": "PGJgvDRwN2dvKAnL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nIs {Cycle 2 Vicuna-HAG bar}[0.127, 0.792, 0.803, 0.865] larger than {Cycle 1 Vicuna-HAG bar}[0.127, 0.720, 0.730, 0.792]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 591 }, { "id": "3W2vyyegey95da39", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nDoes the {text label for Cycle 2}[0.921, 0.859, 0.972, 0.882] appear below or above the {color label for Cycle 2}[0.882, 0.861, 0.910, 0.877]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 591 }, { "id": "fq1co4ZMXe5jxcaM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat do the contents in the region [0.877, 0.815, 0.978, 0.891] describe?" }, { "from": "gpt", "value": "The region [0.877, 0.815, 0.978, 0.891] displays the legend of the bar chart, with two items. The first item indicates Cycle 1 in blue color and its label coordinate at [0.921, 0.824, 0.973, 0.847]. The second item indicates Cycle 2 in orange color and its corresponding label is at [0.921, 0.859, 0.972, 0.882]." } ], "id_raw": 591 }, { "id": "x0CNEyzt5iZyI58y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents in the region [0.488, 0.952, 0.624, 0.975]." }, { "from": "gpt", "value": "The region [0.488, 0.952, 0.624, 0.975] contains the xlabel of the chart, which is \"Resource utilization\"." } ], "id_raw": 591 }, { "id": "4CP3zF7bxOU1XdAC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area defined by [0.123, 0.919, 0.929, 0.942]?" }, { "from": "gpt", "value": "The region represents the x-axis tick labels of the chart from '0' to '160' at intervals of '20'." } ], "id_raw": 591 }, { "id": "T55BywU2YTIVXBnR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the area [0.127, 0.100, 0.899, 0.865]." }, { "from": "gpt", "value": "This area conveys information about the bars of Cycles 1 and 2 regarding education resource distribution and utilization. Each of the four labelled categories (LLAMA-Default, LLAMA-HAG, Vicuna-Default, Vicuna-HAG) has bars for both cycles, illustrating different values for the respective categories." } ], "id_raw": 591 }, { "id": "iEWbagdEDMzWdCim", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat color is associated with {Socialization}<{Socialization legend handle}[0.846, 0.456, 0.874, 0.472] in the legend?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 592 }, { "id": "GnmmkETnqQxYewkm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the {x-axis label}<{x-axis label coordinates}[0.542, 0.952, 0.613, 0.975] read?" }, { "from": "gpt", "value": "Frequency." } ], "id_raw": 592 }, { "id": "cAdious93542qq65", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nWhich category is located at {this point}[0.170, 0.825] on the y-axis?" }, { "from": "gpt", "value": "Pronounced-Increase." } ], "id_raw": 592 }, { "id": "KE22ROVuM4USx644", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Sharp-Decrease for Outdoor}<{Sharp-Decrease Outdoor bar}[0.170, 0.140, 0.817, 0.159] greater or lesser than {Sharp-Decrease for Lifestyle}<{Sharp-Decrease Lifestyle bar}[0.170, 0.159, 0.860, 0.179]?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 592 }, { "id": "Fc6wN6M3o1mO5Wfb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nIs the {first y-axis label}<{first y-axis label coordinates}[0.050, 0.130, 0.160, 0.154] higher or lower than the {last y-axis label}<{last y-axis label coordinates}[0.015, 0.816, 0.160, 0.840] on the chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 592 }, { "id": "HVRfPK59DuBqBYUu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nDoes {Moderate-Growth for Socialization}<{Moderate-Growth Socialization bar}[0.170, 0.316, 0.731, 0.336] have a greater frequency than {Moderate-Growth for Recreation}<{Moderate-Growth Recreation bar}[0.170, 0.296, 0.705, 0.316]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 592 }, { "id": "q4RHMUybkjRtc8ro", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat is detailed in the region [0.840, 0.409, 0.978, 0.556]?" }, { "from": "gpt", "value": "The legend includes four items:\n1. {Recreation}[0.846, 0.421, 0.874, 0.437] is represented by a teal color,\n2. {Socialization}[0.846, 0.456, 0.874, 0.472] is shown in red,\n3. {Outdoor}[0.846, 0.490, 0.874, 0.507] has a green color,\n4. {Lifestyle}[0.846, 0.525, 0.874, 0.542] is in blue." } ], "id_raw": 592 }, { "id": "HByweD65Se1jSBHw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nTell me about the features at [0.170, 0.394, 0.774, 0.414]." }, { "from": "gpt", "value": "This region contains a {Fluctuating-Decline bar for Recreation}[0.170, 0.394, 0.774, 0.414] with a value label of \"70\"[0.842, 0.157, 0.860, 0.181], it's in teal color and corresponds to the Recreation category." } ], "id_raw": 592 }, { "id": "fiWbv59jN2FibeEP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are included in the region given by the coordinates [0.170, 0.806, 0.834, 0.825]?" }, { "from": "gpt", "value": "Within the specified region, there are two main objects:\n1. A {bar for Pronounced-Increase in Socialization}[0.170, 0.806, 0.834, 0.825] in red, with a value label of \"77\"[0.808, 0.451, 0.825, 0.475],\n2. The corresponding part of the {y-axis with the label \"Pronounced-Increase\"}[0.015, 0.816, 0.160, 0.840]." } ], "id_raw": 592 }, { "id": "6UMuWwxValyKuOk3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the structure of bars present at the coordinates [0.499, 0.171, 0.579, 0.213]?" }, { "from": "gpt", "value": "The structure represents the progress of \"Robotic Automation\" in the past\u201c1 year\u201d. The range of x-axis's value for this is from -0.50 to 0.00. The color of these bars is '#fa8072', specified in the \u201c1 year\u201d bar group." } ], "id_raw": 593 }, { "id": "3Pi0exmtjuJZKWzl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat kind of technology is shown in position [0.033, 0.796, 0.131, 0.813]?" }, { "from": "gpt", "value": "The technology shown in these coordinates is \"Nano Technology\"." } ], "id_raw": 593 }, { "id": "EK27Jkam9D7BkYMk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.927, 0.062, 0.964, 0.080]?" }, { "from": "gpt", "value": "It's a label text in the sublegend, indicating the time frame \"1 year\"." } ], "id_raw": 593 }, { "id": "qiNGiOo6l6a2Q49t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nIn terms of the 10-year development outlook, is the confidence in {Artificial Intelligence}[0.419, 0.424, 0.579, 0.466] higher or lower than {Nano Technology}[0.579, 0.845, 0.820, 0.887]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 593 }, { "id": "SwYOMXATWh4PRsyY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nIs {Blockchain}[0.579, 0.508, 0.917, 0.550] at a higher or lower position than {Artificial Intelligence}[0.499, 0.297, 0.579, 0.339] in the \"1 year\" category?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 593 }, { "id": "QDjTvOuDMBI2N0pK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nIs the prediction for {Robotic Automation}[0.178, 0.213, 0.579, 0.255] worse or better than {Blockchain}[0.579, 0.508, 0.917, 0.550] in the \"5 years\" category?" }, { "from": "gpt", "value": "Worse" } ], "id_raw": 593 }, { "id": "ZbBmsNNAtL2c1vv3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat content is inside the region [0.013, 0.375, 0.131, 0.392]?" }, { "from": "gpt", "value": "In this region, there is a label of y-axis named 'Artificial Intelligence'." } ], "id_raw": 593 }, { "id": "WiYG9kRz4gdQJ3d7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe what you see in the area [0.927, 0.062, 0.977, 0.158]?" }, { "from": "gpt", "value": "Within this region, there are four different labels indicating the time frame of the data. The labels are \"1 year\", \"2 years\", \"5 years\", and \"10 years\" respectively. Each label corresponds to a differently colored bar to differentiate data in the chart, with their corresponding colored rectangles located slightly to their left." } ], "id_raw": 593 }, { "id": "HBVP3agZwbVa9LqK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nPlease list and describe the labels included in the area defined by these coordinates [0.068, 0.585, 0.131, 0.813]." }, { "from": "gpt", "value": "In this region, there are two labels positioned horizontally. The first label, \"Blockchain\", indicates the concerning technology for the y-axis, the coordinates of this label are [0.068, 0.585, 0.131, 0.603]. The second label in this region, \"Nano Technology\", also indicates a type of technology for the y-axis. The coordinates for this label are [0.033, 0.796, 0.131, 0.81]." } ], "id_raw": 593 }, { "id": "Lqpj4Kt1oCmrlX1g", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of object with the coordinates [0.383, 0.100, 0.632, 0.143]?" }, { "from": "gpt", "value": "The object is a bar in the bar chart, representing the \"Private Donations\" value for \"Food_Banks\"." } ], "id_raw": 595 }, { "id": "T6GOP52WAhTxgwDb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat name is given to the object located at [0.849, 0.083, 0.973, 0.106]?" }, { "from": "gpt", "value": "The name of the object is \"Private Donations\"." } ], "id_raw": 595 }, { "id": "2Qg20uKZagZRSixc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nWith reference to the {xticks}[0.039, 0.919, 0.060, 0.942], what value does the tick mark represent?" }, { "from": "gpt", "value": "The tick mark represents the value \"-2\"." } ], "id_raw": 595 }, { "id": "kYSk192Vp0DpREoy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for State Funding and Homeless Shelters}[0.383, 0.355, 0.898, 0.398] higher or lower than the {bar for Private Donations and Homeless Shelters}[0.383, 0.313, 0.749, 0.355]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 595 }, { "id": "y6OeAIZ78x80410Z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nComparing {Private Donations for Food Banks}[0.383, 0.100, 0.632, 0.143] and {State Funding for Food Banks}[0.383, 0.143, 0.782, 0.185], which value is greater?" }, { "from": "gpt", "value": "State Funding for Food Banks." } ], "id_raw": 595 }, { "id": "KNnGIsRzOS3xRtsW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nDoes {Private Donations for Animal Welfare}[0.383, 0.737, 0.532, 0.780] have a higher or lower value than the {State Funding for Animal Welfare}[0.383, 0.780, 0.832, 0.822]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 595 }, { "id": "lbenJpbIB3lBuR4E", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat content is presented in the region [0.805, 0.074, 0.978, 0.185]?" }, { "from": "gpt", "value": "This region contains the legend of the bar chart. It displays three labels alongside corresponding colors. The labels are \"Private Donations\"[0.849, 0.083, 0.973, 0.106], \"State Funding\"[0.849, 0.118, 0.946, 0.141], and \"Volunteer Work\"[0.849, 0.153, 0.955, 0.176] with their corresponding color handles being [0.810, 0.085, 0.838, 0.101], [0.810, 0.120, 0.838, 0.136], and [0.810, 0.155, 0.838, 0.171] respectively." } ], "id_raw": 595 }, { "id": "uB8tCaQWiCrkiKow", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.383, 0.737, 0.532, 0.780]." }, { "from": "gpt", "value": "This region includes one blue bar representing \"Private Donations\" for \"Animal_Welfare\" with a value of 0.9." } ], "id_raw": 595 }, { "id": "PYLL76R0kBKGCNpO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.179, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "This region make up the entire plotted area of the chart including the axis labels, ticks, legend, and data bars." } ], "id_raw": 595 }, { "id": "89YDXFGMhnqSbgAi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for the object 'Asia' on the chart?" }, { "from": "gpt", "value": "[0.087, 0.577, 0.116, 0.600]" } ], "id_raw": 596 }, { "id": "eyoIMbqqo99UV0sR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.408, 0.025, 0.692, 0.055]?" }, { "from": "gpt", "value": "It's the subtitle of the chart: 'Consumer Index Scores by Region'." } ], "id_raw": 596 }, { "id": "6fj6OEoXTCNfAXWa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the representation within the coordinates [0.132, 0.074, 0.334, 0.151]?" }, { "from": "gpt", "value": "That would be the {legend}[0.132, 0.074, 0.334, 0.151] of the chart that includes labels for 'OffP_Consumer_Index' and 'OnP_Consumer_Index'." } ], "id_raw": 596 }, { "id": "xuq6tG04wUOurEGp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nComparing {the bar for North America in OffP_Consumer_Index}[0.651, 0.100, 0.974, 0.173] and {the bar for North America in OnP_Consumer_Index}[0.570, 0.173, 0.974, 0.245], which has a lower score?" }, { "from": "gpt", "value": "OffP_Consumer_Index." } ], "id_raw": 596 }, { "id": "EppclBYS1G9aQGfB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nWhich one is higher, {the bar for Europe in OffP_Consumer_Index}[0.489, 0.307, 0.974, 0.379] or {the bar for Europe in OnP_Consumer_Index}[0.435, 0.379, 0.974, 0.452]?" }, { "from": "gpt", "value": "OnP_Consumer_Index." } ], "id_raw": 596 }, { "id": "jY9Ub0DmpJKth08O", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Africa in OffP_Consumer_Index}[0.166, 0.720, 0.974, 0.792] taller or shorter than {the bar for Asia in OnP_Consumer_Index}[0.543, 0.586, 0.974, 0.658]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 596 }, { "id": "M4x0gOABeYXmj71H", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained within the coordinates [0.125, 0.062, 0.974, 0.903]?" }, { "from": "gpt", "value": "The area [0.125, 0.062, 0.974, 0.903] covers the entire bar chart, including the x and y axes labels, axes ticks, the x and y grid lines, chart title, all the bars representing consumer indices, and the legend." } ], "id_raw": 596 }, { "id": "XvSdGxKYtcWE2DoM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the region [0.515, 0.173, 0.974, 0.245]?" }, { "from": "gpt", "value": "The region contains two groups of bars:\n1. The first group {OffP_Consumer_Index for North America}[0.651, 0.100, 0.974, 0.173]\n2. The second group {OnP_Consumer_Index for North America}[0.570, 0.173, 0.974, 0.245]" } ], "id_raw": 596 }, { "id": "k6XRqI3RqADRmSWu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat's within the area [0.132, 0.074, 0.334, 0.151]?" }, { "from": "gpt", "value": "In the area [0.132, 0.074, 0.334, 0.151], there are two legend entries:\n1. The first legend entry {OffP_Consumer_Index}[0.177, 0.083, 0.329, 0.107], which is marked with a color equivalent to '#90ee90'.\n2. The second legend entry {OnP_Consumer_Index}[0.177, 0.118, 0.327, 0.142], which is marked with a color equivalent to '#add8e6'." } ], "id_raw": 596 }, { "id": "tQu2KvUNYtYh3DrU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}'s label [0.178, 0.751, 0.947, 0.808] on the chart?" }, { "from": "gpt", "value": "Rapid Recovery" } ], "id_raw": 597 }, { "id": "SY2RLV3o2khp2WBC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar}[0.178, 0.808, 0.877, 0.865] from the legend box?" }, { "from": "gpt", "value": "Orange(from the object {Community Development}[0.794, 0.118, 0.973, 0.141] in the legend region [0.750, 0.074, 0.978, 0.151])" } ], "id_raw": 597 }, { "id": "1gQWdUBT0WvOu99C", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nWhat is written at the right end of {the x-axis scale} located at [0.866, 0.919, 0.888, 0.942]?" }, { "from": "gpt", "value": "1.2" } ], "id_raw": 597 }, { "id": "gEaMEKBaro5kVGV7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the title} displayed at [0.322, 0.025, 0.840, 0.055]?" }, { "from": "gpt", "value": "Local Government and Community Development Performance" } ], "id_raw": 597 }, { "id": "LmJwbR18MAUXOgVM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the Local Government growth bar for Rapid Recovery}[0.178, 0.751, 0.947, 0.808] extend further than {the Community Development growth bar for Rapid Recovery}[0.178, 0.808, 0.877, 0.865]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 597 }, { "id": "7Z6JGHzGqHgIwc2b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nIs {the orange bar}[0.178, 0.808, 0.877, 0.865] lower than {the blue bar}[0.178, 0.751, 0.947, 0.808] in the Rapid Recovery section?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 597 }, { "id": "jFcnXEhNIzcr4bdF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Local Government\"}[0.178, 0.100, 0.737, 0.157] and {\"Community Development\"}[0.178, 0.157, 0.597, 0.214] which bar is longer in the \"Slow increase\" section?" }, { "from": "gpt", "value": "Local Government." } ], "id_raw": 597 }, { "id": "VlKbXnPpVERaEqiF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nBetween the {\"Local Government\"}[0.178, 0.100, 0.737, 0.157] and {\"Community Development\"}[0.178, 0.157, 0.597, 0.214], which performance bar is higher at the 0.7 mark on the x-axis scale?" }, { "from": "gpt", "value": "Local Government." } ], "id_raw": 597 }, { "id": "h5gUbX5U4zKlh76N", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects identified in the region [0.750, 0.074, 0.978, 0.151]." }, { "from": "gpt", "value": "This area includes the legend. There are two entries: {Local Government}[0.794, 0.083, 0.921, 0.106] represented by a blue color at [0.755, 0.085, 0.783, 0.101], and {Community Development}[0.794, 0.118, 0.973, 0.141] represented by an orange color at [0.755, 0.120, 0.783, 0.136]." } ], "id_raw": 597 }, { "id": "nir0JnXbQJysolpN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the section [0.015, 0.473, 0.168, 0.497] of the chart." }, { "from": "gpt", "value": "This section contains only one part of the y-axis label, which reads \"Gradual Improvement\"." } ], "id_raw": 597 }, { "id": "URerhA8w9Rs6p9HF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region at [0.178, 0.100, 0.737, 0.157]?" }, { "from": "gpt", "value": "Within this region, there is a blue bar representing the performance measurement for \"Slow Increase\" of the \"Local Government\"." } ], "id_raw": 597 }, { "id": "REUpD6qvvIY464YU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.178, 0.808, 0.877, 0.865] contain?" }, { "from": "gpt", "value": "This region contains an orange bar that represents the performance measurement for \"Rapid Recovery\" of \"Community Development\"." } ], "id_raw": 597 }, { "id": "2XjXtFsclIuDkzEj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the main title of this chart?" }, { "from": "gpt", "value": "The main title is \"Visitors' trend at Cultural Heritage Sites\" and the coordinates are [0.416, 0.025, 0.747, 0.055]." } ], "id_raw": 598 }, { "id": "tjtekXFcDzvq6bKP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the lesion denoted with coordinates [0.860, 0.118, 0.944, 0.141]?" }, { "from": "gpt", "value": "Historic Site." } ], "id_raw": 598 }, { "id": "yWefKGIvkhmMSEoq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nWhich visitor numbers is indicated in [0.714, 0.919, 0.740, 0.942]?" }, { "from": "gpt", "value": "100." } ], "id_raw": 598 }, { "id": "3swm5U0qoNNm3J6B", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bars in the coordinates [0.178, 0.173, 0.453, 0.245]?" }, { "from": "gpt", "value": "It represents the number of visitors at a Historic Site in the \"Marginal Growth\" category." } ], "id_raw": 598 }, { "id": "SDJyAjpYUTKz7bKB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nIs the 'National Gallery' bar in the 'Steep Increase' category taller than the 'Historic Site' bar in the same category?" }, { "from": "gpt", "value": "No." } ], "id_raw": 598 }, { "id": "zmKqMaw6Pp3Votut", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nIs the 'National Gallery' bar in the 'Marginal Growth' category to the right of the 'Historic Site' in the same category?" }, { "from": "gpt", "value": "No." } ], "id_raw": 598 }, { "id": "Gzb673VluadRHiia", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nIs the 'Historic Site' bar in the 'Rapid Growth' category taller than the 'National Gallery' bar in the 'Unpredictable Decline' category?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 598 }, { "id": "iZ500rbdWIt5WnkI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nIs the 'Historic Site' in the 'Unpredictable Decline' category to the right of the 'National Gallery' in the same category?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 598 }, { "id": "QYppnhm54IQZzS3z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.816, 0.074, 0.978, 0.151]." }, { "from": "gpt", "value": "This region contains two entries in the legend. The first label describes the 'National Gallery' with color label '#fa8072' at [0.821, 0.085, 0.849, 0.101] and text at [0.860, 0.083, 0.973, 0.106]. The second label describes the 'Historic Site' with color label '#87ceeb' at [0.821, 0.120, 0.849, 0.136] and text at [0.860, 0.118, 0.944, 0.141]." } ], "id_raw": 598 }, { "id": "rUVqbfCssd6WhyAQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.178, 0.062, 0.985, 0.903]." }, { "from": "gpt", "value": "This area contains the main part of the bar chart, including all the bars and the X and Y axis labels. The bars for the 'National Gallery' and the 'Historic Site' are present here, with their respective visitor numbers indicated along the x-axis and the categories along the y-axis." } ], "id_raw": 598 }, { "id": "sNPc9HdcofgPcVVI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.178, 0.100, 0.343, 0.173]?" }, { "from": "gpt", "value": "This area contains a single bar representing the number of visitors at the 'National Gallery' under the 'Marginal Growth' category." } ], "id_raw": 598 }, { "id": "CcGwvbUxcdqpnosl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.517, 0.952, 0.646, 0.975]?" }, { "from": "gpt", "value": "This area contains the x-axis label 'Number of Visitors'." } ], "id_raw": 598 }, { "id": "8JsIcfvXmkfWRYSw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.605, 0.185, 0.974, 0.270] represent?" }, { "from": "gpt", "value": "This object is a bar representing the score of 'Fashion Trend 2' for the Gothic subculture." } ], "id_raw": 599 }, { "id": "y0HeDvFruZI9btO6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in {the legend} at [0.077, 0.409, 0.235, 0.556]?" }, { "from": "gpt", "value": "The legend consists of four items. Each item includes a color code and the corresponding 'Fashion Trend'. The color codes and their respective trends are as follows: {Fashion Trend 1}[0.121, 0.419, 0.229, 0.442], {Fashion Trend 2}[0.121, 0.453, 0.229, 0.477], {Fashion Trend 3}[0.121, 0.488, 0.229, 0.512], and {Fashion Trend 4}[0.121, 0.523, 0.229, 0.547]." } ], "id_raw": 599 }, { "id": "z6GWPw2SlKFp4q0h", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates [0.015, 0.261, 0.060, 0.284] stand for?" }, { "from": "gpt", "value": "This label stands for the 'Gothic' subculture." } ], "id_raw": 599 }, { "id": "JSwHxRAMr9IWQewM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nWhich is wider: {the bar classifying 'Fashion Trend 1' in the 'Gothic' category}[0.113, 0.100, 0.974, 0.185] or {the bar classifying 'Fashion Trend 4' in the 'Punk' category}[0.113, 0.780, 0.974, 0.865]?" }, { "from": "gpt", "value": "They are the same width." } ], "id_raw": 599 }, { "id": "M2aQlpqVuKNC2qhr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Fashion Trend 2' bar for 'Gothic'}[0.605, 0.185, 0.974, 0.270] to the left or right of {the 'Fashion Trend 2' bar for 'Punk'}[0.482, 0.610, 0.974, 0.695]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 599 }, { "id": "m796CPbSWalJCKRP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar representing 'Fashion Trend 1'}[0.113, 0.100, 0.974, 0.185] and {the bar representing 'Fashion Trend 4'}[0.728, 0.355, 0.974, 0.440], which one begins further to the right?" }, { "from": "gpt", "value": "The bar representing 'Fashion Trend 4'." } ], "id_raw": 599 }, { "id": "LWXH2INKvkMn8rkx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what is contained within the region defined by [0.077, 0.409, 0.235, 0.556]." }, { "from": "gpt", "value": "The area contains a legend for the chart. It lists four 'Fashion Trends' each represented by a different color: {Fashion Trend 1}[0.121, 0.419, 0.229, 0.442] is represented by a salmon color, {Fashion Trend 2}[0.121, 0.453, 0.229, 0.477] by a sky blue color, {Fashion Trend 3}[0.121, 0.488, 0.229, 0.512] by orange, and {Fashion Trend 4}[0.121, 0.523, 0.229, 0.547] by a light blue." } ], "id_raw": 599 }, { "id": "n33KwIZCsAFgokBG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region with coordinates [0.482, 0.270, 0.974, 0.355]?" }, { "from": "gpt", "value": "In this region, a bar representing the impact of the Gothic subculture on 'Fashion Trend 3' is located." } ], "id_raw": 599 }, { "id": "nFlIqch589oeTCaS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.113, 0.100, 0.974, 0.185]?" }, { "from": "gpt", "value": "This region contains a bar that demonstrates the score of 'Fashion Trend 1' for the Gothic subculture." } ], "id_raw": 599 }, { "id": "uKOuGro3sdVfFSqz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nWhat object is found at the coordinates [0.199, 0.784, 0.324, 0.887]?" }, { "from": "gpt", "value": "The 2017 Market Share bar for Telecom Industry." } ], "id_raw": 600 }, { "id": "kw9Z4fIS5yyGIoRh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nWhich industry corresponds to the coordinates of [0.082, 0.619, 0.187, 0.647]?" }, { "from": "gpt", "value": "E-Commerce sector." } ], "id_raw": 600 }, { "id": "gftoKyFfcGXtElbz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item located at [0.656, 0.080, 0.821, 0.108] represent?" }, { "from": "gpt", "value": "Market Share for the year 2020." } ], "id_raw": 600 }, { "id": "pApwACpixTUZNO4j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nWhich industry has a higher market share in 2020, {Telecommunication}[0.645, 0.784, 0.810, 0.887] or {Software}[0.788, 0.373, 0.944, 0.476]?" }, { "from": "gpt", "value": "Software." } ], "id_raw": 600 }, { "id": "5GJFjftyQ96z7KV2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nHas {Manufacturing industry in 2018}[0.266, 0.168, 0.315, 0.270] grown or shrunk compared to {Manufacturing industry in 2017}[0.199, 0.168, 0.266, 0.270]?" }, { "from": "gpt", "value": "Grown." } ], "id_raw": 600 }, { "id": "pEABtgCXhMyNokEi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nCompared to {2018 Telecommunication Market Share}[0.471, 0.784, 0.645, 0.887], did the {2019 Telecommunication Market Share}[0.645, 0.784, 0.810, 0.887] increase or decrease?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 600 }, { "id": "YB0FiWemAkvkRXP8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the items in the legend located at [0.352, 0.027, 0.828, 0.119]?" }, { "from": "gpt", "value": "1. 2017 Market Share mark in red color at [0.359, 0.041, 0.394, 0.060];\n2. 2018 Market Share mark in green color at [0.359, 0.082, 0.394, 0.102];\n3. 2019 Market Share mark in lite blue color at [0.607, 0.041, 0.642, 0.060];\n4. 2020 Market Share mark in purple color at [0.607, 0.082, 0.642, 0.102]." } ], "id_raw": 600 }, { "id": "5vTHjZfpdpDSv8MN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.788, 0.373, 0.944, 0.476]?" }, { "from": "gpt", "value": "It contains the 2019 Market Share for the software industry." } ], "id_raw": 600 }, { "id": "Bt57qHcFekK3obOG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nWhat do the vertical bars between coordinates [0.199, 0.431, 0.382, 0.496] represent?" }, { "from": "gpt", "value": "These are the market shares for the Manufacturing Industry from the years 2017 to 2020." } ], "id_raw": 600 }, { "id": "Vz4AACdYyn2Z4fCB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat category does the bar with coordinates [0.174, 0.791, 0.511, 0.888] belong to?" }, { "from": "gpt", "value": "Electronics for the Raw Material Efficiency." } ], "id_raw": 603 }, { "id": "qyMIuG8mhLkGXaFM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat type of data does the bar at [0.701, 0.595, 0.892, 0.693] represent?" }, { "from": "gpt", "value": "It represents the Quality Control data for Automobiles." } ], "id_raw": 603 }, { "id": "GWZGYiJ0DsCE5A7S", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the category \"Quality Control\" in the legend?" }, { "from": "gpt", "value": "The color of \"Quality Control\" in the legend is represented by a leaf at {this position}[0.746, 0.047, 0.780, 0.067] and described as '#a1b5ce'." } ], "id_raw": 603 }, { "id": "ovYBiOICTme9fT1X", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Quality Control\" for \"Textiles\" [0.634, 0.204, 0.780, 0.302] longer than the bar representing \"Production Speed\" for \"Textiles\" [0.432, 0.204, 0.634, 0.302]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 603 }, { "id": "QWQUzDgpq77jpdPX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nIs the \"Raw material efficiency\" score for \"Pharmaceuticals\" [0.174, 0.400, 0.466, 0.497] higher than for \"Automobiles\" [0.174, 0.595, 0.455, 0.693]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 603 }, { "id": "xxXsBnH3D4QJ75SU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nCompared to {the bar of Quality Control for Electronics}[0.735, 0.791, 0.936, 0.888], is {the bar of Production Speed for Electronics}[0.511, 0.791, 0.735, 0.888] shorter or longer?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 603 }, { "id": "PzVdLG7yPjEyuZSW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat are contained in the area [0.174, 0.400, 0.466, 0.497]?" }, { "from": "gpt", "value": "Within this area, there is a rectangle bar representing the \"Raw Material Efficiency\" of the \"Pharmaceuticals\" category." } ], "id_raw": 603 }, { "id": "I4H0wb6swtPXgRE6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents inside [0.217, 0.034, 0.932, 0.084]." }, { "from": "gpt", "value": "This area contains the legend of the chart, which contains descriptors for each color and matching text labels: for example, Raw Material Efficiency is represented by a block at {this coordinate}[0.224, 0.047, 0.259, 0.067] with color '#d66555', and the pairing text located at {this coordinate}[0.273, 0.045, 0.475, 0.073]." } ], "id_raw": 603 }, { "id": "OQ9IY73Fg6N6fzDk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see within the region [0.174, 0.791, 0.936, 0.888]?" }, { "from": "gpt", "value": "Within this region, there are three bars representing the 'Raw Material Efficiency', 'Production Speed' and 'Quality Control' data respectively for the 'Electronics' category. The respective colors of the bars are '#d66555', '#88a27d', and '#a1b5ce'." } ], "id_raw": 603 }, { "id": "65fxskvCTWQxfvco", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.225, 0.660, 0.701, 0.888] on the chart?" }, { "from": "gpt", "value": "A bar representing 'Stock Market' in 'High_Influence_Areas'." } ], "id_raw": 604 }, { "id": "sAh8SeOlhMfOIZDn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat color is the marker in the legend at [0.387, 0.089, 0.422, 0.109]?" }, { "from": "gpt", "value": "The color is '#34495e', which represents 'Unemployment Rate'." } ], "id_raw": 604 }, { "id": "KYSM8FpRxrnNNzQk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat information is placed at [0.827, 0.942, 0.869, 0.970] on the x-axis?" }, { "from": "gpt", "value": "The label '5500'." } ], "id_raw": 604 }, { "id": "xy5uL6CnXNORKKzf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.463, 0.204, 0.554, 0.432] represent?" }, { "from": "gpt", "value": "It represents 'Interest Rates' in 'Weak_Influence_Areas'." } ], "id_raw": 604 }, { "id": "ENVFTINzeEw39Pft", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing 'Unemployment Rate' in 'High_Influence_Areas' taller or shorter than the bar representing 'Unemployment Rate' in 'Weak_Influence_Areas'?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 604 }, { "id": "lRdv2kQGael4kpha", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nIs the 'Stock Market' bar higher in 'High_Influence_Areas' compared to 'Weak_Influence_Areas'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 604 }, { "id": "SejsgAowxu9DDSeV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nWhich bar is to the right of the 'Stock Market' bar in 'High_Influence_Areas'?" }, { "from": "gpt", "value": "The 'Interest Rates' bar." } ], "id_raw": 604 }, { "id": "38Z02bPNZSyzcPRV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nWhich is more towards vertical centre, the 'High_Influence_Areas' label or the 'Weak_Influence_Areas' label?" }, { "from": "gpt", "value": "The 'High_Influence_Areas' label." } ], "id_raw": 604 }, { "id": "30oX8a0UxQXB4z5t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within [0.380, 0.034, 0.826, 0.126]?" }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes four color-text labels: 'Stock Market' is denoted by the color '#d66555' at [0.387, 0.047, 0.422, 0.067]; 'Interest Rates' is denoted by the color '#88a27d' at [0.387, 0.089, 0.422, 0.109]; 'Inflation Rate' is denoted by the color '#a1b5ce' at [0.592, 0.047, 0.626, 0.067]; 'Unemployment Rate' is denoted by the color '#34495e' at [0.592, 0.089, 0.626, 0.109]." } ], "id_raw": 604 }, { "id": "MdUiyvt7CilD5RlK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area [0.225, 0.660, 0.945, 0.888]?" }, { "from": "gpt", "value": "This area contains all the 4 bars from 'High_Influence_Areas' representing 'Stock Market', 'Interest Rates', 'Inflation Rate' and 'Unemployment Rate'. Each of these bars has a unique color assigned as per the legend." } ], "id_raw": 604 }, { "id": "ZOdxYASIxQ8mHZfG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area [0.019, 0.307, 0.213, 0.336]?" }, { "from": "gpt", "value": "This area contains the label 'Weak_Influence_Areas' of the y-axis." } ], "id_raw": 604 }, { "id": "OEi3NwTO2jDUTePJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within [0.225, 0.204, 0.601, 0.432]?" }, { "from": "gpt", "value": "There are four bars in the region corresponding to 'Interest Rates', 'Stock Market', 'Inflation Rate' and 'Unemployment Rate' for 'Weak_Influence_Areas'." } ], "id_raw": 604 }, { "id": "sfmazQB8bsn7OKHW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar of coordinates [0.469, 0.595, 0.784, 0.693] represent?" }, { "from": "gpt", "value": "This bar represents the \"Mutual Funds\" return in the \"Equity\" category." } ], "id_raw": 605 }, { "id": "tB4ahYuROIv7scXt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate is the label for 'Real Estate' located at?" }, { "from": "gpt", "value": "[0.076, 0.438, 0.174, 0.466]" } ], "id_raw": 605 }, { "id": "kMdALjZyKQVayv16", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the scope {x_range} for the bar at coordinates [0.375, 0.791, 0.469, 0.888]?" }, { "from": "gpt", "value": "12.00 -> 18.00" } ], "id_raw": 605 }, { "id": "6c2LgY9XnJeOzjsI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat color does the chart element in [0.846, 0.047, 0.881, 0.067] represent?" }, { "from": "gpt", "value": "{Equity Return (%) color} [0.846, 0.047, 0.881, 0.067] " } ], "id_raw": 605 }, { "id": "1mptkvnjZ2D7rolI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nIs {the Equity Return bar}[0.469, 0.791, 0.784, 0.888] longer than {theReal Estate Return bar}[0.375, 0.791, 0.469, 0.888]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 605 }, { "id": "ezrKzfwJynw9rhlN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nWhich bar is located to the left: {Low Risk Bonds Return bar} [0.186, 0.791, 0.249, 0.888] or {Mutual Funds Return bar} [0.249, 0.791, 0.375, 0.888] ?" }, { "from": "gpt", "value": "The Low Risk Bonds Return bar." } ], "id_raw": 605 }, { "id": "IqbCo9Aq3UfBSTw3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nAre {the Equity Return bars} represented in dark color compared to {the Real Estate Return bars}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 605 }, { "id": "a5BoJp1YCsSacGRw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Low Risk Bonds\"}[0.186, 0.791, 0.249, 0.888] and {\"Equity\"}[0.469, 0.204, 0.784, 0.302], which investment has a higher return rate for {the Equity Return bar}<{Equity Return % bar}[0.469, 0.791, 0.784, 0.888], {Equity Mutual Funds bar}[0.469, 0.595, 0.784, 0.693], {Equity Real Estate bar}[0.469, 0.400, 0.784, 0.497], {Equity Equity bar}[0.469, 0.204, 0.784, 0.302]>?" }, { "from": "gpt", "value": "Equity." } ], "id_raw": 605 }, { "id": "dYrKI9JJ1n76aLJz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in [0.317, 0.045, 0.531, 0.073]." }, { "from": "gpt", "value": "In this area, there is a text element for the Mutual Funds return, the coordinates are [0.317, 0.045, 0.531, 0.073], labeled as \"Mutual Funds Return (%)\" and has the color handle at the coordinate [0.269, 0.047, 0.303, 0.067] which is in the '#88a27d' color." } ], "id_raw": 605 }, { "id": "ae5aFyryUVB22Zct", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects in the region [0.469, 0.204, 0.784, 0.302]?" }, { "from": "gpt", "value": "In this region, there is a rectangle representing the Equity return for the Equity investment type. The bar's coordinates are [0.469, 0.204, 0.784, 0.302]." } ], "id_raw": 605 }, { "id": "q0hauX5ZyLVYBFv5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nDescribe everything you can see in the region [0.186, 0.170, 0.814, 0.923]." }, { "from": "gpt", "value": "This is the main area of the chart and includes several elements. The horizontal and vertical ticks marks, bars representing returns for several investment types (Equity, Real Estate, Mutual Funds, and Low Risk Bonds) and labels." } ], "id_raw": 605 }, { "id": "LYeqsIuwzy9Xi9mo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nCheck the labels in the range [0.181, 0.942, 0.512, 0.970]." }, { "from": "gpt", "value": "There are five labels, ranging from 0 to 20, representing the rate of return:\n1. \"0\": [0.181, 0.942, 0.192, 0.970],\n2. \"5\": [0.259, 0.942, 0.270, 0.970],\n3. \"10\": [0.332, 0.942, 0.354, 0.970],\n4. \"15\": [0.411, 0.942, 0.433, 0.970],\n5. \"20\": [0.490, 0.942, 0.512, 0.970]." } ], "id_raw": 605 }, { "id": "VJo8uzc13KuhXWPx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the label with coordinates [0.563, 0.045, 0.825, 0.073] in the chart represent?" }, { "from": "gpt", "value": "It represents the 'Employment Rate Women (%)'." } ], "id_raw": 606 }, { "id": "LJzly2LdV0CotF4V", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for the title label of the second element in the legend?" }, { "from": "gpt", "value": "The coordinates for the title label of the second element in the legend are [0.185, 0.045, 0.480, 0.073]." } ], "id_raw": 606 }, { "id": "rNWLCZIkzZM1kbSY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}[0.279, 0.660, 0.329, 0.888] representing in the chart?" }, { "from": "gpt", "value": "It represents the 'Gender Wage Gap (%)' for the 'Rights Protection' category." } ], "id_raw": 606 }, { "id": "8JpBbHNAqz6Xf8rX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the specific labels within the coordinates range [0.185, 0.045, 1.141, 0.073]?" }, { "from": "gpt", "value": "These are the labels: {Maternity Leave Duration (weeks)}[0.185, 0.045, 0.480, 0.073], {Employment Rate Women (%)}[0.563, 0.045, 0.825, 0.073] and {Employment Rate Men (%)}[0.908, 0.045, 1.141, 0.073]." } ], "id_raw": 606 }, { "id": "amByMCoh3E3f6Ppq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing 'Employment Rate Women (%)' for 'Rights Protection'}[0.432, 0.660, 0.551, 0.888] have a higher value than {the bar representing 'Employment Rate Men (%)' for 'Rights Protection'}[0.551, 0.660, 0.700, 0.888]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 606 }, { "id": "fYel3VNCNFyU350Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Maternity Leave Duration (weeks)'} for 'Social Inequality'[0.349, 0.204, 0.432, 0.432] wider than the {the bar representing 'Employment Rate Women (%)' for 'Social Inequality'}[0.432, 0.204, 0.535, 0.432]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 606 }, { "id": "9WBBE41XfYqxV6Nq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar}[0.279, 0.660, 0.329, 0.888] higher or shorter than {the subsequent bar}[0.329, 0.660, 0.432, 0.888]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 606 }, { "id": "x7dSEXUd5itjYcrA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nWhich is taller: {First category's bar for 'Employment Rate Men (%)'}[0.551, 0.660, 0.700, 0.888] or {Second category's bar for 'Employment Rate Men (%)'}[0.535, 0.204, 0.688, 0.432]?" }, { "from": "gpt", "value": "First category's bar for 'Employment Rate Men (%)'." } ], "id_raw": 606 }, { "id": "Uu304ADvEA4fEasR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the area with coordinates [-0.148, 0.034, 1.148, 0.084]." }, { "from": "gpt", "value": "The area contains the legend of the chart. It consists of four pairs of symbols and their corresponding labels: \n1. A blue square symbol and the label \"Gender Wage Gap (%)\", placed at [-0.141, 0.047, -0.107, 0.067] and [-0.093, 0.045, 0.102, 0.073] respectively.\n2. A yellow square symbol and the label \"Maternity Leave Duration (weeks)\", located at [0.136, 0.047, 0.171, 0.067] and [0.185, 0.045, 0.480, 0.073] respectively.\n3. A light green square symbol and the label \"Employment Rate Women (%)\", placed at [0.515, 0.047, 0.549, 0.067] and [0.563, 0.045, 0.825, 0.073] respectively.\n4. A turquoise square symbol and the label \"Employment Rate Men (%)\", located at [0.860, 0.047, 0.894, 0.067] and [0.908, 0.045, 1.141, 0.073] respectively." } ], "id_raw": 606 }, { "id": "sFh963eeGCePT1xM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nDescribe what is contained within the region [0.279, 0.170, 0.721, 0.923]." }, { "from": "gpt", "value": "This region contains the main chart, which is a grouped bar chart for two categories ('Rights Protection' and 'Social Inequality'), with four value bars for each category, each bar representing a different data set ('Gender Wage Gap (%)', 'Maternity Leave Duration (weeks)', 'Employment Rate Women (%)', 'Employment Rate Men (%)')." } ], "id_raw": 606 }, { "id": "CABNUvhx5j875ukt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat exists within the region [0.308, 0.942, 0.330, 0.970]?" }, { "from": "gpt", "value": "It contains a tick label on the x-axis representing the value '20'." } ], "id_raw": 606 }, { "id": "MKAoWyFZcPKr96m3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements inside [0.119, 0.763, 0.267, 0.791] and [0.124, 0.307, 0.267, 0.335]?" }, { "from": "gpt", "value": "These regions contain the y-axis labels for two categories: {Rights Protection}[0.119, 0.763, 0.267, 0.791] and {Social Inequality}[0.124, 0.307, 0.267, 0.335]." } ], "id_raw": 606 }, { "id": "Ng6thEbywMAPi6f8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat type of graphical element is located at [0.300, 0.061, 0.324, 0.183]?" }, { "from": "gpt", "value": "{A Video Game Consoles bar for May sales}[0.300, 0.061, 0.324, 0.183]." } ], "id_raw": 607 }, { "id": "sY6PyOdl2GohNbjS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the Dec sales in the legend?" }, { "from": "gpt", "value": "{The Dec Sales handle color}[0.701, 0.924, 0.735, 0.943]." } ], "id_raw": 607 }, { "id": "w9vxArRAQDJ9DNLs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis located at [0.012, 0.367, 0.012, 0.367]?" }, { "from": "gpt", "value": "{The y-axis label}[0.012, 0.367, 0.012, 0.367] is \"Television\"." } ], "id_raw": 607 }, { "id": "uDEw8eAlBhAHRu8i", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the bar for Apr sales of Video Game consoles}[0.280, 0.061, 0.300, 0.183] or {the bar for Nov sales of Video Game consoles}[0.482, 0.061, 0.534, 0.183]?" }, { "from": "gpt", "value": "Nov sales." } ], "id_raw": 607 }, { "id": "gLaXGgDssIyH7A8r", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the Nov sales bar for Mobile Phones}[0.776, 0.305, 0.859, 0.428] extend higher than {the Dec sales bar for Mobile Phones}[0.859, 0.305, 0.945, 0.428]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 607 }, { "id": "lEyzo4LeWZH46VNT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhich brand had the higher start sales, {Television in Jan}[0.217, 0.550, 0.256, 0.673] or {Mobile Phones in Jan}[0.217, 0.305, 0.264, 0.428]?" }, { "from": "gpt", "value": "Mobile Phones." } ], "id_raw": 607 }, { "id": "p8C9e5G1RpyDtmHo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat details are present in the region [0.366, 0.796, 0.831, 0.949]?" }, { "from": "gpt", "value": "The area contains the legend for monthly sales from September to December. The colored handles representing the sales are:\n1. Sep sales: {Color Handle}[0.701, 0.798, 0.735, 0.818]\n2. Oct sales: {Color Handle}[0.701, 0.840, 0.735, 0.859]\n3. Nov sales: {Color Handle}[0.701, 0.882, 0.735, 0.901]\n4. Dec sales: {Color Handle}[0.701, 0.924, 0.735, 0.943]" } ], "id_raw": 607 }, { "id": "wnW27aWrVzPeUt9W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents found in the section [0.217, 0.061, 0.945, 0.183]." }, { "from": "gpt", "value": "This section contains all bar groups corresponding to Video Game Consoles sales across all months:\n1. Jan bar: {Coordinates}[0.217, 0.061, 0.238, 0.183]\n2. Feb bar: {Coordinates}[0.238, 0.061, 0.259, 0.183]\n3. Mar bar: {Coordinates}[0.259, 0.061, 0.280, 0.183]\n4. Apr bar: {Coordinates}[0.280, 0.061, 0.300, 0.183]\n5. May bar: {Coordinates}[0.300, 0.061, 0.324, 0.183]\nand so on through December, each progressively tracking the sales data over the months." } ], "id_raw": 607 }, { "id": "WIOFG3iQ1lJs7zxK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.217, 0.305, 0.945, 0.428]?" }, { "from": "gpt", "value": "This specified area comprises all the bars for Mobile Phones sales from January to December:\n1. Jan bar: {Coordinates}[0.217, 0.305, 0.264, 0.428]\n2. Feb bar: {Coordinates}[0.264, 0.305, 0.312, 0.428]\n3. Mar bar: {Coordinates}[0.312, 0.305, 0.362, 0.428]\n...continuing similarly through December." } ], "id_raw": 607 }, { "id": "djfNMKtxAwwIf0zD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart object located at coordinates [0.249, 0.791, 0.557, 0.888]?" }, { "from": "gpt", "value": "It is a bar representing the \"Donation to Health\" for the past 5 years." } ], "id_raw": 608 }, { "id": "VpEOhC3vW217hEzf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the legend item describing \"Donations - Past 10 Year\"?" }, { "from": "gpt", "value": "The label is at [0.665, 0.045, 0.876, 0.073] and the color representation is at [0.616, 0.047, 0.651, 0.067]." } ], "id_raw": 608 }, { "id": "aaqkpgCoFkHEK9BC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat category does the bar located at [0.358, 0.595, 0.443, 0.693] represent?" }, { "from": "gpt", "value": "It represents the \"Donation to Education\" for the past 10 years." } ], "id_raw": 608 }, { "id": "TeUdpQqmPySHNibh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat is being described by the text at coordinates [0.072, 0.828, 0.237, 0.856]?" }, { "from": "gpt", "value": "The category \"Donation to Health\"." } ], "id_raw": 608 }, { "id": "oyqNhtCdAoLVskkr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Donation to Health - Past 5 Year\"}[0.249, 0.791, 0.557, 0.888] longer than {the bar representing \"Donation to Health - Past 10 Year\"}[0.557, 0.791, 0.831, 0.888]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 608 }, { "id": "sbkjn7KbpSQcZP6V", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nWhich is taller between {the bar for \"Donation to Environment - Past 5 Year\"}[0.249, 0.400, 0.566, 0.497] and {the bar for \"Donation to Environment - Past 10 Year\"}[0.566, 0.400, 0.732, 0.497]?" }, { "from": "gpt", "value": "The bar representing \"Donation to Environment - Past 10 Year\"." } ], "id_raw": 608 }, { "id": "BDIRqtguXlESNZqJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nAmong the bars for \"Donations - Past 5 Year\", is {the bar for \"Donation to Health\"}[0.249, 0.791, 0.557, 0.888] the longest?" }, { "from": "gpt", "value": "No." } ], "id_raw": 608 }, { "id": "yGSQGCfjSpIyFyRq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar for \"Donations - Past 5 Year\" for \"Charity Concerts\"}[0.249, 0.204, 0.401, 0.302] and {the bar for \"Donations - Past 10 Year\" for \"Donation to Education\"}[0.358, 0.595, 0.443, 0.693], which one is taller?" }, { "from": "gpt", "value": "The bar for \"Donations - Past 10 Year\" for \"Donation to Education\"." } ], "id_raw": 608 }, { "id": "VHnUieXo2XHbCwSf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found in the area marked by coordinates [0.326, 0.034, 0.883, 0.084]." }, { "from": "gpt", "value": "This area contains the chart's legend. It includes two entries: the first, labelled \"Donations - Past 5 Year\" with a representation color located at [0.333, 0.047, 0.368, 0.067] and the corresponding text at [0.381, 0.045, 0.581, 0.073]; and the second, labelled \"Donations - Past 10 Year\" with a representation color located at [0.616, 0.047, 0.651, 0.067] and the corresponding text at [0.665, 0.045, 0.876, 0.073]." } ], "id_raw": 608 }, { "id": "FSCelurrxZNdmA1t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are present within the region [0.249, 0.170, 0.959, 0.923]?" }, { "from": "gpt", "value": "The area includes the main body of the chart, which consists of 8 vertical bars, each corresponding to \"Donation to Health\", \"Donation to Education\", \"Donation to Environment\", and \"Charity Concerts\", with two bars for each representing donations for the past 5 years and the past 10 years." } ], "id_raw": 608 }, { "id": "P1SNg4ZCqcd6JQMG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in region [0.043, 0.438, 0.237, 0.466]." }, { "from": "gpt", "value": "This region contains a text label for the \"Donation to Environment\" on the vertical (y) axis." } ], "id_raw": 608 }, { "id": "g6DBJxFYh3x2x0rt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat is within [0.401, 0.204, 0.472, 0.302]?" }, { "from": "gpt", "value": "This region includes a bar that represents \"Charity Concerts\" and their corresponding donation amounts for the past 10 years." } ], "id_raw": 608 }, { "id": "sup4qWK1qdBGKkvu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nCould you tell what the bar at [0.075, 0.168, 0.262, 0.210] represents?" }, { "from": "gpt", "value": "It represents the Global Trade (% of GDP) for 2012." } ], "id_raw": 609 }, { "id": "ulDObWyQ0XRKQl1F", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the coordinate [0.269, 0.038, 0.478, 0.066] in the legend refer to?" }, { "from": "gpt", "value": "It refers to the label \"Global Trade (% of GDP)\" in the legend." } ], "id_raw": 609 }, { "id": "nnetTWaFmCVYhpZQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar at coordinates [0.375, 0.844, 0.796, 0.887]?" }, { "from": "gpt", "value": "It is the representation of the Supply Chain Reliability Index for the year 2020." } ], "id_raw": 609 }, { "id": "Ud4r4V6xPrAFG2IH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Global Trade of 2014}[0.248, 0.337, 0.670, 0.379] longer than {the bar for Global Trade of 2015}[0.281, 0.421, 0.679, 0.464]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 609 }, { "id": "sIEzbM8C0fL9BNUL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat's the relationship between {the bar for Supply Chain Reliability Index of 2018}[0.328, 0.675, 0.745, 0.717] and {the bar for Supply Chain Reliability Index of 2019}[0.347, 0.760, 0.749, 0.802] in terms of length?" }, { "from": "gpt", "value": "The bar for 2019 is longer." } ], "id_raw": 609 }, { "id": "AWqF7coV2TMNydx3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for Global Trade of 2017}[0.318, 0.591, 0.731, 0.633] have a greater value than {the bar for Supply Chain Reliability Index of 2017}[0.318, 0.591, 0.731, 0.633]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 609 }, { "id": "BvbXH73hJCWVi7Ky", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the coordinates [0.075, 0.252, 0.632, 0.294]?" }, { "from": "gpt", "value": "Within these coordinates, there are two bars. The first one corresponds to the Global Trade (% of GDP) for 2013 at coordinates [0.075, 0.252, 0.225, 0.294] and the second one is for the Supply Chain Reliability Index for 2013 at coordinates [0.225, 0.252, 0.632, 0.294]." } ], "id_raw": 609 }, { "id": "AIVg9pwHBJXXrpgC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.075, 0.844, 0.796, 0.887]." }, { "from": "gpt", "value": "This region contains two bars. The first bar with coordinates [0.075, 0.844, 0.375, 0.887] represents the Global Trade (% of GDP) for 2020 while the second bar with coordinates [0.375, 0.844, 0.796, 0.887] represents the Supply Chain Reliability Index for 2020." } ], "id_raw": 609 }, { "id": "IObijcCXaSVs5pA8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information lies within the coordinates [0.213, 0.027, 0.827, 0.077]?" }, { "from": "gpt", "value": "These coordinates contain the legend with its items. The first item is \"Global Trade (% of GDP)\" represented by color '#d66555' at coordinates [0.220, 0.041, 0.255, 0.060] and the second item is \"Supply Chain Reliability Index\" represented by color '#88a27d' at coordinates [0.513, 0.041, 0.548, 0.060]." } ], "id_raw": 609 }, { "id": "V1429ZA83a30Q6aM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.248, 0.626, 0.636, 0.673] in the chart?" }, { "from": "gpt", "value": "This is a bar representing the steady decline in \"Flu Vaccine Production\"." } ], "id_raw": 610 }, { "id": "kuCcgxap54FkfqkJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at [0.248, 0.343, 0.946, 0.390]?" }, { "from": "gpt", "value": "The color of the bar, which represents a \"pronounced increase\" in \"Stem Cell Research\", is yellow (#eacd52)." } ], "id_raw": 610 }, { "id": "zKU8NpvkrXiwHtJX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Y-axis label} at [0.040, 0.638, 0.236, 0.666]?" }, { "from": "gpt", "value": "The label is \"Flu Vaccine Production\"." } ], "id_raw": 610 }, { "id": "1LlcPPXUKD9KH5aR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat indicators or categories are shown in [0.432, 0.796, 0.560, 0.824]?" }, { "from": "gpt", "value": "{Steady decline}[0.432, 0.796, 0.560, 0.824]." } ], "id_raw": 610 }, { "id": "NNwI17b6be9oIoeO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Stem Cell Research\"}[0.248, 0.343, 0.946, 0.390] wider than {the bar representing \"Antibiotics Production\"}[0.248, 0.531, 0.559, 0.578]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 610 }, { "id": "uBMLfrFS935BibYN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Gene Therapy\"}[0.248, 0.155, 0.772, 0.202] end before {the bar representing \"General Pharma Industry\"}[0.248, 0.249, 0.830, 0.296]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 610 }, { "id": "RgLoJaPttJ31aAEm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nBetween {\"robust surgeries\" pattern}[0.432, 0.879, 0.608, 0.907] and {\"stem cell research\" pattern}[0.432, 0.921, 0.613, 0.949], which pattern has a higher value?" }, { "from": "gpt", "value": "\"Stem Cell Research\"." } ], "id_raw": 610 }, { "id": "0hQaSDE1j7Lw1KJo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.376, 0.784, 0.853, 0.960]." }, { "from": "gpt", "value": "This area contains the legend for the chart, describing seven trend patterns each represented by a different color and labeled as follows: \n1. \"Steady decline\" in red (#d66555) at [0.432, 0.796, 0.560, 0.824].\n2. \"Rapid decline\" in green (#88a27d) at [0.432, 0.837, 0.549, 0.865].\n3. \"Periodic fluctuations\" in blue (#a1b5ce) at [0.432, 0.879, 0.608, 0.907].\n4. \"Pronounced increase\" in yellow (#eacd52) at [0.432, 0.921, 0.613, 0.949].\n5. \"Gradual recovery\" in dark blue (#526f7a) at [0.696, 0.796, 0.847, 0.824].\n6. \"Sluggish growth\" in pink (#ee6f7a) at [0.696, 0.837, 0.836, 0.865].\n7. \"Declining pattern\" in olive-green (#a4a482) at [0.696, 0.879, 0.847, 0.907]." } ], "id_raw": 610 }, { "id": "omHSAMUMJBe9lkns", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.248, 0.030, 0.981, 0.703]." }, { "from": "gpt", "value": "This area is where the main visualization of the chart resides, showcasing seven bars each corresponding to a different medical and biotechnology trend such as Flu Vaccine Production, Antibiotics Production, Robotic Surgeries, Stem Cell Research, General Pharma Industry, Gene Therapy, and Biological Implants. Each bar has different length, reflecting the value of these trends." } ], "id_raw": 610 }, { "id": "vaBwXnO2fsAVa8oX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.248, 0.155, 0.772, 0.202]?" }, { "from": "gpt", "value": "There is a single object in this area which is a bar representing sluggish growth in \"Gene Therapy\"." } ], "id_raw": 610 }, { "id": "d9DQmziquAr6zn2X", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.248, 0.343, 0.946, 0.390]?" }, { "from": "gpt", "value": "The area contains a bar on the chart representing the pronounced increase in \"Stem Cell Research\", which ends at a value of 1800. The color of this bar is yellow (#eacd52)." } ], "id_raw": 610 }, { "id": "e4GotKljppCaMMF1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.116, 0.647, 0.343, 0.887]?" }, { "from": "gpt", "value": "They represent the coordinates for the Public Health Awareness (%) bar in the United States." } ], "id_raw": 611 }, { "id": "7e8YAjkFvqD9akF6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.571, 0.168, 0.848, 0.407] represent?" }, { "from": "gpt", "value": "They represent the coordinates for the Medical Equipment Patents bar in the EU." } ], "id_raw": 611 }, { "id": "EK7j53dQWUihTjxw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label at the coordinates [0.073, 0.527, 0.073, 0.527]?" }, { "from": "gpt", "value": "The y-axis label at these coordinates is empty." } ], "id_raw": 611 }, { "id": "5a4gd3HTjnKFF8Fg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nIs {the US Public Health Awareness bar}[0.116, 0.647, 0.343, 0.887] longer than {the EU Public Health Awareness bar}[0.116, 0.168, 0.327, 0.407]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 611 }, { "id": "qgbxyEqWqEzyhcPW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nAre {the EU bars}[[0.116, 0.168, 0.327, 0.407],[0.327, 0.168, 0.571, 0.407],[0.571, 0.168, 0.848, 0.407]] in general longer than {the US bars}[[0.116, 0.647, 0.343, 0.887],[0.343, 0.647, 0.604, 0.887],[0.604, 0.647, 0.799, 0.887]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 611 }, { "id": "BoPhM22HD3ljPjAW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nWhich bar is shorter, {the EU Pharmaceutical Research bar}[0.327, 0.168, 0.571, 0.407] or {the US Medical Equipment Patents bar}[0.604, 0.647, 0.799, 0.887]?" }, { "from": "gpt", "value": "The EU Pharmaceutical Research bar." } ], "id_raw": 611 }, { "id": "MBN3y4QvCgQL6LP6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information represented by the region [0.526, 0.113, 0.869, 0.923]?" }, { "from": "gpt", "value": "This region covers part of the chart where the bars representing the US's Pharmaceutical Research and Medical Equipment Patents are plotted. The Pharmaceutical Research bar is purple, with its coordinate [0.343, 0.647, 0.604, 0.887]. The Medical Equipment Patents bar is light blue, with its coordinate [0.604, 0.647, 0.799, 0.887]." } ], "id_raw": 611 }, { "id": "Zto21yy5JsAedVe8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the region [0.071, 0.038, 0.977, 0.066]." }, { "from": "gpt", "value": "This region contains text labels of three categories: Public Health Awareness (%), Pharmaceutical Research (%), and Medical Equipment Patents, with their corresponding color labels in the legend. The Public Health Awareness label is gold-colored and has the coordinates [0.071, 0.038, 0.319, 0.066]; the Pharmaceutical Research label is purple and has the coordinates [0.402, 0.038, 0.658, 0.066]; the Medical Equipment Patents label is light blue and has the coordinates [0.741, 0.038, 0.977, 0.066]." } ], "id_raw": 611 }, { "id": "XZHXzJ1mcZ5Rx2Td", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained in the area [0.213, 0.070, 0.776, 0.907]?" }, { "from": "gpt", "value": "This area covers most of the grouped bar chart. It includes all the bars representing the amount of Public Health Awareness (%), Pharmaceutical Research (%), and Medical Equipment Patents in both the US and the EU. The x-axis of the area ranges from 0 to about 1200, and the y-axis includes two labels, \"US\" and \"EU\"." } ], "id_raw": 611 }, { "id": "FWwt3v6DX4FZpqg4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat color is the bar for \"Environmental Protection\" Commitments in 2009?" }, { "from": "gpt", "value": "{The bar for \"Environmental Protection\" Commitments in 2009 is colored}[0.211, 0.660, 0.671, 0.888]" } ], "id_raw": 612 }, { "id": "ONYPWs27BE0GRDp6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for the \"Community Investment\" text in the legend?" }, { "from": "gpt", "value": "The coordinates for the \"Community Investment\" text in the legend are [0.659, 0.045, 0.863, 0.073]." } ], "id_raw": 612 }, { "id": "llMC2iS8ZJ2VTrNy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label at [0.019, 0.307, 0.199, 0.335]?" }, { "from": "gpt", "value": "The y-axis label at [0.019, 0.307, 0.199, 0.335] is \"Achievements(2020)\"." } ], "id_raw": 612 }, { "id": "D3GYxNNbQX934pPy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Environmental Protection\" Commitments in 2009}[0.211, 0.660, 0.671, 0.888] wider than {the bar for \"Community Investment\" Commitments in 2009}[0.671, 0.660, 0.767, 0.888]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 612 }, { "id": "vdkkiAvLPhRWdzyg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nIs the value for {Commitments(2009) of \"Community Investment\"}[0.671, 0.660, 0.767, 0.888] significantly greater than {Achievements(2020) of \"Environmental Protection\"}[0.211, 0.204, 0.729, 0.432]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 612 }, { "id": "yrNyIG9IQT3nxGif", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nWhich is further to the right, {the bar for Achievements(2020) of \"Environmental Protection\"}[0.211, 0.204, 0.729, 0.432] or {the bar for Achievements(2020) of \"Community Investment\"}[0.729, 0.204, 0.882, 0.432]?" }, { "from": "gpt", "value": "{The bar for Achievements(2020) of \"Community Investment\"}[0.729, 0.204, 0.882, 0.432]." } ], "id_raw": 612 }, { "id": "HGQIiV8UwanZpvYB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat encompasses the region [0.659, 0.045, 0.863, 0.073]?" }, { "from": "gpt", "value": "The region [0.659, 0.045, 0.863, 0.073] includes the text label of the legend for \"Community Investment\"." } ], "id_raw": 612 }, { "id": "CCxKvyZbVYrh02iM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region covering the coordinates [0.211, 0.204, 0.882, 0.432]?" }, { "from": "gpt", "value": "The region [0.211, 0.204, 0.882, 0.432] has two bars:\n1. {The \"Environment Protection\" bar for \"Achievements(2020)\"}[0.211, 0.204, 0.729, 0.432];\n2. {The \"Community Investment\" bar for \"Achievements(2020)\"}[0.729, 0.204, 0.882, 0.432]." } ], "id_raw": 612 }, { "id": "M8Soz2RtQj7K6Wwb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the plot area from [0.211, 0.660, 0.767, 0.888]." }, { "from": "gpt", "value": "The area [0.211, 0.660, 0.767, 0.888] consists of two bars:\n1. {The \"Environmental Protection\" bar for \"Commitments(2009)\"}[0.211, 0.660, 0.671, 0.888];\n2. {The \"Community Investment\" bar for \"Commitments(2009)\"}[0.671, 0.660, 0.767, 0.888]." } ], "id_raw": 612 }, { "id": "3zqpFzAETAMxsbUZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object being represented in the coordinates [0.273, 0.791, 0.293, 0.888]?" }, { "from": "gpt", "value": "It represents the 'Initial Data' of 'E-Learning Adoption'." } ], "id_raw": 613 }, { "id": "V4sUosFg513R7rvl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell what color represents the 'Later Data' in the chart legend?" }, { "from": "gpt", "value": "The color for 'Later Data' according to the legend is greenish (approx. hex color code: #88a27d) {the legend}[0.460, 0.034, 0.794, 0.084]." } ], "id_raw": 613 }, { "id": "acSrl32gRpsOAtXd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the X-axis entry} at coordinates [0.401, 0.942, 0.434, 0.970]?" }, { "from": "gpt", "value": "The X-axis entry at those coordinates is '400'." } ], "id_raw": 613 }, { "id": "D98jaZZC46qDaLAh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Initial Data bar for 'Online Certification Courses'}[0.273, 0.595, 0.302, 0.693] smaller than or greater than {the Later Data bar for 'Online Certification Courses'}[0.302, 0.595, 0.360, 0.693]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 613 }, { "id": "5hIK77Qq8Rn16Q5Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Initial Data' bar for 'E-Books Downloaded'}[0.273, 0.400, 0.283, 0.497] have the same, lesser, or more value than {the 'Later Data' bar for 'E-Books Downloaded'}[0.283, 0.400, 0.336, 0.497]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 613 }, { "id": "eOcDDZI9ISLxiene", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Initial Data bar located in 'Digital Training Investment'}[0.273, 0.204, 0.514, 0.302] longer than {the Later Data bar located in 'Digital Training Investment'}[0.514, 0.204, 0.948, 0.302]??" }, { "from": "gpt", "value": "No." } ], "id_raw": 613 }, { "id": "8To9mrnHXl4Pb7Ok", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nCan you provide some details about the objects between the coordinate points [0.273, 0.170, 0.981, 0.923]?" }, { "from": "gpt", "value": "The coordinates represent the primary area of the plot. Within this region, there are horizontal gridlines, 4 pairs of bars representing the Initial and Later Data for 4 categories ('E-Learning Adoption', 'Online Certification Courses', 'E-Books Downloaded', and 'Digital Training Investment'), and an X-axis scale showing data ranges from 0 to 1400." } ], "id_raw": 613 }, { "id": "5iObEUApa96gSZhW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the coordinates [0.460, 0.034, 0.794, 0.084]?" }, { "from": "gpt", "value": "This area represents the legend of the bar chart. It consists of two entries: {Initial Data}[0.467, 0.047, 0.502, 0.067] represented by a reddish color (approx.: #d66555) and {Later Data}[0.646, 0.047, 0.681, 0.067] represented by a greenish color (approx.: #88a27d)." } ], "id_raw": 613 }, { "id": "CgJ9SbIvjHKEsEK6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nCan you provide details of the region encompassed by [0.273, 0.400, 0.336, 0.497]?" }, { "from": "gpt", "value": "This region contains two vertical bars belonging to the 'E-Books Downloaded' category. The bar on the left represents the 'Initial Data' and the one on the right represents the 'Later Data'." } ], "id_raw": 613 }, { "id": "Ik6m1LNLakWfqGjW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.180, 0.660, 0.391, 0.888] referred to in the chart?" }, { "from": "gpt", "value": "It is a bar representing Healthy Food Intake (%) for Population." } ], "id_raw": 614 }, { "id": "Z1Hyzw06uw2MHcNG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with the coordinates [0.470, 0.045, 0.626, 0.073] in the chart?" }, { "from": "gpt", "value": "The label is \"Smoking Rate (%)\"." } ], "id_raw": 614 }, { "id": "iggn1DvhFAVB8EGB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.450, 0.204, 0.590, 0.432] depict?" }, { "from": "gpt", "value": "The bar is depicting the Smoking Rate (%) for Urban Population." } ], "id_raw": 614 }, { "id": "qOxAD82MENRHLfKU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the object mentioned at [0.019, 0.307, 0.168, 0.335] represent in the chart?" }, { "from": "gpt", "value": "It's the y-axis label, named \"Urban Population\"." } ], "id_raw": 614 }, { "id": "zeUxDSRwyBqdtKU4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Healthy Food Intake for Population}[0.180, 0.660, 0.391, 0.888] taller than {the bar representing Smoking Rate for Population}[0.391, 0.660, 0.577, 0.888]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 614 }, { "id": "WAsTMdIkDmKGN4if", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of Urban population's Healthy Food Intake}[0.180, 0.204, 0.450, 0.432] longer than {the bar of Urban population's Sports Activity Participation}[0.590, 0.204, 0.897, 0.432]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 614 }, { "id": "c4buGx9Mi8Atr2Vg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the Healthy Food Intake bar for the Population}[0.180, 0.660, 0.391, 0.888] extend further to right than {the Healthy Food Intake bar for Urban Population}[0.180, 0.204, 0.450, 0.432]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 614 }, { "id": "1ZnHRFBfXxNsU6rS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nAre the bars representing urban population typically longer than the bars representing general population?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 614 }, { "id": "XaJG1vw4ExDN9Lpf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is contained within the region [0.121, 0.034, 0.992, 0.084]" }, { "from": "gpt", "value": "This region contains the graph chart legend, with three pairs of color-text labels. Each label consists of a colored square followed by a descriptive text. The labels are: Healthy Food Intake, identified with a red square [0.128, 0.047, 0.163, 0.067]; Smoking Rate, associated with a green square [0.421, 0.047, 0.456, 0.067]; and Sports Activity Participation, marked with a blue square [0.661, 0.047, 0.695, 0.067]." } ], "id_raw": 614 }, { "id": "DUvRPZoPi90Fn2nt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the coordinate range [0.180, 0.204, 0.897, 0.432]" }, { "from": "gpt", "value": "This area contains three bars representing the rates of Healthy Food Intake, Smoking, and Sports Activity Participation for the Urban Population." } ], "id_raw": 614 }, { "id": "8xXZPfGaLxdYD3Hq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area [0.180, 0.660, 0.830, 0.888]?" }, { "from": "gpt", "value": "This area contains three bars, representing the rates of Healthy Food Intake, Smoking and Sports Activity Participation for the general Population." } ], "id_raw": 614 }, { "id": "YvuM3GYXB28ENYc8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.577, 0.660, 0.830, 0.888]" }, { "from": "gpt", "value": "This region contains a single bar that represents the rate of Sports Activity Participation for the general Population." } ], "id_raw": 614 }, { "id": "P9zoq6J4KN4vtPGT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat object represents the y-axis label around the coordinates of [0.078, 0.505, 0.078, 0.505]?" }, { "from": "gpt", "value": "There is no particular object representing the y-axis label around the coordinates [0.078, 0.505, 0.078, 0.505]." } ], "id_raw": 623 }, { "id": "6KefzfegnX72UHdQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the second increment on the x-axis with the coordinates [0.266, 0.902, 0.294, 0.920]?" }, { "from": "gpt", "value": "The tick at [0.266, 0.902, 0.294, 0.920] represents 0.2 on the x-axis." } ], "id_raw": 623 }, { "id": "sXcm8uhPL2Ldvnuk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis scale} at [0.086, 0.575, 0.113, 0.593]?" }, { "from": "gpt", "value": "0.4." } ], "id_raw": 623 }, { "id": "oRnZIpuzdMj3OHjh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nIs {the x-axis tick at 0.6}[0.576, 0.902, 0.604, 0.920] to the left or right of {the x-axis tick at 0.8}[0.731, 0.902, 0.759, 0.920]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 623 }, { "id": "zAWb8ZL2V9ItjGYK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nIs {the y-axis tick at 0.8}[0.085, 0.267, 0.113, 0.285] higher or lower than {the y-axis tick at 0.2}[0.086, 0.729, 0.113, 0.747]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 623 }, { "id": "PlbDQQysXeAF30I5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nAre {the x-axis ticks}[[0.111, 0.902, 0.139, 0.920], [0.266, 0.902, 0.294, 0.920], [0.421, 0.902, 0.449, 0.920], [0.576, 0.902, 0.604, 0.920], [0.731, 0.902, 0.759, 0.920], [0.886, 0.902, 0.914, 0.920]] generally farther from the y-axis than {the y-axis ticks}[[0.085, 0.883, 0.113, 0.901], [0.086, 0.729, 0.113, 0.747], [0.086, 0.575, 0.113, 0.593], [0.085, 0.421, 0.113, 0.439], [0.085, 0.267, 0.113, 0.285], [0.085, 0.113, 0.113, 0.131]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 623 }, { "id": "BhHsqtUFpRoFpdAL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nProvide an overview of the region [0.125, 0.890, 0.900, 0.120]." }, { "from": "gpt", "value": "This region includes the main graphing area of the chart. It includes both the x and y axis with six equally spaced tick marks on each one. The x-axis ticks range from [0.111, 0.902, 0.139, 0.920] to [0.886, 0.902, 0.914, 0.920] while the y-axis ones are located between [0.085, 0.883, 0.113, 0.901] and [0.085, 0.113, 0.113, 0.131]." } ], "id_raw": 623 }, { "id": "MWIFozEA1TZlsjbO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.125, 0.120, 0.900, 0.890]." }, { "from": "gpt", "value": "This rectangle bounds the entire plot, from [0.125, 0.120] to [0.900, 0.890]. Within this area, we can see the x-axis ranging from [0.111, 0.902, 0.139, 0.920] to [0.886, 0.902, 0.914, 0.920] and the y-axis from [0.085, 0.883, 0.113, 0.901] to [0.085, 0.113, 0.113, 0.131]. We also see the blank main plotting area which is currently devoid of any bars or line elements that would normally represent data in such a chart." } ], "id_raw": 623 }, { "id": "2Tby9DJvcF4UahbO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.125, 0.120, 0.900, 0.890]?" }, { "from": "gpt", "value": "This region includes all the primary components of the chart. It encompasses the x-axis with its labels from [0.111, 0.902, 0.913, 0.920], and the y-axis with its labels from [0.085, 0.883, 0.113, 0.131]. However, the central plot area does not currently contain any bars or lines representing data." } ], "id_raw": 623 }, { "id": "8NapwGhKf5tMShTo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at the coordinates [0.782, 0.102, 0.804, 0.120]?" }, { "from": "gpt", "value": "The value is 90." } ], "id_raw": 625 }, { "id": "4dFHQxUTA0Agz7f7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label located at [0.100, 0.478, 0.221, 0.496]?" }, { "from": "gpt", "value": "The label is Public Forums." } ], "id_raw": 625 }, { "id": "ZgBatRPpw8CveESi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the content located at [0.660, 0.352, 0.682, 0.369]?" }, { "from": "gpt", "value": "It's the value of 'Independent Blogs' which is 70." } ], "id_raw": 625 }, { "id": "pKLcB70A5roDxOTG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-coordinate at [0.106, 0.852, 0.221, 0.870]?" }, { "from": "gpt", "value": "The x-coordinate represents 'TV and Radio'." } ], "id_raw": 625 }, { "id": "MUrbwKClA5v2Pck4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nIs {the media type at [0.233, 0.186, 0.904, 0.286]} having higher trust level than {the media type at [0.233, 0.311, 0.660, 0.410]}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 625 }, { "id": "B46lORxfT2dCnxS3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nIs the influence of {Social-media at [0.233, 0.186, 0.904, 0.286]} higher than {Public Forums at [0.233, 0.435, 0.751, 0.535]}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 625 }, { "id": "EYpRZFPN5O9E52Ye", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher influence between {the 'Underground Media' at [0.233, 0.685, 0.629, 0.784]} and {the 'TV and Radio' at [0.233, 0.809, 0.843, 0.909]}?" }, { "from": "gpt", "value": "TV and Radio." } ], "id_raw": 625 }, { "id": "TcpZBBC28Zznyzrc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhich is higher between {the influence of 'Traditional Media' at [0.233, 0.061, 0.782, 0.161]} and {the influence of 'Mainstream News Sites' at [0.233, 0.560, 0.904, 0.660]}?" }, { "from": "gpt", "value": "Mainstream News Sites." } ], "id_raw": 625 }, { "id": "FMEqi279SflyTTCc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be found within the coordinates [0.233, 0.685, 0.629, 0.784]." }, { "from": "gpt", "value": "In this area, there is a bar representing the Underground Media, with a value of 65 represented as a number at coordinates [0.629, 0.726, 0.652, 0.743]." } ], "id_raw": 625 }, { "id": "K1bw9TegEmqUSVkN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is present within the region [0.233, 0.435, 0.751, 0.535]?" }, { "from": "gpt", "value": "Within this region, there is a bar showing the influence of Public Forums that is represented by a value of 85 at the location [0.751, 0.476, 0.773, 0.494]." } ], "id_raw": 625 }, { "id": "lOYQvFQxcrkMaaRD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within the coordinates [0.233, 0.186, 0.904, 0.286]?" }, { "from": "gpt", "value": "Inside these coordinates, we see the data of Social-media which is displayed by a bar. The value of the Social-media is 110, located at [0.904, 0.227, 0.937, 0.244]." } ], "id_raw": 625 }, { "id": "xYdOyiiGYQQ7tkRo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the material that is within the area [0.233, 0.809, 0.843, 0.909]." }, { "from": "gpt", "value": "In this location, there's a bar depicting the influence of 'TV and Radio' that extends until the value 100 at coordinates [0.843, 0.851, 0.876, 0.868]." } ], "id_raw": 625 }, { "id": "SX20FJEO9LP1LesK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the coordinates [0.114, 0.545, 0.121, 0.580]?" }, { "from": "gpt", "value": "The number '4' indicating a measurement." } ], "id_raw": 630 }, { "id": "ubPZbnYVAqzXLMmv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat color is represented at {the legend handle}[0.352, 0.102, 0.376, 0.126]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 630 }, { "id": "Mwpatv5sOeV32r3g", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nWhich axis label is positioned at [0.012, 0.330, 0.028, 0.682]?" }, { "from": "gpt", "value": "Y-axis label, called Integration Level." } ], "id_raw": 630 }, { "id": "pshQksO2GQYk3jTG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar corresponding to \"Historical Monuments\"}[0.093, 0.588, 0.143, 0.915] taller than {the bar corresponding to \"Folk Lore\"}[0.588, 0.343, 0.638, 0.915]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 630 }, { "id": "lxbPbX0mVN3COEd3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nWhich is closer to the y-axis, {Instance 1's Folk Lore bar}[0.588, 0.343, 0.638, 0.915] or {Instance 7's Historical Monuments bar}[0.390, 0.686, 0.440, 0.915]?" }, { "from": "gpt", "value": "{Instance 7's Historical Monuments bar}[0.390, 0.686, 0.440, 0.915]." } ], "id_raw": 630 }, { "id": "5Riz89c3eVbbbbDl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nIs the label {Instance 4}[0.500, 0.099, 0.560, 0.134] positioned to the left of {Instance 5}[0.616, 0.099, 0.676, 0.134]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 630 }, { "id": "f2UE6UYl8V87tWSp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within [0.093, 0.588, 0.440, 0.915]." }, { "from": "gpt", "value": "This region includes several vertical bars representing dataset instances for Historical Monuments. For instance, the bar for Instance 1 is {Instance 1's Historical Monuments bar}[0.093, 0.588, 0.143, 0.915] and the bar for Instance 7 is located at {Instance 7's Historical Monuments bar}[0.390, 0.686, 0.440, 0.915]." } ], "id_raw": 630 }, { "id": "5FgSh16HENC4Rmb5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat elements are located in [0.117, 0.033, 0.912, 0.148]?" }, { "from": "gpt", "value": "This area contains the sublegend with titles for seven instances, from Instance 1 to Instance 7. Each instance has a labeled text and a color handle next to it representing the instance's dataset." } ], "id_raw": 630 }, { "id": "XBlkDhyC7K2uqcSo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [0.012, 0.097, 0.440, 0.915]?" }, { "from": "gpt", "value": "This zone includes the Y-axis with labels ranging from 0 to 10 showing integration levels. It also encompasses the bar representations for Historical Monuments, displaying variable integration levels according to the data." } ], "id_raw": 630 }, { "id": "LlKNavqAnhQ2gAPf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nIdentify the object present at {coordinates}[0.033, 0.252, 0.040, 0.275]." }, { "from": "gpt", "value": "It is the Y-axis label for the value \"4\"." } ], "id_raw": 631 }, { "id": "bKVvZGNyDe6iqmD1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at {position}[0.224, 0.061, 0.252, 0.084] represent on the chart?" }, { "from": "gpt", "value": "It is the text label \"Start\" in the chart legend." } ], "id_raw": 631 }, { "id": "9Tp65mkIsIEFQwxA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nReveal what is located at {location}[0.191, 0.063, 0.215, 0.079]." }, { "from": "gpt", "value": "It is a legend color handle for \"Start\"." } ], "id_raw": 631 }, { "id": "tkIr56qukMyUZoRH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the start bar for \"Urban Regeneration\"}[0.087, 0.773, 0.106, 0.944] have a greater numerical value than {the completion bar for \"Public Transportation\"}[0.497, 0.602, 0.516, 0.944]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 631 }, { "id": "H4PD9zZ2PDrin1YF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nWhich is positioned higher on the Y-axis, {the progress bar for \"Housing Development\"}[0.665, 0.568, 0.684, 0.944] or {the stake involved bar for \"Infrastructure renewal\"}[0.907, 0.398, 0.926, 0.944]?" }, { "from": "gpt", "value": "Stake Involve." } ], "id_raw": 631 }, { "id": "TZPLNTL8Kstw8Shn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nAre {the issues bars}[0.143, 0.688, 0.162, 0.944], [0.330, 0.653, 0.348, 0.944], [0.516, 0.722, 0.535, 0.944], [0.702, 0.705, 0.721, 0.944], [0.889, 0.636, 0.907, 0.944] generally shorter than {the good reviews bars}[0.180, 0.346, 0.199, 0.944], [0.367, 0.261, 0.385, 0.944], [0.553, 0.466, 0.572, 0.944], [0.740, 0.415, 0.758, 0.944], [0.926, 0.244, 0.945, 0.944] in terms of the scale on the chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 631 }, { "id": "6a51pr0SNGvB8dwI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within {the region}[0.187, 0.017, 0.845, 0.093]." }, { "from": "gpt", "value": "The area contains the chart legend including the legend title \"Stages\" and six pairs of color handles and corresponding text labels for different stages such as Start, Progress, Completion, Issues, Stake Involve, and Good Reviews." } ], "id_raw": 631 }, { "id": "RoT882RVXZ5YfJzp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is found in {the bounding box}[0.033, 0.081, 0.040, 0.958]?" }, { "from": "gpt", "value": "This region contains the Y-axis labels with scale values ranging from \"0\" to \"5\" and the corresponding tick marks." } ], "id_raw": 631 }, { "id": "0ZCBiljRm483vDit", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are included in the section marked by {these coordinates}[0.268, 0.952, 0.391, 0.975]?" }, { "from": "gpt", "value": "It contains the X-axis category label for \"Parks & Open Spaces\" and a tick mark for that category." } ], "id_raw": 631 }, { "id": "F4AMOWvUfaidTvwt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar which represents the year 2019 on Facebook?" }, { "from": "gpt", "value": "The color of the bar which represents the year 2019 on Facebook is pinkish-purple, and its exact coordinates are {the bar}[0.148, 0.415, 0.209, 0.915]." } ], "id_raw": 633 }, { "id": "J8LSyecpOkFbDNUI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object with coordinates [0.560, 0.110, 0.589, 0.145] in the legend?" }, { "from": "gpt", "value": "It is the label for the year 2020 in the legend." } ], "id_raw": 633 }, { "id": "xKk6XzMxpMB7uLKW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object appearing within the coordinates [0.332, 0.359, 0.915] represent on the chart?" }, { "from": "gpt", "value": "It is the bar representing the number of Facebook users in 2021." } ], "id_raw": 633 }, { "id": "r06OegOOlJyu9Mrb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nIs the 2021 bar for {Facebook} [0.270, 0.359, 0.332, 0.915] taller than the 2020 bar for {Facebook}[0.209, 0.397, 0.270, 0.915]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 633 }, { "id": "aI5C7EwAXkI1asTD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nAre more users projected for {Instagram}[0.883, 0.489, 0.945, 0.915] or {Facebook}[0.270, 0.359, 0.332, 0.915] in 2021?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 633 }, { "id": "2NWGCSBSz4I1EQB1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nDoes {Twitter}[0.577, 0.749, 0.638, 0.915] have more users in 2021 than it did in 2020[0.516, 0.730, 0.577, 0.915]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 633 }, { "id": "HJQHaUeBPJWR7idc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the data contained within the coordinates [0.044, 0.174, 0.988, 0.915]?" }, { "from": "gpt", "value": "This region contains all of the bars of the chart, which represent the number of users (in billions) for each platform (Facebook, Twitter, Instagram) for the years 2018, 2019, 2020, and 2021." } ], "id_raw": 633 }, { "id": "3qyVuSAkzJtMKmar", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the numbers and information within [0.353, 0.043, 0.678, 0.158]." }, { "from": "gpt", "value": "In this region, there is a legend that corresponds to the colors of the bars for the different years: Blue for 2018, pinkish-purple for 2019, greenish-blue for 2020, and orange for 2021. The text coordinates for these years fall within [0.390, 0.110, 0.419, 0.145], [0.475, 0.110, 0.504, 0.145], [0.560, 0.110, 0.589, 0.145], and [0.644, 0.110, 0.674, 0.145] respectively." } ], "id_raw": 633 }, { "id": "souf3tPMbvatgEwE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the visible features within the region [0.496, 0.730, 0.516, 0.915]?" }, { "from": "gpt", "value": "This region encompasses the bar representing the user count for Twitter in the year 2020, which is a greenish-blue color. The height indicates the number which is expected to be about 1.0 billion." } ], "id_raw": 633 }, { "id": "wYVBAZPGWQNnXoqb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label of the chart in coordinate [0.010, 0.370, 0.022, 0.441]?" }, { "from": "gpt", "value": "Impact." } ], "id_raw": 635 }, { "id": "DfabYY2E6KYAdbOX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the first color in the legend box?" }, { "from": "gpt", "value": "{The color in the legend box}[0.384, 0.905, 0.403, 0.917] is Pink." } ], "id_raw": 635 }, { "id": "307ZdemwHDxz1Kxj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the pink bar represent in [0.086, 0.243, 0.130, 0.351]?" }, { "from": "gpt", "value": "The pink bar represents the \"Covid Cases Decline\" under a \"No Lockdown\" policy." } ], "id_raw": 635 }, { "id": "JxUoCTAyE6wHOhNN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater impact in the \"No Lockdown\" policy, {Covid Cases Decline}[0.086, 0.243, 0.130, 0.351] or {Economic Impact}[0.130, 0.351, 0.175, 0.568]?" }, { "from": "gpt", "value": "Economic Impact." } ], "id_raw": 635 }, { "id": "oFBstiMBuT4OLZPO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nWhat impact is greater in the {Partial Lockdown}[0.383, 0.221, 0.427, 0.351], {Covid Cases Decline}[0.383, 0.221, 0.427, 0.351] or {Healthcare Capacity}[0.516, 0.178, 0.561, 0.351]?" }, { "from": "gpt", "value": "Healthcare Capacity." } ], "id_raw": 635 }, { "id": "sesZuUfBNooheDBW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nUnder the {Complete Lockdown} policy< r>[0.766, 0.792, 0.860, 0.809], is the negative impact greater on {School Disruptions}[0.902, 0.351, 0.947, 0.612] or {Economic Impact} [0.724, 0.351, 0.769, 0.764]?" }, { "from": "gpt", "value": "School Disruptions." } ], "id_raw": 635 }, { "id": "hCmkyXf0Vzk9bRek", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content present in the coordinates [0.381, 0.870, 0.652, 0.980]." }, { "from": "gpt", "value": "This region contains the legend of the chart. The legend presents six different labels each with its corresponding color. Each label represents a different impact of different lockdown policies on various sectors." } ], "id_raw": 635 }, { "id": "IvDl73bFJ4So1mOp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the labels on the X-axis within coordinates [0.026, 0.792, 0.860, 0.809]?" }, { "from": "gpt", "value": "The labels on the X-axis in this region are: \"No Lockdown\", \"Partial Lockdown\", and \"Complete Lockdown\"." } ], "id_raw": 635 }, { "id": "gte2typGioD1AQHU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bars within [0.383, 0.221, 0.427, 0.351] and [0.516, 0.178, 0.561, 0.351]?" }, { "from": "gpt", "value": "The rectangle at [0.383, 0.221, 0.427, 0.351] represents the \"Covid Cases Decline\" impact under \"Partial Lockdown\", while the rectangle at [0.516, 0.178, 0.561, 0.351] represents the impact on \"Healthcare Capacity\" under the same policy." } ], "id_raw": 635 }, { "id": "rRbWpXRAKbQvHUtF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend at [0.055, 0.063, 0.197, 0.129] represent?" }, { "from": "gpt", "value": "The legend contains two items: {Internal Mobility}[0.087, 0.071, 0.168, 0.091] is represented by a blue rectangle, and {International Mobility}[0.087, 0.101, 0.193, 0.121] is represented by a red rectangle." } ], "id_raw": 636 }, { "id": "HJ9wWMFTwAyGWdPI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label mean at the coordinates [0.011, 0.358, 0.021, 0.612]?" }, { "from": "gpt", "value": "It denotes Population (in thousands)." } ], "id_raw": 636 }, { "id": "3KxkZlzwgckXQPaS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the X-axis tick at [0.743, 0.931, 0.768, 0.951]?" }, { "from": "gpt", "value": "It's 2017." } ], "id_raw": 636 }, { "id": "J40jnLsksiPCdEez", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the bar representing \"International Mobility\" in 2016?" }, { "from": "gpt", "value": "The position is [0.652, 0.296, 0.683, 0.917]." } ], "id_raw": 636 }, { "id": "aAMInNHiwNRyFC3B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nComparing the bars of {2010}[0.093, 0.219, 0.124, 0.917] and {2019}[0.885, 0.118, 0.916, 0.917], which year had more cases of Internal Mobility?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 636 }, { "id": "jtTHBdRzgMGhXbRm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar in 2015 representing \"International Mobility\"}[0.564, 0.312, 0.595, 0.917] taller than {the bar in 2015 representing \"Internal Mobility\"}[0.533, 0.094, 0.564, 0.917]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 636 }, { "id": "r19cskXhNKguptq0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 2016 bar for the International Mobility}[0.652, 0.296, 0.683, 0.917] taller than {the 2017 bar for the International Mobility}[0.740, 0.288, 0.771, 0.917]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 636 }, { "id": "wdR67niu7N9QG8Fw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nBetween {2016}[0.621, 0.110, 0.652, 0.917] and {2018}[0.797, 0.141, 0.828, 0.917], which year did the Internal Mobility reach a higher peak?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 636 }, { "id": "ArW7sQU9df3r8IwY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the bounding box [0.093, 0.219, 0.916, 0.917]?" }, { "from": "gpt", "value": "This area features all the blue bars, corresponding to the annual data of Internal Mobility from 2010 to 2019." } ], "id_raw": 636 }, { "id": "UeUz4p9oCa7cfVMF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.124, 0.389, 0.947, 0.917]?" }, { "from": "gpt", "value": "This region contains all red bars from 2010 to 2019, denoting International Mobility information yearly." } ], "id_raw": 636 }, { "id": "Ti0GqVLbzhanJsck", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.050, 0.053, 0.989, 0.917]?" }, { "from": "gpt", "value": "This is the main charting area, which includes rectangular bars representing data trends from two categories - Internal Mobility and International Mobility - over a decade. The y-axis label in this region is \u201cPopulation (in thousands)\u201d, and the x-axis label is \u201cYears\u201d. This region also contains a legend at the top left." } ], "id_raw": 636 }, { "id": "lHzERzPRWd1N1riK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the space [0.353, 0.021, 0.686, 0.047] contain?" }, { "from": "gpt", "value": "It holds the subtitle of the chart: \"Population Migration and Mobility Trends Over a Decade\"." } ], "id_raw": 636 }, { "id": "PKeCe1ltFYYk4lfJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at these coordinates [0.141, 0.593, 0.160, 0.616]?" }, { "from": "gpt", "value": "It is the y-axis tick label \"1.0\"." } ], "id_raw": 637 }, { "id": "oRhYJQ5mImIJRsQD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the color of the bar with the coordinates [0.554, 0.363, 0.590, 0.944]?" }, { "from": "gpt", "value": "The color of the bar {at these coordinates}[0.554, 0.363, 0.590, 0.944] is \"#a3ade5\"." } ], "id_raw": 637 }, { "id": "QZ8sVbg3cK0AX4En", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis tick located at [0.472, 0.952, 0.528, 0.975]?" }, { "from": "gpt", "value": "The label is \"Moderate\"." } ], "id_raw": 637 }, { "id": "yOIkbQ447xJkcbzl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar {representing \"Steady Decline\"}[0.338, 0.636, 0.374, 0.944] of the \"Conservative\" strategy taller than the bar {representing \"Sudden Growth\"}[0.374, 0.466, 0.410, 0.944]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 637 }, { "id": "onjpc77UCNDQ9Esw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is wider, {the bar indicating Moderate for \"Substantial Decrease\"}[0.410, 0.739, 0.446, 0.944] or {the bar indicating Moderate for \"Erratic Behavior\"}[0.446, 0.602, 0.482, 0.944]?" }, { "from": "gpt", "value": "They are the same width." } ], "id_raw": 637 }, { "id": "CC3HwsAWDKqIKjER", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar denoting Aggressive strategy under \"Accelerated Decline\"}[0.662, 0.875, 0.698, 0.944] shorter than {the bar depicting Aggressive strategy under \"Mixed Performance\"}[0.770, 0.432, 0.806, 0.944]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 637 }, { "id": "UcYjOTi0crqQvvRF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nIs the {legend}[0.923, 0.061, 1.034, 0.084]] to the right or left of {ylabel}[0.141, 0.593, 0.160, 0.616]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 637 }, { "id": "Ixtwv4t7nMRqF9qC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is inside the rectangular region [0.770, 0.432, 0.806, 0.944]?" }, { "from": "gpt", "value": "The region includes a bar representing the \"Aggressive\" investment strategy's returns for the category \"Mixed Performance\". This bar has a height representative of a return of 1.5." } ], "id_raw": 637 }, { "id": "k2xxj9Wq0JsjxoBW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.194, 0.602, 0.230, 0.944] contain?" }, { "from": "gpt", "value": "This area contains the bar representing the \"Stable Growth\" returns of the \"Conservative\" financial condition. The height of this bar represents a return of 1.0." } ], "id_raw": 637 }, { "id": "mCGYqUdIsXUGAf9C", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.374, 0.500, 0.410, 0.944]?" }, { "from": "gpt", "value": "This area contains a bar that represents the return of the Moderate investment strategy under Stable Growth which is 1.3." } ], "id_raw": 637 }, { "id": "5XPLDmH4PO0TWSWx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are present in the area [0.680, 0.193, 0.770, 0.944]?" }, { "from": "gpt", "value": "There is a column chart showing the returns of the Aggressive investment strategy for two financial conditions: \"Sudden Growth\" with a value of 2.2 and \"Mixed Performance\" with a value of 1.5." } ], "id_raw": 637 }, { "id": "iOd4b4a74NKg8PSj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart does the main object with coordinates as [0.050, 0.034, 0.989, 0.810] belong to?" }, { "from": "gpt", "value": "Bar Chart." } ], "id_raw": 638 }, { "id": "0WUovvGZGJfzsxpv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinates of the text representing the year 2013 in the legend?" }, { "from": "gpt", "value": "[0.521, 0.943, 0.546, 0.966]." } ], "id_raw": 638 }, { "id": "EITf366yrJAKw7Tz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.011, 0.363, 0.024, 0.481]?" }, { "from": "gpt", "value": "Measure." } ], "id_raw": 638 }, { "id": "Quhx8wlsvrLhWNp6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nWhat labels are shown in [0.271, 0.899, 0.768, 0.976]?" }, { "from": "gpt", "value": "{2010}[0.303, 0.943, 0.328, 0.966], {2011}[0.375, 0.943, 0.401, 0.966], {2012}[0.448, 0.943, 0.473, 0.966], {2013}[0.521, 0.943, 0.546, 0.966], {2014}[0.594, 0.943, 0.619, 0.966], {2015}[0.666, 0.943, 0.691, 0.966], {2016}[0.739, 0.943, 0.764, 0.966]." } ], "id_raw": 638 }, { "id": "x8d6vnktJpP3DzRa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Online Education\" in 2011}[0.143, 0.303, 0.193, 0.810] taller than {the bar representing \"Online Education\" in 2010}[0.093, 0.338, 0.143, 0.810]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 638 }, { "id": "2Hemmpkn32crLBEo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Training Services\" in 2013}[0.494, 0.547, 0.545, 0.810] shorter than {the bar representing \"Training Services\" in 2014}[0.545, 0.529, 0.595, 0.810]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 638 }, { "id": "IVlII5o57Nevkfwc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Edtech Startups\"}[[0.595, 0.780, 0.645, 0.810], [0.695, 0.690, 0.746, 0.810], [0.746, 0.631, 0.796, 0.810], [0.796, 0.541, 0.846, 0.810], [0.846, 0.452, 0.896, 0.810],[0.896, 0.273, 0.947, 0.810]] and {\"Online Education\"}[[0.093, 0.338, 0.143, 0.810], [0.143, 0.303, 0.193, 0.810], [0.193, 0.309, 0.243, 0.810], [0.243, 0.273, 0.294, 0.810], [0.294, 0.159, 0.344, 0.810], [0.344, 0.243, 0.394, 0.810], [0.394, 0.094, 0.444, 0.810]], which category has a higher value for the year 2016?" }, { "from": "gpt", "value": "Online Education." } ], "id_raw": 638 }, { "id": "uocFhkLOh2WnkLn9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nBetween {\"2010\"}[{Online Education}[0.093, 0.338, 0.143, 0.810], {Training Services}[0.344, 0.541, 0.394, 0.810], {Edtech Startups}[0.595, 0.780, 0.645, 0.810]] and {\"2011\"}[{Online Education}[0.143, 0.303, 0.193, 0.810], {Training Services}[0.394, 0.571, 0.444, 0.810], {Edtech Startups}[0.645, 0.750, 0.695, 0.810]], which year has a higher value for {Training Services}<[0.344, 0.541, 0.394, 0.810], [0.394, 0.571, 0.444, 0.810]>?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 638 }, { "id": "9dZB0idvvIXDiZyJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.271, 0.899, 0.768, 0.976]." }, { "from": "gpt", "value": "The region contains a legend displaying seven separate values for each year from 2010 to 2016. Each year is represented by a color label and its corresponding year. The color labels' coordinates range from [0.275, 0.945, 0.295, 0.961] to [0.711, 0.945, 0.731, 0.961], and the labels' text coordinates, in chronological order, start at [0.303, 0.943, 0.328, 0.966] and end at [0.739, 0.943, 0.764, 0.966]." } ], "id_raw": 638 }, { "id": "XXQDnTwLeEEHIaUw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.050, 0.034, 0.989, 0.810]." }, { "from": "gpt", "value": "This region contains the entire bar chart, which includes all the bars representing three categories (Online Education, Training Services, and Edtech Startups) over the years from 2010 to 2016. It also includes the x-axis labels, y-axis labels, and their respective scales." } ], "id_raw": 638 }, { "id": "VfQYxyk5ZwlL7Dj9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.394, 0.094, 0.444, 0.810]?" }, { "from": "gpt", "value": "There is a single bar representing the measure of \"Online Education\" for the year 2016." } ], "id_raw": 638 }, { "id": "Gc1eD5wvTQiL9nUD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.561, 0.514, 0.888, 0.780]?" }, { "from": "gpt", "value": "This area covers a portion of the bar chart, specifically the last three years (2014-2016) of data for the \"Training Services\" and \"Edtech Startups\" categories. It contains six bars in total, three for each category, color-coded according to the years they represent." } ], "id_raw": 638 }, { "id": "oIspCUS9QTdvxhBL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.028, 0.391, 0.046, 0.415]?" }, { "from": "gpt", "value": "The y-axis label \"100\" is positioned here." } ], "id_raw": 640 }, { "id": "v52rmayBBVrIBKHV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.116, 0.400, 0.140, 0.944]?" }, { "from": "gpt", "value": "This is the {2010 bar} highlighting \"Deforestation\"." } ], "id_raw": 640 }, { "id": "bf3vlMFg0SSMLEEL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the object located at [0.763, 0.088, 0.799, 0.101]?" }, { "from": "gpt", "value": "It is the color code for the year 2040 in the chart legend, which is colored #67a9b6." } ], "id_raw": 640 }, { "id": "lF6akIdnJLaNzjwa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Air Pollution\" bar of 2030}[0.790, 0.373, 0.813, 0.944] taller than {the \"Water Pollution\" bar of 2020}[0.297, 0.645, 0.320, 0.944]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 640 }, { "id": "RpLNs01ViFkVCw9b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Deforestation\" in 2010}[0.116, 0.400, 0.140, 0.944] taller or shorter than {the same bar in 2040}[0.187, 0.547, 0.210, 0.944]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 640 }, { "id": "Aew4v1pIoKjH8pKR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Overfishing\" bar for 2040}[0.657, 0.699, 0.680, 0.944] have a higher or lower value than {the \"Water Pollution\" bar for 2030}[0.320, 0.629, 0.343, 0.944]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 640 }, { "id": "xXAUDyHPm7VbF7MM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements located at the coordinates [0.380, 0.018, 0.659, 0.095]." }, { "from": "gpt", "value": "This is the chart's legend which contains four colored squares each representing a different year from 2010 to 2040, and their corresponding label \"2010\" at [0.412, 0.062, 0.437, 0.086], \"2020\" at [0.485, 0.062, 0.510, 0.086], \"2030\" at [0.557, 0.062, 0.582, 0.086], and \"2040\" at [0.630, 0.062, 0.655, 0.086]." } ], "id_raw": 640 }, { "id": "kLv9SoPWvznLVFBD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the area [0.746, 0.563, 0.770, 0.944]." }, { "from": "gpt", "value": "Within this area there is a blue bar - the {Air pollution bar for 2010}[0.743, 0.292, 0.766, 0.944], with a value of 120." } ], "id_raw": 640 }, { "id": "rTeV2YcXtYfhE4cB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.760, 0.952, 0.820, 0.975]?" }, { "from": "gpt", "value": "This is the x-axis label for {Air pollution}[0.760, 0.952, 0.820, 0.975]." } ], "id_raw": 640 }, { "id": "JukZhBhMGP8M4qWM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinate [0.118, 0.350, 0.132, 0.771] in the chart?" }, { "from": "gpt", "value": "{The AI bar for 2022}[0.119, 0.350, 0.132, 0.771]." } ], "id_raw": 641 }, { "id": "q9DeuZs4kddWFAkR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nWhich technology's bar is located at [0.351, 0.474, 0.366, 0.497]?" }, { "from": "gpt", "value": "Cloud Computing." } ], "id_raw": 641 }, { "id": "XfiNFgwUcMoPlkre", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nHow is {the y-label} described in the location [0.011, 0.322, 0.024, 0.483]?" }, { "from": "gpt", "value": "Trend Value." } ], "id_raw": 641 }, { "id": "p62Ytg4WW6vuopVo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nIs {the 2022 bar for IoT}[0.636, 0.487, 0.649, 0.771] taller than {the 2020 bar for IoT}[0.610, 0.540, 0.623, 0.771]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 641 }, { "id": "OWD7wHez8ygNpL2T", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nAre {the markers for '2021'}[0.414, 0.940, 0.434, 0.956] of a darker shade than {the markers for '2024'}[0.632, 0.940, 0.652, 0.956]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 641 }, { "id": "GygkE3fGvJUQZxjv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the 2023 value for AI}[0.132, 0.224, 0.145, 0.771] or {the 2025 value for AI}[0.158, 0.097, 0.171, 0.771]?" }, { "from": "gpt", "value": "2025." } ], "id_raw": 641 }, { "id": "2dAR1d1t0XQsoV6h", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents and layout within the region [0.265, 0.893, 0.762, 0.970]." }, { "from": "gpt", "value": "This area contains the legend for year labels, having several rectangles with different colors from [0.269, 0.940, 0.289, 0.956] to [0.705, 0.940, 0.725, 0.956], and corresponding year labels from 2019 to 2025, overall organized horizontally with equal spacing." } ], "id_raw": 641 }, { "id": "bey9D2qiifgT83Wa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what the coordinate range [0.081, 0.516, 0.182, 0.771] contains?" }, { "from": "gpt", "value": "This area covers three bars, representing values of AI across different years (2019 to 2021), each bar is marked with a value, and gradual height increases indicate rising trends." } ], "id_raw": 641 }, { "id": "CniX8HOAsP1k9oeF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nWhat is presented in the coordinate box [0.028, 0.025, 0.034, 0.785]?" }, { "from": "gpt", "value": "This vertical region lists y-axis values from 0 to 7, meant to represent trend values, and it covers varying locations corresponding to the actual placement of these numerical values beside the y-axis." } ], "id_raw": 641 }, { "id": "ozkmKNMZyvwmACqY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat color is associated with {the \"Arthritis\" label}[0.602, 0.061, 0.649, 0.084] in the legend?" }, { "from": "gpt", "value": "#67a9b6." } ], "id_raw": 643 }, { "id": "yaOfPK0dDZZXvcZ1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nWhich health condition corresponds to {the bar}[0.687, 0.334, 0.702, 0.944] located at the \"60-70\" age group?" }, { "from": "gpt", "value": "Heart Disease." } ], "id_raw": 643 }, { "id": "0OCkgfpoZblcwyB3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the prevalence rate indicated at the top of {the bar}[0.867, 0.724, 0.882, 0.944] for the \"30-40\" age group and \"Cancer\" category?" }, { "from": "gpt", "value": "1.8." } ], "id_raw": 643 }, { "id": "7rZsHE6eUhYp8wlI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nIs the prevalence rate for {Osteoporosis}[0.913, 0.249, 0.928, 0.944] higher in {the \"Over 70\"}[0.867, 0.952, 0.913, 0.975] or {the \"60-70\"}[0.723, 0.952, 0.757, 0.975] age group?" }, { "from": "gpt", "value": "Over 70." } ], "id_raw": 643 }, { "id": "B1Kc6oP51BtACavV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Under 30\" age group}[0.086, 0.822, 0.101, 0.944] have a consistent prevalence rate across all health conditions?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 643 }, { "id": "k49EHWe2oUmR1KNF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nWhich age group has a taller bar for {Depression}, {the second from the left}[0.327, 0.797, 0.342, 0.944] or {the fourth from the left}[0.627, 0.627, 0.642, 0.944]?" }, { "from": "gpt", "value": "50-60." } ], "id_raw": 643 }, { "id": "Ql5WigT3QXTSBtOQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents found within the range specified by these coordinates: [0.273, 0.952, 0.306, 0.975]." }, { "from": "gpt", "value": "This region includes the x-axis label \"30-40\" which marks the age category for the bars in the chart with {its coordinates}[0.273, 0.952, 0.306, 0.975]." } ], "id_raw": 643 }, { "id": "KTy7hkep17g2XXtJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present within the range [0.032, 0.813, 0.040, 0.958]?" }, { "from": "gpt", "value": "This area contains the y-axis labels \"1\" and \"0\" representing prevalence rates. The label \"1\" has {its coordinates}[0.032, 0.813, 0.040, 0.836] and \"0\" has {its coordinates}[0.032, 0.934, 0.040, 0.958]." } ], "id_raw": 643 }, { "id": "ju2YQ4kIJRRSYFop", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.570, 0.063, 0.593, 0.079] indicate in the chart?" }, { "from": "gpt", "value": "This region marks the legend color box corresponding to \"Arthritis,\" which is represented by a rectangle with {its coordinates}[0.570, 0.063, 0.593, 0.079] and the associated color #67a9b6." } ], "id_raw": 643 }, { "id": "n0dWMTHGi7xTlS8q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object responsible for the label 'Revenues (in million dollars)' located at [0.012, 0.359, 0.028, 0.752]?" }, { "from": "gpt", "value": "This refers to the Y-axis label." } ], "id_raw": 644 }, { "id": "NhPJ023DxLhRSMoO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at [0.107, 0.611, 0.160, 0.944] on the chart?" }, { "from": "gpt", "value": "It signifies a bar indicating revenue from the 'Oil' industry in 2008." } ], "id_raw": 644 }, { "id": "pVmXO4gewYhxerPj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.222, 0.024, 0.831, 0.101]?" }, { "from": "gpt", "value": "It refers to the legend, detailing the colors associated with different industries." } ], "id_raw": 644 }, { "id": "5UoGbWz1FuHRKFXb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Oil industry revenue bar for 2008}[0.107, 0.611, 0.160, 0.944] taller or shorter than {the Oil industry revenue bar for 2018}[0.631, 0.633, 0.684, 0.944]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 644 }, { "id": "3ZaSnRKunwoYCxf2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for the Biotechnology industry in 2018}[0.893, 0.500, 0.946, 0.944] have higher or lower height than {the bar for the Electricity industry in 2018}[0.841, 0.389, 0.893, 0.944]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 644 }, { "id": "ascP3rVt44dySv6j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar for the Water industry in 2008}[0.265, 0.555, 0.317, 0.944] and {the bar for Clean Energy industry in 2018}[0.736, 0.689, 0.788, 0.944], which is taller?" }, { "from": "gpt", "value": "The Water industry's bar." } ], "id_raw": 644 }, { "id": "e2yHI5L2ugHcTEJ8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents and structures observed in [0.222, 0.024, 0.831, 0.101]?" }, { "from": "gpt", "value": "The region is where the legend resides. It lists six industry sectors each associated with a different color:\n1. 'Oil' with color represented at [0.227, 0.070, 0.250, 0.086].\n2. 'Gas' symbolized by color at [0.298, 0.070, 0.321, 0.086].\n3. 'Clean Energy' denoted by color at [0.375, 0.070, 0.399, 0.086].\n4. 'Water' signified by color at [0.508, 0.070, 0.531, 0.086].\n5. 'Electricity' with color representation at [0.598, 0.070, 0.621, 0.086].\n6. 'Biotechnology' associated with color at [0.711, 0.070, 0.734, 0.086]." } ], "id_raw": 644 }, { "id": "NCdsnqlbwNPD8GfB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found within the coordinates [0.160, 0.633, 0.212, 0.944]." }, { "from": "gpt", "value": "Located at these coordinates, there's a bar that represents the revenues of the 'Gas' industry for the year 2008." } ], "id_raw": 644 }, { "id": "ZWFo1pPsWdrxvGkp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area between [0.265, 0.555, 0.317, 0.944]." }, { "from": "gpt", "value": "The area is occupied by a bar for the 'Water' industry in the year 2008 reaching up to 3500 million dollars in revenues on the Y-axis." } ], "id_raw": 644 }, { "id": "nyimE31wvHefrr0d", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.174, 0.066, 0.377, 0.094] represent?" }, { "from": "gpt", "value": "This label represents the {Economic Growth}[0.174, 0.066, 0.377, 0.094], which is blue in the chart." } ], "id_raw": 645 }, { "id": "18iteorlb3igodLg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage impact value given at the coordinates [0.877, 0.707, 0.918, 0.735]?" }, { "from": "gpt", "value": "The impact value at these coordinates is \"1.0\"." } ], "id_raw": 645 }, { "id": "LgF5z5g7tpJkeFAJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat's the item at [0.244, 0.153, 0.359, 0.883]?" }, { "from": "gpt", "value": "A {green bar}[0.244, 0.153, 0.359, 0.883] representing Policy Impact for Time Scale \"Years\"." } ], "id_raw": 645 }, { "id": "aICO8HvVRMp3HKn2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the ylabel at coordinates [0.025, 0.324, 0.048, 0.600]?" }, { "from": "gpt", "value": "The ylabel at these coordinates is {Impact Percentages}[0.025, 0.324, 0.048, 0.600]." } ], "id_raw": 645 }, { "id": "WWBZSQUrNy2niV59", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Economic Growth bar for \"Years\"}[0.129, 0.350, 0.244, 0.883] taller than the related {Policy Impact bar}[0.244, 0.153, 0.359, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 645 }, { "id": "AmH9d1uZbdiTVGPl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nIs the {green bar for \"Decades\"}[0.820, 0.743, 0.935, 0.883] to the left or right of the {blue bar}[0.704, 0.532, 0.820, 0.883]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 645 }, { "id": "mPyxp7LVlVGeP1ZM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nIs the value of Economic Growth for Years greater or lesser than for Decades?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 645 }, { "id": "FVJ1OU6Uk0yqXWhQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nBetween {Policy Impact bar for \"Years\"}[0.244, 0.153, 0.359, 0.883] and {Policy Impact bar for \"Decades\"}[0.820, 0.743, 0.935, 0.883], which one shows a higher impact percentage?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 645 }, { "id": "w31XLFr7L6GnE8gZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.100, 0.055, 0.387, 0.147]." }, { "from": "gpt", "value": "This region contains the chart legend, highlighting two categories. The first category is {Economic Growth}[0.174, 0.066, 0.377, 0.094] denoted in blue color. The second category is {Policy Impact}[0.174, 0.108, 0.328, 0.136] represented in green color." } ], "id_raw": 645 }, { "id": "HJQNpzif3RrNJHd6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat can we see in the region [0.704, 0.532, 0.820, 0.883]?" }, { "from": "gpt", "value": "In this region there is a blue bar indicating the Economic Growth for \"Decades\" with a value of 2.5%. The range of value is located at [0.762, 0.496, 0.803, 0.524]." } ], "id_raw": 645 }, { "id": "4BeQ8yUoX3WfiSS5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in region [0.129, 0.350, 0.244, 0.883]." }, { "from": "gpt", "value": "The region contains a blue bar and the corresponding value showing the Economic Growth for \"Years\" is 3.8%, and the value text's coordinate is [0.186, 0.314, 0.227, 0.342]." } ], "id_raw": 645 }, { "id": "O5dAqfA34Swibljj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the area [0.058, 0.030, 0.072, 0.058]." }, { "from": "gpt", "value": "The content within these coordinates is a Y-axis tick label, valued at \"6\"." } ], "id_raw": 645 }, { "id": "NIJkUye9yUxbk74o", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with the coordinates [0.419, 0.942, 0.637, 0.970] in the chart?" }, { "from": "gpt", "value": "X-axis label, named Government Policy." } ], "id_raw": 646 }, { "id": "fZynW5GARrF0mpFq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the Term 'Policy Implementation' in the legend?" }, { "from": "gpt", "value": "[0.174, 0.108, 0.432, 0.136]" } ], "id_raw": 646 }, { "id": "ZsTiI1uUONWtcoZ7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis scale} at [0.058, 0.367, 0.072, 0.395]?" }, { "from": "gpt", "value": "3." } ], "id_raw": 646 }, { "id": "jfo3ENIsowQxvxDd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat indicators or categories are shown in [0.100, 0.055, 0.441, 0.147]?" }, { "from": "gpt", "value": "{Policy Effectiveness}[0.174, 0.066, 0.402, 0.094] and {Policy Implementation}[0.174, 0.108, 0.432, 0.136]." } ], "id_raw": 646 }, { "id": "e02HdQIz9ez8f3dz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Policy Effectiveness'}[0.129, 0.294, 0.243, 0.883] to the left or right of {the bar for 'Policy Implementation'}[0.243, 0.209, 0.357, 0.883] on 'Environment Policy'?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 646 }, { "id": "9uWC2pL5ZYDBlRFe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Policy Implementation'}[0.814, 0.597, 0.928, 0.883] shorter than {the bar for 'Policy Effectiveness'}[0.699, 0.546, 0.814, 0.883] on 'Economic Policy'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 646 }, { "id": "5Wkr81GsW4oW2SlW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher level in 'Environment Policy', {Policy Effectiveness}<{Policy Effectiveness bar}[0.129, 0.294, 0.243, 0.883]> or {Policy Implementation}<{Policy Implementation bar}[0.243, 0.209, 0.357, 0.883]>?" }, { "from": "gpt", "value": "Policy Implementation." } ], "id_raw": 646 }, { "id": "Zc1XB1wx4VVYxIua", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nBetween {Policy Effectiveness on 'Environment Policy'}[0.129, 0.294, 0.243, 0.883] and {Policy Implementation on 'Economic Policy'}[0.814, 0.597, 0.928, 0.883], which one has a higher value?" }, { "from": "gpt", "value": "Policy Effectiveness on 'Environment Policy'." } ], "id_raw": 646 }, { "id": "UY3oOwpwm6oQcTOb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.100, 0.055, 0.441, 0.147]." }, { "from": "gpt", "value": "This area contains the chart's legend, which is composed of two entries has a grey background. Each entry is made up of a label, followed by its textual description: The first entry is 'Policy Effectiveness' with the text coordinates [0.174, 0.066, 0.402, 0.094], and the second entry is 'Policy Implementation' with text coordinates [0.174, 0.108, 0.432, 0.136]" } ], "id_raw": 646 }, { "id": "ly0BcCQH5V6T0fsJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.189, 0.903, 0.964, 0.931]?" }, { "from": "gpt", "value": "This area is the X-axis label area of the chart. It contains two labels, one is 'Environment Policy' at [0.189, 0.903, 0.411, 0.931], and the other one is 'Economic Policy' at [0.778, 0.903, 0.964, 0.931]." } ], "id_raw": 646 }, { "id": "cmTtCmSkFTDHazhk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found within the region [0.129, 0.294, 0.357, 0.883]?" }, { "from": "gpt", "value": "Two vertical bars are located in this area. The bar at [0.129, 0.294, 0.243, 0.883] represents 'Policy Effectiveness' in 'Environment Policy', while the bar at [0.243, 0.209, 0.357, 0.883] represents 'Policy Implementation' in 'Environment Policy'." } ], "id_raw": 646 }, { "id": "PBsGymLZDvYSW0mN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region within [0.699, 0.546, 0.928, 0.883]?" }, { "from": "gpt", "value": "Two vertical bars overlayed on the 'Economic Policy' category are contained within this area. The rectangular bar on the left at [0.699, 0.546, 0.814, 0.883] represents 'Policy Effectiveness' and the bar on the right at [0.814, 0.597, 0.928, 0.883] represents 'Policy Implementation'." } ], "id_raw": 646 }, { "id": "RNgjOpj9lUXlAoz5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the object with the coordinates [0.043, 0.546, 0.087, 0.570]?" }, { "from": "gpt", "value": "The y-axis label 1000." } ], "id_raw": 647 }, { "id": "zy6wO8n5atK4kL2d", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the third bar representing Moderate Growth?" }, { "from": "gpt", "value": "The coordinates are [0.585, 0.503, 0.629, 0.903]." } ], "id_raw": 647 }, { "id": "1m4VuCEBzh7EAQFw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.814, 0.125, 0.938, 0.148] represent?" }, { "from": "gpt", "value": "It represents the category 'Stable Growth'." } ], "id_raw": 647 }, { "id": "0f1EoOfrMJneWuzF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat labels are present in the coordinates [0.814, 0.055, 0.966, 0.078] and [0.814, 0.090, 0.945, 0.113]?" }, { "from": "gpt", "value": "The labels are {Moderate Growth}[0.814, 0.055, 0.966, 0.078] and {Steady Decline}[0.814, 0.090, 0.945, 0.113]." } ], "id_raw": 647 }, { "id": "pZ7VuNCM7yqR1RtQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhich is leftmost among bars for 'Stable Growth'?" }, { "from": "gpt", "value": "The one that represents data for '2020 Q1'[0.228, 0.382, 0.273, 0.903]." } ], "id_raw": 647 }, { "id": "yp5hlvDnEA9OZlFf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nAre the bars of \"Steady Decline\" generally taller than those for \"Moderate Growth\"?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 647 }, { "id": "bVMe1ZBI4DtypvjO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing '2020 Q3' for Moderate Growth taller than the corresponding bar for Stable Growth?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 647 }, { "id": "P7DULuFzFBjaiuue", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nAmong {Moderate Growth bars}<{bar1}[0.139, 0.555, 0.184, 0.903], {bar2}[0.362, 0.521, 0.407, 0.903], {bar3}[0.585, 0.503, 0.629, 0.903], {bar4}[0.808, 0.486, 0.852, 0.903]> and {Steady Decline bars}<{bar1}[0.184, 0.208, 0.228, 0.903], {bar2}[0.407, 0.243, 0.451, 0.903], {bar3}[0.629, 0.277, 0.674, 0.903], {bar4}[0.852, 0.347, 0.897, 0.903]>, which group has a taller bar for '2020 Q2'?" }, { "from": "gpt", "value": "Steady Decline." } ], "id_raw": 647 }, { "id": "m7BhaovHGGDPTcEx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.139, 0.555, 0.184, 0.903]." }, { "from": "gpt", "value": "Within this area there is the first bar of the 'Moderate Growth' category, for '2020 Q1'. Its height represents a unit value of 1000." } ], "id_raw": 647 }, { "id": "B4fqouV82V7KnoCF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat data is shown in the area [0.852, 0.347, 0.897, 0.903]?" }, { "from": "gpt", "value": "This area contains the last bar of the 'Steady Decline' category. It represents '2020 Q4' and the unit value it represents is 1600." } ], "id_raw": 647 }, { "id": "kBRWaXeyVDW8lRE6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained in the area [0.452, 0.376, 0.496, 0.902]?" }, { "from": "gpt", "value": "The area contains the bar of the 'Stable Growth' category for '2020 Q2' with a unit value of 1500." } ], "id_raw": 647 }, { "id": "KxQc9rl5EzQnNkfa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat components occupy the coordinates[0.585, 0.503, 0.718, 0.903]?" }, { "from": "gpt", "value": "That space is occupied by two bars, one from 'Moderate Growth' and the other from 'Steady Decline', representing data for '2020 Q3'. The heights of the bars represent unit values of 1150 and 1800 respectively." } ], "id_raw": 647 }, { "id": "Q0OgGsPsAkvFOAwV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.251, 0.513, 0.355, 0.883]?" }, { "from": "gpt", "value": "A yellow bar representing Machine Learning for Chatbot." } ], "id_raw": 648 }, { "id": "w4qOVnfqHpNElI8d", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at the coordinates [0.196, 0.108, 0.401, 0.136] in the legend?" }, { "from": "gpt", "value": "Machine Learning." } ], "id_raw": 648 }, { "id": "8lUqFjm5TFjMpQDj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the position [0.025, 0.331, 0.048, 0.594] on the y-axis?" }, { "from": "gpt", "value": "Performance Score." } ], "id_raw": 648 }, { "id": "ZTO3B7acYfpahfEz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at the coordinates [0.675, 0.903, 0.972, 0.931]?" }, { "from": "gpt", "value": "x-axis label for Recommendation System." } ], "id_raw": 648 }, { "id": "ajAXMD4NzV33sIm8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nIs {the red bar representing Deep Learning in Chatbot application}[0.147, 0.142, 0.251, 0.883] taller than {the yellow bar representing Machine Learning in Chatbot application}[0.251, 0.513, 0.355, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 648 }, { "id": "wj4yPjACSFBVF9qy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nIs {the yellow bar for Machine learning on Recommendation System}[0.772, 0.176, 0.876, 0.883] to the right of {the red bar for Deep learning on Recommendation System}[0.668, 0.445, 0.772, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 648 }, { "id": "VEVaUzRN9O7IuZVj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nIs {the Machine Learning legend}[0.196, 0.108, 0.401, 0.136] higher than {the Deep Learning legend}[0.196, 0.066, 0.365, 0.094]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 648 }, { "id": "TNPaXCohivKdaImE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Deep Learning}[0.147, 0.142, 0.251, 0.883] and {Machine Learning}[0.251, 0.513, 0.355, 0.883] in the Chatbot application, which has a higher performance score?" }, { "from": "gpt", "value": "Deep Learning." } ], "id_raw": 648 }, { "id": "5y8PEdvcuJOKEfe7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe in the region [0.122, 0.055, 0.410, 0.147]?" }, { "from": "gpt", "value": "This region contains the legend of the graph. The legend has two entries. The first label is 'Deep Learning', with the text located at [0.196, 0.066, 0.365, 0.094] and colored red at [0.131, 0.069, 0.178, 0.088]. The second label is 'Machine Learning', with the text located at [0.196, 0.108, 0.401, 0.136] and colored yellow at [0.131, 0.110, 0.178, 0.130]." } ], "id_raw": 648 }, { "id": "QhNCw9hK77yuloNE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content between the coordinates [0.110, 0.041, 0.912, 0.883]." }, { "from": "gpt", "value": "This contains the entire bar graph with two pairs of bars. The first pair of bars represent the Chatbot at x-axis labels located at [0.257, 0.903, 0.349, 0.931]. The second pair of bars represent the Recommendation System at x-axis labels located at [0.675, 0.903, 0.972, 0.931]. Both pairs include one red bar for 'Deep Learning' and one yellow bar for 'Machine Learning'." } ], "id_raw": 648 }, { "id": "N6nEMxrmFtB714Lj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the space [0.668, 0.445, 0.876, 0.883]?" }, { "from": "gpt", "value": "There are two bars in this area representing the Performance Score for Recommendation System application. One is the red bar representing the score for Deep Learning, and the other is a yellow bar representing the score for Machine Learning." } ], "id_raw": 648 }, { "id": "928Cn40LJkqhI3Mx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe what you observe within the region [0.147, 0.142, 0.355, 0.883]." }, { "from": "gpt", "value": "I identify two bar groups representing Performance Scores for the application type of Chatbot. The red bar, indicating the performance of Deep Learning, spreads from coordinates [0.147, 0.142, 0.251, 0.883], and the yellow bar, indicating the performance of Machine Learning, spreads within [0.251, 0.513, 0.355, 0.883]." } ], "id_raw": 648 }, { "id": "T9kMf7Tb1OgaODVQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.025, 0.238, 0.048, 0.675] on the left side of the chart?" }, { "from": "gpt", "value": "Y-axis label, which is \"Supply Chain Cost (Billion USD)\"." } ], "id_raw": 649 }, { "id": "dPzY2uRpuxQBw32f", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.150, 0.268, 0.262, 0.883] holding information about in the chart?" }, { "from": "gpt", "value": "It is the blue bar representing the supply chain cost of H&M for the year 2020." } ], "id_raw": 649 }, { "id": "qXgCzq3rRQJrqh3o", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhere is {Zara's 2020 data bar} located?" }, { "from": "gpt", "value": "[0.711, 0.586, 0.823, 0.883]" } ], "id_raw": 649 }, { "id": "4kwYaPhDFHapEbmn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhich is the company that corresponds to {the X-axis label}[0.234, 0.903, 0.290, 0.931]?" }, { "from": "gpt", "value": "H&M." } ], "id_raw": 649 }, { "id": "nXdeCyKxxBZfQyV8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhich company's 2021 cost is higher, {H&M}[0.262, 0.586, 0.374, 0.883] or {Zara}[0.823, 0.387, 0.936, 0.883]?" }, { "from": "gpt", "value": "Zara." } ], "id_raw": 649 }, { "id": "9EOrkNwPeOZlaF2R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nDoes {H&M's 2020 bar}[0.150, 0.268, 0.262, 0.883] have a lower cost than {its 2021 bar}[0.262, 0.586, 0.374, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 649 }, { "id": "feDpgp3kRwHXjzrN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller in the x-axis between {H&M bar}[0.150, 0.586, 0.374, 0.883] and {Zara bar}[0.711, 0.387, 0.936, 0.883]?" }, { "from": "gpt", "value": "The H&M bar." } ], "id_raw": 649 }, { "id": "10rHR7UGYyknX2k1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nBetween {Zara's 2020 supply cost}[0.711, 0.586, 0.823, 0.883] and {Zara's 2021 supply cost}[0.823, 0.387, 0.936, 0.883], which is higher?" }, { "from": "gpt", "value": "Zara's 2021 supply cost." } ], "id_raw": 649 }, { "id": "XSm4krWLomiB55xx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.822, 0.044, 0.963, 0.136]." }, { "from": "gpt", "value": "This area contains the chart legend. It has two elements: {2020's legend}[0.831, 0.058, 0.877, 0.077] which is colored blue, and {2021's legend}[0.831, 0.099, 0.877, 0.119] which is colored orange. The labels for these color codes are located at [0.896, 0.055, 0.954, 0.083] and [0.896, 0.097, 0.954, 0.125], respectively." } ], "id_raw": 649 }, { "id": "0mSzb7qRjl5nAohU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhat is within coordinates [0.150, 0.268, 0.374, 0.883]?" }, { "from": "gpt", "value": "There are two bars located within the coordinates. The first is the {blue bar}[0.150, 0.268, 0.262, 0.883] representing H&M's supply chain cost for 2020, and the second one is the {orange bar}[0.262, 0.586, 0.374, 0.883] representing H&M's supply chain cost for 2021." } ], "id_raw": 649 }, { "id": "go0WWLdictUpUhlU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area defined by coordinates [0.711, 0.586, 0.936, 0.883]?" }, { "from": "gpt", "value": "This area accommodates two bars that represent Zara's supply chain costs for the years 2020 (represented by the blue bar) and 2021 (represented by the orange bar). The blue bar is located at [0.711, 0.586, 0.823, 0.883] and the orange bar is found at [0.823, 0.387, 0.936, 0.883]." } ], "id_raw": 649 }, { "id": "ICyz7SK2lBjLQwql", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.058, 0.079, 0.094, 0.900]?" }, { "from": "gpt", "value": "This area contains the Y-axis labels from 4.0 to 0.0. They indicate the scale of supply chain cost in billions of USD." } ], "id_raw": 649 }, { "id": "hDf3ZQiM7YTl89zg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the object located at [0.025, 0.340, 0.048, 0.585] represent?" }, { "from": "gpt", "value": "The y-axis label, which is named 'Level of Influence'." } ], "id_raw": 651 }, { "id": "nu2QKzDDuAsB98P4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at [0.204, 0.903, 0.320, 0.931]?" }, { "from": "gpt", "value": "\"FOX News\"." } ], "id_raw": 651 }, { "id": "CZ4H9eEZJLVXB4zV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted inside the rectangle with coordinates [0.711, 0.378, 0.823, 0.883]?" }, { "from": "gpt", "value": "A bar that signifies the strong influence value of \"CNN\", according to {the legend}[0.690, 0.055, 0.963, 0.147]." } ], "id_raw": 651 }, { "id": "DE3TilsLl2LD6kXG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nIs the {FOX News stronger influence bar}[0.150, 0.715, 0.262, 0.883] higher than the {CNN stronger influence bar}[0.711, 0.378, 0.823, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 651 }, { "id": "XkhoE5lgkz2a2pbF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nWhich one is to the right, {the slight influence bar for FOX News}[0.262, 0.209, 0.374, 0.883] or {the strong influence bar for FOX News}[0.150, 0.715, 0.262, 0.883]?" }, { "from": "gpt", "value": "The slight influence bar for FOX News." } ], "id_raw": 651 }, { "id": "k01v0AwauP5dN2pf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller between {the CNN strong influence bar}[0.711, 0.378, 0.823, 0.883] and {the CNN slight influence bar}[0.823, 0.659, 0.936, 0.883]?" }, { "from": "gpt", "value": "The CNN strong influence bar." } ], "id_raw": 651 }, { "id": "EQR0LuOkQCEWRA5w", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between [0.690, 0.055, 0.963, 0.147]?" }, { "from": "gpt", "value": "This region contains the chart legend, which includes two entries. The first entry has a color label at [0.699, 0.069, 0.746, 0.088] associated with white color, and a text label at [0.764, 0.066, 0.954, 0.094] named as 'Strong Influence'. Correspondingly, the second entry also has a color label at [0.699, 0.110, 0.746, 0.130] with white color, and a text label located at [0.764, 0.108, 0.944, 0.136], which describes 'Slight Influence'." } ], "id_raw": 651 }, { "id": "n2vJAzeo7ce1HSqC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.711, 0.378, 0.936, 0.883]?" }, { "from": "gpt", "value": "Within this region, there are two bars. One of them, located at [0.711, 0.378, 0.823, 0.883], represents the strong influence score of CNN in white color. The other one, at [0.823, 0.659, 0.936, 0.883], represents the slight influence score of CNN, also in white color." } ], "id_raw": 651 }, { "id": "RocBjUJWMEasZG56", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.150, 0.209, 0.374, 0.883]?" }, { "from": "gpt", "value": "This region contains two bars. The first one, located at [0.150, 0.715, 0.262, 0.883], shows the strong influence value for FOX News in white color. The second one, found at [0.262, 0.209, 0.374, 0.883], shows the slight influence value for FOX News, also in white color." } ], "id_raw": 651 }, { "id": "GHitkW9m4e6W4Vvp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.025, 0.357, 0.048, 0.567] mean?" }, { "from": "gpt", "value": "It is the y-axis label, with the title \"Influence Level\"." } ], "id_raw": 652 }, { "id": "lmdYpGXGP9Kv4UGl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the reading at coordinate [0.862, 0.108, 0.954, 0.136]?" }, { "from": "gpt", "value": "The text at these coordinates reads \"Media 2\"." } ], "id_raw": 652 }, { "id": "a9P2wj6EhUE7q6We", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label at [0.459, 0.942, 0.627, 0.970] denote?" }, { "from": "gpt", "value": "It is the x-axis label, named \"Media Sources\"." } ], "id_raw": 652 }, { "id": "K24BAonyteV95R3b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nIn the legend, is {Media 2}[0.862, 0.108, 0.954, 0.136] listed above or below {Media 1}[0.862, 0.066, 0.954, 0.094]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 652 }, { "id": "NF4XAA3lEmUZb2h7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nIs {the influence level for Test 1 in Media 1}[0.150, 0.146, 0.262, 0.883] greater than {the influence level for Test 2 in Media 2}[0.823, 0.673, 0.936, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 652 }, { "id": "JMWnk7nxPE4BP3xl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nIs {Media 1's influence for Test 1}[0.150, 0.146, 0.262, 0.883] higher or lower than {Media 1's influence for Test 2}[0.711, 0.357, 0.823, 0.883]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 652 }, { "id": "l7qD8zmgw6vHPiuK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.150, 0.146, 0.936, 0.883]." }, { "from": "gpt", "value": "There are four bars in the chart, arranged by two different media sources (Media 1 and Media 2) and two test conditions (Test 1 and Test 2). The coordinates are:\n1. Media 1 Test 1: [0.150, 0.146, 0.262, 0.883]\n2. Media 2 Test 1: [0.262, 0.462, 0.374, 0.883]\n3. Media 1 Test 2: [0.711, 0.357, 0.823, 0.883]\n4. Media 2 Test 2: [0.823, 0.673, 0.936, 0.883]." } ], "id_raw": 652 }, { "id": "FGSaW8Zg7lbxIf2s", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the area at [0.788, 0.055, 0.963, 0.147]." }, { "from": "gpt", "value": "It is a legend box consisting of two entries. The first entry is \"Media 1\" with the color handle at [0.797, 0.069, 0.844, 0.088] and the label text at [0.862, 0.066, 0.954, 0.094]. The second entry is \"Media 2\" with the color handle at [0.797, 0.110, 0.844, 0.130] and label text at [0.862, 0.108, 0.954, 0.136]." } ], "id_raw": 652 }, { "id": "qeLKOwzqKyRei1SV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the section of the chart within the region [0.711, 0.357, 0.936, 0.883]?" }, { "from": "gpt", "value": "This region contains two bars. Both bars represent the influence level of the Media 1 and Media 2 for Test 2. The exact coordinates for each bar are:\n1. Bar for Media 1: [0.711, 0.357, 0.823, 0.883]\n2. Bar for Media 2: [0.823, 0.673, 0.936, 0.883]." } ], "id_raw": 652 }, { "id": "U1cXrOm4U7GV2u04", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nWhat's placed at the location [0.019, 0.431, 0.036, 0.506] on the chart?" }, { "from": "gpt", "value": "It's the Y-axis label, named \"Values\"." } ], "id_raw": 653 }, { "id": "EV4u12zHUrATdqn4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nIf I wish to know the name of the state at coordinates [0.231, 0.919, 0.314, 0.942], which one is it?" }, { "from": "gpt", "value": "It's \"California\"." } ], "id_raw": 653 }, { "id": "7zmn43uTxSWQDBQf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nIn case of [0.093, 0.057, 0.128, 0.073], what exactly is represented in there?" }, { "from": "gpt", "value": "It's the color handle in the legend for the \"Forest Coverage\" category." } ], "id_raw": 653 }, { "id": "IgpmCrufar3ZxrXK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nWhich entity does the coordinate [0.502, 0.952, 0.557, 0.975] correspond to?" }, { "from": "gpt", "value": "It corresponds to the X-axis label, known as \"States\"." } ], "id_raw": 653 }, { "id": "5KFuziCqNGswzOUw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nIs the {Forest coverage bar for California} at [0.118, 0.738, 0.221, 0.903] longer or shorter than the {Population Density bar for California} at [0.221, 0.164, 0.324, 0.903]?" }, { "from": "gpt", "value": "It's shorter." } ], "id_raw": 653 }, { "id": "En5qQx8dCntX2O1q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nComparing the {Public Park Coverage bar for California} at [0.324, 0.842, 0.427, 0.903] and the {Public Park Coverage bar for Florida} at [0.837, 0.825, 0.940, 0.903], which is taller?" }, { "from": "gpt", "value": "The bar for Florida is taller." } ], "id_raw": 653 }, { "id": "zHHRexTW71AJWgDc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nAre the {bars for California} [{Forest coverage bar at [0.118, 0.738, 0.221, 0.903]}, {Population Density bar at [0.221, 0.164, 0.324, 0.903]}, {Public Park Coverage bar at [0.324, 0.842, 0.427, 0.903]}] generally higher than the {bars for Florida} [{Forest coverage bar at [0.632, 0.799, 0.735, 0.903]}, {Population Density bar at [0.735, 0.251, 0.837, 0.903]}, {Public Park Coverage bar at [0.837, 0.825, 0.940, 0.903]}]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 653 }, { "id": "G3nXjJgjJ4zOQoSq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nLooking at the {Population Density bar for California} at [0.221, 0.164, 0.324, 0.903] and {Population Density bar for Florida}[0.735, 0.251, 0.837, 0.903], which state seems to have a larger population density?" }, { "from": "gpt", "value": "California." } ], "id_raw": 653 }, { "id": "shykssmZK7gvJK1z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe area [0.086, 0.046, 0.332, 0.157] on the chart?" }, { "from": "gpt", "value": "Yes, it's the legend containing three entries: {Forest Coverage} at [0.142, 0.055, 0.284, 0.078] with color handle at [0.093, 0.057, 0.128, 0.073], {Population Density} at [0.142, 0.090, 0.304, 0.113] with color handle at [0.093, 0.092, 0.128, 0.108], and {Public Park Coverage} at [0.142, 0.125, 0.325, 0.148] with color handle at [0.093, 0.127, 0.128, 0.143]." } ], "id_raw": 653 }, { "id": "VuIuZFjewnEddI27", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [0.118, 0.164, 0.427, 0.903]?" }, { "from": "gpt", "value": "There are three bars representing the values of different categories for the state of California: {Forest coverage bar} at [0.118, 0.738, 0.221, 0.903], {Population Density bar} at [0.221, 0.164, 0.324, 0.903], and {Public Park Coverage bar} at [0.324, 0.842, 0.427, 0.903]." } ], "id_raw": 653 }, { "id": "yQSzynViVVK43s0X", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nWhat information lies within the boundary [0.632, 0.799, 0.940, 0.903]?" }, { "from": "gpt", "value": "Within this boundary, we can see three bars for the state of Florida: {Forest coverage bar} at [0.632, 0.799, 0.735, 0.903], {Population Density bar} at [0.735, 0.251, 0.837, 0.903], and {Public Park Coverage bar} at [0.837, 0.825, 0.940, 0.903]." } ], "id_raw": 653 }, { "id": "e5bsXJXpZWoBiLxt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.077, 0.034, 0.981, 0.903] represent on the chart?" }, { "from": "gpt", "value": "The coordinates represent the entire plotting area of the chart, which includes the axes, the labels on the axes, all bars representing different categories of different states and the space between these bars." } ], "id_raw": 653 }, { "id": "tqQWM6sqnbNGmSc5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the {chart title} at [0.484, 0.942, 0.601, 0.970]?" }, { "from": "gpt", "value": "Platforms." } ], "id_raw": 654 }, { "id": "hWVGXRpiYISGD16g", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the thing located at coordinates [0.771, 0.069, 0.818, 0.088]?" }, { "from": "gpt", "value": "It is a color handle of legend, specifically for Instagram which is colored in #008080 (a teal color)." } ], "id_raw": 654 }, { "id": "PyGVnxDqLNdD3Pe3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.836, 0.108, 0.914, 0.136] represent?" }, { "from": "gpt", "value": "It is the label of Twitter in the legend." } ], "id_raw": 654 }, { "id": "U4mEYv4d9SItSa3L", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nIs {the orange bar}[0.262, 0.135, 0.374, 0.883] for Email shorter or taller than {the teal bar}[0.150, 0.228, 0.262, 0.883] for the same platform?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 654 }, { "id": "GbU3Lj6Ka5ZIaXSU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nWhich is wider on the chart, the {Email bar}[0.150, 0.228, 0.374, 0.883] or the {SMS bar}[0.711, 0.659, 0.936, 0.883]?" }, { "from": "gpt", "value": "They are the same." } ], "id_raw": 654 }, { "id": "uylGxVyn5PAhkpQO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nFrom the legend, does {Twitter color}[0.771, 0.110, 0.818, 0.130] come after or before {Instagram color}[0.771, 0.069, 0.818, 0.088]?" }, { "from": "gpt", "value": "After." } ], "id_raw": 654 }, { "id": "fSPdsG2dygH2LYUt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nProvide a description of what is found within the region [0.836, 0.066, 0.954, 0.136]." }, { "from": "gpt", "value": "There are two text labels in this region: {Instagram label}[0.836, 0.066, 0.954, 0.094] reading \"Instagram\", and {Twitter label}[0.836, 0.108, 0.914, 0.136] reading \"Twitter\". These labels form part of the legend of the chart." } ], "id_raw": 654 }, { "id": "ZkRZmzrmbOfTOz7h", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nWhat content is found within coordinates [0.058, 0.030, 0.094, 0.058]?" }, { "from": "gpt", "value": "It is the label \"4.5\" on the y-axis of the subplot." } ], "id_raw": 654 }, { "id": "nsT5edBWhL5WYd2W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe what data are within the region [0.150, 0.135, 0.374, 0.883]?" }, { "from": "gpt", "value": "This region includes two bars: one for Instagram at coordinates [0.150, 0.228, 0.262, 0.883] which spans from 0 to 3.5 on the y-axis, and one for Twitter at coordinates [0.262, 0.135, 0.374, 0.883] which spans from 0 to 4 on the y-axis. These two bars represent the data for Email on the x-axis." } ], "id_raw": 654 }, { "id": "0mS228wnBOAbMfNn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y-axis in this chart?" }, { "from": "gpt", "value": "The label of the Y-axis is \"Transmission Delay (ms)\"" } ], "id_raw": 655 }, { "id": "LLuNub1aib5V7Ihx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the text on the {the bar with coordinates}[0.262, 0.685, 0.374, 0.883]?" }, { "from": "gpt", "value": "The text represents the application of \"Machine Learning in Medicine\" on \"CRISPR\"" } ], "id_raw": 655 }, { "id": "BM23djDwNfEmgCtT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat kind of application is shown by the bar with coordinates [0.150, 0.526, 0.262, 0.883]?" }, { "from": "gpt", "value": "The bar represents the application of \"Telemedicine\" on \"CRISPR\"." } ], "id_raw": 655 }, { "id": "OOjumMcF7yXj4hX0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the x-axis label is referring to?" }, { "from": "gpt", "value": "The x-axis label is referring to \"Healthcare Tech\"." } ], "id_raw": 655 }, { "id": "fu7lR6rHptzVZDAw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nHow does the height of {the Telemedicine's Gene Therapy bar}[0.711, 0.745, 0.823, 0.883] compare to {the Machine Learning in Medicine's Gene Therapy bar}[0.823, 0.467, 0.936, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 655 }, { "id": "TsjpDAzC8VXDS2lK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nIs there any visible difference between the positioning of {the Machine Learning in Medicine's CRISPR bar}[0.262, 0.685, 0.374, 0.883] and {the Telemedicine's Gene Therapy bar}[0.711, 0.745, 0.823, 0.883] on the chart's x-axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 655 }, { "id": "CSjfx2bm5fV42I31", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nIn terms of transmission delay, does {Machine Learning in Medicine's implementation on CRISPR}[0.262, 0.685, 0.374, 0.883] perform better or worse than {its implementation on Gene Therapy}[0.823, 0.467, 0.936, 0.883]?" }, { "from": "gpt", "value": "Worse." } ], "id_raw": 655 }, { "id": "xep9oTYFZ1F0UNDI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nComparing the bars of {Telemedicine's CRISPR technique}[0.150, 0.526, 0.262, 0.883] and {Machine Learning's CRISPR technique}[0.262, 0.685, 0.374, 0.883], which one demonstrates the higher transmission delay?" }, { "from": "gpt", "value": "Machine Learning's CRISPR technique." } ], "id_raw": 655 }, { "id": "ul4Cd4lM5IXEvbNB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the pattern identified in the region bounded by [0.110, 0.030, 0.975, 0.883]." }, { "from": "gpt", "value": "The area belongs to the main chart region, which comprises multiple bars representing two categories of Healthcare Tech i.e., \"Telemedicine\" and \"Machine Learning in Medicine\" applied on \"CRISPR\" and \"Gene Therapy\". Vertical alignment represents the Transmission delay in ms. The bars corresponding to \"Telemedicine\" are {[0.150, 0.526, 0.262, 0.883], [0.711, 0.745, 0.823, 0.883]} and those corresponding to \"Machine Learning in Medicine\" are {[0.262, 0.685, 0.374, 0.883], [0.823, 0.467, 0.936, 0.883]}." } ], "id_raw": 655 }, { "id": "Yj8vnM18Cmdi03mf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the area in [0.276, 0.903, 0.360, 0.931] signify?" }, { "from": "gpt", "value": "It is the x-axis label for the \"CRISPR\" category of \"Healthcare Tech\"." } ], "id_raw": 655 }, { "id": "JzySEXIz2uvztvwJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is included in the area [0.122, 0.044, 0.549, 0.136]?" }, { "from": "gpt", "value": "This area represents the chart's legend, including the color-text labels denoting the \"Telemedicine\" application and \"Machine Learning in Medicine\" application. The color label for \"Telemedicine\" is located at [0.131, 0.058, 0.178, 0.077], and the text label is at [0.196, 0.055, 0.347, 0.083]. The color label for \"Machine Learning in Medicine\" is located at [0.131, 0.099, 0.178, 0.119], and the text label is at [0.196, 0.097, 0.540, 0.125]." } ], "id_raw": 655 }, { "id": "j4BS5JbpHK3Wi24u", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is in the region [0.025, 0.287, 0.048, 0.626]?" }, { "from": "gpt", "value": "This area represents the y-axis label named \"Transmission Delay (ms)\"." } ], "id_raw": 655 }, { "id": "Osn63escpectAoFL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object within the coordinates [0.118, 0.041, 0.192, 0.059]?" }, { "from": "gpt", "value": "The label for Probability in the legend." } ], "id_raw": 656 }, { "id": "lXeFMKKTR0ieLjN5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.484, 0.964, 0.567, 0.981]?" }, { "from": "gpt", "value": "The x-axis label stating \"Risk Factors\"." } ], "id_raw": 656 }, { "id": "t465XttJL6lrLG4e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.839, 0.476, 0.943, 0.927] represent?" }, { "from": "gpt", "value": "It represents the Manageability risk level for \"Regulatory Changes\"." } ], "id_raw": 656 }, { "id": "qEc6uoSpWqRO9zom", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nWhich measure of risk for \"Economic Conditions\" is greater, {the probability bar}[0.108, 0.206, 0.212, 0.927] or {the manageability bar}[0.317, 0.296, 0.421, 0.927]?" }, { "from": "gpt", "value": "Probability." } ], "id_raw": 656 }, { "id": "AFyokARg12GpK1hS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nIs {the Probability bar for Regulatory Changes}[0.630, 0.386, 0.734, 0.927] lower or higher than {the Manageability bar for Economic Conditions}[0.317, 0.296, 0.421, 0.927]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 656 }, { "id": "dY8NE1J1uelSuPFO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nWhich bar is lower, {the Impact bar for Economic Conditions}[0.212, 0.116, 0.317, 0.927] or {the Probability bar for Regulatory Changes}[0.630, 0.386, 0.734, 0.927]?" }, { "from": "gpt", "value": "The Probability bar for Regulatory Changes." } ], "id_raw": 656 }, { "id": "PpUqBGjOdXegaTIA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nWhat elements are inside the region [0.630, 0.386, 0.943, 0.927]?" }, { "from": "gpt", "value": "Inside this region there are the Probability, Impact, and Manageability bars for \"Regulatory Changes\". The bars are at [0.630, 0.386, 0.734, 0.927], [0.734, 0.161, 0.839, 0.927], and [0.839, 0.476, 0.943, 0.927] respectively." } ], "id_raw": 656 }, { "id": "bOL9bZNwWEGKEMq7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the visuals within [0.108, 0.206, 0.421, 0.927]." }, { "from": "gpt", "value": "This region contains the Probability, Impact, and Manageability bars for \"Economic Conditions\", located at [0.108, 0.206, 0.212, 0.927], [0.212, 0.116, 0.317, 0.927], and [0.317, 0.296, 0.421, 0.927] respectively." } ], "id_raw": 656 }, { "id": "5NJ4B1eo8kiM0OIN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe what can be found within the set of coordinates [0.035, 0.019, 0.057, 0.938]." }, { "from": "gpt", "value": "This area contains the labels for the y-axis scale, reading from 0.0 to 1.0 at intervals of 0.2. The labels are located at the following coordinates: [0.035, 0.920, 0.057, 0.938], [0.035, 0.740, 0.057, 0.757], [0.035, 0.560, 0.057, 0.577], [0.035, 0.379, 0.057, 0.397], [0.035, 0.199, 0.057, 0.217], [0.035, 0.019, 0.057, 0.036]." } ], "id_raw": 656 }, { "id": "HUzG3O6y25xYPZKq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the coordinates [0.150, 0.224, 0.215, 0.883]?" }, { "from": "gpt", "value": "Bar of \"Health Technology\" for \"Year 1\"." } ], "id_raw": 657 }, { "id": "1Q9MNiq5P66info2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis represent, and where is it located in the graph?" }, { "from": "gpt", "value": "The Y-axis, with coordinates [0.025, 0.353, 0.048, 0.560], represents \"Advancements\"." } ], "id_raw": 657 }, { "id": "8UGCCoL4X5cXgrVq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated within the region [0.728, 0.097, 0.954, 0.125]?" }, { "from": "gpt", "value": "The label \"Medical Technology\"." } ], "id_raw": 657 }, { "id": "O79ewLwlSkOHZvD1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the Health Technology bar in Year 2}[0.477, 0.418, 0.543, 0.883] have a lower or higher advancement rate than {the Medical Technology bar in Year 2}[0.543, 0.592, 0.608, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 657 }, { "id": "WuEnGVTAk23fQ6M9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nIs {the Health Technology bar in Year 1}[0.150, 0.224, 0.215, 0.883] taller or shorter than {the Medical Technology bar in Year 1}[0.215, 0.243, 0.281, 0.883]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 657 }, { "id": "dMeJdXoljefO8Im4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nBetween {Year 1 for Health Technology}[0.150, 0.224, 0.215, 0.883] and {Year 3 for Health Technology}[0.805, 0.573, 0.870, 0.883], where are advancements higher?" }, { "from": "gpt", "value": "Year 1." } ], "id_raw": 657 }, { "id": "QUMwxLhCmNX5xL1U", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nWhat information is presented within the region [0.150, 0.224, 0.936, 0.883]?" }, { "from": "gpt", "value": "This region contains all the bars representing advancements in Health and Medical Technology over 3 years. Health Technology bars are at these coordinates: {Year 1}[0.150, 0.224, 0.215, 0.883], {Year 2}[0.477, 0.418, 0.543, 0.883], {Year 3}[0.805, 0.573, 0.870, 0.883]. Medical Technology bars lock at these locations: {Year 1}[0.215, 0.243, 0.281, 0.883], {Year 2}[0.543, 0.592, 0.608, 0.883], {Year 3}[0.870, 0.554, 0.936, 0.883]." } ], "id_raw": 657 }, { "id": "PEeAkja3sr8YEoTu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the spatial information embedded in [0.058, -0.000, 0.094, 0.028]." }, { "from": "gpt", "value": "The number \"4.5\" for the Y-axis label is located at this coordinate subplot indicating the potential maximum value on the Y-axis for \"Advancements\"." } ], "id_raw": 657 }, { "id": "r48o1tYXE855OVmr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the region [0.654, 0.044, 0.963, 0.136]?" }, { "from": "gpt", "value": "This region is the legend for our bar chart. The legend contains color codes and labels for two groups: {Health Technology}[0.728, 0.055, 0.941, 0.083] represented by a white bar, and {Medical Technology}[0.728, 0.097, 0.954, 0.125] again represented by a white bar." } ], "id_raw": 657 }, { "id": "UODVmdlrABvAby5D", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis at [0.025, 0.319, 0.048, 0.594]?" }, { "from": "gpt", "value": "Performance Rating." } ], "id_raw": 658 }, { "id": "ywRlFepShpvvuRkM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the description in the legend at [0.749, 0.055, 0.918, 0.083]?" }, { "from": "gpt", "value": "Deep Learning." } ], "id_raw": 658 }, { "id": "rHNN0g0Ro2e2rR0O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label located at [0.454, 0.942, 0.609, 0.970]?" }, { "from": "gpt", "value": "AI Algorithms." } ], "id_raw": 658 }, { "id": "L0iblez4KEWsjnAM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at [0.749, 0.097, 0.954, 0.125] in the legend section?" }, { "from": "gpt", "value": "Machine Learning." } ], "id_raw": 658 }, { "id": "gyTePwKWPPCt6Mf5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Deep Learning for FastAI}[0.129, 0.513, 0.263, 0.883] higher compared to {Machine Learning for FastAI}[0.263, 0.594, 0.397, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 658 }, { "id": "1rlW4n2QBqjlBZu7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nAre the bars of {Deep Learning}[0.129, 0.513, 0.263, 0.883],[0.666, 0.416, 0.800, 0.883] generally taller than those of {Machine Learning}[0.263, 0.594, 0.397, 0.883],[0.800, 0.094, 0.935, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 658 }, { "id": "pVHvsq3BzhCT6tNF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nCompare the height between {Deep Learning for TensorFlow}[0.666, 0.416, 0.800, 0.883] and {Machine Learning for TensorFlow}[0.800, 0.094, 0.935, 0.883]." }, { "from": "gpt", "value": "Machine Learning for TensorFlow is taller." } ], "id_raw": 658 }, { "id": "3wPJHJKWXGvuVXiV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe the section with the coordinates [0.675, 0.044, 0.963, 0.136]?" }, { "from": "gpt", "value": "It is the legend area containing two items. The first item is \"Deep Learning\" with coordinates [0.749, 0.055, 0.918, 0.083] and the second item is \"Machine Learning\" with coordinates [0.749, 0.097, 0.954, 0.125]." } ], "id_raw": 658 }, { "id": "0RPxSaF8Nf9ZSFUx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.089, 0.030, 0.975, 0.883]?" }, { "from": "gpt", "value": "It's the main plot area where all the bars representing the performance ratings of two AI algorithms, FastAI and TensorFlow, are located with respect to two categories: Deep Learning and Machine Learning." } ], "id_raw": 658 }, { "id": "5sHkHux5XmjrG2s7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's shown at the region [0.129, 0.513, 0.397, 0.883]?" }, { "from": "gpt", "value": "It is the bar section where the performance of FastAI is displayed in two aspects: \"Deep Learning\" with [0.129, 0.513, 0.263, 0.883] and \"Machine Learning\" with [0.263, 0.594, 0.397, 0.883]." } ], "id_raw": 658 }, { "id": "oboybnbmfLncANEQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the title at the top of the y-axis with coordinates [0.025, 0.265, 0.048, 0.660]?" }, { "from": "gpt", "value": "The title is \"Percentage Contribution (%)\"." } ], "id_raw": 659 }, { "id": "tApitOY54eq4yTca", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the first text in the legend, located at [0.189, 0.066, 0.317, 0.094]?" }, { "from": "gpt", "value": "The label name is \"PC Gaming\"." } ], "id_raw": 659 }, { "id": "oIBd142nSxofGdvM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.307, 0.532, 0.389, 0.883]?" }, { "from": "gpt", "value": "A bar representing Mobile Gaming's percentage contribution for 2014." } ], "id_raw": 659 }, { "id": "qPqTjGkeQCKD2lxK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label denoted by the coordinate [0.783, 0.903, 0.842, 0.931]?" }, { "from": "gpt", "value": "The label for the year 2020." } ], "id_raw": 659 }, { "id": "2D73XgPznVp9vWA1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing PC Gaming in 2020}[0.689, 0.322, 0.771, 0.883] taller than {the bar representing Console Gaming in 2020}[0.771, 0.392, 0.853, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 659 }, { "id": "LlDnmw79boUYkWhn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing Console Gaming in 2014} [0.225, 0.322, 0.307, 0.883] have a higher value than {the one representing PC Gaming in 2014} [0.143, 0.392, 0.225, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 659 }, { "id": "DfaOb77KlWAPbtFl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nConsidering {the bar for Mobile Gaming in 2014}[0.307, 0.532, 0.389, 0.883] and {the bar for Mobile Gaming in 2020}[0.853, 0.111, 0.935, 0.883], did Mobile Gaming's contribution increase or decrease from 2014 to 2020?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 659 }, { "id": "a84CHURawssgV16C", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nBetween {PC Gaming}[0.189, 0.066, 0.317, 0.094] and {Console Gaming}[0.189, 0.108, 0.378, 0.136] which had a larger contribution in 2020?" }, { "from": "gpt", "value": "PC Gaming." } ], "id_raw": 659 }, { "id": "qwZdvWCMkETZUGek", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.115, 0.055, 0.388, 0.189]." }, { "from": "gpt", "value": "This area contains the legend which indicates colors for different categories: PC Gaming, Console Gaming, and Mobile Gaming. The labels are associated with their respective white markers." } ], "id_raw": 659 }, { "id": "XaHuGSzzBIScz1OM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what's located in the region [0.689, 0.322, 0.935, 0.883]." }, { "from": "gpt", "value": "This region contains two bars representing the percentage contribution of PC Gaming and Mobile Gaming for the year 2020." } ], "id_raw": 659 }, { "id": "dipRZ9BbarTG4itU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.225, 0.322, 0.389, 0.883]?" }, { "from": "gpt", "value": "There are two bars in this area: one represents the percentage contribution of Console Gaming in 2014, and the other represents the percentage contribution of Mobile Gaming in 2014." } ], "id_raw": 659 }, { "id": "1S4ni96vSjsVJCVN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found at [0.143, 0.392, 0.307, 0.883]?" }, { "from": "gpt", "value": "In this area, there are two stacked bars representing the year 2014. PC Gaming is situated on the bottom with a value of 35%, while Console Gaming is positioned above with a value of 40%." } ], "id_raw": 659 }, { "id": "HJ73AszTDIuDkMzQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label name at the coordinate [0.514, 0.945, 0.603, 0.971]?" }, { "from": "gpt", "value": "The label name is \"Platform\"." } ], "id_raw": 660 }, { "id": "ebLWQCkWVBmb7mR4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat bar is represented by these coordinates [0.334, 0.383, 0.491, 0.898]?" }, { "from": "gpt", "value": "The bar represents \"User Activity\" on \"Facebook\"." } ], "id_raw": 660 }, { "id": "LKf68FmWW7jMSSyp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the handle} at [0.766, 0.106, 0.808, 0.124]?" }, { "from": "gpt", "value": "The color is \"#c38c6a\"." } ], "id_raw": 660 }, { "id": "ip73EiYxsOd4Kc5o", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the bar representing Post Count on Facebook}[0.177, 0.726, 0.334, 0.898], or {the bar representing User Activity on Instagram}[0.782, 0.554, 0.939, 0.898]?" }, { "from": "gpt", "value": "The bar representing Post Count on Facebook." } ], "id_raw": 660 }, { "id": "br2JpZKmHefR9fle", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing User Activity on Facebook}[0.334, 0.383, 0.491, 0.898] higher than {the bar representing User Activity on Instagram}[0.782, 0.554, 0.939, 0.898]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 660 }, { "id": "oxGZ7H6jHOamEt8h", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nIs {the Post Count on Facebook bar}[0.177, 0.726, 0.334, 0.898] to the left or right of {the User Activity on Facebook bar}[0.334, 0.383, 0.491, 0.898]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 660 }, { "id": "2YZzQxTySEFgL9pS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents in the area [0.758, 0.052, 0.967, 0.141]?" }, { "from": "gpt", "value": "There are two color-multi-text labels in the legend. The first one, located at [0.766, 0.066, 0.808, 0.084], is blue and it's called \"Post Count\" at [0.825, 0.063, 0.938, 0.090]; the second one, located at [0.766, 0.106, 0.808, 0.124], is colorized \"#c38c6a\" and it's called \"User Activity\" at [0.825, 0.103, 0.958, 0.130]." } ], "id_raw": 660 }, { "id": "RcneCApnBXFjqxZc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.139, 0.039, 0.977, 0.898]." }, { "from": "gpt", "value": "This area contains a title \"Platform\" at [0.514, 0.945, 0.603, 0.971], a y-axis label \"Count (in thousands)\" at [0.023, 0.329, 0.044, 0.608], two x-axis labels (\"Facebook\" at [0.285, 0.907, 0.384, 0.934] and \"Instagram\" at [0.729, 0.907, 0.836, 0.934]), bars representing \"Post Count\" in color '#5e74a0' and \"User Activity\" in color '#c38c6a' on both platforms, and a legend [0.758, 0.052, 0.967, 0.141] describing the colors of the bars." } ], "id_raw": 660 }, { "id": "A3A3Q3aYZKxhkIYZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.177, 0.726, 0.334, 0.898]?" }, { "from": "gpt", "value": "There is one blue bar that represents a \"Post Count\" of \"50000\" for \"Facebook\"." } ], "id_raw": 660 }, { "id": "CcqhkOkqy3F2PJxr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary subject of the chart, according to the chart's y-axis label at [0.023, 0.361, 0.044, 0.565]?" }, { "from": "gpt", "value": "The main subject is the Percentage (%)." } ], "id_raw": 661 }, { "id": "cbtgp39Ii8FTlEnx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat text exists at the coordinates [0.790, 0.053, 0.958, 0.079] within the chart?" }, { "from": "gpt", "value": "It shows Startup Success." } ], "id_raw": 661 }, { "id": "3bAjSsLHy89cxEeG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.510, 0.945, 0.554, 0.971] of the chart?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 661 }, { "id": "S2wG358Dydc4ECP8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the descriptors or entities represented at [0.722, 0.042, 0.967, 0.130]?" }, { "from": "gpt", "value": "Startup Success label is positioned at {text_coordinates}[0.790, 0.053, 0.958, 0.079] and Startup Failure label located at {text_coordinates}[0.790, 0.093, 0.944, 0.119]." } ], "id_raw": 661 }, { "id": "JBpzVujmXX3Ch0RB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nComparing the bars of 2010, which one is higher, {Startup Success bar}[0.127, 0.663, 0.294, 0.898] or the {Startup Failure bar}[0.294, 0.070, 0.460, 0.898]?" }, { "from": "gpt", "value": "The Startup Failure bar." } ], "id_raw": 661 }, { "id": "fXVJiOYrhspNkNKl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nComparing the bars in 2020, did the {Startup Success bar}[0.603, 0.542, 0.770, 0.898] exceed the {Startup Failure bar}[0.770, 0.191, 0.937, 0.898]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 661 }, { "id": "FSoTBDiO9kBE9AfQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nChecking the bars for Startup Success and Startup Failure, which is generally taller? Refer to {Startup Success bars}[0.127, 0.663, 0.294, 0.898], [0.603, 0.542, 0.770, 0.898] and {Startup Failure bars}[0.294, 0.070, 0.460, 0.898], [0.770, 0.191, 0.937, 0.898]." }, { "from": "gpt", "value": "Startup Failure." } ], "id_raw": 661 }, { "id": "ceMrjzWodxZUN4aS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nBetween the two years 2010 and 2020, which year had higher {Startup Failure}[0.294, 0.070, 0.460, 0.898], [0.770, 0.191, 0.937, 0.898]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 661 }, { "id": "16TqevaEibFaYkki", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is positioned at [0.722, 0.042, 0.967, 0.130]?" }, { "from": "gpt", "value": "There are two groups of text-color labels in the legend. The color label of the first group is found at [0.731, 0.055, 0.773, 0.074], which is of color #5e74a0 and its corresponding text label located at [0.790, 0.053, 0.958, 0.079] is denoted as Startup Success; the color label of the second group is found at [0.731, 0.095, 0.773, 0.114], which is of color #c38c6a and its text label located at [0.790, 0.093, 0.944, 0.119] that is identified as Startup Failure." } ], "id_raw": 661 }, { "id": "JrlK07rQMGvk2h7Q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region [0.086, 0.029, 0.977, 0.898] in the figure?" }, { "from": "gpt", "value": "This area [0.086, 0.029, 0.977, 0.898] includes the whole bar chart area with all the bars, the x-axis, the y-axis, axis labels, and ticks. There are two years labelled on the x-axis: \"2010\" at [0.267, 0.907, 0.320, 0.934] and \"2020\" at [0.744, 0.907, 0.797, 0.934]. Similarly, there are several percentages labelled on the y-axis dotted from 0 to 90 (values out of this range are unrepresented)." } ], "id_raw": 661 }, { "id": "WGrzlLePXEcA8zQu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements present within the range [0.603, 0.542, 0.770, 0.898]?" }, { "from": "gpt", "value": "There are two components:\n1. A bar depicting the Startup Success in the year 2020, with its coordinates being [0.603, 0.542, 0.770, 0.898].\n2. A text label representing the value of the bar above, indicating the number 33.50, and its coordinates are [0.657, 0.515, 0.716, 0.542]." } ], "id_raw": 661 }, { "id": "XsXTeouBM352QAk2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements in the area [0.127, 0.663, 0.294, 0.898]?" }, { "from": "gpt", "value": "This area embodies:\n1. A blue bar named \"Startup Success for the year 2010\" which is positioned at [0.127, 0.663, 0.294, 0.898].\n2. A black text label found at [0.180, 0.636, 0.240, 0.663], stating the value of the bar which is \"22.10\"." } ], "id_raw": 661 }, { "id": "jzOjg80Hf0MuLhkP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the function of {the Y-axis}[0.023, 0.428, 0.044, 0.499] in this chart?" }, { "from": "gpt", "value": "It represents the scale for the averages of dish prices and restaurant popularity." } ], "id_raw": 669 }, { "id": "JdB7mO2LOnSOvL87", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the {blue bar}[0.294, 0.605, 0.460, 0.898] represent?" }, { "from": "gpt", "value": "It represents the average dish price for Japanese cuisine." } ], "id_raw": 669 }, { "id": "KQxiyjgYQoPjBzgm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the identity of the bar at these coordinates [0.770, 0.070, 0.937, 0.898]?" }, { "from": "gpt", "value": "It represents the popularity of Japanese restaurants." } ], "id_raw": 669 }, { "id": "KOFXGPYWA5NFBnra", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the average dish price of Italian cuisine}[0.127, 0.523, 0.294, 0.898] exceed that of {the average dish price of Japanese cuisine}[0.294, 0.605, 0.460, 0.898]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 669 }, { "id": "SuJskBNeFxEPwejK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nIs {the popularity of Japanese restaurants}[0.770, 0.070, 0.937, 0.898] higher than {the popularity of Italian restaurants}[0.603, 0.100, 0.770, 0.898]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 669 }, { "id": "DRnncXLYrA4DBgDI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nComparing to {Japanese restaurants' dish price}[0.294, 0.605, 0.460, 0.898], is {Italian restaurants' dish price}[0.127, 0.523, 0.294, 0.898] higher or lower?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 669 }, { "id": "wtt13yOpyuYfCBlM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in area [0.086, 0.029, 0.977, 0.898]?" }, { "from": "gpt", "value": "The area contains a bar chart representing two cuisine types: Italian and Japanese. Each cuisine is represented by two bars for the average dish price and the restaurant popularity. The scale for these representations is located on the y-axis." } ], "id_raw": 669 }, { "id": "wJVfYLd8hfhytDWi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of content in the area [0.796, 0.042, 0.967, 0.130]?" }, { "from": "gpt", "value": "This area includes a legend that identifies the colours used for the Italian and Japanese cuisines. The colors are represented by rectangular patches followed by the cuisine name." } ], "id_raw": 669 }, { "id": "WoZkxnXjsaqsO9dm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.127, 0.523, 0.937, 0.898]?" }, { "from": "gpt", "value": "This region contains four bars which represent the average dish prices and restaurant popularity for both Italian and Japanese cuisines. Specifically:\n 1. The Italian cuisine's dish price: [0.127, 0.523, 0.294, 0.898],\n 2. The Japanese cuisine's dish price: [0.294, 0.605, 0.460, 0.898],\n 3. The Italian cuisine's restaurant popularity: [0.603, 0.100, 0.770, 0.898],\n 4. The Japanese cuisine's restaurant popularity: [0.770, 0.070, 0.937, 0.898]." } ], "id_raw": 669 }, { "id": "bqsTme0q5CCZ3hMd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.120, 0.363, 0.174, 4.418]?" }, { "from": "gpt", "value": "This is a bar that represents the \"5G\" technology's Outlook Score, which is 92.5." } ], "id_raw": 671 }, { "id": "zCFZw2D4UQanuKif", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the text enclosed in the coordinates [0.748, 0.217, 0.778, 0.240]?" }, { "from": "gpt", "value": "The text is \"95.3\", which is the Outlook Score for the \"Artificial Intelligence\" technology." } ], "id_raw": 671 }, { "id": "WkiOqs9uN7cadx3G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the information presented in the coordinates [0.692, 0.919, 0.834, 0.942]?" }, { "from": "gpt", "value": "This is a label on the X-axis, indicating the \"Artificial Intelligence\" technology." } ], "id_raw": 671 }, { "id": "ucS6VLyFvqIlXA0E", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"5G\"}[0.120, 0.363, 0.174, 4.418] higher than {the bar representing \"IoT\"}[0.890, 0.464, 0.944, 4.418]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 671 }, { "id": "HFefWuewYCOVrkMI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Artificial Intelligence\" technology label}[0.692, 0.919, 0.834, 0.942] located to the right or left of the {IoT technology label}[0.906, 0.919, 0.927, 0.942]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 671 }, { "id": "KPWNIOjSvcy7rAoP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nAmong {Bar \"5G\"}[0.120, 0.363, 0.174, 4.418], {Bar \"Augmented Reality\"}[0.582, 0.582, 0.636, 4.418], and {Bar \"Artificial Intelligence\"}[0.736, 0.240, 0.790, 4.418], which one has the lowest Outlook Score?" }, { "from": "gpt", "value": "Augmented Reality." } ], "id_raw": 671 }, { "id": "3vYGmD0Q5z5wYoH8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nCould you give me a summary of the objects in [0.120, 0.240, 0.944, 4.418]?" }, { "from": "gpt", "value": "This region contains six bars, each representing the corresponding technology's Outlook Score: \nBar \"5G\"[0.120, 0.363, 0.174, 4.418] with Score \"92.5\"; \nBar \"Blockchain\"[0.274, 0.608, 0.328, 4.418] with Score \"86.9\"; \nBar \"Virtual Reality\"[0.428, 0.503, 0.482, 4.418] with Score \"89.3\"; \nBar \"Augmented Reality\"[0.582, 0.582, 0.636, 4.418] with Score \"87.5\"; \nBar \"Artificial Intelligence\"[0.736, 0.240, 0.790, 4.418] with Score \"95.3\"; \nBar \"IoT\"[0.890, 0.464, 0.944, 4.418] with Score \"90.2\"." } ], "id_raw": 671 }, { "id": "2VAsR8OtppcoOdQf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are inside the coordinates [0.792, 0.046, 0.978, 0.088]?" }, { "from": "gpt", "value": "This is the legend of the bar plot. It contains one item: \n1. {\"Technology Outlook\"}[0.836, 0.055, 0.973, 0.078], which is a textual description. It's linked to a color symbol at [0.797, 0.057, 0.825, 0.073] representing the color of the bar." } ], "id_raw": 671 }, { "id": "tdK1KoAzRdiuUcyu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat is visible to see in the top half of this image [0.079, 0.034, 0.985, 0.500]?" }, { "from": "gpt", "value": "Within this range, there is the upper portion of the bars representing the Outlook Score for respective technologies, and also the Y-axis scale." } ], "id_raw": 671 }, { "id": "ArExUZ7bptnVE8Kd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the bar chart?" }, { "from": "gpt", "value": "The title is not specified in the chart." } ], "id_raw": 673 }, { "id": "KjbVbK5tZGIpRJs6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nWhat kind of data is represented by the bar with coordinates [0.146, 0.484, 0.309, 2.615] in the chart?" }, { "from": "gpt", "value": "It represents the issue points for the category \"Equal Rights\"." } ], "id_raw": 673 }, { "id": "VQkEs2P3EP6dXxPB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel} at [0.023, 0.429, 0.044, 0.508]?" }, { "from": "gpt", "value": "Points." } ], "id_raw": 673 }, { "id": "UpxIsDbvna4imCf6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the coordinates [0.809, 0.063, 0.941, 0.090]?" }, { "from": "gpt", "value": "{Text label \"Equal Rights\"}[0.809, 0.063, 0.941, 0.090]." } ], "id_raw": 673 }, { "id": "q2KX1d13OHpJZGzY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher score, {the Equal Rights category}[0.146, 0.484, 0.309, 2.615] or {the Social Welfare category}[0.309, 0.522, 0.472, 2.615] at 100 issue points?" }, { "from": "gpt", "value": "Equal Rights." } ], "id_raw": 673 }, { "id": "BMKQRBxaA2dLOHyv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nWhich category has a higher score at 1000 issue points, {Social Welfare}[0.775, 0.169, 0.938, 2.615] or {Equal Rights}[0.612, 0.420, 0.775, 2.615]?" }, { "from": "gpt", "value": "Social Welfare." } ], "id_raw": 673 }, { "id": "Hgov2zF1eBwHr9de", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nBy comparing the two bars at 1000 issue points, {Equal Rights category}[0.612, 0.420, 0.775, 2.615] and {Social Welfare category}[0.775, 0.169, 0.938, 2.615], which one has a lower base?" }, { "from": "gpt", "value": "Social Welfare." } ], "id_raw": 673 }, { "id": "yozkYvqRgzREsrw8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on the Y-axis, {the 40.0 label} at [0.052, 0.887, 0.099, 0.914] or {the 57.5 label} at [0.052, 0.136, 0.099, 0.163]?" }, { "from": "gpt", "value": "The 40.0 label." } ], "id_raw": 673 }, { "id": "asz8jQ0B5I7srPox", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content of the legend in the area [0.742, 0.052, 0.967, 0.141]?" }, { "from": "gpt", "value": "There are two labels in this area. One is for the category \"Equal Rights\" represented by the color '#5e74a0' in the bar chart at the coordinates [0.809, 0.063, 0.941, 0.090]. The other is for \"Social Welfare\", represented by the color '#c38c6a' at [0.809, 0.103, 0.958, 0.130]." } ], "id_raw": 673 }, { "id": "fwYwZ4OTCjg9CmmH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the labeling on the X-axis within [0.289, 0.907, 0.801, 0.934]." }, { "from": "gpt", "value": "There are two labels: \"100\" at [0.289, 0.907, 0.329, 0.934] and \"1000\" at [0.748, 0.907, 0.801, 0.934]." } ], "id_raw": 673 }, { "id": "PjlhYS6xOS7t6loC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found in the area from [0.146, 0.484, 0.309, 2.615] to [0.309, 0.522, 0.472, 2.615]?" }, { "from": "gpt", "value": "There are two bars in this area. One belongs to the \"Equal Rights\" category and has a score of 49.63 according to its label. This bar is situated at [0.146, 0.484, 0.309, 2.615]. The other belongs to the \"Social Welfare\" category and has a score of 48.75 as per its label, with coordinates [0.309, 0.522, 0.472, 2.615]." } ], "id_raw": 673 }, { "id": "AtjbH3yjidx5Urys", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in [0.612, 0.420, 0.775, 2.615] and [0.775, 0.169, 0.938, 2.615]?" }, { "from": "gpt", "value": "The first object in the region is a bar representing the \"Equal Rights\" category with 51.12 issue points and is located at [0.612, 0.420, 0.775, 2.615]. The second object is another bar, a representation of the \"Social Welfare\" category with 56.97 issue points, which is located at [0.775, 0.169, 0.938, 2.615]." } ], "id_raw": 673 }, { "id": "GkuwF3EqH4SmyuN2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.442, 0.945, 0.622, 0.971]?" }, { "from": "gpt", "value": "X-axis label, called Health Indicators." } ], "id_raw": 674 }, { "id": "7vLc1F97P8qxhFzq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the second text in the legend's coordinate?" }, { "from": "gpt", "value": "[0.757, 0.103, 0.841, 0.130]" } ], "id_raw": 674 }, { "id": "uYkmmQgnogrrDQi6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat value is represented by {the Bar}[0.770, 0.640, 0.937, 1.971]?" }, { "from": "gpt", "value": "62." } ], "id_raw": 674 }, { "id": "RznNmkjIi02z25X7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat are the indicators or categories shown at [0.757, 0.063, 0.958, 0.090] and [0.757, 0.103, 0.841, 0.130]?" }, { "from": "gpt", "value": "{Healthy Individuals}[0.757, 0.063, 0.958, 0.090] and {Patients}[0.757, 0.103, 0.841, 0.130]." } ], "id_raw": 674 }, { "id": "4RFEKBsrp5vrZN2B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar}[0.127, 0.361, 0.294, 1.971] has a higher score than {the orange bar}[0.294, 0.683, 0.460, 1.971]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 674 }, { "id": "PVer287kps8xsuQh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value, {Healthy Individuals bar for Balanced Diet}[0.603, 0.146, 0.770, 1.971] or {Patients bar for Physical Activity}[0.294, 0.683, 0.460, 1.971]?" }, { "from": "gpt", "value": "{Healthy Individuals bar for Balanced Diet}[0.603, 0.146, 0.770, 1.971]." } ], "id_raw": 674 }, { "id": "XQwtM4mfhl78KD3u", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nBetween {Healthy Individuals'[0.757, 0.063, 0.958, 0.090]} and {Patients}[0.757, 0.103, 0.841, 0.130], which one has a higher score for {Balanced diet}[0.694, 0.907, 0.846, 0.934]?" }, { "from": "gpt", "value": "Healthy Individuals." } ], "id_raw": 674 }, { "id": "rCOVRVTll8ZNX6hr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.690, 0.052, 0.967, 0.141]." }, { "from": "gpt", "value": "This region includes two items in the legend. The first item or label is {Healthy Individuals}[0.757, 0.063, 0.958, 0.090] and associated with the color [0.698, 0.066, 0.740, 0.084], which is blue. The second label is {Patients}[0.757, 0.103, 0.841, 0.130] associated with the color [0.698, 0.106, 0.740, 0.124], which is orange." } ], "id_raw": 674 }, { "id": "sH6rxWQFrhK6HbEC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the area [0.086, 0.039, 0.977, 0.898]." }, { "from": "gpt", "value": "This area represents the whole plotting area of the bar chart excluding the legend. It contains 4 bars for two categories on the x-axis: \"Physical Activity\" and \"Balanced Diet\". Each category has two different bars, one for \"Healthy Individuals\" and the other for \"Patients\". The y-axis represents the \"Score\" ranging from 0 to 90." } ], "id_raw": 674 }, { "id": "f3eIqAtn3GncJXKh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.127, 0.361, 0.460, 1.971]?" }, { "from": "gpt", "value": "There are two bars in this area representing the \"Physical_Activity\" indicator for both \"Healthy Individuals\" and \"Patients\". The blue bar for Healthy Individuals is at [0.127, 0.361, 0.294, 1.971] with value 75.00 and the orange bar for Patients is at [0.294, 0.683, 0.460, 1.971] with value 60.00." } ], "id_raw": 674 }, { "id": "ztJ648ttYLCnCKNW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the area [0.052, 0.029, 0.079, 0.914]?" }, { "from": "gpt", "value": "This area includes all y-axis labels which represent the score: '50', '55', '60', '65', '70', '75', '80', '85', '90'." } ], "id_raw": 674 }, { "id": "IUs01MMEXyFWrWrX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar color}{r}[0.046, 0.191, 0.075, 0.543]{r} representing in the ROI chart?" }, { "from": "gpt", "value": "Bitcoin ROI." } ], "id_raw": 675 }, { "id": "Mv1x3d49hfBSEet9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat object is at {r}[0.442, 0.337, 0.470, 0.543]{r}?" }, { "from": "gpt", "value": "Bitcoin ROI bar for 20 years." } ], "id_raw": 675 }, { "id": "qsyYaxcdkne77dIO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the last y-tick}{r}[0.061, 0.080, 0.082, 0.115]{r} value in the last subplot?" }, { "from": "gpt", "value": "100." } ], "id_raw": 675 }, { "id": "2SxEhBAlb7elh59K", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nIs {the label \"Blockchain ROI\"}{r}[0.114, 0.837, 0.189, 0.872]{r} above or below {the Bitcoin ROI label}{r}[0.114, 0.785, 0.169, 0.820]{r}?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 675 }, { "id": "ar0sW0iC0ALi2xD9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nAre {the grid lines}{r}[0.915, 0.093, 0.935, 0.903]{r} darker or lighter than {the labels}{r}[0.854, 0.080, 0.873, 0.115]{r}?" }, { "from": "gpt", "value": "Lighter." } ], "id_raw": 675 }, { "id": "JtfxlLxqIB11LPo5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nIs {the start point of the axis}{r}[0.434, 0.093]{r} for the 5-year ROI of Bitcoin located closer to the x-axis or to the y-axis?" }, { "from": "gpt", "value": "Y-axis." } ], "id_raw": 675 }, { "id": "CUNZwb1vH6ZaUaqe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in {r}[0.830, 0.093, 0.989, 0.903]{r}." }, { "from": "gpt", "value": "The region includes both Bitcoin and Blockchain ROI bars for 10 years, their respective labels, and the x-ticks and y-ticks covering time periods from 5 years, 10 years, and 20 years, where substantial positive and negative values for both investments are displayed." } ], "id_raw": 675 }, { "id": "tNop44rpftKbQmsU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat is visible in {r}[0.236, 0.093, 0.396, 0.903]{r}?" }, { "from": "gpt", "value": "The subplot shows the period for the 10-year ROI including two groups of ROI bars, with the corresponding x-axis labeled for the years 5, 10, and 20, and y-values ranging from -80 to 100 with both positive and negative bars shown for Bitcoin and Blockchain." } ], "id_raw": 675 }, { "id": "bcdpnFKfYPHVP7u0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within {r}[0.039, 0.093, 0.198, 0.903]{r}?" }, { "from": "gpt", "value": "The content includes multiple Bitcoin ROI bars, y-grid lines appearing notably across the bars in patterns, the x-axis with labels for 5, 10, and 20 years, and y-axis ticks marking values from 0 to 100 and down to -80." } ], "id_raw": 675 }, { "id": "rtigaz1M6WX5FWo0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the handle representing \"Economic Conditions\"}[0.053, 0.840, 0.073, 0.864] in the chart?" }, { "from": "gpt", "value": "Orange" } ], "id_raw": 676 }, { "id": "r0wva8mIhI39clRP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-value displayed on the bar at coordinate {the blue bar}[0.046, 0.245, 0.075, 0.498]?" }, { "from": "gpt", "value": "50" } ], "id_raw": 676 }, { "id": "1fxmgVgeCR6PLmvG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis at location {the first tick}[0.057, 0.927, 0.063, 0.962]?" }, { "from": "gpt", "value": "5" } ], "id_raw": 676 }, { "id": "qJxoLchFVmjdw8QZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for the label '5'}[0.046, 0.245, 0.075, 0.498] taller than {the bar for the label '10'}[0.244, 0.144, 0.273, 0.498]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 676 }, { "id": "UWYvgT6PIfLSu0eq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the 20 unit bar in the Dot-Com Bubble}[0.360, 0.195, 0.388, 0.498] have a lower y-value than {the 20 unit bar in Great Recession}[0.162, 0.169, 0.191, 0.498]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 676 }, { "id": "C3Ma5nmuRlnfY9al", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nIs the y-value at the start of {the label '20' in Asian Financial Crisis}[0.557, 0.498, 0.586, 0.498] lower than {the y-value at the start of the label '20' in EU Debt Crisis}[0.953, 0.498, 0.982, 0.498]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 676 }, { "id": "0Hb0XzWPZdQJdfCj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.000, 0.093, 0.300, 0.903]." }, { "from": "gpt", "value": "The region contains two subplots' axes and legends for Great Recession and Dot-com Bubble. It includes elements such as bars representing 'Stock Market Crashes' at {coordinates}[0.046, 0.245, 0.075, 0.498] and 'Economic Conditions' at {coordinates}[0.046, 0.498, 0.075, 0.853], alongside x-ticks and y-ticks indicating various economic figures." } ], "id_raw": 676 }, { "id": "OSAUBZiUPQPfLD7A", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nWhat information can be found within the area [0.621, 0.093, 0.991, 0.903]?" }, { "from": "gpt", "value": "This region captures the bars depicting market responses during the Spanish Property Bubble and the EU Debt Crisis. It includes bars and their corresponding values for both 'Stock Market Crashes' and 'Economic Conditions', as well as the necessary x and y labels and scale labels indicating measurement values and categories." } ], "id_raw": 676 }, { "id": "fO32PDWjWcXFGdLz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the space marked by [0.800, 0.200, 1.000, 0.900]." }, { "from": "gpt", "value": "This section shows the bars representing 'Stock Market Crashes' and 'Economic Conditions' during the EU Debt Crisis, along with y-tick labels from '\u221280' to '80', x-tick labels '5', '10', '20', and axis labels without specified text, reflecting economic measures over these unit intervals. The color-coded bars indicate negative and positive changes." } ], "id_raw": 676 }, { "id": "ZueBHmIhB4Zoj1iw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.435, 0.253, 0.511, 0.323] in the chart?" }, { "from": "gpt", "value": "Subtitle, called Tesla." } ], "id_raw": 682 }, { "id": "29BXdkthL7UUTwpK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second text in the legend?" }, { "from": "gpt", "value": "[0.104, 0.935, 0.191, 0.949]" } ], "id_raw": 682 }, { "id": "zObIBoFKZrkzqcSq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis scale} at [0.364, 0.971, 0.388, 0.985]?" }, { "from": "gpt", "value": "Year 3." } ], "id_raw": 682 }, { "id": "ExeKD0huERG4AEVM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nIs {the blue bar}[0.236, 0.215, 0.261, 0.499] to the left or right of {the orange bar}[0.300, 0.235, 0.325, 0.499]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 682 }, { "id": "IJUjL0BlvyqzRHiJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Year 2\"}[0.300, 0.235, 0.325, 0.499] taller than {the bar representing \"Year 3\"}[0.364, 0.259, 0.389, 0.499]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 682 }, { "id": "b3TMbMdLyxxtkHFd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nAre {the small values in first group of bars}[[0.038, 0.499, 0.063, 0.558], [0.101, 0.499, 0.127, 0.578], [0.165, 0.499, 0.191, 0.578]] generally smaller than {the large values in first group of bars}[[0.038, 0.116, 0.063, 0.499], [0.101, 0.142, 0.127, 0.499], [0.165, 0.189, 0.191, 0.499]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 682 }, { "id": "jZ9b2KxPNoeQuW8L", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.099, 0.015, 0.129, 0.033]." }, { "from": "gpt", "value": "This area contains the subtitle \"Toyota\". It is placed just above the main bar chart graph, oriented horizontally and styled in black font." } ], "id_raw": 682 }, { "id": "F14doDDHScD6NSTy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.204, 0.037, 0.312, 0.991]." }, { "from": "gpt", "value": "This area encompasses the y-axis label \"Ford\", positioned vertically along the left side of the second bar chart graph. Adjacent to it are vertical bars representing data for different years, coupled with their corresponding x-values displayed at the bottom." } ], "id_raw": 682 }, { "id": "cGPnAt3oPM26K9i1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.236, 0.037, 0.325, 0.991]?" }, { "from": "gpt", "value": "This region involves a complete subplot for \"Ford\". It includes y-labels for various data points, x-ticks representing different years, and bars indicating the values for each year. Additionally, each bar has a text label indicating the specific value it represents." } ], "id_raw": 682 }, { "id": "eRlbNjObe3cF8fr8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the \"Customer Satisfaction\" category?" }, { "from": "gpt", "value": "Blue[0.365, 0.387, 0.382, 0.422]." } ], "id_raw": 683 }, { "id": "bByBdDPaMPkAFs8K", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nWhere is the label \"Week 2\" for the \"Sales\" category located?" }, { "from": "gpt", "value": "[0.245, 0.927, 0.274, 0.962]" } ], "id_raw": 683 }, { "id": "vBkpYbChYNFNPXMm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the figure at {the top left corner bar for Operational Efficiency at Week 1}[0.691, 0.699, 0.717, 0.903]?" }, { "from": "gpt", "value": "20.2." } ], "id_raw": 683 }, { "id": "2HxQ3CMeuB1yTbNo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Week 2 in Profitability}[0.908, 0.410, 0.934, 0.903] taller than {the bar for Week 1 in Profitability}[0.856, 0.443, 0.882, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 683 }, { "id": "96Wgmj0UgrVIhcL9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the first week's bar in Product Quality}[0.526, 0.193, 0.552, 0.903] represent a higher value compared to {the third week's bar in the same category}[0.629, 0.240, 0.655, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 683 }, { "id": "IfER6mzlftJlYN3n", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nAre the levels of {Customer Satisfaction}[0.361, 0.343, 0.386, 0.903] higher in {Week 2}[0.412, 0.331, 0.438, 0.903] compared to {Week 3}[0.464, 0.349, 0.489, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 683 }, { "id": "8w5ksiIAa4Ct1Xdi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the segment featuring metrics for Product Quality." }, { "from": "gpt", "value": "This segment contains three bars representing values over three weeks. The first week bar [0.526, 0.193, 0.552, 0.903] shows a value of 70.2 communicated via text [0.530, 0.236, 0.547, 0.271]. The second week bar [0.577, 0.208, 0.603, 0.903] displays a value of 68.7 with corresponding text [0.582, 0.251, 0.599, 0.286], and the third week bar [0.629, 0.240, 0.655, 0.903] represents a value of 65.5 announced by text [0.633, 0.283, 0.650, 0.318]." } ], "id_raw": 683 }, { "id": "xNCJBmqCN02tEWM1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be discovered in the area dedicated to the \"Sales\" subplot?" }, { "from": "gpt", "value": "There are bars for three weeks showing sales data. The first week's data [0.195, 0.493, 0.221, 0.903] displays a value of 40.5 reported by [0.200, 0.537, 0.217, 0.572]. The second week's data [0.247, 0.442, 0.273, 0.903] shows 45.6, indexed at [0.251, 0.485, 0.268, 0.520], while the third week's data [0.298, 0.393, 0.324, 0.903] indicates 50.4, detailed at [0.303, 0.436, 0.320, 0.471]." } ], "id_raw": 683 }, { "id": "4whPEyQAwqJeIu4f", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nProvide details about the operations plotted in the column for Operational Efficiency." }, { "from": "gpt", "value": "This column comprises three weekly bars. Starting with Week 1, the bar [0.691, 0.699, 0.717, 0.903] represents a value of 20.2, detailed via text at [0.695, 0.742, 0.712, 0.777]. Week 2 [0.743, 0.677, 0.768, 0.903] measures up to 22.3, noted at [0.747, 0.721, 0.764, 0.756], and Week 3 ascends to 25.6 with details at [0.799, 0.687, 0.816, 0.722]." } ], "id_raw": 683 }, { "id": "7a2G4b1iSTSeiroB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is displayed in the figure?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 705 }, { "id": "Lgpz8hbtTn1igMZn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.098, 0.118, 0.199, 0.141]?" }, { "from": "gpt", "value": "It is the text label {Trade Equilibrium}[0.098, 0.118, 0.199, 0.141] of the legend." } ], "id_raw": 705 }, { "id": "4FOeDFHDUvN1dbgy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label corresponding to the y-axis coordinate [0.012, 0.242, 0.024, 0.557]?" }, { "from": "gpt", "value": "The label is {Normalized Representation}[0.012, 0.242, 0.024, 0.557]." } ], "id_raw": 705 }, { "id": "G6snT3I2INXEP17H", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Slow Recovery}[0.077, 0.567, 0.108, 0.736] taller than the bar representing {Moderate Growth}[0.165, 0.331, 0.196, 0.736]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 705 }, { "id": "70UW4sfUtpCtVCqn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nIn terms of the Trend {Globalization}[0.065, 0.155, 0.088, 0.171] does the {Slow Recovery - outlier}[0.548, 0.191, 0.578, 0.736] have a higher value than {Moderate Growth - outlier}[0.636, 0.634, 0.666, 0.736]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 705 }, { "id": "KMIIU4mlty3p0nFJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nIs {Sustained Growth}[0.341, 0.698, 0.372, 0.736] situated to the left of {Rapid Decline}[0.429, 0.611, 0.460, 0.736]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 705 }, { "id": "IKEBAFaI46vQIAMI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the items in the region [0.061, 0.074, 0.203, 0.185]." }, { "from": "gpt", "value": "The region contains two items represented in the legend section of the chart. The first is the {Trade Equilibrium}[0.065, 0.120, 0.088, 0.136] with color coded as {#81b4a2}[0.098, 0.118, 0.199, 0.141]. The second item is {Globalization}[0.065, 0.155, 0.088, 0.171], with color coded as {#dd997b}[0.098, 0.153, 0.173, 0.176]." } ], "id_raw": 705 }, { "id": "aeszgYgdqKlWxFMF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.369, 0.025, 0.674, 0.055]?" }, { "from": "gpt", "value": "This area contains the subtitle of the graph, which reads {International Trade and Globalization Trends}[0.369, 0.025, 0.674, 0.055]." } ], "id_raw": 705 }, { "id": "JkF0GKffM4YR0Duh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the coordinates [0.850, 0.744, 0.949, 0.942]?" }, { "from": "gpt", "value": "The region includes the label {Rapid Decline - outlier}[0.850, 0.744, 0.949, 0.942] on the x-axis." } ], "id_raw": 705 }, { "id": "mmMsHB1Ep9KR03RC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.367, 0.025, 0.675, 0.055] in the chart?" }, { "from": "gpt", "value": "It's the chart's subtitle, stating \"Investment Trends in Venture Capital Market\"." } ], "id_raw": 707 }, { "id": "AkpwtTy74fUtqQty", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text content of the last x-axis label}[0.843, 0.803, 0.915, 0.948]?" }, { "from": "gpt", "value": "The label is \"DiamondMiners\"." } ], "id_raw": 707 }, { "id": "XQ0SSWFqB9OAb7mR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {Non_Aggregation}[0.877, 0.118, 0.977, 0.142] in the chart?" }, { "from": "gpt", "value": "The color of Non_Aggregation is blue as indicated by the color box with coordinates [0.845, 0.120, 0.868, 0.136]." } ], "id_raw": 707 }, { "id": "XJUS7L2c1pju4opr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nIs {Non_Aggregation bar of CashCows}[0.727, 0.575, 0.771, 0.795] higher than {Aggregation bar of CashCows}[0.771, 0.130, 0.814, 0.795]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 707 }, { "id": "nbqkwOldDnQfly8s", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"VenturePromotions\" for Non_Aggregation}[0.185, 0.133, 0.228, 0.795] higher than {the bar representing \"VenturePromotions\" for Aggregation}[0.228, 0.461, 0.272, 0.795]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 707 }, { "id": "PrBarTZusGCjqicJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nBetween {\"GoldenEggs\"}[0.525, 0.803, 0.582, 0.917] and {\"RapidGrowers\"}[0.629, 0.803, 0.695, 0.934], which has a higher value for {Non_Aggregation}[0.877, 0.118, 0.977, 0.142]?" }, { "from": "gpt", "value": "RapidGrowers." } ], "id_raw": 707 }, { "id": "M8ptRIKU2IfBW9IN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.840, 0.074, 0.982, 0.186]." }, { "from": "gpt", "value": "This area contains the chart's legend, with the heading \"Investment Type\"[0.863, 0.083, 0.959, 0.106]. There are two entries, {Non_Aggregation label}[0.877, 0.118, 0.977, 0.142] with a blue marker [0.845, 0.120, 0.868, 0.136], and {Aggregation label}[0.877, 0.153, 0.948, 0.176] with a red marker [0.845, 0.155, 0.868, 0.171]." } ], "id_raw": 707 }, { "id": "31sjoHFDItyzM5z4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.036, 0.053, 0.051, 0.809]." }, { "from": "gpt", "value": "This area includes all the y-ticks labels of the vertical axis, which are \"0\", \"20\", \"40\", \"60\", \"80\", and \"100\". The respective coordinates are: \n - \"0\": {0}[0.043, 0.786, 0.051, 0.809]\n - \"20\": {20}[0.036, 0.639, 0.051, 0.663]\n - \"40\": {40}[0.036, 0.493, 0.051, 0.516]\n - \"60\": {60}[0.036, 0.346, 0.051, 0.370]\n - \"80\": {80}[0.036, 0.200, 0.051, 0.223]\n - \"100\": {100}[0.029, 0.053, 0.051, 0.076]" } ], "id_raw": 707 }, { "id": "OzRT8EEO8p1BrK8P", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.771, 0.130, 0.814, 0.795]?" }, { "from": "gpt", "value": "The region contains {the bar representing CashCows of Aggregation}[0.771, 0.130, 0.814, 0.795]. The bar's height indicates that CashCows received 90.7 units of Aggregated Investment." } ], "id_raw": 707 }, { "id": "L4jMGMI1uyfaFAYh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the features in the region [0.077, 0.213, 0.120, 0.795]." }, { "from": "gpt", "value": "The region contains {the bar representing SeedFund of Non_Aggregation}[0.077, 0.243, 0.120, 0.795], the height of the bar indicates that SeedFund received 75.3 units of Non_Aggregation. The region also contains the bar's value label '75.3' at coordinates {75.3}[0.085, 0.213, 0.111, 0.236]." } ], "id_raw": 707 }, { "id": "N43dPsbeGrQdPiYn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by the object located at {coordinates}[0.542, 0.486, 0.587, 0.687]?" }, { "from": "gpt", "value": "It's a bar representing the non-aggregation development level for Sculpture, which has a value of 32.11." } ], "id_raw": 708 }, { "id": "V7t672q5u0mFzisS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nCan you give me the location of the label associated with the tick mark for 'Pop Music'?" }, { "from": "gpt", "value": "{Label 'Pop Music'}[0.900, 0.695, 0.949, 0.792]." } ], "id_raw": 708 }, { "id": "wimqpRFv0pPni6Vj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at {coordinates}[0.710, 0.695, 0.757, 0.787] denote?" }, { "from": "gpt", "value": "Category name 'Ceramics'." } ], "id_raw": 708 }, { "id": "rlj4ss1j5kdz4dxC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value, the bar at {coordinates for 'Theatre and Puppetry'}[0.093, 0.309, 0.138, 0.687] or at {coordinates for 'Painting and Digital Art'}[0.157, 0.446, 0.202, 0.687]?" }, { "from": "gpt", "value": "Theatre and Puppetry." } ], "id_raw": 708 }, { "id": "yNbubOLD8URjKn3D", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar for 'Ballet and Contemporary Dance'}[0.286, 0.215, 0.330, 0.687] represent a larger number than the {bar for 'Fashion'}[0.798, 0.150, 0.843, 0.687]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 708 }, { "id": "CgVdImNDgn73J1H8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Non-Aggregation label}[0.121, 0.118, 0.219, 0.141] and {Aggregation label}[0.121, 0.153, 0.192, 0.176], which is higher in the subplot legend?" }, { "from": "gpt", "value": "Non-Aggregation." } ], "id_raw": 708 }, { "id": "j4pkhcaad6YGzJLy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the section at {coordinates}[0.083, 0.074, 0.225, 0.185]." }, { "from": "gpt", "value": "This region contains the subplot legend of the chart, with two items; one represents {Non-Aggregation}[0.089, 0.120, 0.112, 0.136] with the text at {coordinates}[0.121, 0.118, 0.219, 0.141] and the other is {Aggregation}[0.089, 0.155, 0.112, 0.171] with the text at {coordinates}[0.121, 0.153, 0.192, 0.176]." } ], "id_raw": 708 }, { "id": "rMVO18jk69FoivEY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area between the left side of the chart and {the first bar of the group}[0.093, 0.309, 0.138, 0.687]?" }, { "from": "gpt", "value": "The Y-axis label stating 'Development Level' located at {coordinates}[0.035, 0.262, 0.047, 0.486], and the y-ticks with labels such as '0' at {coordinates}[0.066, 0.677, 0.073, 0.701] and '40' at {coordinates}[0.059, 0.428, 0.073, 0.451]." } ], "id_raw": 708 }, { "id": "yHYkgQ48XOCu0pvH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is displayed in the lower right area of the chart that includes {the label 'Graphic Design'}[0.814, 0.695, 0.885, 0.837] and adjacent elements?" }, { "from": "gpt", "value": "In this region, there are two bars indicating the 'Graphic Design' category development level with the non-aggregation bar at {coordinates}[0.863, 0.527, 0.907, 0.687] and the corresponding text '25.51' just above it at {coordinates}[0.868, 0.497, 0.901, 0.520], along with the aggregation bar at {coordinates}[0.863, 0.448, 0.907, 0.687] and the value '38.2' just above at {coordinates}[0.872, 0.418, 0.898, 0.441]." } ], "id_raw": 708 }, { "id": "WWqiWCbfEAJMoHQs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 'Apr' label on the x-axis?" }, { "from": "gpt", "value": "[0.331, 0.942, 0.355, 0.970]" } ], "id_raw": 709 }, { "id": "4VR8RpwRJjzH1d02", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bars that represent the 'Trending' data?" }, { "from": "gpt", "value": "#dd997b" } ], "id_raw": 709 }, { "id": "xJiPhmRbF7KE2772", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y-axis?" }, { "from": "gpt", "value": "Data Value." } ], "id_raw": 709 }, { "id": "wJVslzXsxJtDgwwr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller in March, the {Non-Volatile bar}[0.250, 0.789, 0.271, 0.932] or the {Trending bar}[0.271, 0.789, 0.293, 0.932]?" }, { "from": "gpt", "value": "Both are equal." } ], "id_raw": 709 }, { "id": "6QoON9zcDrRBD38I", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Non-Volatile bar in October}[0.749, 0.675, 0.771, 0.932] taller than the {Trending bar in October}[0.771, 0.346, 0.792, 0.932]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 709 }, { "id": "FKeRRhcfEFj6P24G", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nWhat color is the {legend label for Trending}[0.064, 0.089, 0.197, 0.223]?" }, { "from": "gpt", "value": "#dd997b." } ], "id_raw": 709 }, { "id": "SVP34hDMv1CyjiNX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content between [0.178, 0.761, 0.200, 0.932]." }, { "from": "gpt", "value": "This area includes the bar for Non-Volatile data in February, which has a height representing a value of 12, as well as the text label \"12\" just above the bar." } ], "id_raw": 709 }, { "id": "eXRfB9I6kyDt2rru", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.415, 0.575, 0.435, 0.932]." }, { "from": "gpt", "value": "There are two bars in this area. The first bar is for Non-Volatile data in June with a height that represents a value of 20. The second bar is for Trending data in June with a height representing a value of 25. On top of each bar, there is a text label showing the corresponding value, \"20\" and \"25\" respectively." } ], "id_raw": 709 }, { "id": "6jWE3X3nuLzrtRKS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you see around [0.064, 0.089, 0.197, 0.223]?" }, { "from": "gpt", "value": "There are two labels in the legend: The first one is 'Non-Volatile' located at [0.108, 0.142, 0.191, 0.170] with a color mark [#81b4a2] at [0.070, 0.144, 0.097, 0.164]. The second one is 'Trending' located at [0.108, 0.183, 0.168, 0.211] with a color mark [#dd997b] at [0.070, 0.186, 0.097, 0.205]. The legend title \"Trends\" is located at [0.108, 0.100, 0.153, 0.128]." } ], "id_raw": 709 }, { "id": "rtR5iUWsxHwlAGMC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the top of the chart in the coordinates [0.186, 0.025, 0.856, 0.055]?" }, { "from": "gpt", "value": "It's the chart subtitle, which reads \"Impact of Environmental Protection and Sustainable Development Policies, 2022.\"" } ], "id_raw": 710 }, { "id": "lMBe1gQWIQOwXPib", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat type of scales are used in {the Y-axis labels}[0.043, 0.142, 0.052, 0.166]?" }, { "from": "gpt", "value": "Percentage scales." } ], "id_raw": 710 }, { "id": "3qu7n4LO6ic7sTxJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nCan I find the unit of the measurements used on the y-axis in {the axis label}[0.015, 0.313, 0.029, 0.526]?" }, { "from": "gpt", "value": "Yes, it states '% Implementation'." } ], "id_raw": 710 }, { "id": "wn0yldl7utBpSVuT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nAre {the bars labeled \"Green Buildings\" from group 'Non-Aggressive'}<[0.264, 0.613, 0.308, 0.776]>, shorter than {those labeled \"Green Buildings\" from group 'Aggressive'}<[0.264, 0.616, 0.308, 0.776]>?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 710 }, { "id": "BDoAZUuStkOYHPzh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Ecological Footprint\" bar for 'Non-Aggressive'}[0.772, 0.776, 0.817, 0.776] as high as {the \"Ecological Footprint\" bar for 'Aggressive'}[0.772, 0.575, 0.817, 0.776]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 710 }, { "id": "KVCGZ3YVowtKxOa4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nWhich is greater in height, {the bar corresponding to 'Wildlife Protection' in 'Non-Aggressive'}[0.327, 0.776, 0.372, 0.776] or {the same in 'Aggressive'}[0.327, 0.519, 0.372, 0.776]?" }, { "from": "gpt", "value": "Aggressive." } ], "id_raw": 710 }, { "id": "DHkJlFcXAU3Cjtf6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the area spanning {the percentages listed under 'Energy Consumption'}[0.200, 0.284, 0.245, 0.776]?" }, { "from": "gpt", "value": "It shows bars to reflect the percentages of Energy Consumption implemented under 'Non-Aggressive' strategies, where values start significantly above the base and extend to the top line representing a large implementation percentage." } ], "id_raw": 710 }, { "id": "TskdkCQ66FNsAL7Q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be seen within the coordinates {for the category of greenhouse gas reduction}[0.073, 0.208, 0.117, 0.776] and [0.136, 0.110, 0.181, 0.776]." }, { "from": "gpt", "value": "These areas include vertical bars for 'Air Pollution' and 'CO2 Emissions', indicating the implementation percentages of these categories. Both show different implemented levels, one nearly filled while the other begins from a lower point." } ], "id_raw": 710 }, { "id": "ICZTWmhjfDPuOqvA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat details are visible in the segment regarding {the bars for 'Ocean Protection'}[0.454, 0.353, 0.499, 0.776]?" }, { "from": "gpt", "value": "This segment shows the percentage of Ocean Protection measures under 'Non-Aggressive' strategies with a starting point much lower than the top, indicating an implementation level just under halfway of the possible maximum." } ], "id_raw": 710 }, { "id": "bAYtvlxVmJkkTubn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text}[0.095, 0.443, 0.112, 0.471] indicate?" }, { "from": "gpt", "value": "Value '50'." } ], "id_raw": 712 }, { "id": "8SLLe7dYTaKXwG7i", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color marked in the legend}[0.816, 0.144, 0.844, 0.164]?" }, { "from": "gpt", "value": "#81b4a2." } ], "id_raw": 712 }, { "id": "xIYI8n0DtAAUQwv3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nWhich product's bar extends to {the coordinate}[0.355, 0.306, 0.377, 0.769]?" }, { "from": "gpt", "value": "Household Goods." } ], "id_raw": 712 }, { "id": "Wz5lQlvFYVOatedV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Non-Aggregation\" bar for \"Fast Food\"}[0.082, 0.480, 0.126, 0.769] have a greater height than {the \"Aggregation\" bar for \"Luxury Goods\"}[0.459, 0.566, 0.503, 0.769]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 712 }, { "id": "Z6shekAs1frSKteC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nIs {the color of the \"Non-Aggregation\" bar}[0.648, 0.422, 0.692, 0.769] similar to {the color denoted for \"Non-Aggregation\" in the legend}[0.816, 0.144, 0.844, 0.164]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 712 }, { "id": "7qwBWO398RwJ7z3y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the aggregation value for \"Makeup\"}[0.648, 0.335, 0.692, 0.769] or {the non-aggregation value for \"Grocery\"}[0.207, 0.364, 0.252, 0.769]?" }, { "from": "gpt", "value": "Makeup." } ], "id_raw": 712 }, { "id": "xANpUKimyra1iDRu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the area between [0.270, 0.364, 0.314, 0.769] and [0.396, 0.595, 0.440, 0.769]." }, { "from": "gpt", "value": "This area contains the \"Car Sales\" bars with values '50' for Non-Aggregation and '60' for Aggregation, and the \"Attire\" bars with values '30' for both Non-Aggregation and Aggregation." } ], "id_raw": 712 }, { "id": "oE52Vy9D5qlfCXb4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.130, 0.779, 0.203, 0.924] depict?" }, { "from": "gpt", "value": "It depicts the category label \"E-Commerce\" with the corresponding x-axis tick nearby." } ], "id_raw": 712 }, { "id": "ADLuSlhang3Rs1RZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nExplain the section [0.517, 0.779, 0.572, 0.889]." }, { "from": "gpt", "value": "This section displays the category label \"Footwear\" accompanied by an x-axis tick, without any graph data or legends." } ], "id_raw": 712 }, { "id": "CwU03Kb4NvIncp6O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated at [0.898, 0.145, 0.962, 0.173]?" }, { "from": "gpt", "value": "The legend of 'No Patent'." } ], "id_raw": 714 }, { "id": "A4hqz2ttRR7Cx9VX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-label} at [0.792, 0.818, 0.825, 0.885]?" }, { "from": "gpt", "value": "The label 'Uber'." } ], "id_raw": 714 }, { "id": "SkTb1MlVbdkRZ3bD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat value is shown at [0.744, 0.522, 0.774, 0.550]?" }, { "from": "gpt", "value": "The number '23.9'." } ], "id_raw": 714 }, { "id": "x3pRng5lavXydksq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled by the y-axis label positioned at [0.015, 0.304, 0.029, 0.580]?" }, { "from": "gpt", "value": "Number of Products" } ], "id_raw": 714 }, { "id": "sQnCbbIJU3gPOoUx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nDid {Facebook's No Patent bar}[0.585, 0.367, 0.613, 0.809] have more products than {Microsoft's Patent bar}[0.105, 0.440, 0.133, 0.809]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 714 }, { "id": "zcNjLCp41iaDCC22", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Oracle}[0.665, 0.251, 0.693, 0.809] taller than {the bar representing Alibaba}[0.745, 0.558, 0.773, 0.809]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 714 }, { "id": "BRke1tXJM1soDnp4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nIs {Snap's Patent bar}[0.905, 0.678, 0.933, 0.809] taller than {Uber's Patent bar}[0.825, 0.618, 0.853, 0.809]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 714 }, { "id": "TBbFTGLUffPqvoob", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nBetween {Microsoft's No Patent bar}[0.077, 0.646, 0.105, 0.809] and {Microsoft's Patent bar}[0.105, 0.440, 0.133, 0.809], which one has a higher number of products?" }, { "from": "gpt", "value": "Microsoft's Patent bar." } ], "id_raw": 714 }, { "id": "g13F4OId9fTRsUbJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.860, 0.089, 0.978, 0.223]." }, { "from": "gpt", "value": "It contains a chart legend with the title 'Patents'. There are two items in the legend. The first item is 'No Patent' with a color label in green located at [0.866, 0.144, 0.894, 0.164]. The second item is 'Patent' with a color label in brown located at [0.866, 0.186, 0.894, 0.205]." } ], "id_raw": 714 }, { "id": "xePEqJJeLskEEf5E", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.140, 0.818, 0.185, 0.908]." }, { "from": "gpt", "value": "This area contains an x-axis label for the company 'Google'." } ], "id_raw": 714 }, { "id": "ba6jpDQDrO3HuBAm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat is present within [0.505, 0.188, 0.533, 0.809]?" }, { "from": "gpt", "value": "There are 2 elements: \n1. Red bar that represents the number of products with a patent for 'Amazon', which ranges from 0 to 59.2; \n2. Text annotation '59.2' at [0.504, 0.152, 0.534, 0.180] to indicate the specific value of the bar." } ], "id_raw": 714 }, { "id": "dN8bFL69xDgZgr3Z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.047, 0.794, 0.095, 0.832]?" }, { "from": "gpt", "value": "It contains 1 object:\n1. The y-axis label '10' positioned at [0.035, 0.693, 0.052, 0.721]." } ], "id_raw": 714 }, { "id": "UuWAkKrgrJIfeOLe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart element corresponds to the coordinates [0.947, 0.131, 0.982, 0.151]?" }, { "from": "gpt", "value": "This is {the legend label for \"Decline\"}[0.947, 0.131, 0.982, 0.151]." } ], "id_raw": 716 }, { "id": "foi3EoxphQkEwAWP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar labeled \"Vietnamese\" for \"Growth\"?" }, { "from": "gpt", "value": "{The bar for \"Vietnamese\" labeled \"Growth\"}[0.491, 0.244, 0.531, 0.875]." } ], "id_raw": 716 }, { "id": "O0OsCaRqrIrA0dbg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhat data does the text at [0.316, 0.271, 0.292, 0.243] refer to?" }, { "from": "gpt", "value": "The text represents the {growth rate for \"Indian\"}[0.316, 0.271, 0.292, 0.243]." } ], "id_raw": 716 }, { "id": "zCuRQL9tC3Dg80kn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {the handle for \"Growth\"}[0.921, 0.103, 0.940, 0.117] and {the handle for \"Decline\"}[0.921, 0.133, 0.940, 0.147] in terms of their color?" }, { "from": "gpt", "value": "Different." } ], "id_raw": 716 }, { "id": "f105dZYCBphTVM5z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nIs the tick label for {the cuisine \"Thai\"}[0.234, 0.881, 0.255, 0.926] above or below {the corresponding bar for \"Decline\"}[0.244, 0.795, 0.285, 0.875]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 716 }, { "id": "E4AogX2Xg0thrSiA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhich has a larger value according to the bar heights, {the \"French\" cuisine for \"Growth\"}[0.090, 0.277, 0.130, 0.875] or {the \"German\" cuisine for \"Decline\"}[0.703, 0.743, 0.743, 0.875]?" }, { "from": "gpt", "value": "French." } ], "id_raw": 716 }, { "id": "N2oQKmsIeeOE5FGF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained within the area [0.108, 0.839, 0.892, 0.978]?" }, { "from": "gpt", "value": "This region includes all the {cuisine x-axis labels}[0.059, 0.881, 0.086, 0.939] {to}[0.856, 0.881, 0.894, 0.963] and the {corresponding ticks}[0.072, 0.875] {to}[0.875, 0.875]." } ], "id_raw": 716 }, { "id": "AdVSOdzMBvVFdWFs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region defined by the coordinates [0.917, 0.063, 0.985, 0.159] include?" }, { "from": "gpt", "value": "This area includes {the legend title \"Rates\"}[0.938, 0.071, 0.964, 0.091] as well as two entries: {the legend entry for \"Growth\"}[0.947, 0.101, 0.981, 0.121] and {the legend entry for \"Decline\"}[0.947, 0.131, 0.982, 0.151] with their respective color handles." } ], "id_raw": 716 }, { "id": "hOF1kM9K0lJublew", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the coordinates [0.556, 0.369, 0.579, 0.389]." }, { "from": "gpt", "value": "The region encompasses the {text element indicating the growth value for \"Turkish\"}[0.558, 0.369, 0.579, 0.389] alongside its corresponding {bar element}[0.548, 0.395, 0.589, 0.875]." } ], "id_raw": 716 }, { "id": "2K4033qTgnrshW0D", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the percentage (%) refer to in the chart at [0.010, 0.312, 0.019, 0.540]?" }, { "from": "gpt", "value": "It refers to the y-axis label." } ], "id_raw": 717 }, { "id": "ti2sSqbvIUVdnjfL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the category represented at coordinate [0.023, 0.787, 0.044, 0.850]?" }, { "from": "gpt", "value": "It's the \"Coal\" category." } ], "id_raw": 717 }, { "id": "MqWpA3f47BqdMBzB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the green object located at [0.882, 0.144, 0.901, 0.164] represent?" }, { "from": "gpt", "value": "It represents the \"Energymax\" parameter of the chart's legend." } ], "id_raw": 717 }, { "id": "nhpq4THdqfYOSIDz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nIs the resource {Hydro}[0.272, 0.292, 0.302, 0.777] more or less efficient in power than {Wind}[0.388, 0.515, 0.418, 0.777]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 717 }, { "id": "bTSV7YAelwXNO8Wj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher energy output, the {Coal}[0.014, 0.108, 0.044, 0.777] or the {Solar}[0.443, 0.484, 0.474, 0.777] resource?" }, { "from": "gpt", "value": "Coal." } ], "id_raw": 717 }, { "id": "XpgvO19NoR0OLsVb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nIn terms of power efficiency, is the {Nuclear}[0.559, 0.258, 0.590, 0.777] energy resource doing better than the {NaturalGas}[0.186, 0.230, 0.216, 0.777] resource?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 717 }, { "id": "zu8Iunp7yoYw9xKX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the legend in the region [0.879, 0.089, 0.985, 0.223]." }, { "from": "gpt", "value": "The region contains the chart's legend, with two entries. The first, labelled \"Energymax\", is depicted with a green color and is located at [0.908, 0.142, 0.961, 0.170] with its color indicator at [0.882, 0.144, 0.901, 0.164]. The second entry, labelled \"Powerefficiency\", is represented with an orange color, located at [0.908, 0.183, 0.981, 0.211], with its color indicator at [0.882, 0.186, 0.901, 0.205]." } ], "id_raw": 717 }, { "id": "2XQiIfoKLvrtaomX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the area [0.044, 0.075, 0.989, 0.777]?" }, { "from": "gpt", "value": "This area contains most of the chart's elements, including the x-ticks and y-ticks coordinates showing the distinct categories of energy resources and the percentages. Moreover, it includes the plot of energy output and power efficiency of various energy resources." } ], "id_raw": 717 }, { "id": "7unXzB97CSqjkpkW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nProvide a description of the objects in the region [0.014, 0.108, 0.044, 0.777]." }, { "from": "gpt", "value": "This area shows a relatively large green bar of the \"Coal\" resource with a high level of energy output represented by \"Energymax\" somewhere around 95.32%, according to the text annotation on top." } ], "id_raw": 717 }, { "id": "QqzLb03E2D72noCB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing 'Apartment Sales' in the 'Non-Aggregation' category?" }, { "from": "gpt", "value": "Green, with specific color code '#81b4a2'. Coordinate information is {the bar}[0.113, 0.222, 0.143, 0.675]." } ], "id_raw": 719 }, { "id": "cNCYmPS6ybHiQ0xC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of 'Commercial Property Sales' under the 'Aggregation' category?" }, { "from": "gpt", "value": "The value is 102000. The information is located at {bar}[0.480, 0.204, 0.509, 0.675] and {value text}[0.468, 0.168, 0.521, 0.196]." } ], "id_raw": 719 }, { "id": "f7kY5QNYSA8Tvxez", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content that appears at the coordinates [0.376, 0.030, 0.701, 0.066]?" }, { "from": "gpt", "value": "This corresponds to the subtitle of the chart known as 'Building and Real Estate Market Trends'." } ], "id_raw": 719 }, { "id": "d59NDs3qq4fGuC2h", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.855, 0.183, 0.940, 0.211] on the subplot?" }, { "from": "gpt", "value": "{The label 'Aggregation'}[0.855, 0.183, 0.940, 0.211] in the legend." } ], "id_raw": 719 }, { "id": "CU768LdUw7MozY8t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for 'Construction Permits' in the 'Non-Aggregation' category have a higher value than in the 'Aggregation' category?" }, { "from": "gpt", "value": "Yes. The coordinates for 'Construction Permits' are: {Non-Aggregation bar}[0.197, 0.606, 0.227, 0.675] and {Aggregation bar}[0.227, 0.617, 0.256, 0.675]." } ], "id_raw": 719 }, { "id": "5WtRC3BDRbceG0xF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nIs the 'Non-Aggregation' bar for 'Rental Market' located on the left or right side of the 'Mortgage Approvals' bar?" }, { "from": "gpt", "value": "Left. Based on their respective coordinates: {Rental Market Non-Aggregation bar}[0.703, 0.444, 0.732, 0.675] and {Mortgage Approvals Non-Aggregation bar}[0.619, 0.282, 0.648, 0.675]." } ], "id_raw": 719 }, { "id": "xBoleRJch6pASBru", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nIs the color of the 'Real Estate Agents' bar the same for 'Non-Aggregation' and 'Aggregation'?" }, { "from": "gpt", "value": "No. The colors are different. The {Non-Aggregation bar color}[0.787, 0.661, 0.817, 0.675] is '#81b4a2' and {Aggregation bar color}[0.817, 0.657, 0.846, 0.675] is '#dd997b'." } ], "id_raw": 719 }, { "id": "5nRdtyzQnI7sR8OV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nAre the x-axis labels 'Commercial Property Sales' and 'Mortgage Approvals' placed next to each other?" }, { "from": "gpt", "value": "Yes. Their location on the x-axis are: {Commercial Property Sales label}[0.337, 0.685, 0.480, 0.970] and {Mortgage Approvals label}[0.539, 0.685, 0.648, 0.902]." } ], "id_raw": 719 }, { "id": "oiUFb7mEm8Sc9iFx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.810, 0.089, 0.978, 0.223]?" }, { "from": "gpt", "value": "Two legend items are within the referred region: the label 'Non-Aggregation' with a green color marker and the coordinates {label}[0.855, 0.142, 0.973, 0.170] and {color marker}[0.816, 0.144, 0.844, 0.164]; and the label 'Aggregation' with an orange color marker at [0.855, 0.183, 0.940, 0.211] and {color marker}[0.816, 0.186, 0.844, 0.205]." } ], "id_raw": 719 }, { "id": "xZQedNHDju6121Fi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the bars between the coordinates [0.113, 0.222, 0.143, 0.675] and [0.901, 0.606, 0.930, 0.675]." }, { "from": "gpt", "value": "These bars represent data from 'Apartment Sales' to 'Property Auctions' of 'Non-Aggregation' and 'Aggregation' categories." } ], "id_raw": 719 }, { "id": "guhqBApIsDdCnHLz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.092, 0.675, 0.985, 0.981]." }, { "from": "gpt", "value": "This area contains the x-axis of the chart, where the labels of the x-axis are situated. They represent categories from 'Apartment Sales' to 'Property Auctions'." } ], "id_raw": 719 }, { "id": "GzTjeqdCc3TKadxE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart within the coordinates [0.043, 0.571, 0.087, 0.599]." }, { "from": "gpt", "value": "This is the y-axis label denoting '20000' which is part of the y-axis scale of the chart." } ], "id_raw": 719 }, { "id": "DKprPVnp9Q7xWYFo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label refer to at [0.478, 0.903, 0.628, 0.950]?" }, { "from": "gpt", "value": "Market Stability Level" } ], "id_raw": 720 }, { "id": "OoJ3rBGldceXm99U", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat's the label for the first y-axis tick mark at [0.015, 0.710, 0.111, 0.756]?" }, { "from": "gpt", "value": "Equity Market" } ], "id_raw": 720 }, { "id": "Hd4o5Hfr2FcEvLAz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat's described by the rectangle/bar at [0.862, 0.668, 0.944, 0.788]?" }, { "from": "gpt", "value": "{It's the 'Extremely Stable' portion of the 'Equity Market'}[0.862, 0.668, 0.944, 0.788]" } ], "id_raw": 720 }, { "id": "bGsvSD4LqbrEg8B3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nWhich bar is wider: {the 'Moderately unstable' portion of the 'Equity Market'}[0.532, 0.668, 0.656, 0.788] or {the 'Extremely Stable' portion of the 'Bond Market'}[0.820, 0.430, 0.944, 0.549]?" }, { "from": "gpt", "value": "The 'Extremely Stable' portion of the 'Bond Market'." } ], "id_raw": 720 }, { "id": "J08XwZ1va7ig06u5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Stable portion of the 'Bond Market'}[0.532, 0.430, 0.615, 0.549] wider than the {Significantly Stable portion of the 'Bond Market'}[0.615, 0.430, 0.820, 0.549]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 720 }, { "id": "sXXWBsUxQhRQ7WsP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nBetween the {extremely unstable rating for the 'Equity Market'}[0.121, 0.668, 0.450, 0.788] and the {extremely unstable rating for the 'Bond Market'}[0.121, 0.430, 0.409, 0.549], which market is seen as more unstable?" }, { "from": "gpt", "value": "Equity Market." } ], "id_raw": 720 }, { "id": "tUns12xzyNKyZYaV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat is comprised within the coordinates [0.248, 0.238, 0.858, 0.392]?" }, { "from": "gpt", "value": "This contains a legend for the colored bars; each color and its corresponding stability level are listed. The colors include: 'Extremely unstable' in red at [0.292, 0.257, 0.427, 0.303], 'Significantly unstable' in orange at [0.292, 0.326, 0.442, 0.373], 'Moderately unstable' in yellow at [0.509, 0.257, 0.652, 0.303], 'Stable' in light blue at [0.509, 0.326, 0.553, 0.373], 'Significantly Stable' in dark blue at [0.718, 0.257, 0.853, 0.303], and 'Extremely Stable' in purple at [0.718, 0.326, 0.838, 0.373]." } ], "id_raw": 720 }, { "id": "ZxucsZ5NfFzD3UyA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.121, 0.668, 0.450, 0.788]?" }, { "from": "gpt", "value": "This is the red bar representing the 'Extremely unstable' portion of the 'Equity Market'." } ], "id_raw": 720 }, { "id": "j4N1acqBWAUhByvO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the area [0.478, 0.903, 0.628, 0.950]." }, { "from": "gpt", "value": "This is the area where the label for the x-axis is located, precisely \"Market Stability Level\"." } ], "id_raw": 720 }, { "id": "J7x9TQFjyyrGiL6V", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the first bar label on the y-axis}[0.066, 0.710, 0.197, 0.756]?" }, { "from": "gpt", "value": "Driverless Delivery." } ], "id_raw": 721 }, { "id": "Z8Al1vo0uMuiDre0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the third legend item}[0.554, 0.257, 0.656, 0.303]?" }, { "from": "gpt", "value": "#ffa500 (Orange)." } ], "id_raw": 721 }, { "id": "OzfLkMNxVbemIbxI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the second label on the x-axis}[0.344, 0.838, 0.366, 0.885]?" }, { "from": "gpt", "value": "0.2." } ], "id_raw": 721 }, { "id": "g9iRaojgVesDqygF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nIs the height of the first bar (the one for Success) in {Driverless Delivery}[0.207, 0.668, 0.800, 0.788] bigger than the first bar in {Unmanned Aerial Delivery}[0.207, 0.430, 0.652, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 721 }, { "id": "h6dFlYwZ4oiRE4YU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Package Damage bar in Driverless Delivery}[0.911, 0.668, 0.933, 0.788] to the left or right of {the Logistical Errors bar in Driverless Delivery}[0.800, 0.668, 0.874, 0.788]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 721 }, { "id": "EdWhVDNWadAE1yqz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the Safety Incidents bar for Unmanned Aerial Delivery}[0.874, 0.430, 0.948, 0.549] or {the Logistical Errors bar for Unmanned Aerial Delivery}[0.652, 0.430, 0.800, 0.549]?" }, { "from": "gpt", "value": "Logistical Errors." } ], "id_raw": 721 }, { "id": "POAEnqHasRYquDhu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the legend section located in [0.332, 0.238, 0.860, 0.392]?" }, { "from": "gpt", "value": "Yes, this area contains color-symbol text pairs for five categories: Success in green at [0.338, 0.261, 0.432, 0.303], Logistical Errors in grey at [0.338, 0.331, 0.488, 0.373], Delivery Delay in orange at [0.515, 0.261, 0.656, 0.303], Package Damage in yellow at [0.515, 0.331, 0.675, 0.373], and Safety Incidents in blue at [0.703, 0.261, 0.854, 0.303]." } ], "id_raw": 721 }, { "id": "XCI2Za2xRCL6BqAd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the range [0.207, 0.430, 0.948, 0.549]?" }, { "from": "gpt", "value": "Yes, this area contains all the bars for the Unmanned Aerial Delivery category: The green bar for Success is at [0.207, 0.430, 0.652, 0.549], the grey bar for Logistical Errors is at [0.652, 0.430, 0.800, 0.549], the orange bar for Delivery Delay is at [0.800, 0.430, 0.837, 0.549], the yellow bar for Package Damage is at [0.837, 0.430, 0.874, 0.549] and the blue bar for Safety Incidents is at [0.874, 0.430, 0.948, 0.549]." } ], "id_raw": 721 }, { "id": "nGmpamNHRPPAUnxL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.800, 0.668, 0.948, 0.788] in the chart." }, { "from": "gpt", "value": "This area contains the last three bars in the Driverless Delivery category, Logistical Errors, Delivery Delay and Package Damage, represented by grey, orange, and yellow colors respectively." } ], "id_raw": 721 }, { "id": "1xjVGLhSviHKbc9J", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing 'Success Prediction'?" }, { "from": "gpt", "value": "The color representing 'Success Prediction' is green, {success prediction color}[0.254, 0.261, 0.282, 0.293]." } ], "id_raw": 722 }, { "id": "tcV5My5yBoNQNuFJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar with coordinates [0.856, 0.668, 0.899, 0.788] represent?" }, { "from": "gpt", "value": "The bar represents the 'Security Risk' performance of 'AI App1'." } ], "id_raw": 722 }, { "id": "5JjfR7NluH6dJ9S2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nWhat category does the bar [0.916, 0.430, 0.933, 0.549] belongs to?" }, { "from": "gpt", "value": "The bar represents 'Capacity Utilization' of 'AI App2'." } ], "id_raw": 722 }, { "id": "HihTf2QqsiBsTHar", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nIs the 'Failure Analysis' value of 'AI App1' greater than that of 'AI App2'?" }, { "from": "gpt", "value": "Yes. 'AI App1' : {Failure Analysis value for AI App1}[0.640, 0.668, 0.856, 0.788], 'AI App2' : {Failure Analysis value for AI App2}[0.726, 0.430, 0.856, 0.549]" } ], "id_raw": 722 }, { "id": "O5OY11UCKLgYlIKz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nBetween 'AI App1' and 'AI App2', which has higher 'Security Risk'?" }, { "from": "gpt", "value": "'AI App2'. 'AI App1' : {Security Risk for AI App1}[0.856, 0.668, 0.899, 0.788], 'AI App2' : {Security Risk for AI App2}[0.856, 0.430, 0.916, 0.549]" } ], "id_raw": 722 }, { "id": "L7uHusDrNmabsHrW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nAre the performance values of 'AI App1' higher than 'AI App2' across all categories?" }, { "from": "gpt", "value": "No, 'Security Risk' value of 'AI App2' is higher than that of 'AI App1'." } ], "id_raw": 722 }, { "id": "akNjNLiKZzX9WkmX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.079, 0.412, 0.985, 0.806]." }, { "from": "gpt", "value": "The area represents the main chart region, containing two sets of colored bars with different heights. Each bar corresponds to a category in the 'AI App1' and 'AI App2' performance metrics measure." } ], "id_raw": 722 }, { "id": "WPQpSDYuVb7bXlrj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nIs there any text in the area [0.4, 0.9, 0.6, 1]?" }, { "from": "gpt", "value": "No, there are no text objects in the described area." } ], "id_raw": 722 }, { "id": "TQ8HhJGCBPhY1Dm2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found in the area [0.24, 0.2, 0.81, 0.39]?" }, { "from": "gpt", "value": "It's the legend of the chart. There are five labeled entities: {Success Prediction}[0.293, 0.257, 0.422, 0.303], {Failure Analysis}[0.293, 0.326, 0.401, 0.373], {Security Risk}[0.489, 0.257, 0.579, 0.303], {Capacity Utilization}[0.489, 0.326, 0.624, 0.373], and {Load Forecasting}[0.691, 0.257, 0.809, 0.303] with corresponding color indicators." } ], "id_raw": 722 }, { "id": "U7eaPucKXwOjr6An", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinate [0.449, 0.903, 0.632, 0.950]?" }, { "from": "gpt", "value": "That is the x-axis label, named \"Percentage of Total Efforts\"." } ], "id_raw": 723 }, { "id": "8bs2lihJW3NmDKOi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at [0.765, 0.575, 0.917, 0.776]?" }, { "from": "gpt", "value": "This bar is also known as {Community Engagement bar}[0.765, 0.575, 0.917, 0.776], and its color is blue." } ], "id_raw": 723 }, { "id": "0ZurJ3foOxHJB2jZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the item at [0.702, 0.154, 0.828, 0.201]?" }, { "from": "gpt", "value": "This is a legend item for {Algorithm Change}[0.702, 0.154, 0.828, 0.201], colored in purple." } ], "id_raw": 723 }, { "id": "hpUXnUeQXhgEM2t5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat's the relation between {Post Engagement Rate bar}[0.095, 0.575, 0.324, 0.776] and {Advertising Cost bar}[0.324, 0.575, 0.519, 0.776]?" }, { "from": "gpt", "value": "The Advertising Cost bar is to the right of the Post Engagement Rate bar." } ], "id_raw": 723 }, { "id": "sWvNsseUwWZCsovo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nIs {Organic Reach bar}[0.519, 0.575, 0.621, 0.776] longer than {Community Engagement bar}[0.765, 0.575, 0.917, 0.776]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 723 }, { "id": "uIhy3RxeNM4iAc40", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Community Engagement bar for Facebook}[0.765, 0.575, 0.917, 0.776] and {Community Engagement bar for Instagram}[0.799, 0.173, 0.917, 0.374], which one is longer?" }, { "from": "gpt", "value": "The Community Engagement bar for Instagram is longer." } ], "id_raw": 723 }, { "id": "zOW0YCnaz8t3c9b3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.199, 0.066, 0.881, 0.220]." }, { "from": "gpt", "value": "This area includes a sublegend of the chart. It contains the labels and color handles for six variables, namely: {Post Engagement Rate}[0.244, 0.085, 0.402, 0.131], {Advertising Cost}[0.244, 0.154, 0.358, 0.201], {Organic Reach}[0.469, 0.085, 0.570, 0.131], {Influencer Collaboration}[0.469, 0.154, 0.636, 0.201], {Community Engagement}[0.702, 0.085, 0.876, 0.131], and {Algorithm Change}[0.702, 0.154, 0.828, 0.201], with corresponding handle colors green, grey, orange, yellow, blue, and purple respectively." } ], "id_raw": 723 }, { "id": "5kMedkTWqBuSlSxF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat is enclosed in the area [0.095, 0.173, 0.943, 0.374]?" }, { "from": "gpt", "value": "This area contains all the bars representing the efforts distribution for the platform Instagram. The bars correspond to six variables: Post Engagement Rate, Advertising Cost, Organic Reach, Influencer Collaboration, Community Engagement, and Algorithm Change." } ], "id_raw": 723 }, { "id": "hxRZSvAq2r0a3Ehw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the coordinates [0.324, 0.575, 0.519, 0.776]." }, { "from": "gpt", "value": "This is the {Advertising Cost Bar}[0.324, 0.575, 0.519, 0.776] which is colored gray and belongs to the Facebook group." } ], "id_raw": 723 }, { "id": "4METKWh3OfXt0DmQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.710, 0.103, 0.756]?" }, { "from": "gpt", "value": "This is the label for the 'TechProjectX' data series on the y-axis." } ], "id_raw": 724 }, { "id": "9k6OQEyCSTZZcdF3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat colour is the element at [0.113, 0.668, 0.694, 0.788]?" }, { "from": "gpt", "value": "The element is coloured with #008000, which represents a shade of green." } ], "id_raw": 724 }, { "id": "NlpTxu79yuM0XD3I", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the x-axis labels at [0.434, 0.838, 0.456, 0.885] and [0.766, 0.838, 0.788, 0.885]?" }, { "from": "gpt", "value": "The x-axis labels at these coordinates are \"0.4\" and \"0.8\" respectively." } ], "id_raw": 724 }, { "id": "tAu5pJDdnRyAuGyz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nAre the bars for 'TechProjectX' at [0.113, 0.668, 0.943, 0.788] longer than the ones for 'TechProjectY' at [0.113, 0.430, 0.943, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 724 }, { "id": "0rWEup6AYPuj1ot8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nIs the \"Technological Feasibility\" bar of {TechProjectX}[0.694, 0.668, 0.819, 0.788] longer than {TechProjectY}[0.611, 0.430, 0.628, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 724 }, { "id": "5zcJiyUSQtBzntSI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nWhich has higher score between 'Strategic Alignment' and 'Business Need' for {TechProjectX}?" }, { "from": "gpt", "value": "'Strategic Alignment'." } ], "id_raw": 724 }, { "id": "hWHMFxRQGopsOuII", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat elements are located in the range of [0.249, 0.238, 0.849, 0.392]?" }, { "from": "gpt", "value": "This range includes a legend with six items, each corresponding to a different category and color: \"Economic Viability\" in green, \"Technological Feasibility\" in grey, \"Business Need\" in orange, \"Market Demand\" in yellow, \"Strategic Alignment\" in blue, and \"End Score\" in purple." } ], "id_raw": 724 }, { "id": "fjGxzIgKAIV9ap4m", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the range of [0.113, 0.412, 0.985, 0.806] contain?" }, { "from": "gpt", "value": "It contains the main chart with bars representing different scores for the categories on two different TechProjects. The height and color of each bar represent the score and category it belongs to." } ], "id_raw": 724 }, { "id": "SRZQ4bTh1vfGXgNI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within the bounds of [0.113, 0.668, 0.943, 0.788]?" }, { "from": "gpt", "value": "There are six segmented bars representing different evaluation scores for the TechProjectX, with each color representing a different category corresponding to the legend." } ], "id_raw": 724 }, { "id": "CNVTuD9QiFVjC2ly", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label in the chart, located at [0.433, 0.952, 0.704, 0.975]?" }, { "from": "gpt", "value": "The x-axis label is \"Effect of factors on business processes\"." } ], "id_raw": 725 }, { "id": "LdlwTtZ3vcms5xq5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the first label in the legend represent, located at [0.309, 0.045, 0.364, 0.069]?" }, { "from": "gpt", "value": "It represents \"Success\"." } ], "id_raw": 725 }, { "id": "FaMgXsOfIgUESyWO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}[0.152, 0.216, 0.707, 0.434] corresponding to in the chart?" }, { "from": "gpt", "value": "The bar corresponds to \"Success\" in the \"Workflow_analysis\" category." } ], "id_raw": 725 }, { "id": "jgGnK5VIWJlncgTh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nIs {the Success bar for Efficiency_check}[0.152, 0.652, 0.311, 0.870] taller than {the Success bar for Workflow_analysis}[0.152, 0.216, 0.707, 0.434]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 725 }, { "id": "Jt1TAjj32qHCJoJW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nIn Workflow_analysis category, is {the \"Success\" bar}[0.152, 0.216, 0.707, 0.434] taller than {the \"Grasping failed\" bar}[0.906, 0.216, 0.945, 0.434]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 725 }, { "id": "on4U3iWEW2ccEQ7Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Success\"}[{Success bar for Efficiency_check}[0.152, 0.652, 0.311, 0.870], {Success bar for Workflow_analysis}[0.152, 0.216, 0.707, 0.434]] and {\"Grasping failed\"}[{Grasping failed bar for Efficiency_check}[0.826, 0.652, 0.945, 0.870], {Grasping failed bar for Workflow_analysis}[0.906, 0.216, 0.945, 0.434]], which factor has a higher impact on Workflow_analysis?" }, { "from": "gpt", "value": "Success." } ], "id_raw": 725 }, { "id": "L49AHWICJsrzMTAf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.264, 0.036, 0.873, 0.113]?" }, { "from": "gpt", "value": "This region contains a legend with six items: \n1. {Success}[0.309, 0.045, 0.364, 0.069] with color green,\n2. {Grounding Error}[0.309, 0.080, 0.420, 0.103] with color gray,\n3. {Occupancy Error}[0.487, 0.045, 0.602, 0.069] with color orange,\n4. {Gripper collision}[0.487, 0.080, 0.599, 0.103] with color yellow,\n5. {Trajectory optimization Error}[0.669, 0.045, 0.867, 0.069] with color blue,\n6. {Grasping failed}[0.669, 0.080, 0.774, 0.103] with color purple." } ], "id_raw": 725 }, { "id": "pN2e9uB9stO6jKNe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nCould you describe the y-axis of the chart within the coordinates [0.028, 0.315, 0.142, 0.775]?" }, { "from": "gpt", "value": "The area contains two labels on the y-axis. They are:\n1. \"Efficiency_check\": [0.028, 0.752, 0.142, 0.775],\n2. \"Workflow_analysis\": [0.015, 0.315, 0.142, 0.339]." } ], "id_raw": 725 }, { "id": "9wv7weIPFZKpP3lJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.906, 0.216, 0.945, 0.434] represent?" }, { "from": "gpt", "value": "It represents a purple bar, indicating the \"Grasping failed\" factor's impact on \"Workflow_analysis\"." } ], "id_raw": 725 }, { "id": "EDqtQCWixJnAMUho", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is depicted in this JSON structured data?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 726 }, { "id": "BdpEKo38HPmdYXqG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y-axis?" }, { "from": "gpt", "value": "Per the JSON structured data, the Y-axis label is not explicitly provided." } ], "id_raw": 726 }, { "id": "qjtVNFer5i2Lpy1I", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the first bar color of the 'bars_of_group_0' as per the bar chart information?" }, { "from": "gpt", "value": "According to the bar chart information, the color of the first bar in 'bars_of_group_0' is #0000ff or Blue." } ], "id_raw": 726 }, { "id": "nytvVsAKX1d0ibpZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nComparing the bars, is the 'Healthy Lifestyle' bar for 2022 {Healthy Lifestyle 2022 bar}[0.060, 0.430, 0.483, 0.549] bigger than for 2017 {Healthy Lifestyle 2017 bar}[0.060, 0.668, 0.451, 0.788]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 726 }, { "id": "SSuqOlbeyUN5Hv9A", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nAre the bars for 'Digital Detachment' larger than those for 'Healthy Lifestyle'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 726 }, { "id": "fswyholUgQrbYyoo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nIs the '2017' {2017 bar on Y-axis}[0.060, 0.668, 0.874, 0.788] bar taller than the '2022' {2022 bar on Y-axis}[0.060, 0.430, 0.940, 0.549] bar in the chart?" }, { "from": "gpt", "value": "No." } ], "id_raw": 726 }, { "id": "5P12FTVzcjLO4fXt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.337, 0.238, 0.706, 0.322]." }, { "from": "gpt", "value": "This region includes the legend of the chart, which contains two items. The first item labeled 'Healthy Lifestyle' is represented by a blue color. The label for this item is positioned at [0.382, 0.257, 0.498, 0.303] and the color patch is at [0.343, 0.261, 0.371, 0.293]. The second item is labeled 'Digital Detachment' and is represented by the color orange. Its label is at [0.565, 0.257, 0.701, 0.303] and the color patch is at [0.526, 0.261, 0.554, 0.293]." } ], "id_raw": 726 }, { "id": "FWQAnrPz4y64UenG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area at [0.060, 0.668, 0.451, 0.788]." }, { "from": "gpt", "value": "This area refers to the bar representing the 'Healthy Lifestyle' category for the year 2017. The bar starts at a x-value of 0.0 and ends at 0.60." } ], "id_raw": 726 }, { "id": "Juk8ogyVJlYTrdMz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.483, 0.430, 0.940, 0.549]?" }, { "from": "gpt", "value": "In the area [0.483, 0.430, 0.940, 0.549], there is a single object which is the bar representing the committee 'Digital Detachment' for the year 2022. The bar begins at a x-value of 0.65 and ends at 1.35." } ], "id_raw": 726 }, { "id": "SNFpFquCUupWNQN0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nWhat's the color of the object at [0.403, 0.261, 0.431, 0.293]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 727 }, { "id": "BHqz6KG7T642cv3W", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nWhat is at coordinates [0.442, 0.257, 0.515, 0.303]?" }, { "from": "gpt", "value": "The label for the innovation category." } ], "id_raw": 727 }, { "id": "RHUumYP5g2XMhMij", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the proportion represented by the bar located at [0.613, 0.430, 0.943, 0.549]?" }, { "from": "gpt", "value": "0.55 -> 0.90." } ], "id_raw": 727 }, { "id": "whZZ2UyJyvyKQQoi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nIs the {Innovation bar for NewTech} at [0.095, 0.668, 0.660, 0.788] longer than the {Infrastructure bar for FutureEdu} at [0.613, 0.430, 0.943, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 727 }, { "id": "pJ73O7PYfJWXvuR5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nWhich category occupies a larger proportion of the total trials for the {NewTech} group, {Innovation} at [0.095, 0.668, 0.660, 0.788] or {Infrastructure} at [0.660, 0.668, 0.896, 0.788]?" }, { "from": "gpt", "value": "Infrastructure." } ], "id_raw": 727 }, { "id": "kaXLfp9xQzbFv6Dt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nIs {FutureEdu's Innovation bar} at [0.095, 0.430, 0.613, 0.549] lower than {NewTech's Infrastructure bar} at [0.660, 0.668, 0.896, 0.788]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 727 }, { "id": "KKXEL065XX8D5FIk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.398, 0.238, 0.683, 0.322]?" }, { "from": "gpt", "value": "It contains the legend of the chart with two entries: {Innovation} with color green at [0.403, 0.261, 0.431, 0.293] and text at [0.442, 0.257, 0.515, 0.303], and {Infrastructure} with color grey at [0.543, 0.261, 0.571, 0.293] and text at [0.582, 0.257, 0.677, 0.303]." } ], "id_raw": 727 }, { "id": "gjUPMUUCcMf149Xp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.095, 0.668, 0.660, 0.788] and [0.660, 0.668, 0.896, 0.788]?" }, { "from": "gpt", "value": "This region includes two horizontal bars for the {NewTech} category. The first bar {Innovation} at [0.095, 0.668, 0.660, 0.788] represents 0.60 of the total trials, and the second bar {Infrastructure} at [0.660, 0.668, 0.896, 0.788] represents from 0.60 to 0.85 of the total trials." } ], "id_raw": 727 }, { "id": "rAlZdQ7VE2F4vMSX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements within [0.095, 0.430, 0.943, 0.549]." }, { "from": "gpt", "value": "There are two bars representing categories {Innovation} and {Infrastructure} for the {FutureEdu} category. The {Innovation bar} is located at [0.095, 0.430, 0.613, 0.549] and represents from 0.00 to 0.55 of the total trials, and the {Infrastructure bar} is located at [0.613, 0.430, 0.943, 0.549] and represents from 0.55 to 0.90 of the total trials." } ], "id_raw": 727 }, { "id": "mRIVhu0fKZFkkZHM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nWhat text can be found at these coordinates [0.333, 0.257, 0.389, 0.303]?" }, { "from": "gpt", "value": "The text is \"Success\"." } ], "id_raw": 728 }, { "id": "xdmOUbWdB6EV8Jn0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nWhat color is represented by the object at [0.294, 0.261, 0.322, 0.293]?" }, { "from": "gpt", "value": "It represents the color green." } ], "id_raw": 728 }, { "id": "2sLkAyCpX0ziTZ7z", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the xlabel coordinate in the chart?" }, { "from": "gpt", "value": "The xlabel has the coordinates: [0.507, 0.903, 0.681, 0.950]." } ], "id_raw": 728 }, { "id": "h2pCz6PxBsqdzLL0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nWhich object existing in the coordinates [0.693, 0.326, 0.800, 0.373]?" }, { "from": "gpt", "value": "The label \"Grasping Failed\"." } ], "id_raw": 728 }, { "id": "fIIVCkYcfM2RCmtx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nIs the {bar}[0.203, 0.668, 0.575, 0.788] wider than the {bar}[0.911, 0.430, 0.948, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 728 }, { "id": "WTeO3vhWL8zhwvmm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nIs {the label 'Gripper Collision'}[0.511, 0.326, 0.626, 0.373] above or below the {label 'Occupancy Error'}[0.511, 0.257, 0.626, 0.303]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 728 }, { "id": "bwJ25cM2uREx9IZO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nFor {the percent value bar of Success}[0.203, 0.668, 0.575, 0.788] in Urban Planning category, is the value greater than the {percent value bar of Success}[0.203, 0.430, 0.650, 0.549] in Community Development category?" }, { "from": "gpt", "value": "No." } ], "id_raw": 728 }, { "id": "mss56pfRV6gNCrsS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nAre the {Occupancy Error's bars}[[0.650, 0.668, 0.724, 0.788], [0.687, 0.430, 0.799, 0.549]] taller than {Grasping Failed's bars}[[0.799, 0.430, 0.836, 0.549], [0.799, 0.430, 0.836, 0.549]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 728 }, { "id": "FBobu0ZPaUbtKJhv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is inside [0.333, 0.257, 0.389, 0.303]." }, { "from": "gpt", "value": "There is a label, which is \"Success\", and it has the color green, the handle's coordinates is at [0.294, 0.261, 0.322, 0.293]" } ], "id_raw": 728 }, { "id": "afRfNyTPeVk6ndqD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.507, 0.903, 0.681, 0.950]?" }, { "from": "gpt", "value": "There is the xlabel of the graph, which reads \"Percentage of Total Trials\"." } ], "id_raw": 728 }, { "id": "LyFptoI72CQNhTag", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area [0.650, 0.430, 0.911, 0.549]?" }, { "from": "gpt", "value": "This area contains one bar of the \"Community Development\" category for the error type \"Occupancy Error\" alongside other error types represented by different colored bars. The percentage value for \"Occupancy Error\" is 0.15." } ], "id_raw": 728 }, { "id": "ILUx2oTsuAxDH0DX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nGive a description for the content in the region [0.294, 0.261, 0.322, 0.293]." }, { "from": "gpt", "value": "It only contains a colored box related to the \"Success\" category in the legend with a green color code." } ], "id_raw": 728 }, { "id": "UMEFKdvuTf7PDJjM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in the chart at the coordinates [0.118, 0.668, 0.441, 0.788]?" }, { "from": "gpt", "value": "It is a bar representing the manufacturing sector for global trends." } ], "id_raw": 729 }, { "id": "hEGmEbzWDDRb17Cc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at [0.629, 0.261, 0.657, 0.293] in the chart?" }, { "from": "gpt", "value": "It is a color handle in the legend, color-coded for \"GDP Increase\"." } ], "id_raw": 729 }, { "id": "tAgtzoEDZcrCpA5J", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat category does the object at the coordinates [0.015, 0.710, 0.108, 0.756] refer to?" }, { "from": "gpt", "value": "It refers to the y-axis label \"Global trends\"." } ], "id_raw": 729 }, { "id": "JbotJwbkovFmN7S8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.548, 0.257, 0.601, 0.303]?" }, { "from": "gpt", "value": "It is a text label for \"Imports\" in the legend." } ], "id_raw": 729 }, { "id": "Eli2LsvMmchXGM93", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nIs {the Manufacturing bar for Asia}[0.118, 0.430, 0.471, 0.549] larger than {the Manufacturing bar for Global trends}[0.118, 0.668, 0.441, 0.788]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 729 }, { "id": "OrH1md22MLox3aWS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the Services bar for Global trends}[0.441, 0.668, 0.688, 0.788] or {the Services bar for Asia}[0.471, 0.430, 0.673, 0.549]?" }, { "from": "gpt", "value": "The Services bar for Global trends." } ], "id_raw": 729 }, { "id": "1awxA33cIH1zizLd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nIs the length of {the Exports bar for Asia}[0.801, 0.430, 0.884, 0.549] greater than the length of {the Imports bar for Global trends}[0.688, 0.668, 0.839, 0.788]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 729 }, { "id": "LXbWcdtvRgRRAMtv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nWhich is shorter, {the GDP Increase bar for Global trends}[0.906, 0.668, 0.921, 0.788] or {the FDI bar for Asia}[0.929, 0.430, 0.944, 0.549]?" }, { "from": "gpt", "value": "The GDP Increase bar for Global trends." } ], "id_raw": 729 }, { "id": "eoftj44bTMhNbDJ8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.336, 0.238, 0.767, 0.392] of the chart." }, { "from": "gpt", "value": "It is the legend of the chart which includes labels for six categories: Manufacturing, Services, Imports, Exports, GDP Increase, and FDI. Each category has a color handle associated with it which is used to identify the color of the bars representing it in the chart." } ], "id_raw": 729 }, { "id": "KgohToMqY4Ki8bWZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.118, 0.668, 0.944, 0.788]?" }, { "from": "gpt", "value": "They are the bar segments of Global trends in the categories of Manufacturing, Services, Imports, Exports, GDP Increase, and FDI." } ], "id_raw": 729 }, { "id": "sYI7GRtb7ypaveNR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the coordinates [0.107, 0.838, 1.030, 0.885]." }, { "from": "gpt", "value": "This region contains labels for the x-axis scale, ranging from 0.0 to 1.2." } ], "id_raw": 729 }, { "id": "56V5jKA9adz5v5WN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you see within [0.015, 0.471, 0.108, 0.756]?" }, { "from": "gpt", "value": "It contains y-axis labels: Global trends and Asia." } ], "id_raw": 729 }, { "id": "s8OkJwiMKPaQF4vd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat kind of data does the object at [0.276, 0.261, 0.304, 0.293] represent?" }, { "from": "gpt", "value": "The object at these coordinates represents the color for \"Success\" in the chart." } ], "id_raw": 730 }, { "id": "WGnSbiMxvJLR1xFr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the category for the bar in coordinates [0.164, 0.668, 0.711, 0.788]?" }, { "from": "gpt", "value": "The category for this bar is \"Self-driving Vehicles\"." } ], "id_raw": 730 }, { "id": "ggQKpZOQUYQ9yvrP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinates [0.488, 0.903, 0.661, 0.950] represent?" }, { "from": "gpt", "value": "This text is the x-axis label, which is \"Percentage of Total Trials\"." } ], "id_raw": 730 }, { "id": "4bQXumL8seLRkJBc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the dataset indicated at [0.636, 0.331, 0.664, 0.363]?" }, { "from": "gpt", "value": "The color at these coordinates represents the dataset for \"Grasping Failed\"." } ], "id_raw": 730 }, { "id": "KmIMK8ajgBCiyKYP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nAre the bars of \"Occupancy Error\" [0.790, 0.668, 0.829, 0.788], [0.711, 0.430, 0.790, 0.549] wider than those of \"Grasping Failed\" [0.930, 0.668, 0.946, 0.788], [0.907, 0.430, 0.946, 0.549]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 730 }, { "id": "qkeeLmTF9gpUHmM4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nIs the bar for \"Self-driving Vehicles\" [0.164, 0.668, 0.711, 0.788] longer than that for \"Drone Delivery\" [0.164, 0.430, 0.555, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 730 }, { "id": "X04YaNI1MuZjPYvN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nIs the \"Success\" dataset [0.164, 0.668, 0.711, 0.788], [0.164, 0.430, 0.555, 0.549] larger than the \"Grasping Failed\" dataset [0.930, 0.668, 0.946, 0.788], [0.907, 0.430, 0.946, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 730 }, { "id": "ZZugnIRwzt9grwxN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nIs the dataset for \"Grounding Error\" [0.711, 0.668, 0.790, 0.788], [0.555, 0.430, 0.711, 0.549] to the right of the \"Occupancy Error\" dataset [0.790, 0.668, 0.829, 0.788], [0.711, 0.430, 0.790, 0.549]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 730 }, { "id": "wny4ebMn5AwZZhY6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.271, 0.238, 0.879, 0.392]." }, { "from": "gpt", "value": "This region contains the legend of the chart. Each rectangle color corresponds to a label, such as \"Success\", \"Grounding Error\", \"Occupancy Error\", \"Gripper collision\", \"Trajectory optimization Error\", \"Grasping Failed\"." } ], "id_raw": 730 }, { "id": "3txDhgucD9jBzufu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region [0.164, 0.668, 0.946, 0.788]?" }, { "from": "gpt", "value": "This is the region of the bars that represent the category of \"Self-driving Vehicles\" with their respective datasets for \"Success\", \"Grounding Error\", \"Occupancy Error\", \"Gripper collision\", \"Trajectory optimization Error\", \"Grasping Failed\"." } ], "id_raw": 730 }, { "id": "D5SiQOYOoenul8jw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area of [0.164, 0.430, 0.946, 0.549]?" }, { "from": "gpt", "value": "This is the region of the bars that represent the \"Drone Delivery\" category with their respective datasets for \"Success\", \"Grounding Error\", \"Occupancy Error\", \"Gripper collision\",\"Trajectory optimization Error\", \"Grasping Failed\"." } ], "id_raw": 730 }, { "id": "6ON0kqykocfpUkf5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is covered in the area [0.015, 0.710, 0.154, 0.756] and [0.050, 0.471, 0.154, 0.518]?" }, { "from": "gpt", "value": "These areas contain labels for the y-axis categories \"Self-driving Vehicles\" and \"Drone Delivery\" respectively." } ], "id_raw": 730 }, { "id": "R4NKBOctDTxS2z56", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat kind of chart is shown in the diagram?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 731 }, { "id": "in8g3mgeKilFK1dp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the text found within the coordinates [0.456, 0.903, 0.650, 0.950]?" }, { "from": "gpt", "value": "Percentage of Total Reviews." } ], "id_raw": 731 }, { "id": "x33te6vu9XRZIPeM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat colors are associated with the labels 'Success' and 'Implementation Errors' in the legend located within the coordinates [0.235, 0.238, 0.871, 0.392]?" }, { "from": "gpt", "value": "{Success}[0.008000] is associated with green color while {Implementation Errors}[0.0000ff] is associated with blue color." } ], "id_raw": 731 }, { "id": "7IL1ak1d496DF2sI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nWhich is longer, the bar representing 'Success' in 'Tax Reform' or 'Drugs Control'?" }, { "from": "gpt", "value": "The bar for 'Drugs Control' is longer." } ], "id_raw": 731 }, { "id": "ePImz197BtgiFtVD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nAre the bars related to 'Policy Ineffectiveness' of the same length in both 'Tax Reform' and 'Drugs Control'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 731 }, { "id": "0vB63miehLnSV9Vt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nIs the bar for 'Unforeseen Consequences' shorter or longer in 'Tax Reform' compared to 'Drugs Control'?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 731 }, { "id": "mucLO8Vme5CMvMal", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.121, 0.668, 0.600, 0.788] contain?" }, { "from": "gpt", "value": "The region contains the green bar that represents the 'Success' category in 'Tax Reform'." } ], "id_raw": 731 }, { "id": "Ik0xgVHH8V3a6eYs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.840, 0.668, 0.880, 0.788]?" }, { "from": "gpt", "value": "In this region, a yellow bar that represents 'Legal Challenges' in 'Tax Reform' is observed." } ], "id_raw": 731 }, { "id": "surt9E2ObONxiNbF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the items found in [0.681, 0.326, 0.865, 0.373]?" }, { "from": "gpt", "value": "The region represents the legend item 'Unforeseen Consequences', colored purple." } ], "id_raw": 731 }, { "id": "2BQfLgoFPhb1HGA5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.327, 0.257, 0.383, 0.303]?" }, { "from": "gpt", "value": "{Success}[0.327, 0.257, 0.383, 0.303]." } ], "id_raw": 734 }, { "id": "IDgKdpzVxEKRc3hU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat is on the x-axis at [0.464, 0.903, 0.638, 0.950]?" }, { "from": "gpt", "value": "The x-axis label, {Percentage of Total Trials}[0.464, 0.903, 0.638, 0.950]." } ], "id_raw": 734 }, { "id": "1pkiw7v3ebVBdPhM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the object at the coordinates [0.289, 0.261, 0.316, 0.293]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 734 }, { "id": "pCToaOfvIHWkOZVK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Water Usage}[0.690, 0.668, 0.772, 0.788] to the right or left of the bar for {Land Degradation}[0.772, 0.668, 0.813, 0.788]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 734 }, { "id": "nGmyfDKqXcma86m5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Air pollution}[0.886, 0.430, 0.944, 0.549] shorter than the bar for {Fossil Fuels}[0.117, 0.430, 0.445, 0.549]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 734 }, { "id": "kcfAUDuLqH7Mz8Dn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nWhich has higher value, {Carbon footprint}[0.445, 0.430, 0.772, 0.549] or {Water Usage}[0.690, 0.668, 0.772, 0.788]?" }, { "from": "gpt", "value": "Carbon footprint." } ], "id_raw": 734 }, { "id": "v7dYjBRKTYFSBBUi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe what's represented in the area [0.283, 0.238, 0.819, 0.392]?" }, { "from": "gpt", "value": "The area [0.283, 0.238, 0.819, 0.392] contains the legend for the chart. Each label in the legend has a color marker and an identifier. The labels include {Success}[0.327, 0.257, 0.383, 0.303], {Carbon footprint}[0.327, 0.326, 0.442, 0.373], {Water Usage}[0.509, 0.257, 0.598, 0.303], {Land Degradation}[0.509, 0.326, 0.634, 0.373], {Biodiversity loss}[0.701, 0.257, 0.814, 0.303], {Air pollution}[0.701, 0.326, 0.785, 0.373]" } ], "id_raw": 734 }, { "id": "v2bpD8jqRPQPdKiy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.015, 0.710, 0.107, 0.756]." }, { "from": "gpt", "value": "This area contains the label for the y-axis category, {Clean Energy}[0.015, 0.710, 0.107, 0.756]." } ], "id_raw": 734 }, { "id": "CcqqRJBvS6QeWird", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.772, 0.430, 0.813, 0.549]?" }, { "from": "gpt", "value": "There are 3 objects:\n1. The gray column representing Carbon footprint for fossil fuel, and its coordinate range is [0.772, 0.430, 0.813, 0.549].\n2. The orange column representing Water Usage for fossil fuel, its coordinate range is [0.772, 0.430, 0.813, 0.549].\n3. The yellow column representing Land Degradation for fossil fuel, its coordinate range is [0.813, 0.430, 0.854, 0.549]." } ], "id_raw": 734 }, { "id": "hQDrh7ONUH8SMYZl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.382, 0.952, 0.707, 0.975]?" }, { "from": "gpt", "value": "X-axis label, saying \"Average Daily User Count (in billions)\"." } ], "id_raw": 735 }, { "id": "X8PncER1zdUvtodw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.455, 0.135, 0.499, 0.158]?" }, { "from": "gpt", "value": "The bar count of Reddit, which is 2.38e9 users." } ], "id_raw": 735 }, { "id": "8Uf8T8VxwPlYGeHP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhat is associated with the area [0.113, 0.782, 0.832, 0.872]?" }, { "from": "gpt", "value": "A blue bar corresponding to Twitter's Average Daily User Count, which is {5.0}[0.832, 0.815, 0.865, 0.839]." } ], "id_raw": 735 }, { "id": "P4UU9N3aJhdDnvb3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information is contained in the area [0.265, 0.025, 0.824, 0.055]?" }, { "from": "gpt", "value": "Subtitle, saying \"Social Media Data Analysis: Average Daily User Count\"." } ], "id_raw": 735 }, { "id": "Zo2qz6eno8uMjcp2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, the bar for {Facebook}[0.113, 0.668, 0.803, 0.759] or for {Instagram}[0.113, 0.555, 0.779, 0.645]?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 735 }, { "id": "2b5EYZ0uB28Rfi47", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nDoes {Instagram}[0.113, 0.555, 0.779, 0.645] have more Daily Users than {LinkedIn}[0.113, 0.441, 0.703, 0.532]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 735 }, { "id": "qfbQEjU0R4VTLA4K", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nAre the Average Daily Users for {Snapchat}[0.113, 0.214, 0.619, 0.305] more than {Reddit}[0.113, 0.101, 0.455, 0.192]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 735 }, { "id": "Zase42QFALVZKLj3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nIs {Pinterest}[0.113, 0.328, 0.659, 0.419] more popular than {LinkedIn}[0.113, 0.441, 0.703, 0.532] based on daily users?" }, { "from": "gpt", "value": "No." } ], "id_raw": 735 }, { "id": "ah2P5RRQP1auM44y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the area [0.113, 0.062, 0.976, 0.911]?" }, { "from": "gpt", "value": "This is the main plot area. It consists of seven horizontal bars, each representing a social media platform's daily user count. The labels of these platforms are {Twitter}[0.049, 0.818, 0.107, 0.841], {Facebook}[0.025, 0.704, 0.107, 0.728], {Instagram}[0.019, 0.591, 0.107, 0.614], {LinkedIn}[0.035, 0.477, 0.107, 0.501], {Pinterest}[0.030, 0.364, 0.107, 0.387], {Snapchat}[0.025, 0.250, 0.107, 0.274], and {Reddit}[0.051, 0.137, 0.107, 0.160]." } ], "id_raw": 735 }, { "id": "7lSuHWagdXmjEFMx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhat is happening within the area [0.265, 0.025, 0.824, 0.055]?" }, { "from": "gpt", "value": "This area contains the subtitle of the chart, which reads \"Social Media Data Analysis: Average Daily User Count\"." } ], "id_raw": 735 }, { "id": "7Q9VaOpMhxiNu1x8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents inside [0.025, 0.250, 0.107, 0.274]." }, { "from": "gpt", "value": "This is the label Snapchat on the Y-axis indicating the platform for which the user daily count bar is representing." } ], "id_raw": 735 }, { "id": "NBVqUiPBRfPVb2Zo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see between these site-specific y-axis labels? [0.035, 0.477, 0.107, 0.501], [0.051, 0.137, 0.107, 0.160]" }, { "from": "gpt", "value": "1. LinkedIn's bar for average daily users which is [0.113, 0.441, 0.703, 0.532].\n2. Pinterest's bar for average daily users which is [0.113, 0.328, 0.659, 0.419].\n3. Snapchat's bar for average daily users which is [0.113, 0.214, 0.619, 0.305]." } ], "id_raw": 735 }, { "id": "Oft2QLwly0sJ8IEW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nWhat contents are present at the coordinates [0.204, 0.093, 0.981, 0.866]?" }, { "from": "gpt", "value": "The main chart area with all bar graphics and axis labels is located at these coordinates." } ], "id_raw": 736 }, { "id": "kR4BIonEFLnoqz9H", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information do the coordinates [0.204, 0.714, 0.833, 0.831] hold?" }, { "from": "gpt", "value": "It is a blue bar signifying the economic indicator strength for {New Businesses}[0.204, 0.714, 0.833, 0.831]." } ], "id_raw": 736 }, { "id": "XMTbdno2pw73Tcz2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nWhat is detailed between the coordinates [0.019, 0.173, 0.198, 0.208]?" }, { "from": "gpt", "value": "It's the label for {Unemployment Rate}[0.019, 0.173, 0.198, 0.208] on the y-axis." } ], "id_raw": 736 }, { "id": "QoLq9rLDb52j9X7W", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nWhat's the relationship between the {bar}[0.204, 0.128, 0.398, 0.246] and the economic indicator strength it represents?" }, { "from": "gpt", "value": "The length of the horizontal bar is proportional to the strength of economic indicator it represents for Unemployment Rate." } ], "id_raw": 736 }, { "id": "HxjfohikBFAaF1CL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nHow does the location of the {bar}[0.204, 0.714, 0.833, 0.831] compare to the location of the {bar}[0.204, 0.568, 0.896, 0.685]?" }, { "from": "gpt", "value": "The bar representing New Businesses is located above the bar representing Stock Market." } ], "id_raw": 736 }, { "id": "zcA239T5q4nNYceh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nHow does the {value}[0.732, 0.462, 0.776, 0.497] compare to the {bar length}[0.204, 0.421, 0.732, 0.538] in the figure for Consumer Spending?" }, { "from": "gpt", "value": "The value represented by the text directly corresponds to the length of the bar in the figure, with the value 0.68 relating to the bar length." } ], "id_raw": 736 }, { "id": "PAWc1GEB9hyI0NiJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the features within coordinates [0.204, 0.093, 0.981, 0.866]." }, { "from": "gpt", "value": "This region mainly comprises the core bar chart plotting the economic indicators strength. Within this area, there are horizontal bars symbolizing strengths of different economic indicators such as New Businesses, Stock Market, Consumer Spending, Inflation Rate, and Unemployment Rate." } ], "id_raw": 736 }, { "id": "4T2FrxYFCGH5XqiG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the area between [0.204, 0.568, 0.896, 0.685]." }, { "from": "gpt", "value": "This region contains a single horizontal bar depicting the indicator strength for the Stock Market. Its bar length extends to the coordinate [0.896, 0.685], indicating the strength value of 0.89 for the Stock Market." } ], "id_raw": 736 }, { "id": "SbnoEI4ZJrbpiTwg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between [0.579, 0.906, 0.606, 0.914]?" }, { "from": "gpt", "value": "This small region contains the x-axis label denoting the economic strength value of 0.5, serving as a reference guide for interpreting the lengths of the indicator bar charts." } ], "id_raw": 736 }, { "id": "9aufYrjZrQmZUthM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.481, 0.855, 0.737, 0.925]?" }, { "from": "gpt", "value": "That is the x-axis label, which is named \"Environmental Impact\"." } ], "id_raw": 737 }, { "id": "2NrHxagRpHmxVWXx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinate [0.692, 0.632, 0.751, 0.702] refer to?" }, { "from": "gpt", "value": "It is the value label of the corresponding bar, which indicates 0.62." } ], "id_raw": 737 }, { "id": "x03BSeJ0Fw678ujO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.261, 0.625, 0.692, 0.708] represent in the {graph}[0.261, 0.187, 0.957, 0.733]?" }, { "from": "gpt", "value": "This bar represents the impact of Deforestation Rate, with a value of 0.62." } ], "id_raw": 737 }, { "id": "OmWMWoGvEjHGH1mM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the category at coordinates [0.035, 0.639, 0.253, 0.709]?" }, { "from": "gpt", "value": "The category is \"Deforestation Rate\"." } ], "id_raw": 737 }, { "id": "hSwbIV1PMH3pRBJz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nIs the impact of {Deforestation Rate}[0.261, 0.625, 0.692, 0.708] more or less than {Global Temperature}[0.261, 0.522, 0.894, 0.605] as per the bars in the chart?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 737 }, { "id": "RpkXh8rBY10EWg4T", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nDoes {Biodiversity Loss}[0.261, 0.211, 0.852, 0.294] have a higher or lower impact than {Water Availability}[0.261, 0.315, 0.769, 0.398]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 737 }, { "id": "cT2oiJIKIER5d01n", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nWhich one has a larger bar in the chart {Global Temperature}[0.261, 0.522, 0.894, 0.605] or {Air Pollution}[0.261, 0.418, 0.832, 0.501]?" }, { "from": "gpt", "value": "Global Temperature." } ], "id_raw": 737 }, { "id": "vjuXZk2TeUyRcqvs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nBetween {Air Pollution}<[0.261, 0.418, 0.832, 0.501]> and {Biodiversity Loss}<[0.261, 0.211, 0.852, 0.294]>, which category has a higher impact?" }, { "from": "gpt", "value": "Biodiversity Loss." } ], "id_raw": 737 }, { "id": "b1rhtAJwwfSWbiUw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in the coordinates [0.261, 0.187, 0.957, 0.733]?" }, { "from": "gpt", "value": "The coordinates hold the entire graph region which includes 5 bars representing 5 different categories. The bars are:\n1. Deforestation Rate: [0.261, 0.625, 0.692, 0.708]\n2. Global Temperature: [0.261, 0.522, 0.894, 0.605]\n3. Air Pollution: [0.261, 0.418, 0.832, 0.501]\n4. Water Availability: [0.261, 0.315, 0.769, 0.398]\n5. Biodiversity Loss: [0.261, 0.211, 0.852, 0.294]" } ], "id_raw": 737 }, { "id": "MFQtEZ6JbE9B84pc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen between the coordinates [0.261, 0.418, 0.852, 0.501]?" }, { "from": "gpt", "value": "The coordinate contains the bar for \"Air Pollution\", which signifies the percentage of impact as 0.82 and its text annotation." } ], "id_raw": 737 }, { "id": "l9fslw5Th4jHE674", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.261, 0.625, 0.692, 0.708]." }, { "from": "gpt", "value": "There is a blue bar representing the Deforestation Rate with a value of 0.62, along with its text annotation with coordinates [0.692, 0.632, 0.751, 0.702]." } ], "id_raw": 737 }, { "id": "clhgsHj81tBBIAR4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the area [0.261, 0.625, 0.894, 0.605]." }, { "from": "gpt", "value": "The area includes two bars with their text annotations:\n1. The Deforestation Rate bar[0.261, 0.625, 0.692, 0.708] with its corresponding text annotation [0.692, 0.632, 0.751, 0.702]\n2. The bar representing Global Temperature[0.261, 0.522, 0.894, 0.605] comes next, with its text annotation at [0.894, 0.528, 0.953, 0.598]." } ], "id_raw": 737 }, { "id": "tA7Ju8eB1bT37I8E", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is represented based on the given JSON data?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 738 }, { "id": "opoorTJU4p3O1UR6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis in the visualization represent?" }, { "from": "gpt", "value": "Happiness Index." } ], "id_raw": 738 }, { "id": "ndlZhuadblD96NTA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at {coordinates}[0.087, 0.631, 0.814, 0.831] represent?" }, { "from": "gpt", "value": "It represents the Happiness Index for Suburban." } ], "id_raw": 738 }, { "id": "lUaRpeHFNDaBLl3I", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis tick at {coordinates}[0.087, 0.731]?" }, { "from": "gpt", "value": "Suburban." } ], "id_raw": 738 }, { "id": "Eg9R6U9Ou0bPvf7U", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher Happiness Index, {Suburban}[0.087, 0.631, 0.814, 0.831] or {Urban}[0.087, 0.379, 0.761, 0.580]?" }, { "from": "gpt", "value": "Suburban." } ], "id_raw": 738 }, { "id": "fFxxbFazXB5ZUH6p", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nIs the Happiness Index for {Suburban}[0.087, 0.631, 0.814, 0.831] less than 1.0?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 738 }, { "id": "W2gHSMSRZ57u6LUZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nComparing {Urban}[0.087, 0.379, 0.761, 0.580] and {Rural}[0.087, 0.128, 0.655, 0.329], which has a lower Happiness Index?" }, { "from": "gpt", "value": "Rural." } ], "id_raw": 738 }, { "id": "MKMQvj5oNkPjLQrr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Suburban}[0.087, 0.631, 0.814, 0.831] wider than that for {Rural}[0.087, 0.128, 0.655, 0.329]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 738 }, { "id": "TMnDuaZCVRDur1HE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nWhat features can be observed in the region [0.087, 0.128, 0.885, 0.831]?" }, { "from": "gpt", "value": "Within this region, there are three horizontal bars which represent the Happiness Index for three different areas: Suburban, Urban, and Rural. Each bar is labeled with its respective value on the right end." } ], "id_raw": 738 }, { "id": "QYQOETsSo9yujyWj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's included in the region [0.076, 0.879, 0.985, 0.914]?" }, { "from": "gpt", "value": "This region includes the x-axis labels for the Happiness Index. The labels range from 0.0 to 1.0 in increments of 0.2." } ], "id_raw": 738 }, { "id": "iZAxFshF9y5Dbncy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nWhat information is presented within the region [0.015, 0.215, 0.082, 0.752]?" }, { "from": "gpt", "value": "This region contains the y-axis labels, which are 'Suburban', 'Urban', and 'Rural'. These labels represent different areas that have been evaluated for their Happiness Index." } ], "id_raw": 738 }, { "id": "hRuPedRpU96tDKmz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nIn the region [0.087, 0.631, 0.849, 0.831], what's depicted?" }, { "from": "gpt", "value": "In this region, there is a bar that represents the happiness index value for the suburban area. The value is labeled as 0.82 at the right side of the bar." } ], "id_raw": 738 }, { "id": "RHKAIRHvoEM2OOrB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart type for the figure?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 739 }, { "id": "EFeSSBp8lnSXmp00", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the box with coordinates [0.164, 0.267, 0.767, 0.391] represent?" }, { "from": "gpt", "value": "Mutual Funds' probability of increase in the market." } ], "id_raw": 739 }, { "id": "6j0LhBfehTG6d1NE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y-tick located at [0.023, 0.628, 0.159, 0.656]?" }, { "from": "gpt", "value": "International Bonds." } ], "id_raw": 739 }, { "id": "OP6yJzws4tWgtBin", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhat values does the X-tick at [0.783, 0.903, 0.805, 0.931] represent?" }, { "from": "gpt", "value": "0.7." } ], "id_raw": 739 }, { "id": "7Mm4fSklJY4f2tUZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"International Bonds\"}[0.164, 0.577, 0.812, 0.701] longer than {the bar representing \"National Bonds\"}[0.164, 0.732, 0.749, 0.856]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 739 }, { "id": "oaJGUzKq4kPedptX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Stock Market Indices\"}[0.164, 0.422, 0.794, 0.546] to the right or left of {the bar representing \"Cryptocurrencies\"}[0.164, 0.112, 0.866, 0.236]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 739 }, { "id": "xIZuRcd2W8mQGgCe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nComparing {the \"International Bonds\" bar}[0.164, 0.577, 0.812, 0.701] with {the \"Cryptocurrencies\" bar}[0.164, 0.112, 0.866, 0.236], which one represents a higher probability of market increase?" }, { "from": "gpt", "value": "Cryptocurrencies." } ], "id_raw": 739 }, { "id": "riCulSDbk7Mc3REV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Mutual Funds bar}[0.164, 0.267, 0.767, 0.391] above or below the {\"Stock Market Indices\" bar}[0.164, 0.422, 0.794, 0.546]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 739 }, { "id": "OZGKx5higFxt6KXY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.392, 0.106, 0.864, 0.884]?" }, { "from": "gpt", "value": "It contains the bars representing probability of market increase for various financial instruments. You can see the bars for the International Bonds, Mutual Funds, and Cryptocurrencies with their corresponding probabilities of market increase." } ], "id_raw": 739 }, { "id": "FKs0fvbygX9sshXP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.164, 0.422, 0.794, 0.546]?" }, { "from": "gpt", "value": "There are two objects: \n1. The bar that signifies the probability of market increase for Stock Market Indices, with the coordinate range [0.164, 0.422, 0.794, 0.546].\n2. The annotation that displays the actual probability number \"0.7\", with the coordinate range [0.794, 0.470, 0.820, 0.498]." } ], "id_raw": 739 }, { "id": "BvUMfMalGnssny7Q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content at [0.306, 0.030, 0.831, 0.066]." }, { "from": "gpt", "value": "It contains the subtitle of the chart, which is \"Probability of Market Increase for Various Financial Instruments\"." } ], "id_raw": 739 }, { "id": "jgvBdxH3O5EySaDH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be found inside the area [0.180, 0.903, 0.963, 0.931]?" }, { "from": "gpt", "value": "The area contains labels representing the x-tick values, which denote the probabilities '0.6', '0.7', '0.8', and '0.9'." } ], "id_raw": 739 }, { "id": "lhzDQzu3tZQuDgpG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the element located at [0.404, 0.025, 0.761, 0.055]?" }, { "from": "gpt", "value": "The subtitle of the chart, which reads \"Emerging Technologies and Trends Outlook\"." } ], "id_raw": 740 }, { "id": "ogerhS6HPGIX82KC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar located at [0.180, 0.809, 0.861, 0.872] represent?" }, { "from": "gpt", "value": "It represents the trend value for \"AI-driven Development\", which is 0.88." } ], "id_raw": 740 }, { "id": "1zjCRTjTGs7O22h9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis?" }, { "from": "gpt", "value": "There isn't any label for the y-axis as indicated by the empty string at the location: {the y-axis label}[0.009, 0.487, 0.009, 0.487]." } ], "id_raw": 740 }, { "id": "aiSSlgrVNzw6GU6L", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"AI-driven Development\"}[0.180, 0.809, 0.861, 0.872] longer than {the bar representing \"Blockchain\"}[0.180, 0.731, 0.792, 0.794]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 740 }, { "id": "bBjYsQDeYNOpoPa5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Augmented Reality\"}[0.180, 0.258, 0.823, 0.321] higher or lower than {the bar representing \"Virtual Reality\"}[0.180, 0.180, 0.792, 0.243] on the chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 740 }, { "id": "vnjNgr2qceAXxhi2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nWhich is longer, {the bar for \"5G Networks\"}[0.180, 0.337, 0.947, 0.400] or {the bar for \"Distributed Cloud\"}[0.180, 0.101, 0.892, 0.164]?" }, { "from": "gpt", "value": "\"5G Networks\"." } ], "id_raw": 740 }, { "id": "xM8ROBjZzE3AU1Zo", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.539, 0.952, 0.626, 0.975]." }, { "from": "gpt", "value": "This region contains the X-axis label, which reads \"Trend Values\"." } ], "id_raw": 740 }, { "id": "R0AAPqG5SoqhS2mG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe any objects within the region [0.180, 0.809, 0.861, 0.872]." }, { "from": "gpt", "value": "This region holds a single object: the bar representing the trend value of 0.88 for \"AI-driven Development\"." } ], "id_raw": 740 }, { "id": "WeDOZNE6JCevCha8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the area [0.100, 0.753, 0.175, 0.776]?" }, { "from": "gpt", "value": "It contains the y-axis label \"Blockchain\"." } ], "id_raw": 740 }, { "id": "dmUYzPgqi3rQd8EY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the percentage increase} at [0.864, 0.370, 0.914, 0.405]?" }, { "from": "gpt", "value": "5.2%" } ], "id_raw": 742 }, { "id": "MjaWRMsvAJa4emSu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar with coordinates [0.193, 0.683, 0.774, 0.831]?" }, { "from": "gpt", "value": "The percentage increase of the Stock Market over a decade." } ], "id_raw": 742 }, { "id": "hfogGCBz8bGJ3gaD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at location [0.100, 0.559, 0.187, 0.594] refer to?" }, { "from": "gpt", "value": "The category Gold Price." } ], "id_raw": 742 }, { "id": "3XaPtNRH2iCPK9N9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nWhat is labelled at {the y-axis} at [0.074, 0.744, 0.187, 0.779]?" }, { "from": "gpt", "value": "Stock Market." } ], "id_raw": 742 }, { "id": "eXbSy7ISNq8hEhdw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Real Estate\"}[0.193, 0.313, 0.864, 0.461] longer or shorter than {the bar representing \"Gold Price\"}[0.193, 0.498, 0.800, 0.646]?" }, { "from": "gpt", "value": "Longer." } ], "id_raw": 742 }, { "id": "gJ57AQkrzdvukuSp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Currency Exchange\"}[0.193, 0.128, 0.813, 0.276] to the left or right of {the \"3%\" label}[0.567, 0.879, 0.594, 0.914]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 742 }, { "id": "wpoH7maI8vrL1vtW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Stock Market\"}[0.193, 0.683, 0.774, 0.831] shorter than {the bar for \"Currency Exchange\"}[0.193, 0.128, 0.813, 0.276]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 742 }, { "id": "b3UNtNELUK3lAn5k", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Stock Market\"}[0.193, 0.683, 0.774, 0.831] and {\"Gold Price\"}[0.193, 0.498, 0.800, 0.646], which one has a higher percentage increase over a decade represented by its bar's length on the x-axis?" }, { "from": "gpt", "value": "Real Estate." } ], "id_raw": 742 }, { "id": "xzBhfti5R05TMfDP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in coordinates [0.019, 0.189, 0.187, 0.224]." }, { "from": "gpt", "value": "This region contains the label for \"Currency Exchange\"." } ], "id_raw": 742 }, { "id": "z4ERLD3ICkWwZLp0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.567, 0.879, 0.594, 0.914]." }, { "from": "gpt", "value": "This region contains the label representing \"3%\" on the x-axis." } ], "id_raw": 742 }, { "id": "CRLdctkzJEIz8rfy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nPlease provide details about the objects located in the area [0.193, 0.498, 0.800, 0.646]." }, { "from": "gpt", "value": "The area contains a bar that represents 'Gold Price' category with a percentage increase of 4.7% over a decade. The bar starts from x-axis representing '0%', stretches to represent '4.7%'." } ], "id_raw": 742 }, { "id": "EkB31spCs2oBSlr8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the area [0.407, 0.927, 0.754, 0.963]?" }, { "from": "gpt", "value": "This area displays the xlabel of the chart which represents the 'Percentage Increase Over a Decade (%)'." } ], "id_raw": 742 }, { "id": "w40STtgwhKcIY58x", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the key object denoted by the coordinates [0.286, 0.685, 0.844, 0.790] in the chart?" }, { "from": "gpt", "value": "A blue bar representing the return rate of the 'High Risk High Return' investment strategy." } ], "id_raw": 743 }, { "id": "Ygj6OwTd0nmNwUVs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the text associated with the 'Aggressive Growth' strategy?" }, { "from": "gpt", "value": "[0.745, 0.318, 0.825, 0.364]" } ], "id_raw": 743 }, { "id": "Q2HO7c7cQlJTdIQj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the string of text represented by the coordinates [0.025, 0.719, 0.278, 0.766]?" }, { "from": "gpt", "value": "High Risk High Return." } ], "id_raw": 743 }, { "id": "tihQj4TmScyrpHEg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by these coordinates [0.351, 0.582, 0.432, 0.629]?" }, { "from": "gpt", "value": "The yield value for the 'Stable Low Yield' strategy, exactly 10%." } ], "id_raw": 743 }, { "id": "WrOQgTgn4ScWlYCD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher yield, {the High Risk High Return bar}[0.286, 0.685, 0.844, 0.790] or {the Stable Low Yield bar}[0.286, 0.552, 0.351, 0.658]?" }, { "from": "gpt", "value": "The High Risk High Return bar." } ], "id_raw": 743 }, { "id": "K8wHSbw6yfoqACzi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nComparing {the Balanced Portfolio bar}[0.286, 0.420, 0.614, 0.526] to {the Aggressive Growth bar}[0.286, 0.288, 0.745, 0.394], which bar is longer?" }, { "from": "gpt", "value": "The Aggressive Growth bar." } ], "id_raw": 743 }, { "id": "IRH7UvsrTxvvyG2r", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the Conservative Mix bar}[0.286, 0.156, 0.483, 0.262] have a higher or lower yield than {the Balanced Portfolio bar}[0.286, 0.420, 0.614, 0.526]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 743 }, { "id": "U7nIvPDvN068amAD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the Stable Low Yield Strategy}[0.286, 0.552, 0.351, 0.658] and {the Aggressive Growth Strategy}[0.286, 0.288, 0.745, 0.394], which has a higher rate of return?" }, { "from": "gpt", "value": "Aggressive Growth Strategy." } ], "id_raw": 743 }, { "id": "Olrcc363Iyoo7RUK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.286, 0.124, 0.942, 0.822]." }, { "from": "gpt", "value": "The region contains five colored bars, each corresponding to a different investment strategy. They are, from top to bottom - {High Risk High Return}[0.286, 0.685, 0.844, 0.790], {Stable Low Yield}[0.286, 0.552, 0.351, 0.658], {Balanced Portfolio}[0.286, 0.420, 0.614, 0.526], {Aggressive Growth}[0.286, 0.288, 0.745, 0.394] and {Conservative Mix}[0.286, 0.156, 0.483, 0.262]. Each bar's length indicates the average return rate of the corresponding strategy and has a number label at the edge showing the exact rate percentage." } ], "id_raw": 743 }, { "id": "NM3Vpj3ZD6LTmHsj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nCan you write down what exists within the region [0.050, 0.800, 0.660, 0.950]?" }, { "from": "gpt", "value": "It contains the chart's title, represented by the string \"Average Return Rates of Different Investment Strategies\" with coordinates [0.222, 0.050, 1.006, 0.110]." } ], "id_raw": 743 }, { "id": "nUlIoVAL7tGjjfdd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the area bounded by [0.286, 0.685, 0.844, 0.790]?" }, { "from": "gpt", "value": "This region contains a blue bar representing the 'High Risk High Return' investment strategy's return rate with a label on the right side showing the value as '85%'." } ], "id_raw": 743 }, { "id": "D4QmDuEGcpdwao7j", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you find inside the region [0.856, 0.838, 0.942, 0.822]?" }, { "from": "gpt", "value": "It's the representation of 100% on the X-axis." } ], "id_raw": 743 }, { "id": "g4zbbx7rB8RKDNTh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar with coordinates [0.134, 0.714, 0.784, 0.831]?" }, { "from": "gpt", "value": "The bar represents the Influence Score of TV News." } ], "id_raw": 744 }, { "id": "iKX1gtsVAU6gtELn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.676, 0.316, 0.720, 0.351] in the chart?" }, { "from": "gpt", "value": "The value of the Influence Score for Radio News, \"0.65\"." } ], "id_raw": 744 }, { "id": "NkFCpu1RTBcB2HP5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nWhat is signified by the bar at [0.134, 0.421, 0.901, 0.538]?" }, { "from": "gpt", "value": "This bar signifies the Influence Score of Social Media." } ], "id_raw": 744 }, { "id": "A9pRQAXDQQuh7Fqc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nIs the influence score of {Online News bar}[0.134, 0.568, 0.843, 0.685] higher or lower than the influence score of {TV News bar}[0.134, 0.714, 0.784, 0.831]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 744 }, { "id": "gREvXZDSrHlM0gW4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nBetween {TV News bar}[0.134, 0.714, 0.784, 0.831] and {Print Media bar}[0.134, 0.128, 0.718, 0.246], which media has the lower influence score?" }, { "from": "gpt", "value": "Print Media." } ], "id_raw": 744 }, { "id": "nafT14XDLombTI61", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater impact, the {Social Media bar}[0.134, 0.421, 0.901, 0.538] or the {Radio News bar}[0.134, 0.275, 0.676, 0.392]?" }, { "from": "gpt", "value": "Social Media." } ], "id_raw": 744 }, { "id": "uEPMJ3Cz0a1B6WAX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what you can observe in [0.134, 0.128, 0.901, 0.831]." }, { "from": "gpt", "value": "The region contains five bars, each representing the influence score of different media channels: \n1. {TV News bar}[0.134, 0.714, 0.784, 0.831] with an influence score of 0.78.\n2. {Online News bar}[0.134, 0.568, 0.843, 0.685] with an influence score of 0.85.\n3. {Social Media bar}[0.134, 0.421, 0.901, 0.538] with an influence score of 0.92.\n4. {Radio News bar}[0.134, 0.275, 0.676, 0.392] with an influence score of 0.65.\n5. {Print Media bar}[0.134, 0.128, 0.718, 0.246] with an influence score of 0.7." } ], "id_raw": 744 }, { "id": "LgO59Yh7LREDjgMT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements included in the region [0.484, 0.927, 0.618, 0.963]?" }, { "from": "gpt", "value": "It contains the x-axis label \"Influence Score\"." } ], "id_raw": 744 }, { "id": "yx8OHNTKoJSFn9f5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.020, 0.613, 0.128, 0.648]?" }, { "from": "gpt", "value": "The content within this area is a y-axis category label, {Online News}[0.020, 0.613, 0.128, 0.648]." } ], "id_raw": 744 }, { "id": "sitxppUiM1hpppXU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the bar representing \"Process Improvement\"?" }, { "from": "gpt", "value": "{Process Improvement bar}[0.296, 0.492, 0.811, 0.589]" } ], "id_raw": 745 }, { "id": "7rnZpXTWsC2X5Lg8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.904, 0.765, 0.962, 0.800] represent?" }, { "from": "gpt", "value": "The numeric value 0.92 corresponding to the \"Automation\" task." } ], "id_raw": 745 }, { "id": "fkeXbT8wb1lgJPC9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.434, 0.927, 0.818, 0.963]?" }, { "from": "gpt", "value": "The x-axis label \"Efficiency Improvement Potential\"." } ], "id_raw": 745 }, { "id": "8nTwwSRCqQj3hHIS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the parameters being compared, indicated by the labels in the coordinates [0.161, 0.648, 0.288, 0.683],[0.147, 0.163, 0.288, 0.198],[0.037, 0.527, 0.288, 0.562]?" }, { "from": "gpt", "value": "The parameters being compared are {Delegation}[0.161, 0.648, 0.288, 0.683], {Outsourcing}[0.147, 0.163, 0.288, 0.198],, and {Process Improvement}[0.037, 0.527, 0.288, 0.562]." } ], "id_raw": 745 }, { "id": "X5jmZq3j6trnMs9Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nDoes the length of the bar showing \"Technological Upgrade\" exceed the length of the bar showing \"Remote Working\"?" }, { "from": "gpt", "value": "Using their coordinates: {Technological Upgrade bar}[0.296, 0.371, 0.752, 0.468] and {Remote Working bar}[0.296, 0.250, 0.725, 0.347] - The answer is No." } ], "id_raw": 745 }, { "id": "QjOQd8ftKEfiLa3x", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for \"Process Improvement\" have a higher efficiency improvement potential compared to \"Delegation\"?" }, { "from": "gpt", "value": "Given their coordinates: {Process Improvement bar}[0.296, 0.492, 0.811, 0.589] and {Delegation bar}[0.296, 0.613, 0.858, 0.710] - The answer is No." } ], "id_raw": 745 }, { "id": "CKwTPstxkGuD8N7j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher efficiency improvement potential, \"Outsourcing\" or \"Automation\"?" }, { "from": "gpt", "value": "If we look at their bar coordinates: {Outsourcing bar}[0.296, 0.128, 0.679, 0.225], and {Automation bar}[0.296, 0.734, 0.904, 0.831], the answer is \"Automation\"." } ], "id_raw": 745 }, { "id": "wd4qgmysSPizipQl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhat information is represented in the area [0.296, 0.128, 0.811, 0.831]?" }, { "from": "gpt", "value": "Within this large rectangular area, six horizontal bars representing different tasks are found. These include Automation, Delegation, Process Improvement, Technological Upgrade, Remote Working, and Outsourcing. Each bar's length represents the corresponding task's Efficiency Improvement Potential." } ], "id_raw": 745 }, { "id": "XfLGCsGWgNdnVn9r", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the area ranging from [0.434, 0.927, 0.818, 0.963]?" }, { "from": "gpt", "value": "This area includes only the x-axis label \"Efficiency Improvement Potential\"." } ], "id_raw": 745 }, { "id": "TRGGabCh8EQV5c79", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhat is presented in the region [0.309, 0.037, 0.943, 0.083]?" }, { "from": "gpt", "value": "This region includes the chart's subtitle \"Efficiency Improvement Potential Assessment\"." } ], "id_raw": 745 }, { "id": "UFSGPmCJzKeno4QO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nWhat's the name of the media company corresponding to the bar with coordinates [0.182, 0.128, 0.889, 0.192]?" }, { "from": "gpt", "value": "CBC News." } ], "id_raw": 746 }, { "id": "bpC0gSj9qkmkTFOw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the axis label at [0.486, 0.927, 0.663, 0.963]?" }, { "from": "gpt", "value": "Influence Index." } ], "id_raw": 746 }, { "id": "VjYIWsx0QKT0JPzS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the entity located at the coordinates [0.182, 0.767, 0.931, 0.831] and what it represents?" }, { "from": "gpt", "value": "It is a bar representing the Influence Index for {BBC}[0.126, 0.786, 0.174, 0.821]." } ], "id_raw": 746 }, { "id": "Uv4qS8XXsHfSu4pp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at coordinate [0.689, 0.622, 0.748, 0.657] representing?" }, { "from": "gpt", "value": "It represents the value (0.61) of {Fox News}[0.064, 0.626, 0.174, 0.661] in the Influence Index." } ], "id_raw": 746 }, { "id": "A8eoXts7S7IWJu4H", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nIs the Influence Index for {Al Jazeera}[0.059, 0.546, 0.174, 0.581] higher or lower than {Russia Today}[0.025, 0.466, 0.174, 0.501]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 746 }, { "id": "hf2eCYPTitqpDbIq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing {NBC News}[0.055, 0.227, 0.174, 0.262] in Influence Index extend further in x-direction than the bar representing {Sky News}[0.063, 0.386, 0.174, 0.421]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 746 }, { "id": "VrHrAYZDTcpTs10v", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nIs {ABC News}[0.056, 0.306, 0.174, 0.341] more influential than {CBC News}[0.056, 0.147, 0.174, 0.182] according to the Influence Index?" }, { "from": "gpt", "value": "No." } ], "id_raw": 746 }, { "id": "YejNKOPbJ9f9XO1J", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nWhich media outlet has a lower Influence Index, {Fox News}[0.064, 0.626, 0.174, 0.661] or {Russia Today}[0.025, 0.466, 0.174, 0.501]?" }, { "from": "gpt", "value": "Fox News." } ], "id_raw": 746 }, { "id": "u9VLe8qQNQpEIRwu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the area {0.182, 0.093, 0.968, 0.866}?" }, { "from": "gpt", "value": "This area includes the bar plot area. It contains nine horizontal bars each representing a media outlet's Influence Index. From bottom to top, they are {CBC News}[0.182, 0.128, 0.889, 0.192], {NBC News}[0.182, 0.208, 0.831, 0.272], {ABC News}[0.182, 0.288, 0.806, 0.352], {Sky News}[0.182, 0.368, 0.847, 0.432], {Russia Today}[0.182, 0.448, 0.739, 0.512], {Al Jazeera}[0.182, 0.528, 0.881, 0.592], {Fox News}[0.182, 0.608, 0.689, 0.672], {CNN}[0.182, 0.688, 0.914, 0.751] and {BBC}[0.182, 0.767, 0.931, 0.831]." } ], "id_raw": 746 }, { "id": "pGMx2yQbrZUlqls8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is contained in the section [0.123, 0.706, 0.174, 0.741] of the chart." }, { "from": "gpt", "value": "This section contains the y-axis label for {CNN}[0.123, 0.706, 0.174, 0.741]." } ], "id_raw": 746 }, { "id": "wqL5trvBXibGuD4a", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's represented in the region [0.197, 0.037, 0.953, 0.083]?" }, { "from": "gpt", "value": "The region contains the subtitle of the chart, which is \"Comparing Media Influence & Public Opinion Guidance.\"" } ], "id_raw": 746 }, { "id": "X9t8tz0cJnFVCe9c", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.330, 0.879, 0.366, 0.914]." }, { "from": "gpt", "value": "This region contains the x-axis tick value \"0.2\"." } ], "id_raw": 746 }, { "id": "XH4Rk940CBKWOrLX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at coordinate [0.260, 0.487, 0.873, 0.708]?" }, { "from": "gpt", "value": "The bar representing the level of Public Interest in Cultural Heritage." } ], "id_raw": 747 }, { "id": "foOLJFOtSaCIUwP5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.938, 0.757, 0.975, 0.827]?" }, { "from": "gpt", "value": "X-tick label, 1.0 indicating the maximum level of public interest." } ], "id_raw": 747 }, { "id": "Tyi53ZIp6dhR64gc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in [0.025, 0.294, 0.251, 0.364]?" }, { "from": "gpt", "value": "Y-tick label, Cultural Integration." } ], "id_raw": 747 }, { "id": "j6yas5VZVAmkv0jC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Cultural Heritage'}[0.260, 0.487, 0.873, 0.708] longer than {the bar representing 'Cultural Integration'}[0.260, 0.211, 0.748, 0.432]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 747 }, { "id": "qsRTJxU4AcHAqkf6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nIs the {y-axis label for 'Cultural Heritage'}[0.053, 0.570, 0.251, 0.640] located higher than the {y-axis label for 'Cultural Integration'}[0.025, 0.294, 0.251, 0.364]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 747 }, { "id": "xZBOdV7CYrAi2k8E", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar of 'Cultural Heritage'}[0.260, 0.487, 0.873, 0.708] extend further than {x-tick 0.6}[0.659, 0.757, 0.696, 0.827]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 747 }, { "id": "44FRsMxfGKDWuRva", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region defined by [0.260, 0.211, 0.873, 0.708]." }, { "from": "gpt", "value": "There are two bars:\n1. The bar of 'Cultural Heritage'{Cultural Heritage Bar}[0.260, 0.487, 0.873, 0.708] with a length representing the public interest level of 0.88\n2. The bar of 'Cultural Integration'{Cultural Integration Bar}[0.260, 0.211, 0.748, 0.432] with a length representing the public interest level of 0.7 and the corresponding y-axis labels." } ], "id_raw": 747 }, { "id": "L5vmYduhI427qnzn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.260, 0.733, 0.957, 0.827]?" }, { "from": "gpt", "value": "The x-axis of the chart with tick labels from 0 to 1 indicating the levels of public interest." } ], "id_raw": 747 }, { "id": "isR9vy5jfRXD9Fv8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.477, 0.855, 0.740, 0.925]?" }, { "from": "gpt", "value": "Label for the x-axis reading \"Level of Public Interest\"." } ], "id_raw": 747 }, { "id": "oH8KWHvXBy23Mszq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the main subtitle of the chart located at [0.258, 0.030, 0.870, 0.068]?" }, { "from": "gpt", "value": "Key Parameters of Global Financial System and Policies." } ], "id_raw": 748 }, { "id": "jyZHnAeoXbetkdFh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at the coordinate [0.494, 0.934, 0.635, 0.970]?" }, { "from": "gpt", "value": "Associated Value." } ], "id_raw": 748 }, { "id": "7D1x8JLzIP1ggUgj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nWhat parameter is shown at {the bar at the coordinates} [0.154, 0.638, 0.638, 0.698]?" }, { "from": "gpt", "value": "Interest Rates." } ], "id_raw": 748 }, { "id": "qvMhqKkibBb3ma7g", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nWhat value is found just right side of the bar with coordinates [0.154, 0.788, 0.884, 0.848]?" }, { "from": "gpt", "value": "0.89." } ], "id_raw": 748 }, { "id": "uCYYR7yBUd95X72d", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nIs the bar showing {Monetary Policy}[0.154, 0.788, 0.884, 0.848] longer than the bar showing {Quantitative Easing}[0.154, 0.713, 0.703, 0.773]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 748 }, { "id": "JNunKUCHGuxgkbFu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of Subprime Mortgages}[0.154, 0.263, 0.581, 0.323] shorter than {the bar of Commodities}[0.154, 0.188, 0.720, 0.248]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 748 }, { "id": "PASsKLbaIPFXLwH6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nBetween {Exchange-traded Fund}[0.154, 0.113, 0.662, 0.173] and {Trade Policies}[0.154, 0.413, 0.605, 0.473], which has a higher value?" }, { "from": "gpt", "value": "Trade Policies." } ], "id_raw": 748 }, { "id": "mMmGMUqAVfxRlVzF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Subprime Mortgages}[0.154, 0.263, 0.581, 0.323] longer than half of {the bar representing Monetary Policy}[0.154, 0.788, 0.884, 0.848]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 748 }, { "id": "47L3M9pUepUUd0Xs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.494, 0.934, 0.635, 0.970]." }, { "from": "gpt", "value": "It is the x-axis label, named 'Associated Value'." } ], "id_raw": 748 }, { "id": "IWIJ8IVMSqwAVHck", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region covered by the coordinates [0.154, 0.263, 0.662, 0.173]?" }, { "from": "gpt", "value": "There are 2 objects. {The bar of Exchange-traded Fund}[0.154, 0.113, 0.662, 0.173] and its associated text '0.62' with coordinates [0.662, 0.129, 0.698, 0.157]." } ], "id_raw": 748 }, { "id": "BGupxlnW3hFP9lDH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nWhat could be found in the area [0.154, 0.788, 0.919, 0.832]?" }, { "from": "gpt", "value": "There are two items in the area: \n1. the blue rectangle with coordinates [0.154, 0.788, 0.884, 0.848] represents the 'Monetary Policy' bar. \n2. The text '0.89' at [0.884, 0.804, 0.919, 0.832] that describes the value of Monetary Policy." } ], "id_raw": 748 }, { "id": "BEvDGYScM8MQnJ9E", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nWhat is stated in the subtitle at [0.071, 0.037, 1.118, 0.085]?" }, { "from": "gpt", "value": "\"Proportion of Companies Adopting Different Social Responsibility Indicators\"." } ], "id_raw": 749 }, { "id": "KK9x64rYvaAi6dG5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the category corresponding to the bar with coordinates [0.221, 0.423, 0.841, 0.540]?" }, { "from": "gpt", "value": "{The bar corresponds to the \"Fair Trade\"}[0.221, 0.423, 0.841, 0.540];" } ], "id_raw": 749 }, { "id": "D8eyRN1fUEtLHGRj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nWhat portion of the x-axis is represented by the coordinate [0.406, 0.927, 0.782, 0.963]?" }, { "from": "gpt", "value": "\"Proportion of Sustainable Business Practice\"." } ], "id_raw": 749 }, { "id": "uzEpD5eRxguYSboX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nWhat bars are within the horizontal range of [0.221, 0.5, 0.5, 0.9]?" }, { "from": "gpt", "value": "{bar indicating Biodiversity Protection}[0.221, 0.715, 0.400, 0.831] and {bar indicating Emission Reduction}[0.221, 0.569, 0.661, 0.685]." } ], "id_raw": 749 }, { "id": "ksBzcjAWWUNFjhIg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Charitable Giving\"}[0.221, 0.277, 0.893, 0.394] longer than {the bar representing \"Emission Reduction\"}[0.221, 0.569, 0.661, 0.685]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 749 }, { "id": "y0v8iqI3fEYchby2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar indicating \"Fair Trade\"}[0.221, 0.423, 0.841, 0.540] have a greater x-value than the {bar indicating \"Biodiversity Protection\"}[0.221, 0.715, 0.400, 0.831]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 749 }, { "id": "fB3drlzDaYwOZXyQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nAre the {bars}[[0.221, 0.715, 0.400, 0.831], [0.221, 0.569, 0.661, 0.685], [0.221, 0.423, 0.841, 0.540], [0.221, 0.277, 0.893, 0.394], [0.221, 0.131, 0.542, 0.248]] uniformly colored?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 749 }, { "id": "NJQyRVKuLG7I5VN6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nComparing the {highest bar}[0.221, 0.277, 0.893, 0.394] and the {lowest bar}[0.221, 0.131, 0.542, 0.248], which one represents a higher value?" }, { "from": "gpt", "value": "The highest bar." } ], "id_raw": 749 }, { "id": "MzWZGvJRDNAoxP6B", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects and texts found along x-axis from coordinates [0.221, 0.866, 0.967, 0.914]." }, { "from": "gpt", "value": "Along this section of the x-axis are tick labels representing decimal fractions from 0.0 to 1.0 in increments of 0.2. Specifically, '0.0' ranges from [0.207, 0.879, 0.234, 0.914], '0.2' ranges from [0.356, 0.879, 0.384, 0.914], '0.4' ranges from [0.506, 0.879, 0.533, 0.914], '0.6' ranges from [0.655, 0.879, 0.683, 0.914], '0.8' ranges from [0.804, 0.879, 0.832, 0.914], and '1.0' ranges from [0.954, 0.879, 0.981, 0.914]." } ], "id_raw": 749 }, { "id": "OexYzyuxVgPQ8l12", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the area inside the coordinates [0.221, 0.096, 0.967, 0.866]." }, { "from": "gpt", "value": "This area contains five bars each representing different Social Responsibility Indicators. From high to low, they represent \"Charitable Giving\"[0.221, 0.277, 0.893, 0.394], \"Fair Trade\"[0.221, 0.423, 0.841, 0.540], \"Emission Reduction\"[0.221, 0.569, 0.661, 0.685], \"Sustainable Packaging\"[0.221, 0.131, 0.542, 0.248], and \"Biodiversity Protection\"[0.221, 0.715, 0.400, 0.831]. The x-axis length of the bars represents the Proportion of Sustainable Business Practice." } ], "id_raw": 749 }, { "id": "QSbPEuQSLbTksS43", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located between [0.221, 0.400, 0.967, 0.500]?" }, { "from": "gpt", "value": "There's one bar:\n1. {Bar representing \"Fair Trade\"}[0.221, 0.423, 0.841, 0.540]." } ], "id_raw": 749 }, { "id": "lDrALBVeuvSTcpLK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content that falls within [0.221, 0.400, 0.9, 0.9]?" }, { "from": "gpt", "value": "Within this region, there are three category bars:\n1. {Bar representing \"Fair Trade\"}[0.221, 0.423, 0.841, 0.540].\n2. {Bar representing \"Emission Reduction\"}[0.221, 0.569, 0.661, 0.685].\n3. {Bar representing \"Biodiversity Protection\"}[0.221, 0.715, 0.400, 0.831]." } ], "id_raw": 749 }, { "id": "4Xbu1G0S1Mp8BWvH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nInside the coordinates [0.464, 0.623, 0.479, 0.637], what is displayed?" }, { "from": "gpt", "value": "The x-axis label for chunk size 5." } ], "id_raw": 751 }, { "id": "2G2LUjTRniQnxxDG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nWhat color is denoted by the coordinates [0.143, 0.363, 0.190, 0.372] in the chart's legend?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 751 }, { "id": "VUIaNMSsNcBnpGov", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nAt which coordinate can I find the value of -10 on the y-axis in the second subplot?" }, { "from": "gpt", "value": "{The Y-axis tick label '-10'}[0.058, 0.502, 0.106, 0.516]." } ], "id_raw": 751 }, { "id": "zTuoMBJDOhmbvvN9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at x-label '7', the {blue bar}[0.603, 0.359, 0.626, 0.455] or the {purple bar}[0.626, 0.455, 0.650, 0.592]?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 751 }, { "id": "bdbeSwKDhCVAduIF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nIs {the first chunk's bar for ours}[0.138, 0.100, 0.161, 0.126] taller than {the first chunk's bar for saliency-based sampling}[0.161, 0.126, 0.185, 0.206]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 751 }, { "id": "o3gbaiweaRhmZJ9g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nDoes the {10th chunk's blue bar}[0.836, 0.677, 0.859, 0.783] represent a higher or lower rate of gain change than its corresponding {purple bar}[0.859, 0.783, 0.882, 0.942]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 751 }, { "id": "GA32QiPhsGAlpJxm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.485, 0.314, 0.613, 0.328] entail?" }, { "from": "gpt", "value": "It contains the x-axis title \"Chunk Size\", labeled at {Chunk Size}[0.485, 0.314, 0.613, 0.328]." } ], "id_raw": 751 }, { "id": "VsROMZ60lE0a0glR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the coordinate range [0.025, 0.069, 0.106, 0.237]?" }, { "from": "gpt", "value": "This region includes the y-axis label \"Rate of Gain Change[%]\", with its coordinates at {Rate of Gain Change[%]}[0.025, 0.069, 0.048, 0.237]." } ], "id_raw": 751 }, { "id": "fuTxJMSqfPrbZw4M", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the key elements of the legend within [0.134, 0.356, 0.508, 0.402]." }, { "from": "gpt", "value": "The legend contains two elements. One is the label \"Ours\" with a {blue color square}[0.143, 0.363, 0.190, 0.372] next to the corresponding {text}[0.208, 0.361, 0.262, 0.375], and the other is \"Saliency-based Sampling\" with a {purple color square}[0.143, 0.384, 0.190, 0.393] next to the corresponding {text}[0.208, 0.382, 0.499, 0.396]." } ], "id_raw": 751 }, { "id": "BuMTDwM95sJV4dBw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color associated with {the series handle}[0.318, 0.026, 0.364, 0.035] in the chart?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 752 }, { "id": "qsX1Fj9kvcF3cded", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the metric name placed at the coordinates [0.457, 0.314, 0.626, 0.328]?" }, { "from": "gpt", "value": "Data Accuracy." } ], "id_raw": 752 }, { "id": "lZBIKKRK4ntzcvpk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nWhich series does the bar at [0.323, 0.444, 0.498, 0.543] belong to?" }, { "from": "gpt", "value": "Series B." } ], "id_raw": 752 }, { "id": "ozYbDq46H7POMSSJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the blue colored bar}[0.147, 0.101, 0.260, 0.115] have a higher or lower value than {the lavender bar}[0.260, 0.115, 0.373, 0.215]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 752 }, { "id": "ROIY2m5VDH0oipbR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nWhich series bar is taller between [0.585, 0.441, 0.760, 0.444] and [0.710, 0.058, 0.823, 0.115]?" }, { "from": "gpt", "value": "Series A." } ], "id_raw": 752 }, { "id": "loZ6SixDrrRfxldm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nIs {the y-axis label}[0.025, 0.120, 0.048, 0.205] indicating a higher value for the red bar located at [0.429, 0.094, 0.541, 0.115] compared to the blue bar at [0.147, 0.368, 0.323, 0.444]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 752 }, { "id": "xlvsHjY7s7VjT4j4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is included in the region [0.025, 0.120, 0.325, 0.215]?" }, { "from": "gpt", "value": "This region includes a y-axis label indicating 'Metric Value', along with two vertical bars from Series A, one representing Data Cleanliness and another representing Data Accuracy." } ], "id_raw": 752 }, { "id": "wqnAcKZd7QEk8xIB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nExplain the elements that can be observed within the coordinates [0.429, 0.094, 0.541, 0.328]." }, { "from": "gpt", "value": "The area contains a bar from Series A representing Data Accuracy, backed by the x-axis label directly beneath it reading 'Data Accuracy'." } ], "id_raw": 752 }, { "id": "3fD8IrsFpljLxZhB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.192, 0.652, 0.537, 0.757]." }, { "from": "gpt", "value": "This space encompasses bars from Series A and B for the metric Data Consistency and all their respective y-axis values ranging from \u22124 to 4, and the corresponding parts of y-axis marked by grid lines." } ], "id_raw": 752 }, { "id": "i6jISNQL5ZID4YMR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the Y-axis label of the top subplot?" }, { "from": "gpt", "value": "{Sales}[0.025, 0.225, 0.048, 0.262]." } ], "id_raw": 755 }, { "id": "lU2oLH0ID45Frn5W", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is marked in the subplot by the coordinates [0.118, 0.037, 0.975, 0.449]?" }, { "from": "gpt", "value": "Sales trend for product X." } ], "id_raw": 755 }, { "id": "KbMVlv2IGtfMzA6w", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWhere is the indicator '2021 Q2' located on the x-axis scale?" }, { "from": "gpt", "value": "{2021 Q2}[0.655, 0.459, 0.754, 0.473]." } ], "id_raw": 755 }, { "id": "SSeWpA108g73XwMz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar at [0.157, 0.702, 0.199, 0.942] in the second subplot?" }, { "from": "gpt", "value": "The sales of product Y in 2020 Q1, indicated by the purple bar." } ], "id_raw": 755 }, { "id": "OC1EupLb2DtPa6zE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, is the bar for {2020 Q4}[0.473, 0.605, 0.515, 0.942] higher or lower than the bar for {2021 Q1}[0.578, 0.549, 0.620, 0.942]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 755 }, { "id": "GdBggCpJf1oQJpVZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, is {2020 Q2}'s bar[0.262, 0.193, 0.304, 0.449] taller than {2020 Q1}'s bar[0.157, 0.275, 0.199, 0.449]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 755 }, { "id": "gUdQFPUzAsVW26bc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nComparing the bars of 2020's Q1 and Q4, is the height of the bar in the second subplot greater than the one in first subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 755 }, { "id": "dKhU6oVxYHqCwlVf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nDoes the sales trend of {product X} generally increase or decrease from the beginning of 2020 to the end of 2021?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 755 }, { "id": "ntFEhd6i9eVRcM6V", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat data can you see in the region [0.118, 0.037, 0.975, 0.449]?" }, { "from": "gpt", "value": "This region covers the first subplot, illustrating the sales trend of product X from 2020 Q1 to 2021 Q4. It has 8 bars, with varying heights indicating different sales volumes in each quarter. This region also includes a y-axis labeled as 'Sales' and an x-axis representing time periods." } ], "id_raw": 755 }, { "id": "7Qdu4RDURj2eUNU0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is described within the coordinates [0.375, 0.508, 0.718, 0.526]?" }, { "from": "gpt", "value": "It describes the subtitle \"Sales trend for product Y\" for the second subplot." } ], "id_raw": 755 }, { "id": "0qwaQESpetYWzk2D", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the main features within the coordinate range [0.118, 0.530, 0.975, 0.942]?" }, { "from": "gpt", "value": "The second subplot showing the sales trend for product Y, including 8 bars varying in height, each representing the sales of product Y from 2020 Q1 to 2021 Q4. The region covered also contains labels for time on the X-axis and 'Sales' on the Y-axis." } ], "id_raw": 755 }, { "id": "3KIvhaU6RoGKlOIR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is marked in the area [0.262, 0.193, 0.304, 0.449]?" }, { "from": "gpt", "value": "It's the bar representing sales of Product X in the second quarter of 2020." } ], "id_raw": 755 }, { "id": "aFCn8dN5bimJtmCp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing \"Network Security\" in this chart?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 756 }, { "id": "JIZT6wfHGEdaLlFu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar with coordinates [0.439, 0.515, 0.510, 0.697] referring to?" }, { "from": "gpt", "value": "It refers to the \"Awareness Programme\" implemented with \"Our\" method." } ], "id_raw": 756 }, { "id": "int6Q1kBfDId0aNb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.707, 0.278, 0.957, 0.308] refer to?" }, { "from": "gpt", "value": "It refers to the label \"Awareness Programme\" in the chart's legend." } ], "id_raw": 756 }, { "id": "k5MTSx3FUeXNaXQu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nWhich is higher: {the bar representing \"Tools Changed\"}[0.510, 0.551, 0.581, 0.697], or {the bar representing \"Network Security\"}[0.652, 0.697, 0.724, 0.842]?" }, { "from": "gpt", "value": "The bar representing \"Network Security\" is higher." } ], "id_raw": 756 }, { "id": "olHUmyThu1RDfQ8i", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing \"Tools Used\"}[0.225, 0.114, 0.296, 0.697], [0.581, 0.260, 0.652, 0.697] taller than {the bars representing \"Policy Review\"}[0.510, 0.405, 0.581, 0.697], [0.866, 0.369, 0.937, 0.697]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 756 }, { "id": "Ly9q587CpFvhLWut", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nCompare {the \"Risk-based\" \"Network Security\" bar}[0.652, 0.697, 0.724, 0.842] to {the \"Ours\" \"Physical Security\" bar}[0.368, 0.587, 0.439, 0.697], which bar is higher?" }, { "from": "gpt", "value": "The \"Risk-based\" \"Network Security\" bar." } ], "id_raw": 756 }, { "id": "I3B1M2PQxlGSWCw3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content enclosed by [0.072, 0.103, 0.100, 0.132]?" }, { "from": "gpt", "value": "The area contains the y-axis label of '80'." } ], "id_raw": 756 }, { "id": "JmB3vTgm7ooJyTa0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.638, 0.092, 0.966, 0.363]?" }, { "from": "gpt", "value": "This is the location of the legend, which contains labels and colors for the bars grouped by the different parameters evaluated: 'Tools Changed', 'Tools Used', 'Network Security', 'Physical Security', 'Awareness Programme', and 'Policy Review'." } ], "id_raw": 756 }, { "id": "dlRzxd52OJkVmZ1r", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about region [0.154, 0.587, 0.225, 0.697]?" }, { "from": "gpt", "value": "This region contains the \"Our\" method's \"Tools Changed\" bar indicating a 15% rate of gain change." } ], "id_raw": 756 }, { "id": "tffWg7H85ZSKo948", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled on the y-axis of the first subplot?" }, { "from": "gpt", "value": "Billion Tons, as indicated by the y-axis label located at {the y-axis label}[0.040, 0.114, 0.063, 0.191]." } ], "id_raw": 757 }, { "id": "SR4YsnRiuJsHLru9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color representation for \"CO2 Emissions\" in the legend?" }, { "from": "gpt", "value": "Blue, located at {the CO2 Emissions color handle}[0.195, -0.016, 0.241, -0.006]." } ], "id_raw": 757 }, { "id": "PdzsqcRJ2s52Oau3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the text label for the handle at the coordinates [0.195, 0.005, 0.241, 0.014]?" }, { "from": "gpt", "value": "Mean Temperature, as indicated by {the text label of Mean Temperature}[0.588, 0.003, 0.805, 0.017]." } ], "id_raw": 757 }, { "id": "DA5sXoNk3u385Nxb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 2005 in CO2 Emissions}[0.448, 0.038, 0.478, 0.287] taller than {the bar for 2006 in CO2 Emissions}[0.524, 0.031, 0.554, 0.287]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 757 }, { "id": "aa60kPBZOEGe7YVv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher y-axis value, {the bar representing 2002 in Mean Temperature}[0.246, 0.712, 0.275, 0.942] or {the bar representing 2009 in Mean Temperature}[0.759, 0.694, 0.789, 0.942]?" }, { "from": "gpt", "value": "2009." } ], "id_raw": 757 }, { "id": "lKZb33nr4A6EmIxA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nAre {the y-axis numbers}[0.072, 0.016, 0.087, 0.030] in {subplot 0}[0.103, 0.018, 0.975, 0.287] or {subplot 2}[0.103, 0.673, 0.975, 0.942]?" }, { "from": "gpt", "value": "Subplot 2." } ], "id_raw": 757 }, { "id": "Qm64beQrzCYTMWew", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the coordinates [0.103, 0.018, 0.975, 0.287]." }, { "from": "gpt", "value": "This region contains the first subplot which includes bars representing CO2 Emissions across various years. It includes a background grid, x-axis labels for the years, y-axis labels for billion tons, and individual bars with coordinates like {2001 CO2 bar}[0.143, 0.063, 0.173, 0.287]." } ], "id_raw": 757 }, { "id": "eNUWL13bVjLKFeFQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the bounding box ranging from [0.103, 0.673, 0.975, 0.942]?" }, { "from": "gpt", "value": "This area contains the third subplot, including combined bars for CO2 Emissions and Mean Temperature across years. Each year, such as 2001 to 2011, has two adjacent bars, for instance, {2001 CO2 bar}[0.143, 0.824, 0.172, 0.942] and {2001 Temp bar}[0.172, 0.697, 0.202, 0.942]." } ], "id_raw": 757 }, { "id": "Tn999XivNTgnR9Zr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat is visible within the range [0.103, 0.345, 0.975, 0.614]?" }, { "from": "gpt", "value": "This second subplot area depicts bars indicating Mean Temperature over the years. It also has a grid, x-axis labeled by years, y-axis marked in degrees Celsius, and colorful bars representing the data points like {2005 Temp bar}[0.466, 0.693, 0.495, 0.942]." } ], "id_raw": 757 }, { "id": "5XGUTTqvuZXbfn48", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at coordinates [0.143, 0.197, 0.202, 0.285]?" }, { "from": "gpt", "value": "This is a bar representing {Persistent Growth}[0.189, 0.027, 0.394, 0.041] for the year 2008." } ], "id_raw": 758 }, { "id": "d9234lJjywdhuiiQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.877, 0.685, 0.935, 0.942] in the chart?" }, { "from": "gpt", "value": "It is a bar indicating {Substantial Increase}[0.189, 0.684, 0.424, 0.698] for the year 2010." } ], "id_raw": 758 }, { "id": "SWWIpez3lbSlVKfV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nIdentify the object at [0.583, 0.951, 0.642, 0.965]?" }, { "from": "gpt", "value": "It is the label for the year 2016 on {the x-axis}[0.515, 0.971, 0.564, 0.985]." } ], "id_raw": 758 }, { "id": "2LJYRj8b6mPUzoZ6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Erratic Increase\" in 2008}[0.143, 0.799, 0.202, 0.942] taller than {the bar for \"Marginal Improvement\" in 2008}[0.143, 0.491, 0.202, 0.613]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 758 }, { "id": "xoRZvlKbBdnhdEU2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the blue bar for \"Persistent Growth\" in 2008}[0.143, 0.197, 0.202, 0.285] have a higher value than {the blue bar for \"Erratic Increase\" in 2008}[0.143, 0.799, 0.202, 0.942]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 758 }, { "id": "pCu7EZRozbUkPbph", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nAre the values represented by {the red bars in 2010}[0.877, 0.028, 0.935, 0.285], [0.877, 0.685, 0.935, 0.942] greater than those represented by {the green and yellow bars in the same year}[0.877, 0.356, 0.935, 0.613], [0.877, 0.480, 0.935, 0.613]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 758 }, { "id": "5R8XZYVwvMr8FWjV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.025, 0.086, 0.087, 0.125]." }, { "from": "gpt", "value": "This region includes part of {the y-axis}[0.025, 0.086, 0.048, 0.214] with labels such as \"20\" {y-tick label}[0.058, 0.111, 0.087, 0.125]." } ], "id_raw": 758 }, { "id": "mVZfDAA2HcNBts9L", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.103, 0.915, 0.935, 0.965]?" }, { "from": "gpt", "value": "This region shows all {x-axis labels}[0.515, 0.643, 0.564, 0.657] from 2008 to 2022, like: \"2008\" {x-tick label}[0.143, 0.951, 0.201, 0.965], \"2010\" {x-tick label}[0.877, 0.951, 0.935, 0.965]." } ], "id_raw": 758 }, { "id": "ZbNVAjrN2F8YZant", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nExplain what is found between the coordinates [0.100, 0.020, 0.500, 0.100]." }, { "from": "gpt", "value": "Within this region, you find the {subplot title}[0.539, 0.007, 0.539, 0.007] and part of {the legend}[0.115, 0.022, 0.403, 0.068], which names growth types like \"Persistent Growth\" {legend label}[0.189, 0.027, 0.394, 0.041]." } ], "id_raw": 758 }, { "id": "ValUJXqQCySB7ixe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the label \"Technological Breakthrough\"}[0.181, -0.018, 0.503, -0.004] in the legend?" }, { "from": "gpt", "value": "Blue[0.116, -0.016, 0.163, -0.006]." } ], "id_raw": 762 }, { "id": "3NMIR71vgyaiMMVE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nIdentify the color associated with {the first bar's indicator}[0.161, 0.434, 0.214, 0.446] on the bottom chart?" }, { "from": "gpt", "value": "Lavender[0.549, -0.016, 0.595, -0.006]." } ], "id_raw": 762 }, { "id": "AO6cxt0sgDJuxpEm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nWhere can we find {the y-axis label for the top chart}[0.030, 0.166, 0.053, 0.295]?" }, { "from": "gpt", "value": "Rate of Growth[%][0.030, 0.166, 0.053, 0.295]." } ], "id_raw": 762 }, { "id": "bdIyYvjjWSXHTF6b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 2011 bar of \"Technological Breakthrough\"}[0.358, 0.356, 0.411, 0.446] higher than {the 2015 bar of \"Innovation Introduction\"}[0.621, 0.726, 0.674, 0.823]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 762 }, { "id": "FptJZVv4eZ71wFC7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nWhat comes first on the x-axis, {the tick labeled \"2012\"}[0.355, 0.456, 0.414, 0.470] or {the tick labeled \"2019\"}[0.815, 0.456, 0.874, 0.470]?" }, { "from": "gpt", "value": "2012[0.355, 0.456, 0.414, 0.470]." } ], "id_raw": 762 }, { "id": "YK9qTVQgBBGbPyhK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the one for \"2014\" under \"Technological Breakthrough\"}[0.555, 0.241, 0.608, 0.446] or {the one for \"2014\" under \"Innovation Introduction\"}[0.490, 0.726, 0.542, 0.855]?" }, { "from": "gpt", "value": "Technological Breakthrough[0.555, 0.241, 0.608, 0.446]." } ], "id_raw": 762 }, { "id": "cs7btHZBid2qebGs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the region marked by {the top right corner of the top chart}[0.123, 0.015, 0.975, 0.446]." }, { "from": "gpt", "value": "This region contains a series of blue bars for each year from 2008 to 2019, representing the \"Technological Breakthrough\" rate of growth, with a gradient from light for lower values to dark for the highest value." } ], "id_raw": 762 }, { "id": "5zz1FN7ka4OyVPIK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nNarrate what you see in {the area containing the y-axis labels for negative growth rates}[0.025, 0.662, 0.048, 0.790] of the bottom chart." }, { "from": "gpt", "value": "Within this region, there are labels for negative growth rates ranging from 0 to -30, alongside corresponding bars for \"Innovation Introduction,\" which are colored in shades of lavender indicating negative values." } ], "id_raw": 762 }, { "id": "xkBY2AhFlLXP6Euu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area including {the y-axis label}[0.030, 0.166, 0.053, 0.295] and {the bar for the year 2015 under \"Technological Breakthrough\"}[0.555, 0.241, 0.608, 0.446] show?" }, { "from": "gpt", "value": "The area features the label \"Rate of Growth[%]\" on the y-axis and a blue bar indicating the value for the year 2015 for \"Technological Breakthrough,\" which extends upwards to show a growth rate of 50%." } ], "id_raw": 762 }, { "id": "aIAOOoScgpkkjFXi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat phase does the x-tick at [0.331, 0.789, 0.506, 0.903] refer to?" }, { "from": "gpt", "value": "It refers to the \"Development\" phase." } ], "id_raw": 765 }, { "id": "R8ni2EswelW5zVZI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label color of 'R&D Spend' from the legend?" }, { "from": "gpt", "value": "The color is orange found at {legend}[0.083, 0.120, 0.111, 0.136]." } ], "id_raw": 765 }, { "id": "G2MAsfAf7p9660cq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label with the coordinates [0.015, 0.427, 0.029, 0.538]?" }, { "from": "gpt", "value": "The label for the y-axis at these coordinates is \"Measures\"." } ], "id_raw": 765 }, { "id": "p6CxHo4BIdBvoB9F", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat series does the bar with these coordinates [0.768, 0.102, 0.943, 0.492] signify?" }, { "from": "gpt", "value": "It represents the series 'Market Response' in the 'Evaluation' phase." } ], "id_raw": 765 }, { "id": "zDlDUt5YtIT03Rrr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nAre {the bars for the series 'User Acceptance'}<[0.112, 0.679, 0.287, 0.771], [0.331, 0.469, 0.506, 0.663], [0.550, 0.374, 0.725, 0.613], [0.768, 0.492, 0.943, 0.606]> generally higher than {the bars for the series 'Market Response'}<[0.112, 0.565, 0.287, 0.679], [0.331, 0.321, 0.506, 0.469], [0.550, 0.168, 0.725, 0.374], [0.768, 0.102, 0.943, 0.492]>?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 765 }, { "id": "OiWUvvAV664Bd37w", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nBetween {the 'Lightbulb Moment'}[0.112, 0.846, 0.287, 0.903] in 'Conceptualization' and {the 'R&D Spend'}[0.112, 0.771, 0.287, 0.846] bars in the same phase, which one is taller?" }, { "from": "gpt", "value": "The 'Lightbulb Moment' bar is taller." } ], "id_raw": 765 }, { "id": "EXGXQKwYN4SFMEpm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nAmong the four measures {Lightbulb Moment}[0.112, 0.846, 0.287, 0.903], {R&D Spend}[0.331, 0.789, 0.506, 0.903], {User Acceptance}[0.550, 0.711, 0.725, 0.903] and {Market Response}[0.768, 0.857, 0.943, 0.903] during the 'Conceptualization' phase, which one has the highest value?" }, { "from": "gpt", "value": "The 'Market Response' has the highest value." } ], "id_raw": 765 }, { "id": "E9E5aRl9FrOcS8Og", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for 'User Acceptance'<[0.331, 0.469, 0.506, 0.663]> have a higher or lower value than the 'Market Response' bar<[0.331, 0.321, 0.506, 0.469]> in the 'Development' phase?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 765 }, { "id": "DCExYrd300WONa3G", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates [0.331, 0.321, 0.506, 0.663]?" }, { "from": "gpt", "value": "This region contains two bars:\n1. The 'R&D Spend' bar with coordinates {Bar}[0.331, 0.663, 0.506, 0.789] and the encoded value is '55'.\n2. The 'User Acceptance' bar with coordinates {Bar}[0.331, 0.469, 0.506, 0.663] and the encoded value is '85'." } ], "id_raw": 765 }, { "id": "BD3nGbqGf7FeDgiU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe area [0.078, 0.074, 0.254, 0.220]?" }, { "from": "gpt", "value": "This region contains the chart legend with the following items: \n1. 'Lightbulb Moment' with color-coordinate {Colorbox}[0.083, 0.085, 0.111, 0.101] and label-coordinate {label}[0.122, 0.083, 0.248, 0.106]\n2. 'R&D Spend' with color-coordinate {Colorbox}[0.083, 0.120, 0.111, 0.136] and label-coordinate {label}[0.122, 0.118, 0.201, 0.141]\n3. 'User Acceptance' with color-coordinate {Colorbox}[0.083, 0.155, 0.111, 0.171] and label-coordinate {label}[0.122, 0.153, 0.239, 0.176]\n4. 'Market Response' with color-coordinate {Colorbox}[0.083, 0.190, 0.111, 0.206] and label-coordinate {Label}[0.122, 0.188, 0.241, 0.211]" } ], "id_raw": 765 }, { "id": "oy6xzzkGVVsv3i8f", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in [0.668, 0.050, 0.868, 0.932]?" }, { "from": "gpt", "value": "This region contains:\n1. X-tick labels: 'Implementation' located at {X-tick label}[0.582, 0.919, 0.692, 0.942] and 'Evaluation' at {X-tick label}[0.819, 0.919, 0.893, 0.942];\n2. Two groups of Stacked Bars: for 'Implementation' we have 'Lightbulb Moment', 'R&D Spend', 'User Acceptance' and 'Market Response' at {Bars}[0.550, 0.168, 0.725, 0.903]; for 'Evaluation' we have 'Lightbulb Moment', 'R&D Spend', 'User Acceptance' and 'Market Response' at {Bars}[0.768, 0.102, 0.943, 0.903]." } ], "id_raw": 765 }, { "id": "SwA8qtDUgl8mnXYU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.768, 0.857, 0.943, 0.903]?" }, { "from": "gpt", "value": "This space contains a blue bar from the 'Lightbulb Moment' series. The range of the y-value is '0.00 -> 20.00' and the x-label is 'Evaluation'. The data label located at [0.847, 0.868, 0.865, 0.892] on the bar reads '20'." } ], "id_raw": 765 }, { "id": "WYtPummdHRA2dtsP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the given coordinates [0.346, 0.083, 0.679, 0.113] represent?" }, { "from": "gpt", "value": "It represents the title of the chart, \"Bar Chart Showing Food Security Trends\"." } ], "id_raw": 766 }, { "id": "CytNkSmqtnJqGd6W", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat kind of bar does the bounding box at [0.618, 0.385, 0.770, 0.903] refer to?" }, { "from": "gpt", "value": "It corresponds to a bar indicating the usage of \"Mathematical Modelling\" in {Deep Learning}[0.618, 0.385, 0.770, 0.903]." } ], "id_raw": 766 }, { "id": "0bRbYBnNWI2d4yrc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nWith coordinates [0.253, 0.919, 0.456, 0.942], what is written on the x-axis?" }, { "from": "gpt", "value": "The label \"Natural Language Processing\" is written there." } ], "id_raw": 766 }, { "id": "OKj8teXREMltClj1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat is signified by the text within [0.850, 0.083, 0.951, 0.106]?" }, { "from": "gpt", "value": "It signifies the description of the dark blue color in the legend, labeled as {Deep Learning}[0.850, 0.083, 0.951, 0.106]." } ], "id_raw": 766 }, { "id": "KgTNTPuZ5V9E9NW8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Deep Learning\" bar for \"Computer Vision\"}[0.112, 0.646, 0.251, 0.903] taller than {the \"Deep Learning\" bar for \"Natural Language Processing\"}[0.285, 0.737, 0.424, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 766 }, { "id": "JXBTVpYjLWNFLJO1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nLooking at {the \"Machine Learning\" bar for \"Statistical Analysis\"}[0.459, 0.665, 0.597, 0.805] and {the \"Machine Learning\" bar for \"Mathematical Modelling\"}[0.632, 0.102, 0.770, 0.385], which one is higher?" }, { "from": "gpt", "value": "The bar for \"Mathematical Modelling\" is higher." } ], "id_raw": 766 }, { "id": "rFaUavpMXze9OUOF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nComparing {the \"Deep Learning\" bar for \"Anomaly Detection\"}[0.805, 0.748, 0.943, 0.903] and {the \"Machine Learning\" bar for \"Anomaly Detection\"}[0.805, 0.514, 0.943, 0.748], which one is taller?" }, { "from": "gpt", "value": "The \"Deep Learning\" bar is taller." } ], "id_raw": 766 }, { "id": "1hrEveaMqvQ7z3Nx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Deep Learning\" bar for \"Statistical Analysis\"}[0.459, 0.805, 0.597, 0.903] shorter than {the \"Machine Learning\" bar for \"Statistical Analysis\"}[0.459, 0.665, 0.597, 0.805]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 766 }, { "id": "84C2Rg1GYJ2evvLH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area delineated by the coordinates [0.805, 0.074, 0.978, 0.151] convey?" }, { "from": "gpt", "value": "This area encompasses the chart's legend. It encompasses two entries: the first is a blue color label located at [0.811, 0.085, 0.839, 0.101] representing {Deep Learning}[0.850, 0.083, 0.951, 0.106], and the second one is a light blue color label located at [0.811, 0.120, 0.839, 0.136] representing {Machine Learning}[0.850, 0.118, 0.972, 0.141]." } ], "id_raw": 766 }, { "id": "3OODAV3Ft760abCN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.112, 0.646, 0.251, 0.646]." }, { "from": "gpt", "value": "This area incorporates a blue bar on the chart signifying the usage of the \"Computer Vision\" application in {Deep Learning}[0.112, 0.646, 0.251, 0.646]. It also possesses a text label showing the usage number \"68\"." } ], "id_raw": 766 }, { "id": "Q0VxkH6E9mfrQemm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.459, 0.665, 0.597, 0.805]?" }, { "from": "gpt", "value": "The region contains a light blue bar illustrating the usage of \"Statistical Analysis\" in {Machine Learning}[0.459, 0.665, 0.597, 0.805]. A textual label inside the bar shows the number \"37\"." } ], "id_raw": 766 }, { "id": "VPLZGfDkGH8K1yil", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nBriefly describe the area [0.632, 0.102, 0.770, 0.385]." }, { "from": "gpt", "value": "This area displays a light blue bar that corresponds to the usage of \"Mathematical Modelling\" in {Machine Learning}[0.632, 0.102, 0.770, 0.385]. Inside this bar, there's a text label presenting the number \"75\"." } ], "id_raw": 766 }, { "id": "sShR4bt8No8NTElv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat is described at coordinates [0.015, 0.445, 0.029, 0.520]?" }, { "from": "gpt", "value": "Y-axis label, named Values." } ], "id_raw": 767 }, { "id": "Y2fN6fgVmwpDAKBO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the category \"Civil Rights\"?" }, { "from": "gpt", "value": "[0.817, 0.919, 0.895, 0.942]." } ], "id_raw": 767 }, { "id": "avJDuQFcDOUBdzGI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the area [0.122, 0.118, 0.224, 0.141]?" }, { "from": "gpt", "value": "The text \"Reinforcement\" from the legend." } ], "id_raw": 767 }, { "id": "mUfPvKRfh8IOAvMV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the handle in the legend show at [0.083, 0.120, 0.111, 0.136]?" }, { "from": "gpt", "value": "It shows a color, specifically '#fb6a4a'." } ], "id_raw": 767 }, { "id": "rN6bNz9zmfVQCm2V", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Social Inequality\" for \"Routine Monitoring\"}[0.550, 0.770, 0.725, 0.903] higher than {the bar of \"Social Inequality\" for \"Reinforcement\"}[0.550, 0.569, 0.725, 0.770]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 767 }, { "id": "qvjT2ZHTKYEW4EUx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Civil Rights\" for \"Reinforcement\"}[0.768, 0.102, 0.943, 0.517] taller than {the bar of \"Civil Rights\" for \"Routine Monitoring\"}[0.768, 0.517, 0.943, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 767 }, { "id": "1yCfjSVR0Npbf4JU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nOn the vertical axis, is the label for \"80\"[0.043, 0.433, 0.061, 0.456] above the label for \"100\"[0.035, 0.318, 0.061, 0.341]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 767 }, { "id": "mDzkQKj5BQzSAOVF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Economic Inequality\" in \"Reinforcement\"}[0.331, 0.471, 0.506, 0.644] broader than {the bar for \"Economic Inequality\" in \"Routine Monitoring\"}[0.331, 0.644, 0.506, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 767 }, { "id": "TsBAy1GRl8QAoHZC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the space [0.078, 0.074, 0.259, 0.151]." }, { "from": "gpt", "value": "It is the legend of the bar chart. The first entry is {Routine Monitoring}[0.122, 0.083, 0.254, 0.106] with color handle {#7f0000}[0.083, 0.085, 0.111, 0.101] and the second entry is {Reinforcement}[0.122, 0.118, 0.224, 0.141] with color handle {#fb6a4a}[0.083, 0.120, 0.111, 0.136]." } ], "id_raw": 767 }, { "id": "8CtJgepQUgVKdPsk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.112, 0.701, 0.287, 0.903] represent?" }, { "from": "gpt", "value": "The region references a bar in the bar chart for the category \"Right Protection\" in the legend entry \"Routine Monitoring\" with a value of 35." } ], "id_raw": 767 }, { "id": "nxpcNx5ubJBgqy2P", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nCan you tell me about the space [0.071, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "This area encompasses the main plot of the chart and contains all the bars and axis labels. The bars indicate two categories \u201cRoutine Monitoring\u201d and \u201cReinforcement\u201d across the categories \"Right Protection\", \"Economic Inequality\", \"Social Inequality\", and \"Civil Rights.\"" } ], "id_raw": 767 }, { "id": "8JevCAT24bm47LtF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.279, 0.025, 0.776, 0.055]?" }, { "from": "gpt", "value": "It is the subtitle of the chart, which states \"Stacked Bar Chart of Social Inequality and Rights Protection\"." } ], "id_raw": 767 }, { "id": "SNYkearOyHAjuiDP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content represented by the coordinates [0.895, 0.118, 0.972, 0.141]?" }, { "from": "gpt", "value": "This is the text label for \"Risk-taking\" in the legend." } ], "id_raw": 768 }, { "id": "JRPh02LhILVN3Cny", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat object is at the coordinates [0.459, 0.445, 0.597, 0.903] in the chart?" }, { "from": "gpt", "value": "{The \"Motivation\" bar for \"Exploit\"}[0.459, 0.445, 0.597, 0.903]" } ], "id_raw": 768 }, { "id": "dQAO8LN6E8u1DAiP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat's the object with coordinates [0.490, 0.952, 0.565, 0.975]?" }, { "from": "gpt", "value": "The xlabel, labeled as \"Categories\"." } ], "id_raw": 768 }, { "id": "qr0oLeVLQwgynK1e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat can I find in the coordinates [0.805, 0.274, 0.943, 0.388]?" }, { "from": "gpt", "value": "{The \"Risk-taking\" bar for \"Exceed\"}[0.805, 0.274, 0.943, 0.388]" } ], "id_raw": 768 }, { "id": "vk1ag2kGgUZbfrcK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nIs {the motivation bar for Expand}[0.632, 0.503, 0.770, 0.903] higher or lower than {the risk-taking bar for Expand}[0.632, 0.274, 0.770, 0.503]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 768 }, { "id": "MiRqWeC7zfVdes74", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nIs {the motivation bar for Experiment}[0.285, 0.617, 0.424, 0.903] to the left or right of {the risk-taking bar for Experiment}[0.285, 0.331, 0.424, 0.617]?" }, { "from": "gpt", "value": "They are in the same x position." } ], "id_raw": 768 }, { "id": "qoKcUGlOmdZlx5Pr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nAre {the Motivation bars}[[0.112, 0.731, 0.251, 0.903], [0.285, 0.617, 0.424, 0.903], [0.459, 0.445, 0.597, 0.903], [0.632, 0.503, 0.770, 0.903], [0.805, 0.388, 0.943, 0.903]] generally higher than {the Risk-taking bars}[[0.112, 0.331, 0.251, 0.731], [0.285, 0.331, 0.424, 0.617], [0.459, 0.102, 0.597, 0.445], [0.632, 0.274, 0.770, 0.503], [0.805, 0.274, 0.943, 0.388]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 768 }, { "id": "qBnEtEW928HPqMKS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nAre {bars of \"Motivation\"}[0.112, 0.731, 0.251, 0.903], [0.285, 0.617, 0.424, 0.903], [0.459, 0.445, 0.597, 0.903], [0.632, 0.503, 0.770, 0.903], [0.805, 0.388, 0.943, 0.903] taller or shorter than {bars of \"Risk-taking\"}[0.112, 0.331, 0.251, 0.731], [0.285, 0.331, 0.424, 0.617], [0.459, 0.102, 0.597, 0.445], [0.632, 0.274, 0.770, 0.503], [0.805, 0.274, 0.943, 0.388]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 768 }, { "id": "1XS6ogVrGNPCdv2r", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents within [0.145, 0.919, 0.218, 0.942]?" }, { "from": "gpt", "value": "There is a category label, \"Innovation\", situated in this area." } ], "id_raw": 768 }, { "id": "ZI2KdbbL5YvqwsXS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.459, 0.445, 0.597, 0.903]?" }, { "from": "gpt", "value": "This area contains a section of the \"Motivation\" bar and represents the value of the \"Exploit\" category, which is 80." } ], "id_raw": 768 }, { "id": "Gwkakhtoig9kQQs2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.632, 0.503, 0.770, 0.903]." }, { "from": "gpt", "value": "The region contains the \"Motivation\" bar for the \"Expand\" category, valued at 70." } ], "id_raw": 768 }, { "id": "MOrT4Mpc7QTqtAh5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.112, 0.331, 0.251, 0.731]?" }, { "from": "gpt", "value": "The region includes the \"Risk-taking\" bar under the category \"Innovation\", with a value of 70." } ], "id_raw": 768 }, { "id": "g9YMXMWw3xAl5Fi5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the text at coordinates [0.302, 0.025, 0.752, 0.055]?" }, { "from": "gpt", "value": "It's the subtitle of the chart, \"Stacked Bar Chart of Creativity and Design Innovation\"." } ], "id_raw": 769 }, { "id": "mywfHQMaaZdoIr80", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat color does the legend item {the legend item} [0.817, 0.085, 0.844, 0.101] correspond to?" }, { "from": "gpt", "value": "'#097138' (Green)." } ], "id_raw": 769 }, { "id": "c6M05Qq5jsxHQRlU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat category does the bar at {the bar} [0.767, 0.254, 0.942, 0.600] represent?" }, { "from": "gpt", "value": "Cross-Disciplinary Collaboration." } ], "id_raw": 769 }, { "id": "u6Rw5sQJuti11LtA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label say at {the y-axis label} [0.015, 0.445, 0.029, 0.520]?" }, { "from": "gpt", "value": "Values." } ], "id_raw": 769 }, { "id": "rd8LAOUH7PL3iVhy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Design Thinking Process}[0.331, 0.189, 0.505, 0.557] higher on the chart than {Brainstorming Use}[0.112, 0.340, 0.287, 0.600]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 769 }, { "id": "j1xSoUrN8HGToF73", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing {Prototyping and Simulation}[0.549, 0.102, 0.724, 0.513] have a higher value than that of {Design Thinking Process}[0.331, 0.557, 0.505, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 769 }, { "id": "VNr1g8JYSfbCoiVe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nWhich bar is wider, {Design Thinking Process}[0.331, 0.557, 0.505, 0.903] or {Cross-Disciplinary Collaboration}[0.767, 0.254, 0.942, 0.600]?" }, { "from": "gpt", "value": "They are the same." } ], "id_raw": 769 }, { "id": "EOCZXFPFR3psqOPG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nIs the legend item {Creativity Levels}[0.855, 0.118, 0.971, 0.141] located below the legend item {Design Quality}[0.855, 0.083, 0.957, 0.106]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 769 }, { "id": "NhuLguQneviY1UvV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the visual contents within {the specified area}[0.112, 0.340, 0.287, 0.600]?" }, { "from": "gpt", "value": "{\"Creativity Levels bar representing Brainstorming Use\"}[0.112, 0.340, 0.287, 0.600] and the corresponding value 60 are displayed in this area." } ], "id_raw": 769 }, { "id": "3hQr8ug08ZBZHToQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are included within the coordinates {the specified coordinates}[0.767, 0.254, 0.942, 0.600]?" }, { "from": "gpt", "value": "{\"Cross-Disciplinary Collaboration Creativity Levels bar\"}[0.767, 0.254, 0.942, 0.600] and the corresponding value 80 are located in this area." } ], "id_raw": 769 }, { "id": "njvfI5qOsR7Qxwl3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the information enclosed by the bounding box {the bounding box}[0.811, 0.074, 0.977, 0.151]." }, { "from": "gpt", "value": "This area contains the chart's legend. It includes two items: {\"Design Quality\"}[0.855, 0.083, 0.957, 0.106] in color '#097138' and {\"Creativity Levels\"}[0.855, 0.118, 0.971, 0.141] in color '#74c476'." } ], "id_raw": 769 }, { "id": "AAdb1kxJelVrKaYd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area {the specified area}[0.112, 0.340, 0.942, 0.903]?" }, { "from": "gpt", "value": "This area encloses all the bars in the chart, including those for Brainstorming Use, Design Thinking Process, Prototyping and Simulation, and Cross-Disciplinary Collaboration, with their respective values visually indicated." } ], "id_raw": 769 }, { "id": "ZWrG2M9XfX8xtNJD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.550, 0.569, 0.725, 0.903] in the chart?" }, { "from": "gpt", "value": "It's a bar representing \"Urbanization level\" category for \"Human Life\" data." } ], "id_raw": 770 }, { "id": "YfgHaevEqKLv6Vmi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the place of the legend of \"Living Environment\" in the chart?" }, { "from": "gpt", "value": "The legend of \"Living Environment\" is located at [0.122, 0.118, 0.256, 0.141]." } ], "id_raw": 770 }, { "id": "SuBQcel7X9xWEjzy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the value indicated by [0.628, 0.458, 0.646, 0.481] mean in the chart?" }, { "from": "gpt", "value": "It represents a value of 45 in \"Urbanization level\" category for \"Living Environment\" data." } ], "id_raw": 770 }, { "id": "r2Fy9zs7BQOvErZf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nReferring to the items in the area [0.078, 0.074, 0.262, 0.151], what do you see?" }, { "from": "gpt", "value": "I see two legend entries: {Human Life}<[0.122, 0.083, 0.202, 0.106]> and {Living Environment}<[0.122, 0.118, 0.256, 0.141]>." } ], "id_raw": 770 }, { "id": "vyLk7m9cTbB0d2Ra", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nComparing {the bar of \"Urbanization level\" for Human Life}[0.550, 0.569, 0.725, 0.903] with {the bar of \"Urbanization level\" for Living Environment}[0.550, 0.369, 0.725, 0.569], which one is higher?" }, { "from": "gpt", "value": "The one for \"Human Life\"." } ], "id_raw": 770 }, { "id": "dBT67UbzYiVzdNAM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nLooking at {the bars of \"Population Growth\"}[0.112, 0.680, 0.287, 0.903] and [0.112, 0.391, 0.287, 0.680], which data set they belong to has a higher value?" }, { "from": "gpt", "value": "Human Life." } ], "id_raw": 770 }, { "id": "4lWLkHb4mDEAlEYW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Green Areas\" under \"Living Environment\"}[0.331, 0.525, 0.506, 0.769] taller than {the bar of \"Urbanization level\" under \"Living Environment\"} [0.550, 0.369, 0.725, 0.569]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 770 }, { "id": "v8Z70ep26inbwh1u", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Population Growth\"}[0.136, 0.919, 0.264, 0.942] and {\"Carbon Footprint\"}[0.797, 0.919, 0.915, 0.942], which category's bars under \"Living Environment\" {[0.112, 0.391, 0.287, 0.680], [0.768, 0.102, 0.943, 0.480]} have a higher total?" }, { "from": "gpt", "value": "Carbon Footprint." } ], "id_raw": 770 }, { "id": "qL8HvLtTUykKTpB1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nCould you describe the information in [0.078, 0.074, 0.262, 0.151]?" }, { "from": "gpt", "value": "This area contains a legend for two categories: {Human Life}<[0.122, 0.083, 0.202, 0.106]>, depicted in dark blue, and {Living Environment}<[0.122, 0.118, 0.256, 0.141]>, depicted in bright blue." } ], "id_raw": 770 }, { "id": "nj3LmpjFikH5wEXD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nWhat's included in the region from [0.331, 0.369, 0.725, 0.903]?" }, { "from": "gpt", "value": "It includes two stacked bars in the category \"Urbanization level\", where the bottom {bar}<[0.550, 0.369, 0.725, 0.569]> represents \"Living Environment\" with a value of 45, and the top {bar}<[0.550, 0.569, 0.725, 0.903]> represents \"Human Life\" with a value of 75." } ], "id_raw": 770 }, { "id": "HAlEDZ7J2gUzYgvn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.112, 0.391, 0.287, 0.903]?" }, { "from": "gpt", "value": "In the \"Population Growth\" category, the area contains two overlapping bars, the {upper bar}<[0.112, 0.680, 0.287, 0.903]> represents \"Human Life\" with a value of 50 and the {lower bar}<[0.112, 0.391, 0.287, 0.680]> represents \"Living Environment\" with a value of 65." } ], "id_raw": 770 }, { "id": "DMBTbGNj2C2H5aoi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the area from [0.768, 0.102, 0.943, 0.903]." }, { "from": "gpt", "value": "This area includes two columns of a stacked bar in the category \"Carbon Footprint\". The {lower bar}<[0.768, 0.102, 0.943, 0.480]> represents the \"Living Environment\" data with a value of 85, and the {upper bar}<[0.768, 0.480, 0.943, 0.903]> represents the \"Human Life\" data with a value of 95." } ], "id_raw": 770 }, { "id": "RT4xlec0cbS8m603", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the graph legend located at [0.857, 0.074, 0.978, 0.151] showing?" }, { "from": "gpt", "value": "The legend lists two categories: \"{Expansion}\"[0.901, 0.083, 0.972, 0.106] marked in dark blue and \"{Decline}\"[0.901, 0.118, 0.953, 0.141] marked in light purple." } ], "id_raw": 771 }, { "id": "KdgbIe3QF47PngX7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.331, 0.342, 0.506, 0.903]?" }, { "from": "gpt", "value": "It is an Expansion bar for the Healthcare category." } ], "id_raw": 771 }, { "id": "dybHv1at82PxKEdR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the information contained in the coordinates [0.381, 0.919, 0.456, 0.942]?" }, { "from": "gpt", "value": "It is the x-tick, labelled \"{Healthcare}\"[0.381, 0.919, 0.456, 0.942]" } ], "id_raw": 771 }, { "id": "BZMkTWni8iUSil6j", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat category does the area [0.112, 0.422, 0.287, 0.903] represent?" }, { "from": "gpt", "value": "It represents the \"{Expansion}\"[0.901, 0.083, 0.972, 0.106] category in \"{Schooling}\"[0.166, 0.919, 0.233, 0.942] with a value of 60." } ], "id_raw": 771 }, { "id": "XRtui310JpkD2wNK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nIs the bar for \"{Expansion}\"[0.901, 0.083, 0.972, 0.106] in \"{Schooling}\"[0.166, 0.919, 0.233, 0.942] taller or shorter than the bar for \"{Decline}\"[0.901, 0.118, 0.953, 0.141] in \"{Schooling}\"[0.166, 0.919, 0.233, 0.942]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 771 }, { "id": "HgBDp4yDEwp1iOSE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nIs the \"{Decline}\"[0.901, 0.118, 0.953, 0.141] bar larger in \"Housing\" or \"Healthcare\"?" }, { "from": "gpt", "value": "\"Housing\"." } ], "id_raw": 771 }, { "id": "bXFaJIjw3xzv2vuk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nWhich of the \"{Expansion}\"[0.901, 0.083, 0.972, 0.106] bars is the shortest?" }, { "from": "gpt", "value": "\"{Housing}\"[0.828, 0.919, 0.884, 0.942]" } ], "id_raw": 771 }, { "id": "W5KlWxMUvN2bMg7Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nCompare the size of \"{Expansion}\"[0.901, 0.083, 0.972, 0.106] and \"{Decline}\"[0.901, 0.118, 0.953, 0.141] in \"{Food Stamps}\"[0.592, 0.919, 0.682, 0.942]." }, { "from": "gpt", "value": "They are equal." } ], "id_raw": 771 }, { "id": "283FDMSrrnSqO395", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the bars in the region [0.550, 0.102, 0.943, 0.903]." }, { "from": "gpt", "value": "Within the region, there are two stacked bars indicating the categories \"Expansion\" and \"Decline\" for \"Food Stamps\" and \"Housing\". The blue bar representing \"{Expansion}\"[0.901, 0.083, 0.972, 0.106] in \"{Food Stamps}\"[0.592, 0.919, 0.682, 0.942] is [0.550, 0.503, 0.725, 0.903] with a value of 50, and the purple bar representing \"{Decline}\"[0.901, 0.118, 0.953, 0.141] in \"{Food Stamps}\"[0.592, 0.919, 0.682, 0.942] is [0.550, 0.102, 0.725, 0.503]. The blue bar for \"{Expansion}\"[0.901, 0.083, 0.972, 0.106] in \"{Housing}\"[0.828, 0.919, 0.884, 0.942] is [0.768, 0.583, 0.943, 0.903] with a value of 40, and the purple bar for \"{Decline}\"[0.901, 0.118, 0.953, 0.141] in \"{Housing}\"[0.828, 0.919, 0.884, 0.942] is [0.768, 0.102, 0.943, 0.583]." } ], "id_raw": 771 }, { "id": "Je7QCRlNKisKEMlA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.857, 0.074, 0.978, 0.151]." }, { "from": "gpt", "value": "This region contains the legend of the chart. \"{Expansion}\"[0.901, 0.083, 0.972, 0.106] is represented with dark blue color and \"{Decline}\"[0.901, 0.118, 0.953, 0.141] is represented with light purple color." } ], "id_raw": 771 }, { "id": "rTcbRJHbhrsi09QP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat do the objects in the region [0.033, 0.593, 0.065, 0.760] represent?" }, { "from": "gpt", "value": "These are y-axis tick labels representing values: \"80\" at [0.043, 0.253, 0.061, 0.277] , \"60\" at [0.043, 0.413, 0.061, 0.437], and \"40\" at [0.043, 0.573, 0.061, 0.597]." } ], "id_raw": 771 }, { "id": "M1ZysJZwyl7XRTs1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.381, 0.025, 0.675, 0.055]?" }, { "from": "gpt", "value": "This is the subtitle of the chart: \"{Stacked Bar Chart of Social Welfare}\"[0.381, 0.025, 0.675, 0.055]." } ], "id_raw": 771 }, { "id": "dKo0RRKXsjG3U8EB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at coordinates [0.510, 0.952, 0.546, 0.975]?" }, { "from": "gpt", "value": "The x-axis label titled \"Years\"." } ], "id_raw": 772 }, { "id": "iq6dXIm4cUTOUaeA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the color representation for \"Google\" is at [0.083, 0.155, 0.111, 0.171]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 772 }, { "id": "uxcGcAhMj5MqKN87", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinates [0.161, 0.837, 0.178, 0.860] signify?" }, { "from": "gpt", "value": "It represents the value '15' for {Apple's bar}[0.112, 0.795, 0.227, 0.903] in the year 2015." } ], "id_raw": 772 }, { "id": "BVIDv446L0eFNGnf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Apple\"}[0.112, 0.795, 0.227, 0.903] taller than {the bar representing \"Microsoft\"}[0.112, 0.470, 0.227, 0.506]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 772 }, { "id": "Axm8t3KBYwNo5BZA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nAre {the y-axis labels}[0.043, 0.605, 0.061, 0.629] located to the left or right of {the main chart area}[0.071, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 772 }, { "id": "euApDMO8ygGESqhO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is shorter, {the red bar for Samsung in 2018}[0.542, 0.427, 0.657, 0.730] or {the blue bar for Google in 2019}[0.685, 0.283, 0.800, 0.463]?" }, { "from": "gpt", "value": "Samsung." } ], "id_raw": 772 }, { "id": "Vf82Oii1y2Gkzbsd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.112, 0.470, 0.943, 0.903]." }, { "from": "gpt", "value": "This region contains multiple bars representing market share percentages for different companies from 2015 to 2020, each labeled with a year and specific value. Corner labels for the area are {first Apple bar}[0.112, 0.795, 0.227, 0.903] for '2015' and {last Others bar}[0.829, 0.182, 0.943, 0.232] for '2020'." } ], "id_raw": 772 }, { "id": "HyqNNpBpPvrcqMnR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area bounding [0.071, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "The main chart area includes axes, bars grouped by years, and grid lines. Specifically, it includes {y-axis}[0.015, 0.391, 0.029, 0.574] and {Samsung's bar in 2020}[0.829, 0.448, 0.943, 0.759], along with other companies' data." } ], "id_raw": 772 }, { "id": "341wQE88jODL7iTV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nPlease detail the objects within [0.078, 0.074, 0.192, 0.255]." }, { "from": "gpt", "value": "This is the chart's legend area including color labels and text for different companies such as {Apple's legend item}[0.083, 0.085, 0.111, 0.101] and {Others' legend item}[0.083, 0.225, 0.111, 0.241], describing their represented color and company name." } ], "id_raw": 772 }, { "id": "9EGgjOPJrXEJjZwG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for the figure near [0.015, 0.448, 0.029, 0.517]?" }, { "from": "gpt", "value": "Count." } ], "id_raw": 773 }, { "id": "g9HqijHKy3ibir53", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the text shown in the legend located at this coordinate [0.131, 0.083, 0.249, 0.106]?" }, { "from": "gpt", "value": "Papers Published." } ], "id_raw": 773 }, { "id": "U7hfC6Glw0tRhZZF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat attribute represented by this bar covers the range [0.771, 0.833, 0.944, 0.903]?" }, { "from": "gpt", "value": "The research field called \"Robotics Innovation\" in the category of \"Papers Published\"." } ], "id_raw": 773 }, { "id": "sHPcnfdvmHmiv4aH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat value can be read off at coordinates [0.194, 0.868, 0.220, 0.891]?" }, { "from": "gpt", "value": "400." } ], "id_raw": 773 }, { "id": "wLoQkZkLtXZhXAOs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar represent {Physics Research in \"Papers Published\"}[0.121, 0.856, 0.294, 0.903] below or above {Physics Research in \"Citations\"}[0.121, 0.276, 0.294, 0.856]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 773 }, { "id": "hSgFSrQnxzqMcJhC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nLooking at the two bars at Robotics Innovation, is the bar for {\u201cPapers Published\u201d}[0.771, 0.833, 0.944, 0.903] taller or shorter than the bar for {\u201cCitations\u201d}[0.771, 0.102, 0.944, 0.833]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 773 }, { "id": "ClEDMljOr104kG5t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nBetween \"Papers Published\" and \"Citations\", which one has a higher value at {Chemistry Research}[0.337, 0.851, 0.511, 0.903; 0.337, 0.363, 0.511, 0.851]?" }, { "from": "gpt", "value": "Citations." } ], "id_raw": 773 }, { "id": "JpPJ8SQDtD4Oszo9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nWhich field has more citations {Physics Research}[0.121, 0.276, 0.294, 0.856] or {Robotics Innovation}[0.771, 0.102, 0.944, 0.833]?" }, { "from": "gpt", "value": "Robotics Innovation." } ], "id_raw": 773 }, { "id": "mMJRSEWOmNCimrNx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.086, 0.074, 0.254, 0.151] contain?" }, { "from": "gpt", "value": "This area contains the legend of the chart, including color labels and text labels for \"Papers Published\" and \"Citations\", specifically:\n 1. \"Papers Published\" with a color label at [0.092, 0.085, 0.120, 0.101] and a textual label at [0.131, 0.083, 0.249, 0.106];\n 2. \"Citations\" with a color label at [0.092, 0.120, 0.120, 0.136] and a textual label at [0.131, 0.118, 0.192, 0.141]." } ], "id_raw": 773 }, { "id": "BENJo7cxjMK1XBkb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nHow about the content at [0.771, 0.833, 0.944, 0.903; 0.771, 0.102, 0.944, 0.833]?" }, { "from": "gpt", "value": "This region is showing data for \"Robotics Innovation\". There are two stacked bars:\n 1. \"Papers Published\" with a bar at [0.771, 0.833, 0.944, 0.903] showing 600 papers;\n 2. \"Citations\" with a bar at [0.771, 0.102, 0.944, 0.833] showing 6300 citations." } ], "id_raw": 773 }, { "id": "8eYSfh5DNpFVm2ke", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.554, 0.845, 0.727, 0.903; 0.554, 0.195, 0.727, 0.845]?" }, { "from": "gpt", "value": "The region contains 2 stacked bars for \"Biology Research\":\n 1. \"Papers Published\" with a bar located at [0.554, 0.845, 0.727, 0.903] indicating 500 papers;\n 2. \"Citations\" with a bar located at [0.554, 0.195, 0.727, 0.845] indicating 5600 citations." } ], "id_raw": 773 }, { "id": "eokRvbiuh8Frh6UE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nCan you explain what's found in the range of [0.771, 0.833, 0.944, 0.903; 0.771, 0.102, 0.944, 0.833]?" }, { "from": "gpt", "value": "Within this range, there are 2 bars presenting data for the \"Robotics Innovation\" field:\n 1. A bar showing \"Papers Published\" is located at [0.771, 0.833, 0.944, 0.903] with a value of 600;\n 2. Another bar showing \"Citations\" is located at [0.771, 0.102, 0.944, 0.833] with a value of 6300." } ], "id_raw": 773 }, { "id": "HXNCyYuo4d7TrcGm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the object labeled \"Facebook\" displayed in {the legend}[0.857, 0.074, 0.978, 0.220]?" }, { "from": "gpt", "value": "Light blue." } ], "id_raw": 774 }, { "id": "2OlzsgwG3EGq4aKx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.112, 0.667, 0.482, 0.903] represent in the chart?" }, { "from": "gpt", "value": "It represents the Facebook trend during a continuous decrease period." } ], "id_raw": 774 }, { "id": "ysI5Ds7yPHBr24mf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label in the chart?" }, { "from": "gpt", "value": "User Count." } ], "id_raw": 774 }, { "id": "1HjlTDNs0X3QtXBp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nIs the height of the {Facebook bar in the Continuous Decrease trend}[0.112, 0.667, 0.482, 0.903] greater than the {Twitter bar in the Continuous Decrease trend}[0.112, 0.455, 0.482, 0.667]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 774 }, { "id": "dusQb6gyGHGzGb4t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nDoes the {LinkedIn bar in the Steep Decline trend}[0.574, 0.432, 0.943, 0.479] represent a larger or smaller user count than that of the {Instagram bar in the Steep Decline trend}[0.574, 0.479, 0.943, 0.573]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 774 }, { "id": "fwYapJXdga6WIXfs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Facebook bar in the Continuous Decrease trend}[0.112, 0.667, 0.482, 0.903] taller than the {Facebook bar in the Steep Decline trend}[0.574, 0.714, 0.943, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 774 }, { "id": "M5ThdxeZCB7kB7jc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the coordinates [0.112, 0.102, 0.482, 0.903]?" }, { "from": "gpt", "value": "This area contains the bars representing the data trends for Facebook, Twitter, Instagram, and LinkedIn during the Continuous Decrease time period. Their respective heights indicate user counts during this period, with Facebook's bar being the tallest." } ], "id_raw": 774 }, { "id": "7d8ZztcocHyan5dl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.574, 0.432, 0.943, 0.903]." }, { "from": "gpt", "value": "This area includes the bars representing the data trends for Facebook, Twitter, Instagram, and LinkedIn during the Steep Decline time period. The heights of these bars reflect their respective user counts." } ], "id_raw": 774 }, { "id": "OCwghGA9Dg8Q2o40", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe what you see within [0.857, 0.074, 0.978, 0.220]?" }, { "from": "gpt", "value": "There are four components within this range; these correspond to the legend of the chart. They are each labeled as Facebook, Twitter, Instagram, and LinkedIn, with their respective color coding." } ], "id_raw": 774 }, { "id": "JLObHznplChuTBUl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text at coordinates}[0.388, 0.025, 0.667, 0.055]?" }, { "from": "gpt", "value": "Socioeconomic and Policy Making." } ], "id_raw": 775 }, { "id": "hJbdBRo2u7wPLcvd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nWhere is {the label for 'Increase in National Debt'} located in the chart legend?" }, { "from": "gpt", "value": "[0.122, 0.083, 0.298, 0.106]" } ], "id_raw": 775 }, { "id": "HgsW4vefywARIPaa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nWhere is the bar for {Government Spending} under {Policy Initiatives} located?" }, { "from": "gpt", "value": "The bar is located at [0.112, 0.220, 0.482, 0.352]." } ], "id_raw": 775 }, { "id": "5e7XE5JmZqZNKSV7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {the y-axis label}?" }, { "from": "gpt", "value": "[0.015, 0.445, 0.029, 0.520]" } ], "id_raw": 775 }, { "id": "C7d1CSDx8knDljNW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Consumer Price Index' under 'Political Unrest'}[0.574, 0.483, 0.943, 0.719] taller than {the similar bar under 'Policy Initiatives'}[0.112, 0.548, 0.482, 0.732]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 775 }, { "id": "RKGEM7R1GyBmnuvm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat color is associated with {Increase in National Debt} at [0.122, 0.083, 0.298, 0.106] in the legend?" }, { "from": "gpt", "value": "#08306b" } ], "id_raw": 775 }, { "id": "p4R6rWSHHYGW0UG5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Increase in National Debt' bar} under 'Policy Initiatives' at [0.112, 0.732, 0.482, 0.903] shorter than {the 'Increase in National Debt' bar} under 'Political Unrest' at [0.574, 0.719, 0.943, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 775 }, { "id": "iRZkDMMtSSMe0mbx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nIn comparison to {the 'Government Spending' bar under 'Policy Initiatives'}[0.112, 0.220, 0.482, 0.352], is {the 'Government Spending' bar under 'Political Unrest'}[0.574, 0.102, 0.943, 0.260] taller or shorter?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 775 }, { "id": "LgZymjdMItb5Dk1V", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed within the coordinates [0.112, 0.220, 0.482, 0.352]?" }, { "from": "gpt", "value": "It includes the bar representing 'Government Spending' under 'Policy Initiatives', with a value of 50 represented by the text at [0.288, 0.274, 0.306, 0.298]." } ], "id_raw": 775 }, { "id": "M1vlGnniy5iEQEmB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.574, 0.719, 0.943, 0.903] show?" }, { "from": "gpt", "value": "It shows the bar representing 'Increase in National Debt' under 'Political Unrest', and the value of this category is 70, represented by the text at [0.750, 0.799, 0.767, 0.823]." } ], "id_raw": 775 }, { "id": "SFLLKH36Tpyj1rTd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the items within the region specified by [0.078, 0.074, 0.304, 0.220]." }, { "from": "gpt", "value": "This region contains the legend of the chart, which contains four items. Each item includes two parts, a color-label and a text-label: \n1. The first item is represented by color '#08306b' at [0.083, 0.085, 0.111, 0.101] and is labeled 'Increase in National Debt' at [0.122, 0.083, 0.298, 0.106]. \n2. The second item is represented by color '#2171b5' at [0.083, 0.120, 0.111, 0.136] and is labeled 'Consumer Price Index' at [0.122, 0.118, 0.273, 0.141]. \n3. The third item is represented by color '#6baed6' at [0.083, 0.155, 0.111, 0.171] and is labeled 'Tax Revenue' at [0.122, 0.153, 0.210, 0.176]. \n4. The last item is represented by color '#bdd7e7' at [0.083, 0.190, 0.111, 0.206] and is labeled 'Government Spending' at [0.122, 0.188, 0.278, 0.211]." } ], "id_raw": 775 }, { "id": "VSBBYa4SQlXxF5km", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.574, 0.102, 0.943, 0.260]." }, { "from": "gpt", "value": "This region includes the bar for 'Government Spending' under 'Political Unrest', with a value of 60 represented by the text at [0.750, 0.169, 0.768, 0.193]." } ], "id_raw": 775 }, { "id": "f9L2eEJT6RBL1Qpr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label located at [0.069, 0.437, 0.083, 0.573]?" }, { "from": "gpt", "value": "The y-axis label is called {Prominence}[0.069, 0.437, 0.083, 0.573]." } ], "id_raw": 776 }, { "id": "au99XLlDEF7cu3pQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate for the second legend item in the chart?" }, { "from": "gpt", "value": "The second legend item, {Trend 2020}[0.810, 0.176, 0.887, 0.199]." } ], "id_raw": 776 }, { "id": "i1Rz7oZQSYIp9Sxm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the second tick on the x-axis at [0.366, 0.890]?" }, { "from": "gpt", "value": "The second tick is {Deep Learning}[0.315, 0.906, 0.416, 0.930]." } ], "id_raw": 776 }, { "id": "TH1KMkt0daLNH6M5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nWhich concepts are indicated in the legend at [0.765, 0.132, 0.893, 0.208]?" }, { "from": "gpt", "value": "The concepts indicated are {Trend 2015}[0.810, 0.141, 0.887, 0.164] and {Trend 2020}[0.810, 0.176, 0.887, 0.199]." } ], "id_raw": 776 }, { "id": "MExvihMmH7rAA1tz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nWhich trend has a higher value for {Deep Learning}[0.315, 0.906, 0.416, 0.930], {Trend 2015}[0.810, 0.141, 0.887, 0.164] or {Trend 2020}[0.810, 0.176, 0.887, 0.199]?" }, { "from": "gpt", "value": "{Trend 2020}[0.810, 0.176, 0.887, 0.199]." } ], "id_raw": 776 }, { "id": "WE4CwXGyjXF4kBj8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nIs the {bar representing \"Neural Networks\"}[0.160, 0.680, 0.278, 0.890] taller than the {bar representing \"Machine Learning\"}[0.454, 0.618, 0.571, 0.890]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 776 }, { "id": "S6Rlyw9n5vh8LKyF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nAmong the {Trend 2015 bars}[0.160, 0.680, 0.278, 0.890], [0.307, 0.576, 0.424, 0.890], [0.454, 0.618, 0.571, 0.890], [0.601, 0.722, 0.718, 0.890], [0.747, 0.660, 0.865, 0.890]], and the {Trend 2020 bars}[0.160, 0.534, 0.278, 0.680], [0.307, 0.157, 0.424, 0.576], [0.454, 0.240, 0.571, 0.618], [0.601, 0.492, 0.718, 0.722], [0.747, 0.450, 0.865, 0.660]], which trend generally demonstrates greater prominence?" }, { "from": "gpt", "value": "{Trend 2020}[0.810, 0.176, 0.887, 0.199]." } ], "id_raw": 776 }, { "id": "Qbf4tZeN2X6OQ9aA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nComparing the {Trend 2015 bar for Neural Networks}[0.160, 0.680, 0.278, 0.890] and the {Trend 2020 bar for Neural Networks}[0.160, 0.534, 0.278, 0.680], which trend shows a higher prominence for Neural Networks?" }, { "from": "gpt", "value": "{Trend 2015}[0.810, 0.141, 0.887, 0.164]." } ], "id_raw": 776 }, { "id": "i5cG6QpFnhbLzDY3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the chart components within [0.765, 0.132, 0.893, 0.208]." }, { "from": "gpt", "value": "The area [0.765, 0.132, 0.893, 0.208] contains the legend of the chart, which includes two trend labels: {Trend 2015}[0.810, 0.141, 0.887, 0.164] with a colour handle at [0.771, 0.143, 0.799, 0.159] and {Trend 2020}[0.810, 0.176, 0.887, 0.199] with a colour handle at [0.771, 0.178, 0.799, 0.194]." } ], "id_raw": 776 }, { "id": "bKXKRNFS1QkVEFqU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located in the coordinate range [0.160, 0.680, 0.278, 0.890]." }, { "from": "gpt", "value": "The range contains the red bar for the Neural Networks category in {Trend 2015}[0.160, 0.680, 0.278, 0.890], indicating a prominence of 50." } ], "id_raw": 776 }, { "id": "gN308lMxnY3SZMhB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements found in [0.307, 0.157, 0.424, 0.576]?" }, { "from": "gpt", "value": "The range contains the orange bar representing {Deep Learning}[0.307, 0.157, 0.424, 0.576] in Trend 2020, indicating a prominence of 100." } ], "id_raw": 776 }, { "id": "e8ODBBPyADV10cF3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat's observed in the coordinates [0.160, 0.534, 0.278, 0.680]?" }, { "from": "gpt", "value": "This is the area of the Orange bar representing {Neural Networks for Trend 2020}[0.160, 0.534, 0.278, 0.680] with a prominence value of 35." } ], "id_raw": 776 }, { "id": "AzMKoc6ymokrt3HY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.381, 0.029, 0.584] on the chart?" }, { "from": "gpt", "value": "Y-axis label, also known as \"Indicators' values\"." } ], "id_raw": 777 }, { "id": "60lOc2Q0w2dZ7Q9R", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the second label in the legend on the chart, can I have its coordinate?" }, { "from": "gpt", "value": "The second label in the legend is \"People Satisfaction Index\" with the coordinates [0.122, 0.118, 0.297, 0.141]." } ], "id_raw": 777 }, { "id": "c70rfyt7nBVDAcMu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nWhere can I find \"{Economic Indicator}\" in the chart, and what it stands for?" }, { "from": "gpt", "value": "\"{Economic Indicator}\" is an x-axis label located at [0.133, 0.919, 0.266, 0.942]. It denotes the type of indicator being evaluated in the chart." } ], "id_raw": 777 }, { "id": "uLZxtLVA0XhnobNf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell what the object at [0.768, 0.507, 0.943, 0.903] is and what information it conveys?" }, { "from": "gpt", "value": "That's a bar from the \"City Quality of Life Index\". The bar represents the \"Health Indicator\", with a value of 90." } ], "id_raw": 777 }, { "id": "INIy5q0ftfCXXDG2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Environmental Indicator\" in \"People Satisfaction Index\"}[0.550, 0.181, 0.725, 0.525] stacked on top of {the bar of \"Environmental Indicator\" in \"City Quality of Life Index\"}[0.550, 0.525, 0.725, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 777 }, { "id": "n7VZ4MHrn9S7UczM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value, {the \"Social Indicator\" of the \"City Quality of Life Index\"}[0.331, 0.617, 0.506, 0.903] or {the \"Social Indicator\" of the \"People Satisfaction Index\"}[0.331, 0.243, 0.506, 0.617]?" }, { "from": "gpt", "value": "The \"Social Indicator\" of the \"People Satisfaction Index\" is higher." } ], "id_raw": 777 }, { "id": "wB47hF9v2xp6Rn7k", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nIs {the \"People Satisfaction Index\"}[0.768, 0.102, 0.943, 0.507] wider than {the \"City Quality of Life Index\"}[0.768, 0.507, 0.943, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 777 }, { "id": "QGgIUGGY0spI2Noq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nAre the bars related to {the \"Environmental Indicator\"} [0.550, 0.181, 0.725, 0.525], [0.550, 0.525, 0.725, 0.903] taller than those related to {the \"Health Indicator\"} [0.768, 0.102, 0.943, 0.507], [0.768, 0.507, 0.943, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 777 }, { "id": "6CNO0wPVb9DvtTvn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region [0.078, 0.074, 0.302, 0.151] in the chart?" }, { "from": "gpt", "value": "This region consists of the legend of the chart. It contains two labels: the {first label \"City Quality of Life Index\"}[0.122, 0.083, 0.293, 0.106] with a {colour representation}[0.083, 0.085, 0.111, 0.101] in dark blue, and the {second label \"People Satisfaction Index\"}[0.122, 0.118, 0.297, 0.141] with a {colour representation}[0.083, 0.120, 0.111, 0.136] in light blue." } ], "id_raw": 777 }, { "id": "Vp4CSyiFvmAZaKd7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nCan you give a description of what is within the area [0.112, 0.595, 0.287, 0.903]?" }, { "from": "gpt", "value": "There is a bar within this area representing the \"Economic Indicator\" for the \"City Quality of Life Index\", it has a height that corresponds to a value of 70. The bar's value label {70} is located at [0.191, 0.737, 0.208, 0.761]." } ], "id_raw": 777 }, { "id": "GriT6u9v4ef1x0D7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.550, 0.181, 0.725, 0.903] in the chart." }, { "from": "gpt", "value": "This area accommodates two stacked bars that correspond to the \"Environmental Indicator\". The {lower one}[0.550, 0.525, 0.725, 0.903] represents the \"City Quality of Life Index\" with a value of 86. The {upper one}[0.550, 0.181, 0.725, 0.525] denotes the \"People Satisfaction Index\" with a value of 78." } ], "id_raw": 777 }, { "id": "pQgCbD1stkF2HccZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the region [0.768, 0.102, 0.943, 0.507] on the chart?" }, { "from": "gpt", "value": "This region contains one of the bars that represent the \"Health Indicator\". It is part of the \"People Satisfaction Index\" and has a value label {92} at [0.847, 0.293, 0.865, 0.316]. The bar's height corresponds to a value of 92, which is denoted by the label." } ], "id_raw": 777 }, { "id": "27HWydGYLafk3bjh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the object located at [0.554, 0.768, 0.727, 0.903] represent?" }, { "from": "gpt", "value": "It represents the viewership number for the streaming platform Amazon Prime in 2019." } ], "id_raw": 778 }, { "id": "NdisrnM0zDFrqZdC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat is annotated at the coordinate [0.621, 0.824, 0.660, 0.847]?" }, { "from": "gpt", "value": "It is the viewership number for Amazon Prime in 2019, which is \"220.0\"." } ], "id_raw": 778 }, { "id": "JS7ga5IaXb5c0GWT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat information is provided by the coordinate [0.937, 0.118, 0.972, 0.141]?" }, { "from": "gpt", "value": "It is the year \"2020\" in the legend." } ], "id_raw": 778 }, { "id": "ifFTdDDLgfE9XB7X", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at the coordinates [0.771, 0.823, 0.944, 0.903] represent?" }, { "from": "gpt", "value": "It is the viewership of Disney+ in 2019." } ], "id_raw": 778 }, { "id": "xkrNRKpwM1cRMumJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nHow does the viewership of Amazon Prime in 2020 at {the bar}[0.554, 0.585, 0.727, 0.768] compare to its viewership in 2019 at{the bar}[0.554, 0.768, 0.727, 0.903] ?" }, { "from": "gpt", "value": "It was higher in 2020." } ], "id_raw": 778 }, { "id": "erPjO9XwdDAoXFVP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nWhich platform had a higher viewership in 2019, {Netflix}[0.121, 0.567, 0.294, 0.903] or {Disney+}[0.771, 0.823, 0.944, 0.903]?" }, { "from": "gpt", "value": "Netflix." } ], "id_raw": 778 }, { "id": "VqcJFdF9USU1YDl6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nIn 2020, did {Disney+}[0.771, 0.683, 0.944, 0.823] have a higher or lower viewership than {HBO}[0.337, 0.102, 0.511, 0.524]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 778 }, { "id": "dNsyGqG4sEXkODUA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nBetween {Netflix}[0.121, 0.148, 0.294, 0.567] and {HBO}[0.337, 0.102, 0.511, 0.524], which platform had a higher viewership in 2020?" }, { "from": "gpt", "value": "HBO." } ], "id_raw": 778 }, { "id": "XgBuVlBHns8hHAV9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nPlease provide a description of the content in the legend at [0.893, 0.074, 0.978, 0.151]." }, { "from": "gpt", "value": "There are two labeled color marks. The first color mark at [0.898, 0.085, 0.926, 0.101] is blue if it represents \"2019\", and the second color mark at [0.898, 0.120, 0.926, 0.136] is a darker shade of blue representing \"2020\"." } ], "id_raw": 778 }, { "id": "DwEkSIqZz4UC2Dnk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained within the region defined by coordinates [0.121, 0.567, 0.294, 0.903] and [0.337, 0.524, 0.511, 0.903]?" }, { "from": "gpt", "value": "These regions contain the viewership data for Netflix and HBO in 2019. The viewership for Netflix was 550 million and for HBO it was 620 million." } ], "id_raw": 778 }, { "id": "aVuGWJ0n0NPJCWcz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is represented within the area of [0.771, 0.683, 0.944, 0.823]." }, { "from": "gpt", "value": "This area represents the viewership for Disney+ in 2020, which is annotated as 230 million." } ], "id_raw": 778 }, { "id": "1wocqv5m7O6BWy32", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.121, 0.148, 0.944, 0.567]?" }, { "from": "gpt", "value": "This area represents the viewership numbers for four streaming platforms (Netflix, HBO, Amazon Prime, and Disney+) in 2020." } ], "id_raw": 778 }, { "id": "nVdhDQLcVHPTtR1t", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar on coordinates [0.112, 0.691, 0.227, 0.903] represent?" }, { "from": "gpt", "value": "It represents the economic impact of \"Subdued Expansion\" policy, with a value of 70." } ], "id_raw": 779 }, { "id": "DX7iWNGzegXa0m3o", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color code of {the label in the legend} at [0.845, 0.190, 0.873, 0.206]?" }, { "from": "gpt", "value": "#bdd7e7, representing \"Tourism\"." } ], "id_raw": 779 }, { "id": "IBaobOH5Zty3Uphn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat label is at the coordinates [0.256, 0.919, 0.372, 0.942] on the x-axis label of the chart?" }, { "from": "gpt", "value": "\"Gradual Increase\"." } ], "id_raw": 779 }, { "id": "wapukxChyVBKHZsI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the description on the coordinates [0.884, 0.083, 0.948, 0.106] in the legend?" }, { "from": "gpt", "value": "It is a label that tells us that \"Economy\" corresponds to the color \"#08306b\" in the legend." } ], "id_raw": 779 }, { "id": "F9NXl3hJmmlBSNmO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nBetween [{the \"Subdued Expansion\" for the Environment} [0.112, 0.465, 0.227, 0.601]] and [{the \"Gradual Increase\" for the Environment} [0.256, 0.583, 0.370, 0.679]], which policy has a higher impact on environment?" }, { "from": "gpt", "value": "\"Gradual Increase\"." } ], "id_raw": 779 }, { "id": "wYpQ0YwUvHIcnaJD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nIs {the color label for \"Health\"}[0.845, 0.120, 0.873, 0.136] located above or below {the color label for \"Tourism\"}[0.845, 0.190, 0.873, 0.206] in the legend?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 779 }, { "id": "kmXXZjwAvi1d6ytz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Health\" bar for the \"Gradual Increase\" policy}[0.256, 0.679, 0.370, 0.752] longer than {the \"Health\" bar for the \"Subdued Expansion\" policy}[0.112, 0.601, 0.227, 0.691]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 779 }, { "id": "VdF70xQswH8YxoQf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Environment\" impact for \"Rapid Expansion\" policy} [0.542, 0.284, 0.657, 0.480] have higher or lower impact than {the \"Tourism\" impact for the same policy} [0.542, 0.102, 0.657, 0.284]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 779 }, { "id": "6pgMdGuP1nJft4n8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nKindly describe coordinates [0.839, 0.074, 0.978, 0.220]." }, { "from": "gpt", "value": "This region primarily covers the legend of the chart. It shows 4 different categories each represented with unique color and text labels: \n\"Economy\"[0.884, 0.083, 0.948, 0.106] using color [0.845, 0.085, 0.873, 0.101],\n\"Health\"[0.884, 0.118, 0.929, 0.141] using color [0.845, 0.120, 0.873, 0.136],\n\"Environment\"[0.884, 0.153, 0.972, 0.176] using color [0.845, 0.155, 0.873, 0.171], and\n\"Tourism\"[0.884, 0.188, 0.937, 0.211] using color [0.845, 0.190, 0.873, 0.206]." } ], "id_raw": 779 }, { "id": "w7TB6CFB2Tw2ZXyk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area from [0.112, 0.691, 0.227, 0.903]." }, { "from": "gpt", "value": "The area includes a stacked bar representing various impacts of the \"Subdued Expansion\" policy concerning \"Economy\" effect, with a coordinate [0.112, 0.691, 0.227, 0.903] and its value." } ], "id_raw": 779 }, { "id": "JytYPAUPXKE3XxVg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within coordinates [0.256, 0.447, 0.370, 0.583]." }, { "from": "gpt", "value": "This area only contains one object, which is the \"Tourism\" stack of the \"Gradual Increase\" bar. It represents that the \"Gradual Increase\" policy with a value of 45 has an impact on \"Tourism\"." } ], "id_raw": 779 }, { "id": "59WYpbKJ9vtF9znG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.303, 0.025, 0.753, 0.055]?" }, { "from": "gpt", "value": "This region contains the title of the chart: \"Stacked Bar Chart of Government Policy Effectiveness\"." } ], "id_raw": 779 }, { "id": "nIHGzJ7Uv0Qso6Zq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat specific object can be found at this given coordinates [0.107, 0.800, 0.876, 0.918]?" }, { "from": "gpt", "value": "It is the blue bar which represents GDP values." } ], "id_raw": 787 }, { "id": "cetBpyRWAirr9Djh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.519, 0.964, 0.557, 0.981] on the chart?" }, { "from": "gpt", "value": "It's the x-axis label, called \"Values\"." } ], "id_raw": 787 }, { "id": "3bhBgpHM5gJ04fKj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the object at [0.042, 0.557, 0.103, 0.649] corresponds to?" }, { "from": "gpt", "value": "It is the y-axis label for \"Inflation rate\"." } ], "id_raw": 787 }, { "id": "UMLN8S3cmP3bkLEu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis scale} at [0.951, 0.939, 0.988, 0.957]?" }, { "from": "gpt", "value": "The value is 40000." } ], "id_raw": 787 }, { "id": "B4FeqWjJahMZpwNZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Interest rates\"}[0.107, 0.061, 0.107, 0.180] shorter than {the bar representing \"Trade balance\"}[0.107, 0.209, 0.144, 0.327]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 787 }, { "id": "xYLvzBkibcPBzZRA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"GDP\"}[0.107, 0.800, 0.876, 0.918] taller than {the bar representing \"Retail sales\"}[0.107, 0.357, 0.660, 0.475]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 787 }, { "id": "PkcEM77y7aTAmp4V", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller - {the bar representing \"Unemployment rate\"}[0.107, 0.652, 0.107, 0.771] or {the bar representing \"Inflation rate\"}[0.107, 0.505, 0.107, 0.623]?" }, { "from": "gpt", "value": "The bar representing \"Unemployment rate\"." } ], "id_raw": 787 }, { "id": "jkKSpPvwTaz6iunT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Retail sales\"}[0.107, 0.357, 0.660, 0.475] shorter than the {the bar representing \"GDP\"}[0.107, 0.800, 0.876, 0.918]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 787 }, { "id": "l1s53lRVJXCmNp1Y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.107, 0.061, 0.876, 0.180]." }, { "from": "gpt", "value": "This area includes the bar corresponding to the \"Interest rates\" economic indicator. The coordinates of the bar are found at [0.107, 0.061, 0.107, 0.180]." } ], "id_raw": 787 }, { "id": "gcUGaZxDgRqvcscv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the coordinates [0.107, 0.800, 0.876, 0.918]?" }, { "from": "gpt", "value": "Within these coordinates, one can identify the bar portraying the \"GDP\" values." } ], "id_raw": 787 }, { "id": "kbbMOihbYzLexSac", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is displayed within [0.107, 0.652, 0.107, 0.771]." }, { "from": "gpt", "value": "This area is home to the small economic indicator bar corresponding to \"Unemployment rate\" located at [0.107, 0.652, 0.107, 0.771]." } ], "id_raw": 787 }, { "id": "CMNXGDeJIEiIAzzr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's in [0.346, 0.083, 0.679, 0.113]?" }, { "from": "gpt", "value": "The coordinates [0.346, 0.083, 0.679, 0.113] denote the location of the chart subtitle, captioned as \"Economic Indicators Presentation\"." } ], "id_raw": 787 }, { "id": "vuUKvRBgQOchUnmn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.871, 0.072, 0.889]?" }, { "from": "gpt", "value": "It is a y-axis label which reads \"Period 1\"." } ], "id_raw": 790 }, { "id": "YfthnkZgkhVrewGd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements found at the coordinates [0.795, 0.088, 0.964, 0.106]?" }, { "from": "gpt", "value": "This is a part of the legend - \"Design Innovation Index\"." } ], "id_raw": 790 }, { "id": "WbiG4PPlIeDYjuNX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the notation at [0.488, 0.964, 0.569, 0.981]?" }, { "from": "gpt", "value": "It is an x-axis label, titled \"Index Value\"." } ], "id_raw": 790 }, { "id": "C3VuQsqiBSrYSi5t", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the element at [0.081, 0.849, 0.148, 0.878] represent?" }, { "from": "gpt", "value": "It is a bar representing the \"Creativity Index\" value for \"Period 1\"." } ], "id_raw": 790 }, { "id": "R78TUoZ3AOpbn0wP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhich of the bars is taller, the one at [0.081, 0.653, 0.468, 0.683] or the one at [0.081, 0.555, 0.560, 0.585]?" }, { "from": "gpt", "value": "The bar at [0.081, 0.555, 0.560, 0.585] is taller." } ], "id_raw": 790 }, { "id": "XQFB5EGKSWx5pel9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nIs the {Design Innovation Index bar for Period 3}[0.081, 0.683, 0.256, 0.712] taller than the {Creativity Index bar for Period 3}[0.081, 0.653, 0.468, 0.683]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 790 }, { "id": "lB1T2YWmgPymmiaW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhich bar is farther to the right, {Period 1's bar of Design Innovation Index}[0.081, 0.878, 0.202, 0.908] or {Period 1's bar of Creativity Index}[0.081, 0.849, 0.148, 0.878]?" }, { "from": "gpt", "value": "The bar of Design Innovation Index." } ], "id_raw": 790 }, { "id": "wv3CiFobSq19CXvO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nAs compared to the {Design Innovation Index bar for Period 2}[0.081, 0.780, 0.148, 0.810], is {Creativity Index bar for Period 2}[0.081, 0.751, 0.301, 0.780] longer?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 790 }, { "id": "mf58fUC2FGuLcFoh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the highlighting elements within the region [0.751, 0.055, 0.969, 0.113]." }, { "from": "gpt", "value": "This region contains the legend enumerating the two plotted indexes: {Creativity Index}[0.795, 0.062, 0.906, 0.080] represented by a purple color and {Design Innovation Index}[0.795, 0.088, 0.964, 0.106] represented by the same color." } ], "id_raw": 790 }, { "id": "FriLvrIBJCChIrKL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat elements exist in the region [0.077, 0.939, 0.985, 0.957]?" }, { "from": "gpt", "value": "The area encompasses the x-axis scale or labels written as: \"0\" at [0.077, 0.939, 0.086, 0.957], \"2\" at [0.256, 0.939, 0.265, 0.957], \"4\" at [0.435, 0.939, 0.444, 0.957], \"6\" at [0.614, 0.939, 0.623, 0.957], \"8\" at [0.793, 0.939, 0.802, 0.957], and \"10\" at [0.967, 0.939, 0.985, 0.957]." } ], "id_raw": 790 }, { "id": "UaxVfWiqw4iuIjrm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nCan you detail what can be found in the area [0.081, 0.164, 0.798, 0.193]?" }, { "from": "gpt", "value": "In this area, we can see a bar representing the Creativity Index value for \"Period 8\", with a value extending up to 8.01 on the Index Value x-axis. There is also another smaller bar next to it representing the Design Innovation Index for the same period." } ], "id_raw": 790 }, { "id": "OeR3dVqAWzBtAzZM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat is seen in the region [0.015, 0.284, 0.072, 0.302]?" }, { "from": "gpt", "value": "The area contains a y-axis label that reads \"Period 7\"." } ], "id_raw": 790 }, { "id": "Wv2QsMzHS36TSfSt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}[0.157, 0.812, 0.498, 0.888] representing?" }, { "from": "gpt", "value": "Urban Movement for North America." } ], "id_raw": 795 }, { "id": "rY77PeLypaE9SVZ4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the bar}[0.782, 0.356, 0.942, 0.432]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 795 }, { "id": "QZp5xJgLHUcWwLLN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nAt what x-range is {the bar}[0.635, 0.508, 0.832, 0.584] located?" }, { "from": "gpt", "value": "14.30 -> 20.20." } ], "id_raw": 795 }, { "id": "9Ms4wGsnAPCLpeiy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Urban Movement for North America}[0.157, 0.812, 0.498, 0.888] longer or shorter than {the bar representing Rural Movement for Australia}[0.240, 0.204, 0.317, 0.280]?" }, { "from": "gpt", "value": "Longer." } ], "id_raw": 795 }, { "id": "B2YdwSz9Wou2aPQM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing Rural Movement for Africa}[0.782, 0.356, 0.942, 0.432] have a higher value than {the bar representing Urban Movement for Europe}[0.157, 0.660, 0.394, 0.736]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 795 }, { "id": "N4kKklsU28wYWftA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Asia's urban movement}[0.157, 0.508, 0.635, 0.584] located higher on the y-axis than {the bar representing Europe's rural movement}[0.394, 0.660, 0.571, 0.736]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 795 }, { "id": "1HDS6H08XnqBmTp4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.240, 0.204, 0.317, 0.280]." }, { "from": "gpt", "value": "It includes a bar representing the data of Rural Movement in Australia." } ], "id_raw": 795 }, { "id": "nErvKB7Ks44ucKpT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nElucidate the visual elements in [0.498, 0.812, 0.661, 0.888]." }, { "from": "gpt", "value": "It includes a green bar that indicates the Rural Movement for North America." } ], "id_raw": 795 }, { "id": "aldysMAxNdpReXGN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in [0.782, 0.356, 0.942, 0.432]?" }, { "from": "gpt", "value": "Yes, there is a green bar that represents the Rural Movement in Africa." } ], "id_raw": 795 }, { "id": "kHyOi7yQO5slXkZN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the first bar}[0.222, 0.821, 0.267, 0.887]?" }, { "from": "gpt", "value": "Tomato." } ], "id_raw": 796 }, { "id": "NNIkR5ABEaN4B7B6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nWhich category does {the bar with the highest value}[0.759, 0.168, 0.945, 0.233] represent?" }, { "from": "gpt", "value": "Big Data from IoT." } ], "id_raw": 796 }, { "id": "QAyWn94ZK889EV0j", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the leftmost tick on the x-axis}[0.216, 0.942, 0.227, 0.970]?" }, { "from": "gpt", "value": "0." } ], "id_raw": 796 }, { "id": "TW4VWbfBjkGI6OKU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nIs {the finance label}[0.743, 0.038, 0.810, 0.066] above or below {its color handle}[0.695, 0.041, 0.729, 0.060] in the legend?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 796 }, { "id": "YxIwHz03jqMxj0f6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nWhich is wider, {the healthcare bar}[0.501, 0.560, 0.571, 0.625] or {the finance bar}[0.501, 0.298, 0.562, 0.364]?" }, { "from": "gpt", "value": "The healthcare bar." } ], "id_raw": 796 }, { "id": "vCQJNg8dID7VSrab", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nIs {the tick labeled \"30\"}[0.550, 0.942, 0.572, 0.970] further to the right than {the tick labeled \"10\"}[0.324, 0.942, 0.346, 0.970] on the x-axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 796 }, { "id": "GdKXoMBUhecEZJ0c", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region that contains the {second and third bars}[0.267, 0.691, 0.344, 0.887]." }, { "from": "gpt", "value": "There are {two bars}[0.267, 0.821, 0.299, 0.887],[0.299, 0.821, 0.318, 0.887] with two different colors - one {wheat}[0.267, 0.821, 0.299, 0.887] and one {light steel blue}[0.299, 0.821, 0.318, 0.887]. They represent {ML with Big Data}[0.062, 0.712, 0.210, 0.740] and {AI with Big Data}[0.070, 0.843, 0.210, 0.871] with x-range values 4.70 -> 8.30 and 6.80 -> 8.50, respectively." } ], "id_raw": 796 }, { "id": "x1gtMdKNeIo2ND9B", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found in the area above {the third bar from the left}[0.267, 0.691, 0.344, 0.756]." }, { "from": "gpt", "value": "The region contains the {label Transport}[0.400, 0.080, 0.482, 0.108] in the legend and two {bars}[0.275, 0.691, 0.316, 0.756],[0.316, 0.691, 0.344, 0.756], where the first bar has a {wheat color}[0.275, 0.691, 0.316, 0.756] representing ML with Big Data with an x-range of 4.70 -> 8.30, and the second has a {light steel blue color}[0.316, 0.691, 0.344, 0.756] representing ML with Big Data with an x-range of 8.30 -> 10.80." } ], "id_raw": 796 }, { "id": "Iif3HfZFDJbENMoz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the region where the {axes meet}[0.216, 0.821, 0.267, 0.970]?" }, { "from": "gpt", "value": "This region includes the {first bar in the Retail group}[0.222, 0.821, 0.267, 0.887] at {the bottom}[0.222, 0.887], which is {tomato}[0.222, 0.821, 0.267, 0.887] in color and represents AI with Big Data from 0.00 to 4.00. Directly above, there is a {label coordinate}[0.070, 0.843, 0.210, 0.871] for AI with Big Data with a color match to the bar and an {x-axis tick labeled \"0\"}[0.216, 0.942, 0.227, 0.970]." } ], "id_raw": 796 }, { "id": "sueZnhuuUVepGTeQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the coordinates [0.237, 0.780, 0.550, 0.847]?" }, { "from": "gpt", "value": "The bar representing the number of studies in the field of Quantum Computing." } ], "id_raw": 797 }, { "id": "K6vWUcGgASOu2HUs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinate [0.518, 0.941, 0.680, 0.970]?" }, { "from": "gpt", "value": "The x-axis label, which is titled \"Number of Studies\"." } ], "id_raw": 797 }, { "id": "89JlBeSkUI3whWGk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} located at [0.019, 0.447, 0.036, 0.511]?" }, { "from": "gpt", "value": "Field." } ], "id_raw": 797 }, { "id": "m8PkxMyKsfT3cplP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat exactly does the region [0.237, 0.646, 0.923, 0.713] contain?" }, { "from": "gpt", "value": "{The bar representing Artificial Intelligence field}[0.237, 0.646, 0.923, 0.713]." } ], "id_raw": 797 }, { "id": "AgdtRBCb1dw2tuK1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Chemistry}[0.237, 0.245, 0.788, 0.312] longer or shorter than {the one for Computer Science}[0.237, 0.512, 0.670, 0.579]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 797 }, { "id": "qsjHQAtb4pJBEoT5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Biology bar}[0.237, 0.379, 0.851, 0.446] closer to the {Physics bar}[0.237, 0.111, 0.839, 0.178] or {Artificial Intelligence bar}[0.237, 0.646, 0.923, 0.713]?" }, { "from": "gpt", "value": "Physics bar." } ], "id_raw": 797 }, { "id": "pk6H0IsI9jeKMGpN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nComparing the lengths of {the bar representing Physics studies}[0.237, 0.111, 0.839, 0.178] and {the bar representing Quantum Computing studies}[0.237, 0.780, 0.550, 0.847], which field has a higher number of studies?" }, { "from": "gpt", "value": "Physics." } ], "id_raw": 797 }, { "id": "kcZZnAqZdeRqrqWP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhich subject has more studies, the one represented by {the bar}[0.237, 0.246, 0.789, 0.312] or the one represented by {the bar}[0.237, 0.646, 0.923, 0.713]?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 797 }, { "id": "xh60tJhWaZvGordr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.237, 0.780, 0.550, 0.847]?" }, { "from": "gpt", "value": "It contains a single bar representing the number of studies in Quantum Computing. The bar spans from 0 (the y-axis) to 2600 on the x-axis." } ], "id_raw": 797 }, { "id": "ctVChev7EURMSw5g", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.517, 0.942, 0.679, 0.970]?" }, { "from": "gpt", "value": "This region encapsulates the x-axis label, which is \"Number of Studies\"." } ], "id_raw": 797 }, { "id": "tIt6kjM9GuTCW78T", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region found in [0.019, 0.447, 0.036, 0.511]?" }, { "from": "gpt", "value": "This region encompasses the y-axis label, which is titled \"Field\"." } ], "id_raw": 797 }, { "id": "OHPxuIRvQtnXJJva", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.237, 0.111, 0.839, 0.178] contain?" }, { "from": "gpt", "value": "The region contains one single bar, which represents the number of studies within the field of Physics. The bar extends from 0, which is the y-axis, to 5000 on the x-axis." } ], "id_raw": 797 }, { "id": "nYIxvku7HZ4Gml7t", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nCan you tell what color represents the label \"Gradual Downturn\"?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 799 }, { "id": "MYMUlrkycb8ERILT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at coordinates [0.336, 0.548, 0.445, 0.629]?" }, { "from": "gpt", "value": "It is a {blue bar}[0.336, 0.548, 0.445, 0.629] representing the \"TeleCommunication Advancements\" category under the trend \"Fluctuating Decline\"." } ], "id_raw": 799 }, { "id": "KJrR2XyuMtsKOQOv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nWhat trend is represented by the color given by the coordinates [0.821, 0.893, 0.856, 0.912] in the legend?" }, { "from": "gpt", "value": "Stable growth." } ], "id_raw": 799 }, { "id": "SSASXKcPBsLuX2iy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"TeleCommunication Advancements\"}[0.632, 0.548, 0.762, 0.629] longer than {the bar representing \"Alternative Energy Consumption\"}[0.494, 0.388, 0.553, 0.468]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 799 }, { "id": "T6V9vojMpSc01RMB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nBetween the \"AI Adoption\"[0.224, 0.256, 0.324, 0.284] and \"Smart Home Usage\"[0.155, 0.095, 0.324, 0.123] bars which trend does the longer bar represent?" }, { "from": "gpt", "value": "\"Inexorable Rise\"." } ], "id_raw": 799 }, { "id": "ef8AM5j7zODA2JwV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nIn the category \"Online Education Participation\", is the bar indicating \"Fluctuating Decline\"[0.336, 0.709, 0.409, 0.789] smaller than the bar indicating \"Stable Growth\"[0.634, 0.709, 0.712, 0.789]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 799 }, { "id": "gsQ0nvgR1wOfOpPa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nPlease tell me what items do you see in the area [0.291, 0.879, 1.026, 0.971]?" }, { "from": "gpt", "value": "This area contains the chart legend with six pairs of color-text labels depicting different trends; the color labels with corresponding text are located as shown: \"Fluctuating Decline\" - [0.298, 0.893, 0.333, 0.912], \"Gradual Downturn\" - [0.298, 0.935, 0.333, 0.954], \"Marginal Improvement\" - [0.541, 0.893, 0.575, 0.912], \"Inexorable Rise\" - [0.541, 0.935, 0.575, 0.954], \"Stable Growth\" - [0.821, 0.893, 0.856, 0.912], and \"Declining Pattern\" - [0.821, 0.935, 0.856, 0.954]." } ], "id_raw": 799 }, { "id": "A2a4l5dlYwPSEtfZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found within the coordinates [0.474, 0.709, 0.551, 0.789]?" }, { "from": "gpt", "value": "This area represents a single bar for the \"Online Education Participation\" category. The bar is green, indicating the \"Marginal Improvement\" trend." } ], "id_raw": 799 }, { "id": "B8MRwXzPKnqlOzhO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is contained within the area [0.336, 0.388, 0.440, 0.468]?" }, { "from": "gpt", "value": "This is a single blue bar representing the trend \"Fluctuating Decline\" for the category \"Alternative Energy Consumption\"." } ], "id_raw": 799 }, { "id": "GC7OM4AnWrXym3VQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at coordinates [0.282, 0.827, 0.313, 0.885] in the chart represent?" }, { "from": "gpt", "value": "It represents the count of OTT platforms for the year 2019." } ], "id_raw": 800 }, { "id": "O4dlzjZ5u1SSkiDD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first text in the legend?" }, { "from": "gpt", "value": "The coordinate of the first text in legend is [0.570, 0.035, 0.614, 0.063]." } ], "id_raw": 800 }, { "id": "IpoN7BCK8zX2HXN1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-title} at [0.020, 0.729, 0.270, 0.757]?" }, { "from": "gpt", "value": "Third-Party Content Creators." } ], "id_raw": 800 }, { "id": "4vfNVj1YmTjKkXTK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Online Ad Spend\"}[0.282, 0.363, 0.355, 0.421] smaller or larger than {the bar representing \"Social Media Engagement\"}[0.282, 0.479, 0.386, 0.537] for 2019?" }, { "from": "gpt", "value": "It's smaller." } ], "id_raw": 800 }, { "id": "z1nX2UnzxcI9jpBF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Podcast Listeners for 2020\"} [0.365, 0.247, 0.532, 0.305] taller than {the bar representing \"Internet Users for 2020\"} [0.327, 0.131, 0.577, 0.189]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 800 }, { "id": "u9IeJQKzGP9yEbT1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nIs the {2019 bar for Internet Users}[0.282, 0.131, 0.327, 0.189] shorter than the {2020 bar for Internet Users}[0.327, 0.131, 0.577, 0.189]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 800 }, { "id": "GQYQfaLiREWmVTCd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.515, 0.024, 0.748, 0.074]." }, { "from": "gpt", "value": "The area contains the legend of the chart which includes two entries. The first entry has a color label at [0.522, 0.038, 0.556, 0.057] representing the year 2019 with the color #20b2aa while the corresponding text label is at [0.570, 0.035, 0.614, 0.063]. The second entry represents the year 2020 at [0.649, 0.038, 0.684, 0.057] with the color #da70d6 and its corresponding text label is at [0.698, 0.035, 0.741, 0.063]." } ], "id_raw": 800 }, { "id": "4OZLjWWvudsg0DtV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.282, 0.093, 0.981, 0.923]." }, { "from": "gpt", "value": "The area contains the main plot of the chart which includes all the x-ticks, y-ticks and bars. The x-ticks range from 0 to 300 in intervals of 50 and the y-ticks include labels such as \"OTT Platforms\", \"Third-Party Content Creators\", \"Video Streaming Subscribers\", \"Social Media Engagement\", \"Online Ad Spend\", \"Podcast Listeners\", and \"Internet Users\". The bars represent the data of these categories for the years 2019 and 2020." } ], "id_raw": 800 }, { "id": "w3CcSsYv1wiTYYIP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.282, 0.711, 0.636, 0.769]?" }, { "from": "gpt", "value": "There are 2 objects in this coordinate range: \n1. A blue bar representing the count of \"Third-Party Content Creators\" for the year 2019, and the coordinate range is [0.282, 0.711, 0.323, 0.769].\n2. A purple bar representing the count of \"Third-Party Content Creators\" for the year 2020, and the coordinate range is [0.323, 0.711, 0.636, 0.769]." } ], "id_raw": 800 }, { "id": "v5lUzO6cDIsVKXwr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.282, 0.363, 0.625, 0.421]?" }, { "from": "gpt", "value": "This region contains 2 bars: \n1. A blue bar for 2019 representing \"Online Ad Spend\" with a value around 35 (coordinate: [0.282, 0.363, 0.355, 0.421]).\n2. A purple bar for 2020 representing \"Online Ad Spend\" with a value around 165 (coordinate: [0.355, 0.363, 0.625, 0.421])." } ], "id_raw": 800 }, { "id": "lTU3l1PBgMLGIW2V", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.245, 0.660, 0.297, 0.888] represent?" }, { "from": "gpt", "value": "It is the bar representing the US's Technological Innovation data, ranging from 0 to 3.60 units." } ], "id_raw": 801 }, { "id": "tzVvRA9JKxm62inn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.488, 0.087, 0.511, 0.115]?" }, { "from": "gpt", "value": "It is the label of the EU in the chart's legend." } ], "id_raw": 801 }, { "id": "vaRgb3q1RU3r1Isa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nCould you identify the label at [0.887, 0.942, 0.909, 0.970]?" }, { "from": "gpt", "value": "It is the X-axis label for 45." } ], "id_raw": 801 }, { "id": "xPYEMsAGN6WI2yy3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.069, 0.307, 0.233, 0.335] denote?" }, { "from": "gpt", "value": "It is the Y-axis label for \"Scientific Research\"." } ], "id_raw": 801 }, { "id": "1xefCgInyrEq8KcS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Brazil's bar for Scientific Research}[0.448, 0.204, 0.477, 0.432] taller or shorter than {India's bar for Scientific Research}[0.371, 0.204, 0.448, 0.432]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 801 }, { "id": "ZqQQAJbleNpRQ3Pd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nIs {US's bar for Technological Innovation}[0.245, 0.660, 0.297, 0.888] on the left or right of the {EU's bar for Technological Innovation}[0.297, 0.660, 0.402, 0.888]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 801 }, { "id": "d6J2Ci1lDlB4DkCp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nDoes the {China's bar for Technological Innovation}[0.402, 0.660, 0.424, 0.888] or the {India's bar for Technological Innovation}[0.424, 0.660, 0.516, 0.888] start first?" }, { "from": "gpt", "value": "China's bar for Technological Innovation." } ], "id_raw": 801 }, { "id": "8bg6G1jXSAKWpFgT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nDoes the {India's bar for Scientific Research}[0.371, 0.204, 0.448, 0.432] extend further right than {Brazil's bar for Scientific Research}[0.448, 0.204, 0.477, 0.432]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 801 }, { "id": "6g2AkRO1e9L7yGRP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found within the coordinates [0.432, 0.034, 0.783, 0.126]." }, { "from": "gpt", "value": "There are five different labels in the legend showing different countries' colors. From top to bottom, these are the labels: US with a color tag at [0.439, 0.047, 0.474, 0.067], EU with a color tag at [0.439, 0.089, 0.474, 0.109], China with a color tag at [0.546, 0.047, 0.581, 0.067], India with a color tag at [0.546, 0.089, 0.581, 0.109], Brazil with a color tag at [0.679, 0.047, 0.714, 0.067]." } ], "id_raw": 801 }, { "id": "ezQP9yJvdbSO5c2C", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.245, 0.660, 0.297, 0.888]?" }, { "from": "gpt", "value": "There is a rectangular plot representing US's Technological Innovation data with a range from 0.00 to 3.60 units." } ], "id_raw": 801 }, { "id": "7QTzbNwGvngp18XJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the area [0.245, 0.170, 0.970, 0.923]?" }, { "from": "gpt", "value": "This is the main plot area of the bar chart. It contains all the bars denoting the Scientific Research and Technological Innovation data for US, EU, China, India, and Brazil." } ], "id_raw": 801 }, { "id": "zcyijTRH8RVYEoDX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat items are encompassed in the region defined by [0.432, 0.034, 0.783, 0.126]?" }, { "from": "gpt", "value": "This region contains the chart's legend, including five labels and color tags indicating their representation of countries \u2014 US, EU, China, India, and Brazil." } ], "id_raw": 801 }, { "id": "t3qVyzE8igYpvRek", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the rectangle located at [0.186, 0.827, 0.303, 0.897] in the chart?" }, { "from": "gpt", "value": "It is a bar showing the internet speed in the US." } ], "id_raw": 802 }, { "id": "EhRRTALTib70KbDZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} located at [0.052, 0.155, 0.175, 0.178]?" }, { "from": "gpt", "value": "5G Connectivity." } ], "id_raw": 802 }, { "id": "8PoPPXrqJoETyelb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend at [0.391, 0.129, 0.461, 0.199] represent?" }, { "from": "gpt", "value": "It denotes the data for 5G connectivity in India." } ], "id_raw": 802 }, { "id": "vFAyPeDjPao675Cf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nIs {EU's Bar on Artificial Intelligence}[0.219, 0.688, 0.268, 0.757] higher than {Brazil's Bar on Big Data}[0.578, 0.408, 0.675, 0.478]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 802 }, { "id": "04hxXeGdHqDllOD0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nIs {China's Bar on Cybersecurity}[0.399, 0.548, 0.572, 0.618] taller than {India's Bar on Cloud Computing}[0.412, 0.269, 0.506, 0.338]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 802 }, { "id": "9jJjm5du3GkAsaLT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Cybersecurity\"}[[0.186, 0.548, 0.278, 0.618],[0.278, 0.548, 0.399, 0.618],[0.399, 0.548, 0.572, 0.618],[0.572, 0.548, 0.706, 0.618],[0.706, 0.548, 0.821, 0.618]] and {\"Big Data\"}[[0.186, 0.408, 0.252, 0.478], [0.252, 0.408, 0.337, 0.478], [0.337, 0.408, 0.452, 0.478], [0.452, 0.408, 0.578, 0.478], [0.578, 0.408, 0.675, 0.478]], which has generally higher values?" }, { "from": "gpt", "value": "Cybersecurity." } ], "id_raw": 802 }, { "id": "mKOOnxh6UYOHYne2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the coordinates [0.303, 0.827, 0.487, 0.897]." }, { "from": "gpt", "value": "'US Internet Speed' bar with a coordinate ranging from [0.303, 0.827, 0.487, 0.897] are found here. This bar represents the EU's investment in Internet Speed which lies on x-range 29.30 to 75.00." } ], "id_raw": 802 }, { "id": "YM03p836wj1iq7U7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.186, 0.713, 0.219, 0.757]." }, { "from": "gpt", "value": "Within this region, we have the Artificial Intelligence bar for US, range from 0.00 to 8.20, which is located at [0.186, 0.713, 0.219, 0.757]." } ], "id_raw": 802 }, { "id": "KryiNufklx6PB7td", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.372, 0.924, 0.528, 0.946]?" }, { "from": "gpt", "value": "This region contains the \"Cybersecurity\" category name of the Y-axis at [0.068, 0.574, 0.175, 0.597]." } ], "id_raw": 802 }, { "id": "1ibyuSuC4DzRfyjh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area contained within [0.186, 0.827, 0.945, 0.897]?" }, { "from": "gpt", "value": "This area exhibits the values for 'Internet Speed' amongst different countries. It has several bars, including the US's bar at [0.186, 0.827, 0.303, 0.897], EU's bar at [0.303, 0.827, 0.487, 0.897], China's bar at [0.487, 0.827, 0.748, 0.897], India's bar at [0.748, 0.827, 0.897, 0.897], and Brazil's bar at [0.897, 0.827, 0.945, 0.897]." } ], "id_raw": 802 }, { "id": "uTwO58jTtwUHMYrU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the text at {the coordinate}[0.580, 0.953, 0.615, 0.973]?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 803 }, { "id": "YOgS4zqY9BGcNJxk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat color corresponds to {the handle located at}[0.643, 0.895, 0.670, 0.909]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 803 }, { "id": "xe39bmUFWP9LAEqN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label at}[0.164, 0.304, 0.265, 0.324] indicate?" }, { "from": "gpt", "value": "Patents Issued." } ], "id_raw": 803 }, { "id": "SzRkI2LpuOyfW15I", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar at}[0.293, 0.396, 0.313, 0.452] wider than {the bar at}[0.332, 0.396, 0.352, 0.452]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 803 }, { "id": "2n7MpkrzqVmBTDKp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the tick mark at}[0.965, 0.827] represent a higher value than {the tick mark at}[0.735, 0.827]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 803 }, { "id": "VJ5Jmj6TYbln93uo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nAre {the text color for 2016 at}[0.541, 0.895, 0.568, 0.909] and {the text color for 2020 at}[0.643, 0.925, 0.670, 0.939] different?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 803 }, { "id": "2IBQhPBCm2ERy278", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the area bound by {these coordinates}[0.270, 0.583, 0.485, 0.900]." }, { "from": "gpt", "value": "This region includes several bars of varying heights representing different years' values across various health-related sectors. Key visible bars include BioTech Market Cap (spanning approximately from [0.275, 0.621, 0.275, 0.678]) and Private Investments in Biotech (extended roughly around [0.278, 0.734, 0.276, 0.790])." } ], "id_raw": 803 }, { "id": "3G0YpNt2kXP3bAZV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.270, 0.583, 0.980, 0.900]?" }, { "from": "gpt", "value": "This region visualizes the performance of multiple sectors over consecutive years plotted as bars. Notable within this space are bars for Legal Suits for Patent Infringements and Pharmaceutical R&D Spent, with visible disclosures of their escalating trends each year, discernible through bars about [0.293, 0.396, 0.313, 0.452] and wider for subsequent years." } ], "id_raw": 803 }, { "id": "nJnOLRjTUvIg9J0Z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nExplain what is within the described bounds [0.270, 0.160, 0.485, 0.900]." }, { "from": "gpt", "value": "This space illustrates bars of two sectors namely Medical Devices Revenue with a starting point around [0.274, 0.171, 0.277, 0.227] for 2016 and extends wider annually, and BioTech IPO that also starts narrower with approximately [0.274, 0.509, 0.274, 0.565] for the year 2016 and progressively increases." } ], "id_raw": 803 }, { "id": "YRPzCS3H9MlqadZ0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {\"China\"}[0.467, 0.087, 0.517, 0.115] in the sublegend?" }, { "from": "gpt", "value": "Olive." } ], "id_raw": 805 }, { "id": "sPu9hjRTtHngTJER", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the current position {Cloud Services label}[0.072, 0.325, 0.201, 0.353] located at?" }, { "from": "gpt", "value": "On the y-axis." } ], "id_raw": 805 }, { "id": "PmbJhMDHGOJA2rWM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nWhat text is located at these coordinates {the third x-axis label}[0.292, 0.942, 0.314, 0.970]?" }, { "from": "gpt", "value": "40." } ], "id_raw": 805 }, { "id": "qSOKCnwigSnsPrys", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nWhich is wider, {the US bar for Blockchain}[0.213, 0.836, 0.323, 0.888] or {the China bar for Blockchain}[0.323, 0.836, 0.451, 0.888]?" }, { "from": "gpt", "value": "The China bar for Blockchain." } ], "id_raw": 805 }, { "id": "xB4WQ7QIxmO0waCh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nIs the {bar of India in Quantum Computing}[0.307, 0.625, 0.343, 0.678] higher or lower than the {bar of Brazil in Quantum Computing}[0.343, 0.625, 0.354, 0.678]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 805 }, { "id": "k39FL6CPylywWWxV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nBetween the bars representing {5G Coverage of India}[0.626, 0.204, 0.734, 0.257] and {5G Coverage of Brazil}[0.734, 0.204, 0.792, 0.257], which one has a greater value?" }, { "from": "gpt", "value": "5G Coverage of India." } ], "id_raw": 805 }, { "id": "JwffpnayqTXewScv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content you can see in the region defined by the range [0.323, 0.836, 0.451, 0.888]." }, { "from": "gpt", "value": "This region contains the bar which represents the 'Blockchain' of 'China'. Its value range goes from '48.60 -> 105.40'." } ], "id_raw": 805 }, { "id": "iih7lLCqopop3Ht3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.412, 0.034, 0.782, 0.126]?" }, { "from": "gpt", "value": "It's a sublegend that includes five tech trends. The five tech trends are \"US\", \"China\", \"EU\", \"India\", \"Brazil\"." } ], "id_raw": 805 }, { "id": "RcNaJrCLDApE6z69", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the area [0.213, 0.204, 0.792, 0.257]?" }, { "from": "gpt", "value": "It contains 5 bars that represent the '5G Coverage' in different countries (US, China, EU, India, Brazil) with varying lengths that reflect the statistical value of each country. The respective country colors are tomato, olive, deep sky blue, plum and khaki." } ], "id_raw": 805 }, { "id": "CRcLitrXQjZRkiEZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the third graphical text at [0.110, 0.581, 0.303, 0.609]?" }, { "from": "gpt", "value": "Michelin Starred Restaurant." } ], "id_raw": 806 }, { "id": "tg5EZTKZfRtJvJ1B", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color swatch representation of {Europe} at [0.479, 0.082, 0.507, 0.102]?" }, { "from": "gpt", "value": "Brown." } ], "id_raw": 806 }, { "id": "OXkEqXb4EDeGQa6U", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis scale} at [0.569, 0.942, 0.587, 0.970]?" }, { "from": "gpt", "value": "20." } ], "id_raw": 806 }, { "id": "h7NyHqQ9feKCUjvV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nCould you identify the object at [0.313, 0.821, 0.392, 0.887]?" }, { "from": "gpt", "value": "It's a bar for America in the category of num_of_TVShows_TalkAbout_Gastronomy with a range of 0.00 -> 6.00." } ], "id_raw": 806 }, { "id": "el5Z1UJMqjp3Wn7b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Europe in num_of_TVShows_TalkAbout_Gastronomy}[0.392, 0.821, 0.493, 0.887] taller than the {bar for America in the same category}[0.313, 0.821, 0.392, 0.887]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 806 }, { "id": "zorBaszrLo722ONa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the bar representing Africa in num_of_Gastronomy_Books at [0.520, 0.691, 0.536, 0.756]} or {the bar representing Europe in the same category at [0.363, 0.691, 0.432, 0.756]}?" }, { "from": "gpt", "value": "Europe." } ], "id_raw": 806 }, { "id": "WF6bxXPjobHlWZ9W", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nAre bars of {Asia}[0.493, 0.821, 0.613, 0.887][0.432, 0.691, 0.520, 0.756][0.448, 0.560, 0.537, 0.625][0.471, 0.429, 0.571, 0.494][0.524, 0.298, 0.643, 0.364][0.514, 0.168, 0.637, 0.233] generally taller than those of {Australia}[0.652, 0.821, 0.703, 0.887][0.536, 0.691, 0.562, 0.756][0.557, 0.560, 0.590, 0.625][0.618, 0.429, 0.675, 0.494][0.731, 0.298, 0.810, 0.364][0.706, 0.168, 0.796, 0.233]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 806 }, { "id": "vwQsLBFQsddBngdI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Asia\"}[[0.493, 0.821, 0.613, 0.887][0.432, 0.691, 0.520, 0.756][0.448, 0.560, 0.537, 0.625][0.471, 0.429, 0.571, 0.494][0.524, 0.298, 0.643, 0.364][0.514, 0.168, 0.637, 0.233]] and {\"Europe\"}[[0.392, 0.821, 0.493, 0.887][0.363, 0.691, 0.432, 0.756][0.371, 0.560, 0.448, 0.625][0.382, 0.429, 0.471, 0.494][0.414, 0.298, 0.524, 0.364][0.399, 0.168, 0.514, 0.233]], which area has higher values for Gourmet Festival Attendance?" }, { "from": "gpt", "value": "Asia." } ], "id_raw": 806 }, { "id": "BzDUna4XzxiUUwsB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.473, 0.027, 0.816, 0.119]." }, { "from": "gpt", "value": "The region houses the chart's legend. It contains five pairs of color-text labels, representing America[0.479, 0.041, 0.507, 0.060], Europe[0.479, 0.082, 0.507, 0.102], Asia[0.603, 0.041, 0.631, 0.060], Africa[0.603, 0.082, 0.631, 0.102], and Australia[0.710, 0.041, 0.737, 0.060]." } ], "id_raw": 806 }, { "id": "87HfKWfkImUSrGEI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.313, 0.132, 0.976, 0.923]." }, { "from": "gpt", "value": "The whole chart is within this region, except the title and the frame. It includes the bars for America, Europe, Asia, Africa, and Australia, each in six different categories. It also includes the x-axis from 0 to 50 and the y-axis with six category labels." } ], "id_raw": 806 }, { "id": "uDOCYWbQipAPFCTY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within the area [0.392, 0.821, 0.493, 0.887]?" }, { "from": "gpt", "value": "It is a bar representing Europe in the category \"num_of_TVShows_TalkAbout_Gastronomy,\" with a range of 6.00 to 13.60." } ], "id_raw": 806 }, { "id": "GyhiXIHXaXQCT7Fz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.642, 0.038, 0.671, 0.066]?" }, { "from": "gpt", "value": "I can see the word 'Asia' which is part of the chart legend indicating the color that represents Asia on the chart." } ], "id_raw": 806 }, { "id": "OBJzANlsTpIK8Hru", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the first bar [0.202, 0.660, 0.346, 0.888] in the Consumer Behavior category?" }, { "from": "gpt", "value": "Red or specifically tomato." } ], "id_raw": 807 }, { "id": "TpkObjQoZrGQkixm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nAre there any labels on the chart at coordinates [0.202, 0.204, 0.346, 0.432]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 807 }, { "id": "PXULuXD8C9AzcWJv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label written at {the y-axis}[0.032, 0.307, 0.190, 0.335]?" }, { "from": "gpt", "value": "Emerging Markets." } ], "id_raw": 807 }, { "id": "wjAMZTis2Ii3DXpe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nIs {the US bar}[0.202, 0.660, 0.346, 0.888] longer or shorter than {the EU bar}[0.346, 0.660, 0.476, 0.888] in the Consumer Behavior category?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 807 }, { "id": "HE3JT7kwgj3cUC1Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nFor {the bar representing \"China\"}[0.476, 0.204, 0.664, 0.432], in comparison to {the bar representing \"India\"}[0.664, 0.204, 0.828, 0.432], which one is longer for the Emerging Markets category?" }, { "from": "gpt", "value": "China." } ], "id_raw": 807 }, { "id": "brC0s0maE57XmBnM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nBetween {the EU bar}<[0.346, 0.660, 0.476, 0.888], [0.346, 0.204, 0.476, 0.432]> and {the Brazil bar}<[0.828, 0.660, 0.944, 0.888], [0.828, 0.204, 0.944, 0.432]>, which one has the longer cumulative length?" }, { "from": "gpt", "value": "EU." } ], "id_raw": 807 }, { "id": "Si43nxCR3haz1RNz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.202, 0.204, 0.944, 0.888]." }, { "from": "gpt", "value": "The content is a bar chart representing the Emerging Markets and Consumer Behavior. It contains 5 bars each for Emerging Markets, and 5 bars for Consumer Behavior sorted horizontally. The bars are colored and represent different regions: US (Tomato), EU (Wheat), China (Steel Blue), India (Dark Sea Green), and Brazil (Cornflower Blue)." } ], "id_raw": 807 }, { "id": "hbMK0OWfzkj3ggjJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.416, 0.034, 0.767, 0.126]." }, { "from": "gpt", "value": "The area [0.416, 0.034, 0.767, 0.126] contains five color-text labels representing different regions: US (Tomato), EU (Wheat), China (Steel Blue), India (Dark sea green), and Brazil (Cornflower Blue)." } ], "id_raw": 807 }, { "id": "Myiy7Boj4gftuQRK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nIn the area [0.202, 0.204, 0.944, 0.432], what indicators are there?" }, { "from": "gpt", "value": "In the area [0.202, 0.204, 0.944, 0.432], there are five colored bars representing different regions: US (Tomato), EU (Wheat), China (Steel Blue), India (Dark sea green), and Brazil (Cornflower Blue). These represent the Emerging Markets category." } ], "id_raw": 807 }, { "id": "l50irNPuWVkG8ers", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nIf you consider [0.346, 0.660, 0.476, 0.888] and [0.346, 0.204, 0.476, 0.432] as a single region, what does it include?" }, { "from": "gpt", "value": "It includes two bars representing the EU, one in the Consumer Behavior category and the other in the Emerging Markets category, both colored Wheat." } ], "id_raw": 807 }, { "id": "AFvBocbPMqgWiwM3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.068, 0.224, 0.216, 0.252]?" }, { "from": "gpt", "value": "The text is Online Education." } ], "id_raw": 808 }, { "id": "h9fltveuMgodkf6r", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar with coordinates [0.461, 0.702, 0.590, 0.764]?" }, { "from": "gpt", "value": "The color of the bar is beige represented as the color code '#f5deb3'." } ], "id_raw": 808 }, { "id": "MBbU2CrD3Ae5uD0Z", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart entity at coordinates [0.019, 0.722, 0.216, 0.750]?" }, { "from": "gpt", "value": "It is the y-axis label for Education Expenditure." } ], "id_raw": 808 }, { "id": "YZhPlreRtIyxtUMj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat is described by the object having coordinates [0.294, 0.045, 0.459, 0.073]?" }, { "from": "gpt", "value": "It is the text label {Continuous decline}[0.294, 0.045, 0.459, 0.073] of the legend." } ], "id_raw": 808 }, { "id": "Tz7QKAb6mJudFvq5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Classroom Education\"}[0.408, 0.329, 0.519, 0.391] taller than {the bar representing \"Online Education\"}[0.365, 0.204, 0.445, 0.266]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 808 }, { "id": "Am82frlgXY2IkE5R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {Slow growth bar of Classroom Education}[0.669, 0.329, 0.766, 0.391] or {Slow growth bar of Online Education}[0.561, 0.204, 0.622, 0.266]?" }, { "from": "gpt", "value": "The slow growth bar of Classroom Education." } ], "id_raw": 808 }, { "id": "GJaJstAacPeaXlaS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Continuous decrease bar of Student-Teacher Ratio}[0.228, 0.577, 0.430, 0.640] shorter than the {Continuous decrease bar of Education Expenditure}[0.228, 0.702, 0.461, 0.764]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 808 }, { "id": "SUKSS06N2BtzF69P", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nIs the object at coordinates [0.578, 0.045, 0.708, 0.073] left to the object at [0.791, 0.045, 0.964, 0.073]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 808 }, { "id": "gj7ESFue4QlyxS2A", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.228, 0.702, 0.870, 0.764]." }, { "from": "gpt", "value": "There are 3 bars representing Education Expenditure in different aspects. The coordinates of the bars from left to right are: {Continuous decline bar}[0.228, 0.702, 0.461, 0.764], {Marginal improvement bar}[0.461, 0.702, 0.590, 0.764], and {Slight decrease bar}[0.590, 0.702, 0.749, 0.764]." } ], "id_raw": 808 }, { "id": "kOqYSNPU8efGwAe4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat elements are found within the coordinates [0.238, 0.034, 0.971, 0.126]?" }, { "from": "gpt", "value": "This area contains the legend of the chart, which includes 5 color-text labels. It elucidates the meaning of different colors used in the bars." } ], "id_raw": 808 }, { "id": "C0R1AT9rVVcORWo2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat is seen in the region from [0.228, 0.826, 0.945, 0.888]?" }, { "from": "gpt", "value": "This region contains 5 bars under the College Education category, each of different colors representing Continuous decline[ranging from 0.228 to 0.350], Marginal improvement[ranging from 0.350 to 0.455], Slight decrease[ranging from 0.455 to 0.558], Slow growth[ranging from 0.558 to 0.645] and Inconsistent decline[ranging from 0.645 to 0.691]." } ], "id_raw": 808 }, { "id": "r4xr9bKKgEsU334y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area between [0.228, 0.204, 0.654, 0.266]." }, { "from": "gpt", "value": "This area contains different bars representing different aspects of Online Education. The bars are ordered from left to right as follows: Continuous decline, Marginal improvement, Slight decrease, Slow growth, Inconsistent decline." } ], "id_raw": 808 }, { "id": "AKsj0N5twV3kDXp3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis?" }, { "from": "gpt", "value": "The label of the y-axis is {Statistic}[0.019, 0.438, 0.036, 0.508]." } ], "id_raw": 810 }, { "id": "GAKSN3A1rhpELMbD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the first midpoint in the x-axis correspond to?" }, { "from": "gpt", "value": "The first midpoint on the x-axis corresponds to {a trend value of 2}[0.452, 0.939, 0.463, 0.957]." } ], "id_raw": 810 }, { "id": "mkQQFA9IKHkb1u84", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at coordinates [0.319, 0.818, 0.894, 0.886] represent?" }, { "from": "gpt", "value": "The bar at coordinates [0.319, 0.818, 0.894, 0.886] represents the {Gross Domestic Product (GDP) value of 8.3}[0.894, 0.843, 0.927, 0.861] in the trend." } ], "id_raw": 810 }, { "id": "ioSxyfwDkcOwZ0C1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinates [0.783, 0.759, 0.816, 0.777] represent?" }, { "from": "gpt", "value": "The text at coordinates [0.783, 0.759, 0.816, 0.777] represents a {value of 6.7}[0.783, 0.759, 0.816, 0.777] for the \"Spending\" statistic." } ], "id_raw": 810 }, { "id": "hJZ6CYUsEniB8wU4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nIs the {bar representing GDP}[0.319, 0.818, 0.894, 0.886] larger than the {bar representing Spending}[0.319, 0.734, 0.783, 0.802]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 810 }, { "id": "SpiYUzk3DM0ek0xH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nAre {unemployment trend values}[0.319, 0.060, 0.402, 0.127] lower than {exports trend values}[0.319, 0.313, 0.555, 0.380]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 810 }, { "id": "7qjbUkmSe9QKIXqG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nBetween {Income}[0.319, 0.397, 0.839, 0.465] and {Factory Orders}[0.319, 0.144, 0.818, 0.212], which has a higher trend value?" }, { "from": "gpt", "value": "Income." } ], "id_raw": 810 }, { "id": "iPy2lWw3iCqswQed", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nBetween {Inflation}[0.319, 0.566, 0.707, 0.633] and {Interest Rates}[0.319, 0.481, 0.513, 0.549], which has a lower trend value?" }, { "from": "gpt", "value": "Interest Rates." } ], "id_raw": 810 }, { "id": "1pUXtoeZK2fclbwT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.319, 0.818, 0.894, 0.886] contain?" }, { "from": "gpt", "value": "The area [0.319, 0.818, 0.894, 0.886] contains the bar representing {Gross Domestic Product (GDP)}[0.319, 0.818, 0.894, 0.886] with a trend value of {8.3}[0.894, 0.843, 0.927, 0.861]." } ], "id_raw": 810 }, { "id": "QlnU9oAUOqXiPgAd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.319, 0.060, 0.402, 0.127]." }, { "from": "gpt", "value": "The area [0.319, 0.060, 0.402, 0.127] contains the bar representing {Unemployment}[0.319, 0.060, 0.402, 0.127] with a trend value of {1.2}[0.402, 0.085, 0.435, 0.102]." } ], "id_raw": 810 }, { "id": "7ydLRJtwLgl0yLy1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.043, 0.845, 0.307, 0.863]?" }, { "from": "gpt", "value": "The object within [0.043, 0.845, 0.307, 0.863] is the {y-axis marker for the Gross Domestic Product (GDP)}[0.043, 0.845, 0.307, 0.863]." } ], "id_raw": 810 }, { "id": "2xfsGFZ3YBWezUIF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.091, 0.727, 0.169, 0.746]?" }, { "from": "gpt", "value": "There is no information available in the area [0.091, 0.727, 0.169, 0.746]." } ], "id_raw": 810 }, { "id": "csQvs6slgLS67PsH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed on the y-axis of the chart?" }, { "from": "gpt", "value": "Innovation Indicator." } ], "id_raw": 811 }, { "id": "MAPh4FUAWNh8PpT4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart located at [0.333, 0.025, 0.902, 0.055]?" }, { "from": "gpt", "value": "Innovation Indicators in Entrepreneurship and Innovation Ecosystem." } ], "id_raw": 811 }, { "id": "o0B0tUFFbZy8pxlg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nWhich indicator is located at [0.196, 0.824, 0.248, 0.848]?" }, { "from": "gpt", "value": "Patents." } ], "id_raw": 811 }, { "id": "EQHi6bODcOqJiHOa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nIs the value for {Venture Capital Investment}[0.258, 0.490, 0.669, 0.553] higher than {Scientific Publications}[0.258, 0.724, 0.467, 0.787]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 811 }, { "id": "XV5neC5Crt6O7Ngm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nAmong these indicators {Patents}[0.258, 0.802, 0.687, 0.865], {Scientific Publications}[0.258, 0.724, 0.467, 0.787], {R&D Personnel}[0.258, 0.646, 0.783, 0.709], and {Startup Creation}[0.258, 0.568, 0.812, 0.631], which has the highest value?" }, { "from": "gpt", "value": "Startup Creation." } ], "id_raw": 811 }, { "id": "f8BNCkPwBCqbi16Z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nDoes {Technological Infrastructure}[0.258, 0.100, 0.419, 0.163] have a higher value than {Knowledge Transfer}[0.258, 0.334, 0.741, 0.397]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 811 }, { "id": "7QuQdMRXqWAk2xPu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area [0.258, 0.062, 0.978, 0.903]?" }, { "from": "gpt", "value": "It's a bar chart that depicts the percentages of different Innovation Indicators. Each bar represents an Innovation Indicator, and the length of the bar represents the percentage value of each indicator. The x-axis represents the percentage values, and the y-axis represents different Innovation Indicators." } ], "id_raw": 811 }, { "id": "d5ucSDkc2jste4rO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the set of objects within the x-axis label region with boundaries [0.565, 0.952, 0.671, 0.975]." }, { "from": "gpt", "value": "It's the x-axis label called \"Percentage (%)\"." } ], "id_raw": 811 }, { "id": "vR6WbjmoVP6cgzIN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.015, 0.366, 0.029, 0.599] show?" }, { "from": "gpt", "value": "It shows the y-axis label, which is titled \"Innovation Indicator\"." } ], "id_raw": 811 }, { "id": "ZYBy6FLmZDB2L900", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the social issue located at [0.079, 0.298, 0.191, 0.316]?" }, { "from": "gpt", "value": "Gender Equality." } ], "id_raw": 812 }, { "id": "FRdrHIaHKT6TBQFz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nWhich social issue is located at [0.093, 0.116, 0.191, 0.134] in the chart?" }, { "from": "gpt", "value": "Human Rights." } ], "id_raw": 812 }, { "id": "uJJsQLIq7koXdUUF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar at coordinates [0.201, 0.269, 0.816, 0.341]?" }, { "from": "gpt", "value": "It represents the Frequency of the social issue {Gender Equality}[0.079, 0.298, 0.191, 0.316] in the dataset." } ], "id_raw": 812 }, { "id": "qRbWJANE5AOZm94u", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nIs the frequency of {Climate Change}[0.080, 0.844, 0.191, 0.861] lower or higher than {Waste Management}[0.052, 0.753, 0.191, 0.770]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 812 }, { "id": "vEZZTHnn0AgMZuNU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nDoes {Diversity & Inclusion}[0.048, 0.389, 0.191, 0.407] have a higher frequency than {Gender Equality}[0.079, 0.298, 0.191, 0.316]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 812 }, { "id": "BPR7yxqnar42qgQA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nWhich one is higher, {Animal Welfare}[0.086, 0.207, 0.191, 0.225] frequency or {Human Rights}[0.093, 0.116, 0.191, 0.134] frequency?" }, { "from": "gpt", "value": "Human Rights." } ], "id_raw": 812 }, { "id": "k5fRMvUblXi0xkzF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.201, 0.087, 0.945, 0.159]." }, { "from": "gpt", "value": "This area contains the longest bar in the chart, representing the issue of {Human Rights}[0.093, 0.116, 0.191, 0.134], with a frequency of 9.2%." } ], "id_raw": 812 }, { "id": "YLoRE3GWd6FsBizP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nTell me about the items in [0.201, 0.814, 0.565, 0.887]." }, { "from": "gpt", "value": "This area contains a single bar representing the frequency of {Climate Change}[0.080, 0.844, 0.191, 0.861]. The frequency of this issue is 4.5%, making it the least frequent issue on the chart." } ], "id_raw": 812 }, { "id": "syaOKspXxKz0qVfu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area from [0.201, 0.360, 0.767, 0.432]?" }, { "from": "gpt", "value": "It contains a bar that represents the {Diversity & Inclusion}[0.048, 0.389, 0.191, 0.407] social issue frequency, which score is 7.0%." } ], "id_raw": 812 }, { "id": "hDkDvxz4AOpnrJdl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart located at [0.455, 0.019, 0.858, 0.041]?" }, { "from": "gpt", "value": "Main Causes of Death in US Population." } ], "id_raw": 813 }, { "id": "grwB2s5j9jB5EzxC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the ranges of percentages located at [0.965, 0.939, 0.987, 0.957] and [0.350, 0.939, 0.361, 0.957]?" }, { "from": "gpt", "value": "The range is from 0% to 25%." } ], "id_raw": 813 }, { "id": "xZDykjmusx02vXwt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented by the data at [0.355, 0.495, 0.482, 0.560]?" }, { "from": "gpt", "value": "This is a bar showing the percentage of deaths caused by Stroke." } ], "id_raw": 813 }, { "id": "HFk7ztYcWhDBkyAs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nIs {the percentage of deaths caused by Heart Disease}[0.355, 0.822, 0.929, 0.887] lower or higher than {the percentage of deaths caused by Chronic lower respiratory diseases}[0.355, 0.577, 0.494, 0.642]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 813 }, { "id": "XEjYnxioxJyrBiTl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nWhich one has a higher death percentage, {Cancer}[0.355, 0.740, 0.914, 0.805] or {Stroke}[0.355, 0.495, 0.482, 0.560]?" }, { "from": "gpt", "value": "Cancer." } ], "id_raw": 813 }, { "id": "OauZveUUti2zQv5g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nComparing causes with coordinates [0.355, 0.495, 0.482, 0.560] and [0.355, 0.577, 0.494, 0.642], which cause occurs more commonly?" }, { "from": "gpt", "value": "Chronic lower respiratory diseases." } ], "id_raw": 813 }, { "id": "SaIVUX9pAPe18vQn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in coordinates [0.355, 0.047, 0.958, 0.927]." }, { "from": "gpt", "value": "This region represents the main plot of the chart where 10 hamming bars were drawn. Each bar corresponds to a cause of death like Heart Disease, Cancer, Accidents, etc. The bars are arranged in descending order of their percentages." } ], "id_raw": 813 }, { "id": "EETF47lcvF1caHFw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.355, 0.495, 0.482, 0.560]?" }, { "from": "gpt", "value": "This region contains a single bar that measures the percentage of Stroke-related deaths." } ], "id_raw": 813 }, { "id": "tQUs7dpEx4uWEqIh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region [0.355, 0.495, 0.482, 0.642]?" }, { "from": "gpt", "value": "There are two objects present. The stroke and Alzheimer's disease bars, representing their respective percentages of total deaths." } ], "id_raw": 813 }, { "id": "LyYoRP3re8Glxuc2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the category under the coordinates [0.146, 0.087, 0.224, 0.104]?" }, { "from": "gpt", "value": "e-Library." } ], "id_raw": 814 }, { "id": "aN7sYVnPmgnrvQ54", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar depicting the utilization rate of 'e-Business'?" }, { "from": "gpt", "value": "[0.236, 0.566, 0.834, 0.633]." } ], "id_raw": 814 }, { "id": "Zj9VirPPGRU8Rpv0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the category}<{category label}[0.175, 0.677, 0.224, 0.694]> at these coordinates?" }, { "from": "gpt", "value": "Email." } ], "id_raw": 814 }, { "id": "WDhO9gCEQVw2JX0k", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nWhich category does the bar located at [0.236, 0.650, 0.904, 0.717] represent?" }, { "from": "gpt", "value": "{Email}[0.175, 0.677, 0.224, 0.694]." } ], "id_raw": 814 }, { "id": "wU40IOMI6meRJLCB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Internet Games}[0.236, 0.397, 0.658, 0.465] shorter than that of {Email}[0.236, 0.650, 0.904, 0.717]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 814 }, { "id": "wkJ3JNi57H7KN9rB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nAre the bars of {Internet Applications}[0.236, 0.818, 0.939, 0.886] and {Chat Room}[0.236, 0.734, 0.939, 0.802] of the same height?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 814 }, { "id": "OxiI75iC2MNo6o1b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nBetween {VoIP}[0.236, 0.481, 0.834, 0.549] and {e-Business}[0.236, 0.566, 0.834, 0.633], which has a higher utilization rate?" }, { "from": "gpt", "value": "Both are the same." } ], "id_raw": 814 }, { "id": "NGE5MfvK8pVNvQg5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nIs {Internet TV}[0.236, 0.229, 0.588, 0.296] more utilized than {Telecommuting}[0.236, 0.144, 0.517, 0.212]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 814 }, { "id": "5nw3kaoU34VHogaj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.236, 0.019, 0.974, 0.927]." }, { "from": "gpt", "value": "This is a bar chart representing the utilization rate of different internet and communication services. It includes the bars denoting the utilization rates, the labels of these services on the y-axis, and the percentage scale on the x-axis." } ], "id_raw": 814 }, { "id": "B93vYF3NghnPzXRR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.236, 0.566, 0.834, 0.633]." }, { "from": "gpt", "value": "This region represents the utilization rate of the \"e-Business\" service. It includes a horizontal bar that extends to 17% on the x-axis, indicating that the utilization rate of e-Business is 17%." } ], "id_raw": 814 }, { "id": "78zEjC9GLeOPafVj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nWhat lies within the coordinates [0.236, 0.397, 0.658, 0.465]?" }, { "from": "gpt", "value": "Within these coordinates, there is a horizontal bar pertaining to the \"Internet Games\" label. This bar extends up to 12% on the x-axis, illustrating a 12% utilisation rate for Internet Games." } ], "id_raw": 814 }, { "id": "52tA7FzFGuZobbqT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nWhat content does the region [0.236, 0.313, 0.623, 0.380] encompass?" }, { "from": "gpt", "value": "This region encompasses a horizontal bar related to the label \"Video Conferencing\", signifying an 11% utilization rate, since the bar extends to 11% on the x-axis." } ], "id_raw": 814 }, { "id": "h6qIqIAN39UXNrvY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at the coordinates [0.019, 0.437, 0.036, 0.509]?" }, { "from": "gpt", "value": "It is the Y-axis label, named 'Industry'." } ], "id_raw": 815 }, { "id": "lMVYzrFuGQFgnYjG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinate of the bar representing 'Biotechnology'?" }, { "from": "gpt", "value": "The coordinates are [0.301, 0.717, 0.767, 0.792]." } ], "id_raw": 815 }, { "id": "q1yob6QNsw8G44Tw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated within the coordinates [0.725, 0.652, 0.774, 0.669]?" }, { "from": "gpt", "value": "It denotes the value of 'Semiconductors' as ' $20K'." } ], "id_raw": 815 }, { "id": "ByUBOhQ7QIvOPlp5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the entity observed at the coordinates [0.149, 0.654, 0.289, 0.671]?" }, { "from": "gpt", "value": "It's the Y-axis label for 'Semiconductors'." } ], "id_raw": 815 }, { "id": "Upn537uuhjwxF70g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nIs the {Sales of 'Information Technology'}[0.301, 0.811, 0.831, 0.886] greater than the {Sales of 'Communications Equipment'}[0.301, 0.435, 0.682, 0.510]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 815 }, { "id": "2JBlAGCRz0kbIBRQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nIs {Pharmaceuticals}[0.301, 0.154, 0.873, 0.229] below or above {Data processing}[0.301, 0.060, 0.894, 0.135] on the chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 815 }, { "id": "5OVDqgDhZuhWj5d8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nComparing {Software}[0.301, 0.529, 0.937, 0.604] and {Media}[0.301, 0.248, 0.788, 0.323], which one has a higher sales volume?" }, { "from": "gpt", "value": "Software." } ], "id_raw": 815 }, { "id": "bMcPqFNXs8br1efs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nAmong the given industries, does {Biotechnology}[0.301, 0.717, 0.767, 0.792] or {Telecommunications}[0.301, 0.342, 0.746, 0.417] have higher sales?" }, { "from": "gpt", "value": "Biotechnology." } ], "id_raw": 815 }, { "id": "eBfjSyxFPfLkHIx8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.301, 0.019, 0.968, 0.927] contain?" }, { "from": "gpt", "value": "This region contains the complete bar chart representing sales volumes for different industries. Each bar signifies sales for a particular sector, including 'Information Technology', 'Biotechnology', 'Semiconductors', 'Software', 'Communications Equipment', 'Telecommunications', 'Media', 'Pharmaceuticals', and 'Data processing'." } ], "id_raw": 815 }, { "id": "JVxPv6eJTyN5heDz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.149, 0.185, 0.289, 0.202]." }, { "from": "gpt", "value": "In this region, there is a label for 'Pharmaceuticals' on the Y-axis." } ], "id_raw": 815 }, { "id": "EuBYhvKt70LfLZWs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the area [0.685, 0.464, 0.732, 0.482]?" }, { "from": "gpt", "value": "A text label for the 'Communications Equipment' bar, indicating its sales value as ' $18K', is visible in this area." } ], "id_raw": 815 }, { "id": "a7Rjk8nRuv41zVch", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nWhat information is available within the region [0.301, 0.811, 0.831, 0.886]?" }, { "from": "gpt", "value": "Within this region, a 'turquoise' colored bar stands for 'Information Technology' sales value. The length of the bar visually represents the volume of sales." } ], "id_raw": 815 }, { "id": "bQDW4HhB4wjlGPBQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the text label at these coordinates [0.507, 0.015, 0.760, 0.033]?" }, { "from": "gpt", "value": "The label is \"Probability of Future Scenarios\"." } ], "id_raw": 816 }, { "id": "AhxShcNREsHoKsDF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what category is represented at the y-axis coordinates [0.171, 0.110, 0.272, 0.124]?" }, { "from": "gpt", "value": "The category is \"Living on Mars\"." } ], "id_raw": 816 }, { "id": "VQYTJa14KmLL0ame", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label located at [0.015, 0.459, 0.029, 0.520]?" }, { "from": "gpt", "value": "The label is \"Scenario\"." } ], "id_raw": 816 }, { "id": "uxjIJvnYD4zzAnjJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at the specific spot [0.582, 0.971, 0.684, 0.985]?" }, { "from": "gpt", "value": "The label is \"Probability (%)\"." } ], "id_raw": 816 }, { "id": "WoHRYKzNg4dki6Jn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nDo we have a higher probability for {E-learning}[0.281, 0.732, 0.893, 0.807] or {Automated Transport}[0.281, 0.546, 0.772, 0.620]?" }, { "from": "gpt", "value": "E-learning." } ], "id_raw": 816 }, { "id": "xvntwKhUBeNvMApU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nComparing {Remote Healthcare}[0.281, 0.359, 0.603, 0.433] and {Robot Companions}[0.281, 0.265, 0.515, 0.340], which one has lower probability?" }, { "from": "gpt", "value": "Robot Companions." } ], "id_raw": 816 }, { "id": "jecBOLyTNlqO3HQU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nIs {Artificial Intelligence in Household}[0.281, 0.452, 0.662, 0.527] more probable than {Virtual Reality Entertainment}[0.281, 0.639, 0.834, 0.714]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 816 }, { "id": "WjdgYMZ2L9OqxwML", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nBetween {Time Travel}[0.281, 0.172, 0.375, 0.247] and {Living on Mars}[0.281, 0.078, 0.338, 0.153], which scenario has a higher probability?" }, { "from": "gpt", "value": "Time Travel." } ], "id_raw": 816 }, { "id": "cDL9eqVfAcCwFgkP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe within the region [0.125, 0.577, 0.272, 1.094]?" }, { "from": "gpt", "value": "This region contains three probablity bars, namely \"Automated Transport\"[0.281, 0.546, 0.772, 0.620], \"Telework\"[0.281, 0.826, 0.951, 0.901] and \"E-learning\"[0.281, 0.732, 0.893, 0.807] with their corresponding probability percentages and the axis label \"Probability (%)\"[0.582, 0.971, 0.684, 0.985] located at the bottom of the region. The bars present relative probabilities of these future scenarios." } ], "id_raw": 816 }, { "id": "VXZJud7cFjaIKZmY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what objects are located between [0.277, 0.951, 0.960, 0.965]?" }, { "from": "gpt", "value": "Between these coordinates are the numerical labels for the x-axis, which range from \"0\"[0.277, 0.951, 0.286, 0.965] to \"25\"[0.943, 0.951, 0.960, 0.965], signifying the probability range for the different scenarios." } ], "id_raw": 816 }, { "id": "FUMmXbxDkftZUZS6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's inside [0.281, 0.359, 0.951, 0.901]?" }, { "from": "gpt", "value": "This region represents scenarios and their respective probabilities. The scenarios include \"Telework\"[0.281, 0.826, 0.951, 0.901], \"E-learning\"[0.281, 0.732, 0.893, 0.807], \"Virtual Reality Entertainment\"[0.281, 0.639, 0.834, 0.714], \"Automated Transport\"[0.281, 0.546, 0.772, 0.620] and \"Remote Healthcare\"[0.281, 0.359, 0.603, 0.433] and the associated probabilities for each scenario are provided." } ], "id_raw": 816 }, { "id": "tBCJ3tPCqcbmBNOc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.160, 0.172, 0.360, 0.601]?" }, { "from": "gpt", "value": "In this region, there are three scenarios: \"Robot Companions\"[0.281, 0.265, 0.515, 0.340], \"Time Travel\"[0.281, 0.172, 0.375, 0.247] and \"Automated Transport\"[0.281, 0.546, 0.772, 0.620], along with their corresponding probabilities." } ], "id_raw": 816 }, { "id": "NUOxG4LkpE2mokMY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the element in the chart at coordinates [0.274, 0.822, 0.723, 0.887]?" }, { "from": "gpt", "value": "It's a bar representing the \"Number of Friends\" with a value of 20." } ], "id_raw": 817 }, { "id": "1BzcOfWvJV44Tbhm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinate [0.723, 0.846, 0.751, 0.863]?" }, { "from": "gpt", "value": "This is the label \"20\" for the bar of \"Number of Friends\"." } ], "id_raw": 817 }, { "id": "UwRRHv3HpAVKvsCn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the item at the coordinate [0.043, 0.766, 0.262, 0.783]?" }, { "from": "gpt", "value": "It's the y-axis label for \"Number of Active Friends\"." } ], "id_raw": 817 }, { "id": "WKWOmERj7tC3bjb9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.274, 0.577, 0.916, 0.642] represent?" }, { "from": "gpt", "value": "This bar represents the \"Messages Received\" category with a value of 28.6." } ], "id_raw": 817 }, { "id": "YXmQ9tJSYTqtVhOb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Messages Sent}[0.274, 0.658, 0.851, 0.724] longer than {Number of Active Friends}[0.274, 0.740, 0.622, 0.805]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 817 }, { "id": "8auhAvQ8EfJUrWa9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat bar is immediately above {Group Chats Participated}[0.274, 0.332, 0.773, 0.397]?" }, { "from": "gpt", "value": "{Text Conversations}[0.274, 0.413, 0.685, 0.479]" } ], "id_raw": 817 }, { "id": "bq7fGW7pwAH8ECdd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nAre the values for {Number of Friends}[0.274, 0.822, 0.723, 0.887] and {Number of Active Friends}[0.274, 0.740, 0.622, 0.805] equal?" }, { "from": "gpt", "value": "No." } ], "id_raw": 817 }, { "id": "hnDXRriA4I4opWQc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Number of Active Friends}[0.274, 0.740, 0.622, 0.805] greater than {Text Conversations}[0.274, 0.413, 0.685, 0.479]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 817 }, { "id": "R5BRvB73kbU8soa3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the area [0.274, 0.822, 0.751, 0.863]." }, { "from": "gpt", "value": "It contains one bar and one label. The bar represents the category \"Number of Friends\" with a value of 20, and its coordinate is [0.274, 0.822, 0.723, 0.887]. The label \"20\", represented by the coordinates [0.723, 0.846, 0.751, 0.863], indicates the value of the bar." } ], "id_raw": 817 }, { "id": "Dmyg0QpVQBSBKFb4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area [0.274, 0.577, 0.960, 0.618]?" }, { "from": "gpt", "value": "This area contains a bar and a label. The bar represents the category \"Messages Received\" with a value of 28.6, the coordinates for this bar are [0.274, 0.577, 0.916, 0.642]. The label \"28.6\", indicated by [0.916, 0.601, 0.960, 0.618], represents the value of the bar." } ], "id_raw": 817 }, { "id": "jGSaJYRkXBDx4o1m", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the region [0.274, 0.168, 0.523, 0.210]?" }, { "from": "gpt", "value": "The area contains a bar and a label. The bar, with coordinates [0.274, 0.168, 0.490, 0.234], represents the category \"Relationship Initiation\" with a value of 9.6. The label \"9.6\", indicated by [0.490, 0.192, 0.523, 0.210], represents the value of the bar." } ], "id_raw": 817 }, { "id": "qRy5MIIfw1nktXlL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.274, 0.332, 0.775, 0.373]." }, { "from": "gpt", "value": "It contains a bar and a corresponding text value. The bar, located at [0.274, 0.332, 0.773, 0.397], represents the \"Group Chats Participated\" category with a value of 22.2. The value \"22.2\" is denoted by the label located at [0.773, 0.356, 0.816, 0.373]." } ], "id_raw": 817 }, { "id": "5UnN34m44MYG6yro", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinate [0.019, 0.435, 0.036, 0.511]?" }, { "from": "gpt", "value": "Y-axis label, named Indicator." } ], "id_raw": 818 }, { "id": "zADSGtsf7Epkl86m", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the object at coordinates [0.721, 0.845, 0.749, 0.862]?" }, { "from": "gpt", "value": "The number 5 as an x-axis tick label." } ], "id_raw": 818 }, { "id": "PqgS1Q1nR5YoAZbZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat value does the block with coordinates [0.321, 0.154, 0.468, 0.229] represent?" }, { "from": "gpt", "value": "The indicator \"Housing\" with a value of 1.8." } ], "id_raw": 818 }, { "id": "F6i5FpA6t1kjHOzc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhich text is located at [0.321, 0.277, 0.627, 0.294]?" }, { "from": "gpt", "value": "The value 3.4." } ], "id_raw": 818 }, { "id": "XUQ4KMPhGMmd1WhB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Housing}[0.321, 0.154, 0.468, 0.229] shorter or taller than the bar for {Currency}[0.321, 0.248, 0.599, 0.323]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 818 }, { "id": "reDxNXO2PxEJNK19", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {GDP}[0.656, 0.840, 0.684, 0.857] or {Inflation}[0.501, 0.746, 0.528, 0.763] bar in the chart?" }, { "from": "gpt", "value": "GDP." } ], "id_raw": 818 }, { "id": "2RIDNbCtJsIQOWuq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Consumer Confidence}[0.321, 0.060, 0.419, 0.135] nearer to the origin compared to {Unemployment}[0.321, 0.623, 0.820, 0.698]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 818 }, { "id": "L847IMqUpi0zuImr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhich is shorter, the {Trade Balance} bar<{Trade Balance bar}[0.280, 0.342, 0.321, 0.417]> or the {Interest Rate} bar<{Interest Rate bar}[0.321, 0.529, 0.341, 0.604]>?" }, { "from": "gpt", "value": "Interest Rate." } ], "id_raw": 818 }, { "id": "jrVAXU7GMntekuTl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the area [0.280, 0.342, 0.321, 0.417]?" }, { "from": "gpt", "value": "The area contains one object - a bar indicating the Trade Balance value at -0.5. Also, the numerical value is labeled on the bar." } ], "id_raw": 818 }, { "id": "2atQgAK9MtVAQb1L", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within the coordinates [0.435, 0.248, 0.943, 0.510]." }, { "from": "gpt", "value": "The area includes two objects: \n1. The bar of Stock Market with coordinates [0.321, 0.435, 0.943, 0.510] illustrates a value of 7.6.\n2. The label \"7.6\" is positioned at [0.943, 0.464, 0.970, 0.482], indicating the value represented by the bar." } ], "id_raw": 818 }, { "id": "I7Ge2e92l6n7mpMc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area defined by [0.197, 0.811, 0.235, 0.859]?" }, { "from": "gpt", "value": "The area contains the label \"GDP\" for the y-axis, which is one of the economic indicators displayed on the chart." } ], "id_raw": 818 }, { "id": "RntYYOaKOtS1T1ie", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.315, 0.939, 0.326, 0.957]?" }, { "from": "gpt", "value": "The region contains an x-axis label, specifically the number \"0\"." } ], "id_raw": 818 }, { "id": "6rai2iiG2NazYUhd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y-axis?" }, { "from": "gpt", "value": "{Activity}[0.019, 0.440, 0.036, 0.506]" } ], "id_raw": 819 }, { "id": "6rOB4bxccVbjjD5N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat activity is represented by the bar at [0.183, 0.818, 0.921, 0.886]?" }, { "from": "gpt", "value": "Exercise" } ], "id_raw": 819 }, { "id": "Bdau1daSBse2nVaB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat percentage value does the bar for {Cooking} at [0.183, 0.481, 0.510, 0.549] represent?" }, { "from": "gpt", "value": "14.4%" } ], "id_raw": 819 }, { "id": "nsz9a6MeV0ZYd22w", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat element is located at [0.376, 0.254, 0.426, 0.271]?" }, { "from": "gpt", "value": "The percentage value of the Studying activity bar." } ], "id_raw": 819 }, { "id": "c87Sw2SiA3LMIsjW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Studying}[0.183, 0.229, 0.376, 0.296] longer than the bar for {Watching_TV}[0.183, 0.144, 0.288, 0.212]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 819 }, { "id": "j34R56bEbEN1xLja", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nWhich bar is longer, the one representing {Socializing}[0.183, 0.734, 0.819, 0.802] or {Exercise}[0.183, 0.818, 0.921, 0.886]?" }, { "from": "gpt", "value": "Exercise." } ], "id_raw": 819 }, { "id": "klEwJh4C6OZAvTgH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Traveling}[0.183, 0.397, 0.456, 0.465] shorter than the one for {Gaming}[0.183, 0.566, 0.587, 0.633]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 819 }, { "id": "ndvcI7bS20JfO0eh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nBetween {Working}[0.183, 0.313, 0.419, 0.380] and {Cooking}[0.183, 0.481, 0.510, 0.549], which activity has a higher percentage?" }, { "from": "gpt", "value": "Cooking." } ], "id_raw": 819 }, { "id": "ftvmlAHZ5c3o0mxZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements present in coordinates [0.183, 0.818, 0.981, 0.861]." }, { "from": "gpt", "value": "This region encompasses the bar for the Exercise activity and its accompanying percentage text. The bar is shaded in a color denoted by '#f08080', positioned at [0.183, 0.818, 0.921, 0.886] and the text displays '32.5%', which is located at [0.921, 0.843, 0.981, 0.861]." } ], "id_raw": 819 }, { "id": "se0ZvcGSYirc5CHw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.183, 0.060, 0.307, 0.102]." }, { "from": "gpt", "value": "The area contains a bar and its corresponding percentage value. The bar coordinates are [0.183, 0.060, 0.258, 0.127]. This represents the activity 'Doing_Nothing' and its associated percentage, 3.3%. The percentage text is located at [0.258, 0.085, 0.307, 0.102]." } ], "id_raw": 819 }, { "id": "G6JUujIaPYF15L7a", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within the area [0.183, 0.397, 0.571, 0.524]?" }, { "from": "gpt", "value": "This area includes the bar that represents the activity 'Cooking' with coordinates [0.183, 0.397, 0.510, 0.549], as well as its corresponding percentage number '14.4%' which is located at [0.510, 0.506, 0.571, 0.524]." } ], "id_raw": 819 }, { "id": "JWicYJeD0ToIfIAg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within [0.183, 0.566, 0.648, 0.608]?" }, { "from": "gpt", "value": "1. A bar representing the 'Gaming' activity, measured at 17.8%, located at [0.183, 0.566, 0.587, 0.633]\n2. Text displaying the value '17.8%', also representing the 'Gaming' activity, located at [0.587, 0.591, 0.648, 0.608]." } ], "id_raw": 819 }, { "id": "zpR9esg7UrlscDyX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar at the coordinates [0.182, 0.833, 0.947, 0.901]?" }, { "from": "gpt", "value": "The investment in Fintech industry, amounting to 780 million dollars." } ], "id_raw": 820 }, { "id": "6Kxq36mKJBrm9R3z", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat label is located at the coordinates [0.134, 0.778, 0.172, 0.792]?" }, { "from": "gpt", "value": "The label is \"AI/ML\"." } ], "id_raw": 820 }, { "id": "KOXJ1xAi0oSlzMdz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the value indicated at the coordinates [0.260, 0.107, 0.275, 0.117]?" }, { "from": "gpt", "value": "80 million dollars." } ], "id_raw": 820 }, { "id": "OkFTMuliX54vzGRC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled at the coordinates [0.035, 0.442, 0.172, 0.456]?" }, { "from": "gpt", "value": "The industry \"Enterprise Software\"." } ], "id_raw": 820 }, { "id": "7KgZ8pGRz72nJzLh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nIs the investment in {Manufacturing Industry}[0.182, 0.330, 0.456, 0.397] higher than that in the {Education Industry}[0.182, 0.246, 0.407, 0.313]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 820 }, { "id": "zH7IL4hf9X8f1y7R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nIs the {Fintech bar}[0.182, 0.833, 0.947, 0.901] taller than the {HealthCare bar}[0.182, 0.666, 0.721, 0.733]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 820 }, { "id": "hsCfDd7MdEguvSpV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nBetween the {AI/ML bar}[0.182, 0.750, 0.770, 0.817] and the {Mobility bar}[0.182, 0.498, 0.549, 0.565], which industry received more investment?" }, { "from": "gpt", "value": "AI/ML." } ], "id_raw": 820 }, { "id": "GjoH2dx9Oup9bqvG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nCompare the investment in {Ecommerce}[0.182, 0.582, 0.672, 0.649] and {Enterprise Software}[0.182, 0.414, 0.495, 0.481], which one is higher?" }, { "from": "gpt", "value": "Ecommerce." } ], "id_raw": 820 }, { "id": "wU8DtIKUPY6m8qv1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.182, 0.037, 0.985, 0.942]." }, { "from": "gpt", "value": "There is a bar chart depiction of the investment trend in venture capital markets across multiple industries and their investment values, including Fintech, AI/ML, HealthCare, Ecommerce, Mobility, Enterprise Software, Manufacturing, Education, AR/VR and Gaming. Each bar in the chart corresponds to an industry and the length of the bar represents the amount of investment in million dollars." } ], "id_raw": 820 }, { "id": "xwvofhmIvU4Dzqpe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the area between coordinates [0.182, 0.414, 0.495, 0.481]?" }, { "from": "gpt", "value": "There is a bar that represents the investment in the Enterprise Software industry, amounting to 320 million dollars." } ], "id_raw": 820 }, { "id": "CKcltyYFc7RfM0UZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.182, 0.582, 0.672, 0.649]." }, { "from": "gpt", "value": "In this area, there is a blue bar representing the investment in Ecommerce, amounting to 500 million dollars." } ], "id_raw": 820 }, { "id": "p08uXn8wgNDxJ4v1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within [0.182, 0.246, 0.407, 0.313]?" }, { "from": "gpt", "value": "The area contains a bar showcasing the investment in the Education sector, which is valued at 230 million dollars." } ], "id_raw": 820 }, { "id": "d1G8ZeGzX3i0PNEU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.015, 0.449, 0.029, 0.508] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Location." } ], "id_raw": 821 }, { "id": "x2xgKi9sbBoKII9X", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the eighth tick on the y-axis?" }, { "from": "gpt", "value": "[0.185, 0.263]" } ], "id_raw": 821 }, { "id": "Nz2K4yFHK1ellIP7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the {the x-axis value} at [0.185, 0.375, 0.219, 0.395]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 821 }, { "id": "e8OWjnkwDetGFeoY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat office or location is shown in [0.091, 0.688, 0.175, 0.702]?" }, { "from": "gpt", "value": "{Tokyo Office}[0.091, 0.688, 0.175, 0.702]." } ], "id_raw": 821 }, { "id": "nRiTtVbsVx03XhOQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Bangkok Office\"}[0.185, 0.401, 0.511, 0.470] shorter or taller than {the bar representing \"Seoul Office\"}[0.185, 0.573, 0.625, 0.642]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 821 }, { "id": "y42Rtw7ZU0azxHiJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nIs {the visit from \"Seoul Office\"}[0.185, 0.573, 0.625, 0.642] larger than that from {\"Jakarta Office\"}[0.185, 0.315, 0.474, 0.384]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 821 }, { "id": "l1OImF4MnM8r0yTB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing Offfices}[[0.185, 0.401, 0.511, 0.470], [0.185, 0.573, 0.625, 0.642], [0.185, 0.315, 0.474, 0.384]] consistently taller than {the bar representing \"Hanoi Office\"}[0.185, 0.057, 0.343, 0.126]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 821 }, { "id": "Kcj3pHXfC5G8ZxUq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Beijing Office\"}[0.185, 0.745, 0.805, 0.814] and {\"Tokyo Office\"}[0.185, 0.659, 0.664, 0.728], which one has a higher Visitation percentage?" }, { "from": "gpt", "value": "Beijing Office." } ], "id_raw": 821 }, { "id": "USqr0oWJGWTmGyFn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.185, 0.831, 0.936, 0.900]." }, { "from": "gpt", "value": "There is one bar in the region. The bar represents the visitation rate at the Shanghai office which is 75.6%." } ], "id_raw": 821 }, { "id": "gU732kzEhJO0HHjJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.185, 0.343, 0.391, 0.099]." }, { "from": "gpt", "value": "This area includes one bar which represents the visitation percentage for the Hanoi office. The visitation rate is 15.9%." } ], "id_raw": 821 }, { "id": "InRkzWfqrloUee2A", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.185, 0.745, 0.805, 0.814]?" }, { "from": "gpt", "value": "There is 1 object:\n1. The bar represents that the visitation rate of the Beijing Office is 62.4%, and the coordinate range is [0.185, 0.745, 0.805, 0.814]." } ], "id_raw": 821 }, { "id": "kqDBPEFDMUSqluQV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.185, 0.057, 0.343, 0.126]?" }, { "from": "gpt", "value": "1. Hanoi Office bar that shows visitation percentage, and its coordinate range is [0.185, 0.057, 0.343, 0.126].\n2. The text annotation (15.9%) next to the bar, which indicates the visitation percentage." } ], "id_raw": 821 }, { "id": "HzVEvYUIHp41aQmf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary object within the coordinates [0.229, 0.047, 0.958, 0.927]?" }, { "from": "gpt", "value": "It is the main plot area where the bars are plotted." } ], "id_raw": 822 }, { "id": "eJF9TUOChnNU9Mz1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.417, 0.019, 0.769, 0.041] refer to?" }, { "from": "gpt", "value": "This text is the subtitle, \"Investment Strategy Performance\"." } ], "id_raw": 822 }, { "id": "rZlmmkiIn8DfLvMf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nWhich investment strategy is represented at the coordinate [0.229, 0.740, 0.699, 0.805]?" }, { "from": "gpt", "value": "{The strategy}[0.229, 0.740, 0.699, 0.805] is Low Risk." } ], "id_raw": 822 }, { "id": "87Oc7l7eucqfNrE1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nHow is the bar [0.229, 0.822, 0.923, 0.887] labeled?" }, { "from": "gpt", "value": "The bar is labeled as {High Risk}[0.229, 0.822, 0.923, 0.887]." } ], "id_raw": 822 }, { "id": "8ikiJqIScalEhmHq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nIs the {Return Rate} Bar for {High Risk Investing}[0.229, 0.822, 0.923, 0.887] larger than that for {Low Risk Investing}[0.229, 0.740, 0.699, 0.805]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 822 }, { "id": "cocSaHi3tFi9jYD0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nWhat item is next to the {Income Investing} bar [0.229, 0.332, 0.726, 0.397] in a downward direction?" }, { "from": "gpt", "value": "{Small-Cap Investing}[0.229, 0.250, 0.644, 0.315]." } ], "id_raw": 822 }, { "id": "kaJFjTjr5rfrgnow", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nIs the return from {Value Investing}[0.229, 0.413, 0.661, 0.479] higher than {Income Investing}[0.229, 0.332, 0.726, 0.397]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 822 }, { "id": "N7Dz2QHJAB2jfLBj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nWhat investment strategy has a lower return rate, {High-Risk}[0.229, 0.822, 0.923, 0.887] or {Low-Risk}[0.229, 0.740, 0.699, 0.805]?" }, { "from": "gpt", "value": "Low Risk." } ], "id_raw": 822 }, { "id": "UeA9TliwRRyiPO1a", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects present in the region [0.229, 0.047, 0.958, 0.927]?" }, { "from": "gpt", "value": "This area is the main region of the graph that contains the horizontal barsof each investing strategy's Return Rate, ranging from Bond Investing to High Risk." } ], "id_raw": 822 }, { "id": "iZ3Ub80zqLCrOdui", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the region from [0.229, 0.740, 0.699, 0.805] consist of?" }, { "from": "gpt", "value": "This region consists of a single bar representing the return rate of \"Low Risk\" investing strategy." } ], "id_raw": 822 }, { "id": "HRqJ1bGGaTAUNtDm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.692, 0.216, 0.746, 0.419] contain?" }, { "from": "gpt", "value": "The area contains no objects. It's an empty space between the bars representing the return rates." } ], "id_raw": 822 }, { "id": "ByNfhdZMeJ6yYgM1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents found within the region [0.524, 0.964, 0.663, 0.981]?" }, { "from": "gpt", "value": "The region contains the x-axis label, which is \"Return Rate (%)\"." } ], "id_raw": 822 }, { "id": "F87vWRzgUCocCNGT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the vertical axis represent in the chart?" }, { "from": "gpt", "value": "The vertical axis represents {Social Network}[0.019, 0.408, 0.036, 0.538]." } ], "id_raw": 823 }, { "id": "8AEmYFu4q63xIKGK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the bar that refers to the performance of LinkedIn?" }, { "from": "gpt", "value": "The coordinates are [0.144, 0.566, 0.758, 0.633]." } ], "id_raw": 823 }, { "id": "k3HoZJpV2Wqft5Ef", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of the percentage scale on the x-axis?" }, { "from": "gpt", "value": "The range of the percentage scale is from {0%}[0.139, 0.939, 0.150, 0.957] to {10%}[1.036, 0.939, 1.058, 0.957]." } ], "id_raw": 823 }, { "id": "1MOzvTbUpC1rE8zt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nDoes the performance of {Twitter}[0.144, 0.734, 0.821, 0.802] exceed that of {Instagram}[0.144, 0.650, 0.803, 0.717]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 823 }, { "id": "iNUnCdXN9hZ0EPPD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform performed the lowest, {WeChat}[0.144, 0.144, 0.505, 0.212] or {Tumblr}[0.144, 0.060, 0.370, 0.127]?" }, { "from": "gpt", "value": "Tumblr." } ], "id_raw": 823 }, { "id": "jQxqQzPBiJZRDTpo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nIs {Pinterest's performance}[0.144, 0.313, 0.532, 0.380] closer to the performance of {LinkedIn}[0.144, 0.566, 0.758, 0.633] or {WeChat}[0.144, 0.144, 0.505, 0.212]?" }, { "from": "gpt", "value": "WeChat." } ], "id_raw": 823 }, { "id": "R4a32k5c3nqjgDzS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area broadcasting the performance of each social network [0.144, 0.019, 0.969, 0.927]?" }, { "from": "gpt", "value": "The area represents ten horizontal bars reflecting the performance of ten social networks. From top to bottom they are: Facebook, Twitter, Instagram, LinkedIn, Snapchat, Reddit, Pinterest, WhatsApp, WeChat, and Tumblr. The length of each bar corresponds to its specific rating out of 10. Moreover, each bar is labeled with its respective percentage." } ], "id_raw": 823 }, { "id": "BRMQ0oT425s7AtWR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.144, 0.818, 0.930, 0.886] tell us?" }, { "from": "gpt", "value": "The area contains a blue bar that represents the performance of Facebook, which is 8.7%." } ], "id_raw": 823 }, { "id": "wzL7OkrvjNWgEn13", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nProvide a description of the area from [0.144, 0.566, 0.758, 0.633] to [0.144, 0.060, 0.370, 0.127]?" }, { "from": "gpt", "value": "This area covers five social networks, starting from LinkedIn and going down to Tumblr. Each has been given a performance rating out of 10, represented by the length of the horizontal bar for each network." } ], "id_raw": 823 }, { "id": "ixexdHQBhkICupre", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content located at [0.015, 0.450, 0.029, 0.524]?" }, { "from": "gpt", "value": "It is the Y-axis label, named Platform." } ], "id_raw": 824 }, { "id": "n2XIfbiVIf9K2uOG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the third bar from the top in the chart?" }, { "from": "gpt", "value": "[0.115, 0.658, 0.765, 0.724]" } ], "id_raw": 824 }, { "id": "zww2LMNIJd4MBx6L", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.357, 0.019, 0.723, 0.041] refer to?" }, { "from": "gpt", "value": "These are the coordinates for the subtitle, which reads \"Content Sharing Rate on Different Platforms\"." } ], "id_raw": 824 }, { "id": "IwLZJ500hOZSq3nt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at [0.925, 0.764, 0.981, 0.782]?" }, { "from": "gpt", "value": "It refers to text marking the percentage value of the bar, which reads \"67.25%\"." } ], "id_raw": 824 }, { "id": "EvcEeHwdAenUhJH9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, is {the bar for Instagram}[0.115, 0.740, 0.924, 0.805] higher or lower than {the bar for Facebook}[0.115, 0.822, 0.867, 0.887]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 824 }, { "id": "Y90yvqogdfeah20H", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nIs the {TikTok bar}[0.115, 0.168, 0.732, 0.234] longer or shorter than the {WhatsApp bar}[0.115, 0.332, 0.825, 0.397]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 824 }, { "id": "i1Q6YIBe0NBJkalY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nIn terms of their bar lengths, how does {Email}[0.115, 0.087, 0.389, 0.152] perform in comparison to {LinkedIn}[0.115, 0.577, 0.470, 0.642]?" }, { "from": "gpt", "value": "It is lower." } ], "id_raw": 824 }, { "id": "PEIy45TxClBGgsBQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Instagram bar}[0.115, 0.740, 0.924, 0.805] on the left or right of the {Snapchat bar}[0.115, 0.658, 0.765, 0.724]?" }, { "from": "gpt", "value": "Neither, they are aligned vertically." } ], "id_raw": 824 }, { "id": "PJgAf7z1rhGCGBMn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nCan you explain what is represented in the region from [0.115, 0.087, 0.389, 0.152]?" }, { "from": "gpt", "value": "This area features a single light blue bar representing the platform \"Email\". Its height signifies the sharing rate per user, which is \"22.75%\", as visible by an adjacent text label on the right of the bar." } ], "id_raw": 824 }, { "id": "fHvaoFDo2c0gpuoH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.035, 0.766, 0.106, 0.783]?" }, { "from": "gpt", "value": "It is the label for the platform category \"Instagram\", situated on the Y-axis." } ], "id_raw": 824 }, { "id": "OeA46zyz1VUo9I2G", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what the area [0.111, 0.939, 1.087, 0.957] encompasses." }, { "from": "gpt", "value": "This area contains the labels for the X-axis, which mark the \"Shares per user (%)\" ranging from 0% up to 80% in increments of 10." } ], "id_raw": 824 }, { "id": "NlEdZgXan4NN8Zvv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted within [0.115, 0.740, 0.924, 0.805]?" }, { "from": "gpt", "value": "It includes a bar in the plot representing the content sharing rate on the \"Instagram\" platform. The bar stretches to a length representing \"67.25%\" as text displays this number beside the bar's end point." } ], "id_raw": 824 }, { "id": "6rz1a7z1ObN3Fozj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.019, 0.190, 0.036, 0.239]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 825 }, { "id": "zavU8u0EvfIRiI49", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the volume of Airway transportation} at [0.437, 0.952, 0.631, 0.975]?" }, { "from": "gpt", "value": "Volume (in thousands)." } ], "id_raw": 825 }, { "id": "Hbe3MsjBGjuR7HLZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat do {the first x-axis tick label and coordinate} in the Shipping subplot indicate at [0.103, 0.327, 0.114, 0.351]?" }, { "from": "gpt", "value": "0." } ], "id_raw": 825 }, { "id": "PAexOUXMmU3K4ffv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nIs {the first blue bar in the \"Rail\" subplot}[0.109, 0.422, 0.449, 0.443] longer than {the last blue bar in the \"Airways\" subplot}[0.109, 0.873, 0.636, 0.894]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 825 }, { "id": "aWLRDrxmGJVwfCQx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nIs {the x-tick labeled \"0\"}[0.109, 0.311] on the left side of {the x-tick labeled \"1000\"}[0.959, 0.311]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 825 }, { "id": "CCbGaVtOUgRsbSWO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the top bar in the \"Shipping\" subplot}[0.109, 0.126, 0.304, 0.147] represent a greater range than {the middle bar in the \"Airways\" subplot}[0.109, 0.795, 0.534, 0.816]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 825 }, { "id": "1endGovd7uGWo3X4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within [0.109, 0.709, 0.959, 0.903]." }, { "from": "gpt", "value": "This region contains the \"Airways\" subplot showing annual transportation volumes over 7 years. The bars illustrate the volume for each year, starting from a range of 0 to 900 thousand, located from the leftmost side of the x-axis to the endpoint rightmost." } ], "id_raw": 825 }, { "id": "Abj3UI6uxHnKSUvW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in [0.109, 0.413, 0.959, 0.607]?" }, { "from": "gpt", "value": "This area captures the \"Rail\" subplot, illustrating yearly transportation data over 7 years with bars mapped from values ranging from 200 to 430. The years are labeled along the vertical axis from \"Year 1\" to \"Year 7\"." } ], "id_raw": 825 }, { "id": "4Dx6drQ1gmWQD3QZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.109, 0.117, 0.959, 0.311] include?" }, { "from": "gpt", "value": "This section displays the \"Shipping\" subplot which plots yearly shipping volumes. It includes seven blue bars representing volumes for seven consecutive years starting from year 1 to year 7, showing a fluctuation in volume across different years." } ], "id_raw": 825 }, { "id": "419gyhfyV2dBJ3jT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nIdentify the data series represented by {this bar}[0.213, 0.822, 0.701, 0.853]." }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 835 }, { "id": "0NhaJM0VVuuTet82", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the textual content at {these coordinates}[0.184, 0.867, 0.201, 0.890]?" }, { "from": "gpt", "value": "AI." } ], "id_raw": 835 }, { "id": "NeLNkRemnNLq29lz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nProvide the label for the y-axis tick at coordinates [0.213, 0.915]." }, { "from": "gpt", "value": "Quantum Computing." } ], "id_raw": 835 }, { "id": "3kQeH8hDXP4CvqTN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar at}[0.213, 0.861, 0.762, 0.892] have a higher value than {the one at}[0.213, 0.899, 0.798, 0.930]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 835 }, { "id": "RPuCyofIepr9Hg0p", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 2021 AI bar}[0.213, 0.106, 0.701, 0.137] wider than {the 2025 AI bar}[0.213, 0.861, 0.762, 0.892]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 835 }, { "id": "inLm1oz9n4J5UyWE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nCompare the y-coordinate start position of {Blockchain bar for 2021}[0.213, 0.068, 0.823, 0.098] with that of {Blockchain bar for 2023}[0.213, 0.445, 0.774, 0.476]. Which is higher?" }, { "from": "gpt", "value": "{2023}[0.445]." } ], "id_raw": 835 }, { "id": "DdGQAGcTFhaANxtu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nDetail the content encompassed within [0.213, 0.251, 0.981, 0.369]." }, { "from": "gpt", "value": "This area holds a bar chart section for the year 2022. It includes three bars corresponding to Blockchain [0.213, 0.256, 0.798, 0.287], AI [0.213, 0.295, 0.725, 0.326], and Quantum Computing [0.213, 0.333, 0.920, 0.364], as well as their associated y-axis labels." } ], "id_raw": 835 }, { "id": "NjWkDDVGWlq4QPwg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what appears in the bounds of [0.213, 0.440, 0.981, 0.558]?" }, { "from": "gpt", "value": "This region contains the 2023 graph part of the bar chart with three bars labeled Blockchain [0.213, 0.445, 0.774, 0.476], AI [0.213, 0.483, 0.737, 0.514], and Quantum Computing [0.213, 0.522, 0.908, 0.553], and the year label \"2023\" [0.571, 0.402, 0.623, 0.432]." } ], "id_raw": 835 }, { "id": "KBGIg5M6RAe91I3q", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the section demarcated by [0.213, 0.628, 0.981, 0.747]?" }, { "from": "gpt", "value": "This section features a bar chart for 2024 with bars dedicated to Blockchain [0.213, 0.634, 0.737, 0.664], AI [0.213, 0.672, 0.749, 0.703], and Quantum Computing [0.213, 0.711, 0.823, 0.741], including a subtitle placed at [0.571, 0.591, 0.623, 0.621]." } ], "id_raw": 835 }, { "id": "7ri8GYYwUrs6stm7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bars for the indicator \"Wildlife Conservation\"}[0.103, 0.321, 0.128, 0.445]?" }, { "from": "gpt", "value": "The color of the bars for \"Wildlife Conservation\" is light beige (#f4d3b4)." } ], "id_raw": 841 }, { "id": "Hycn7HqYrSjsMYVs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nWhere is the label for {the y-axis}[0.015, 0.422, 0.029, 0.552] located?" }, { "from": "gpt", "value": "The label for the y-axis is located to the left of the chart, in a vertical orientation." } ], "id_raw": 841 }, { "id": "jmhKTICBrgnaF37C", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the {year label}[0.227, 0.939, 0.252, 0.957] under the second group of bars from the left?" }, { "from": "gpt", "value": "The year label under the second group of bars from the left is \"2016\"." } ], "id_raw": 841 }, { "id": "BbPNav49LrhPOB0m", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nWhat kind of trend does {the blue bar for \"Carbon Footprint\"}[0.227, 0.593, 0.252, 0.927] have compared with {the adjacent blue bar for \"Carbon Footprint\"}[0.103, 0.575, 0.128, 0.927]?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 841 }, { "id": "il5OQGqZbG9sT2jw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the bar for \"Wildlife Conservation\" in 2020}[0.723, 0.223, 0.748, 0.321] or {the bar for \"Green Energy\" in 2020}[0.723, 0.321, 0.748, 0.533]?" }, { "from": "gpt", "value": "The bar for \"Green Energy\"." } ], "id_raw": 841 }, { "id": "SDCkrPk9K80jr2tu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Forestation\" bar in 2017}[0.351, 0.522, 0.376, 0.610] have higher value than {the \"Forestation\" bar in 2016}[0.227, 0.522, 0.252, 0.593]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 841 }, { "id": "I1vfRB81UFsQWP50", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.787, 0.055, 0.985, 0.165]." }, { "from": "gpt", "value": "The area includes the chart's legend, which features four color-text labels. Each label represents a different environmental protection indicator: Carbon Footprint (blue), Forestation (light-green), Green Energy (orange), and Wildlife Conservation (light-beige)." } ], "id_raw": 841 }, { "id": "6aUE4MrhrxuyNR1O", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.346, 0.568, 0.381, 0.957]?" }, { "from": "gpt", "value": "The area contains the bars representing the values for every indicator in the year 2017, and their respective labels." } ], "id_raw": 841 }, { "id": "TRstcCGXQGtZpKJU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements in the box [0.071, 0.047, 0.780, 0.927]." }, { "from": "gpt", "value": "The box contains the actual bar chart. It's in this region where the four environmental indicators (Carbon Footprint, Forestation, Green Energy, and Wildlife Conservation) are displayed by the height of shaded bars for each year from 2015 to 2020." } ], "id_raw": 841 }, { "id": "aFliQIhRZvaK1Wo2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the plot represented by the coordinates [0.209, 0.019, 0.648, 0.041]?" }, { "from": "gpt", "value": "Progress of Clean Energy Technologies (2000 - 2016)." } ], "id_raw": 842 }, { "id": "I1Nfd3ySfMcEkaLu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the item referenced by the coordinates [0.873, 0.088, 0.939, 0.106] in the subplot legend?" }, { "from": "gpt", "value": "Solar Cell." } ], "id_raw": 842 }, { "id": "kZSRETZm8GPbCyC8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat is denoted by the text located at the coordinates [0.699, 0.583, 0.730, 0.601]?" }, { "from": "gpt", "value": "It denotes the progress of Hydro Power in 2016 as 20%." } ], "id_raw": 842 }, { "id": "VTmXzGeCPEVdrqhF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nIs {Wind Turbine in 2004}[0.239, 0.634, 0.266, 0.927] better progressed than {Hydro Power in 2008}[0.428, 0.707, 0.455, 0.927]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 842 }, { "id": "NjP7DB5i8CwamPEy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nDoes {Solar Cell in 2012}[0.537, 0.487, 0.564, 0.927] have a higher progress percentage than {Thermal Power in 2000}[0.184, 0.634, 0.211, 0.927]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 842 }, { "id": "OEmkMNnt7N6eoloW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nBetween {Thermal Power in 2012}[0.591, 0.414, 0.618, 0.927] and {Solar Cell in 2016}[0.672, 0.340, 0.699, 0.927], which technology has better progress?" }, { "from": "gpt", "value": "Solar Cell." } ], "id_raw": 842 }, { "id": "S2SeERrSDLWkpZUB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.828, 0.055, 0.982, 0.165]." }, { "from": "gpt", "value": "This region portrays the legend of the subplot. It includes four types of energies, each represented by a distinct color: \n1. {Wind Turbine}[0.873, 0.062, 0.963, 0.080] in blue, \n2. {Solar Cell}[0.873, 0.088, 0.939, 0.106] in orange,\n3. {Hydro Power}[0.873, 0.115, 0.961, 0.132] in light blue, and \n4. {Thermal Power}[0.873, 0.141, 0.976, 0.158] in light orange." } ], "id_raw": 842 }, { "id": "tciKQUOnfXAz73gC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.103, 0.560, 0.672, 0.854]?" }, { "from": "gpt", "value": "This region includes the progress bars of Wind Turbine technology for all years ranging from 2000 to 2016." } ], "id_raw": 842 }, { "id": "wHd0SgKfRLirODzS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat can we see in the area defined by [0.015, 0.432, 0.029, 0.542]?" }, { "from": "gpt", "value": "This region highlights the Y-axis label of the chart, which refers to the 'Progress (%)'." } ], "id_raw": 842 }, { "id": "yXXQ7SqevdUssYmg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart title indicated by [0.334, 0.019, 0.713, 0.041]?" }, { "from": "gpt", "value": "The chart title is \"Market Competition Analysis and Comparison\"." } ], "id_raw": 843 }, { "id": "x35PvqiGltrdUzmH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nCould you please tell me the color of the rectangle labeled as Apple located at [0.870, 0.064, 0.898, 0.076] in the legend?" }, { "from": "gpt", "value": "The rectangle color representing Apple is blue." } ], "id_raw": 843 }, { "id": "OtBBT6hCEfi7NdrQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat label does the coordinates [0.909, 0.088, 0.958, 0.106] in the legend correspond to?" }, { "from": "gpt", "value": "The label is for {Google}[0.909, 0.088, 0.958, 0.106]." } ], "id_raw": 843 }, { "id": "kmlTeFzu3QtCAmG6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.493, 0.964, 0.554, 0.981] refer to in the chart?" }, { "from": "gpt", "value": "It refers to {the x-axis label}[0.493, 0.964, 0.554, 0.981] which is named \"Quarters\"." } ], "id_raw": 843 }, { "id": "dX6AtGClMRjb8DJa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nComparing the bar of Google in Q2 [0.369, 0.560, 0.413, 0.927] with Microsoft in Q2 [0.457, 0.634, 0.501, 0.927], which one recorded higher sales?" }, { "from": "gpt", "value": "{Microsoft}[0.457, 0.634, 0.501, 0.927] recorded higher sales in Q2 compared to {Google}[0.369, 0.560, 0.413, 0.927]." } ], "id_raw": 843 }, { "id": "e2xm6cOzSWHnc9eS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nConsidering the bars representing the sales for Apple in Q1 [0.104, 0.193, 0.148, 0.927] and Q4 [0.766, 0.267, 0.811, 0.927], did the sales increase or decrease?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 843 }, { "id": "8WfjFp0IUflzcVKK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nAre {Google's sales in Q3}[0.590, 0.516, 0.634, 0.927] higher than {Amazon's sales in Q3}[0.634, 0.370, 0.678, 0.927]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 843 }, { "id": "oRMxFZet0fKt3mQ0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nOf the two bars {Microsoft in Q1}[0.236, 0.707, 0.281, 0.927] and {Apple in Q1}[0.104, 0.193, 0.148, 0.927], which is taller signifying higher sales?" }, { "from": "gpt", "value": "Apple." } ], "id_raw": 843 }, { "id": "WO6MRVPwohzTh9aw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nDescribe what you see in the area [0.865, 0.055, 0.978, 0.165]." }, { "from": "gpt", "value": "In this region, there is a legend with color-text labels for four entities: {Apple is blue}[0.870, 0.064, 0.898, 0.076]; {Google is orange}[0.870, 0.090, 0.898, 0.102]; {Amazon is in a greyish-brown color}[0.870, 0.116, 0.898, 0.128], and {Microsoft is in a teal color}[0.870, 0.142, 0.898, 0.155]." } ], "id_raw": 843 }, { "id": "WXcQhahLF40be2FG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.043, 0.040, 0.052, 0.938]." }, { "from": "gpt", "value": "This region corresponds to the y-axis and includes numerical values ranging from 0 up to 60, which represent the 'Sales (in millions)'. Each major tick on this axis represents an interval of 10 units." } ], "id_raw": 843 }, { "id": "4VNIKUG77w1vKcPZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.634, 0.663, 0.722, 0.927]?" }, { "from": "gpt", "value": "In this region, the Microsoft's Q3 bar is located, which refers to sales of 18 million." } ], "id_raw": 843 }, { "id": "CVHiOhdMVXaq405j", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.104, 0.193, 0.148, 0.927]?" }, { "from": "gpt", "value": "Inside this area is located the bar representing Q1 sales of Apple having a value of 50 million." } ], "id_raw": 843 }, { "id": "iRhd24ItAUcHVfcu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat is positioned at coordinates [0.129, 0.603, 0.205, 0.903]?" }, { "from": "gpt", "value": "It's the bar representing Life Expectancy for the Rural category." } ], "id_raw": 845 }, { "id": "alSZL3VO9mRyPC0D", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object located at [0.043, 0.293, 0.076, 0.317]?" }, { "from": "gpt", "value": "It's the y-axis label representing the value '150'." } ], "id_raw": 845 }, { "id": "VECYChIXWL9wBac3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label represented by the coordinates [0.828, 0.153, 0.939, 0.176] in the legend area?" }, { "from": "gpt", "value": "It's the label 'Water Usage'." } ], "id_raw": 845 }, { "id": "1ySqizH9F4GEZYrF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for rural life expectancy}[0.129, 0.603, 0.205, 0.903] taller than {the bar for urban life expectancy}[0.636, 0.631, 0.712, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 845 }, { "id": "1J2m5HIG6Sh3AKSn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for CO2 Emission in rural areas}[0.205, 0.807, 0.281, 0.903] higher than {the bar for Water Usage in rural areas}[0.281, 0.102, 0.357, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 845 }, { "id": "qkZW9D6Nm2a4uxOD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {the bar for 'Plastic Waste' in urban areas}[0.865, 0.443, 0.941, 0.903] relative to {the bar for 'Life Expectancy' in urban areas}[0.636, 0.631, 0.712, 0.903]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 845 }, { "id": "WEO3ztQgS7VbuZYR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.772, 0.074, 0.973, 0.220]." }, { "from": "gpt", "value": "It is a legend area containing several color-text pairs, each includes a colored rectangular patch and a label: {Life Expectancy}[0.828, 0.083, 0.966, 0.106], {CO2 Emission}[0.828, 0.118, 0.947, 0.141], {Water Usage}[0.828, 0.153, 0.939, 0.176] and {Plastic Waste}[0.828, 0.188, 0.943, 0.211]." } ], "id_raw": 845 }, { "id": "QkEqSdMClNTfObIA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat lies within the area [0.636, 0.631, 0.712, 0.903]?" }, { "from": "gpt", "value": "It is {the bar representing 'Life Expectancy' in the 'Urban' category}[0.636, 0.631, 0.712, 0.903]." } ], "id_raw": 845 }, { "id": "uIwyfSqQ8DpL8Snl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in the region [0.281, 0.102, 0.357, 0.903]?" }, { "from": "gpt", "value": "It is {the bar for 'Water Usage' in the 'Rural' category}[0.281, 0.102, 0.357, 0.903]." } ], "id_raw": 845 }, { "id": "8HVSlWzUhfGsOTv6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the textual labels at the coordinates [0.143, 0.919, 0.209, 0.942] and [0.866, 0.919, 0.944, 0.942]?" }, { "from": "gpt", "value": "They are {x-axis labels for the categories \"Ancient\"}[0.143, 0.919, 0.209, 0.942] and {\"Future AI\"}[0.866, 0.919, 0.944, 0.942], respectively." } ], "id_raw": 846 }, { "id": "ltSHR8x3oAQOUQ8z", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the text \"Living Population (millions)\" located at [0.731, 0.083, 0.966, 0.106] refer to?" }, { "from": "gpt", "value": "That's the name of the {first series in the legend}[0.731, 0.083, 0.966, 0.106]." } ], "id_raw": 846 }, { "id": "FWcupXKy1VEScJjP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the nature of the data based on the text at [0.019, 0.172, 0.036, 0.793]?" }, { "from": "gpt", "value": "It's the {y-axis label}[0.019, 0.172, 0.036, 0.793], titled \"Units (In millions for Population and Index for Quality)\"." } ], "id_raw": 846 }, { "id": "DK8K4BHco6xPnUYH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nHow does the size of the bar representing the \"Ancient\" category at [0.139, 0.182, 0.176, 0.903] relate to the bar representing the \"Future AI\" category at [0.868, 0.753, 0.905, 0.903]?" }, { "from": "gpt", "value": "The {bar for the \"Ancient\" category}[0.139, 0.182, 0.176, 0.903] is taller than the {bar for the \"Future AI\" category}[0.868, 0.753, 0.905, 0.903]." } ], "id_raw": 846 }, { "id": "Xl5IYx3ifJEcXtWv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Living Quality bar}[0.358, 0.876, 0.394, 0.903] for the \"Medieval\" era higher or lower than the {Living Quality bar}[0.722, 0.855, 0.759, 0.903] for the \"Modern\" era?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 846 }, { "id": "sfSo4rMPXaAzNK9M", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nIn the legend, is the color box for {Living Quality}[0.683, 0.120, 0.717, 0.136] located higher or lower than the color box for {Living Population (millions)}[0.683, 0.085, 0.717, 0.101]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 846 }, { "id": "mP6tJKlCUl6wVgvU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.676, 0.074, 0.973, 0.151]." }, { "from": "gpt", "value": "This area contains the legend of the chart. It lists two datasets, {Living Population (millions)}[0.731, 0.083, 0.966, 0.106] with a corresponding color box at [0.683, 0.085, 0.717, 0.101] and {Living Quality}[0.731, 0.118, 0.851, 0.141] with a corresponding color box at [0.683, 0.120, 0.717, 0.136]." } ], "id_raw": 846 }, { "id": "3WmPJEIt2ptUF9bF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nDiscuss the components included within the region from [0.504, 0.453, 0.540, 0.903]." }, { "from": "gpt", "value": "This region features a single bar from the chart. It's the {Living Population (millions)}[0.504, 0.453, 0.540, 0.903] bar for the \"Industrial\" scenario." } ], "id_raw": 846 }, { "id": "nKm1y30xRjShwzZZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the area marked by [0.043, 0.053, 0.087, 0.076]?" }, { "from": "gpt", "value": "It includes a numeric label for the y-axis: {\"1400\"}[0.043, 0.053, 0.087, 0.076]." } ], "id_raw": 846 }, { "id": "YLT1EhhraRTy9Pkl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in the chart located at [0.743, 0.919, 0.886, 0.942]?" }, { "from": "gpt", "value": "The x-axis label, Discouragement." } ], "id_raw": 854 }, { "id": "HzM4Mo2P5iJmDABf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nWhere is the fourth y-axis label located?" }, { "from": "gpt", "value": "[0.054, 0.435, 0.076, 0.459]" } ], "id_raw": 854 }, { "id": "fiTTp5pGJGrnQcSS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color representation of \"Innovative Thinking\" in the legend?" }, { "from": "gpt", "value": "Red {the Innovative Thinking}[0.746, 0.155, 0.781, 0.171]." } ], "id_raw": 854 }, { "id": "0f1js5FtSpHV45dA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat labels are specified at [0.795, 0.083, 0.898, 0.106]?" }, { "from": "gpt", "value": "{Imagination}[0.795, 0.083, 0.898, 0.106]." } ], "id_raw": 854 }, { "id": "VOnYAOlimY8Z3xZZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Imagination bar for Encouragement}[0.129, 0.307, 0.213, 0.903] taller than {the Innovative Thinking bar for Discouragement}[0.857, 0.658, 0.941, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 854 }, { "id": "8aqgu2DhsNnNSByx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nAre {the Ongoing Discovery bars}[[0.213, 0.123, 0.297, 0.903], [0.773, 0.574, 0.857, 0.903]] generally taller than {the Innovative Thinking bars}[[0.297, 0.253, 0.381, 0.903], [0.857, 0.658, 0.941, 0.903]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 854 }, { "id": "0OuIzmPdjiEzP95I", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nBetween {Imagination bar for Encouragement}[0.129, 0.307, 0.213, 0.903] and {Imagination bar for Discouragement}[0.689, 0.521, 0.773, 0.903], which bar is taller?" }, { "from": "gpt", "value": "Encouragement." } ], "id_raw": 854 }, { "id": "rJ0jTl42ebUwX7Bi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nIs the color handle of {Ongoing Discovery}[0.746, 0.120, 0.781, 0.136] placed higher than that of {Innovative Thinking}[0.746, 0.155, 0.781, 0.171] in the legend?" }, { "from": "gpt", "value": "No." } ], "id_raw": 854 }, { "id": "CV6lv9FSRKymY0F8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.739, 0.074, 0.973, 0.185]." }, { "from": "gpt", "value": "This region contains the legend with three items. The first item is {Imagination}[0.795, 0.083, 0.898, 0.106] with color blue; the second item is {Ongoing Discovery}[0.795, 0.118, 0.959, 0.141] with color green; the third one is {Innovative Thinking}[0.795, 0.153, 0.966, 0.176] with color red." } ], "id_raw": 854 }, { "id": "qIw2tdz3f1FVDXPe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is included in [0.129, 0.307, 0.381, 0.903]?" }, { "from": "gpt", "value": "Within this rectangular area, there are bars representing the Encouragement's effect on three factors: {Imagination}[0.129, 0.307, 0.213, 0.903], {Ongoing Discovery}[0.213, 0.123, 0.297, 0.903], and {Innovative Thinking}[0.297, 0.253, 0.381, 0.903]." } ], "id_raw": 854 }, { "id": "iGeal4apNaCtjEFL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within [0.689, 0.521, 0.941, 0.903]?" }, { "from": "gpt", "value": "Inside this region there are 3 bars. These bars represent the results of Discouragement on three areas: {Imagination}[0.689, 0.521, 0.773, 0.903], {Ongoing Discovery}[0.773, 0.574, 0.857, 0.903], and {Innovative Thinking}[0.857, 0.658, 0.941, 0.903]." } ], "id_raw": 854 }, { "id": "z4xv7z6miA9Aommq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area covered by [0.104, 0.025, 0.965, 0.055]?" }, { "from": "gpt", "value": "This area contains the subtitle, which says \"Effect of Encouragement and Discouragement on Creativity and Design Innovation\"" } ], "id_raw": 854 }, { "id": "MIRy5xuOGByiNiuH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nWhich category is at the coordinate [0.194, 0.806, 0.213, 0.973] on the x-axis?" }, { "from": "gpt", "value": "Product Development." } ], "id_raw": 855 }, { "id": "aoMq5ruqO70hyWAa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the y_label in the chart at the coordinate [0.011, 0.275, 0.024, 0.539]?" }, { "from": "gpt", "value": "Innovation Design (%)." } ], "id_raw": 855 }, { "id": "iPFR0IPf3WpR8NYe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat value corresponds to the bar [0.666, 0.140, 0.708, 0.793] on the y-axis?" }, { "from": "gpt", "value": "This bar belongs to {Aesthetic Sensibility} and it reflects a value of 62.3%." } ], "id_raw": 855 }, { "id": "BnWxqiv5CeaHrpgH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar located at [0.194, 0.096, 0.232, 0.793] represent?" }, { "from": "gpt", "value": "This bar represents {Product Development}[0.194, 0.096, 0.232, 0.793] with a value of 63.2%." } ], "id_raw": 855 }, { "id": "d8IXtusxdqUk0TW3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Risk-taking}[0.623, 0.397, 0.661, 0.793] higher or lower than that representing {Problem Solving}[0.337, 0.291, 0.375, 0.793]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 855 }, { "id": "sjjXwOZvZE8f8oVI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nIs {Idea Generation}[0.099, 0.401, 0.137, 0.793] to the left or right of {Strategy Creation}[0.242, 0.238, 0.280, 0.793]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 855 }, { "id": "thNzK03yJoQNue2o", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nBetween {Collaborative Thinking}[0.385, 0.177, 0.423, 0.793] and {Product Development}[0.194, 0.096, 0.232, 0.793], which has a higher value?" }, { "from": "gpt", "value": "Product Development." } ], "id_raw": 855 }, { "id": "NWtKrreSYN5HmWrz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nIs {System Thinking}[0.909, 0.463, 0.947, 0.793] to the left or right of {Visionary Thinking}[0.289, 0.344, 0.328, 0.793]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 855 }, { "id": "u76mYzPEPuOPx1FA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.526, 0.0, 0.566, 0.793]." }, { "from": "gpt", "value": "This region contains {Prototyping}[0.528, 0.127, 0.566, 0.793] bar, reflecting a value of 60.4%." } ], "id_raw": 855 }, { "id": "guMHV06E9FdiJhc9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.385, 0.0, 0.423, 1.0] consist of?" }, { "from": "gpt", "value": "This region contains the bar of {Collaborative Thinking}[0.385, 0.177, 0.423, 0.793] with a value of 55.9% and the corresponding x-axis label located at [0.318, 0.806, 0.404, 0.979]." } ], "id_raw": 855 }, { "id": "meFnOulLuZ6qz10g", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat's within the coordinate [0.804, 0.806, 0.880, 0.958]?" }, { "from": "gpt", "value": "This coordinates contain {Pattern Recognition}[0.804, 0.806, 0.880, 0.958] label which is on the x-axis." } ], "id_raw": 855 }, { "id": "pyKXqc0p9rDGCnIu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.713, 0.00, 0.785, 1.00] entail?" }, { "from": "gpt", "value": "This region includes the segment of {Artistic Expression}[0.713, 0.265, 0.785, 0.793] bar, corresponding to a value of 47.9%, and the associated x-axis label at [0.713, 0.806, 0.785, 0.950]." } ], "id_raw": 855 }, { "id": "bMC0ZKW1rixGbDJE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat energy source is specified by the coordinates [0.322, 0.761, 0.371, 0.859] on the x-axis?" }, { "from": "gpt", "value": "Hydro." } ], "id_raw": 856 }, { "id": "HwV0fLG6nze3utpF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the Year 3 bar}[0.213, 0.267, 0.296, 0.403]?" }, { "from": "gpt", "value": "The color is #ffa600, which corresponds to orange." } ], "id_raw": 856 }, { "id": "52j6zVcIuEHctNbk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the area [0.121, 0.297, 0.204, 0.431]?" }, { "from": "gpt", "value": "This area represents the amount of Fossil Fuels used in Year 3, which is 73.2%." } ], "id_raw": 856 }, { "id": "0X02y5bb4Aicc1Eq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the text label of {the legend item}[0.662, 0.077, 0.710, 0.112]?" }, { "from": "gpt", "value": "Year 3." } ], "id_raw": 856 }, { "id": "tfXiQxyqqriRByn0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Hydro bar in Year 3}[0.305, 0.284, 0.388, 0.445] taller or shorter than {the Hydro bar in Year 1}[0.305, 0.610, 0.388, 0.737]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 856 }, { "id": "qOhx804KMb4VqVWv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Biomass bar in Year 1}[0.489, 0.721, 0.572, 0.737] to the left or the right of {the avg bar for Year 1}[0.857, 0.606, 0.940, 0.737]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 856 }, { "id": "2DOC9LSJ6w7m84zm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Year 1 Coal bar}[0.581, 0.433, 0.664, 0.737] wider than {the Year 2 Coal bar}[0.581, 0.238, 0.664, 0.433]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 856 }, { "id": "v5CkWD28UPR0TVdy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhich bar has greater value, {Year 2 Fossil Fuels bar}[0.121, 0.431, 0.204, 0.523] or {Year 2 Coal bar}[0.581, 0.238, 0.664, 0.433]?" }, { "from": "gpt", "value": "Year 2 Coal bar." } ], "id_raw": 856 }, { "id": "N0YxbPjKFWL9lC1B", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area within [0.345, 0.063, 0.717, 0.126] represent?" }, { "from": "gpt", "value": "This area contains the chart's legend, including three entries with their respective color markers. The entries are: Year1 with the color #003f5c[0.352, 0.080, 0.386, 0.104], Year2 with the color #bc5090[0.483, 0.080, 0.517, 0.104], and Year3 with the color #ffa600[0.614, 0.080, 0.648, 0.104]." } ], "id_raw": 856 }, { "id": "IGQZmLAxhkaE1Rp3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the area [0.673, 0.551, 0.756, 0.638]." }, { "from": "gpt", "value": "This area contains a bar from the Year 1 group, representing the usage of Natural gas, which accounts for 16.5%." } ], "id_raw": 856 }, { "id": "xYfMovTuZ1vpT0WN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the range from [0.792, 0.761, 0.821, 0.819]?" }, { "from": "gpt", "value": "This is a label on the x-axis for the energy source \"Oil\"." } ], "id_raw": 856 }, { "id": "JTYflzXxTR6wIEra", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the area [0.213, 0.267, 0.296, 0.403]." }, { "from": "gpt", "value": "This area contains a bar for \"Nuclear Power\" from the group \"Year 3\". The bar represents the proportion of Nuclear Power for that year, which is 22.6%." } ], "id_raw": 856 }, { "id": "lKoNMBLPeATTD2Os", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat social media platform has the coordinates [0.166, 0.812, 0.241, 0.962] along the X-axis?" }, { "from": "gpt", "value": "Instagram" } ], "id_raw": 857 }, { "id": "yEGg8IrjHGx8MnWc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the bar representing \"Ambiversion\"}[0.452, 0.136, 0.527, 0.263] correspond to?" }, { "from": "gpt", "value": "Blue" } ], "id_raw": 857 }, { "id": "l16vWygQguYyt6G1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.263, 0.072, 0.297, 0.097] in the legend represent?" }, { "from": "gpt", "value": "It represents the color for the \"Introversion\" bars." } ], "id_raw": 857 }, { "id": "nlz6PqYPFHe0pjGG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Extroversion bar of \"Skype\"}[0.866, 0.261, 0.940, 0.533] taller than {the Introversion bar of \"Skype\"}[0.866, 0.533, 0.940, 0.788]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 857 }, { "id": "zenLod54XHrH9Za6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nIn terms of \"Ambiversion\", is {Twitter}[0.287, 0.136, 0.361, 0.262] or {Snapchat}[0.452, 0.136, 0.527, 0.263] rated higher?" }, { "from": "gpt", "value": "They have the same height, indicating the same value for Ambiversion." } ], "id_raw": 857 }, { "id": "lmBWC0aCCQpoNxQE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nAre {the Introversion bars} generally taller than {the Extroversion bars} across all platforms?" }, { "from": "gpt", "value": "No." } ], "id_raw": 857 }, { "id": "aRRu0nDSr885ikRl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within [0.520, 0.483, 0.609, 0.788]." }, { "from": "gpt", "value": "There are 3 objects, indicating Introversion, Extroversion, and Ambiversion's values for Reddit. They are ordered from top to bottom as: {Introversion bar of Reddit}[0.535, 0.483, 0.609, 0.788], {Extroversion bar of Reddit}[0.535, 0.262, 0.609, 0.483] and {Ambiversion bar of Reddit}[0.535, 0.136, 0.609, 0.262]." } ], "id_raw": 857 }, { "id": "JU51Vzh86k0smN82", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within the coordinates [0.256, 0.055, 0.806, 0.118]?" }, { "from": "gpt", "value": "This region contains the chart legend, with three categories labelled: Introversion represented in green {color box}[0.263, 0.072, 0.297, 0.097], Extroversion represented in a yellow color {color box}[0.450, 0.072, 0.484, 0.097], and Ambiversion represented in blue {color box}[0.642, 0.072, 0.677, 0.097]." } ], "id_raw": 857 }, { "id": "wEQtBXRTbfX8OmcO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is shown in the region [0.080, 0.136, 0.981, 0.788]?" }, { "from": "gpt", "value": "This region is the main body of the chart showing the stacked bars. Each set of bars corresponds to a different social media platform, listed along the x-axis. Each bar corresponds to the Introversion, Extroversion and Ambiversion values of the social media platforms." } ], "id_raw": 857 }, { "id": "BImVFyXys3jgcFMz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label present at the coordinates [0.127, 0.823, 0.190, 0.947]?" }, { "from": "gpt", "value": "Region1." } ], "id_raw": 860 }, { "id": "pjwe54PT78Sp9I33", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing Param2 at coordinates [0.204, 0.568, 0.278, 0.798]?" }, { "from": "gpt", "value": "White." } ], "id_raw": 860 }, { "id": "vitY3Cc294ydChRa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nWhich Param is represented with the handle coordinates [0.851, 0.177, 0.885, 0.201] in the subplot legend?" }, { "from": "gpt", "value": "Param3." } ], "id_raw": 860 }, { "id": "6iYLs14d0Ex1XdlL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Param1}<{Param1}[0.121, 0.576, 0.196, 0.798]> in Region1 taller than the bar of {Param2}<{Param2}[0.121, 0.362, 0.196, 0.576]> in the same region?" }, { "from": "gpt", "value": "No." } ], "id_raw": 860 }, { "id": "GMIgQyeHvC4eopbl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nIn {Region3}, does {Param3}<{Param3}[0.287, 0.074, 0.361, 0.215]> have a higher value than {Param2}<{Param2}[0.287, 0.293, 0.361, 0.596]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 860 }, { "id": "Lv62zP1i7GGIL27E", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller in Region7 - {Param1}<{Param1}[0.618, 0.606, 0.692, 0.798]> or {Param3}<{Param3}[0.618, 0.074, 0.692, 0.359]>?" }, { "from": "gpt", "value": "Param1." } ], "id_raw": 860 }, { "id": "nJ4nkEHdHPTVij6E", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects residing in the area [0.121, 0.074, 0.196, 0.798]." }, { "from": "gpt", "value": "There are 3 stacked bars. The top bar representing the measure Param1 extends from 60.19% to 100.00%, the middle bar for Param2 ranges from 30.71% to 60.19%, and the bottommost bar for Param3 spans from 0% to 30.71%. All three bars are white." } ], "id_raw": 860 }, { "id": "wW7iqmAj83PWAGbj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area of [0.844, 0.055, 0.973, 0.222]." }, { "from": "gpt", "value": "It's the subplot legend area with three entries: Param1 with a handle at [0.851, 0.072, 0.885, 0.096], Param2 with a handle at [0.851, 0.124, 0.885, 0.149], and Param3 with a handle at [0.851, 0.177, 0.885, 0.201]. All handles are white." } ], "id_raw": 860 }, { "id": "anvG9qLtXMlA9Bfc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nTell me about the elements found in the region [0.204, 0.074, 0.278, 0.798]." }, { "from": "gpt", "value": "There are 3 stacked bars. The top bar stands for the measure Param1 with coordinates [0.204, 0.568, 0.278, 0.798], the middle bar represents Param2 and extends from point [0.204, 0.293, 0.278, 0.568], and the bottom bar represents Param3 with coordinates [0.204, 0.074, 0.278, 0.293]. All these bars are colored white." } ], "id_raw": 860 }, { "id": "Yacpi5CFuCsBUiui", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by the coordinates [0.329, 0.275, 0.422, 0.634] in the chart?" }, { "from": "gpt", "value": "It is a white bar in the first group of bars (Option1) indicating the \"Home Area\" with a value of 72." } ], "id_raw": 862 }, { "id": "jctOcZkPLe1P7w0B", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nCould you locate the label of the third bar group?" }, { "from": "gpt", "value": "The label {Option3}[0.673, 0.092, 0.742, 0.127] is represented in the legend." } ], "id_raw": 862 }, { "id": "uKZRnpMrPb0GMfwP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat label is at the coordinates of [0.738, 0.658, 0.842, 0.865] on the x-axis?" }, { "from": "gpt", "value": "It is the label {School_District}[0.738, 0.658, 0.842, 0.865]." } ], "id_raw": 862 }, { "id": "8yCrmZ0bKNGPLFHj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nAre the bars for {Option2}[0.847, 0.041, 0.940, 0.285] generally taller than those for {Option1}[0.847, 0.285, 0.940, 0.634]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 862 }, { "id": "iKLabQZVqCHzOCR1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nComparing the bars for {Option1} and {Option3}, which has a higher value for {Number_of_rooms}<{Option1 Number_of_rooms bar}[0.225, 0.335, 0.318, 0.634], {Option3 Number_of_rooms bar}[0.225, -0.143, 0.318, 0.041]>?" }, { "from": "gpt", "value": "Option3." } ], "id_raw": 862 }, { "id": "jDcgo48XaNtXIu1d", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Family_Size in Option2}[0.121, 0.121, 0.215, 0.345] taller than the bar for {Public_Transport in Option1}[0.640, 0.410, 0.733, 0.634]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 862 }, { "id": "7ZW3PMvvVPvr6lGm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the area [0.313, 0.078, 0.749, 0.141]." }, { "from": "gpt", "value": "It contains the legend of the chart with three entries: {Option1}[0.368, 0.092, 0.437, 0.127] with a white handle at [0.320, 0.095, 0.354, 0.120], {Option2}[0.521, 0.092, 0.590, 0.127] with a white handle at [0.472, 0.095, 0.507, 0.120], and {Option3}[0.673, 0.092, 0.742, 0.127] with a white handle at [0.624, 0.095, 0.659, 0.120]." } ], "id_raw": 862 }, { "id": "Db999koaJEDfS5yN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the area [0.121, -0.079, 0.215, 0.634]." }, { "from": "gpt", "value": "It comprises three bars corresponding to the categories 'Option1', 'Option2', 'Option3' for the 'Family_Size' label. The {Option1 bar}[0.121, 0.345, 0.215, 0.634] has a value of 58, the {Option2 bar}[0.121, 0.121, 0.215, 0.345] a value of 45, and the {Option3 bar}[0.121, -0.079, 0.215, 0.121] a value of 40." } ], "id_raw": 862 }, { "id": "9Hs5c8xGfw4cs6yz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.472, 0.096, 0.526, 0.360] contain?" }, { "from": "gpt", "value": "It contains the fourth bar of the Option2 group of bars, representing the category {Number_of_bathrooms}[0.432, -0.138, 0.526, 0.096] with a value range of 108 to 155." } ], "id_raw": 862 }, { "id": "6Y80c5ObahPIM0Yk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value on the Y-axis at the coordinates [0.020, 0.314, 0.045, 0.337]?" }, { "from": "gpt", "value": "80%." } ], "id_raw": 863 }, { "id": "bvjUfAqRHEM6c9UK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the chart bar at [0.339, 0.525, 0.379, 0.901]?" }, { "from": "gpt", "value": "AI Research for the year 2013." } ], "id_raw": 863 }, { "id": "kmYLypvDNALEtLGx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the ticks at [0.183, 0.917, 0.212, 0.975] on the X-axis?" }, { "from": "gpt", "value": "The year 2011." } ], "id_raw": 863 }, { "id": "1fI1Gvu5cKTVBu9h", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nIs the line representing AI Research at [0.339, 0.525, 0.379, 0.901] higher than the line representing Automation Applications at [0.339, 0.092, 0.379, 0.525] for the year 2013?" }, { "from": "gpt", "value": "No." } ], "id_raw": 863 }, { "id": "UGcyLWjkGQNeMoS8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nIs the AI Research bar for the year 2010 at [0.096, 0.648, 0.136, 0.901] higher than for the year 2011 at [0.177, 0.626, 0.217, 0.901]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 863 }, { "id": "Z9LOCXkF4XW3bGna", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nIs the Automation Applications line for 2014 at [0.419, 0.034, 0.460, 0.504] higher than the one for 2013 at [0.339, 0.092, 0.379, 0.525]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 863 }, { "id": "l2IoyJnIYkaxAaAw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.339, 0.034, 0.460, 0.901]?" }, { "from": "gpt", "value": "This is a region representing the year 2014. It consists of two bars. The upper bar indicates the value of AI Research is 55%, and the lower bar indicates that the value of Automation Applications is 120%." } ], "id_raw": 863 }, { "id": "5ZSfNlEwhfVA7Xvt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat appears in the area [0.027, 0.892, 0.045, 0.915]?" }, { "from": "gpt", "value": "The Y-axis label showing the value of 0% is present in this area." } ], "id_raw": 863 }, { "id": "RIUimUWp4WnDodX6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the area [0.096, 0.648, 0.136, 0.901]?" }, { "from": "gpt", "value": "This area represents the graph bar for AI Research in the year 2010 which is at 35%." } ], "id_raw": 863 }, { "id": "4ojBRIHd3ccpQHKK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the axes} at [0.088, 0.129, 0.985, 0.740]?" }, { "from": "gpt", "value": "The coordinate plane of the bar chart." } ], "id_raw": 864 }, { "id": "zRy2ksT2MMW1ulwI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the xlabel called Categories?" }, { "from": "gpt", "value": "[0.492, 0.945, 0.581, 0.975]" } ], "id_raw": 864 }, { "id": "CHjGxjS47L9zW7Eb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-label} at [0.015, 0.357, 0.033, 0.511]?" }, { "from": "gpt", "value": "Indices (%)." } ], "id_raw": 864 }, { "id": "KqbQsh115Y0tZCK6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nWhich categories are listed in this coordinates [0.123, 0.756, 0.196, 0.879],[0.192, 0.756, 0.264, 0.877],[0.256, 0.756, 0.337, 0.891],[0.322, 0.756, 0.408, 0.900]?" }, { "from": "gpt", "value": "{Public Safety}[0.123, 0.756, 0.196, 0.879], {Environment}[0.192, 0.756, 0.264, 0.877], {Transportation}[0.256, 0.756, 0.337, 0.891], and {Health Services}[0.322, 0.756, 0.408, 0.900]." } ], "id_raw": 864 }, { "id": "dHVsn3Gr7XuPMStz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Public Transport\"}[0.883, -0.794, 0.944, -0.317] taller than {the bar representing \"Public Safety\"}[0.129, 0.208, 0.190, 0.740]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 864 }, { "id": "uG48eG9bjQxBWBk9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nAre {the bars representing Quality}[[0.129, 0.208, 0.190, 0.740],[0.197, 0.184, 0.259, 0.740],[0.266, 0.166, 0.327, 0.740],[0.334, 0.178, 0.396, 0.740]] generally shorter than {the bars representing Equity}[[0.129, -0.818, 0.190, -0.354],[0.197, -0.904, 0.259, -0.397],[0.266, -0.983, 0.327, -0.433],[0.334, -0.916, 0.396, -0.397]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 864 }, { "id": "JkQRoApEpP0ijMB9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Public Safety\"}[0.123, 0.756, 0.196, 0.879] and {\"Environment\"}[0.192, 0.756, 0.264, 0.877],[0.197, 0.184, 0.259, 0.740]], which category has higher {Quality}<{Quality bar for Public Safety}[0.129, 0.208, 0.190, 0.740], {Quality bar for Environment}[0.197, 0.184, 0.259, 0.740]>?" }, { "from": "gpt", "value": "Environment." } ], "id_raw": 864 }, { "id": "yGVE1Aw8rpQ5ZkTH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nDescribe content in [0.129, -0.818, 0.190, -0.354]." }, { "from": "gpt", "value": "This coordinate area comprises a single white bar representing the Quality index of \"Public Safety\" from 179% to 255%." } ], "id_raw": 864 }, { "id": "IO0FRYMsT1tc0pgg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.047, 0.242, 0.078, 0.265]." }, { "from": "gpt", "value": "The y-label \"80%\" is located within these coordinates." } ], "id_raw": 864 }, { "id": "7mtkkFHXKnLw70ND", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.197, -0.904, 0.259, -0.397]?" }, { "from": "gpt", "value": "There is only 1 object: a Quality bar representing the \"Environment\" category with the range from 186% to 269%." } ], "id_raw": 864 }, { "id": "NQschnKWzVvH79BN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.865, 0.756, 0.944, 0.740]?" }, { "from": "gpt", "value": "In this area, you can see a label for the category \"Public Transport\"." } ], "id_raw": 864 }, { "id": "K8AHim6kTNdVjAbi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart element at the coordinates [0.015, 0.651, 0.055, 0.675]?" }, { "from": "gpt", "value": "It is the y-axis label, specifically the label for \"100%\"." } ], "id_raw": 865 }, { "id": "Z69kEDvlkfmkLMXQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bars represented by the legend at [0.320, 0.041, 0.409, 0.065]?" }, { "from": "gpt", "value": "The bars are colored white." } ], "id_raw": 865 }, { "id": "dfoxnc7sB78SuNF5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat group does the bar at [0.619, 0.208, 0.772, 0.373] belong to?" }, { "from": "gpt", "value": "The bar belongs to the group titled \"Sharp Rise\"." } ], "id_raw": 865 }, { "id": "8twzT1yHQUeXH0cB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing the category \"Electronics\" higher or lower than the bar representing \"Automobiles\" for the trend \"Volatile Growth\"?" }, { "from": "gpt", "value": "The bar for the category \"Electronics\"[0.106, 0.515, 0.260, 0.531] is lower than \"Automobiles\"[0.619, 0.538, 0.772, 0.628]." } ], "id_raw": 865 }, { "id": "68JcPLkJhCcfePpK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nIs the \"Steady Increase\" trend higher for \"Grocery\" or \"Fashion\"?" }, { "from": "gpt", "value": "The \"Steady Increase\" trend is higher for \"Fashion\"[0.277, 0.342, 0.431, 0.504] than for \"Grocery\"[0.448, 0.375, 0.601, 0.535]." } ], "id_raw": 865 }, { "id": "YrXGX1tXRAl1XS0c", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nIs \"Slow Decline\" more for \"Stationery\" or for \"Fashion\"?" }, { "from": "gpt", "value": "\"Slow Decline\" is more for \"Electronics\"[0.106, 0.675, 0.260, 0.840] than for \"Fashion\"[0.277, 0.688, 0.431, 0.840]." } ], "id_raw": 865 }, { "id": "a6m0qzf8grMwK4xW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents inside [0.775, 0.839, 0.947, 0.961]?" }, { "from": "gpt", "value": "It contains a x-axis label called \"Stationary\"[0.836, 0.857, 0.897, 0.958]." } ], "id_raw": 865 }, { "id": "XKP7O6l7RmjepfH7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the bars and their respective categories within the region [0.106, 0.184, 0.260, 0.357]." }, { "from": "gpt", "value": "This region contains part of the \"Electronics\" category for \"Sharp Rise\". The respective bar is located at [0.106, 0.184, 0.260, 0.357]." } ], "id_raw": 865 }, { "id": "k7qq5RewhmOloClH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.275, 0.032, 0.774, 0.109] represent in the chart?" }, { "from": "gpt", "value": "It includes the legend for the bar chart with three color-text labels, each representing a different trend. The trends are \"Slow Decline\"[0.320, 0.041, 0.409, 0.065], \"Inexorable Decline\"[0.320, 0.076, 0.449, 0.100], and \"Volatile Growth\"[0.516, 0.041, 0.622, 0.065]." } ], "id_raw": 865 }, { "id": "U7rFvaUQfS6qCLHl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color connected to \"Tech Startups\" in the legend?" }, { "from": "gpt", "value": "White, connected to {Tech Startups}[0.340, 0.040, 0.418, 0.060]." } ], "id_raw": 866 }, { "id": "EBppkhii1VoZyLhY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nHow is the x-axis labeled directly below {the bar for \"FinTech\"}[0.293, 0.584, 0.353, 0.827]?" }, { "from": "gpt", "value": "FinTech, located at {x-axis label for FinTech}[0.304, 0.841, 0.342, 0.907]." } ], "id_raw": 866 }, { "id": "BG9gNLmFtBRYHimF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nWhat percentage is shown by the y-axis label aligned with the bottom of {the Green Tech bar from Venture Capital}[0.096, 0.458, 0.155, 0.766]?" }, { "from": "gpt", "value": "8.8%, as indicated by {the y-axis label}[0.027, 0.820, 0.045, 0.840]." } ], "id_raw": 866 }, { "id": "9wfZz9hTeMtVshWi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Average\"}[0.886, 0.617, 0.945, 0.827] higher than {the bar for \"On-Demand\"}[0.820, 0.558, 0.879, 0.827]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 866 }, { "id": "x5jMDLySxkWq10P1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nAre {the bars for \"Space Tech\"}[[0.688, 0.610, 0.748, 0.827], [0.688, 0.406, 0.748, 0.610], [0.688, 0.127, 0.748, 0.406]] arranged in descending order from top to bottom?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 866 }, { "id": "Bev3ppk9BVpwF1wG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nWhich has a wider range in the chart, {the labels on the x-axis}[0.170, 0.841, 0.212, 0.913] or {the menu in the sublegend}[0.303, 0.032, 0.738, 0.068]?" }, { "from": "gpt", "value": "Sublegend." } ], "id_raw": 866 }, { "id": "qXnZrSLUfPDwAcIS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content between the coordinates [0.096, 0.127, 0.221, 0.458] covering multiple bars." }, { "from": "gpt", "value": "This region contains three segments of bars for Green Tech across different fundings: {Tech Startups}[0.096, 0.766, 0.155, 0.827], {Venture Capital}[0.096, 0.458, 0.155, 0.766], {Government Grants}[0.096, 0.127, 0.155, 0.458] showing increasing funding amounts." } ], "id_raw": 866 }, { "id": "HD1PZEtzVbsR8LxH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nDetail the area occupied by y-axis percentage labels from [0.013, 0.119, 0.045, 0.840]." }, { "from": "gpt", "value": "It includes the vertical spectrum of percentages from 0% to 100%, each labeled correspondingly at intervals of 20%: {0%}[0.027, 0.820, 0.045, 0.840], {20%}[0.020, 0.679, 0.045, 0.699], {40%}[0.020, 0.539, 0.045, 0.559], {60%}[0.020, 0.399, 0.045, 0.419], {80%}[0.020, 0.259, 0.045, 0.279], {100%}[0.013, 0.119, 0.045, 0.139]." } ], "id_raw": 866 }, { "id": "iaib9ZPN64rI4yLe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.427, 0.841, 0.748, 0.937]?" }, { "from": "gpt", "value": "This region includes labels for Health Tech, e-Commerce, and Space Tech located along the x-axis representing different areas of focus for data shown by {Health Tech}[0.427, 0.841, 0.483, 0.937], {e-Commerce}[0.490, 0.841, 0.551, 0.945], and {Space Tech}[0.691, 0.841, 0.745, 0.934] on the bar chart." } ], "id_raw": 866 }, { "id": "957u0EJFsEPh7Q8m", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the label 'Recovery'}[0.375, 0.049, 0.440, 0.072] have in the legend?" }, { "from": "gpt", "value": "White." } ], "id_raw": 867 }, { "id": "qLHJlBKCXNPYkFzH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate range showing the percentage values for {\"Theatre's highest section in 'Decline' category\"}[0.106, 0.129, 0.151, 0.230]?" }, { "from": "gpt", "value": "85.00 to 100.00." } ], "id_raw": 867 }, { "id": "DijU4jBbHXhxksX6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat represents the label located at [0.656, 0.820, 0.696, 0.895] in the x-axis?" }, { "from": "gpt", "value": "Writing." } ], "id_raw": 867 }, { "id": "JMFjsxmquedmbfWW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nIs {\"the highest value section of 'Museums'\"}[0.700, 0.129, 0.745, 0.223] higher than {\"the lowest value section of 'Photography'\"}[0.552, 0.601, 0.596, 0.804]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 867 }, { "id": "JhxuGAIOa5qBl3FU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nIs {the start point for 'theatre'}[0.106, 0.804] located to the left of {the start point for 'comedy'}[0.800, 0.804]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 867 }, { "id": "AsmqlcuxDQ5z1ULe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nWhich x-axis label is nearer to {the tick for 'Novels'}[0.921, 0.804]?" }, { "from": "gpt", "value": "Novels." } ], "id_raw": 867 }, { "id": "cGKGh8Ib7sF7T28w", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content and structure within [0.046, 0.049, 0.330, 0.078]." }, { "from": "gpt", "value": "This region includes a series of shaded backgrounds for several x-axis tick labels for categories 'Theatre' through 'Opera', denoting different cultural sectors." } ], "id_raw": 867 }, { "id": "ge6WoGkxLqyufAGG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the zone from [0.106, 0.129, 0.943, 0.804] represent in the chart?" }, { "from": "gpt", "value": "It contains all the grouped bar segments for each industry sector across the categories 'Recovery', 'Performance', and 'Decline', showing the gradual rise or fall in values." } ], "id_raw": 867 }, { "id": "GvqMATPp1qc1TPj6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.331, 0.039, 0.718, 0.081]." }, { "from": "gpt", "value": "This area contains the chart's legend specifying the three types of categories \u2014 'Recovery', 'Performance', and 'Decline', with each category presented alongside its associated color box." } ], "id_raw": 867 }, { "id": "WbWOE2S9g33vj2Uv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bars with coordinates [0.106, 0.554, 0.234, 0.742]?" }, { "from": "gpt", "value": "It represents the \"Debt Management\" category in \"Regulation Compliance\"." } ], "id_raw": 868 }, { "id": "eZLLQunHkSifPyno", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nWhat category is identified by the label at [0.749, 0.759, 0.879, 0.975]?" }, { "from": "gpt", "value": "The category is \"Operational Risk Control\"." } ], "id_raw": 868 }, { "id": "TZMSPHGCxOn8BaRe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nWhat part of the chart can be found at [0.768, 0.025, 0.978, 0.137]?" }, { "from": "gpt", "value": "It's the legend part of the chart containing labels for \"Regulation Compliance\", \"Operational Oversight\", and \"Market Risk Control\"." } ], "id_raw": 868 }, { "id": "fTYAtiAl5gFLsyIu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nIs the bar group representing \"Debt Management\"{bar_coordinates: [0.106, 0.554, 0.234, 0.742]} taller than the bar group representing \"Operational Risk Control\"{bar_coordinates: [0.815, 0.530, 0.943, 0.742]}?" }, { "from": "gpt", "value": "No." } ], "id_raw": 868 }, { "id": "NfAGwE26xe5g5pk4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Operational Risk Control\" in \"Operational Oversight\"{bar_coordinates: [0.815, 0.365, 0.943, 0.530]} higher than that in \"Market Risk Control\"{bar_coordinates: [0.815, 0.224, 0.943, 0.365]}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 868 }, { "id": "CIZsuxzAapumLAlN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nAre the y-scale values for Credit Risk Control greater { Coordinate of 'Credit Risk Control': [0.352, 0.759, 0.454, 0.928]} or less than that of Reputation Risk Control { Coordinate of 'Reputation Risk Control': [0.532, 0.542, 0.659] } in Market Risk Control?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 868 }, { "id": "sq7GMO3HuNSWJkOe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area of [0.768, 0.025, 0.978, 0.137]." }, { "from": "gpt", "value": "This region contains the chart legend. The first label, for \"Regulation Compliance\"{text_coordinates: [0.812, 0.034, 0.973, 0.058]}, has a color label of white {coordinates: [0.773, 0.036, 0.801, 0.053]}. The second label, \"Operational Oversight\"{text_coordinates: [0.812, 0.069, 0.966, 0.093]}, similarly has a color label of white {coordinates: [0.773, 0.071, 0.801, 0.088]}. Lastly, the label for \"Market Risk Control\"{text_coordinates: [0.812, 0.104, 0.948, 0.127]} also has a color label of white {coordinates: [0.773, 0.106, 0.801, 0.122]}." } ], "id_raw": 868 }, { "id": "wZNKi9KN0G2C1fZs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.064, 0.148, 0.985, 0.742]." }, { "from": "gpt", "value": "This area covers the entire plot of the chart including all the bar groups for each category - \"Debt Management\", \"Equity Management\", \"Credit Risk Control\", \"Reputation Risk Control\", \"Financial Reporting\", \"Operational Risk Control\" and the labels for each percentage on the y-axis." } ], "id_raw": 868 }, { "id": "iO9PAOMcdLq5OHe3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region [0.674, 0.365, 0.801, 0.742]?" }, { "from": "gpt", "value": "It includes the bars for \"Financial Reporting\" in \"Regulation Compliance\", \"Operational Oversight\", and \"Market Risk Control\"." } ], "id_raw": 868 }, { "id": "0pxq3Lqrd0SXznEr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the blue bar at [0.121, 0.241, 0.509, 0.662] represent?" }, { "from": "gpt", "value": "It represents the value of \"Artificial Intelligence\" in \"RegOpt\", which is 80%." } ], "id_raw": 869 }, { "id": "RRy3jaQtenK7BF6x", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the text inside {the second option in the legend}[0.526, 0.088, 0.575, 0.123]?" }, { "from": "gpt", "value": "BIOpt." } ], "id_raw": 869 }, { "id": "FvJtLrZdQ6yjoDzU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the green bar at [0.552, 0.136, 0.940, 0.241] show?" }, { "from": "gpt", "value": "The green bar represents \"Machine Learning\" in \"LoopOpt\" and its value is 80% to 100%." } ], "id_raw": 869 }, { "id": "1TKPNmNHIWRtOtfM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the indicator of {the x-axis}[0.246, 0.686, 0.384, 0.963] at the first tick?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 869 }, { "id": "EKFyapeGISjiSdKq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nIs the {blue bar}[0.121, 0.241, 0.509, 0.662] higher or lower than the {red bar}[0.121, 0.188, 0.509, 0.241]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 869 }, { "id": "XdOpgSXIrnKbNpLU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nIs the {green bar}[0.552, 0.136, 0.940, 0.241] to the left or right of the {red bar}[0.552, 0.241, 0.940, 0.556]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 869 }, { "id": "exvw6S4hZmI6MwEs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nAre the green bars generally shorter than the red bars?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 869 }, { "id": "69WEaTHdU0Tu4S4D", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nWhich category has a higher value for \"LoopOpt\", {\"Artificial Intelligence\"}[0.121, 0.136, 0.509, 0.188] or {\"Machine Learning\"}[0.552, 0.136, 0.940, 0.241]?" }, { "from": "gpt", "value": "Both have the same range from 80% to 100%." } ], "id_raw": 869 }, { "id": "F4385ZJKoqddWOP2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.323, 0.074, 0.739, 0.137]." }, { "from": "gpt", "value": "This is the legend area, which includes three sets of color-label pairs. The first pair is blue and labeled as \"RegOpt\", located at [0.330, 0.091, 0.365, 0.116] and [0.379, 0.088, 0.443, 0.123] respectively. The second pair is red and labeled as \"BIOpt\", located at [0.478, 0.091, 0.513, 0.116] and [0.526, 0.088, 0.575, 0.123] respectively. The third pair is green and labeled as \"LoopOpt\", located at [0.610, 0.091, 0.644, 0.116] and [0.658, 0.088, 0.732, 0.123] respectively." } ], "id_raw": 869 }, { "id": "ebsThlxAQ6NRjqmi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.246, 0.686, 0.384, 0.963]." }, { "from": "gpt", "value": "This region contains the label for \"Artificial Intelligence\"." } ], "id_raw": 869 }, { "id": "KVe7FHtNBJYM0ENv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.552, 0.136, 0.940, 0.662]?" }, { "from": "gpt", "value": "This region consists of two bars and their corresponding labels. The red bar signifies \"Machine Learning\" in \"BIOpt\" with a range of 20% to 80%, while the green bar represents \"Machine Learning\" in \"LoopOpt\" with a range of 80% to 100%." } ], "id_raw": 869 }, { "id": "fkQu2aei0oTNdvQ3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what you observe in the region [0.121, 0.136, 0.940,0.662]." }, { "from": "gpt", "value": "This region contains three bars which are representative of \"Artificial Intelligence\" and \"Machine Learning\". The blue bar annotates \"Artificial Intelligence\" in \"RegOpt\" with a range of 0% to 80%, and green bar portrays \"LoopOpt\" from 80% to 100%. For \"Machine Learning\", the red bar shows \"BIOpt\" from 20% to 80% and the green bar depicts \"LoopOpt\" from 80% to 100%." } ], "id_raw": 869 }, { "id": "sS6Yoowm0hJpXCur", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nWhat specific topic does the title {the subtitle}[0.322, 0.118, 0.689, 0.163] refer to?" }, { "from": "gpt", "value": "Healthcare and Medical Technology." } ], "id_raw": 870 }, { "id": "wHsXsIPFLu31kuAA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the data being represented by the chart in the area bounded by [0.126, 0.354, 0.179, 0.903]?" }, { "from": "gpt", "value": "It represents the Device Accuracy of Anesthesia, which is 75.3." } ], "id_raw": 870 }, { "id": "3P6d2O9W8QlVBqL2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the legend item is represented in the area [0.153, 0.060, 0.297, 0.095]?" }, { "from": "gpt", "value": "It's the label for Device Accuracy, which is colored blue." } ], "id_raw": 870 }, { "id": "Se6NvQHIhRdsGSiO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing Device Accuracy for Anesthesia}[0.126, 0.354, 0.179, 0.903] have a higher value than {the bar representing Device Accuracy2 for Neurology}[0.778, 0.666, 0.832, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 870 }, { "id": "78tsbBhaxzpurYOC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Misleading Information for Radiology}[0.432, 0.174, 0.486, 0.687] taller than {the bar representing Device Accuracy for Radiology}[0.326, 0.445, 0.379, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 870 }, { "id": "0l7C3ErQlJXEqf6q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {the bar for Device Accuracy for Cardiology}[0.526, 0.460, 0.579, 0.903] and {the bar for Device Accuracy2 for Cardiology}[0.579, 0.513, 0.632, 0.903] in terms of value?" }, { "from": "gpt", "value": "The value of Device Accuracy2 for Cardiology is less." } ], "id_raw": 870 }, { "id": "tCQgRB75xXEQUlgs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.097, 0.046, 0.315, 0.161]." }, { "from": "gpt", "value": "This area contains the legend for the chart. There are two labels present, Device Accuracy with its corresponding color blue at [0.104, 0.063, 0.139, 0.087], and Device Accuracy2 with its corresponding color at [0.104, 0.115, 0.139, 0.139]." } ], "id_raw": 870 }, { "id": "x9D3mNFY6oedCiht", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.158, 0.927, 0.849, 0.962]." }, { "from": "gpt", "value": "This region contains labels for the X-axis categories. They are Anesthesia at [0.158, 0.927, 0.254, 0.962], Radiology at [0.363, 0.927, 0.449, 0.962], Cardiology at [0.559, 0.927, 0.652, 0.962], and Neurology at [0.761, 0.927, 0.849, 0.962]." } ], "id_raw": 870 }, { "id": "A0MWoVhjvHs0GVfl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is covered in the area [0.126, 0.174, 0.885, 0.903]." }, { "from": "gpt", "value": "This area covers all the bars in the chart, it includes Device Accuracy bars for Anesthesia, Radiology, Cardiology, and Neurology as well as Device Accuracy2 bars for the same categories. Additionally, it also includes the bars representing misleading information for all the mentioned categories." } ], "id_raw": 870 }, { "id": "67tAQYHrfZN2Et45", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of the chart?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 871 }, { "id": "QUPvVZKrQdqnIYnE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the title} at [0.313, 0.030, 0.687, 0.066] of the chart is?" }, { "from": "gpt", "value": "AI Models: Advanced Automation Comparison." } ], "id_raw": 871 }, { "id": "7ojxRX7QUdtBtHYj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the color represents {at this point} [0.726, 0.100, 0.926, 0.128]?" }, { "from": "gpt", "value": "Advanced Automation Recall." } ], "id_raw": 871 }, { "id": "q5ZY9prE0ZI9aVoG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of Model 1}[0.102, 0.101, 0.143, 0.499] higher than {the bar of Model 8}[0.817, 0.286, 0.858, 0.499]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 871 }, { "id": "K7xzT4XFyO0aJRKp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the bar of Model 3}[0.306, 0.302, 0.347, 0.499] or {the bar of Model 5}[0.510, 0.265, 0.551, 0.499]?" }, { "from": "gpt", "value": "Model 5." } ], "id_raw": 871 }, { "id": "SfO30qokO0NBxgoo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nAre the bars of {AI Models} [ generally higher or shorter than the bars of {Advanced Automation} [?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 871 }, { "id": "UwKBe6T6n7x0uZLv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.069, 0.089, 0.231, 0.139]?" }, { "from": "gpt", "value": "In this area, there is a label named \"AI Models Recall\" at [0.113, 0.100, 0.226, 0.128], which has a corresponding color badge in dark blue at [0.074, 0.102, 0.102, 0.122]." } ], "id_raw": 871 }, { "id": "9ofLIVmam3Ob7via", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.102, 0.101, 0.143, 0.499]." }, { "from": "gpt", "value": "This area contains a bar representing the recall rate of Model 1 in AI Models, which is 56.3%. The mark is located at [0.107, 0.100, 0.138, 0.128]." } ], "id_raw": 871 }, { "id": "iM6UBFgCxQhldwU1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.510, 0.265, 0.551, 0.499]?" }, { "from": "gpt", "value": "This area contains a bar representing the recall of Model 5 in AI Models. The height of the bar represents a recall rate of 33%, and the corresponding mark is at [0.522, 0.265, 0.539, 0.293]." } ], "id_raw": 871 }, { "id": "0QLVPIE2FwHPJyI3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.810, 0.942, 0.865, 0.970]?" }, { "from": "gpt", "value": "The label for the Model 8 is located in this area." } ], "id_raw": 871 }, { "id": "MNxgsAEORMmGL13F", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart as presented in the coordinates?" }, { "from": "gpt", "value": "The title of the chart at [0.242, 0.130, 0.828, 0.160] is \"Media Influence and Public Opinion Guidance in a Month\"." } ], "id_raw": 872 }, { "id": "nwnvYB6PlMNqSf3c", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar located at coordinates [0.359, 0.333, 0.420, 0.647] represent?" }, { "from": "gpt", "value": "The bar at coordinates [0.359, 0.333, 0.420, 0.647] represents the Positive Influence of FOX News, which has a value of 65.5." } ], "id_raw": 872 }, { "id": "11j8l6EpagN3MdNm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nWhat is being described in the box located at [0.097, 0.025, 0.323, 0.102]?" }, { "from": "gpt", "value": "The box located at [0.097, 0.025, 0.323, 0.102] contains the legend of the chart, which distinguishes between Positive Influence and Negative Influence with colors." } ], "id_raw": 872 }, { "id": "fXaoGg7UdFnucIWS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nIs the Influence Score of {CNN's Positive Influence}[0.129, 0.238, 0.190, 0.647] higher than its {Negative Influence}[0.190, 0.333, 0.251, 0.647]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 872 }, { "id": "19T4RCyOU5HNKYqU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nAmong the four media outlets, does {BBC's Negative Influence}[0.650, 0.386, 0.711, 0.647] have the lowest Influence Score?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 872 }, { "id": "qGFebBtT5dAHzoJv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nWhich one of the media outlets has a higher Negative Influence Score, {FOX News}[0.420, 0.246, 0.481, 0.647] or {Al Jazeera}[0.879, 0.263, 0.941, 0.647]?" }, { "from": "gpt", "value": "Al Jazeera." } ], "id_raw": 872 }, { "id": "hbDmaRW7W39S6gy4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects located in the region [0.818, 0.342, 0.879, 0.647]." }, { "from": "gpt", "value": "The area [0.818, 0.342, 0.879, 0.647] contains two bars. The higher blue bar that stretches from [0.818, 0.342, 0.879, 0.647] represents Al Jazeera's Positive Influence with a score of 63.7. Below it, the red bar that spreads from [0.879, 0.263, 0.941, 0.647] indicates Al Jazeera's Negative Influence, which has a score of 80.1." } ], "id_raw": 872 }, { "id": "KcAuYMAIr4NB7sqJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area [0.065, 0.638, 0.076, 0.662]?" }, { "from": "gpt", "value": "The area [0.065, 0.638, 0.076, 0.662] contains the label for the 0 mark of the Influence Score on the Y-axis." } ], "id_raw": 872 }, { "id": "vGmxi4u3WBEkTbaa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nCould you describe everything in the region [0.129, 0.238, 0.251, 0.647]?" }, { "from": "gpt", "value": "The region [0.129, 0.238, 0.251, 0.647] encompasses two bars. The one from [0.129, 0.238, 0.190, 0.647] shows CNN's Positive Influence with a score of 85.4, and the one from [0.190, 0.333, 0.251, 0.647] represents CNN\u2019s Negative Influence with a score of 65.5." } ], "id_raw": 872 }, { "id": "Vqxs0qYuPMrn54z4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nWhich label does the location {top left}[0.113, 0.927, 0.124, 0.962] refer to on the x-axis?" }, { "from": "gpt", "value": "0." } ], "id_raw": 873 }, { "id": "LErv7fFoyNWnHxW3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nAt what {specific y-coordinate}[0.064, 0.763] does the labeled grid line align with the y-axis label \"20\"?" }, { "from": "gpt", "value": "0.763." } ], "id_raw": 873 }, { "id": "NkiDInv7wsfFnXXh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nWhat color does the legend key at {this coordinate}[0.079, 0.063, 0.114, 0.087] represent?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 873 }, { "id": "5Ur7ptb5gRuhKdFW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {text label \"99.1%\"}[0.835, 0.181, 0.890, 0.216] correspond to the {bar whose bottom left is at}[0.847, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 873 }, { "id": "GQZrfnrL7D4eTabh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nIs the {blue bar}[0.104, 0.443, 0.134, 0.903] associated with the x-label {closer to}[0.119, 0.903] taller than the {green bar}[0.134, 0.525, 0.163, 0.903] associated with the same x-label?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 873 }, { "id": "T4IYRsR9WgGMUCm3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nWhich of the labeled bars is higher, {Economic Development}[0.104, 0.443, 0.134, 0.903] or {Social Consciousness}[0.877, 0.209, 0.907, 0.903] at the \"10\" position on the x-axis?" }, { "from": "gpt", "value": "Economic Development." } ], "id_raw": 873 }, { "id": "pNc0qn3zlU6TRg0k", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the features between {the whole x-axis range}[0.113, 0.927, 0.873, 0.962]." }, { "from": "gpt", "value": "There are labeled tick marks, with numerical labels from 0 to 10, with each tick spaced evenly and marked at regular intervals, corresponding to each bar's position on the x-axis." } ], "id_raw": 873 }, { "id": "HmoN9qvx03a5GaHU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nCan you illustrate the chart features within {this specified range}[0.333, 0.118, 0.678, 0.163]?" }, { "from": "gpt", "value": "Within this range, there's a subtitle \"Social Cultural and Value Change\" with centered alignment, and it has a black font color." } ], "id_raw": 873 }, { "id": "beHhonm5e0ES6kzR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area {left side of the plotting area}[0.064, 0.174, 0.505, 0.903]?" }, { "from": "gpt", "value": "This area includes half of the plot with the x-axis labels from 0 to 5, the corresponding blue bars and their percentages from Economic Development, and the left half of the grid background in light grey." } ], "id_raw": 873 }, { "id": "KoTn32Zjfonoy9Wd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent in the chart given that its coordinates are [0.019, 0.414, 0.036, 0.664]?" }, { "from": "gpt", "value": "Sales Volumes." } ], "id_raw": 874 }, { "id": "AwQuUnElZT34fzfa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the following coordinates [0.379, 0.927, 0.438, 0.962]?" }, { "from": "gpt", "value": "The label 'Vicuna'." } ], "id_raw": 874 }, { "id": "eN3NYUptFXnVVuTA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at coordinates [0.843, 0.209, 0.940, 0.903] on the chart?" }, { "from": "gpt", "value": "A bar representing the sales volume of 'WizardLM'." } ], "id_raw": 874 }, { "id": "hrDWOAZSAUwlEQ1U", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nWhich animal is associated with the coordinates [0.621, 0.927, 0.680, 0.962]?" }, { "from": "gpt", "value": "Alpaca." } ], "id_raw": 874 }, { "id": "F85BNb1n0jWP3bJd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Vicuna\"}[0.360, 0.457, 0.457, 0.903] shorter or taller than {the bar representing \"Alpaca\"}[0.602, 0.368, 0.698, 0.903]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 874 }, { "id": "TQkPQbfW9KJCBqfb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nAre {the bars}[0.118, 0.407, 0.215, 0.903],[0.360, 0.457, 0.457, 0.903],[0.602, 0.368, 0.698, 0.903],[0.843, 0.209, 0.940, 0.903] arranged in ascending or descending order?" }, { "from": "gpt", "value": "Ascending." } ], "id_raw": 874 }, { "id": "7TT0LFSzJHbV0yra", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"WizardLM\"}[0.843, 0.209, 0.940, 0.903] positioned to the right or left of {the bar for \"Alpaca\"}[0.602, 0.368, 0.698, 0.903]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 874 }, { "id": "xlezbymxSRQfmDUc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Alpaca\"}[0.602, 0.368, 0.698, 0.903] bar closer to {the \"Vicuna\"}[0.360, 0.457, 0.457, 0.903] bar or {the \"WizardLM\"}[0.843, 0.209, 0.940, 0.903] bar?" }, { "from": "gpt", "value": "Vicuna." } ], "id_raw": 874 }, { "id": "j27oYc3pdlThxi4J", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nPlease provide information about the section [0.118, 0.407, 0.215, 0.903]." }, { "from": "gpt", "value": "It contains a blue bar that represents the sales volume of 'LLaMA', which is '50'." } ], "id_raw": 874 }, { "id": "KUq97SBjFGUtiE4o", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nCould you describe the information located in the coordinates [0.043, 0.790, 0.065, 0.825]?" }, { "from": "gpt", "value": "The y-axis has a tick mark representing the value '10'." } ], "id_raw": 874 }, { "id": "ZSsbZ6OlSSo2KkUf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents in the location [0.843, 0.209, 0.940, 0.903]?" }, { "from": "gpt", "value": "A vertical bar is presented which signifies the sales volume of 'WizardLM' to be '70'." } ], "id_raw": 874 }, { "id": "xTTFaSnGY7QfQfrB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the area contained by [0.043, 0.096, 0.065, 0.131]." }, { "from": "gpt", "value": "There is a y-axis label '80' located between these coordinates." } ], "id_raw": 874 }, { "id": "VMdN2rqQ8z891rI3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object located at [0.386, 0.118, 0.649, 0.163] in the chart?" }, { "from": "gpt", "value": "Subtitle, called \"Statistical Data Overview\"." } ], "id_raw": 875 }, { "id": "0QtNkNa3poulS7uM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nWhere can I find the text of 'Trend Analysis'?" }, { "from": "gpt", "value": "In the legendary label at [0.117, 0.060, 0.242, 0.095]." } ], "id_raw": 875 }, { "id": "2p6f4mn9RNCMXe71", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nWhere can I locate the bar labeled 'End' in the chart?" }, { "from": "gpt", "value": "[0.752, 0.235, 0.939, 0.539]." } ], "id_raw": 875 }, { "id": "dciVcpSs11eaw0JS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at [0.019, 0.403, 0.041, 0.438]?" }, { "from": "gpt", "value": "Y-axis label, called \"20\"." } ], "id_raw": 875 }, { "id": "fg1vmcaD6zXcbGeR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of trend analysis at End}[0.564, 0.205, 0.752, 0.539] higher than {the bar of variance measurement at End}[0.752, 0.235, 0.939, 0.539]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 875 }, { "id": "mfRaUzKYXlPyRRji", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of 'Trend Analysis' at Initial}[0.095, 0.265, 0.283, 0.539] in the left or right of {the bar of 'Variance Measurement' at Initial}[0.283, 0.387, 0.470, 0.539]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 875 }, { "id": "XXhfSLtFUqcHCnBh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nAre {bars of 'Trend Analysis'}[{Trend Analysis at Initial}[0.095, 0.265, 0.283, 0.539],{Trend Analysis at End}[0.564, 0.205, 0.752, 0.539]] generally higher than {bars of 'Variance Measurement'}[{Variance Measurement at Initial}[0.283, 0.387, 0.470, 0.539],{Variance Measurement at End}[0.752, 0.235, 0.939, 0.539]]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 875 }, { "id": "2qGACgvaKm012k8L", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of 'Variance Measurement' at Initial}[0.283, 0.387, 0.470, 0.539] taller or shorter than {the bar of 'Variance Measurement' at End}[0.752, 0.235, 0.939, 0.539]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 875 }, { "id": "R26TGiaojQ20vRRV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located at [0.062, 0.046, 0.324, 0.161]." }, { "from": "gpt", "value": "This part of the chart has a legend containing two pairs of color-text labels; The first pair consists of a color label with a blue color located at [0.069, 0.063, 0.103, 0.087], and a text label indicating \"Trend Analysis\" located at [0.117, 0.060, 0.242, 0.095]; The second pair consists of a color label with a color near dark cyan located at [0.069, 0.115, 0.103, 0.139], and a text label indicating \"Variance Measurement\" located at [0.117, 0.112, 0.317, 0.147]." } ], "id_raw": 875 }, { "id": "d5lGTiS3KVQij7oc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents in the area [0.352, 0.927, 0.401, 0.962] and [0.829, 0.927, 0.862, 0.962]." }, { "from": "gpt", "value": "They are the labels of the x-axis. The label 'Initial' is located at [0.352, 0.927, 0.401, 0.962] and the 'End' label is located at [0.829, 0.927, 0.862, 0.962]." } ], "id_raw": 875 }, { "id": "uih3l2v2Tgdqv0yu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the area [0.095, 0.265, 0.283, 0.539] and [0.564, 0.205, 0.752, 0.539]?" }, { "from": "gpt", "value": "These areas consist of two bars representing 'Trend Analysis', with the coordinates [0.095, 0.265, 0.283, 0.539] for the value at 'Initial' and [0.564, 0.205, 0.752, 0.539] for the value at 'End'. Both bars are colored in a shade of blue." } ], "id_raw": 875 }, { "id": "OtvctHqsQ0VjoU4W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the items within the area [0.564, 0.165, 0.669, 0.200] and [0.834, 0.195, 0.856, 0.230]." }, { "from": "gpt", "value": "The positions contain the texts, '55' at [0.564, 0.165, 0.669, 0.200] belonging to {the bar of 'Trend Analysis' at End}[0.564, 0.205, 0.752, 0.539], and '50' at [0.834, 0.195, 0.856, 0.230] belonging to {the bar of 'Variance Measurement' at End}[0.752, 0.235, 0.939, 0.539]. These figures represent the data of each statistical graph." } ], "id_raw": 875 }, { "id": "cOrkuxnrRKFPaIrc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by [0.318, 0.293, 0.399, 0.935]?" }, { "from": "gpt", "value": "A bar representing the Media Growth for Streaming Media at 11.4%" } ], "id_raw": 876 }, { "id": "8F9ovRIDPR0Xaw0e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the object at [0.142, 0.027, 0.262, 0.050]?" }, { "from": "gpt", "value": "This is a label named Media Growth in the legend." } ], "id_raw": 876 }, { "id": "yYx5Rs06wDIW8I8t", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nWhat is identified by [0.354, 0.952, 0.444, 0.975]?" }, { "from": "gpt", "value": "This is the label for Streaming Media on the x-axis of the plot." } ], "id_raw": 876 }, { "id": "KLDEgKefg9cfCPeL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nWhat role does the object at [0.043, 0.359, 0.981, 0.667] play?" }, { "from": "gpt", "value": "This is the label for Entertainment Decline (%) on the y-axis of the plot." } ], "id_raw": 876 }, { "id": "KUGlEqBeHg8u5LQW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Media Growth bar for Traditional Media}[0.116, 0.637, 0.197, 0.935] higher than the {Entertainment Decline bar for Traditional Media}[0.197, 0.090, 0.278, 0.361]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 876 }, { "id": "vipZYipQXzpWbjMl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nDo the {Media Growth bar for Interactive Media}[0.722, 0.721, 0.803, 0.935] and the {Entertainment Decline bar for Interactive Media}[0.803, 0.090, 0.884, 0.319] have the same height?" }, { "from": "gpt", "value": "No." } ], "id_raw": 876 }, { "id": "Jg7rEO03lmvGeRfd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nIs {Entertainment Decline for Streaming Media}[0.399, 0.090, 0.480, 0.724] higher than {Media Growth for Streaming Media}[0.318, 0.293, 0.399, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 876 }, { "id": "hMFB1pGQkSj3Luww", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nDoes {Interactive Media}[0.757, 0.952, 0.850, 0.975] have a higher level of Entertainment Decline [0.803, 0.090, 0.884, 0.319] than that of Media Growth [0.722, 0.721, 0.803, 0.935]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 876 }, { "id": "FUbRPDIq3I9JDi0l", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the coordinates [0.077, 0.090, 0.923, 0.935]?" }, { "from": "gpt", "value": "The content within these coordinates includes all of the plotted bars representing Media Growth and Entertainment Decline for different types of Media, as well as the plot labels and ticks on the x and y axes." } ], "id_raw": 876 }, { "id": "zivujHIiJXIW4CsZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements within [0.656, 0.017, 0.914, 0.059]?" }, { "from": "gpt", "value": "This region contains the legend for Entertainment Decline with the text label located at [0.711, 0.027, 0.907, 0.050] and the color handle located at [0.663, 0.029, 0.697, 0.045]." } ], "id_raw": 876 }, { "id": "R4vAq2f9DAiADwvk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nWhat is notable in the region [0.043, 0.081, 0.065, 0.105]?" }, { "from": "gpt", "value": "This is a label for the 0 value on the Y-axis (Entertainment Decline axis)." } ], "id_raw": 876 }, { "id": "qrvAtHJRni9NYUiv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nWhat's significant about the region [0.722, 0.721, 0.803, 0.935]?" }, { "from": "gpt", "value": "This region contains a blue bar representing Media Growth for Interactive Media which is said to be at 3.8%." } ], "id_raw": 876 }, { "id": "h7ihUMrPYH3DYMgy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}<{a bar}[0.646, 0.556, 0.708, 0.903]> representing in the chart?" }, { "from": "gpt", "value": "It represents the \"Adaptable Leadership\" score of the \"DynamicInc\" organization." } ], "id_raw": 877 }, { "id": "jaB5EL0fxeFRMFrO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label of the chart?" }, { "from": "gpt", "value": "{Y-axis label}[0.019, 0.490, 0.036, 0.587], it is \"Score\"." } ], "id_raw": 877 }, { "id": "7lzuRdh9MgyxWPNa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the handle color of the \"Adaptable Leadership\" in the legend of the chart?" }, { "from": "gpt", "value": "It is represented by \"{the legend handle}[0.093, 0.115, 0.128, 0.139]\", which is in the color \"#33756f\"." } ], "id_raw": 877 }, { "id": "ayuAzTURQuE1Mxh6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nIs {the score bar of SoftCorp for Effective Management}[0.118, 0.209, 0.180, 0.903] higher than {the score bar of FlexCorp for Effective Management}[0.351, 0.260, 0.413, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 877 }, { "id": "d95z02p3OycVeoI5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is higher among {the Adaptable Leadership score for DynamicInc}[0.646, 0.556, 0.708, 0.903] and {the Adaptable Leadership score for SteadFastLLC}[0.878, 0.620, 0.940, 0.903]?" }, { "from": "gpt", "value": "The bar for DynamicInc." } ], "id_raw": 877 }, { "id": "LqeD0S89jy8FJOhY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nAre {the effective management bars}[0.118, 0.209, 0.180, 0.903],[0.351, 0.260, 0.413, 0.903],[0.584, 0.447, 0.646, 0.903],[0.816, 0.490, 0.878, 0.903] generally higher than {the adaptable leadership bars}[0.180, 0.302, 0.242, 0.903],[0.413, 0.421, 0.475, 0.903],[0.646, 0.556, 0.708, 0.903],[0.878, 0.620, 0.940, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 877 }, { "id": "9lOkTkj4wJpsh1Ic", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.086, 0.046, 0.345, 0.161]." }, { "from": "gpt", "value": "This is the legend area of the chart. In this area, two pairs of color-text labels represent two types of scoring categories. The first pair \"{Effective Management}[0.142, 0.060, 0.338, 0.095]\" has its color in #3f81bb; the second pair \"{Adaptable Leadership}[0.142, 0.112, 0.331, 0.147]\" has its color in #33756f." } ], "id_raw": 877 }, { "id": "iw93WBVvnWFnNDZK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat content does the area [0.626, 0.927, 0.728, 0.962] contain?" }, { "from": "gpt", "value": "This area contains the label \"{DynamicInc}[0.626, 0.927, 0.728, 0.962]\", which represents the name of one of the organizations." } ], "id_raw": 877 }, { "id": "a7KS16U5f6mE65oh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.077, 0.174, 0.981, 0.903]?" }, { "from": "gpt", "value": "This is the main chart area. It includes 4 x-axis labels representing the names of organizations, several y-axis tick marks indicating the scoring range from 0 to 80, and 8 bars, each representing a score for Effective Management or Adaptable Leadership of a certain organization." } ], "id_raw": 877 }, { "id": "ZoI3vua8itu2yr4J", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat do the two bars labeled in the coordinates [0.104, 0.269, 0.144, 0.581] and [0.144, 0.316, 0.185, 0.581] represent?" }, { "from": "gpt", "value": "They represent Global Trade and Supply Chain Management values for Asia respectively." } ], "id_raw": 878 }, { "id": "50xBLlTeythFm4Yv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the text located in the coordinates [0.113, 0.045, 0.200, 0.073]?" }, { "from": "gpt", "value": "It represents the label \"Global Trade\" in the chart's legend." } ], "id_raw": 878 }, { "id": "JwD6Ic1uoIlsuoKK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at [0.015, 0.501, 0.029, 0.592] indicate?" }, { "from": "gpt", "value": "It indicates \"Values\" which are represented on the y-axis of the chart." } ], "id_raw": 878 }, { "id": "FeVKGjNtLBjbQ2iz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the {Supply Chain Management bar for Europe}[0.448, 0.290, 0.488, 0.581] relative to the {Global Trade bar for Europe}[0.407, 0.343, 0.448, 0.581]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 878 }, { "id": "MzzcHH1VHPu9fcU9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nDo {Africa's Global Trade values}[0.559, 0.392, 0.599, 0.581] exceed its {Supply Chain Management values}[0.599, 0.340, 0.640, 0.581]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 878 }, { "id": "eDPQWAJQdz3kJuSZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nBetween {Global Trade}[0.104, 0.269, 0.144, 0.581] and {Supply Chain Management}[0.144, 0.316, 0.185, 0.581] values for Asia, which is higher?" }, { "from": "gpt", "value": "Global Trade." } ], "id_raw": 878 }, { "id": "WQYzFMqmexaFVs4I", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the coordinates [0.069, 0.034, 0.306, 0.126]?" }, { "from": "gpt", "value": "This region contains the chart's legend with two entries. The first is labeled as \"Global Trade\" with coordinates [0.113, 0.045, 0.200, 0.073] and a blue rectangle at [0.074, 0.047, 0.102, 0.067]. The second entry is labeled as \"Supply Chain Management\" with coordinates [0.113, 0.087, 0.301, 0.115] and a green rectangle at [0.074, 0.089, 0.102, 0.109]." } ], "id_raw": 878 }, { "id": "s5gG4mRbMG8jG7Yl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat data is present in the region [0.872, 0.942, 0.933, 0.970]?" }, { "from": "gpt", "value": "The label \"Australia\" at [0.872, 0.942, 0.933, 0.970], indicating Australia as a category on the x-axis." } ], "id_raw": 878 }, { "id": "Gw1DZcc6SNXCqZIX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within [0.104, 0.269, 0.144, 0.581]." }, { "from": "gpt", "value": "This area has a blue bar which represents the Global Trade values for Asia at 45.6." } ], "id_raw": 878 }, { "id": "WpozHPsVF3i3GVu0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat color is the bar at {Company A's Value1}[0.116, 0.325, 0.197, 0.903]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 879 }, { "id": "ZnI5lrwRxEACFRMP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart type used for the figure based on the JSON structured information?" }, { "from": "gpt", "value": "Bar." } ], "id_raw": 879 }, { "id": "qUz3YZWdvWnMun55", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the Y-axis labels in the chart?" }, { "from": "gpt", "value": "{Company A}[0.019, 0.398, 0.036, 0.598] and {Company B}[0.964, 0.398, 0.981, 0.599]." } ], "id_raw": 879 }, { "id": "zwv6mzxmgMqPtQO4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nIs {Company B's Value3 bar}[0.803, 0.440, 0.884, 0.903] higher than {Company A's Value2 bar}[0.419, 0.498, 0.500, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 879 }, { "id": "KaYYlcw3XU6v8fPN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nAre {Company A's bars}[[0.116, 0.325, 0.197, 0.903], [0.419, 0.498, 0.500, 0.903], [0.722, 0.614, 0.803, 0.903]] generally higher than {Company B's bars}[[0.197, 0.209, 0.278, 0.903], [0.500, 0.383, 0.581, 0.903], [0.803, 0.440, 0.884, 0.903]]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 879 }, { "id": "Dj4y8sl14d1qZDKC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Value1 bar for Company A}[0.116, 0.325, 0.197, 0.903] positioned to the left or right of the {Value2 bar for Company A}[0.419, 0.498, 0.500, 0.903]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 879 }, { "id": "okk85BQVQ6aMD0Df", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the region [0.077, 0.093, 0.923, 0.903]." }, { "from": "gpt", "value": "Two groups of bars are located in this region. \n Group 1: {Company A's Value1} [0.116, 0.325, 0.197, 0.903], {Company A's Value2} [0.419, 0.498, 0.500, 0.903], and {Company A's Value3} [0.722, 0.614, 0.803, 0.903].\n Group 2: {Company B's Value1} [0.197, 0.209, 0.278, 0.903], {Company B's Value2} [0.500, 0.383, 0.581, 0.903], and {Company B's Value3} [0.803, 0.440, 0.884, 0.903]." } ], "id_raw": 879 }, { "id": "CW4rh9slFMK0yp3p", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects located in the region [0.116, 0.209, 0.278, 0.903]?" }, { "from": "gpt", "value": "There are two objects:\n 1. Bars corresponding to Value1 of both companies; {Company A's Value1 bar}[0.116, 0.325, 0.197, 0.903], and {Company B's Value1 bar}[0.197, 0.209, 0.278, 0.903]." } ], "id_raw": 879 }, { "id": "lpXgDMkFpLGBsUMq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region labeled by [0.364, 0.037, 0.636, 0.083]?" }, { "from": "gpt", "value": "This region contains the subtitle of the chart, \"Financial Data Exploration\"." } ], "id_raw": 879 }, { "id": "f9caSEHqS6q1vt4X", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nCan you please tell me what is denoted by the object in the chart located at [0.115, 0.250, 0.168, 0.903]?" }, { "from": "gpt", "value": "The bar for BlueChip in Week1." } ], "id_raw": 880 }, { "id": "blYB0AHupdtFh6j9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at [0.329, 0.290, 0.351, 0.325] represent?" }, { "from": "gpt", "value": "The value of BlueChip in Week3." } ], "id_raw": 880 }, { "id": "GTa04YVsxDyGVrS3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend at [0.086, 0.046, 0.234, 0.161] indicate?" }, { "from": "gpt", "value": "It points out the color codes for BlueChip and Gold ETFs." } ], "id_raw": 880 }, { "id": "1Fc2BDXuKcGEPLyR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nIs {the BlueChip bar for Week 1}[0.115, 0.250, 0.168, 0.903] higher or lower than {the BlueChip bar for Week 2}[0.314, 0.291, 0.367, 0.903]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 880 }, { "id": "8oVab7o20IjBIIkk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nHow does {the Gold ETFs bar for Week 1}[0.168, 0.386, 0.221, 0.903] compare with {the Gold ETFs bar for Week 4}[0.764, 0.508, 0.817, 0.903] in height?" }, { "from": "gpt", "value": "The Gold ETFs bar for Week 1 is shorter." } ], "id_raw": 880 }, { "id": "vyMlLfNhV7bha6si", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nIs {the BlueChip bar for week 1}[0.115, 0.250, 0.168, 0.903] to the left of {the Gold ETFs bar for week 1}[0.168, 0.386, 0.221, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 880 }, { "id": "ulxTJ2VoLRG02ket", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe the items within the area [0.086, 0.046, 0.234, 0.161]?" }, { "from": "gpt", "value": "The area contains the legend listing two items: \n\n1. The label 'BlueChip' with a corresponding color indicator in a shade of dark teal, situated at [0.142, 0.060, 0.219, 0.095] for the label and [0.093, 0.063, 0.128, 0.087] for the color indicator.\n\n2. The label 'Gold ETFs' with a color indicator in blue, location at [0.142, 0.112, 0.228, 0.147] for the label and [0.093, 0.115, 0.128, 0.139] for the color indicator." } ], "id_raw": 880 }, { "id": "757pg2jK32QHQsbY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information within the region [0.513, 0.209, 0.566, 0.903]?" }, { "from": "gpt", "value": "The area includes one bar for 'BlueChip' for 'Week3', with it's top text marking the value as 45. The coordinates of the bar are [0.513, 0.209, 0.566, 0.903] while the top text is placed at [0.329, 0.290, 0.351, 0.325]." } ], "id_raw": 880 }, { "id": "FbAmXlrn9FNnupJV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the main elements in the region [0.659, 0.046, 0.899, 0.108]?" }, { "from": "gpt", "value": "The region contains the legend for 'Market Manipulation' situated at [0.714, 0.060, 0.892, 0.095] with corresponding color label on the left at [0.666, 0.063, 0.701, 0.087] in a shade of pink." } ], "id_raw": 880 }, { "id": "4jpGrcPMt96fkMhz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.964, 0.372, 0.981, 0.706] on the chart?" }, { "from": "gpt", "value": "Y-axis label, which is \"Globalization Index\"." } ], "id_raw": 881 }, { "id": "r1fYB0ZZK56PzDXj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the specific content at [0.793, 0.060, 0.907, 0.095]?" }, { "from": "gpt", "value": "The legend text that represents the \"Globalization\" trend." } ], "id_raw": 881 }, { "id": "q9r9sCR6eLziAxsN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed at the coordinate [0.116, 0.257, 0.170, 0.539]?" }, { "from": "gpt", "value": "A blue bar representing the \"International Trade\" trend of USA." } ], "id_raw": 881 }, { "id": "UlhxUPuCW2K5mfOx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the positioned at [0.179, 0.927, 0.215, 0.962] represent?" }, { "from": "gpt", "value": "An X-axis coordinate label, which is \"USA\"." } ], "id_raw": 881 }, { "id": "G4FliRE2yX6X7ODi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing \"International Trade\" trend for China [0.318, 0.193, 0.372, 0.539] have a higher value than that for UK [0.520, 0.212, 0.574, 0.539]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 881 }, { "id": "HGD2frH54KUFlca7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nIs the text label for Globalization [0.793, 0.060, 0.907, 0.095] located on the left or right of the color handle [0.745, 0.063, 0.780, 0.087]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 881 }, { "id": "pzQaDlYDGeP916ET", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing the \"Globalization\" trend for India [0.830, 0.539, 0.884, 0.684] taller than the bar representing the \"International Trade\" trend for India [0.723, 0.421, 0.776, 0.539]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 881 }, { "id": "GPSydQ0mxjtS0dbP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nIs {bar}[0.318, 0.193, 0.372, 0.539] associated with {China}[0.374, 0.927, 0.424, 0.962] higher or lower than 40?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 881 }, { "id": "sSQC83ynOmCRRV2s", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.738, 0.046, 0.914, 0.108]." }, { "from": "gpt", "value": "There is one label for the \"Globalization\" trend within this region. The color handle of \"Globalization\" is located at [0.745, 0.063, 0.780, 0.087], and the text label is at [0.793, 0.060, 0.907, 0.095]." } ], "id_raw": 881 }, { "id": "4tV6Ul6opiQlg1jQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in [0.224, 0.539, 0.278, 0.758] region?" }, { "from": "gpt", "value": "There is 1 object:\n1. The purple bar represents the \"Globalization\" trend for the USA and its coordinate range is [0.224, 0.539, 0.278, 0.758]." } ], "id_raw": 881 }, { "id": "4d10qOQupj6xnJ0Z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside [0.077, 0.174, 0.923, 0.903]." }, { "from": "gpt", "value": "This region contains the whole chart with all bars and labels. For instance, the bar of USA in \"International Trade\" trend with coordinates [0.116, 0.257, 0.170, 0.539], and the bar of China in \"Globalization\" trend with coordinates [0.426, 0.539, 0.480, 0.787] are inside this region." } ], "id_raw": 881 }, { "id": "kKfGoUEU1z8RA6X6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.043, 0.282, 0.065, 0.317]?" }, { "from": "gpt", "value": "The Y-axis label of the value \"40\" is in this area." } ], "id_raw": 881 }, { "id": "vWa74it68db2ATTv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the content shown at coordinates [0.328, 0.025, 0.719, 0.055]?" }, { "from": "gpt", "value": "The subtitle of the chart, which reads \"Healthy Habits: Healthy vs Freelance Lifestyles\"." } ], "id_raw": 882 }, { "id": "dJkLNbYLfbq47h5G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the rectangle at [0.104, 0.209, 0.153, 0.795]?" }, { "from": "gpt", "value": "A bar in the bar chart, representing the 'Healthy Lifestyle' percentage (53.1 %) of individuals who use 'Morning Run' as a healthy habit." } ], "id_raw": 882 }, { "id": "lVEYRiG3FHe864h2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.843, 0.083, 0.959, 0.106] signify?" }, { "from": "gpt", "value": "These are the coordinates for the label 'Healthy Lifestyle' in the chart's legend." } ], "id_raw": 882 }, { "id": "IRRhPSjx0r1xrbG3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nWhich bar is higher, {the bar representing 'Healthy Eating for a Healthy Lifestyle'}[0.289, 0.097, 0.338, 0.795] or {the bar representing 'Healthy Eating for a Freelance Lifestyle'}[0.338, 0.377, 0.388, 0.795]?" }, { "from": "gpt", "value": "The bar representing 'Healthy Eating for a Healthy Lifestyle'." } ], "id_raw": 882 }, { "id": "azkM0saEhfUZbJYW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Healthy Lifestyle' for 'Regular Checkups'} bar[0.474, 0.240, 0.523, 0.795] have a higher percentage than {the 'Freelance Lifestyle' for 'Regular Checkups'} bar[0.523, 0.481, 0.573, 0.795]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 882 }, { "id": "qIxLDmEGJyYM7O5Z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nIs {the legend entry for 'Healthy Lifestyle'}[0.843, 0.083, 0.959, 0.106] positioned above {the legend entry for 'Freelance Lifestyle'}[0.843, 0.118, 0.972, 0.141]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 882 }, { "id": "cp7MAv63LnjRsH4z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nDescribe what is inside the area [0.798, 0.074, 0.978, 0.151]." }, { "from": "gpt", "value": "This is the area where the chart's legend is located. It consists of two entries: 'Healthy Lifestyle', represented by a blue color at [0.804, 0.085, 0.832, 0.101] and the text at [0.843, 0.083, 0.959, 0.106]; and 'Freelance Lifestyle', represented by an orange color at [0.804, 0.120, 0.832, 0.136] and the text at [0.843, 0.118, 0.972, 0.141]." } ], "id_raw": 882 }, { "id": "3Td247BFbyRFu6MC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat elements are included in [0.043, 0.786, 0.052, 0.809]?" }, { "from": "gpt", "value": "This region contains the y-axis label \"0%\"." } ], "id_raw": 882 }, { "id": "fxB6UqhrOnFw3wvc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nCould you please describe the region [0.104, 0.209, 0.153, 0.795]?" }, { "from": "gpt", "value": "The region represents a blue bar on the bar chart, which signifies the percentage of individuals living a Healthy Lifestyle who have the habit of a 'Morning Run'. The Percentage is given as 53.1% in the text area at [0.126, 0.188, 0.156, 0.211]." } ], "id_raw": 882 }, { "id": "4bcdNEfBU4w9Jv86", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object seen at coordinates [0.118, 0.281, 0.261, 0.903]?" }, { "from": "gpt", "value": "It is a bar from the category 'Job Stability' for 'Software Engineer' profession with a percentage of 61.5." } ], "id_raw": 883 }, { "id": "I8BmdppB4eGBYzUX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nCould you please tell me the profession present at coordinate range [0.741, 0.927, 0.853, 0.962]?" }, { "from": "gpt", "value": "The profession at the coordinates is 'Data Analyst'." } ], "id_raw": 883 }, { "id": "oT3SMbhacqqlvHWc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} in the area [0.019, 0.365, 0.036, 0.632]?" }, { "from": "gpt", "value": "The y-axis label is 'Percentage (%)'." } ], "id_raw": 883 }, { "id": "30ZMOlc7LSrihmZO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the text within [0.314, 0.098, 0.352, 0.133] represent?" }, { "from": "gpt", "value": "It represents the job growth rate of 75.3 for {Data Analyst}[0.654, 0.395, 0.797, 0.903]." } ], "id_raw": 883 }, { "id": "UgmQrQbTySdYDiaw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Job Growth' bar for 'Software Engineer'}[0.261, 0.141, 0.404, 0.903] taller or shorter than {the 'Job Stability' bar for 'Data Analyst'}[0.654, 0.395, 0.797, 0.903]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 883 }, { "id": "Kia3Q4dNz9VGQh1H", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Job Growth' bar for 'Data Analyst'}[0.797, 0.248, 0.940, 0.903] located to the right of {the 'Job Stability' bar for 'Software Engineer'}[0.118, 0.281, 0.261, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 883 }, { "id": "1bl7bMregLTvrVx2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nWhich is greater, the {value of 'Job Stability' for 'Software Engineer'}[0.171, 0.238, 0.209, 0.273] or the {value of 'Job Growth' for 'Data Analyst'}[0.849, 0.206, 0.888, 0.241]?" }, { "from": "gpt", "value": "Job Growth for Data Analyst." } ], "id_raw": 883 }, { "id": "ZdYZ7xUZolE5hcs8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nAre the bars representing 'Job Stability'[0.118, 0.281, 0.261, 0.903], [0.654, 0.395, 0.797, 0.903] taller than the bars representing \"Job Growth\"[0.261, 0.141, 0.404, 0.903], [0.797, 0.248, 0.940, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 883 }, { "id": "uiCEjtLieSINUBue", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is present in area [0.086, 0.111, 0.252, 0.226]?" }, { "from": "gpt", "value": "It is the legend of the graph. It contains two entries: {Job Stability}[0.142, 0.125, 0.245, 0.160], represented by a blue color, and {Job Growth}[0.142, 0.177, 0.237, 0.212], represented by a green color." } ], "id_raw": 883 }, { "id": "PvcFOZDtIZARuXdq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.261, 0.141, 0.940, 0.903]?" }, { "from": "gpt", "value": "There are two bar pairs each representing 'Job Growth' for the professions 'Software Engineer' and 'Data Analyst', correspondingly." } ], "id_raw": 883 }, { "id": "sPdbks2xu2lb7LlI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.077, 0.093, 0.981, 0.903] represent?" }, { "from": "gpt", "value": "It encloses the main plot area of the bar chart including the bars, the x-axis with labels indicating professions, and the y-axis indicating the percentage values." } ], "id_raw": 883 }, { "id": "QaozKawDoFijogcP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.304, 0.037, 0.755, 0.083]." }, { "from": "gpt", "value": "That is the subtitle area of the chart, reading 'Career and Workplace Development Trends'." } ], "id_raw": 883 }, { "id": "WNhQKgoD7vzjXRrH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing Road Freight in 2021?" }, { "from": "gpt", "value": "The color is blue; {the bar representing Road Freight in 2021}[0.702, 0.222, 0.762, 0.570]." } ], "id_raw": 884 }, { "id": "LJwl1RGyTMx7THvU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar representing Air Freight in 2020?" }, { "from": "gpt", "value": "The coordinate is [0.465, 0.415, 0.524, 0.570]." } ], "id_raw": 884 }, { "id": "NtaxNTN6shV0uxTB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat value is noted for Rail Freight in 2019?" }, { "from": "gpt", "value": "The noted value is -36.70; {the note}[0.772, 0.325, 0.811, 0.360]." } ], "id_raw": 884 }, { "id": "ckgwxKzHEwbYIzPv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color and coordinates associated with the Rail Freight in the chart legend?" }, { "from": "gpt", "value": "The color is purpulish; {the color handle}[0.083, 0.212, 0.118, 0.236] and the coordinates for the label are [0.132, 0.209, 0.229, 0.244]." } ], "id_raw": 884 }, { "id": "l4eN3i2yXxTodr4f", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nWhich year among {Road Freight}[0.109, 0.171, 0.168, 0.570], {Air Freight}[0.168, 0.337, 0.228, 0.570], {Sea Freight}[0.228, 0.570, 0.287, 0.868], {Rail Freight}[0.287, 0.570, 0.346, 0.757] had the highest value in 2019?" }, { "from": "gpt", "value": "Sea Freight." } ], "id_raw": 884 }, { "id": "Yu2SRM2WnVqAYPEC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nAre {the values of Road Freight}[0.109, 0.171, 0.168, 0.570] generally higher than {the values of Air Freight}[0.168, 0.337, 0.228, 0.570]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 884 }, { "id": "GNDm05FPdixZWrCR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nBetween {Sea Freight}[0.228, 0.570, 0.287, 0.868] and {Rail Freight}[0.287, 0.570, 0.346, 0.757], which has seen an increase from 2019 to 2021?" }, { "from": "gpt", "value": "Rail Freight." } ], "id_raw": 884 }, { "id": "PpN2YjuXvIYojFN0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Rail Freight in 2019}[0.287, 0.570, 0.346, 0.757] higher or lower than {the bar representing Road Freight in 2020}[0.465, 0.415, 0.524, 0.570]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 884 }, { "id": "mAfPZEddgmKYETAq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what you see in the legend area at [0.076, 0.038, 0.248, 0.258]." }, { "from": "gpt", "value": "The area contains four separate entries, each with a square color handle and a text label: \n1. {Road Freight with color blue}[0.132, 0.052, 0.241, 0.087][0.083, 0.055, 0.118, 0.079], \n2. {Air Freight with color green}[0.132, 0.104, 0.221, 0.139][0.083, 0.107, 0.118, 0.132], \n3. {Sea Freight with color red}[0.132, 0.156, 0.230, 0.191][0.083, 0.160, 0.118, 0.184], \n4. {Rail Freight with color purple}[0.132, 0.209, 0.229, 0.244][0.083, 0.212, 0.118, 0.236]." } ], "id_raw": 884 }, { "id": "MDXfU2hdtZkoI0tr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what the y-axis lables include in the region [0.019, 0.046, 0.055, 0.999]." }, { "from": "gpt", "value": "This region includes the text for the y-axis labels. They are, from top to bottom:\n1. \"-80\"[0.019, 0.964, 0.055, 0.999],\n2. \"-60\"[0.019, 0.862, 0.055, 0.897],\n3. \"-40\"[0.019, 0.760, 0.055, 0.795],\n4. \"-20\"[0.019, 0.658, 0.055, 0.693],\n5. \"0\"[0.044, 0.556, 0.055, 0.591],\n6. \"20\"[0.033, 0.454, 0.055, 0.489],\n7. \"40\"[0.033, 0.352, 0.055, 0.387],\n8. \"60\"[0.033, 0.250, 0.055, 0.285],\n9. \"80\"[0.033, 0.148, 0.055, 0.183], \n10. \"100\"[0.022, 0.046, 0.055, 0.081]." } ], "id_raw": 884 }, { "id": "fddEW3WEBKHleaKK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.406, 0.171, 0.465, 0.570]?" }, { "from": "gpt", "value": "The region has one object within it: the blue colored bar representing {Road Freight}[0.406, 0.283, 0.465, 0.570] in the year 2020." } ], "id_raw": 884 }, { "id": "dV0qNnfFyo60ffzh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the chart region [0.295, 0.080, 0.753, 0.125]?" }, { "from": "gpt", "value": "This region contains the chart {subtitle}[0.295, 0.080, 0.753, 0.125] \"Transportation and Logistics Industry Trends\"." } ], "id_raw": 884 }, { "id": "SZ8aPpQsm2YB4rrF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the object at coordinates [0.829, 0.083, 0.938, 0.106] in the chart?" }, { "from": "gpt", "value": "It is the legend label for Rapid Recovery." } ], "id_raw": 885 }, { "id": "NUyX1F7UVEqJl5uz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nFrom the given JSON structured information, could you tell me what is the color of {the first bar of the second group}[0.186, 0.483, 0.265, 0.865]?" }, { "from": "gpt", "value": "The color is '#efc958', meaning it has a yellowish-orange color." } ], "id_raw": 885 }, { "id": "FcvaEX4TTF7sBI1g", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nCan you tell what the Y-axis label {ylabel}[0.015, 0.416, 0.029, 0.549] is referring to in the chart?" }, { "from": "gpt", "value": "The Y-axis label is referring to the 'Trend Scale'." } ], "id_raw": 885 }, { "id": "KoR5c1ULudu3vQ3f", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 'Podcast Subscribers' under the 'Rapid Recovery' trend}[0.107, 0.196, 0.186, 0.483] above or below zero on the Trend Scale?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 885 }, { "id": "wO7vOJsX9wjLkOdg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nComparatively, is {the 'Accelerating Decline' bar for 'Live Streaming'}[0.864, 0.483, 0.943, 0.769] longer than {the 'Rapid Recovery' bar for 'Live Streaming'}[0.785, 0.291, 0.864, 0.483]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 885 }, { "id": "6xE4b5qIMSOwwMta", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nIn terms of height, how does {the 'Rapid Recovery' bar for 'Online Webinars'}[0.333, 0.483, 0.412, 0.674] compare to {the 'Accelerating Decline' bar for 'Online Webinars'}[0.412, 0.483, 0.491, 0.483]?" }, { "from": "gpt", "value": "The 'Rapid Recovery' bar is taller." } ], "id_raw": 885 }, { "id": "JXLcTmjGVTAoKVGU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nWhat content does the region [0.785, 0.074, 0.978, 0.151] contain?" }, { "from": "gpt", "value": "The region contains the legend of the chart. It includes two items: the first, labeled as 'Rapid Recovery', is associated with a color coded as '#007acc' (a shade of blue), its coordinates are [0.829, 0.083, 0.938, 0.106]; the second, labeled as 'Accelerating Decline', is associated with a color coded as '#efc958' (a shade of yellowish-orange), its coordinates are [0.829, 0.118, 0.972, 0.141]." } ], "id_raw": 885 }, { "id": "3eFvZBbe9WR0g93t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.116, 0.919, 0.255, 0.942]?" }, { "from": "gpt", "value": "This area contains the x-axis label for 'Podcast Subscribers'." } ], "id_raw": 885 }, { "id": "e8LaIMoiMM3YcS4s", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nPlease provide a description of what the region [0.107, 0.196, 0.186, 0.483] includes?" }, { "from": "gpt", "value": "The region includes a bar representing the trend 'Rapid Recovery' for 'Podcast Subscribers'. The bar's height indicates a positive trend scale of 3." } ], "id_raw": 885 }, { "id": "PTtVlFadzWc2xyN8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the text {at the coordinates [0.105, 0.183, 0.200, 0.211]} represent?" }, { "from": "gpt", "value": "It indicates the label for the green bar; it is called Gasoline Tech." } ], "id_raw": 886 }, { "id": "hP4XsJ2NP0AgjtTW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the axis label} at the coordinates [0.483, 0.942, 0.555, 0.970] name?" }, { "from": "gpt", "value": "It labels the x-axis as Fuel Types." } ], "id_raw": 886 }, { "id": "0wKJv7qt4918NAHY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the {ylabel} at coordinates [0.015, 0.440, 0.029, 0.518]?" }, { "from": "gpt", "value": "The y-axis label is Score." } ], "id_raw": 886 }, { "id": "D47KEwhGQU2Dm53a", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nAre the {bars in Hybrid Technology} at positions [[0.096, 0.745, 0.143, 0.788], [0.331, 0.449, 0.378, 0.788], [0.566, 0.196, 0.613, 0.788], [0.801, 0.111, 0.849, 0.788]] generally higher or lower than {the bars in Diesel Tech} at positions [[0.143, 0.720, 0.190, 0.788], [0.378, 0.280, 0.425, 0.788], [0.613, 0.280, 0.660, 0.788], [0.849, 0.280, 0.896, 0.788]]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 886 }, { "id": "iwr8UFRWk2Mc5oja", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the blue bar titled \"Diesel Tech\"} at [0.143, 0.720, 0.190, 0.788] have a higher or lower value than the {green bar titled \"Gasoline Tech\"} at [0.190, 0.669, 0.237, 0.788] for Type 1?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 886 }, { "id": "WSOsrplYuu3HjlMD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar of Type 2 in the Hybrid Technology series} at [0.331, 0.449, 0.378, 0.788] have higher value than {the bar of Type 3 in the same series} at [0.566, 0.196, 0.613, 0.788]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 886 }, { "id": "5dngIFyD9etco40O", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area from [0.060, 0.089, 0.238, 0.264]?" }, { "from": "gpt", "value": "The area contains a key explanation for the color coding of the bars in the chart. The Hybrid Technology corresponds to the blue color at location [0.066, 0.102, 0.093, 0.122], Diesel Tech corresponds to the orange color at location [0.066, 0.144, 0.093, 0.164], Gasoline Tech corresponds to the green color at location [0.066, 0.186, 0.093, 0.205], and Outliers corresponds to the red color at location [0.066, 0.228, 0.093, 0.247]." } ], "id_raw": 886 }, { "id": "pDiHhYJCwrHo0sY1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nWhat kind of objects can we see in the area of [0.060, 0.089, 0.238, 0.264]?" }, { "from": "gpt", "value": "This region includes the color-coded legend for the four types of bars, which include Hybrid Technology, Diesel Tech, Gasoline Tech, and Outliers." } ], "id_raw": 886 }, { "id": "7XlHsTSipDV36Hgs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region from [0.096, 0.745, 0.143, 0.788]." }, { "from": "gpt", "value": "This region covers the first blue bar standing for the Hybrid Technology for the Type 1 fuel category." } ], "id_raw": 886 }, { "id": "dEw8b88EIvttF5Mg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object located at [0.108, 0.595, 0.157, 0.883]?" }, { "from": "gpt", "value": "This is the bar for \"EdTech Adoption\" in the feature \"Artificial Intelligence\"." } ], "id_raw": 887 }, { "id": "MCJLPk61Ql0x4g5v", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate is the label for \"Machine Learning\" at?" }, { "from": "gpt", "value": "The coordinate is [0.341, 0.903, 0.464, 0.931]." } ], "id_raw": 887 }, { "id": "7fIpux0JZExG30By", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.015, 0.441, 0.029, 0.517]?" }, { "from": "gpt", "value": "The y-axis label is \"Value\"." } ], "id_raw": 887 }, { "id": "p36T24NQn7GEq0IJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nWhat labels for color are in the legend found at [0.073, 0.089, 0.287, 0.181]?" }, { "from": "gpt", "value": "The labels are {EdTech Adoption}[0.118, 0.100, 0.233, 0.128] and {Online Learning Growth}[0.118, 0.142, 0.282, 0.170]." } ], "id_raw": 887 }, { "id": "2XjzU03ufCP1an3g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, {the bar representing \"EdTech Adoption\"}[0.108, 0.595, 0.157, 0.883] or {the bar representing \"Online Learning Growth\"}[0.157, 0.498, 0.206, 0.883] for \"Artificial Intelligence\"?" }, { "from": "gpt", "value": "Online Learning Growth." } ], "id_raw": 887 }, { "id": "AzuJJdlI1j1DKOLQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nAre {the bars for \"EdTech Adoption\"}[0.108, 0.595, 0.157, 0.883], [0.354, 0.498, 0.403, 0.883], [0.599, 0.402, 0.648, 0.883], [0.845, 0.306, 0.894, 0.883] generally taller than {the bars for \"Online Learning Growth\"}[0.157, 0.498, 0.206, 0.883], [0.403, 0.402, 0.452, 0.883], [0.648, 0.306, 0.698, 0.883], [0.894, 0.113, 0.943, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 887 }, { "id": "7wMN0TARmA2MjCJI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Cloud-based Learning\" in EdTech Adoption} [0.599, 0.402, 0.648, 0.883] taller than {the bar for \"Cloud-based Learning\" in Online Learning Growth}[0.648, 0.306, 0.698, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 887 }, { "id": "nbTuV4u1RZtLfHsw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nWhich feature has more value for {Online Learning Growth}<{Online Learning Growth in Artificial Intelligence}[0.157, 0.498, 0.206, 0.883], {Online Learning Growth in Machine Learning}[0.403, 0.402, 0.452, 0.883], {Online Learning Growth in Cloud-based Learning}[0.648, 0.306, 0.698, 0.883], {Online Learning Growth in VR/MR Technology}[0.894, 0.113, 0.943, 0.883]>?" }, { "from": "gpt", "value": "VR/MR Technology." } ], "id_raw": 887 }, { "id": "SEXooj0Of9fb1LnY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.073, 0.089, 0.287, 0.181]." }, { "from": "gpt", "value": "This is a legend containing two labels. The first label, {EdTech Adoption}[0.118, 0.100, 0.233, 0.128] associated with the color '#8ecfc9', and the second label is {Online Learning Growth}[0.118, 0.142, 0.282, 0.170], associated with the color '#ffbe7a'." } ], "id_raw": 887 }, { "id": "0INMCszpfgVn0AiR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the area [0.066, 0.075, 0.985, 0.883]." }, { "from": "gpt", "value": "This is the main plot area, where the bars representing values for different features (\"Artificial Intelligence\", \"Machine Learning\", \"Cloud-based Learning\", and \"VR/MR Technology\") in \"EdTech Adoption\" and \"Online Learning Growth\" can be found." } ], "id_raw": 887 }, { "id": "6Dp9jv2ZV9noTb34", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within [0.354, 0.498, 0.452, 0.883]?" }, { "from": "gpt", "value": "Within these coordinates, there are two objects:\n1. The {bar for \"EdTech Adoption\"} in {Machine Learning}[0.354, 0.498, 0.403, 0.883]\n2. The {bar for \"Online Learning Growth\"} in {Machine Learning}[0.403, 0.402, 0.452, 0.883]." } ], "id_raw": 887 }, { "id": "HFl8MgswtCLWXexq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nDescribe what is visible in the region [0.830, 0.903, 0.958, 0.931]." }, { "from": "gpt", "value": "This region contains the x-axis label for {VR/MR Technology}[0.830, 0.903, 0.958, 0.931]." } ], "id_raw": 887 }, { "id": "ENmsNQgscW67Olwp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object located at [0.355, 0.295, 0.603] represent in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Sales (thousands)." } ], "id_raw": 888 }, { "id": "YXNQiJ1LLYOXIU8A", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third text in the legend?" }, { "from": "gpt", "value": "[0.926, 0.183, 0.954, 0.211]." } ], "id_raw": 888 }, { "id": "iXizHLCBqiJUtOt0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled at the tick coordinates [0.643, 0.883] on the x-axis?" }, { "from": "gpt", "value": "Q3." } ], "id_raw": 888 }, { "id": "N13oIAY65fiUj7yf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat do the labels in the coordinates [0.881, 0.089, 0.978, 0.264] represent?" }, { "from": "gpt", "value": "{iPhone}[0.926, 0.100, 0.972, 0.128], {iPad}[0.926, 0.142, 0.955, 0.170], {Mac}[0.926, 0.183, 0.954, 0.211], and {Watch}[0.926, 0.225, 0.969, 0.253]." } ], "id_raw": 888 }, { "id": "Pq2ox6MbvQ4wno8t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nIs {the Mac bar in Q1}[0.181, 0.691, 0.216, 0.883] higher or lower than {the iPhone bar in Q1}[0.112, 0.421, 0.147, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 888 }, { "id": "Dv5Ez5ypCJpJITFc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nIs {the iPad bar representing \"Q4\"}[0.840, 0.575, 0.874, 0.883] taller than {the Watch bar representing \"Q2\"}[0.447, 0.691, 0.482, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 888 }, { "id": "AD9zlc9Wjah5cZJy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nAre {the iPhone bars}[[0.112, 0.421, 0.147, 0.883], [0.343, 0.383, 0.378, 0.883], [0.574, 0.306, 0.609, 0.883], [0.805, 0.113, 0.840, 0.883]] generally taller than {the Mac bars}[[0.181, 0.691, 0.216, 0.883], [0.412, 0.614, 0.447, 0.883], [0.643, 0.595, 0.678, 0.883], [0.874, 0.498, 0.909, 0.883]] in each corresponding quarter?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 888 }, { "id": "ExityCBdysIPsyjO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Q1\"}[0.172, 0.903, 0.191, 0.931] and {\"Q3\"}[0.633, 0.903, 0.653, 0.931], which quarter has a higher sales figure for {Watch}[0.926, 0.225, 0.969, 0.253] considering the Watch bars [{Watch Q1 bar}[0.216, 0.768, 0.251, 0.883], {Watch Q2 bar}[0.447, 0.691, 0.482, 0.883], {Watch Q3 bar}[0.678, 0.614, 0.713, 0.883], {Watch Q4 bar}[0.909, 0.498, 0.943, 0.883]." }, { "from": "gpt", "value": "Q3." } ], "id_raw": 888 }, { "id": "ZlHPq7BfWxs1MVu3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.881, 0.089, 0.978, 0.264]." }, { "from": "gpt", "value": "There are four pairs of color-text labels in the legend. The first pair's color label is located at [0.887, 0.102, 0.915, 0.122] displaying blue, and its text label is located at [0.926, 0.100, 0.972, 0.128] and it is called {iPhone}; The second pair's color label is located at [0.887, 0.144, 0.915, 0.164] displaying purple, and its text label is located at [0.926, 0.142, 0.955, 0.170], corresponding with the label {iPad}; The third pair's color label is located at [0.887, 0.186, 0.915, 0.205], rendering in green, and its text label is located at [0.926, 0.183, 0.954, 0.211] indicating the label {Mac}; The fourth pair's color label is displayed at [0.887, 0.228, 0.915, 0.247], showing an orange color, and its text label is located at [0.926, 0.225, 0.969, 0.253] bearing the label {Watch};" } ], "id_raw": 888 }, { "id": "Df59VlJf55U4pLM1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.071, 0.075, 0.985, 0.883]." }, { "from": "gpt", "value": "The area [0.071, 0.075, 0.985, 0.883] contains the chart's primary plotting area with the following components: X-axis label {Quarters}[0.497, 0.942, 0.558, 0.970]; Y-axis label {Sales (thousands)}[0.015, 0.355, 0.029, 0.603]; all the bars representing sales of different products in different quarters such as {iPhone bars}[[0.112, 0.421, 0.147, 0.883], [0.343, 0.383, 0.378, 0.883], [0.574, 0.306, 0.609, 0.883], [0.805, 0.113, 0.840, 0.883]], and x-axis ticks labels for each quarter including {Q1}[0.172, 0.903, 0.191, 0.931], {Q2}[0.403, 0.903, 0.422, 0.931], {Q3}[0.633, 0.903, 0.653, 0.931], {Q4}[0.864, 0.903, 0.884, 0.931]." } ], "id_raw": 888 }, { "id": "p0KApozRFuqJ9YGy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the location [0.805, 0.113, 0.840, 0.883]?" }, { "from": "gpt", "value": "There are 4 objects:\n\n1. A blue bar that represents the sales of iPhones in Q4, and its coordinates are [0.805, 0.113, 0.840, 0.883].\n2. A purple bar representing the sales of iPads in Q4, and its coordinates are [0.840, 0.575, 0.874, 0.883].\n3. A green bar indicating the sales of Mac in Q4, located in the coordinates of [0.874, 0.498, 0.909, 0.883].\n4. An orange bar showing the sales of Watch in Q4, with coordinates [0.909, 0.498, 0.943, 0.883]." } ], "id_raw": 888 }, { "id": "0aY9XwgHGT7w33UY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.353, 0.185, 0.643, 0.883]?" }, { "from": "gpt", "value": "In the belted area, there are bars indicating Q2 and Q3 sales for different products, bars and labels for {iPad}[0.926, 0.142, 0.955, 0.170] in Q2[0.403, 0.903, 0.422, 0.931] and Q3[0.633, 0.903, 0.653, 0.931], along with bars and labels for {Mac}[0.926, 0.183, 0.954, 0.211] in Q2 and Q3 are visible. Tick marks for Q2 and Q3 on the x-axis also fall within this area." } ], "id_raw": 888 }, { "id": "JOjr9yH7MjxDgCzg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the coordinate [0.357, 0.030, 0.699, 0.066]?" }, { "from": "gpt", "value": "It's the subtitle \"Human Health and Disease Management\"." } ], "id_raw": 889 }, { "id": "O0MMrtSWfMSFe7FX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color coded legend label at [0.460, 0.100, 0.634, 0.128]?" }, { "from": "gpt", "value": "It's labeled as \"Efficient Care Continuum\"." } ], "id_raw": 889 }, { "id": "Ald2NwNhLzVFLz1x", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the item located at [0.591, 0.903, 0.710, 0.931]?" }, { "from": "gpt", "value": "It's the Management Area \"Accident & Injury\"" } ], "id_raw": 889 }, { "id": "w4U6uOQ7dmjziJXw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nWhich one is taller, the {Efficient Care Continuum bar}[0.112, 0.344, 0.161, 0.883] or the {Personalized Care bar}[0.161, 0.113, 0.210, 0.883] for Minor Illness Management?" }, { "from": "gpt", "value": "Personalized Care bar." } ], "id_raw": 889 }, { "id": "ZbO9IDYs1agnk9qT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nIs {the Efficient Care Continuum bar for Mental Health}[0.846, 0.421, 0.895, 0.883] shorter than {the Personalized Care bar for Mental Health}[0.895, 0.152, 0.943, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 889 }, { "id": "hiwzKDcRqDfxKSFK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nIs the position of {the Efficient Care Continuum bar for Chronic Disease Management}[0.357, 0.190, 0.406, 0.883] to the left or right of {the Personalized Care bar for Chronic Disease Management}[0.406, 0.306, 0.454, 0.883]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 889 }, { "id": "PqVK2DPw5hjxnSlz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area of [0.416, 0.089, 0.640, 0.181]." }, { "from": "gpt", "value": "This area includes two groups of coordinate colors and text indicators. {The first set}[0.460, 0.100, 0.634, 0.128] indicates that the \"Efficient Care Continuum\" is color-coded in '#8ecfc9'. {The second set}[0.460, 0.142, 0.585, 0.170] indicates that the \"Personalized Care\" is color-coded in '#ffbe7a'." } ], "id_raw": 889 }, { "id": "Oucghxzp2pTBDjq1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.071, 0.075, 0.985, 0.883]." }, { "from": "gpt", "value": "This area mainly contains the plot of the bar chart, showing the efficiency score of different management areas between efficient care continuum and personalized care. It contains bars representing the efficiency score of different types of disease management, collated by two healthcare delivery strategies: Efficient Care Continuum and Personalized Care. There are four types of disease management represented: Minor Illness, Chronic Disease, Accident & Injury and Mental Health." } ], "id_raw": 889 }, { "id": "5orZzSP5GawUZB9S", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nWhat is in [0.846, 0.421, 0.943, 0.883]?" }, { "from": "gpt", "value": "Two bars are there. The bar to the left represents Mental Health management in Efficient Care Continuum with ranges of efficiency scores from 0.00 to 60.00 located at [0.846, 0.421, 0.895, 0.883]. The bar to the right represents Mental Health management in Personalized Care with ranges of efficiency scores from 0.00 to 95.00 located at [0.895, 0.152, 0.943, 0.883]." } ], "id_raw": 889 }, { "id": "MLtL7fqKwHdFqadq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis located at coordinates [0.015, 0.342, 0.029, 0.616]?" }, { "from": "gpt", "value": "The label is 'Development Score'." } ], "id_raw": 890 }, { "id": "nJL76lqWvilj2uYA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar representing \"Performing Arts\"} at [0.400, 0.411, 0.449, 0.883] for the Emerging group?" }, { "from": "gpt", "value": "It represents a Development Score of 0 to 46." } ], "id_raw": 890 }, { "id": "gWuinuqDcTm2VBk7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nHow is the handle color represented in the legend for 'Emerging' located at [0.859, 0.144, 0.887, 0.164] on the chart?" }, { "from": "gpt", "value": "The color '#f4a261' represents 'Emerging'." } ], "id_raw": 890 }, { "id": "DtDlYCInFDB1Bsg7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nWhich developed culture aspect bar is taller, {the bar representing \"Visual Arts\"}[0.104, 0.113, 0.153, 0.883] or {the bar representing \"Sculpture\"}[0.597, 0.195, 0.647, 0.883]?" }, { "from": "gpt", "value": "Visual Arts." } ], "id_raw": 890 }, { "id": "R2DR9UhKTgXcUBKC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Writing\" category for the Emerging group}[0.894, 0.185, 0.943, 0.883] taller than {the bar representing \"Performing Arts\" category for the Emerging group}[0.400, 0.411, 0.449, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 890 }, { "id": "iQDeZG76aVnxsFhK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Writing\"}[0.844, 0.380, 0.894, 0.883] and {\"Performing Arts\"}[0.351, 0.185, 0.400, 0.883] for the Developed group, which has higher Development Score?" }, { "from": "gpt", "value": "Performing Arts." } ], "id_raw": 890 }, { "id": "R6Hrm3YNXbWmCRtU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.104, 0.113, 0.203, 0.883]." }, { "from": "gpt", "value": "There are two vertical bars within these coordinates. The blue bar at [0.104, 0.113, 0.153, 0.883] represents a score of 0 to 75 for the Visual Arts aspect in the Developed group. The orange bar at [0.153, 0.319, 0.203, 0.883] represents a score of 0 to 55 for the Visual Arts aspect in the Emerging group." } ], "id_raw": 890 }, { "id": "3mhO4zJleAGTLcrb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.869, 0.903, 0.919, 0.931]." }, { "from": "gpt", "value": "It contains the X-axis label \"Writing\" for both the Developed and Emerging groups of the Culture Aspects." } ], "id_raw": 890 }, { "id": "d3pcAT7IRiazsetV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.597, 0.195, 0.696, 0.883]?" }, { "from": "gpt", "value": "There are 2 bars within these coordinates representing the Sculpture aspect. The bar at [0.597, 0.195, 0.647, 0.883] is for the Developed group with a score from 0 to 67. The bar at [0.647, 0.524, 0.696, 0.883] belongs to the Emerging group with a score from 0 to 35." } ], "id_raw": 890 }, { "id": "bBX68BCHmPfidL1w", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordiantes [0.148, 0.903, 0.184, 0.931] in the chart?" }, { "from": "gpt", "value": "The year \"2019\" on the x-axis." } ], "id_raw": 891 }, { "id": "c3Mz64YO3k9ZfiMe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the object located at [0.105, 0.100, 0.188, 0.128]?" }, { "from": "gpt", "value": "It's the legend label for the term {CSR-Default}[0.105, 0.100, 0.188, 0.128]." } ], "id_raw": 891 }, { "id": "GTlei1KiaCfxjDXD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.731, 0.242, 0.766, 0.883] refer to?" }, { "from": "gpt", "value": "They refer to a {Sustainable-HAG}[0.731, 0.242, 0.766, 0.883] bar for the year 2022." } ], "id_raw": 891 }, { "id": "svBQoKC6FF2Z26xJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat text label exists within the coordinate [0.015, 0.348, 0.029, 0.610]?" }, { "from": "gpt", "value": "The label is {Performance Score}[0.015, 0.348, 0.029, 0.610] which represents the y-axis label." } ], "id_raw": 891 }, { "id": "neVT5c4JQdUZDum5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nIs {the CSR-Default bar of 2019}[0.096, 0.242, 0.131, 0.883] taller than {the Sustainable-Default bar of the same year}[0.166, 0.370, 0.201, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 891 }, { "id": "AiuzH83mLMxoRBoj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nHow does the height of {the CSR-HAG bar for 2021}[0.484, 0.177, 0.519, 0.883] compare to {the Sustainable-HAG bar for 2021}[0.554, 0.242, 0.590, 0.883]?" }, { "from": "gpt", "value": "It is taller." } ], "id_raw": 891 }, { "id": "VX6DwCdBDL3nzqPZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nWhich is closer to the top, {CSR-HAG 2023 bar}[0.837, 0.113, 0.872, 0.883] or {Sustainable-HAG 2023 bar}[0.907, 0.177, 0.943, 0.883]?" }, { "from": "gpt", "value": "The CSR-HAG 2023 bar." } ], "id_raw": 891 }, { "id": "EkcqdDTqRVp6oZL4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nBetween the bars for 2020, is the {CSR-Default bar}[0.272, 0.370, 0.307, 0.883] higher or lower than {Sustainable-HAG bar}[0.378, 0.370, 0.413, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 891 }, { "id": "FaCgSOAmlYWfIPF2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects located in the region [0.060, 0.089, 0.247, 0.264]?" }, { "from": "gpt", "value": "This region holds the chart legend. The legend includes four items: {CSR-Default}[0.105, 0.100, 0.188, 0.128] in green color, {CSR-HAG}[0.105, 0.142, 0.167, 0.170] in orange color, {Sustainable-Default}[0.105, 0.183, 0.242, 0.211] in blue color, and {Sustainable-HAG}[0.105, 0.225, 0.221, 0.253] in light peach color. The color labels for each are located at [0.066, 0.102, 0.093, 0.122], [0.066, 0.144, 0.093, 0.164], [0.066, 0.186, 0.093, 0.205], and [0.066, 0.228, 0.093, 0.247] respectively." } ], "id_raw": 891 }, { "id": "seLhcDooagViPPdh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the region [0.448, 0.177, 0.554, 0.883]." }, { "from": "gpt", "value": "The region includes two bars for the year 2021. They are: \n1. {CSR-HAG bar for 2021}[0.484, 0.177, 0.519, 0.883] in orange color, \n2. {Sustainable-Default bar for 2021}[0.519, 0.498, 0.554, 0.883] in blue color." } ], "id_raw": 891 }, { "id": "6zvBZfqG1Aw2C9tK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.855, 0.903, 0.890, 0.931]?" }, { "from": "gpt", "value": "It contains the year {2023}[0.855, 0.903, 0.890, 0.931] label on the x-axis." } ], "id_raw": 891 }, { "id": "gA9vwaA6VC5hHL3X", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of the objects within the boundaries [0.676, 0.177, 0.766, 0.883]?" }, { "from": "gpt", "value": "This region includes bars from different groups for 2022: \n1. {CSR-Default bar for 2022}[0.625, 0.627, 0.660, 0.883] in green color,\n2. {CSR-HAG bar for 2022}[0.660, 0.177, 0.696, 0.883] in orange color,\n3. {Sustainable-Default bar for 2022}[0.696, 0.563, 0.731, 0.883] in blue color,\n4. {Sustainable-HAG bar for 2022}[0.731, 0.242, 0.766, 0.883] in light peach color." } ], "id_raw": 891 }, { "id": "92WjSRcnrXQ0fTn0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the product category located at [0.169, 0.903, 0.277, 0.931] on the x-axis?" }, { "from": "gpt", "value": "Cosmeceuticals" } ], "id_raw": 892 }, { "id": "3DrOjoUeLCGWxkNd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat color does the legend indicate for the 'Apr' sales at [0.101, 0.228, 0.128, 0.247]?" }, { "from": "gpt", "value": "Red" } ], "id_raw": 892 }, { "id": "ja2uziuTTw1Oa7eh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat text does the y-axis label at [0.015, 0.418, 0.029, 0.540] represent in the chart?" }, { "from": "gpt", "value": "Sales ($)" } ], "id_raw": 892 }, { "id": "FWLVfmYtvCecKiRe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at the coordinates [0.818, 0.235, 0.849, 0.883] represent in terms of product and month?" }, { "from": "gpt", "value": "{Battery's June sales}[0.913, 0.140, 0.944, 0.883]" } ], "id_raw": 892 }, { "id": "rhsDssamC2irn011", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nUsing the bars for the 'Battery' product, does the sales of 'Feb' at [0.787, 0.140, 0.819, 0.883] exceed that of 'Jun'[0.913, 0.140, 0.944, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 892 }, { "id": "JtbV4xv3sF7LjXAO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nFor 'Superfoods', are the sales in 'Jan'[0.547, 0.151, 0.578, 0.883] higher compared to 'Jun'[0.704, 0.347, 0.735, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 892 }, { "id": "gw2wwYwKvSHZlMRD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhich month has higher sales for 'Natural Products': 'May'[0.463, 0.140, 0.495, 0.883] or 'Feb'[0.369, 0.219, 0.401, 0.883]?" }, { "from": "gpt", "value": "May." } ], "id_raw": 892 }, { "id": "zZTQxM4nMCieRwx4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nComparing the sales of 'Cosmeceuticals' in 'Mar'[0.192, 0.328, 0.223, 0.883] with 'Apr'[0.223, 0.321, 0.254, 0.883], which month has higher sales?" }, { "from": "gpt", "value": "March." } ], "id_raw": 892 }, { "id": "9xSiwEkU4UfCTXG6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe what the visual elements within the axes coordinates [0.088, 0.075, 0.985, 0.883] are about?" }, { "from": "gpt", "value": "The area defined by these coordinates contains the main bar chart which illustrates the sales trends of 4 different product categories ('Cosmeceuticals', 'Natural Products', 'Superfoods', 'Battery') over the period of 6 months ('Jan' to 'Jun')." } ], "id_raw": 892 }, { "id": "xocjdFR1Mn4gN4Ys", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region between [0.095, 0.089, 0.174, 0.348]?" }, { "from": "gpt", "value": "This region hosts the legend of the chart, which contains six colored markers, each representing a different month from January to June, along with their corresponding labels." } ], "id_raw": 892 }, { "id": "e2KZBaPhnuGlUKc8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the region [0.756, 0.166, 0.787, 0.883]?" }, { "from": "gpt", "value": "This region encompasses the January sales bar for the product category \"Battery\"." } ], "id_raw": 892 }, { "id": "D1mCEbohTUkUgdBc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area of [0.035, 0.117, 0.078, 0.145]." }, { "from": "gpt", "value": "This region contains the y-axis tick label \"50000\", indicating the sales value." } ], "id_raw": 892 }, { "id": "ZSGv9pE2N5pLJK1i", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in {the green bar} at this location [0.112, 0.463, 0.191, 0.883]?" }, { "from": "gpt", "value": "Local News influence on Media Type 1." } ], "id_raw": 893 }, { "id": "PK2uIPigPQXJx5jl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the text at coordinates [0.145, 0.903, 0.237, 0.931]?" }, { "from": "gpt", "value": "The label for Media Type 1." } ], "id_raw": 893 }, { "id": "FvKacExDZNVxu51Q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value that corresponds to {the yellow bar}[0.865, 0.218, 0.943, 0.883]?" }, { "from": "gpt", "value": "190." } ], "id_raw": 893 }, { "id": "CvSaDKDnFI8dDZAT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the identifier for the orange color shown in the legend at [0.846, 0.144, 0.874, 0.164]?" }, { "from": "gpt", "value": "Social Media." } ], "id_raw": 893 }, { "id": "RnRLcD5NWAF9y278", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nIs {the Local News influence on Media Type 2}[0.337, 0.358, 0.415, 0.883] higher than {the Social Media influence on Media Type 2}[0.415, 0.428, 0.494, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 893 }, { "id": "K7bP2ZiWLz6KbJWB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nAre {Media Type 2's coordinates}[0.370, 0.903, 0.461, 0.931] positioned at a higher x-axis level compared to {Media Type 1}[0.145, 0.903, 0.237, 0.931]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 893 }, { "id": "Qh5DEFcf5K3rBnPV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhich type of media has a greater influence covered by the bars, {Local News}[0.112, 0.463, 0.191, 0.883]; [0.337, 0.358, 0.415, 0.883]; [0.562, 0.603, 0.640, 0.883]; [0.786, 0.428, 0.865, 0.883] or {Social Media}[0.191, 0.113, 0.269, 0.883]; [0.415, 0.428, 0.494, 0.883]; [0.640, 0.533, 0.719, 0.883]; [0.865, 0.218, 0.943, 0.883]?" }, { "from": "gpt", "value": "Social Media." } ], "id_raw": 893 }, { "id": "LBj9k8bXfv7P847Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhich has more influence on Media Type 1, {Local News}[0.112, 0.463, 0.191, 0.883] or {Social Media}[0.191, 0.113, 0.269, 0.883]?" }, { "from": "gpt", "value": "Social Media." } ], "id_raw": 893 }, { "id": "X5GFrwq7Jn63DbFe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe what's in the bounding box with these coordinates [0.841, 0.089, 0.978, 0.181]?" }, { "from": "gpt", "value": "There are two labels for color text within this region, each associated with a color block. The first label, Local News, has the color green and is situated at [0.885, 0.100, 0.963, 0.128] with the color block at [0.846, 0.102, 0.874, 0.122], while the second label, Social Media, is associated with an orange color at [0.885, 0.142, 0.972, 0.170] with the color block at [0.846, 0.144, 0.874, 0.164]." } ], "id_raw": 893 }, { "id": "KNaCphJxxsWhNkxg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what I would see in the region with the following coordinates [0.562, 0.533, 0.719, 0.883]?" }, { "from": "gpt", "value": "Within this region, you would find a bar that displays the influence score of Social Media on Media Type 3." } ], "id_raw": 893 }, { "id": "JAglsPxFrz16dpnZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be represented by the area that lies within these coordinates [0.071, 0.075, 0.985, 0.883]?" }, { "from": "gpt", "value": "This region contains the main chart area for the graph that shows the influence scores of Local News and Social Media across four different media types. It includes the bars indicating the influence score along with the x-axis and y-axis." } ], "id_raw": 893 }, { "id": "gSQiQOeGV1OtR0o7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat's inside the bounding box with these coordinates [0.340, 0.030, 0.716, 0.066]?" }, { "from": "gpt", "value": "This area contains the subtitle for the chart, which reads 'Media Influence and Public Opinion Guidance.'" } ], "id_raw": 893 }, { "id": "JH63xaNHcduIHv5W", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat color corresponds to the label \"Data Science\" in the legend?" }, { "from": "gpt", "value": "The color for {Data Science}[0.053, 0.142, 0.073, 0.155] is {beige}[0.053, 0.142, 0.073, 0.155]." } ], "id_raw": 894 }, { "id": "kbGRIo93aXqSVSBA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the yAxis label present at the coordinates [0.011, 0.401, 0.021, 0.572]?" }, { "from": "gpt", "value": "Development Score." } ], "id_raw": 894 }, { "id": "ZcyLmST3jOu3a2wW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for the label \"Quantum Computing\" in the plotting legend?" }, { "from": "gpt", "value": "The coordinates for {Quantum Computing}[0.081, 0.088, 0.185, 0.106] are [0.081, 0.088, 0.185, 0.106]." } ], "id_raw": 894 }, { "id": "dsunnvujAKsKDp64", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nIs the {2015 bar for Artificial Intelligence}[0.492, 0.596, 0.505, 0.814] higher than the {2015 bar for 5G}[0.517, 0.778, 0.529, 0.814]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 894 }, { "id": "Nl6JLOcr14LWQUye", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nDoes the {first year bar of Data Science}[0.124, 0.742, 0.136, 0.814] start lower than the {first year bar of 5G}[0.111, 0.814, 0.124, 0.887]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 894 }, { "id": "3ohUkQ6M67USexkv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nAre {2011 bars}[0.180, 0.669, 0.193, 0.814], [0.205, 0.705, 0.217, 0.814] aligned vertically with the {2011 x-tick}[0.193, 0.927]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 894 }, { "id": "xQQYpMK6YWy7ltAO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.010, 0.350, 0.050, 0.600]." }, { "from": "gpt", "value": "This area contains {y-axis label}[0.011, 0.401, 0.021, 0.572] and several {y-axis ticks}[[0.044, 0.378], [0.044, 0.523], [0.044, 0.669]]. The label is \"Development Score\" and the tickvalues are from 6 to 0, indicating increasing Development Scores down the axis." } ], "id_raw": 894 }, { "id": "EJgMiqrUAJZ2Cjm8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the visual content between [0.080, 0.060, 0.190, 0.160]?" }, { "from": "gpt", "value": "This region holds the {legend}[0.049, 0.055, 0.189, 0.165] including labels for different technologies such as {Artificial Intelligence}[0.081, 0.062, 0.183, 0.080], {Quantum Computing}[0.081, 0.088, 0.185, 0.106], and their corresponding {color indicators}[0.053, 0.064, 0.073, 0.076], [0.053, 0.090, 0.073, 0.102]." } ], "id_raw": 894 }, { "id": "J85DXpuqMRUvM9bN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nCover the details found within the rectangular section [0.500, 0.940, 0.600, 0.960]." }, { "from": "gpt", "value": "This area contains the {x-tick label \"2016\"}[0.585, 0.939, 0.610, 0.957], which is part of the x-axis representing the years." } ], "id_raw": 894 }, { "id": "W14ooaRFTrUiQ743", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the label is for the coordinate [0.611, 0.903, 0.676, 0.931]?" }, { "from": "gpt", "value": "The label is 'Webinars'." } ], "id_raw": 895 }, { "id": "c9ULwl8PLkEcnRNT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.105, 0.100, 0.228, 0.128]?" }, { "from": "gpt", "value": "It's a text label in the legend reading 'Sustained Growth'." } ], "id_raw": 895 }, { "id": "Pq6a30re09F41uAb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat's the numeric range value for the rectangle at [0.843, 0.521, 0.893, 0.883]?" }, { "from": "gpt", "value": "The numeric range is '0.00 -> 4.00'" } ], "id_raw": 895 }, { "id": "Ld3LYKlYWF1gPLHU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing 'Online Classes' of 'Sustained Growth'[0.096, 0.702, 0.145, 0.883] taller than the bar representing 'Online Classes' of 'Rapid Expansion'[0.145, 0.793, 0.195, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 895 }, { "id": "zzMgAdxYJ6TjZGf1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nWhich is higher in the chart, {the 'Sustained Growth' bar for 'Webinars'}[0.594, 0.566, 0.644, 0.883] or {the 'Rapid Expansion' bar for 'Webinars'}[0.644, 0.340, 0.693, 0.883]?" }, { "from": "gpt", "value": "The 'Rapid Expansion' bar for 'Webinars'." } ], "id_raw": 895 }, { "id": "83Fuc0dKl8G0eA8q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Rapid Expansion' bar for 'Online Classes'}[0.145, 0.793, 0.195, 0.883] to the left or right of {the 'Sustained Growth' bar for 'Online Classes'}[0.096, 0.702, 0.145, 0.883]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 895 }, { "id": "neINRnhdpALh5Ejj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.060, 0.089, 0.234, 0.181]?" }, { "from": "gpt", "value": "This is the area of the legend. It contains labels and color squares for two categories: 'Sustained Growth'{color square}[0.066, 0.102, 0.093, 0.122], labelled at [0.105, 0.100, 0.228, 0.128], and 'Rapid Expansion'{color square}[0.066, 0.144, 0.093, 0.164], labelled at [0.105, 0.142, 0.220, 0.170]." } ], "id_raw": 895 }, { "id": "aV5cPuqRqB0KA07z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.344, 0.113, 0.943, 0.883]?" }, { "from": "gpt", "value": "That region houses most of the bars depicting the growth units of different Educational Technologies in the categories of 'Sustained Growth' and 'Rapid Expansion'. Examples include the 'Sustained Growth' bar for 'Virtual Reality'[0.843, 0.521, 0.893, 0.883] and the 'Rapid Expansion' bar for 'Webinars'[0.644, 0.340, 0.693, 0.883]." } ], "id_raw": 895 }, { "id": "i5uxDTKROQNcTzE2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.015, 0.389, 0.029, 0.569] include?" }, { "from": "gpt", "value": "The region includes the y-axis label, which is 'Growth Units'." } ], "id_raw": 895 }, { "id": "QelRpOhCWib5yomG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the horizontal axis indicator for the coordinate [0.172, 0.903, 0.200, 0.931]?" }, { "from": "gpt", "value": "SEO" } ], "id_raw": 896 }, { "id": "fYsuzHDmJitpBLGe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bars designated at coordinates [0.107, 0.380, 0.186, 0.524]?" }, { "from": "gpt", "value": "Red" } ], "id_raw": 896 }, { "id": "F2SZpVEB8C16qbs4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the plot located at [0.448, 0.030, 0.602, 0.066]?" }, { "from": "gpt", "value": "Media Comparison" } ], "id_raw": 896 }, { "id": "gV3q1TY507vYGkmG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar having the coordinate [0.559, 0.524, 0.638, 0.775]?" }, { "from": "gpt", "value": "{the value of video production for Traditional Media}[0.559, 0.524, 0.638, 0.775]" } ], "id_raw": 896 }, { "id": "x70CAiMeJ4BtADaE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the Digital Media bar for SEO}[0.186, 0.309, 0.265, 0.524] have a higher score than {the Traditional Media bar for SEO}[0.107, 0.380, 0.186, 0.524]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 896 }, { "id": "OwpEzT0Mmzab0y0F", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nIs {the Graphic Design value of Digital Media}[0.864, 0.111, 0.943, 0.524] higher than {the Video Production value of Traditional Media}[0.559, 0.524, 0.638, 0.775]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 896 }, { "id": "eH5c1TPteeXRthS9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the Content Editing value for Traditional Media}[0.333, 0.327, 0.412, 0.524] have a higher value than {the SEO value for Digital Media}[0.186, 0.309, 0.265, 0.524]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 896 }, { "id": "747kGItZxUpqDhYg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nIs {the Traditional Media bar for Graphic Design}[0.785, 0.255, 0.864, 0.524] on the left of {the Digital Media bar for Graphic Design}[0.864, 0.111, 0.943, 0.524]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 896 }, { "id": "PumO11YgcoSqRFVn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects present in the coordinate [0.072, 0.089, 0.241, 0.181]." }, { "from": "gpt", "value": "This area contains a legend for the graph represented by two color-coded labels for Traditional Media and Digital Media. {Traditional Media} is represented by a red box at [0.077, 0.102, 0.105, 0.122], and {Digital Media} is represented by a blue box at [0.077, 0.144, 0.105, 0.164]." } ], "id_raw": 896 }, { "id": "0eOOvZVryXLaKgHc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nWhich objects are within the bounds of [0.107, 0.380, 0.186, 0.524]?" }, { "from": "gpt", "value": "The object in this area is a {red bar}[0.107, 0.380, 0.186, 0.524] representing the score (0.8) for SEO under Traditional Media." } ], "id_raw": 896 }, { "id": "FVFxdENcvxtQcYI8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.579, 0.524, 0.697, 0.931] contain?" }, { "from": "gpt", "value": "This area contains the {bar for Video Production of Traditional Media}[0.559, 0.524, 0.638, 0.775] and its title located at [0.579, 0.903, 0.697, 0.931]." } ], "id_raw": 896 }, { "id": "1VxKMkmxpoHOd4Hp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat information does the region from [0.046, 0.154, 0.055, 0.182] on the y-axis display?" }, { "from": "gpt", "value": "The label \"2\" indicating the score of 2 on the y-axis." } ], "id_raw": 896 }, { "id": "tHJ5K50zPm9Lm1vh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the specific academic field represented by the X-axis label at [0.340, 0.903, 0.460, 0.931]?" }, { "from": "gpt", "value": "Natural_Sciences." } ], "id_raw": 897 }, { "id": "dl4JaywJZ5pbGaZL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color for the bar series PhD as specified in the given legend coordinates [0.808, 0.144, 0.835, 0.164]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 897 }, { "id": "77RGEWgBsu63K15q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat label is present at the coordinate [0.046, 0.872, 0.052, 0.900] on the Y-axis?" }, { "from": "gpt", "value": "5." } ], "id_raw": 897 }, { "id": "SRWxwmEYtaLhsYVJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat region of research is specified in the coordinates [0.839, 0.903, 0.948, 0.931]?" }, { "from": "gpt", "value": "Social_Sciences." } ], "id_raw": 897 }, { "id": "m1wdzweEWqucoEyf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for Humanities Bachelors_Degree}[0.104, 0.370, 0.153, 0.883] higher or lower than the {bar for Humanities PhD}[0.153, 0.498, 0.203, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 897 }, { "id": "4O3D2cU9iOttD9xX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar for Natural_Sciences Bachelors_Degree}[0.351, 0.241, 0.400, 0.883] and {the bar for Formal_Sciences Bachelors_Degree}[0.597, 0.113, 0.647, 0.883], which one is taller?" }, { "from": "gpt", "value": "Formal_Sciences." } ], "id_raw": 897 }, { "id": "DjucmS8Sar7ehKfT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nIs {PhD for Social_Sciences}[0.894, 0.293, 0.943, 0.883] more conducted than {PhD for Natural_Sciences}[0.400, 0.318, 0.449, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 897 }, { "id": "XjvkyDnnbasSKzEt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhich is greater, {Bachelors_Degree in Formal_Sciences}[0.597, 0.113, 0.647, 0.883] or {PhD in Formal_Sciences}[0.647, 0.164, 0.696, 0.883]?" }, { "from": "gpt", "value": "Bachelors_Degree." } ], "id_raw": 897 }, { "id": "F2J0ilS3zX7O4FAk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the coordinates range [0.802, 0.089, 0.978, 0.181]?" }, { "from": "gpt", "value": "This region contains the legend for the chart. The legend has two items: \n1. The first entry titled \"Bachelors_Degree\"[0.846, 0.100, 0.972, 0.128] is associated with a color marker in Sea Green[0.808, 0.102, 0.835, 0.122].\n2. The second entry with the title \"PhD\" at[0.846, 0.142, 0.874, 0.170] is associated with a color marker in Orange[0.808, 0.144, 0.835, 0.164]." } ], "id_raw": 897 }, { "id": "GgT3nBiMNKIafnZa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the labels present in the area [0.468, 0.942, 0.579, 0.970]?" }, { "from": "gpt", "value": "The X-axis label \"Academic Fields\" is present in the specified area." } ], "id_raw": 897 }, { "id": "MYLROF5188MmjWsz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements within the coordinates range [0.597, 0.113, 0.696, 0.883]?" }, { "from": "gpt", "value": "This area contains two bars:\n1. The bar representing the Bachelors_Degree in Formal_Sciences research quantity, with coordinates [0.597, 0.113, 0.647, 0.883].\n2. The bar representing the PhD in Formal_Sciences research quantity, with coordinates [0.647, 0.164, 0.696, 0.883]." } ], "id_raw": 897 }, { "id": "4AqB5XMolP2ODZHQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the region at [0.328, 0.030, 0.719, 0.066] consist of?" }, { "from": "gpt", "value": "It consists of the subtitle of the chart, which is \"Research Quantity in Different Academic Fields\"." } ], "id_raw": 897 }, { "id": "jDwbM9S620Zcy1zc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the Dell XPS bar at July}[0.571, 0.165, 0.585, 0.917]?" }, { "from": "gpt", "value": "Peach." } ], "id_raw": 899 }, { "id": "NDdrZHP0e1C42FS2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label corresponding to {the bar for 'June' for Lenovo Thinkpad}[0.484, 0.670, 0.498, 0.917]?" }, { "from": "gpt", "value": "June." } ], "id_raw": 899 }, { "id": "GTuNo58ZWC3Bwk3A", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of sales for {the Apple Macbook bar in September}[0.672, 0.729, 0.686, 0.917]?" }, { "from": "gpt", "value": "0.00 -> 80.00." } ], "id_raw": 899 }, { "id": "gDwV7tzli7KeDdFw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Lenovo ThinkPad bar in April}[0.339, 0.647, 0.354, 0.917] taller than {the Samsung Chromebook bar in April}[0.325, 0.635, 0.339, 0.917]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 899 }, { "id": "KuReBGyCCcqIe9yy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar of Samsung Chromebook for May}[0.397, 0.564, 0.411, 0.917] represent higher sales compared to {the Apple Macbook bar for May}[0.382, 0.635, 0.397, 0.917]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 899 }, { "id": "JtepSzTKuIxtDq6e", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nWhich bar has a higher vertical position in October, {Dell XPS}[0.787, 0.200, 0.802, 0.917] or {Apple Macbook}[0.744, 0.741, 0.759, 0.917]?" }, { "from": "gpt", "value": "Dell XPS." } ], "id_raw": 899 }, { "id": "j1FvpMd4FnsPYnr8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.611, 0.105, 0.688, 0.917]." }, { "from": "gpt", "value": "This area includes vertically oriented bars for several products for the month of August. Each bar represents different sales values of products, clearly defined by distinct colors that specify different brands: Apple Macbook, Samsung Chromebook, Lenovo Thinkpad, and Dell XPS." } ], "id_raw": 899 }, { "id": "nGZERekZJE84tcOn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat content is noticeable in the region [0.160, 0.377, 0.542, 0.917]?" }, { "from": "gpt", "value": "The region depicts several different product sales bars for the first seven months of the year. Each month houses a set of colored bars categorized by product brands and shows various heights indicating the sales values." } ], "id_raw": 899 }, { "id": "ngskRa7qwp1XB57Y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat do you observe in the portion of the graph marked by [0.839, 0.063, 0.984, 0.189]?" }, { "from": "gpt", "value": "This is the legend explaining the various products displayed in the graph. It includes product names attached to color indicators displaying which colors represent which product on the bar graph. Specifically, it shows 'Apple Macbook' linked to a red color and 'Samsung Chromebook' linked to a blue color." } ], "id_raw": 899 }, { "id": "ElYyvLByvSAcfFHk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the text displayed at [0.504, 0.881, 0.533, 0.898] in the chart?" }, { "from": "gpt", "value": "The year 2013." } ], "id_raw": 930 }, { "id": "L5zxlsiHnhuDEDIp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar located at [0.558, 0.274, 0.572, 0.564]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 930 }, { "id": "s6Ew6hTnPb19siA2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nWhere is the index label for 'NASDAQ-Default' located in the chart?" }, { "from": "gpt", "value": "[0.548, 0.965, 0.644, 0.983]." } ], "id_raw": 930 }, { "id": "S2pZ0GjNZmLGuUuL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for SPX-Default}[0.093, 0.430, 0.106, 0.564] taller than {the bar for CRSP-Default}[0.133, 0.440, 0.146, 0.564] in 2007?" }, { "from": "gpt", "value": "No." } ], "id_raw": 930 }, { "id": "JboBUG2H6Z1BT9kM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar at}[0.559, 0.274, 0.572, 0.564] represent a higher value than {the bar at}[0.865, 0.212, 0.878, 0.564]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 930 }, { "id": "XL189XcikP1PSBuv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nIs the y-coordinate higher for {the DOW-Default label}[0.417, 0.965, 0.493, 0.983] compared to {the CRSP-Default label}[0.700, 0.965, 0.777, 0.983]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 930 }, { "id": "JcWq4CREfNKRFqvP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the information presented between coordinates [0.676, 0.019, 0.952, 0.041]." }, { "from": "gpt", "value": "This region includes part of the subtitle 'Index Performance Comparison (2007-2019)'." } ], "id_raw": 930 }, { "id": "GYdeBo4s8zJKqJ9z", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the region [0.029, 0.040, 0.046, 0.885]?" }, { "from": "gpt", "value": "This region accommodates the y-axis labels for the chart which ranges from '\u22123' to '5'." } ], "id_raw": 930 }, { "id": "beQ4w2KeJEJ9dVSu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are found within the region [0.013, 0.880, 0.936, 0.920]?" }, { "from": "gpt", "value": "This area includes all the tick labels of the years from 2007 to 2019, marking each year's representation in the chart." } ], "id_raw": 930 }, { "id": "oicTMHOaKAyGdAwG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located at these coordinates [0.012, 0.402, 0.024, 0.578]?" }, { "from": "gpt", "value": "The y-axis label, \"Percentage Increase\"." } ], "id_raw": 931 }, { "id": "PtTX84zVutbXAMVr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nWhat information does the bar at [0.657, 0.592, 0.705, 0.933] represent?" }, { "from": "gpt", "value": "It represents the \"Innovation Increase\" for the \"Genomics\" tech area." } ], "id_raw": 931 }, { "id": "e8DLmVd4K5mYskFz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item with position [0.867, 0.115, 0.977, 0.132] represent?" }, { "from": "gpt", "value": "It's the legend entry for \"Innovation Increase\", indicated with a {color: #fa8072}[0.835, 0.116, 0.858, 0.128]." } ], "id_raw": 931 }, { "id": "XKSGEzRgFYpzdHkj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nWhat does this object at [0.868, 0.962, 0.977, 0.132] correspond to in the chart?" }, { "from": "gpt", "value": "It's {the label for the first tech area: \"Quantum\"}[0.128, 0.939, 0.182, 0.957]." } ], "id_raw": 931 }, { "id": "3lb4YOVCMH2IKpkU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nIs the height of {the bar representing \"Quantum\" in \"Research Growth\"}[0.131, 0.524, 0.179, 0.933] more than the height of {the bar representing \"Quantum\" in \"Innovation Increase\"}[0.179, 0.729, 0.227, 0.933]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 931 }, { "id": "w4qAcsre2TiI2HvR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Research Growth\" bar for \"Genomics\"}[0.610, 0.251, 0.657, 0.933] taller than {the \"Research Growth\" bar for \"Robotics\"}[0.370, 0.388, 0.418, 0.933]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 931 }, { "id": "QOJorjF6K7mOQoUW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nIs there a {bar in the TechArea category}[0.083, 0.729, 0.131, 0.933] for the Quantum tech area that is taller than {the bar in the Innovation Increase category}[0.179, 0.729, 0.227, 0.933]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 931 }, { "id": "g9jmEDuM3HYzHV81", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nBetween {Research Growth}[0.867, 0.088, 0.962, 0.106] and {Innovation Increase}[0.867, 0.115, 0.977, 0.132], which one has a higher value for {Genomics}[0.605, 0.939, 0.662, 0.957]?" }, { "from": "gpt", "value": "Research Growth." } ], "id_raw": 931 }, { "id": "wb5AZsvIsu3SBeQd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is included within this area [0.830, 0.055, 0.982, 0.139]?" }, { "from": "gpt", "value": "This area contains the legend of the chart. It has three items. The first item is {TechArea}[0.867, 0.062, 0.920, 0.080] with color {#cd5c5c}[0.835, 0.064, 0.858, 0.076], the second item is {ResearchGrowth}[0.867, 0.088, 0.962, 0.106] with color {#f08080}[0.835, 0.090, 0.858, 0.102], and the third item is {InnovationIncrease}[0.867, 0.115, 0.977, 0.132] with color {#fa8072}[0.835, 0.116, 0.858, 0.128]." } ], "id_raw": 931 }, { "id": "jd7VUU3eEGDfF2qO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.083, 0.729, 0.849, 0.933]?" }, { "from": "gpt", "value": "There are 12 bars representing the values for 4 tech areas across 3 categories: TechArea, Research Growth, and Innovation Increase. For example, within the Quantum tech area, the values for the three categories are represented by the following bars: {TechArea Quantum}[0.083, 0.729, 0.131, 0.933], {Research Growth Quantum}[0.131, 0.524, 0.179, 0.933], and {Innovation Increase Quantum}[0.179, 0.729, 0.227, 0.933]." } ], "id_raw": 931 }, { "id": "kEWcWeTOgp86NnDZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the region [0.128, 0.939, 0.914, 0.957]?" }, { "from": "gpt", "value": "Inside the said region are the labels of the technology areas on the x-axis which are {Quantum}[0.128, 0.939, 0.182, 0.957], {Robotics}[0.370, 0.939, 0.419, 0.957], {Genomics}[0.605, 0.939, 0.662, 0.957], and {CyberSecurity}[0.832, 0.939, 0.914, 0.957]." } ], "id_raw": 931 }, { "id": "03T56LVwOJ3q8hXB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the region [0.323, 0.524, 0.418, 0.933]." }, { "from": "gpt", "value": "This area covers two bar charts representing the values in the 'Research Growth' category for the 'Robotics' tech area. These include the {Research Growth bar for Robotics}[0.370, 0.388, 0.418, 0.933] and the {Innovation Increase bar for Robotics}[0.418, 0.660, 0.466, 0.933]." } ], "id_raw": 931 }, { "id": "hFYxLewti4BXAqS2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object inside the coordinates [0.015, 0.323, 0.029, 0.603]?" }, { "from": "gpt", "value": "It's the y-axis label named Investment or Interest Level." } ], "id_raw": 932 }, { "id": "qwzK6tFzBjH2KApU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nWhere is the label with \"Health Apps\" located?" }, { "from": "gpt", "value": "The label \"Health Apps\" is at [0.445, 0.958, 0.529, 0.978]." } ], "id_raw": 932 }, { "id": "nkP4Zb7Cqxw3ZctF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis tick at [0.035, 0.045, 0.061, 0.065]?" }, { "from": "gpt", "value": "It's 100." } ], "id_raw": 932 }, { "id": "DkQBms01mzKBxUcl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nWhat information is provided at [0.691, 0.958, 0.802, 0.978] in the chart?" }, { "from": "gpt", "value": "It's a label named AI in Healthcare." } ], "id_raw": 932 }, { "id": "8j4FOGstTqHl1rGQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nBetween {the bars representing 2016}[0.108, 0.627, 0.142, 0.873] and {the bars representing 2020}[0.804, 0.258, 0.839, 0.873], which year has higher interest in Telemedicine?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 932 }, { "id": "XEZ7svaP1w7wsdkz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nIs the {Health Apps bar}[0.839, 0.340, 0.874, 0.873] for 2020 higher than the {AI in Healthcare bar}[0.908, 0.545, 0.943, 0.873] for the same year?" }, { "from": "gpt", "value": "No." } ], "id_raw": 932 }, { "id": "1Z9m5bPTMRAGdbl5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the EHR bar in 2018}[0.525, 0.627, 0.560, 0.873] or {the EHR bar in 2020}[0.874, 0.504, 0.908, 0.873]?" }, { "from": "gpt", "value": "2018 bar." } ], "id_raw": 932 }, { "id": "SPU5dQzgsMVP77lo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nIn comparison between {the interest in Health Apps in 2016}[0.142, 0.586, 0.177, 0.873] and {the same in 2020}[0.839, 0.340, 0.874, 0.873], which one is higher?" }, { "from": "gpt", "value": "The interest in Health Apps is higher in 2020." } ], "id_raw": 932 }, { "id": "Z1FBRBViwS9gGqdG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.243, 0.950, 0.808, 0.986]." }, { "from": "gpt", "value": "The area includes the graph's legend with four items. It has color and text labels for each type of technology: Telemedicine with color label at [0.249, 0.960, 0.277, 0.974], Health Apps with color label at [0.406, 0.960, 0.434, 0.974], EHR with color label at [0.557, 0.960, 0.585, 0.974], and AI in Healthcare with color label at [0.652, 0.960, 0.680, 0.974]." } ], "id_raw": 932 }, { "id": "oIcjupBIRLl5f4d1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.066, 0.053, 0.985, 0.873]." }, { "from": "gpt", "value": "This region mostly includes the main chart plotting area, with an x-axis displaying the years from 2016 to 2020 at intervals. The y-axis shows the level of investment or interest, from 0 to 100. The chart consists of bars representing four types of healthcare technologies: Telemedicine, Health apps, EHR, and AI in Healthcare, for each year." } ], "id_raw": 932 }, { "id": "T6o2CG2f808yYJlf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are found within the region [0.108, 0.627, 0.142, 0.873]?" }, { "from": "gpt", "value": "The area contains the Telemedicine bar for the year 2016, showing an investment or interest level of 30." } ], "id_raw": 932 }, { "id": "IQGpPjnuYfeVbqBJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.043, 0.373, 0.061, 0.393]." }, { "from": "gpt", "value": "The area contains one y-axis label, which is the number \"60\"." } ], "id_raw": 932 }, { "id": "HqJlb9LccOldifMn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the main object} at [0.015, 0.386, 0.029, 0.540] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Projected Value." } ], "id_raw": 933 }, { "id": "dp7imLFq46y68kem", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text that corresponds to \"AI Adoption\" in the legend?" }, { "from": "gpt", "value": "[0.832, 0.958, 0.912, 0.978]" } ], "id_raw": 933 }, { "id": "IoC65fOtHiiTxi15", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nWhat do {the bars in green color}[0.194, 0.600, 0.238, 0.873],[0.412, 0.490, 0.456, 0.873], [0.630, 0.381, 0.674, 0.873], [0.848, 0.217, 0.891, 0.873] represent?" }, { "from": "gpt", "value": "Renewable Energy Production." } ], "id_raw": 933 }, { "id": "hhriBY6FsTQqc2Qk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by [0.043, 0.756, 0.061, 0.776] on the Y-axis?" }, { "from": "gpt", "value": "Value 20." } ], "id_raw": 933 }, { "id": "6EKpQBizHR5Jjosl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Renewable Energy Production\" in the year 2035}[0.630, 0.381, 0.674, 0.873] higher than {the bar of \"Oil Production\" in the year 2035}[0.586, 0.463, 0.630, 0.873]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 933 }, { "id": "J5jweCsFfh7eNF6m", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 2040 for \"AI Adoption\"}[0.891, 0.327, 0.935, 0.873] taller than {the bar representing 2040 for \"Oil Production\"}[0.804, 0.545, 0.848, 0.873]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 933 }, { "id": "mcGT5GOlEnjUUrRK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nAre {the bars of \"Automobile Production\"}[[0.107, 0.217, 0.151, 0.873], [0.325, 0.245, 0.369, 0.873], [0.543, 0.272, 0.586, 0.873], [0.761, 0.327, 0.804, 0.873]] generally taller than {the bars of \"Renewable Energy Production\"}[[0.194, 0.600, 0.238, 0.873], [0.412, 0.490, 0.456, 0.873], [0.630, 0.381, 0.674, 0.873], [0.848, 0.217, 0.891, 0.873]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 933 }, { "id": "RsgQc74s8Vzfghxm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Automobile Production\"}[[0.107, 0.217, 0.151, 0.873], [0.325, 0.245, 0.369, 0.873], [0.543, 0.272, 0.586, 0.873], [0.761, 0.327, 0.804, 0.873]] and {\"AI Adoption\"}[[0.238, 0.764, 0.281, 0.873], [0.456, 0.654, 0.499, 0.873], [0.674, 0.545, 0.717, 0.873], [0.891, 0.327, 0.935, 0.873]], which category has a higher projected value for year 2040?" }, { "from": "gpt", "value": "AI Adoption." } ], "id_raw": 933 }, { "id": "QF7kCZxptYeY1Zc8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.125, 0.950, 0.917, 0.986]." }, { "from": "gpt", "value": "This region holds the chart legend with 4 items. They are:\n1. \"Automobile Production\" with a green handle at [0.131, 0.960, 0.158, 0.974].\n2. \"Oil Production\" with a brown handle at [0.355, 0.960, 0.383, 0.974].\n3. \"Renewable Energy Production\" with a bright green handle at [0.519, 0.960, 0.546, 0.974].\n4. \"AI Adoption\" with a purple handle at [0.793, 0.960, 0.821, 0.974]." } ], "id_raw": 933 }, { "id": "gsdofgApS1KgXIf1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the area defined by [0.066, 0.053, 0.976, 0.873]." }, { "from": "gpt", "value": "This region is mainly composed of the chart area showing the \"Projected Value\" against the \"Year\". It contains:\n1. Along the X-axis, there are 4 tick labels indicating the years: 2025, 2030, 2035, and 2040.\n2. On the Y-axis, there are values ranging from 0 to 160, representing the \"Projected Value\".\n3. The central area showcases individual bars for the 4 categories of interest: \"Automobile Production\", \"Oil Production\", \"Renewable Energy Production\", and \"AI Adoption\", over the years." } ], "id_raw": 933 }, { "id": "dcOtjB1eB1d96Rcs", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the region [0.543, 0.272, 0.717, 0.873]?" }, { "from": "gpt", "value": "Within the region:\n1. The bar for \"Automobile Production\" in the year 2035 is [0.543, 0.272, 0.586, 0.873].\n2. The bar for \"Oil Production\" in the year 2035 is [0.586, 0.463, 0.630, 0.873].\n3. The bar for \"Renewable Energy Production\" in the year 2035 is [0.630, 0.381, 0.674, 0.873].\n4. The bar for \"AI Adoption\" in the year 2035 is [0.674, 0.545, 0.717, 0.873]." } ], "id_raw": 933 }, { "id": "4EYi0H3FxXgiOQ8h", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.369, 0.021, 0.673, 0.047]?" }, { "from": "gpt", "value": "This area contains the subtitle of the chart: \"Future Forecast and Trend Prediction\"." } ], "id_raw": 933 }, { "id": "joaKF1LRqNY99QLH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of this chart?" }, { "from": "gpt", "value": "\"Company Financial Risk Control & Economic Affairs Scores\" located at coordinates {title}[0.356, 0.021, 0.678, 0.047]." } ], "id_raw": 934 }, { "id": "q4QcQGYp9HNkdRek", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the sixth company on the x-axis?" }, { "from": "gpt", "value": "It is {Citigroup}[0.876, 0.818, 0.912, 0.896]." } ], "id_raw": 934 }, { "id": "Uy9fpbnS9IOLoNmc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the text inside the rectangle with coordinates {bar label}[0.904, 0.071, 0.982, 0.091] refer to?" }, { "from": "gpt", "value": "This refers to the bar category \"Economic Affairs\"." } ], "id_raw": 934 }, { "id": "kuv3eb3VnVKBFi7d", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nIs {Goldman Sachs} bar of \"Economic Affairs\"[0.087, 0.412, 0.140, 0.804] taller than {Goldman Sachs} bar of \"Risk Control\"[0.140, 0.274, 0.193, 0.804]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 934 }, { "id": "MW0eeNBIbG7fnDqe", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, the {Wells Fargo} bar of \"Economic Affairs\"[0.691, 0.366, 0.743, 0.804] or the {Wells Fargo} bar of \"Risk Control\"[0.743, 0.458, 0.796, 0.804]?" }, { "from": "gpt", "value": "The bar of \"Risk Control\" is taller." } ], "id_raw": 934 }, { "id": "RTamkRxf7JZo1BSB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nWhat color represents \"Risk Control\"? Is it the color for the {Citigroup}[0.894, 0.343, 0.947, 0.804] bar?" }, { "from": "gpt", "value": "Yes, the color which is at coordinates {color}[0.878, 0.103, 0.897, 0.117] in the legend and represents \"Risk Control\" is the same for the Citigroup bar." } ], "id_raw": 934 }, { "id": "DsQ0DdY60O3K0uqQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the area [0.238, 0.228, 0.344, 0.804]?" }, { "from": "gpt", "value": "There are two bars located within the area. The first bar represents the score of \"Economic Affairs\" of {J.P. Morgan}[0.238, 0.228, 0.291, 0.804] and the second bar represents the score of \"Risk Control\" of {J.P. Morgan}[0.291, 0.089, 0.344, 0.804]." } ], "id_raw": 934 }, { "id": "izSWWWG4saLp68DZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.875, 0.063, 0.985, 0.129]." }, { "from": "gpt", "value": "This area contains the legend of the chart. The legend has two entries. The first is \"Economic Affairs\" with color code shown at {color}[0.878, 0.073, 0.897, 0.087], and the second entry is \"Risk Control\" with color code shown at {color}[0.878, 0.103, 0.897, 0.117]." } ], "id_raw": 934 }, { "id": "NI81K3Chq28K7gyn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.540, 0.320, 0.645, 0.804]?" }, { "from": "gpt", "value": "These are two bars representing {Bank Of America}'s scores in \"Economic Affairs\"[0.540, 0.320, 0.593, 0.804] and \"Risk Control\"[0.593, 0.366, 0.645, 0.804]." } ], "id_raw": 934 }, { "id": "g0WoyY6ik0UEetVq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the subplot on this chart?" }, { "from": "gpt", "value": "{'subplot_0'}[0.337, 0.021, 0.705, 0.047]" } ], "id_raw": 935 }, { "id": "HXPT8SC1aWT6qypt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent?" }, { "from": "gpt", "value": "{'Scores'}[0.015, 0.430, 0.029, 0.496]" } ], "id_raw": 935 }, { "id": "uU08b6lut51j5kIj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing R&D Investment in 2021?" }, { "from": "gpt", "value": "{'The color of the R&D Investment bar in 2021'}[0.893, 0.144, 0.943, 0.873] is purple." } ], "id_raw": 935 }, { "id": "dK9WNN7ejRZfSAJf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nIs the score for {Community Engagement}[0.099, 0.782, 0.149, 0.873] in 2018 higher than the score for {R&D Investment}[0.149, 0.327, 0.198, 0.873]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 935 }, { "id": "NTbQtKjp438HrHVQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nDoes the score for {R&D Investment}[0.645, 0.199, 0.695, 0.873] increase from 2020 to 2021 {R&D Investment}[0.893, 0.144, 0.943, 0.873]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 935 }, { "id": "Xr0dTovrbLhYejKg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {R&D Investment in 2019}[0.397, 0.235, 0.447, 0.873] taller than the bar for {Community Engagement in 2019}[0.347, 0.654, 0.397, 0.873]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 935 }, { "id": "3EtDe5AuIkq3hDtI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the color indicators in the legend [0.319, 0.950, 0.723, 0.986]?" }, { "from": "gpt", "value": "The legend includes two color indicators. The first one is established for Community Engagement and is pink [[0.325, 0.960, 0.352, 0.974]]. The second one displays the R&D Investment and is purple [[0.565, 0.960, 0.592, 0.974]]." } ], "id_raw": 935 }, { "id": "7fJBMhW13VnlzPJM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region starting from the bottom [[0.057, 0.053]] to the top [[0.985, 0.873]]." }, { "from": "gpt", "value": "The main chart area contains bars that represent Community Engagement and R&D investment for various years. Each pair of bars shares the same 'x' axis value, which denotes the year, with one ('Community Engagement') being pink and the other ('R&D Investment') being purple." } ], "id_raw": 935 }, { "id": "DJUHyOG7KNRS25oL", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [[0.844, 0.144, 0.943, 0.873]]?" }, { "from": "gpt", "value": "This area contains two bar charts for the year 2021. One represents Community Engagement with the height corresponding to a score of 40, the other one represents R&D Investment with the same height, indicating a score of 40 as well." } ], "id_raw": 935 }, { "id": "qPOLe69crrP4a6rV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis that can be located at the coordinates [0.015, 0.390, 0.029, 0.536]?" }, { "from": "gpt", "value": "Progress Score." } ], "id_raw": 936 }, { "id": "e4njeMDTgcOoAw49", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the text content within the box [0.180, 0.958, 0.304, 0.978] in the legend?" }, { "from": "gpt", "value": "Planning Strategy." } ], "id_raw": 936 }, { "id": "NVM7QaEDoyLJoAFr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the object at the coordinates [0.142, 0.960, 0.169, 0.974] in the legend?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 936 }, { "id": "23poLOvn20W4MYqb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nIs the height of {the bar graph for Planning Strategy in 2010}[0.128, 0.641, 0.157, 0.873] greater than the height of {the bar graph for Wildlife Conservation in 2025}[0.623, 0.723, 0.652, 0.873]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 936 }, { "id": "edr97egYUl5K3npl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nIn the legend, is the {the color icon of Sustainable Energy}[0.513, 0.960, 0.541, 0.974] located to the left or right of {the color icon of Wildlife Conservation}[0.714, 0.960, 0.742, 0.974]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 936 }, { "id": "XJQHUbBSl7x275cX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nCompare the bar heights, is the {the bar graph for Sustainable Energy in 2025}[0.594, 0.258, 0.623, 0.873] higher than {the bar graph for Planning Strategy in 2025}[0.536, 0.395, 0.565, 0.873]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 936 }, { "id": "HVT7mui9WGlxqJAX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nWhat's inside the region [0.099, 0.559, 0.902, 0.873]?" }, { "from": "gpt", "value": "This region contains all the bar graphs for each category from 2010 to 2035. Each year has four bars that represent the Progress Score for Planning Strategy, Pollution Control, Sustainable Energy and Wildlife Conservation respectively." } ], "id_raw": 936 }, { "id": "CwUtHpdv7HLNGTO7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area enclosed by [0.136, 0.950, 0.906, 0.986]?" }, { "from": "gpt", "value": "This is the area where the legend of the bar chart is situated. It contains four color-text pairs, each represents a different label: Planning Strategy, Pollution Control, Sustainable Energy, and Wildlife Conservation. Each pair consists of a colored square and corresponding label text." } ], "id_raw": 936 }, { "id": "n7AjoZ73gFsqzFi4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is displayed within the area from [0.448, 0.327, 0.506, 0.873]?" }, { "from": "gpt", "value": "Within this region, you can find two vertical bars. The blue bar marking the Sustainable Energy's Progress Score in 2020 and the red bar represents the Wildlife Conservation's Progress Score in 2020." } ], "id_raw": 936 }, { "id": "3O13rZC99KMVu7aY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinate [0.425, 0.019, 0.618, 0.041]?" }, { "from": "gpt", "value": "It's the subtitle of the graph, titled 'Health Status by Age Group'." } ], "id_raw": 937 }, { "id": "HUzTFt80XFCr269F", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label with coordinates [0.012, 0.394, 0.024, 0.527] stand for?" }, { "from": "gpt", "value": "It stands for 'Percentage (%)'." } ], "id_raw": 937 }, { "id": "F8Nhquy36ioHHbGV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the green element at the coordinates [0.226, 0.967, 0.249, 0.979] represent?" }, { "from": "gpt", "value": "It represents 'Healthy Lifestyle'." } ], "id_raw": 937 }, { "id": "dBA5pm7oHn06DSPt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nWhich category corresponds to the coordinate [0.097, 0.212, 0.125, 0.874]?" }, { "from": "gpt", "value": "It corresponds to the category 'Healthy Lifestyle' for the age group '20-30'." } ], "id_raw": 937 }, { "id": "iVWEBor3XmOuSLuY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nIs the percentage of 'Chronic Disease' higher or lower than 'Severe Disease' for the age group '40-50' ({Chronic Disease bar}[0.521, 0.626, 0.549, 0.874] vs {Severe Disease bar}[0.549, 0.750, 0.576, 0.874])?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 937 }, { "id": "SDuOgC0Mh0kUiCUD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nIs the 'Healthy Lifestyle' percentage for '30-40' age group({Healthy Lifestyle bar}[0.282, 0.295, 0.309, 0.874]) higher than the '60 and above' age group({Healthy Lifestyle bar}[0.835, 0.461, 0.862, 0.874])?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 937 }, { "id": "untjv1wvlmFXYmGX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nWhich lifestyle has a higher percentage in the '20-30' years age group, the 'Healthy Lifestyle'({Healthy Lifestyle bar}[0.097, 0.212, 0.125, 0.874]) or the 'Sedentary Lifestyle' ({Sedentary Lifestyle bar}[0.125, 0.709, 0.153, 0.874])?" }, { "from": "gpt", "value": "Healthy Lifestyle." } ], "id_raw": 937 }, { "id": "NZaonqWKqADkFCiZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nIs the 'Chronic Disease' percentage for '50-60'({Chronic Disease bar}[0.705, 0.585, 0.733, 0.874]) higher than '40-50'({Chronic Disease bar}[0.521, 0.626, 0.549, 0.874])?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 937 }, { "id": "ddD4zJ59i6ihPgNJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be seen in [0.055, 0.047, 0.988, 0.874]?" }, { "from": "gpt", "value": "This is the main chart area containing all the age groups and their respective lifestyle and disease visualizations." } ], "id_raw": 937 }, { "id": "llQBiunMwOHFhj1h", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is found in the region [0.221, 0.959, 0.821, 0.990]?" }, { "from": "gpt", "value": "The area contains the legend indicating the color representation of 'Healthy Lifestyle', 'Sedentary Lifestyle', 'Chronic Disease', and 'Severe Disease'." } ], "id_raw": 937 }, { "id": "AOD4takq8nFeQ86n", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in area [0.097, 0.212, 0.125, 0.874]?" }, { "from": "gpt", "value": "It is a bar showing the percentage of 'Healthy Lifestyle' in the age group '20-30'." } ], "id_raw": 937 }, { "id": "sgkGKEyEpiAxWNLR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements within [0.029, 0.040, 0.051, 0.057]?" }, { "from": "gpt", "value": "This is the y-axis label for the 100% mark." } ], "id_raw": 937 }, { "id": "ompivZRJj82sbQso", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart's main title say, which can be found in the coordinates [0.391, 0.021, 0.667, 0.047]?" }, { "from": "gpt", "value": "The main title says \"Social Cultural and Value Change\"." } ], "id_raw": 938 }, { "id": "jDOcBQPcGgtBf4Aw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nIn the chart, which color does the label at [0.656, 0.960, 0.684, 0.974] correspond to in the legend?" }, { "from": "gpt", "value": "The color corresponds to is red." } ], "id_raw": 938 }, { "id": "45UX9l6xMevEnDAs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nWhat variable is shown at y-axis label located at [0.015, 0.435, 0.029, 0.491]?" }, { "from": "gpt", "value": "The variable at the y-axis is called \"Score\"." } ], "id_raw": 938 }, { "id": "GdgfpoCSbRQdX1Jr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the text in the second tick label on the x-axis?" }, { "from": "gpt", "value": "The second tick label on the x-axis reads \"2020\"." } ], "id_raw": 938 }, { "id": "5LlvRpz1cpfbJRuv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Environmental Awareness in 1980}[0.245, 0.053, 0.376, 0.756] taller than {the bar representing Globalization in 1980}[0.114, 0.053, 0.245, 0.405]?" }, { "from": "gpt", "value": "Yes, it is." } ], "id_raw": 938 }, { "id": "Vf76llPiulGSQ3Lv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Globalization in 2020}[0.551, 0.053, 0.682, 0.288] higher or lower than its {equivalent in 1980}[0.114, 0.053, 0.245, 0.405]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 938 }, { "id": "UxD4rvS7ct7s8E93", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nAre {the Information Age bars}[[0.376, 0.053, 0.507, 0.170],[0.813, 0.053, 0.944, 0.217]] generally taller than {the Environmental Awareness bars}[[0.245, 0.053, 0.376, 0.756],[0.682, 0.053, 0.813, 0.709]]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 938 }, { "id": "JT8LvkllDsqkeZuw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nWithin the coordinates [0.114, 0.053, 0.507, 0.405], which bar is the highest: {Globalization}[0.114, 0.053, 0.245, 0.405] or {Information Age}[0.376, 0.053, 0.507, 0.170]?" }, { "from": "gpt", "value": "Globalization." } ], "id_raw": 938 }, { "id": "1UYAFqjln8FFCI1W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are there in the area with coordinates [0.114, 0.053, 0.507, 0.756]?" }, { "from": "gpt", "value": "This area contains three bars: a Light Blue bar representing \"Globalization\" in 1980 [0.114, 0.053, 0.245, 0.405], a Green bar representing \"Environmental Awareness\" in 1980 [0.245, 0.053, 0.376, 0.756] and a Red bar representing \"Information Age\" in 1980 [0.376, 0.053, 0.507, 0.170]." } ], "id_raw": 938 }, { "id": "hjsAmRpUZ2eX21Uv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the legend box located at [0.246, 0.950, 0.812, 0.986]?" }, { "from": "gpt", "value": "This area includes three legend keys: Light Blue for \"Globalization\" located at [0.252, 0.960, 0.279, 0.974], Green for \"Environmental Awareness\" located at [0.409, 0.960, 0.437, 0.974], and Red for \"Information Age\" located at [0.656, 0.960, 0.684, 0.974]." } ], "id_raw": 938 }, { "id": "Ihp8jrCt0WnR1aqy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.551, 0.053, 0.944, 0.709]?" }, { "from": "gpt", "value": "This area contains three bars corresponding to the year 2020: a Light blue bar representing \"Globalization\" with a height reaching -1 on the y-axis [0.551, 0.053, 0.682, 0.288], a Green bar representing \"Environmental Awareness\" with a height reaching -2.8 on the y-axis [0.682, 0.053, 0.813, 0.709], and a Red bar representing \"Information Age\" with a height reaching -0.7 on the y-axis [0.813, 0.053, 0.944, 0.217]." } ], "id_raw": 938 }, { "id": "F3ESSP2GlVDiIuRy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nIn the y-axis labels, what values are shown in the region with coordinates [0.035, 0.163, 0.068, 0.885]?" }, { "from": "gpt", "value": "The y-axis labels located in this box are \"-3.5\", \"-3.0\", \"-2.5\", \"-2.0\", \"-1.5\", \"-1.0\", and \"-0.5\"." } ], "id_raw": 938 }, { "id": "UFhP4ql1fAo6rmPr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nWhat does this bar chart represent with the title coordinates [0.343, 0.021, 0.717, 0.047]?" }, { "from": "gpt", "value": "The chart represents statistics on 'Human History and Civilization Development'." } ], "id_raw": 939 }, { "id": "EW93Ws1TCItjrszu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at coordinates [0.518, 0.959, 0.541, 0.979]?" }, { "from": "gpt", "value": "The x-axis label is 'Era'." } ], "id_raw": 939 }, { "id": "7S89SrKiuZ5ypu7v", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item found at coordinates [0.126, 0.161, 0.219, 0.181] refer to?" }, { "from": "gpt", "value": "The legend item refers to the Culture Index." } ], "id_raw": 939 }, { "id": "zbFyc7vK9RKSalUt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nHow does the Life Expectancy bar [0.116, 0.880, 0.143, 0.884] for Bronze Age compare with Iron Age [0.296, 0.879, 0.323, 0.884]?" }, { "from": "gpt", "value": "They are almost the same height." } ], "id_raw": 939 }, { "id": "6nEe8OPFv3diEhxv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nIs the Technology Index for Industrial Revolution bar [0.710, 0.884, 0.737, 0.884] higher than the Culture Index bar for the same era [0.737, 0.884, 0.764, 0.884]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 939 }, { "id": "PcDF9KxRM7TMPusY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the Human Population}[0.863, 0.093, 0.890, 0.884] in the Contemporary Era have larger value than the Life Expectancy {bar}[0.836, 0.877, 0.863, 0.884] in the same Era?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 939 }, { "id": "ti7UZ203O1A45BJC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what is present in the legends' area, [0.081, 0.063, 0.259, 0.189]." }, { "from": "gpt", "value": "The area includes four legend items: Life Expectancy with the color handle at [0.087, 0.073, 0.115, 0.087], Human Population with the color handle at [0.087, 0.103, 0.115, 0.117], Technology Index with the color handle at [0.087, 0.133, 0.115, 0.147], and Culture Index with the color handle at [0.087, 0.163, 0.115, 0.177]." } ], "id_raw": 939 }, { "id": "VRFZ4c675OZ9Aske", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe what's seen in the range [0.530, 0.880, 0.584, 0.884]." }, { "from": "gpt", "value": "This range corresponds to a bar representing the Culture Index during the Middle Ages Era." } ], "id_raw": 939 }, { "id": "ztuamoySbPeJl94n", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nPlease identify and expound the objects within the area [0.836, 0.877, 0.890, 0.884]." }, { "from": "gpt", "value": "There are three bars: \n1. The Life Expectancy bar with coordinates at [0.836, 0.877, 0.863, 0.884]\n2. Human Population bar with coordinates at [0.863, 0.093, 0.890, 0.884]\n3. Culture Index bar with coordinates at [0.917, 0.884, 0.944, 0.884] and \n4. Technology Index bar with coordinates at [0.890, 0.884, 0.917, 0.884], all representing the Contemporary Era." } ], "id_raw": 939 }, { "id": "PBCWSEDNvBlWn0pz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the label of the Y-axis signify within the coordinates [0.012, 0.300, 0.024, 0.621]?" }, { "from": "gpt", "value": "Number of Individuals (in thousands)." } ], "id_raw": 940 }, { "id": "md2iHB2n0AGU5OXD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nWhat is described by the bar at position [0.892, 0.047, 0.946, 0.169]?" }, { "from": "gpt", "value": "This bar represents the Graduate Employment for the year 2019 and the count is approximately -2000." } ], "id_raw": 940 }, { "id": "RhTHX6mUFUPM43WK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the text in the area [0.740, 0.965, 0.872, 0.983] represent?" }, { "from": "gpt", "value": "It represents the label {Graduate Employment}[0.740, 0.965, 0.872, 0.983] in the legend." } ], "id_raw": 940 }, { "id": "9W0mpYeZY7poj6q0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nIn the label for 2019, is the {bar representing \"Online Education\"}[0.838, 0.047, 0.865, 0.549] taller than the {bar representing \"Graduate Employment\"}[0.919, 0.047, 0.946, 0.169]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 940 }, { "id": "sx91ERMAV544ienr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar of \"Traditional Education\"}[0.865, 0.047, 0.892, 0.752] for 2019 located to the left or right of {the bar of \"Online Education\"}[0.838, 0.047, 0.865, 0.549]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 940 }, { "id": "DCeJ6pA3cc2mH85D", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nIs the {Online Education bar in 2015}[0.120, 0.047, 0.147, 0.476] shorter than the {Education Technology bar in 2019}[0.892, 0.047, 0.919, 0.105]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 940 }, { "id": "w3DcV64NwsK0eoLi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what the region [0.708, 0.967, 0.731, 0.979] contains?" }, { "from": "gpt", "value": "This area contains a color label in the legend that represents Graduate Employment and is the color red." } ], "id_raw": 940 }, { "id": "82sqrrAf5cII26tr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what the region [0.120, 0.047, 0.147, 0.476] contains." }, { "from": "gpt", "value": "This region contains a green bar representing \"Online Education,\" specifically for the year \"2015\", where the count is about -7000." } ], "id_raw": 940 }, { "id": "GZEgl7irM2lV1KJv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the area containing [0.190, 0.959, 0.877, 0.990]?" }, { "from": "gpt", "value": "This area contains the legend for the bar chart. It includes 4 different labels: {Online Education}[0.227, 0.965, 0.326, 0.983] with green color, {Traditional Education}[0.381, 0.965, 0.503, 0.983] with yellow color, {Education Technology}[0.559, 0.965, 0.685, 0.983] with dark blue color, and {Graduate Employment}[0.740, 0.965, 0.872, 0.983] with red color." } ], "id_raw": 940 }, { "id": "A1dmSwwF0cmuLiTa", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the vertical label}[0.015, 0.397, 0.029, 0.452] on the chart?" }, { "from": "gpt", "value": "The vertical label is Score." } ], "id_raw": 941 }, { "id": "lJZiYdGiWKVY8dWt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar for Apple Hardware?" }, { "from": "gpt", "value": "The color of the bar is orange, specified by the legend item {Apple Hardware}[0.544, 0.916, 0.572, 0.930]." } ], "id_raw": 941 }, { "id": "TP8fZeGUcamZBnq0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.108, 0.573, 0.152, 0.796] represent in the chart?" }, { "from": "gpt", "value": "It represents the score of Google Hardware in 2017." } ], "id_raw": 941 }, { "id": "KErCLxMGHZSbE4eW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of this chart?" }, { "from": "gpt", "value": "The title is \"Tech Industry Growth\" at coordinates [0.437, 0.021, 0.614, 0.047]." } ], "id_raw": 941 }, { "id": "sVxJ3Se9aysf9ciO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for Apple Hardware in 2019}[0.635, 0.202, 0.679, 0.796] have a higher score than {the bar for Google Software in 2019}[0.591, 0.350, 0.635, 0.796]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 941 }, { "id": "udVvQi6gQF8jv8aq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nIs the {Score of Google Hardware in 2020}[0.767, 0.350, 0.811, 0.796] higher than its {Score in 2017}[0.108, 0.573, 0.152, 0.796]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 941 }, { "id": "LiBuMchVzOf0fHuO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nBetween {Google Hardware}[0.395, 0.914, 0.516, 0.934] and {Apple Software}[0.583, 0.944, 0.688, 0.964], which one had a higher score in 2018?" }, { "from": "gpt", "value": "Apple Software." } ], "id_raw": 941 }, { "id": "TtOvX2Gs9BS4o8yP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.351, 0.906, 0.700, 0.972]." }, { "from": "gpt", "value": "The content in the mentioned region is the legend of the chart, which contains the information of four groups: Google Hardware, Google Software, Apple Hardware, and Apple Software. The corresponding color and labels of the four groups are as follows:\n 1. Google Hardware is represented by a blue color {handle}[0.357, 0.916, 0.384, 0.930] and the matching label is located at [0.395, 0.914, 0.516, 0.934].\n 2. Google Software is represented by a lighter blue color {handle}[0.357, 0.946, 0.384, 0.960] and the matching label is located at [0.395, 0.944, 0.510, 0.964].\n 3. Apple Hardware is highlighted by an orange color {handle}[0.544, 0.916, 0.572, 0.930] and the matching label is located at [0.583, 0.914, 0.694, 0.934].\n 4. Apple Software is represented by a light orange color {handle}[0.544, 0.946, 0.572, 0.960] and the matching label is located at [0.583, 0.944, 0.688, 0.964]." } ], "id_raw": 941 }, { "id": "FQwR7nQd4CGgH1lk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.066, 0.053, 0.985, 0.796]." }, { "from": "gpt", "value": "This area outlines all features of the chart, including the title labeled 'Tech Industry Growth\" at the top, the two sets of x and y axis labels, the bar graph representing the industry growth data with one pair of bars for each year from 2017 to 2020 and the legend at the top right of the area detailing the color coding information for each group." } ], "id_raw": 941 }, { "id": "vcEFuWV1gBwWj924", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.152, 0.165, 0.283, 0.796]?" }, { "from": "gpt", "value": "This area contains the bar for Google Software and Apple Hardware for 2017." } ], "id_raw": 941 }, { "id": "TuqSYEfVmAAJsfce", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in [0.108, 0.350, 0.811, 0.796]?" }, { "from": "gpt", "value": "This region contains eight vertical bars and their labels indicating Google Hardware, Google Software, Apple Hardware, and Apple Software for the years 2017, 2018, 2019 and 2020." } ], "id_raw": 941 }, { "id": "0olbRceJOUWzRDMJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.117, 0.880, 0.139, 0.900] represent on the x-axis?" }, { "from": "gpt", "value": "It represents the month of January." } ], "id_raw": 942 }, { "id": "QB9IDx8eBzLl5oUD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the handle corresponding to \"Order\" in the legend?" }, { "from": "gpt", "value": "{the color of the handle for \"Order\"}[0.546, 0.960, 0.574, 0.974] is green." } ], "id_raw": 942 }, { "id": "tQObu8rfUogeTfhy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.643, 0.958, 0.749, 0.978] in the chart?" }, { "from": "gpt", "value": "{The label for \"Delivery\"}[0.691, 0.958, 0.749, 0.978]." } ], "id_raw": 942 }, { "id": "TaeQd8Gxdj6N2Za0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"March\"}[0.271, 0.190, 0.285, 0.873] higher than {the bar for \"June\"}[0.485, 0.272, 0.500, 0.873]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 942 }, { "id": "IJFaFd4fqwiIJJ5Z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nAre the values represented by {the yellow handle}[0.408, 0.960, 0.436, 0.974] lower than those by {the green handle}[0.546, 0.960, 0.574, 0.974]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 942 }, { "id": "nheNz6BN6KxD0aMs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nWhich is taller: {the bar indicating orders in September}[0.700, 0.217, 0.714, 0.873] or {the bar indicating deliveries in the same month}[0.714, 0.490, 0.728, 0.873]?" }, { "from": "gpt", "value": "{Order bar in September}[0.700, 0.217, 0.714, 0.873]." } ], "id_raw": 942 }, { "id": "WLmwjrl9UQYz36DH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.128, 0.873, 0.171, 0.928]." }, { "from": "gpt", "value": "This area contains the x-axis tick mark and label for January. The tick mark is at [0.128, 0.873] and the label \"Jan\" spans [0.117, 0.880, 0.139, 0.900]." } ], "id_raw": 942 }, { "id": "wpuIS50GEjDw8QvU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you find within the coordinates [0.018, 0.434, 0.044, 0.493]?" }, { "from": "gpt", "value": "This region includes the y-axis label \"Count\" displayed vertically, spanning [0.015, 0.434, 0.029, 0.493]." } ], "id_raw": 942 }, { "id": "TWMGdbNIT4IQcrmz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nExplain what is depicted within the boundaries [0.408, 0.949, 0.624, 0.984]." }, { "from": "gpt", "value": "This section contains the legend entries for \"Complaint\" and \"Order,\" with respective color handles and text labels. {Complaint handle}[0.408, 0.960, 0.436, 0.974] is yellow and {Order handle}[0.546, 0.960, 0.574, 0.974] is green; their text descriptions are [0.447, 0.958, 0.518, 0.978] and [0.585, 0.958, 0.624, 0.978] respectively." } ], "id_raw": 942 }, { "id": "MXDazD3MVACPLJrc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.404, 0.021, 0.656, 0.047] represent in the chart?" }, { "from": "gpt", "value": "It represents the subtitle of the chart, which is \"Quarterly Profit Trend Analysis\"." } ], "id_raw": 943 }, { "id": "GjtqU0OVItdYU0on", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.499, 0.852, 0.560, 0.872]?" }, { "from": "gpt", "value": "The X-axis label, which is \"Quarters\"." } ], "id_raw": 943 }, { "id": "V4SnP0Nf3SncSeIY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object located at [0.328, 0.950, 0.374, 0.970]?" }, { "from": "gpt", "value": "The label \"Mobile\" in the legend." } ], "id_raw": 943 }, { "id": "H8fIXdEX5hmwqKOX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Q1 profit bar of Electronics}[0.159, 0.187, 0.203, 1.104] higher than {the Q1 profit bar of Mobile}[0.116, 0.531, 0.159, 1.104]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 943 }, { "id": "hFxksfrwaMJwepVD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Q4 profit bar of Furniture}[0.856, 0.569, 0.900, 1.104] taller than {the Q4 profit bar of Supplies}[0.900, 0.760, 0.944, 1.104]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 943 }, { "id": "qMex6CkAaP2ezdEN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nBetween {Electronics}[0.401, 0.952, 0.429, 0.965] and {Furniture}[0.544, 0.952, 0.572, 0.965], which category has a higher Q2 profit bar?" }, { "from": "gpt", "value": "Electronics." } ], "id_raw": 943 }, { "id": "YwZD0cew2ubuJZM7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the coordinates [0.284, 0.942, 0.776, 0.978]?" }, { "from": "gpt", "value": "This area contains the chart's legend. It lists four categories: {Mobile}[0.328, 0.950, 0.374, 0.970], {Electronics}[0.440, 0.950, 0.516, 0.970], {Furniture}[0.583, 0.950, 0.645, 0.970], and {Supplies}[0.712, 0.950, 0.770, 0.970], along with their corresponding color codes." } ], "id_raw": 943 }, { "id": "GalzTPsk8ibvqlpJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content positioned at [0.075, 0.053, 0.985, 0.817]?" }, { "from": "gpt", "value": "This region encompasses the entire graphical area of the bar chart, which displays the quarterly profit trend analysis of four different categories (Mobile, Electronics, Furniture, and Supplies), each represented by different colored bars." } ], "id_raw": 943 }, { "id": "QKk3zkrjgcqku0uW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nCould you explain what is shown in the region [0.847, 0.824, 0.866, 0.844]?" }, { "from": "gpt", "value": "The label for the last quarter \"Q4\" on the x-axis is positioned in this region." } ], "id_raw": 943 }, { "id": "92wyTgRalr3b86nr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.057, 0.053, 0.985, 0.924]?" }, { "from": "gpt", "value": "This is the main chart area, which contains the bars representing the data, as well as the axes labels." } ], "id_raw": 944 }, { "id": "RiV9vm6fCHP0ETtR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at coordinates [0.395, 0.021, 0.647, 0.047]?" }, { "from": "gpt", "value": "The text at this coordinates is the subtitle \"Big Data Integration Overview\"." } ], "id_raw": 944 }, { "id": "tagNPhxnYHwPMe7e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the bar at [0.451, 0.240, 0.521, 0.924]?" }, { "from": "gpt", "value": "This bar indicates that the \"Number of Tasks\" for \"Partial Integration\" is 55." } ], "id_raw": 944 }, { "id": "a0Wwbi941JZQ0fMZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nWhich object has coordinates [0.873, 0.178, 0.943, 0.924]?" }, { "from": "gpt", "value": "This is a bar representing the \"Time to Complete (days)\" for \"Manual Integration\", which is 60 days." } ], "id_raw": 944 }, { "id": "KbsmwtuhcwStuJdN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \u201cNumber of Tasks for Partial Integration\u201d}[0.451, 0.240, 0.521, 0.924] taller than {the bar representing \u201cTime to Complete for Partial Integration\u201d}[0.521, 0.551, 0.591, 0.924]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 944 }, { "id": "zqi3ALstUTp0y29J", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {the bar for \u201cNumber of Tasks for Full Integration\u201d}[0.099, 0.489, 0.170, 0.924] and {the bar for \u201cTime to Complete for Full Integration\u201d}[0.170, 0.737, 0.240, 0.924] in terms of height?" }, { "from": "gpt", "value": "The \"Number of Tasks for Full Integration\" bar is taller than the \"Time to Complete for Full Integration\" bar." } ], "id_raw": 944 }, { "id": "z7cSKb4PsDi8Ow85", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nAre the bars for \"Number of Tasks\"{Number of Tasks for Full Integration}[0.099, 0.489, 0.170, 0.924], {Number of Tasks for Partial Integration}[0.451, 0.240, 0.521, 0.924] and {Number of Tasks for Manual Integration}[0.802, 0.675, 0.873, 0.924] generally taller than the bars for \"Time to Complete\"{Time to Complete for Full Integration}[0.170, 0.737, 0.240, 0.924], {Time to Complete for Partial Integration}[0.521, 0.551, 0.591, 0.924] and {Time to Complete for Manual Integration}[0.873, 0.178, 0.943, 0.924]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 944 }, { "id": "4B2XPujIP4hZLYOn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nIs {the \u201cNumber of Tasks for Full Integration\u201d bar}[0.099, 0.489, 0.170, 0.924] to the left of {the \u201cTime to Complete for Full Integration\u201d bar}[0.170, 0.737, 0.240, 0.924]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 944 }, { "id": "UUorSwXIulyXjoMZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region [0.756, 0.063, 0.978, 0.129]?" }, { "from": "gpt", "value": "This is the chart legend with two entries: \"Number of Tasks\" with a pink square as a symbol (coordinates: [0.762, 0.073, 0.790, 0.087]) and \"Time to Complete (days)\" with a mid-blue square as a symbol(coordinates: [0.762, 0.103, 0.790, 0.117])." } ], "id_raw": 944 }, { "id": "pPaMsIq3wpiwL6YK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are identified in the area of [0.057, 0.053, 0.985, 0.924]?" }, { "from": "gpt", "value": "This is the main area of the chart where the bar graphs representing the \"Number of Tasks\" and \"Time to Complete (days)\" for each \"Integration Type\" are displayed, along with the axis labels and the legend." } ], "id_raw": 944 }, { "id": "ZBshLjhBlNHBZfMx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.099, 0.489, 0.240, 0.924]?" }, { "from": "gpt", "value": "In this area, we have two bars: the pink bar represents \"Number of Tasks\" for \"Full Integration\" (35 tasks) with the coordinates [0.099, 0.489, 0.170, 0.924]; the mid-blue bar represents \"Time to Complete (days)\" for \"Full Integration\"(15 days) with the coordinates [0.170, 0.737, 0.240, 0.924]." } ], "id_raw": 944 }, { "id": "87aFH2aO5E4GGhW3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the area marked by the coordinates [0.465, 0.959, 0.578, 0.979]." }, { "from": "gpt", "value": "This area refers to the x-axis label, which is titled \"Integration Type\"." } ], "id_raw": 944 }, { "id": "rUkfRoQn93HWTLKo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in this chart?" }, { "from": "gpt", "value": "The y-axis label is \"Sales\"{the y-axis label}[0.019, 0.428, 0.036, 0.520]." } ], "id_raw": 945 }, { "id": "5ZgHPQmGxCnsK2sT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the text in the coordinate [0.366, 0.037, 0.703, 0.083] refer to?" }, { "from": "gpt", "value": "The text is the subplot's subtitle, which reads as \"Product Sales Trend Observation\"." } ], "id_raw": 945 }, { "id": "C9jpXl5Jpf5M2jDx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the blue bar at [0.129, 0.492, 0.264, 0.854] represent?" }, { "from": "gpt", "value": "It represents the sales of Apple in January, which totalled 400 units." } ], "id_raw": 945 }, { "id": "jR6CzcQlTnCVA3Mm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nWhat's plotted at the coordinate [0.467, 0.401, 0.602, 0.854]?" }, { "from": "gpt", "value": "It's a green bar representing the sales of 600 units of Cherry in March." } ], "id_raw": 945 }, { "id": "E4p70qxbscpFICoX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nIs the sales of Durian in April represented by the red bar at [0.636, 0.447, 0.772, 0.854] more than the sales of Eggplant in the same month represented by the purple bar at [0.636, 0.356, 0.772, 0.854]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 945 }, { "id": "UY5LvXZi5MREL3L7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nComparing the sales of Banana in January represented by orange bar {at}[0.129, 0.130, 0.264, 0.854] and that in February represented by orange bar {at}[0.298, 0.175, 0.433, 0.854], which one is greater?" }, { "from": "gpt", "value": "The sales in January is higher." } ], "id_raw": 945 }, { "id": "DGxGgplkVLOBuVEh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nBetween Apple's sales in March represented by the blue bar at [0.467, 0.401, 0.602, 0.854] and the sales in April at [0.636, 0.311, 0.772, 0.854], which month experienced more sales?" }, { "from": "gpt", "value": "April." } ], "id_raw": 945 }, { "id": "LEnepPtkJwHkR5eG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nComparing the sales of Cherry in January (green bar at [0.129, 0.220, 0.264, 0.854]) and the sales in February (green bar at [0.298, 0.265, 0.433, 0.854]), which month sold more?" }, { "from": "gpt", "value": "January." } ], "id_raw": 945 }, { "id": "Hgmgt229oqNpApMZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.298, 0.175, 0.433, 0.854]." }, { "from": "gpt", "value": "The region contains two bars. The taller orange bar indicates the sales of Banana in February, which was 750 units, and this bar is located at [0.298, 0.175, 0.433, 0.854], while the shorter green bar indicates the sales of Cherry in February, which was 650 units, and it is located at [0.298, 0.265, 0.433, 0.854]." } ], "id_raw": 945 }, { "id": "MThMiCbiD9NtFaYj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.805, 0.130, 0.941, 0.854]?" }, { "from": "gpt", "value": "The region shows five bars. These represent the sales in May for each product: Apple (blue) sold 700 units, Banana (orange) sold 800 units, Cherry (green) sold 500 units, Durian (red) sold 500 units, and Eggplant (purple) sold 500 units." } ], "id_raw": 945 }, { "id": "daUptJXV4jgtkEMM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area marked by [0.043, 0.025, 0.076, 0.060]?" }, { "from": "gpt", "value": "There is a y-axis label of \"900\" located at [0.043, 0.025, 0.076, 0.060]." } ], "id_raw": 945 }, { "id": "QVBLDCdekSHx8eTN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.129, 0.130, 0.264, 0.854]?" }, { "from": "gpt", "value": "The area contains five bars, each representing the sales of a specific product in January. The highest bar (orange) represents 800 Banana sales, the green bar is 700 Cherry sales, the red bar represents 300 Durian sales, the purple bar indicates 500 Eggplant sales, and the lowest bar (blue) represents 400 Apple sales." } ], "id_raw": 945 }, { "id": "orXx2goBhrk6RC9m", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.144, 0.785, 0.579, 0.877]?" }, { "from": "gpt", "value": "The bar representing the patent count for Robotic Technology." } ], "id_raw": 946 }, { "id": "prieeXjDn7YLPNbB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis?" }, { "from": "gpt", "value": "It's \"Number of Patents\"." } ], "id_raw": 946 }, { "id": "2JobFdiXiZZw6up3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the range of patents being displayed on the x-axis for the object at [0.144, 0.208, 0.611, 0.300]?" }, { "from": "gpt", "value": "It's ranging from 0 to 2900." } ], "id_raw": 946 }, { "id": "RzFgvmbvSX6pZadx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the description or title located above the graph?" }, { "from": "gpt", "value": "It's \"Innovation and Intellectual Property in Tech Sectors\"." } ], "id_raw": 946 }, { "id": "YCv6ePRDhGPAI67l", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nIs {Cybersecurity bar}[0.144, 0.093, 0.885, 0.185] higher than {Robotic Technology bar}[0.144, 0.785, 0.579, 0.877] on the horizontal axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 946 }, { "id": "dVjena7vj7omZOau", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nIs the {BioTech bar}[0.144, 0.208, 0.611, 0.300] longer than the {Quantum Computing bar}[0.144, 0.323, 0.354, 0.416]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 946 }, { "id": "0m89ur6aNUOP3qSH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nComparing the bars for {Wireless Communication bar}[0.144, 0.439, 0.950, 0.531] and {BlockChain bar}[0.144, 0.554, 0.499, 0.647], which one extends further on the x-axis?" }, { "from": "gpt", "value": "Wireless Communication." } ], "id_raw": 946 }, { "id": "FOZOQpaF1FrQSXO1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nBetween {AI Technology bar}[0.144, 0.670, 0.660, 0.762] and {Robotic Technology bar}[0.144, 0.785, 0.579, 0.877], which bar represents a greater number of patents?" }, { "from": "gpt", "value": "AI Technology." } ], "id_raw": 946 }, { "id": "10a6gOrrqYwbp5Ks", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the section of the chart with bounds [0.144, 0.554, 0.499, 0.647]?" }, { "from": "gpt", "value": "Yes, this is the bar for the 'BlockChain' category. It indicates that the number of patents in this category ranges from 0 to 2200." } ], "id_raw": 946 }, { "id": "lI2gYJ1ATQitKiGU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.144, 0.785, 0.579, 0.877] encapsulate?" }, { "from": "gpt", "value": "These coordinates encapsulate the bar for 'Robotic Technology' category. The bar shows that the number of patents range from 0 to 2700 for this category." } ], "id_raw": 946 }, { "id": "dMEzieFG8t7TWCiu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area of [0.541, 0.959, 0.593, 0.979]?" }, { "from": "gpt", "value": "This area contains the label for the x-axis, which is 'Technology.'" } ], "id_raw": 946 }, { "id": "9THqzEwXa64ZGhZI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.424, 0.021, 0.710, 0.047] contain?" }, { "from": "gpt", "value": "This area contains the subtitle of the graph, which reads 'Innovation and Intellectual Property in Tech Sectors.\"" } ], "id_raw": 946 }, { "id": "SZkBxTvMMXB74bcN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the significant text at coordinates [0.381, 0.030, 0.666, 0.066] in the chart?" }, { "from": "gpt", "value": "It is the subtitle which reads \"Internal Business Process Analysis\"." } ], "id_raw": 947 }, { "id": "J18vLNBFCncsSxrT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nIf we observe the y-axis, What does the tick text at [0.043, 0.872, 0.052, 0.900] represent?" }, { "from": "gpt", "value": "It represents the value 0 on the y-axis scale." } ], "id_raw": 947 }, { "id": "uGQ3zYdc3jxtipTw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nCan you identify the bar in the chart with coordinates [0.227, 0.130, 0.326, 0.883]?" }, { "from": "gpt", "value": "Yes, it's the green bar representing the effectiveness of \"Marketing\" process." } ], "id_raw": 947 }, { "id": "S9MAiXVbk21pPptW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nWhat labels are shown in coordinates [0.863, 0.903, 0.924, 0.931] and [0.026, 0.035, 0.052, 0.063]?" }, { "from": "gpt", "value": "The labels are {the x-axis label \"Financial\"}[0.863, 0.903, 0.924, 0.931] and {the y-axis label \"100\"}[0.026, 0.035, 0.052, 0.063]." } ], "id_raw": 947 }, { "id": "MTDjbjMyyVDGM9XE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nIs {the bar associated with \"Product Development\"}[0.104, 0.172, 0.203, 0.883] lower in height than {the bar associated with \"Marketing\"}[0.227, 0.130, 0.326, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 947 }, { "id": "CywpAGhPan7DoLcc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nLooking at the bar chart, is {the \"Customer Service\" bar}[0.597, 0.113, 0.696, 0.883] higher than {the \"Financial\" bar}[0.844, 0.138, 0.943, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 947 }, { "id": "MdRQHtaT8Zjg76VU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nConsidering {the bar representing \"Sales\"}[0.351, 0.230, 0.449, 0.883] and {the bar representing \"Management\"}[0.474, 0.147, 0.573, 0.883], which one is taller?" }, { "from": "gpt", "value": "The \"Management\" bar." } ], "id_raw": 947 }, { "id": "TWqHR9k2P95N5hJI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nBetween {the \"Manufacturing\" bar}[0.721, 0.214, 0.820, 0.883] and {the \"Product Development\" bar}[0.104, 0.172, 0.203, 0.883], which process dominated in terms of effectiveness?" }, { "from": "gpt", "value": "Product Development." } ], "id_raw": 947 }, { "id": "krCTvTG0ycbcZH3A", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nBriefly explain about the components specified in the area [0.062, 0.075, 0.985, 0.883]." }, { "from": "gpt", "value": "This is the primary region where the bar chart is plotted. It contains effectiveness bars for different processes like Product Development, Marketing, Sales, Management, Customer Service, Manufacturing, and Financial. Each bar's height represents the effectiveness of the respective process." } ], "id_raw": 947 }, { "id": "pAGJ58A49Qpf7TlT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is enclosed within the frame [0.474, 0.147, 0.573, 0.883]?" }, { "from": "gpt", "value": "It includes a single chart element, {the green bar representing the \"Management\" process}[0.474, 0.147, 0.573, 0.883], indicating an effectiveness score of 88." } ], "id_raw": 947 }, { "id": "e2R6Riwpxn9y5LUH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nCan you tell about the contents in the region [0.585, 0.903, 0.708, 0.931]?" }, { "from": "gpt", "value": "This region contains a single object, the label for {the \"Customer Service\" category}[0.585, 0.903, 0.708, 0.931] on the x-axis." } ], "id_raw": 947 }, { "id": "c6hjOH6eZW660h5o", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nWhat is presented in the rectangular area [0.497, 0.942, 0.550, 0.970]?" }, { "from": "gpt", "value": "The rectangular area hosts the x-axis label \"Process\"." } ], "id_raw": 947 }, { "id": "HQ0XQz7QD6fFeMfD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.897, 0.100, 0.926, 0.122] indicate in the chart figure?" }, { "from": "gpt", "value": "It is the color label in the legend, with a light blue color." } ], "id_raw": 948 }, { "id": "lUoGpIgqwtUZiqTp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at [0.699, 0.457, 0.762, 0.883]?" }, { "from": "gpt", "value": "It's a bar representing \"Inconsistent decline\" for the year 2020." } ], "id_raw": 948 }, { "id": "uDkxqa9PpCGh6sbm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.015, 0.434, 0.029, 0.524] refer to in the chart?" }, { "from": "gpt", "value": "It is the Y-axis label, named as \"Values\"." } ], "id_raw": 948 }, { "id": "xMTk6qtDd9B2eHYd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nWhich of the bars is taller - {the bar representing \"Slow expansion\" in 2000}[0.096, 0.429, 0.159, 0.883] or {the bar representing \"Slow expansion\" in 2020}[0.159, 0.113, 0.222, 0.883]?" }, { "from": "gpt", "value": "The bar representing \"Slow expansion\" in 2020." } ], "id_raw": 948 }, { "id": "CmBnWEZRm5lUGKc2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Downward trend\" in 2000}[0.816, 0.113, 0.880, 0.883] taller than {the bar for \"Downward trend\" in 2020}[0.880, 0.317, 0.943, 0.883]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 948 }, { "id": "a4lfTLKBnQb26Ytk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar for \"Unstable behavior\" in 2000}[0.276, 0.317, 0.339, 0.883] and {the bar for \"Unstable behavior\" in 2020}[0.339, 0.252, 0.402, 0.883], which one is taller?" }, { "from": "gpt", "value": "The bar for \"Unstable behavior\" in 2020." } ], "id_raw": 948 }, { "id": "wMtR0mNjatmkl51u", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nWhat do you see in the region [0.105, 0.903, 0.937, 0.931]?" }, { "from": "gpt", "value": "This region includes all the labels of the X-axis, which are {\"Slow expansion\"}[0.105, 0.903, 0.213, 0.931], {\"Unstable behavior\"}[0.275, 0.903, 0.402, 0.931], {\"Slight increase\"}[0.468, 0.903, 0.570, 0.931], {\"Inconsistent decline\"}[0.630, 0.903, 0.769, 0.931], and {\"Downward trend\"}[0.822, 0.903, 0.937, 0.931]." } ], "id_raw": 948 }, { "id": "N5aGhOqXuzHHZTyb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained in the region [0.096, 0.113, 0.943, 0.883]?" }, { "from": "gpt", "value": "There are ten bars in total, each representing a trend in 2000 and 2020 for each category of Trends, including \"Slow expansion\", \"Unstable behavior\", \"Slight increase\", \"Inconsistent decline\", and \"Downward trend\"." } ], "id_raw": 948 }, { "id": "CJ1G5VfeaeYpcVL3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nWhat content exists within the coordinates [0.348, 0.030, 0.690, 0.066]?" }, { "from": "gpt", "value": "It is the subtitle of the chart, labeled as \"Time Series Data Comparison Over Years\"." } ], "id_raw": 948 }, { "id": "GKKFwTlY1ST3wko0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at the coordinates [0.366, 0.025, 0.694, 0.055]?" }, { "from": "gpt", "value": "The title, \"Market Research of BMW Vehicle Series\"." } ], "id_raw": 949 }, { "id": "MHlpmNTmr9SVcXPg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at these coordinates [0.043, 0.683, 0.065, 0.706]?" }, { "from": "gpt", "value": "The y-axis label \"5.0\"." } ], "id_raw": 949 }, { "id": "9MwS9HEdGAur1hDc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nAt the coordinates, [0.360, 0.102, 0.457, 0.903], what is the object and its value?" }, { "from": "gpt", "value": "Bar representing \"5 Series\" BMW vehicles, with a market share of 19.0%." } ], "id_raw": 949 }, { "id": "fFB9vszzGVYDf3F2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"1 Series\"}[0.116, 0.397, 0.214, 0.903] taller or shorter than {the bar representing \"3 Series\"}[0.238, 0.229, 0.335, 0.903]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 949 }, { "id": "uAQnZRlonctqE7S5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nBetween {the \"5 Series\" bar}[0.360, 0.102, 0.457, 0.903] and {the \"Z Series\" bar}[0.725, 0.313, 0.822, 0.903], which one shows a higher market share?" }, { "from": "gpt", "value": "The \"5 Series\" bar." } ], "id_raw": 949 }, { "id": "jpOGuatabpl7CQg9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nIs the {bar representing the \"x Series market share\"}[0.603, 0.271, 0.700, 0.903] taller than the {bar representing the \"i Series market share\"}[0.846, 0.271, 0.944, 0.903]?" }, { "from": "gpt", "value": "They are the same height." } ], "id_raw": 949 }, { "id": "7GQaFNtZMExKZGdv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nCould you provide a description of what is contained within the area [0.116, 0.397, 0.214, 0.903]?" }, { "from": "gpt", "value": "This area contains the bar representing the market share of the \"1 Series\" BMW vehicles, which is 12.0%." } ], "id_raw": 949 }, { "id": "bK4oEw1X5ZWwBiTT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.725, 0.313, 0.822, 0.903]?" }, { "from": "gpt", "value": "The area contains the bar representing the market share of the \"Z Series\" BMW vehicles which is 14.0%." } ], "id_raw": 949 }, { "id": "7994StqyNr2eOkMR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.481, 0.524, 0.579, 0.903]." }, { "from": "gpt", "value": "Within these coordinates is the bar indicating the market share of \"7 Series\" BMW vehicles, which stands at 9.0%." } ], "id_raw": 949 }, { "id": "tQ1FmlUqZKbv9mal", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the category indicated by the x-axis label at [0.463, 0.927, 0.596, 0.963]?" }, { "from": "gpt", "value": "Family Member." } ], "id_raw": 950 }, { "id": "hzx2p5o1epPaRwZI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the second bar in the chart with bounding box [0.260, 0.194, 0.373, 0.854]?" }, { "from": "gpt", "value": "Mom." } ], "id_raw": 950 }, { "id": "4YY9Vwo9ql3VlDzx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the title at coordinates [0.323, 0.037, 0.736, 0.083]?" }, { "from": "gpt", "value": "Family and Household Living Conditions." } ], "id_raw": 950 }, { "id": "r4gxA730tAFoPsVq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis signify at [0.019, 0.346, 0.036, 0.601]?" }, { "from": "gpt", "value": "Conditions (%)." } ], "id_raw": 950 }, { "id": "krAgW69Ti9owWGEK", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar representing \"Dad\"}[0.118, 0.210, 0.232, 0.854] and {the bar representing \"Carer\"}[0.827, 0.291, 0.940, 0.854], which one is higher?" }, { "from": "gpt", "value": "Dad." } ], "id_raw": 950 }, { "id": "UJbqxPJ8YisEEQpd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Brother\"}[0.402, 0.130, 0.515, 0.854] at a higher position than {the bar representing \"Sister\"}[0.543, 0.170, 0.657, 0.854]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 950 }, { "id": "HQ3lB2EFnaGTZMqo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Mom\"}[0.260, 0.194, 0.373, 0.854] to the right of {the bar representing \"Dad\"}[0.118, 0.210, 0.232, 0.854]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 950 }, { "id": "pqyixbKd3SiotjFv", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar representing \"Pet\"}[0.685, 0.130, 0.798, 0.854] and {the bar representing \"Carer\"}[0.827, 0.291, 0.940, 0.854], which one is taller?" }, { "from": "gpt", "value": "Pet." } ], "id_raw": 950 }, { "id": "9eNWJLjuJadI9biI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information does the area with coordinates [0.077, 0.093, 0.981, 0.854] contain?" }, { "from": "gpt", "value": "This area contains the bar chart that represents the living conditions of each family member. The x-axis represents family members and the y-axis stands for the conditions percentage." } ], "id_raw": 950 }, { "id": "5P3Zp3XeRZDmqehG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the slice between [0.260, 0.194, 0.373, 0.854]?" }, { "from": "gpt", "value": "This slice contains a bar representing the living conditions of 'Mom' at 82%." } ], "id_raw": 950 }, { "id": "4NyX1iASJI1QNPYO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region [0.827, 0.291, 0.940, 0.854]?" }, { "from": "gpt", "value": "This region includes a bar depicting the living conditions for 'Carer' which is 70%." } ], "id_raw": 950 }, { "id": "8Nvorn2laRCorfjg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained between [0.543, 0.170, 0.657, 0.854]?" }, { "from": "gpt", "value": "The region contains a data bar indicating that the living conditions for 'Sister' are at 85%." } ], "id_raw": 950 }, { "id": "UwsCZr6Bl9tzEDRC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent based on the label at coordinates [0.015, 0.374, 0.029, 0.584]?" }, { "from": "gpt", "value": "The y-axis represents the Industry Bloom." } ], "id_raw": 952 }, { "id": "3LsFDQJJRrQ4G9Ua", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nWhat sector is indicated at the x-axis coordinates [0.414, 0.903, 0.488, 0.931]?" }, { "from": "gpt", "value": "The sector indicated is E-Learning." } ], "id_raw": 952 }, { "id": "dbkzdnqoxLwiWbco", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the value for the sector located at [0.393, 0.113, 0.509, 0.883]?" }, { "from": "gpt", "value": "The value for the E-Learning sector is 32." } ], "id_raw": 952 }, { "id": "eA9VazzIGff4o23j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {E-Learning}[0.393, 0.113, 0.509, 0.883] higher than the bar for {Language Learning}[0.683, 0.426, 0.798, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 952 }, { "id": "nNkPsyZmIscJKpjM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Software Development}[0.104, 0.402, 0.220, 0.883] on the left or right of the bar for {Instructional Design}[0.249, 0.282, 0.364, 0.883]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 952 }, { "id": "sTrYkg65MSWcD6cp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, the one representing {Career Counseling}[0.827, 0.595, 0.943, 0.883] or {Corporate Training}[0.538, 0.378, 0.654, 0.883]?" }, { "from": "gpt", "value": "Corporate Training." } ], "id_raw": 952 }, { "id": "xc5JZLiFkaETGjYI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nDescribe what's inside the area [0.035, 0.030, 0.052, 0.058]." }, { "from": "gpt", "value": "There is the label '35' from the y-axis scale inside this area." } ], "id_raw": 952 }, { "id": "rH0BD2w8NHIzTzUT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.249, 0.282, 0.364, 0.883]?" }, { "from": "gpt", "value": "This region includes the bar for Instructional Design, with a height indicating its value of 25." } ], "id_raw": 952 }, { "id": "gBsmvgcdJJRfuQ2F", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the area [0.821, 0.903, 0.949, 0.931]?" }, { "from": "gpt", "value": "The label \u2018Career Counseling\u2019 from the x-axis scale is located in this area." } ], "id_raw": 952 }, { "id": "CQpWgeildugNfahX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar with these coordinates [0.160, 0.492, 0.291, 0.854]?" }, { "from": "gpt", "value": "Facebook's content views." } ], "id_raw": 953 }, { "id": "KX4JmNMfXtr0s2RX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nWhat category is associated with the coordinates [0.486, 0.564, 0.616, 0.854]?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 953 }, { "id": "lig2yVxdreEIVjcD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nWhat numerical value in views is indicated for Instagram whose coordinates for the bar are [0.323, 0.347, 0.453, 0.854]?" }, { "from": "gpt", "value": "350,000 views." } ], "id_raw": 953 }, { "id": "zEthwHZUN01qGtos", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nIs {Facebook's bar}[0.160, 0.492, 0.291, 0.854] higher or lower than {Twitter's bar}[0.486, 0.564, 0.616, 0.854]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 953 }, { "id": "f66IxxzYnlldzkxR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nOut of {YouTube's bar}[0.649, 0.130, 0.779, 0.854] and {Instagram's bar}[0.323, 0.347, 0.453, 0.854], which one represents more views?" }, { "from": "gpt", "value": "YouTube's bar." } ], "id_raw": 953 }, { "id": "uzbm4rq9lmOQRMvJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nAre {LinkedIn's views}[0.812, 0.637, 0.942, 0.854] less than {Facebook's views}[0.160, 0.492, 0.291, 0.854]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 953 }, { "id": "ASSjrHHtZLPiPamW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's displayed in the region [0.160, 0.130, 0.942, 0.854]?" }, { "from": "gpt", "value": "This area covers all the social media platforms' bars. In sequence, from left to right, we have {Facebook}[0.160, 0.492, 0.291, 0.854], {Instagram}[0.323, 0.347, 0.453, 0.854], {Twitter}[0.486, 0.564, 0.616, 0.854], {YouTube}[0.649, 0.130, 0.779, 0.854], and {LinkedIn}[0.812, 0.637, 0.942, 0.854]." } ], "id_raw": 953 }, { "id": "4yqt6QDkt5M7olFu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the figures in this region [0.290, 0.037, 0.812, 0.083]?" }, { "from": "gpt", "value": "This region contains the subtitle of the chart, which is \"Content Views on Different Social Media Platforms\"." } ], "id_raw": 953 }, { "id": "aT9pYZRNLAtwXY73", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nWhat's represented in the region [0.019, 0.184, 0.036, 0.764]?" }, { "from": "gpt", "value": "This area contains the Y-axis label which indicates \"{Views (in hundreds of thousands)}\"[0.019, 0.184, 0.036, 0.764]." } ], "id_raw": 953 }, { "id": "Fe6L80DpMXwvUEGn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart at coordinates [0.297, 0.030, 0.750, 0.066]?" }, { "from": "gpt", "value": "Internal Business Process Analysis (time consumption)." } ], "id_raw": 954 }, { "id": "ZtTAAxwBOxqxoMkf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the x-axis label at coordinates [0.489, 0.942, 0.558, 0.970]?" }, { "from": "gpt", "value": "Processes." } ], "id_raw": 954 }, { "id": "auD93T720Bgg3sAe", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar in the \"Delivery\" category?" }, { "from": "gpt", "value": "[0.454, 0.113, 0.593, 0.883]." } ], "id_raw": 954 }, { "id": "0ctfWhCsU1zMZdsr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value range for {Quality Assurance}[0.803, 0.587, 0.943, 0.883]?" }, { "from": "gpt", "value": "0.00 -> 9.90." } ], "id_raw": 954 }, { "id": "4zWb7R6VX5X1o7p5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Order Processing\"}[0.104, 0.428, 0.244, 0.883] shorter than {the bar representing \"Delivery\"}[0.454, 0.113, 0.593, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 954 }, { "id": "0RumxAbkEaswlx6b", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, does {the bar representing \"Customer Service\"}[0.628, 0.299, 0.768, 0.883] consume more time than {the bar representing \"Quality Assurance\"}[0.803, 0.587, 0.943, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 954 }, { "id": "KPrs0DTtO6d0NPl5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nOn the y-axis, is the point representing 10 hours closer to the starting point, {0}[0.043, 0.872, 0.052, 0.900], or the end point, {30}[0.035, -0.027, 0.052, 0.001]?" }, { "from": "gpt", "value": "Starting point." } ], "id_raw": 954 }, { "id": "q0GlwmuFPVIuMSTw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content located between the coordinates [0.062, 0.075, 0.985, 0.883]." }, { "from": "gpt", "value": "There are five bars representing different business processes in this area. The bars from left to right represent the following processes: {Order Processing}[0.104, 0.428, 0.244, 0.883] with a time consumption range of 0.00 -> 15.20 hours, {Inventory Management}[0.279, 0.560, 0.419, 0.883] with a time consumption range of 0.00 -> 10.80 hours, {Delivery}[0.454, 0.113, 0.593, 0.883] with a range of 0.00 -> 25.70 hours, {Customer Service}[0.628, 0.299, 0.768, 0.883] with a range of 0.00 -> 19.50 hours, and finally {Quality Assurance}[0.803, 0.587, 0.943, 0.883] with a range of 0.00 -> 9.90 hours." } ], "id_raw": 954 }, { "id": "cIWNHmSE5vVQiLWM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained within the region [0.035, 0.123, 0.052, 0.151]?" }, { "from": "gpt", "value": "It is a tick mark on the y-axis that denotes the value of 25 hours." } ], "id_raw": 954 }, { "id": "qYXFGEMEPBuBL7kb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects located at [0.810, 0.903, 0.936, 0.931]?" }, { "from": "gpt", "value": "It is a label on the x-axis that represents the category \"Quality Assurance\"." } ], "id_raw": 954 }, { "id": "ifoJBGRF1w6c6HqY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at [0.798, 0.145, 0.840, 0.883]?" }, { "from": "gpt", "value": "Blue, it represents the \"CO2 Emission (Million Metric Tons)\" for the year 2010." } ], "id_raw": 955 }, { "id": "T5ZhDSq4jjnhEBvs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the label located at [0.522, 0.942, 0.551, 0.970] refer to?" }, { "from": "gpt", "value": "It's the x-axis label indicating \"Year\"." } ], "id_raw": 955 }, { "id": "PXZ3F4aC660GOzAG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nWhat data point is described by the green bar located at [0.861, 0.883, 0.902, 0.883]?" }, { "from": "gpt", "value": "It indicates the \"Deforestation (% of Forest Area)\" for the year 2015." } ], "id_raw": 955 }, { "id": "80cE25gaqnxDJXAb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nComparing the blue bar at [0.693, 0.171, 0.735, 0.883] and [0.798, 0.145, 0.840, 0.883], which year has a higher \"CO2 Emission (Million Metric Tons)\"?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 955 }, { "id": "Gbt032hXvxWmfuGG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nIs the deforestation rate in 1990 at [0.338, 0.883, 0.380, 0.883] higher than the rate in 1985 at [0.233, 0.883, 0.275, 0.883]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 955 }, { "id": "PL2KN1BkTg3kxumw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nBetween the green bar at [0.443, 0.883, 0.484, 0.883] and the green bar at [0.547, 0.883, 0.589, 0.883], which one represents a greater rate of deforestation?" }, { "from": "gpt", "value": "The green bar at [0.547, 0.883, 0.589, 0.883] for the year \"2000\"." } ], "id_raw": 955 }, { "id": "Ab3wAoFOyDsx33cY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within [0.095, 0.089, 0.383, 0.181]." }, { "from": "gpt", "value": "This is the region containing the legend for the chart. It includes two labels: \n1. {CO2 Emission (Million Metric Tons)}[0.140, 0.100, 0.377, 0.128], represented in blue color [0.101, 0.102, 0.128, 0.122]. \n2. {CO2 Emission (Million Metric Tons)}[0.140, 0.142, 0.364, 0.170], represented in green color [0.101, 0.144, 0.128, 0.164]." } ], "id_raw": 955 }, { "id": "egsJFLtkoOQvLhsi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the area [0.015, 0.004, 0.029, 0.955]?" }, { "from": "gpt", "value": "This is the vertical axis label area, representing \"CO2 Emission (Million Metric Tons) / Deforestation (% of Forest Area)\"." } ], "id_raw": 955 }, { "id": "wTq3Zs2iB72pxQrw", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region [0.171, 0.268, 0.213, 0.883]?" }, { "from": "gpt", "value": "This region contains a blue bar, indicating the \"CO2 Emission (Million Metric Tons)\" for the year 1980." } ], "id_raw": 955 }, { "id": "sKaSgMCJEC1pVGnF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nWhat kind of chart is represented according to the JSON structured information?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 956 }, { "id": "W2a0QPRoXqDMw1Ph", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the caption written on the x-axis?" }, { "from": "gpt", "value": "Country." } ], "id_raw": 956 }, { "id": "6PJ9XcR1vadjOEyV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the bar graph} represent at [0.115, 0.471, 0.230, 0.903]?" }, { "from": "gpt", "value": "It represents the \"USA\" country's Greenhouse Gas Emissions equivalent to 6341 million tons." } ], "id_raw": 956 }, { "id": "qFiIZphOk1wX387e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bars} displayed on the plot?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 956 }, { "id": "dYH0ktz6L21AX4mF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nIs the {Germany bar}[0.831, 0.850, 0.946, 0.903] shorter or taller than the {India bar} [0.402, 0.736, 0.516, 0.903] ?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 956 }, { "id": "cEUUkx02tb7Rj4HA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar that represents China}[0.258, 0.102, 0.373, 0.903] produce more or less Greenhouse Gas emissions than {the bar that represents Russia}[0.545, 0.731, 0.659, 0.903]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 956 }, { "id": "QZrqObNSZ588jhxn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nWhich has more greenhouse gas emissions between ({the USA} [0.115, 0.471, 0.230, 0.903] and {Russia}[0.545, 0.731, 0.659, 0.903])?" }, { "from": "gpt", "value": "The USA." } ], "id_raw": 956 }, { "id": "WY2yNShdTGT3Oui1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nAre {Japan's emissions} [0.688, 0.816, 0.803, 0.903] lower than {Germany's emissions} [0.831, 0.850, 0.946, 0.903] ?" }, { "from": "gpt", "value": "No." } ], "id_raw": 956 }, { "id": "2gansFtj8xnYyDDZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the coordinates [0.396, 0.025, 0.665, 0.055]?" }, { "from": "gpt", "value": "This region contains the subtitle of the chart which reads \"Greenhouse Gas Emissions by Country\"." } ], "id_raw": 956 }, { "id": "lwpGueIz3THOw2va", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the region [0.073, 0.062, 0.988, 0.903]." }, { "from": "gpt", "value": "This area represents the main plot in which the bars of the Greenhouse gas emissions of different countries are plotted. The bars range from abbreviated country name labels on the x-axis from \"USA\" [0.160, 0.919, 0.184, 0.942] to \"Germany\" [0.862, 0.919, 0.915, 0.942], and the y-tick labels range from \"0\" [0.058, 0.894, 0.065, 0.917] to \"14000\" [0.029, -0.059, 0.065, -0.036]." } ], "id_raw": 956 }, { "id": "dEZedEP4Zfm7Zqeh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nWhat information does the region [0.507, 0.952, 0.554, 0.975] provide?" }, { "from": "gpt", "value": "The region contains the label for the x-axis, which is \"Country\"." } ], "id_raw": 956 }, { "id": "07LQhH5dhHtYDp0t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the information present within the coordinates [0.688, 0.816, 0.803, 0.903]." }, { "from": "gpt", "value": "The given coordinates represent the bar corresponding to Japan's Greenhouse Gas emissions, which is at 1272 million tons." } ], "id_raw": 956 }, { "id": "CSivMVwt0IBtDOeq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nWhat type of graph is represented by the data?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 957 }, { "id": "wRRZmqRATICeFyiF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent in the chart?" }, { "from": "gpt", "value": "Efficiency Improvement Potential (%)." } ], "id_raw": 957 }, { "id": "rjYsSVYMNfQtPPje", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nWhat {data point}[0.461, 0.239, 0.598, 0.854] is the highest on the chart according to the relavant x-axis and y-axis labels?" }, { "from": "gpt", "value": "Logistics." } ], "id_raw": 957 }, { "id": "XKmtgh9kxVOqFW1G", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for \"Sales\"}[0.803, 0.130, 0.940, 0.854] indicate more Efficiency Improvement Potential (%) than {the bar for \"Manufacturing\"}[0.118, 0.415, 0.255, 0.854]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 957 }, { "id": "XgNeZu6H2hAO42Va", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for \"Distribution\"}[0.290, 0.525, 0.427, 0.854] have lower or higher Efficiency Improvement Potential (%) compared to {the bar for \"Management\"}[0.632, 0.635, 0.769, 0.854]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 957 }, { "id": "loM8LgGctrVM3OA8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nAre the data points for {Sales and Manufacturing}{Sales: [0.803, 0.130, 0.940, 0.854]}, {Manufacturing: [0.118, 0.415, 0.255, 0.854]} higher or lower than the data point for {Logistics}[0.461, 0.239, 0.598, 0.854]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 957 }, { "id": "FELeFDa064kwIxwx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the pattern of the Efficiency Improvement Potential represented by the bars within the area [0.118, 0.130, 0.940, 0.854]?" }, { "from": "gpt", "value": "The bar chart within the specified area [0.118, 0.130, 0.940, 0.854] shows the Efficiency Improvement Potential (%) for five processes including Manufacturing, Distribution, Logistics, Management, and Sales. The highest potential is for Logistics at 28%, followed by Sales at 33%, with the lowest potential for Management at 10%." } ], "id_raw": 957 }, { "id": "voxRz3kLeo3VAur8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the Efficiency Improvement Potential of \"Distribution\" in the area [0.290, 0.525, 0.427, 0.854]?" }, { "from": "gpt", "value": "In the specified area [0.290, 0.525, 0.427, 0.854], the \"Distribution\" process shows a relatively moderate Efficiency Improvement Potential of 15%." } ], "id_raw": 957 }, { "id": "Ph8DSAFLrmWuJlFu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.796, 0.854] to [0.865, 0.879]?" }, { "from": "gpt", "value": "In the area specified as [0.796, 0.854] to [0.865, 0.879], no information is contained in the chart as it lies outside the visualized region of the bar chart." } ], "id_raw": 957 }, { "id": "ORy1FGzUQQfJwkuX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis}[0.019, 0.309, 0.036, 0.639] on the chart represent?" }, { "from": "gpt", "value": "The y-axis represents the 'Environment Score'." } ], "id_raw": 958 }, { "id": "2EGxPlvyvWKbLh89", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the category shown at [0.845, 0.879, 0.922, 0.914]?" }, { "from": "gpt", "value": "The category is 'Australia'." } ], "id_raw": 958 }, { "id": "drMtBMm5Z1Tffe9A", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.260, 0.130, 0.373, 0.854] indicate?" }, { "from": "gpt", "value": "The bar represents the 'Environment Score' for 'Germany', which is 84.5." } ], "id_raw": 958 }, { "id": "jbBwcCWwxTv4nubZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nAre the environment scores for {USA}[0.118, 0.180, 0.232, 0.854] and {India}[0.543, 0.333, 0.657, 0.854] the same?" }, { "from": "gpt", "value": "No." } ], "id_raw": 958 }, { "id": "Xc1LCzoBcVoMRXSH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nIs {Germany}'s[0.260, 0.130, 0.373, 0.854] environment score higher or lower than {Australia}'s[0.827, 0.150, 0.940, 0.854]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 958 }, { "id": "mRYMVIwGGnuarqPS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher environment score, {China}[0.402, 0.320, 0.515, 0.854] or {Brazil}[0.685, 0.201, 0.798, 0.854]?" }, { "from": "gpt", "value": "Brazil." } ], "id_raw": 958 }, { "id": "O8d4DedKkUvQqfyx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the area [0.043, 0.154, 0.065, 0.189]?" }, { "from": "gpt", "value": "The area marks the '80' line on the y-axis." } ], "id_raw": 958 }, { "id": "gySf1C7X8Ytx5FIb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the components in the region [0.118, 0.180, 0.657, 0.854]?" }, { "from": "gpt", "value": "This region includes several bars that represent the 'Environment Scores' for the countries 'USA', 'Germany', 'China', and 'India'. The coordinates for these bars are {USA}[0.118, 0.180, 0.232, 0.854], {Germany}[0.260, 0.130, 0.373, 0.854], {China}[0.402, 0.320, 0.515, 0.854], and {India}[0.543, 0.333, 0.657, 0.854]." } ], "id_raw": 958 }, { "id": "XzHghS4cNNJcIyAc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.260, 0.130, 0.515, 0.854]" }, { "from": "gpt", "value": "This region contains two bars that represent the 'Environment Scores' for 'Germany' and 'China'. The coordinates for these bars are {Germany}[0.260, 0.130, 0.373, 0.854] and {China}[0.402, 0.320, 0.515, 0.854]." } ], "id_raw": 958 }, { "id": "t3jGpcFg37ESZtN4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary object located at coordinates [0.104, 0.717, 0.244, 0.903]?" }, { "from": "gpt", "value": "It is the bar representing the return rate for Bonds." } ], "id_raw": 959 }, { "id": "SyuM8vufwAbZr0bK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nWhat exactly is the label for the y-axis in the coordinates [0.015, 0.390, 0.029, 0.575]?" }, { "from": "gpt", "value": "The label is \"Return Rate (%)\"." } ], "id_raw": 959 }, { "id": "es1LAHowENiJWlbd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the \"Investment Vehicles\" represent at coordinates [0.454, 0.952, 0.593, 0.975]?" }, { "from": "gpt", "value": "It's the x-axis label." } ], "id_raw": 959 }, { "id": "nLKpP2xQ8dKafErn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the object is within the coordinates [0.372, 0.025, 0.675, 0.055]?" }, { "from": "gpt", "value": "It is a subtitle that states \"Investment Return Rate Comparison\"." } ], "id_raw": 959 }, { "id": "npaVLPZHPEsSv9YI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nComparing the bars representing the \"Bonds\" at [0.104, 0.717, 0.244, 0.903] and the \"Stocks\" at [0.279, 0.463, 0.419, 0.903], which bar is higher?" }, { "from": "gpt", "value": "The \"Stocks\" bar is higher." } ], "id_raw": 959 }, { "id": "T3Y7d4VkXd6jpqxP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nCompared to {the bar representing \"Real Estates\"}[0.454, 0.574, 0.593, 0.903], is {the bar representing \"Cryptocurrencies\"}[0.803, 0.102, 0.943, 0.903] taller or shorter?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 959 }, { "id": "qbhIXWk2PyHrMCST", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Commodities\"}[0.628, 0.760, 0.768, 0.903] have greater or lesser return rate than {the bar representing \"Bonds\"}[0.104, 0.717, 0.244, 0.903]?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 959 }, { "id": "apZVOBVND7VyVmHt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Cryptocurrencies bar}[0.803, 0.102, 0.943, 0.903] positioned to the right or left of the {Real Estates bar}[0.454, 0.574, 0.593, 0.903]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 959 }, { "id": "RkL4Gx8Z9aPl8mEO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained in the area [0.062, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "It contains the entire graph body including the bars representing various investment vehicles and their corresponding return rates. For instance, the bar for Bonds is found at [0.104, 0.717, 0.244, 0.903] and for Cryptocurrencies at [0.803, 0.102, 0.943, 0.903]." } ], "id_raw": 959 }, { "id": "deyU5NjEm3IL3aTh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region [0.629, 0.458, 0.769, 0.903]?" }, { "from": "gpt", "value": "This region contains the bar for \"Commodities\" at [0.628, 0.760, 0.768, 0.903] which spans a range from 0.00 to 2.70 on the y-axis." } ], "id_raw": 959 }, { "id": "p4iMUfcwyPuzHibc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about what is inside the region [0.279, 0.463, 0.419, 0.903]?" }, { "from": "gpt", "value": "The region is occupied by the bar for \"Stocks\" and it spans from 0.00 to 8.30 which represents the return rate for \"Stocks\"." } ], "id_raw": 959 }, { "id": "8Dj8iR716ogr3Ik8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the region [0.803, 0.102, 0.943, 0.903]?" }, { "from": "gpt", "value": "Within this region you can find the bar for \"Cryptocurrencies\" which ranges from 0.00 up to 15.10, thus representing the return rate for \"Cryptocurrencies\"." } ], "id_raw": 959 }, { "id": "p6pX3FpDOcGSL6MG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.005, 0.075, 0.017, 0.241]?" }, { "from": "gpt", "value": "{the subplot's y-axis label}[0.005, 0.075, 0.017, 0.241] labelled as \"Stocks Return Rate\"." } ], "id_raw": 960 }, { "id": "xZ3idOPXF5TY6YoC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for {the x-axis label} in the third subplot?" }, { "from": "gpt", "value": "{the x-axis label}[0.472, 0.964, 0.571, 0.981]." } ], "id_raw": 960 }, { "id": "RU3jGh0oWSbUeH4c", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nWhat label is associated with the coordinate range [0.022, 0.291, 0.047, 0.308]?" }, { "from": "gpt", "value": "It is a {y-axis label}[0.022, 0.291, 0.047, 0.308] stating \"0.00\"." } ], "id_raw": 960 }, { "id": "pCh4AzY9s98b0MGW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 2020}[0.098, 0.827, 0.160, 0.927] taller than {the bar for 2030}[0.882, 0.749, 0.945, 0.927]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 960 }, { "id": "0ezmo1fjyt94KrRU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar labelled 2028}[0.725, 0.771, 0.788, 0.927] have a smaller y-range than {the bar labelled 2026}[0.568, 0.788, 0.631, 0.927]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 960 }, { "id": "k9lm6xVKSnfHJLL2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nIs {the y-coordinate for \"0.35\" label}[0.022, 0.039, 0.047, 0.057] closer to {the y-coordinate of \"0.30\" label}[0.022, 0.075, 0.047, 0.093] than to {the y-coordinate of \"0.40\" label}[0.022, 0.004, 0.047, 0.021]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 960 }, { "id": "JyZN5UDpKISFDrQV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the items in the region [0.055, 0.649, 0.988, 0.927]?" }, { "from": "gpt", "value": "This region encompasses the third subplot comprising bars for Real Estate Return Rate from 2020 to 2030. Each bar represents the annual return rates for real estate investments, with the investment years labeled at the bottom and specific yearly return rates labeled on the y-axis on the left." } ], "id_raw": 960 }, { "id": "DzHnLSQLsnS8Il1b", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the bounded area of [0.055, 0.019, 0.988, 0.297]." }, { "from": "gpt", "value": "This area includes a set of bars representing the Stocks Return Rate from the years 2020 to 2030. Each year has a corresponding bar along the x-axis showing the increase in return rates from 0.12 in 2020 to a higher rate in subsequent years, with these results displayed on the y-axis to the left." } ], "id_raw": 960 }, { "id": "NIXbMpBxaYI1RsHD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements found between [0.055, 0.334, 0.988, 0.612]?" }, { "from": "gpt", "value": "The region contains bars that illustrate Bond Investments Return Rates from 2020 to 2030. Each bar correlates to specific years shown at the base. These bars show an increase from lower rates in earlier years towards higher rates in the last years, with ticks and labels on the y-axis indicating these values in increments of 0.05." } ], "id_raw": 960 }, { "id": "Y17mGNdwvvo1em0E", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nWhat color is represented by the first bar in the subplot for Offline Interaction?" }, { "from": "gpt", "value": "{Purple}[0.116, 0.648, 0.184, 0.808]." } ], "id_raw": 962 }, { "id": "rFLFFRON0SP6TD9y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate range displayed next to the y-axis in the Online Interaction plot?" }, { "from": "gpt", "value": "{0.00 to 0.50}[0.035, 0.522, 0.065, 0.545]." } ], "id_raw": 962 }, { "id": "WO12nnufZvsOfgnR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nWhat is located at {the coordinates}[0.623, 0.625, 0.690, 0.808] in the Offline Interaction subplot?" }, { "from": "gpt", "value": "WhatsApp bar reflecting Offline Interaction." } ], "id_raw": 962 }, { "id": "VcUSQvcLWz4DD3sH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nWhich is longer, {the Duration of Reading bar for Instagram}[0.285, 0.048, 0.353, 0.254] or {the Online Interaction bar for Instagram}[0.285, 0.326, 0.353, 0.531]?" }, { "from": "gpt", "value": "The Online Interaction bar." } ], "id_raw": 962 }, { "id": "UuqHyq8HRiBOlXz1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the LinkedIn bar in Offline Interaction}[0.454, 0.682, 0.522, 0.808] extend higher than {the LinkedIn bar in Online Interaction}[0.454, 0.377, 0.522, 0.531]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 962 }, { "id": "6xLXqlWnharowEqq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nIs the first mark on the y-axis of {Online Interaction}[0.075, 0.531] higher than the first bar of {Twitter in Online Interaction}[0.201, 0.403, 0.268, 0.531]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 962 }, { "id": "5LQo1lep6isgyBgn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the subplot bounds [0.075, 0.025, 0.985, 0.254]." }, { "from": "gpt", "value": "This region comprises bars indicating the Duration of Reading for multiple social networks, each bar colored in shades of blue, with the first bar's coordinates at [0.116, 0.036, 0.184, 0.254] and the last one at [0.876, 0.121, 0.944, 0.254]." } ], "id_raw": 962 }, { "id": "BvdPCG9MKIxja7H8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the visual space framed by [0.075, 0.302, 0.985, 0.531]?" }, { "from": "gpt", "value": "This constitutes the Online Interaction subplot with various bars representing interaction scores, such as the LinkedIn bar situated at [0.454, 0.377, 0.522, 0.531] and marked by an orange hue." } ], "id_raw": 962 }, { "id": "Ao3KQzW6j5JSy7od", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.075, 0.579, 0.985, 0.808]?" }, { "from": "gpt", "value": "This area features the Offline Interaction subplot, showcasing vertical bars corresponding to different social networks' offline engagement metrics. Key identifying features include a label for Instagram at [0.312, 0.824, 0.326, 0.942] and a corresponding bar at [0.285, 0.636, 0.353, 0.808]." } ], "id_raw": 962 }, { "id": "eHdMydpHB4e1BRlJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.024, -0.001, 0.038, 0.317] for the second sub-chart?" }, { "from": "gpt", "value": "French Cuisine Appreciation." } ], "id_raw": 963 }, { "id": "VI57qHpFL0ZqWooc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nWhat color are {the bars}[0.116, 0.721, 0.184, 0.903] in the third sub-chart?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 963 }, { "id": "8jbIbU29eswuPaOG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nWhat does {the xlabel}[0.462, 0.952, 0.598, 0.975] indicate in the chart?" }, { "from": "gpt", "value": "Rank Over 10 Years." } ], "id_raw": 963 }, { "id": "mhDTIMLL52tzyE68", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nWhich subplot has a higher maximum y-value, the first {subplot}[0.075, 0.025, 0.985, 0.292] or the third {subplot}[0.075, 0.636, 0.985, 0.903]?" }, { "from": "gpt", "value": "First." } ], "id_raw": 963 }, { "id": "SBBONHcTa0XGs2Y4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nAre {the blue bars}[0.116, 0.038, 0.184, 0.292] from the Italian category positioned above {the y-tick 0.6}[0.043, 0.122, 0.065, 0.145]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 963 }, { "id": "VEWxs6hUM7JiOqME", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the first bar}[0.116, 0.343, 0.184, 0.597] in subplot_1 have a rank associated with {xtick 1}[0.146, 0.919, 0.154, 0.942] from subplot_3?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 963 }, { "id": "3KhvSuwqLyQc4yK2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bars in the subplot labeled \"Genre 2\"?" }, { "from": "gpt", "value": "The color of the bars in {Genre 2}[0.066, 0.332, 0.985, 0.596] subplot is light orange." } ], "id_raw": 964 }, { "id": "9NoS4YvdwTdaAEdZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.015, 0.111, 0.029, 0.203] in the first subplot indicate?" }, { "from": "gpt", "value": "Genre 1." } ], "id_raw": 964 }, { "id": "vBh0m50795trTPiu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nWhat year corresponds to the furthest right tick on the X-axis in the third subplot?" }, { "from": "gpt", "value": "The year is 2029 in {the X-axis tick}[0.892, 0.919, 0.927, 0.942]." } ], "id_raw": 964 }, { "id": "0p6ctjX7MKnI5g9f", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nIs the bar for 2020 taller in {Genre 1}[0.108, 0.195, 0.176, 0.289] or in {Genre 3}[0.108, 0.779, 0.176, 0.815]?" }, { "from": "gpt", "value": "Genre 1." } ], "id_raw": 964 }, { "id": "u8XJ3mOGYdxc4IER", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the first bar of Genre 1}[0.108, 0.195, 0.176, 0.289] reach a higher value than {the first bar of Genre 2}[0.108, 0.344, 0.176, 0.527]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 964 }, { "id": "GDFkZjXn1pvBQa5Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nAre the annual trends decreasing or increasing from 2020 to 2029 in {Genre 2}[0.066, 0.332, 0.985, 0.596]?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 964 }, { "id": "zcQavwabVgI9Kvy5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the trends observed in {Genre 3 region}[0.066, 0.638, 0.985, 0.903]." }, { "from": "gpt", "value": "The genre shows a generally increasing trend across the years with values starting around 0.4 in 2020 and reaching up to around 1.4 by 2029 with each year relayed by corresponding bars increasing in height." } ], "id_raw": 964 }, { "id": "X01LYUmBAlHmxjPF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the area spanning from the bottom of the {ylabel of Genre 1}[0.015, 0.111, 0.029, 0.203] to the top of {xlabel of Genre 1}[0.526, 0.307, 0.526, 0.307]?" }, { "from": "gpt", "value": "This area primarily contains the vertically aligned category label for 'Genre 1,' individual bars corresponding to each year from 2020 to 2029 with lower values at the start and increasing towards the end, and associated Y-ticks indicating scale." } ], "id_raw": 964 }, { "id": "o5QNt1BuLZ3DyDiM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nAnalyze and describe the data in the vicinity of {the X-axis labels}[0.125, 0.919, 0.927, 0.942] in the bottom subplot." }, { "from": "gpt", "value": "This region includes the year labels from 2020 to 2029 aligned along the X-axis below Genre 3's plot, each year marked with a tick beneath its label, overseeing a consistent growth in bars correlating to increasing values per year." } ], "id_raw": 964 }, { "id": "56uDsM6TIKHXnqJ2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.105, 0.029, 0.205] in the first subplot signify?" }, { "from": "gpt", "value": "Revenue" } ], "id_raw": 965 }, { "id": "Zw8j5GCkQu6o6weE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nWhich year corresponds to {the x-axis label}[0.894, 0.919, 0.929, 0.942] in the profits subplot?" }, { "from": "gpt", "value": "2020" } ], "id_raw": 965 }, { "id": "pdPIMo0EPguO4L3B", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label}[0.153, 0.919, 0.188, 0.942] in the profits subplot represent?" }, { "from": "gpt", "value": "The year 2011" } ], "id_raw": 965 }, { "id": "jjowBVsXIsZNQ8s6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nIs {the revenue bar for 2011}[0.137, 0.110, 0.203, 0.285] taller than {the costs bar for 2011}[0.137, 0.399, 0.203, 0.594]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 965 }, { "id": "gfbWeYn9iEFluOqU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the final profit bar}[0.879, 0.825, 0.945, 0.903] have a higher value than {the initial profit bar}[0.137, 0.859, 0.203, 0.903]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 965 }, { "id": "tuqs5v9udkHvvtmc", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nIs {the costs bar for 2018}[0.714, 0.360, 0.780, 0.594] lower than {the revenue bar for 2018}[0.714, 0.057, 0.780, 0.285]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 965 }, { "id": "kcW6ZUYjcuyt3wgG", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.097, 0.334, 0.985, 0.594]." }, { "from": "gpt", "value": "This area visualizes costs over the years with bars. Each bar represents a particular year's cost, ranging from 2011 to 2020, gradually increasing from {2011 costs}[0.137, 0.399, 0.203, 0.594] to {2020 costs}[0.879, 0.346, 0.945, 0.594]." } ], "id_raw": 965 }, { "id": "o8vlHOpLSuWeFMmt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.097, 0.642, 0.985, 0.903]?" }, { "from": "gpt", "value": "This region displays the profits from 2011 to 2020. It begins with a lower profit in 2011 and gradually increases with each bar until peaking in 2020." } ], "id_raw": 965 }, { "id": "SScv60wlByyApMPD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.097, 0.025, 0.985, 0.285]." }, { "from": "gpt", "value": "The region shows a series of bars, each representing annual revenue from 2011 to 2020. The bars indicate an increasing trend in revenue, starting from 2011 {2011 revenue bar}[0.137, 0.110, 0.203, 0.285] up to 2020 {2020 revenue bar}[0.879, 0.037, 0.945, 0.285]." } ], "id_raw": 965 }, { "id": "yFEqU1Nu9banCdXh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} of the second subplot at [0.024, 0.407, 0.038, 0.521]?" }, { "from": "gpt", "value": "Reliability." } ], "id_raw": 966 }, { "id": "UYepcfOUlUy16NAN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first xtick in the third subplot?" }, { "from": "gpt", "value": "[0.257, 0.919, 0.344, 0.942]." } ], "id_raw": 966 }, { "id": "UPH7ge48IaIRFVBt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the {the bar} at [0.576, 0.343, 0.944, 0.597] in the second subplot?" }, { "from": "gpt", "value": "The bar represents that the value of the reliability of the Supply Chain Management is 0.65." } ], "id_raw": 966 }, { "id": "avsSnriG1B2D1RpG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in [0.024, 0.102, 0.038, 0.214]?" }, { "from": "gpt", "value": "An Efficiency {label}[0.024, 0.102, 0.038, 0.214]." } ], "id_raw": 966 }, { "id": "nA5cJxnwe8MVC2eG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nIs {the first bar of the first subplot}[0.116, 0.041, 0.484, 0.292] higher or lower than {the second bar of the third subplot}[0.576, 0.649, 0.944, 0.903]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 966 }, { "id": "IGnx0klQkDrtZI7B", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nWhich one has larger y range, {the first bar of the first subplot}[0.116, 0.041, 0.484, 0.292] or {the first bar of the third subplot}[0.116, 0.688, 0.484, 0.903]?" }, { "from": "gpt", "value": "The first bar of the third subplot." } ], "id_raw": 966 }, { "id": "Axcql7wNIyliDIA0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nAre {the bars of the first subplot}[[0.116, 0.041, 0.484, 0.292],[0.576, 0.038, 0.944, 0.292]] generally taller than {the bars of the second subplot}[[0.116, 0.429, 0.484, 0.597],[0.576, 0.343, 0.944, 0.597]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 966 }, { "id": "MwvS4f2Zru840JCR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nWhich one is more to the right, {\"Third subplot Global Trade bar\"}[0.116, 0.688, 0.484, 0.903] or {\"Third subplot Supply Chain Management bar\"}[0.576, 0.649, 0.944, 0.903]?" }, { "from": "gpt", "value": "Third subplot Supply Chain Management bar." } ], "id_raw": 966 }, { "id": "2CfCR6wCKPGX0lyr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.075, 0.331, 0.985, 0.597]." }, { "from": "gpt", "value": "This is the second subplot of the chart. It has two bars representing \"Global Trade\" and \"Supply Chain Management\", with parameters for \"Reliability\", a grey background and color codes are used to identify different subjects. The color for \"Global Trade\" is #e39c90, while the color for \"Supply Chain Management\" is #e39c90." } ], "id_raw": 966 }, { "id": "yFw9WKf2Po6OUjI8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.075, 0.025, 0.985, 0.292]." }, { "from": "gpt", "value": "It is a subplot with the y-axis label \"Efficiency\" located at [0.024, 0.102, 0.038, 0.214] and two bars. One bar corresponds to the \"Global Trade\" category at [0.116, 0.041, 0.484, 0.292] and the another bar corresponds to \"Supply Chain Management\" category at [0.576, 0.038, 0.944, 0.292]." } ], "id_raw": 966 }, { "id": "Jnhl78H9KrapobqD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.075, 0.636, 0.985, 0.903]?" }, { "from": "gpt", "value": "There are 3 main objects in this area, they are:\n1. The y-axis label \"Diversity\" [0.024, 0.718, 0.038, 0.821].\n2. The bar representing the value for \"Global Trade\" [0.116, 0.688, 0.484, 0.903].\n3. The bar representing the value for \"Supply Chain Management\" [0.576, 0.649, 0.944, 0.903]." } ], "id_raw": 966 }, { "id": "c9AwOZaTRMYwPZHq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained in the region [0.469, 0.952, 0.591, 0.975]?" }, { "from": "gpt", "value": "This region contains xlabel reading \"Areas of Business\" [0.469, 0.952, 0.591, 0.975]." } ], "id_raw": 966 }, { "id": "ghPe79YSxAaezFfz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with {the y-axis}[0.015, 0.034, 0.029, 0.276]?" }, { "from": "gpt", "value": "Charitable donations." } ], "id_raw": 967 }, { "id": "dIldpWOO8tZ1pKv3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the bars for the subplot of 'Volunteer hours'?" }, { "from": "gpt", "value": "{Purple}[0.121, 0.903, 0.188, 0.903]" } ], "id_raw": 967 }, { "id": "75TaDSdwM3dZgp8v", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nWhat year is indicated by the label at [0.557, 0.919, 0.592, 0.942]?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 967 }, { "id": "p5t2Vu5LpUqwhcXM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nIs {the peak year for charitable donations}[0.877, 0.037, 0.944, 0.285] later than {the peak year for social services}[0.205, 0.356, 0.272, 0.594]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 967 }, { "id": "GF8oHE0ovGU4fZ3r", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the initial year on 'Volunteer hours' subplot}[0.121, 0.903, 0.188, 0.903] precede {the initial year on 'Charitable donations' subplot}[0.121, 0.065, 0.188, 0.285]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 967 }, { "id": "4F6IaAfUKDITFYOi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar coordinate for 2012}[0.289, 0.062, 0.356, 0.285] shorter than {the bar coordinate for 2019}[0.877, 0.037, 0.944, 0.285]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 967 }, { "id": "SBcVEckUq8jTlO2d", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.079, 0.025, 0.985, 0.285]." }, { "from": "gpt", "value": "This area contains the subplot for 'Charitable donations' which includes a series of bars ranging from 2010 to 2019, with respective ends and values labeled. The y-axis at [0.015, 0.034, 0.029, 0.276] is labeled 'Charitable donations', and two shades of blue are visible in the bars with various heights representing differing donation values over the years." } ], "id_raw": 967 }, { "id": "w24G6g0yWU3mKULY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects and labels present in [0.079, 0.642, 0.985, 0.903]." }, { "from": "gpt", "value": "This is the subplot for 'Volunteer Hours' with a y-axis labeled 'Volunteer Hours' at [0.015, 0.681, 0.029, 0.864] and x-axis labels indicating years from 2010 to 2019. The bars are uniformly shaded in purple, representing volunteer hours for each year, incrementally increasing from left to right, and year labels are aligned beneath each bar." } ], "id_raw": 967 }, { "id": "vT2Do2JsGTybqOsZ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.079, 0.334, 0.985, 0.594]?" }, { "from": "gpt", "value": "It contains the 'Social Services' subplot with bars in a peach tone, indicating values from the year 2010 to 2019. The y-axis labeled 'Social services' at [0.015, 0.379, 0.029, 0.549] shows values ranging from 0 to 8000 with each bar's height proportional to the value of social services recorded each year." } ], "id_raw": 967 }, { "id": "rm5Scz9NXSCOI1Gm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar for the \"Decision Tree\" algorithm in the efficiency chart?" }, { "from": "gpt", "value": "Light blue, located in {the bar for Decision Tree}[0.285, 0.091, 0.353, 0.292]." } ], "id_raw": 968 }, { "id": "K5dFjDo34h9ndNCP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value range indicated by the first tick mark on the y-axis of the reliability chart?" }, { "from": "gpt", "value": "{0.0}[0.043, 0.894, 0.065, 0.917]." } ], "id_raw": 968 }, { "id": "xkeVgQi5M0OFy325", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the x-axis in the reliability chart?" }, { "from": "gpt", "value": "AI Algorithms, located in {the xlabel area}[0.483, 0.952, 0.577, 0.975]." } ], "id_raw": 968 }, { "id": "JDklble86hGeLYvz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nIs {the \"XGBoost\" bar}[0.876, 0.038, 0.944, 0.292] taller than {the \"Naive Bayes\" bar}[0.370, 0.072, 0.437, 0.292] in the efficiency chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 968 }, { "id": "kBi1OG5yc0PS9qt1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nIn the performance chart, does {the bar for \"Deep Learning\"}[0.454, 0.367, 0.522, 0.597] outperform {the bar for \"Random Forest\"}[0.538, 0.407, 0.606, 0.597]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 968 }, { "id": "4DUviftHh17HMsRE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nIs the performance of {the \"Support Vector Machine\" in the reliability chart}[0.201, 0.782, 0.268, 0.903] higher than its representation in {the performance chart}[0.201, 0.437, 0.268, 0.597]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 968 }, { "id": "gMr4IKzpLjGpA7kg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the visualization within the area [0.075, 0.025, 0.985, 0.292]." }, { "from": "gpt", "value": "The region contains a graph displaying efficiency of different AI algorithms. Each algorithm, from Neural Network to XGBoost, has a corresponding light blue bar showing its individual efficiency score. The y-axis is labeled \"Efficiency\" with ticks ranging from 0.0 to 1.0 and corresponding bars span from top to bottom in varying lengths based on the efficiency value." } ], "id_raw": 968 }, { "id": "KY9bYmMIAYUpHT85", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nWhat details can be observed in the area [0.075, 0.331, 0.985, 0.597]?" }, { "from": "gpt", "value": "This area of the chart relates to the performance of AI algorithms. Each algorithm is represented by a coral bar, and the y-axis on the left is marked from 0.0 to 1.0 indicating performance levels. Each bar's height varies according to the respective algorithm's performance metric." } ], "id_raw": 968 }, { "id": "efEO5Zh2zx0vkgz2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the features found in the region [0.075, 0.636, 0.985, 0.903]?" }, { "from": "gpt", "value": "This particular region showcases the reliability of AI algorithms. It is populated with purple bars, each corresponding to an AI algorithm listed along the x-axis, which is clearly labeled \"AI Algorithms\". The y-axis is marked at intervals from 0.0 to 1.0 to indicate the reliability scale. Each bar\u2019s height directly corresponds to its reliability measure." } ], "id_raw": 968 }, { "id": "YFsoTRdoGhGGPbUS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.024, 0.105, 0.038, 0.211]?" }, { "from": "gpt", "value": "Y-axis label in the first subplot, denoting \"Accuracy\"." } ], "id_raw": 969 }, { "id": "VaNN1iOX5DWWS2NB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nWhat is plotted at coordinates [0.116, 0.151, 0.230, 0.292]?" }, { "from": "gpt", "value": "It's a bar representing the \"Accuracy\" of the \"Size of data\" stage with a value of 0.50." } ], "id_raw": 969 }, { "id": "a8sWIkfdiqbsmqj3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.829, 0.919, 0.944, 0.942] in the chart figure?" }, { "from": "gpt", "value": "This is the X-axis label \"Data integration\" in the last subplot." } ], "id_raw": 969 }, { "id": "R1kNgFzuMePj5XWB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar at [0.402, 0.755, 0.516, 0.903] represent?" }, { "from": "gpt", "value": "It represents the \"Precision\" of the \"Data storage\" stage." } ], "id_raw": 969 }, { "id": "V7PKVksoRLzqgXUp", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing Precision of the Data storage stage}[0.402, 0.755, 0.516, 0.903] higher than {the bar representing Precision of the Data processing stage}[0.259, 0.772, 0.373, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 969 }, { "id": "XeZpLT123xRIIm7j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Data retrieval\" Accuracy}[0.544, 0.122, 0.658, 0.292] taller than {the bar for \"Data integration\" Accuracy}[0.830, 0.066, 0.944, 0.292]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 969 }, { "id": "ij2Ve3vtB4JTuWku", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nDid {the \"Data analysis\" stage}[0.687, 0.721, 0.801, 0.903] achieve a higher precision than {the \"Size of data\" stage}[0.116, 0.709, 0.230, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 969 }, { "id": "e0yXsCJoCiyHVfMG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nComparing the Accuracy of {Data storage}[0.402, 0.086, 0.516, 0.292] and {Data analysis}[0.687, 0.038, 0.801, 0.292], which one is higher?" }, { "from": "gpt", "value": "Data storage." } ], "id_raw": 969 }, { "id": "amv8dP6wSPbRNIZN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is in the box [0.075, 0.636, 0.985, 0.903]?" }, { "from": "gpt", "value": "This region represents the third subplot, which illustrates Precision scores across different stages of Big Data Integration. It has 6 colored bars representing the precision values for different stages, along with y-axis labels and ticks for measurement and x-axis labels naming stages." } ], "id_raw": 969 }, { "id": "clPhPevfucjm7M4a", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nWhat kind of elements can you see in the region [0.043, 0.588, 0.065, 0.611]?" }, { "from": "gpt", "value": "It's part of the y-axis of the second subplot, specifically the tick label showing the value \"0.0\"." } ], "id_raw": 969 }, { "id": "Iw90BN1HwBwVEpsu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area in the range of [0.259, 0.772, 0.373, 0.903]?" }, { "from": "gpt", "value": "In this area, there is a bar representing the precision of \"Data processing\" in the last subplot. The height of the bar is proportional to the precision value it represents." } ], "id_raw": 969 }, { "id": "1Q2ENzWz20eLFNg1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the area of [0.075, 0.025, 0.985, 0.292]?" }, { "from": "gpt", "value": "This area encompasses the first subplot, which visualizes the Accuracy measures across different stages of Big Data Integration. There are a total of 6 colored bars representing the accuracy measures, with the object height representing the measure value. Also, x-axis and y-axis with their labels and ticks are included." } ], "id_raw": 969 }, { "id": "Wd9fmtBG0uDlaGV3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nCan you identify {the label} for the y-axis[0.024, 0.015, 0.038, 0.295] in the second subplot?" }, { "from": "gpt", "value": "Efficiency - Second Quarter." } ], "id_raw": 970 }, { "id": "1noyj0ETUQRME7CY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the third bar}[0.285, 0.051, 0.353, 0.285] in the first subplot?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 970 }, { "id": "wz3UUMfjXYSSUmJG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nWhat is placed at {coordinates}[0.146, 0.919, 0.154, 0.942] in the third subplot?" }, { "from": "gpt", "value": "X-axis label, \"1\"." } ], "id_raw": 970 }, { "id": "0RCwOw4FKinMH3m2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nIs {bar \"HR\" in the second subplot}[0.285, 0.392, 0.353, 0.594] shorter or taller than {bar \"HR\" in the first subplot}[0.285, 0.051, 0.353, 0.285]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 970 }, { "id": "u8xBPrSnUeYD03e5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the y-axis label \"0.8\"}[0.043, 0.056, 0.065, 0.079] refer to a higher value than {the y-axis label \"0.6\"}[0.043, 0.111, 0.065, 0.134] in the first subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 970 }, { "id": "c0J5JNVVziRv6x7I", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nIs the {first bar}[0.116, 0.079, 0.184, 0.285] located to the left of {the tenth bar}[0.876, 0.098, 0.944, 0.285] in the first subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 970 }, { "id": "d2nHOwOqFVXGzgQP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the horizontal label at the coordinates [0.396, 0.952, 0.655, 0.975]?" }, { "from": "gpt", "value": "It is the label of the horizontal axis, called Local Government Funding Sequence." } ], "id_raw": 971 }, { "id": "FeFEYK5rysYY7VBU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nWhat represents the coordinates of the bar labeled '3'?" }, { "from": "gpt", "value": "The coordinates of the bar labeled '3' are [0.278, 0.596, 0.347, 0.903]." } ], "id_raw": 971 }, { "id": "QaWPg3iWIeKKsIWr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar labeled '10' representing?" }, { "from": "gpt", "value": "The bar labeled '10' represents a value of 0.96 with coordinates [0.875, 0.540, 0.943, 0.903]." } ], "id_raw": 971 }, { "id": "yCCarA4cI8yNoqpP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nWhat represents the coordinates [0.035, 0.591, 0.057, 0.614]?" }, { "from": "gpt", "value": "It is a y-axis label with a value of 0.8." } ], "id_raw": 971 }, { "id": "KCJoP9rw9C1Xj3mF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nIs the bar labeled '7'[0.619, 0.596, 0.688, 0.903] higher than the bar labeled '4'[0.364, 0.608, 0.432, 0.903]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 971 }, { "id": "dABTg4RseWU7sjr1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nIs the bar labeled '6' [0.534, 0.608, 0.602, 0.903] at the same height as the bar labeled '10' [0.875, 0.540, 0.943, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 971 }, { "id": "DO2GxRuCsgLQuVrh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nDoes the bar labeled '1' [0.108, 0.562, 0.176, 0.903] have a higher or lower value than the bar labeled '9' [0.790, 0.562, 0.858, 0.903]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 971 }, { "id": "7n8qArPtuERMqdax", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nIs the bar labeled '2' [0.193, 0.585, 0.261, 0.903] to the left or right of bar labeled '3' [0.278, 0.596, 0.347, 0.903]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 971 }, { "id": "qQoZVhnpfIytnWqg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area [0.066, 0.031, 0.985, 0.448]?" }, { "from": "gpt", "value": "This area contains a bar chart that represents Local Government funds, with bars labeled 1 to 10. Each bar represents a specific funding sequence and its corresponding funds amount." } ], "id_raw": 971 }, { "id": "xwrf9mfUb6SZqJnd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region in [0.263, 0.487, 0.677, 0.903] include?" }, { "from": "gpt", "value": "It includes the main part of a bar chart that shows Community Development Funds plotted against a Local Government Funding Sequence. The bar heights reflect the amount of funds, with each bar labeled 1 to 10 representing a specific sequence in the funding." } ], "id_raw": 971 }, { "id": "Gu3sD4xgdGYsrWxK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.449, 0.612, 0.517, 0.903]?" }, { "from": "gpt", "value": "There is a single bar located in this area. It's labeled '5' and represents a value of 0.77 for the Community Development Funds in the Local Government Funding Sequence." } ], "id_raw": 971 }, { "id": "I2N5nUOdSfH2OyLq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the area [0.066, 0.487, 0.985, 0.903]?" }, { "from": "gpt", "value": "This area contains the secondary bar chart in the figure, which represents the Community Development Funds. Each of the bars labeled from 1 to 10 represents a specific sequence in the funding. The height of each bar corresponds to the amount of funds." } ], "id_raw": 971 }, { "id": "8k1Qg223M4SgQMgU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar representing the year 2007}[0.707, 0.038, 0.775, 0.292] in the GDP Growth subplot?" }, { "from": "gpt", "value": "Light blue." } ], "id_raw": 972 }, { "id": "W79fcc9Sp6YT7TPj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the highest bar in the Inflation subplot}[0.370, 0.343, 0.437, 0.597]?" }, { "from": "gpt", "value": "[0.370, 0.343, 0.437, 0.597]." } ], "id_raw": 972 }, { "id": "OcitMbfxeFjYYE8o", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nWhat labels can be seen for {ticks on the y-axis}[0.043, 0.845, 0.065, 0.869] in the Unemployment Rate subplot?" }, { "from": "gpt", "value": "0.4." } ], "id_raw": 972 }, { "id": "wQOuYsA6msil5sI5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 2004}[0.454, 0.372, 0.522, 0.597] higher than {the bar for 2006}[0.623, 0.428, 0.690, 0.597] in the Inflation subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 972 }, { "id": "ea0bntj1Mx80nR37", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nIs {the lowest labeled value on the y-axis}[0.043, 0.894, 0.065, 0.917] in the Unemployment Rate subplot less than {the highest labeled value}[0.043, 0.651, 0.065, 0.675]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 972 }, { "id": "DlS5ZBpEfb9nKw92", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nIs {the first year labeled on the x-axis}[0.133, 0.919, 0.168, 0.942] earlier than {the last year labeled}[0.892, 0.919, 0.927, 0.942] in the Unemployment Rate subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 972 }, { "id": "k7Q2bX1dEvzmxfgm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.043, 0.362, 0.065, 0.386]." }, { "from": "gpt", "value": "This area contains the y-axis label for 0.8 in the Inflation subplot, indicating a value of 0.8 on the y-axis." } ], "id_raw": 972 }, { "id": "XDPGPV733AdVGsAi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.133, 0.758, 0.944, 0.903]?" }, { "from": "gpt", "value": "The entire x-axis label set for years 2000 to 2009 can be found, with the tick marks and labels visible for each year." } ], "id_raw": 972 }, { "id": "78FQf3GGbUyJSGqP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nExplain the contents within [0.319, 0.372, 0.353, 0.597]." }, { "from": "gpt", "value": "This smaller region captures the middle section of a bar showing 0.8 inflation for the year 2002. The full bar is actually taller, beginning below this region at a higher inflation value." } ], "id_raw": 972 }, { "id": "2XreNinoJAp9ZfUq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar corresponding to 2014 in the subplot indicating Mobility Rate}[0.449, 0.143, 0.517, 0.292]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 973 }, { "id": "WwOWQ954SGSoxdqG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of {the ylabel \"Political Protests\"}[0.015, 0.675, 0.029, 0.865]?" }, { "from": "gpt", "value": "[0.015, 0.675, 0.029, 0.865]." } ], "id_raw": 973 }, { "id": "2EtHAD9iroIrxzCJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {the first label on the y-axis in the Internet Users subplot}[0.035, 0.282, 0.057, 0.306] represent?" }, { "from": "gpt", "value": "0.0." } ], "id_raw": 973 }, { "id": "wx5rN6Kli1qX2N0i", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nIs {the political protest bar for 2015}[0.534, 0.831, 0.602, 0.903] taller than {the political protest bar for 2013}[0.364, 0.800, 0.432, 0.903]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 973 }, { "id": "6ioLZYxdRIM6san1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing the Internet Users in 2010}[0.108, 0.228, 0.176, 0.292] represent a higher value than {the bar representing Internet Users in 2019}[0.875, 0.038, 0.943, 0.292]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 973 }, { "id": "eFbO1LpaMUzkWn1P", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nAre {the Internet Users' bars from 2010 to 2012}[[0.108, 0.228, 0.176, 0.292], [0.193, 0.196, 0.261, 0.292], [0.278, 0.186, 0.347, 0.292]] of increasing height?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 973 }, { "id": "FHcBMww9AtmpvuN4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the range between {0.898, 0.919, 0.927, 0.942} in the subplot for Political Protests consist of?" }, { "from": "gpt", "value": "This region includes the year label \"2019\"[0.892, 0.919, 0.927, 0.942] situated on the x-axis." } ], "id_raw": 973 }, { "id": "GhVBQUg8u5yg2UKn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within {the section of the chart representing the x-axis for Internet Users}[0.066, 0.292, 0.985, 0.309]." }, { "from": "gpt", "value": "The section contains several tick marks at intervals reflecting the years from 2010 to 2019. Most year labels are not visible due to overlap, but the beginning and end of the axis are marked." } ], "id_raw": 973 }, { "id": "0PuWQ1vJgk0wcV0T", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates {0.015, 0.05, 0.029, 0.865} in the subplot for Political Protests?" }, { "from": "gpt", "value": "This region contains the y-axis[0.015, 0.675, 0.029, 0.865] labeled with \"Political Protests\" and includes labels ranging from 0.0 to 1.2." } ], "id_raw": 973 }, { "id": "N0SkMbgIwtIDTKsH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at coordinates [0.116, 0.108, 0.184, 0.292]?" }, { "from": "gpt", "value": "It is a light blue bar representing the efficiency of Product 1 at point 1." } ], "id_raw": 974 }, { "id": "wpqqsJlhrRh1WMeD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at [0.201, 0.697, 0.268, 0.903]?" }, { "from": "gpt", "value": "It's an orange bar corresponding to the efficiency of Product 2 at point 2." } ], "id_raw": 974 }, { "id": "O2MCdwL5B9FVwTxY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nIdentify the object at [0.876, 0.675, 0.944, 0.903]." }, { "from": "gpt", "value": "That's a purple bar that represents Product 3's efficiency at point 10." } ], "id_raw": 974 }, { "id": "uksLl3mNfiQ0HoI2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nIs {the purple bar}[0.116, 0.687, 0.184, 0.903] taller than {the orange bar}[0.116, 0.366, 0.184, 0.597]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 974 }, { "id": "shDa8JOgOzR9WxqA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nCompare the height of {bar 1 of Product 1}[0.116, 0.108, 0.184, 0.292] with {bar 1 of Product 3}[0.116, 0.687, 0.184, 0.903]. Which is taller?" }, { "from": "gpt", "value": "Product 3." } ], "id_raw": 974 }, { "id": "1GL3cfsHsSBAdfYD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nWhich bar is shorter, {bar 9 of Product 2}[0.792, 0.407, 0.859, 0.597] or {bar 5 of Product 1}[0.454, 0.080, 0.522, 0.292]?" }, { "from": "gpt", "value": "Product 1." } ], "id_raw": 974 }, { "id": "KDtm37DpRrBalYTX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area spanning from [0.075, 0.636, 0.985, 0.903]." }, { "from": "gpt", "value": "This region contains the third subplot, titled \"Efficiency Product 3\", with purple bars representing efficiency levels across ten production series. The ylabel reads \"Efficiency Product 3\", and the bars' values increase from left to right, peaking at the bar for the 8th series." } ], "id_raw": 974 }, { "id": "lSrfoGBBwtDZWdqj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.075, 0.025, 0.985, 0.292] contain?" }, { "from": "gpt", "value": "This region encompasses the first subplot detailing \"Efficiency Product 1\". It comprises light blue bars plotted against labels denoting ten production series, with a ylabel captioned \"Efficiency Product 1\". The bar heights vary, demonstrating differing efficiency values across series." } ], "id_raw": 974 }, { "id": "0Hdweobp4L0puqv4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nCan you provide information about the area [0.075, 0.331, 0.985, 0.597]?" }, { "from": "gpt", "value": "The middle subplot contained in this area relates to \"Efficiency Product 2\", featuring orange bars against a backdrop of production series ranging from 1 to 10. The ylabel is \"Efficiency Product 2\", and similar to other subplots, the heights of the bars illustrate the efficiency scores differentiating each production series." } ], "id_raw": 974 }, { "id": "UQC2KW6EIUEacFyt", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the [0.528, 0.984, 0.528, 0.984] coordinate represent in the chart?" }, { "from": "gpt", "value": "It represents the x-axis label, which is blank." } ], "id_raw": 975 }, { "id": "qFCAZXT0n8ZV8ZWl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the object at [0.347, 0.025, 0.709, 0.055] in the chart?" }, { "from": "gpt", "value": "It is the subtitle of the chart, labeled \"Technological Industry Development Trends\"." } ], "id_raw": 975 }, { "id": "i5PAXarnzs2sKyL3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nWhat labels are represented by the coordinates [0.043, 0.781, 0.061, 0.804] and [0.043, 0.635, 0.061, 0.659]?" }, { "from": "gpt", "value": "They are y-axis labels called \"20\" and \"40\", respectively." } ], "id_raw": 975 }, { "id": "wObsDlQ78WaHkyoM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.195, 0.717, 0.215, 0.935] and [0.882, 0.171, 0.902, 0.935] of the bar graph refer to?" }, { "from": "gpt", "value": "The first coordinates refer to a bar that represents the development level of \"IoT\" in 2015 and the second coordinates refer to a bar that represents the same in 2030." } ], "id_raw": 975 }, { "id": "p2mT2Jkb4tXBqXhR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nIs the trend in {AR}[0.673, 0.644, 0.694, 0.935] higher or lower than the trend in {Quantum Computing}[0.611, 0.426, 0.632, 0.935] in the 2025 projections?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 975 }, { "id": "GdlKqwRgdFrUfOud", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nBetween 2020 and 2030, did the trend in {Mobile Tech}[0.403, 0.426, 0.424, 0.935, 0.861, 0.353, 0.882, 0.935] increase or decrease?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 975 }, { "id": "dUQO6SeydeBNadRH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nIs the 2015 trend in {AI}[0.112, 0.535, 0.133, 0.935] higher or lower than the trend for {Cloud Computing}[0.133, 0.499, 0.153, 0.935]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 975 }, { "id": "x4Hp7GC0pVQXNRSL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nIs {IoT}<{IoT 2025 bar}[0.653, 0.208, 0.673, 0.935]> at a higher or lower level than {VR}<{VR 2025 bar}[0.694, 0.790, 0.714, 0.935]> in 2025 projections?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 975 }, { "id": "4LXK2V2L8UrNhzm1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects inside the area [0.078, 0.074, 0.273, 0.325]." }, { "from": "gpt", "value": "It contains a legend with seven entries. Each entry includes a color swatch and a text label, representing the trends in different technology sectors: AI, Cloud Computing, Quantum Computing, Mobile Technology, Internet of Things (IoT), Augmented Reality (AR), and Virtual Reality (VR)." } ], "id_raw": 975 }, { "id": "5waQjMjU0y5RxizV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents inside the region [0.570, 0.135, 0.591, 0.935]." }, { "from": "gpt", "value": "This region includes the {2025 bar for AI}[0.570, 0.135, 0.591, 0.935], indicating a development level of 110." } ], "id_raw": 975 }, { "id": "16rAZhWSHxylHql4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen from the region [0.361, 0.390, 0.403, 0.935]?" }, { "from": "gpt", "value": "There is a green rectangle representing Cloud Computing trend in 2020." } ], "id_raw": 975 }, { "id": "94fl0qozQXGBXeuy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nWhat are visual elements present in the area located at [0.112, 0.535, 0.133, 0.935]?" }, { "from": "gpt", "value": "This region contains a blue bar representing the trend level of AI in 2015." } ], "id_raw": 975 }, { "id": "e2pZSELDcVXUQ3qw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the y-axis}[0.015, 0.398, 0.029, 0.600]?" }, { "from": "gpt", "value": "Market Share (%)." } ], "id_raw": 976 }, { "id": "cwodFTRe0Y1uZiHN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nWhat category is located at the coordinates [0.737, 0.952, 0.791, 0.975] on the x-axis?" }, { "from": "gpt", "value": "Fortnite." } ], "id_raw": 976 }, { "id": "uo1ebKxk31M4Q1XF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.898, 0.120, 0.926, 0.136] in the sublegend?" }, { "from": "gpt", "value": "The legend color for the year 2018." } ], "id_raw": 976 }, { "id": "9HcLCJky8bcH3xjj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nWhich year does the text representation at [0.937, 0.083, 0.972, 0.106] in the legend refer to?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 976 }, { "id": "t9WnDFegQmlSqRUa", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nLooking at the bars for the game 'WoW', is the market share {in 2016}[0.104, 0.312, 0.126, 0.935] higher or lower than {in 2018}[0.148, 0.416, 0.170, 0.935]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 976 }, { "id": "G568OOBCr43yZQy5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nBased on the coordinates, which game has a higher market share {in 2016}[0.104, 0.312, 0.126, 0.935], [0.250, 0.364, 0.272, 0.935], [0.396, 0.416, 0.418, 0.935], [0.542, 0.883, 0.564, 0.935], [0.688, 0.832, 0.710, 0.935], [0.834, 0.468, 0.855, 0.935]: 'WoW' or 'LoL'?" }, { "from": "gpt", "value": "WoW." } ], "id_raw": 976 }, { "id": "vrR5vTB5rBrVMoUh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nIn {2022}[0.191, 0.572, 0.213, 0.935], [0.337, 0.104, 0.359, 0.935], [0.483, 0.260, 0.505, 0.935], [0.629, 0.468, 0.651, 0.935], [0.775, 0.520, 0.797, 0.935], [0.921, 0.520, 0.943, 0.935], which game has a higher market share, 'LoL' or 'DOTA'?" }, { "from": "gpt", "value": "LoL." } ], "id_raw": 976 }, { "id": "2nbUiSrli1d8QDUk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the markers, which year has a higher market share for the Fortnite game {in 2020}[0.753, 0.520, 0.775, 0.935] or {in 2022}[0.775, 0.520, 0.797, 0.935]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 976 }, { "id": "KVrQcCnmdVcTpgYE", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents located in the region [0.893, 0.074, 0.978, 0.220] of the chart?" }, { "from": "gpt", "value": "This area contains the chart legend, labeled with years 2016, 2018, 2020, 2022 and their corresponding colors." } ], "id_raw": 976 }, { "id": "r76jWQtaXnb6YFmi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the area covering [0.316, 0.952, 0.490, 0.975]?" }, { "from": "gpt", "value": "This area includes the x-axis labels for the categories 'LoL', 'DOTA' and 'PUBG'." } ], "id_raw": 976 }, { "id": "lFWC38fjRfVmuE0G", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within the range [0.876, 0.468, 0.943, 0.935]?" }, { "from": "gpt", "value": "This area includes two bars: one represents Overwatch's market share in 2020 with coordinates [0.899, 0.468, 0.921, 0.935], and the other represents Overwatch's market share in 2022 with coordinates [0.921, 0.520, 0.943, 0.935]." } ], "id_raw": 976 }, { "id": "BkzRf2jvrKhKxeDA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the visuals located in the region [0.635, 0.312, 0.775, 0.935]?" }, { "from": "gpt", "value": "The region covers the bars related to the market share of the game 'PUBG' and 'Fortnite' for the years 2020 and 2022." } ], "id_raw": 976 }, { "id": "V5Mj0K2azoKL7DI9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar representing 'Sales' in the 'Value Creation' category?" }, { "from": "gpt", "value": "Blue. This is represented by this bar with coordinates {Value Creation - Sales bar}[0.733, 0.196, 0.775, 0.923]." } ], "id_raw": 977 }, { "id": "lHmTUkCpqvimHhpW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label of the chart?" }, { "from": "gpt", "value": "'Value'. This can be seen at these coordinates {Y-axis label}[0.021, 0.460, 0.041, 0.537]." } ], "id_raw": 977 }, { "id": "qYR0qhT3zKf5pnIA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the 'Distribution' process in 'Value Erosion'?" }, { "from": "gpt", "value": "The value is 6. The 'Value Erosion' bar for 'Distribution' has coordinates {Value Erosion - Distribution bar}[0.654, 0.850, 0.697, 0.923]." } ], "id_raw": 977 }, { "id": "YveTm8anVjIl0mE7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nIs the Value Creation bar for the 'Manufacturing' process higher than the Value Erosion bar for the same process?" }, { "from": "gpt", "value": "Yes. This can be determined by comparing the height of {Value Creation - Manufacturing bar}[0.491, 0.317, 0.534, 0.923] and {Value Erosion - Manufacturing bar}[0.534, 0.826, 0.576, 0.923]." } ], "id_raw": 977 }, { "id": "v9C6qzZEFtctoCts", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher, 'Value Creation' for 'Product Design' or 'Value Erosion' for 'After Sales'?" }, { "from": "gpt", "value": "'Value Creation' for 'Product Design'. This can be inferred by comparing the height of the bars {Value Creation - Product Design bar}[0.370, 0.377, 0.413, 0.923] and {Value Erosion - After Sales bar}[0.896, 0.886, 0.938, 0.923]." } ], "id_raw": 977 }, { "id": "1TQbkuQ4W6k7oD1O", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nIs the 'Value Creation' bar for the 'Raw Materials' process taller than its corresponding 'Value Erosion' bar?" }, { "from": "gpt", "value": "Yes. It can be demonstrated by comparing the heights of these bars: {Value Creation - Raw Materials bar}[0.250, 0.414, 0.292, 0.923] and {Value Erosion - Raw Materials bar}[0.292, 0.801, 0.334, 0.923]." } ], "id_raw": 977 }, { "id": "1g0ULDlHxlyVPL95", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.098, 0.089, 0.315, 0.181]." }, { "from": "gpt", "value": "This area contains the chart's legend. There are two entries: 'Value Creation', represented by a blue color, with coordinates at {Value Creation Label}[0.162, 0.100, 0.307, 0.128] and 'Value Erosion', represented by a red color, with its text label at {Value Erosion Label}[0.162, 0.142, 0.295, 0.170]." } ], "id_raw": 977 }, { "id": "tNFWoa3gqh0Qz4vc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the region [0.338, 0.942, 0.488, 0.970]?" }, { "from": "gpt", "value": "This region contains one of the x-axis labels: 'Product Design'. Its coordinates are {Product Design Label}[0.338, 0.942, 0.488, 0.970]." } ], "id_raw": 977 }, { "id": "Wen06rYT3xFpZLsk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the area [0.896, 0.886, 0.938, 0.923]?" }, { "from": "gpt", "value": "The area contains the 'Value Erosion' bar for 'After Sales', along with a value of 3. The bar's coordinates are {Value Erosion - After Sales bar}[0.896, 0.886, 0.938, 0.923]." } ], "id_raw": 977 }, { "id": "skzOJMTYOab6WC10", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.596, 0.942, 0.712, 0.970]." }, { "from": "gpt", "value": "This area includes an x-axis label: 'Distribution'. The coordinate of this label is {Distribution Label}[0.596, 0.942, 0.712, 0.970]." } ], "id_raw": 977 }, { "id": "ahdCGGeD0bbCijhi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.174, 0.183, 0.230, 0.211] in the chart?" }, { "from": "gpt", "value": "The object is the text label for the third sequence, {Seq 3}[0.174, 0.183, 0.230, 0.211]." } ], "id_raw": 978 }, { "id": "B1i35rvRLJP2cWW5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar in the category \"KNN\" for \"Seq 4\"?" }, { "from": "gpt", "value": "The coordinate of the bar in the category \"KNN\" for {Seq 4} is [0.484, 0.456, 0.505, 0.923]." } ], "id_raw": 978 }, { "id": "mydZxNbcBIgnfUgg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.021, 0.367, 0.041, 0.630] in the chart?" }, { "from": "gpt", "value": "It is {the y-axis label}[0.021, 0.367, 0.041, 0.630], which is labeled as \"Performance Score\"." } ], "id_raw": 978 }, { "id": "LH3LPvkBVmOICojz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nWhat is in the legend area located at [0.111, 0.089, 0.239, 0.264]?" }, { "from": "gpt", "value": "There are four different text-color label pairs in {the legend}[0.111, 0.089, 0.239, 0.264] representing four sequences: {Seq 1}[0.174, 0.100, 0.231, 0.128], {Seq 2}[0.174, 0.142, 0.230, 0.170], {Seq 3}[0.174, 0.183, 0.230, 0.211], {Seq 4}[0.174, 0.225, 0.230, 0.253], with corresponding color labels mapped." } ], "id_raw": 978 }, { "id": "a8dPVa7De2bcmnBY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the performance score of {Seq 1} [0.140, 0.287, 0.161, 0.923] or {Seq 4}[0.203, 0.329, 0.224, 0.923] for the model \"Perceptron\"?" }, { "from": "gpt", "value": "{Seq 4}." } ], "id_raw": 978 }, { "id": "tah22VSyMtmgBSnq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nAre the performance scores of Seq 1 higher than Seq 2 for all models?" }, { "from": "gpt", "value": "No." } ], "id_raw": 978 }, { "id": "eT9lcRs3TqqY3bzb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nFor the DecisionTree model, which performance score sequence is the lowest?" }, { "from": "gpt", "value": "It\u2019s {Seq 1} with the corresponding bar [0.701, 0.287, 0.722, 0.923]." } ], "id_raw": 978 }, { "id": "8AbIrGolcpxIYz6K", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nBetween {the SVM model}[0.280, 0.371, 0.301, 0.923] and {the GradientBoosting model}[0.841, 0.244, 0.862, 0.923], which one has a higher score for Seq 1?" }, { "from": "gpt", "value": "The GradientBoosting model." } ], "id_raw": 978 }, { "id": "JKkdOXNLgHO5sDCY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.111, 0.089, 0.239, 0.264]." }, { "from": "gpt", "value": "The legend for the sequences is within this region. It contains four pairs of colored boxes and text:\n1. The box for {Seq 1}[0.119, 0.102, 0.158, 0.122] is colored '#98ddca', and the label is {Seq 1 label}[0.174, 0.100, 0.231, 0.128].\n2. The box for {Seq 2}[0.119, 0.144, 0.158, 0.164] is colored '#d5ecc2', and the label is {Seq 2 label}[0.174, 0.142, 0.230, 0.170].\n3. The box for {Seq 3}[0.119, 0.186, 0.158, 0.205] is colored '#ffe156', and the label is {Seq 3 label}[0.174, 0.183, 0.230, 0.211].\n4. The box for {Seq 4}[0.119, 0.228, 0.158, 0.247] is colored '#ffad69', and the label is {Seq 4 label}[0.174, 0.225, 0.230, 0.253]." } ], "id_raw": 978 }, { "id": "qfv0eyRPB1DWGcb9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.140, 0.287, 0.926, 0.923]." }, { "from": "gpt", "value": "This area covers all the performance bars for all sequences of models. There are four bars for each model, each representing a different sequence. Each group of bars representing the same model is aligned along the x-axis at the corresponding tick." } ], "id_raw": 978 }, { "id": "IyxXZLevQ3UqlzR7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.421, 0.244, 0.505, 0.923]?" }, { "from": "gpt", "value": "There are 4 bars within the area:\n1. {Seq 1 Bar for KNN}[0.421, 0.414, 0.442, 0.923];\n2. {Seq 2 Bar for KNN}[0.442, 0.329, 0.463, 0.923];\n3. {Seq 3 Bar for KNN}[0.463, 0.244, 0.484, 0.923];\n4. {Seq 4 Bar for KNN}[0.484, 0.456, 0.505, 0.923]." } ], "id_raw": 978 }, { "id": "UIz6h8Ob7G8KwUmW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.296, 0.030, 0.770, 0.066]?" }, { "from": "gpt", "value": "The area [0.296, 0.030, 0.770, 0.066] contains the chart's subtitle: \"AI Models Performance Over Sequences\"." } ], "id_raw": 978 }, { "id": "CfItYIwfqWHb0FSE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nCan you spot the label of y-axis in the chart from this data?" }, { "from": "gpt", "value": "\"Count and Funds Raised (in million USD).\" Located at { coordinates }[0.021, 0.218, 0.041, 0.779]." } ], "id_raw": 979 }, { "id": "EryVkyyd87i1mkHv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nWhere can we find the information for \"Patents Filed\"?" }, { "from": "gpt", "value": "This is identified in the legend at { coordinates }[187, 0.142, 0.313, 0.170]." } ], "id_raw": 979 }, { "id": "vQ2Ry38m8JwsDu7N", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nWhere is the marker for the year \"2010\" on the x-axis?" }, { "from": "gpt", "value": "The marker is at the specified location { coordinates }[0.534, 0.942, 0.584, 0.970]." } ], "id_raw": 979 }, { "id": "bW64LqRlroW0Ymkw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the blue bar at { coordinates }[0.495, 0.541, 0.520, 0.923] represent?" }, { "from": "gpt", "value": "Startups Founded in the year 2010." } ], "id_raw": 979 }, { "id": "shlzEdlnRuHdi5Kr", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nIs { the bar of \"Startups Founded\" }[0.495, 0.541, 0.520, 0.923] higher than { the bar of \"Investments Received\" }[0.546, 0.371, 0.572, 0.923] in 2010?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 979 }, { "id": "40ykA7bgTJJhIr7z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nAre there more { \"Startups Founded\" }[0.495, 0.541, 0.520, 0.923] or { \"Patents Filed\" }[0.520, 0.448, 0.546, 0.923] during 2010?" }, { "from": "gpt", "value": "Startups Founded." } ], "id_raw": 979 }, { "id": "AxKrf7AzHxoMh4Mm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nIs there more investment or startups in 2015? Given { \"Investments Received\" bar }[0.717, 0.202, 0.743, 0.923] and { \"Startups Founded\" bar }[0.666, 0.414, 0.691, 0.923]." }, { "from": "gpt", "value": "Investments Received." } ], "id_raw": 979 }, { "id": "uEB49U3qsJySMhIM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nIn 2020, are there more { \"Patents Filed\" }[0.862, 0.261, 0.888, 0.923] or { \"Startups Founded\" }[0.837, 0.371, 0.862, 0.923]?" }, { "from": "gpt", "value": "Patents Filed." } ], "id_raw": 979 }, { "id": "3kw6hJtPcinu1YSC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content of this area [0.436, 0.906, 0.512, 0.930]." }, { "from": "gpt", "value": "This area contains the x-axis label for the category \"2010\", located at the specified coordinates [0.534, 0.942, 0.584, 0.970]." } ], "id_raw": 979 }, { "id": "xmUcH1UdlU2KIoZ4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nTell me about the area [0.123, 0.089, 0.413, 0.264]." }, { "from": "gpt", "value": "This area contains the legend of the chart which includes four items:\n1. { \"Startups Founded\" label }[0.187, 0.100, 0.363, 0.128]\n2. { \"Patents Filed\" label }[0.187, 0.142, 0.313, 0.170]\n3. { \"Investments Received\" label }[0.187, 0.183, 0.405, 0.211]\n4. { \"Funds Raised\" label }[0.187, 0.225, 0.318, 0.253]" } ], "id_raw": 979 }, { "id": "QzJKku4TSuiJ0UBQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.324, 0.795, 0.349, 0.923]?" }, { "from": "gpt", "value": "That area presents the number of startups founded in 2005, represented as a green bar at { bar coordinates for Startups Founded in 2005 }[0.324, 0.795, 0.349, 0.923]." } ], "id_raw": 979 }, { "id": "XO8xthT1lU2MuiXq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.923, 0.071, 0.942, 0.908]?" }, { "from": "gpt", "value": "It is the Y-axis, marked with ticks at intervals of 200, and ranging from 0 to 1000. It also has the y-label \"Count and Funds Raised (in million USD)\". It does not have any bars or other plot-specific items." } ], "id_raw": 979 }, { "id": "qAUlUCl0avjsT24M", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.471, 0.029, 0.527]?" }, { "from": "gpt", "value": "Score." } ], "id_raw": 980 }, { "id": "DeaPXHw64UdF0rQN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the legend text}[0.903, 0.071, 0.948, 0.091]?" }, { "from": "gpt", "value": "Rating." } ], "id_raw": 980 }, { "id": "6l5U6Sh90kPcZTpA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nWhat granularity is shown for {the y-axis}[0.035, 0.045, 0.061, 0.065]?" }, { "from": "gpt", "value": "It is divided into increments of 20, with a range from 0 to 100." } ], "id_raw": 980 }, { "id": "dLnvbTSrE5k02vdk", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nIs the {the \u201cRating\u201d bar for Mediterranean cuisine}[0.112, 0.187, 0.148, 0.945] higher than the {\u201cRating\u201d bar for African cuisine}[0.835, 0.454, 0.871, 0.945]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 980 }, { "id": "IYYIfINtDyGnMhga", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nDoes the {\u201cDiversity\u201d bar for Latin America cuisine}[0.727, 0.187, 0.763, 0.945] have a higher value than the {\u201cDiversity\u201d bar for Anglo Saxon cuisine}[0.546, 0.321, 0.582, 0.945]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 980 }, { "id": "icY2twpQKJ6ErVy9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nIs the {\u201cPopularity\u201d bar for Asian cuisine}[0.329, 0.187, 0.365, 0.945] taller than the {\u201cPopularity\u201d bar for Latin America cuisine}[0.690, 0.321, 0.727, 0.945]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 980 }, { "id": "AybaV1RYiu7DNjsq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nWhat do you see in the region [0.858, 0.063, 0.978, 0.159]?" }, { "from": "gpt", "value": "That region contains the chart legend. It has 3 entries: {Rating}[0.903, 0.071, 0.948, 0.091] with a color representation of '#f7b2b2', {Popularity}[0.903, 0.101, 0.972, 0.121] with a color representation of '#b2d8f7' and {Diversity}[0.903, 0.131, 0.964, 0.151] with a color representation of '#b2f7c6'." } ], "id_raw": 980 }, { "id": "dzmZ1YNcJ5bjK8GD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the chart elements within the region [0.293, 0.187, 0.365, 0.945]" }, { "from": "gpt", "value": "Within the area, we have two important elements:\n 1. {The Popularity bar for Asian cuisine}[0.329, 0.187, 0.365, 0.945] which is colored '#b2d8f7'\n 2. {The Rating bar for Asian cuisine}[0.293, 0.232, 0.329, 0.945] which is colored '#f7b2b2'." } ], "id_raw": 980 }, { "id": "QUcbAwsmlIE0jAOd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [0.356, 0.021, 0.700, 0.047]?" }, { "from": "gpt", "value": "The area contains the chart's subtitle, which is \"Cuisine and Gastronomy Culture Analysis\"." } ], "id_raw": 980 }, { "id": "cPQ0hEBLmopXqtvr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the bar for \"AI Research\" in 2020}[0.930, 0.165, 0.943, 0.945]?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 982 }, { "id": "2Uyx7H9o6YVWHQoh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted at {the position}[0.119, 0.959, 0.154, 0.979] on the x-axis?" }, { "from": "gpt", "value": "The year 2011." } ], "id_raw": 982 }, { "id": "leVRnIDycpTirjBP", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nWhat category does the text at {coordinates}[0.113, 0.191, 0.195, 0.211] refer to in the legend?" }, { "from": "gpt", "value": "AI Research." } ], "id_raw": 982 }, { "id": "QV3lXxBmpY119ROw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Data Analysis\" in 2014}[0.401, 0.343, 0.414, 0.945] taller than {the bar for \"Information Security\" in the same year}[0.388, 0.655, 0.401, 0.945]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 982 }, { "id": "L5sqjqErzsX1oaA7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the purple bar for \"AI Research\"}[0.930, 0.165, 0.943, 0.945] represent a higher value than {the blue bar for \"Data Analysis\"}[0.917, 0.076, 0.930, 0.945] in 2020?" }, { "from": "gpt", "value": "No." } ], "id_raw": 982 }, { "id": "GTmAPpdjeK7RIcRM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nAre the values indicated by {the red bars}[0.104, 0.610, 0.117, 0.945] consistently increasing over the years compared to {the green bars}[0.130, 0.722, 0.143, 0.945]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 982 }, { "id": "GWhQ6hruAKkvJm8w", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhich type of energy is represented by the coordinates [0.141, 0.626, 0.205, 0.923]?" }, { "from": "gpt", "value": "Solar energy." } ], "id_raw": 983 }, { "id": "bLsg4bV7ZhwPABCn", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the bar representing nuclear energy?" }, { "from": "gpt", "value": "[0.874, 0.414, 0.939, 0.923]" } ], "id_raw": 983 }, { "id": "8ktf1XFulHPbVWl9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis scale} at [0.331, 0.942, 0.381, 0.970]?" }, { "from": "gpt", "value": "Wind." } ], "id_raw": 983 }, { "id": "PH7eAzcPjAInbKzF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage for {Geothermal}[0.508, 0.583, 0.572, 0.923]?" }, { "from": "gpt", "value": "40%." } ], "id_raw": 983 }, { "id": "ukSJttmb27NuMseB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhich is more, the {Solar energy resource allocation}[0.141, 0.626, 0.205, 0.923] or the {Wind energy resource allocation}[0.324, 0.711, 0.388, 0.923]?" }, { "from": "gpt", "value": "Solar energy resource allocation." } ], "id_raw": 983 }, { "id": "iCahpgqLmhX8JNTd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nAre the resource allocations for {Nuclear}[0.874, 0.414, 0.939, 0.923] and {Wind}[0.324, 0.711, 0.388, 0.923] the same?" }, { "from": "gpt", "value": "No." } ], "id_raw": 983 }, { "id": "4pbUNCYFkbYV2VrG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhich one has a greater resource allocation, the {Hydro} energy at [0.691, 0.753, 0.755, 0.923] or the {Geothermal} energy at [0.508, 0.583, 0.572, 0.923]?" }, { "from": "gpt", "value": "Geothermal energy." } ], "id_raw": 983 }, { "id": "Wz1ZfyV2epyQY1U0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.101, 0.075, 0.979, 0.923]." }, { "from": "gpt", "value": "This area represents the main chart area. It includes five sets of color bars, each representing a type of energy: Solar, Wind, Geothermal, Hydro, and Nuclear. Their respective coordinates are: {Solar}[0.141, 0.626, 0.205, 0.923], {Wind}[0.324, 0.711, 0.388, 0.923], {Geothermal}[0.508, 0.583, 0.572, 0.923], {Hydro}[0.691, 0.753, 0.755, 0.923], and {Nuclear}[0.874, 0.414, 0.939, 0.923]." } ], "id_raw": 983 }, { "id": "QFVQHPd0mnI8aLK3", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.702, 0.089, 0.969, 0.139]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It consists of a colored square indicating the color used for resource allocation, labeled 'Resource Allocation'[0.766, 0.100, 0.961, 0.128]." } ], "id_raw": 983 }, { "id": "ZmPGTuEUQy0ZQ8Js", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.874, 0.414, 0.939, 0.923]?" }, { "from": "gpt", "value": "There is only one object within these coordinates:\n1. A bar that represents the resource allocation for Nuclear energy within 0.00 -> 60.00%." } ], "id_raw": 983 }, { "id": "ZMCtjKZGDCsKTQcu", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nWhere is the label of the first bar representing the \"Fluctuating Decline Series\" located?" }, { "from": "gpt", "value": "{\"Fluctuating Decline Series\"}[0.112, 0.383, 0.174, 0.945]" } ], "id_raw": 984 }, { "id": "Qso8IBExDKLOGy5x", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nWhere is the Y-axis label located within the chart?" }, { "from": "gpt", "value": "{\"Y-axis\"}[0.015, 0.449, 0.029, 0.549]" } ], "id_raw": 984 }, { "id": "ZH0N6VWRnmxmphTh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "Education Resource Distribution and Utilization located at {\"title\"}[0.332, 0.021, 0.723, 0.047]." } ], "id_raw": 984 }, { "id": "wA5CxKPaP94rz16R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nIs the height of {the first bar in Continuous Rise Series}[0.174, 0.633, 0.236, 0.945] higher than {the first bar in Fluctuating Decline Series}[0.112, 0.383, 0.174, 0.945]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 984 }, { "id": "xugV50BN1DcgAWh3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, where does {the bar representing \"Tutorials\"}[0.145, 0.959, 0.203, 0.979] locate in relation to {the bar representing \"Videos\"}[0.328, 0.959, 0.374, 0.979]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 984 }, { "id": "gryrnLfLWIqEZcVT", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nIs the {label of \"Fluctuating Decline Series\"}[0.122, 0.071, 0.303, 0.091] above {label of \"Continuous Rise Series\"}[0.122, 0.101, 0.281, 0.121]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 984 }, { "id": "4nOefsNoGqnNElgA", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements within the area [0.143, 0.633, 0.882, 0.945]." }, { "from": "gpt", "value": "Within the specified box, there are all five sets of data for each series. This includes five bars for both the \"Fluctuating Decline Series\" and the \"Continuous Rise Series\", corresponding to the categories of \"Tutorials\", \"Videos\", \"Exercises\", \"RealCases\", and \"SampleCodes\"." } ], "id_raw": 984 }, { "id": "HgE9WCqPQJFqQdXS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.078, 0.063, 0.309, 0.129]." }, { "from": "gpt", "value": "This box contains the legend of the chart. The legend includes two entries. The first one is {\"Fluctuating Decline Series\"}[0.122, 0.071, 0.303, 0.091] with a green color indicator [0.083, 0.073, 0.111, 0.087], and the second one is {\"Continuous Rise Series\"}[0.122, 0.101, 0.281, 0.121] with a blue color indicator [0.083, 0.103, 0.111, 0.117]." } ], "id_raw": 984 }, { "id": "fFmIZL25jwRN3mRI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.145, 0.959, 0.929, 0.979]." }, { "from": "gpt", "value": "This area encompasses labels of the categories on the x-axis. From left to right, they are: {\"Tutorials\"} [0.145, 0.959, 0.203, 0.979], {\"Videos\"} [0.328, 0.959, 0.374, 0.979], {\"Exercises\"} [0.495, 0.959, 0.560, 0.979], {\"RealCases\"} [0.669, 0.959, 0.740, 0.979], and {\"SampleCodes\"} [0.834, 0.959, 0.929, 0.979]." } ], "id_raw": 984 }, { "id": "NJk11UzVbmCEEy5K", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nIn the given chart, what is the object at the coordinates [0.162, 0.100, 0.280, 0.128]?" }, { "from": "gpt", "value": "The text label \"Return Rate\"" } ], "id_raw": 985 }, { "id": "uxIH3HNeMV5nm844", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nWhat color does the figure in the coordinates [0.106, 0.102, 0.146, 0.122] shows?" }, { "from": "gpt", "value": "Green color(#c0e4b8) representing \"Return Rate\"." } ], "id_raw": 985 }, { "id": "qKSKzmIOrwc79wm7", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nPoint out the object associated with the coordinates [0.050, 0.064, 0.075, 0.092]." }, { "from": "gpt", "value": "It is the y-axis label \"40\"." } ], "id_raw": 985 }, { "id": "6npOd16xeY2BFv7t", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nIs the Blue Chip return rate bar at [0.129, 0.711, 0.179, 0.923] shorter or taller than the Growth Stock return rate bar at [0.271, 0.499, 0.321, 0.923]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 985 }, { "id": "kETH7kDYt1V62meU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nIn the chart, does the Income Stock risk level bar at [0.604, 0.838, 0.654, 0.923] have a higher or lower position compared to the Value Stock risk level bar at [0.463, 0.668, 0.512, 0.923]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 985 }, { "id": "YAkapuLQ4a0l2pYh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nComparing the coordinates, is Speculative Stock's return rate at [0.697, 0.541, 0.746, 0.923] higher or lower than Cyclical Stock's risk level at [0.888, 0.499, 0.938, 0.923]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 985 }, { "id": "kGtmloNTwaVf1aYI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nCan you illustrate the content within [0.098, 0.089, 0.287, 0.181]?" }, { "from": "gpt", "value": "It is a legend area. The first element is a text label that says \"Return Rate\", located at [0.162, 0.100, 0.280, 0.128], next to a green color square [0.106, 0.102, 0.146, 0.122]. The second element is another label that says \"Risk Level\" located at [0.162, 0.142, 0.261, 0.170], next to a blue color square [0.106, 0.144, 0.146, 0.164]." } ], "id_raw": 985 }, { "id": "D2EKYaErPxUco3xf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nWhat's inside the area [0.254, 0.942, 0.387, 0.970]?" }, { "from": "gpt", "value": "A label showing \"Growth Stock\" on the x-axis." } ], "id_raw": 985 }, { "id": "u3FcIpIx9lq2FKjH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the rectangle defined by the coordinates [0.413, 0.605, 0.512, 0.923]." }, { "from": "gpt", "value": "There are two stacked bars. The first bar showing the return rate of the Value Stock is green, with coordinates [0.413, 0.605, 0.463, 0.923]. The second bar showing the risk level Value Stock is blue, with coordinates [0.463, 0.668, 0.512, 0.923]." } ], "id_raw": 985 }, { "id": "PEDGHxCYqQiipi7B", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the text inside the box with coordinates [0.122, 0.161, 0.245, 0.181]?" }, { "from": "gpt", "value": "It represents the label of the legend, \"Private Donations\"." } ], "id_raw": 986 }, { "id": "l7Pr6OwZkGOXbFFN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the value being represented by {the bar of Book Sale for Private Donations}[0.771, 0.588, 0.800, 0.944]?" }, { "from": "gpt", "value": "The value represented is 40, for the book sale activity funded by Private Donations." } ], "id_raw": 986 }, { "id": "5XIuFmw3kS2dKiK1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the role of the object located at the coordinates [0.083, 0.073, 0.111, 0.087]?" }, { "from": "gpt", "value": "It's a color label in the legend, representing the funding type \"Local Giving\"." } ], "id_raw": 986 }, { "id": "xFPZgaezUurAsSje", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nWhat event is noted by the x-axis coordinates of [0.542, 0.958, 0.658, 0.979]?" }, { "from": "gpt", "value": "The event represented is \"Coastal_Cleanup\"." } ], "id_raw": 986 }, { "id": "tgppAXPQNh4ZJuQN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nWhat's the relationship between the height of the {bar for Book Sale from Foreign Aid}[0.714, 0.410, 0.743, 0.944] and the height of {the bar for Church Opening from Foreign Aid}[0.284, 0.454, 0.313, 0.944]?" }, { "from": "gpt", "value": "The bar for Book Sale from Foreign Aid is shorter." } ], "id_raw": 986 }, { "id": "O7MCorPc160y1MS1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for Local Giving at Coastal_Cleanup}[0.542, 0.855, 0.571, 0.944] is higher than {the bar for Local Giving at Canister_Collection}[0.112, 0.499, 0.141, 0.944]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 986 }, { "id": "V3KTYYF0EpcyE9n7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nAmong the bars representing Public Funding, which bar is the highest: {Public Funding for Canister_Collection}[0.170, 0.633, 0.198, 0.944] or {Public Funding for Coastal_Cleanup}[0.599, 0.766, 0.628, 0.944]?" }, { "from": "gpt", "value": "Canister_Collection." } ], "id_raw": 986 }, { "id": "cjM6lbgy8BhJPOJD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nWhich bar shows higher value, the {bar of Local Giving for Social Service}[0.399, 0.187, 0.428, 0.944] or the {bar of Foreign Aid for Social Service}[0.428, 0.142, 0.456, 0.944]?" }, { "from": "gpt", "value": "Foreign Aid." } ], "id_raw": 986 }, { "id": "v30yAyn9vlzfjzss", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the area denoted by [0.406, 0.958, 0.506, 0.979]." }, { "from": "gpt", "value": "This area contains the label for the category \"Social_Service\" on the horizontal axis." } ], "id_raw": 986 }, { "id": "VG2itCcbjRq7xvVV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe what's within the coordinates [0.083, 0.103, 0.111, 0.117]?" }, { "from": "gpt", "value": "These coordinates enclose a color label in the legend, which is a blue square that signifies the Foreign Aid funding type." } ], "id_raw": 986 }, { "id": "2Lg8lW0hUBfuVnPh", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the area between [0.071, 0.053, 0.985, 0.944] represent?" }, { "from": "gpt", "value": "This is the main chart area, where bars showing the amounts of funding in different funding methods for different charity activities are plotted." } ], "id_raw": 986 }, { "id": "nP98xOHOA4IXPrLK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nPlease provide description of region [0.078, 0.063, 0.251, 0.189]." }, { "from": "gpt", "value": "This region contains the legend of the chart which explains the color coding for different types of funding: 'Local Giving' in light green color coded at [0.083, 0.073, 0.111, 0.087], 'Foreign Aid' in light blue color at [0.083, 0.103, 0.111, 0.117], 'Public Funding' in light red color at [0.083, 0.133, 0.111, 0.147] and 'Private Donations' in light purple color at [0.083, 0.163, 0.111, 0.177]." } ], "id_raw": 986 }, { "id": "8JsjaIgRyaj9ha00", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.035, 0.148, 0.052, 0.168] in the chart?" }, { "from": "gpt", "value": "The Y-axis label, indicating a trend value of 70." } ], "id_raw": 987 }, { "id": "PAIA7xyMFncHJxZS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color square of the first item in the legend?" }, { "from": "gpt", "value": "{Color}[0.074, 0.073, 0.102, 0.087]." } ], "id_raw": 987 }, { "id": "k7Y8RS1V6NPvGKyz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.296, 0.774, 0.310, 0.930]?" }, { "from": "gpt", "value": "\"Skill Acquisition\"." } ], "id_raw": 987 }, { "id": "7qRhi5CGHOHVLUTT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nWhat bar or category is shown in [0.398, 0.346, 0.450, 0.761]?" }, { "from": "gpt", "value": "{2019 Networking bar} [0.398, 0.346, 0.450, 0.761]." } ], "id_raw": 987 }, { "id": "eeFfeB3rUGijGyKW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 2020 bar for \"Skill Acquisition\"}[0.303, 0.484, 0.354, 0.761] higher or lower than {the 2019 bar for \"Skill Acquisition\"}[0.251, 0.458, 0.303, 0.761]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 987 }, { "id": "MYx5E3raLkHyk52X", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 2019 \"Entrepreneurship\" bar}[0.546, 0.303, 0.597, 0.761] to the left or right of {the 2020 \"Entrepreneurship\" bar}[0.597, 0.260, 0.649, 0.761]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 987 }, { "id": "GsKWMo1WeDQRvlPB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nBetween {\"2019 Professional Training\"}[0.104, 0.242, 0.155, 0.761] and {\"2020 Professional Training\"}[0.155, 0.208, 0.207, 0.761], which year does the higher bar represent?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 987 }, { "id": "xzaKZSkjCa3syOEY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nIs {the \"Mentoring\" category in 2019}[0.693, 0.182, 0.744, 0.761] exceeded by {the \"Mentoring\" category in 2020} [0.744, 0.156, 0.796, 0.761]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 987 }, { "id": "TZfIEfHyAVvOYTkb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.069, 0.063, 0.154, 0.129]." }, { "from": "gpt", "value": "This region contains the legend of the chart. It consists of two entries; The first one is a color label located at [0.074, 0.073, 0.102, 0.087] which is blue, and is accompanied by the text \"2019\" found at [0.113, 0.071, 0.149, 0.091]. The second one is another color label located at [0.074, 0.103, 0.102, 0.117] which is red, and its corresponding text \"2020\" is located at [0.113, 0.101, 0.148, 0.121]." } ], "id_raw": 987 }, { "id": "McJhDSZVSLI11LZM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.104, 0.242, 0.155, 0.761]." }, { "from": "gpt", "value": "This area primarily contains the blue bar for \"Professional Training\" for the year 2019. The bar reaches a height indicative of a value of 60, as per the y-axis." } ], "id_raw": 987 }, { "id": "8vDROK1CwCQV1zll", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.155, 0.208, 0.207, 0.761]?" }, { "from": "gpt", "value": "There is a single object in these coordinates: a bar. It is the red bar for the \"Professional Training\" category in 2020. The height of the bar represents a value of 64 according to the y-axis." } ], "id_raw": 987 }, { "id": "iIBlGw5vqRGFHZZR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.343, 0.021, 0.704, 0.047]?" }, { "from": "gpt", "value": "In this area, there is the subtitle of the chart: \"Career and Workplace Development Trends\". It situates centrally indicating its importance as the subtitle of the chart." } ], "id_raw": 987 }, { "id": "qjmyXQgjF0N2A6ot", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label in the chart with coordinates [0.021, 0.457, 0.041, 0.540]?" }, { "from": "gpt", "value": "The Y-axis label is \"Count\"." } ], "id_raw": 988 }, { "id": "G4ZvYfgKXQzeeKq6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of object with coordinates [0.725, 0.609, 0.729, 0.923]?" }, { "from": "gpt", "value": "The object label is \"Robotics\"." } ], "id_raw": 988 }, { "id": "8fqQ8PKtGOSoYPX6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nWhat is at [0.162, 0.225, 0.355, 0.253] in the bar chart?" }, { "from": "gpt", "value": "It's the label of a legend element, and it's called \"Abandoned Patents\"." } ], "id_raw": 988 }, { "id": "IfqMr2hINlO4x5S7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nIs {the bar indicating AR's Innovation}[0.273, 0.725, 0.295, 0.923] taller than {the bar indicating Blockchain's Innovation}[0.418, 0.698, 0.440, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 988 }, { "id": "CqgEDyto9YwAEHVY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nIs {the VR's Patents Infrienged}[0.151, 0.609, 0.172, 0.923] lower than {the IoT's Patents Infrienged}[0.873, 0.115, 0.895, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 988 }, { "id": "sNYkJq1vTGFqmvIz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nComparing {Robotics Failed Patents}[0.750, 0.788, 0.772, 0.923] and {AI Failed Patents}[0.606, 0.815, 0.627, 0.923], which one is higher?" }, { "from": "gpt", "value": "Robotics Failed Patents." } ], "id_raw": 988 }, { "id": "kwEJ3IUMDtdYolMF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.098, 0.089, 0.363, 0.264]." }, { "from": "gpt", "value": "This is the legend area of the chart. It contains color-coded labels for \"Innovation\" {color}[0.106, 0.102, 0.146, 0.122], \"Patents Infrienged\"{color}[0.106, 0.144, 0.146, 0.164], \"Failed Patents\"{color}[0.106, 0.186, 0.146, 0.205], and \"Abandoned Patents\"{color}[0.106, 0.228, 0.146, 0.247]." } ], "id_raw": 988 }, { "id": "PsE5IteMCPzFqWya", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe the items contained in the horizontal labels area [0.169, 0.942, 0.921, 0.970]?" }, { "from": "gpt", "value": "This area represents the x-axis category labels, and the labels include \"VR\"[0.169, 0.942, 0.197, 0.970], \"AR\"[0.314, 0.942, 0.341, 0.970], \"Blockchain\"[0.418, 0.942, 0.526, 0.970], \"AI\"[0.607, 0.942, 0.626, 0.970], \"Robotics\"[0.719, 0.942, 0.803, 0.970], and \"IoT\"[0.891, 0.942, 0.921, 0.970]." } ], "id_raw": 988 }, { "id": "lY7SmVp56y40tuHQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.129, 0.564, 0.216, 0.923]?" }, { "from": "gpt", "value": "This area contains two bars:\n1. One indicating \"Innovation\" in VR technology, colored green, coordinate range [0.129, 0.788, 0.151, 0.923].\n2. Other four bars are of different techs, \n - Patents Infrienged colored blue, coordinate range [0.151, 0.609, 0.172, 0.923],\n - Failed Patents colored red, coordinate range [0.172, 0.878, 0.194, 0.923], \n - Abandoned Patents colored purple, coordinate range [0.194, 0.833, 0.216, 0.923]" } ], "id_raw": 988 }, { "id": "gYTetlhxhTElbdyv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nWhat is at {the coordinate}[0.019, 0.419, 0.036, 0.579] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Efficiency (%)." } ], "id_raw": 989 }, { "id": "agu26fTLSw6UZDtC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nWhere is the second bar for \"Unstructured Data\" located?" }, { "from": "gpt", "value": "{the second bar for \"Unstructured Data\"}[0.268, 0.618, 0.296, 0.935]." } ], "id_raw": 989 }, { "id": "xe98QVdNHj2DjvnV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label} at [0.548, 0.952, 0.633, 0.975]?" }, { "from": "gpt", "value": "AWS-Glue." } ], "id_raw": 989 }, { "id": "iPiuuEEl9efiHsEF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.104, 0.120, 0.139, 0.136]?" }, { "from": "gpt", "value": "{The handle for the \"Semi-Structured Data\"}[0.104, 0.120, 0.139, 0.136] in the legend." } ], "id_raw": 989 }, { "id": "xPGVogp3Ira9dHqd", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Cloudera under Unstructured Data}[0.128, 0.459, 0.156, 0.935] taller than {the bar for Hortonworks under the same category}[0.268, 0.618, 0.296, 0.935]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 989 }, { "id": "qfdskHV1nzf5yyUb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for AWS-Glue under Structured Data}[0.605, 0.364, 0.633, 0.935] to the left or right of {the bar for the same company under Semi-Structured Data}[0.577, 0.181, 0.605, 0.935]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 989 }, { "id": "09DzOqbxvdmJvjzP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nAmong the three bars representing {Google Cloud Dataprep}[[0.829, 0.261, 0.857, 0.935], [0.857, 0.142, 0.885, 0.935], [0.885, 0.324, 0.913, 0.935]], which one is the tallest?" }, { "from": "gpt", "value": "{The second bar for Google Cloud Dataprep}[0.857, 0.142, 0.885, 0.935]." } ], "id_raw": 989 }, { "id": "hsmumN46ttI0XEqj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the first bar for MapR}[0.408, 0.499, 0.436, 0.935] or {the first bar for AWS-Glue}[0.548, 0.300, 0.577, 0.935]?" }, { "from": "gpt", "value": "AWS-Glue." } ], "id_raw": 989 }, { "id": "SAB6R3mg6S8Hvvhz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.097, 0.074, 0.348, 0.185]." }, { "from": "gpt", "value": "It includes the chart legend that contains three pairs of color-text labels. The first pair's color label is located at [0.104, 0.085, 0.139, 0.101], which is blue, and the text label is located at [0.153, 0.083, 0.313, 0.106], which represents Unstructured Data. The second pair's color label is located at [0.104, 0.120, 0.139, 0.136], which is orange, and the text label is located at [0.153, 0.118, 0.341, 0.141], representing Semi-Structured Data. The third pair's color label is located at [0.104, 0.155, 0.139, 0.171], which is green, and the text represents Structured Data, located at [0.153, 0.153, 0.292, 0.176]." } ], "id_raw": 989 }, { "id": "5PNOHHkzo5Rbh1Wc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.128, 0.459, 0.156, 0.935]." }, { "from": "gpt", "value": "This area contains the bar for Cloudera under the category of Unstructured Data. The bar's height represents an efficiency of 60 percent." } ], "id_raw": 989 }, { "id": "FjRf7qLtm7uneJZj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.268, 0.618, 0.296, 0.935]?" }, { "from": "gpt", "value": "There is 1 object, the orange bar representing \"Hortonworks\" under \"Unstructured Data. Its height represents an efficiency of 40 percent." } ], "id_raw": 989 }, { "id": "jDO7kiWdOFqAser4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.363, 0.025, 0.678, 0.055]?" }, { "from": "gpt", "value": "The subtitle of the chart: \"Big Data Integration Overview\"." } ], "id_raw": 989 }, { "id": "P1fObv8XGLoQT6He", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the Y-axis in the chart?" }, { "from": "gpt", "value": "The label is \"Change in Value\". It is found at coordinates {Y-axis label}[0.015, 0.403, 0.029, 0.564]." } ], "id_raw": 992 }, { "id": "LbAMqUrF55nnY0yM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the cryptocurrency labeled \"Ripple\" in the chart?" }, { "from": "gpt", "value": "The color of \"Ripple\" is {Ripple's handle color}#ff006e." } ], "id_raw": 992 }, { "id": "lJwGGfVrVphzAym8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the range for the Y-axis values of the bar element for \"Ethereum\" at position '1'?" }, { "from": "gpt", "value": "It's from 0.00 to 0.60, located at {Ethereum's bar at '1'}[0.441, 0.216, 0.467, 0.674]." } ], "id_raw": 992 }, { "id": "glfKa9mm8E7Fdhm9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nDoes the bar for \"Bitcoin\" at position '2' has higher value than the bar for \"Tether\" at the same position?" }, { "from": "gpt", "value": "Yes, because the {Bitcoin's bar at '2'}[0.667, 0.636, 0.692, 0.674]" } ], "id_raw": 992 }, { "id": "Ptg7ZcVMg59a3iSX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nAre the values for \"Stellar\" increasing or decreasing over the positions '0' to '3'?" }, { "from": "gpt", "value": "{Stellar}[0.241, 0.674, 0.266, 0.674], [0.492, 0.659, 0.517, 0.674], [0.742, 0.651, 0.767, 0.674], [0.993, 0.643, 1.018, 0.674] values are increasing over the positions '0' to '3'." } ], "id_raw": 992 }, { "id": "JNzU9aUdpbwrEliM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nIs the value of \"Ethereum\" at position '0' equal to that of \"Tether\" at the same position?" }, { "from": "gpt", "value": "Yes, because the {Ethereum's bar at '0'}[0.191, 0.674, 0.216, 0.674] has the same value as the {Tether's bar at '0'}[0.266, 0.674, 0.291, 0.674]." } ], "id_raw": 992 }, { "id": "ahFtmx3fJkyvEjYV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the legend." }, { "from": "gpt", "value": "The legend is located within {Legend}[0.085, 0.031, 0.202, 0.187]. It contains five entries for different cryptocurrencies - Bitcoin, {Bitcoin's handle}[0.090, 0.041, 0.118, 0.055] Ethereum, {Ethereum's handle}[0.090, 0.071, 0.118, 0.085] Ripple, {Ripple's handle}[0.090, 0.101, 0.118, 0.115] Stellar, {Stellar's handle}[0.090, 0.131, 0.118, 0.145] and Tether, {Tether's handle}[0.090, 0.161, 0.118, 0.175]. The legend provides a color reference for the individual bars representing each cryptocurrency in the graph." } ], "id_raw": 992 }, { "id": "IpX1fQeZwK1TLd5F", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area between [0.166, 0.643, 1.043, 0.712]." }, { "from": "gpt", "value": "This area houses the fourth bar group of the chart showing the data for the position '3' of all five cryptocurrencies: Bitcoin, {Bitcoin's bar}[0.918, 0.651, 0.943, 0.674] Ethereum, {Ethereum's bar}[0.943, 0.063, 0.968, 0.674] Ripple, {Ripple's bar}[0.968, 0.674, 0.993, 0.903] Stellar, {Stellar's bar}[0.993, 0.643, 1.018, 0.674] and Tether, {Tether's bar}[1.018, 0.674, 1.043, 0.712]." } ], "id_raw": 992 }, { "id": "QsHwiDjBEmL6COHb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.166, 0.674, 0.293, 0.674] contain?" }, { "from": "gpt", "value": "This area contains the set of bars for the first position '0' of the bar chart. It includes only the base values for \"Bitcoin\", {Bitcoin's bar at '0'}[0.166, 0.674, 0.191, 0.674] \"Ethereum\", {Ethereum's bar at '0'}[0.191, 0.674, 0.216, 0.674] \"Ripple\", {Ripple's bar at '0'}[0.216, 0.674, 0.241, 0.674] \"Stellar\", {Stellar's bar at '0'}[0.241, 0.674, 0.266, 0.674] and \"Tether\", {Tether's bar at '0'}[0.266, 0.674, 0.291, 0.674] which are all zeros, therefore there are no vertical bars visible on the plot in this area." } ], "id_raw": 992 }, { "id": "LIu23XY4FibtMwfC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinate [0.019, 0.340, 0.036, 0.620]?" }, { "from": "gpt", "value": "It is the Y-axis label, which is \"User Engagement Score\"." } ], "id_raw": 993 }, { "id": "tTXDAv6mHqz0T5xV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the third score on the X-axis?" }, { "from": "gpt", "value": "The location is [0.625, 0.952, 0.691, 0.975]." } ], "id_raw": 993 }, { "id": "34qW5F72SwcOm9tO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nWhat social media platform does the color #3b5998 in the legend [0.086, 0.037, 0.237, 0.218] represent?" }, { "from": "gpt", "value": "It represents {Facebook}[0.142, 0.046, 0.223, 0.069]." } ], "id_raw": 993 }, { "id": "5tO9YMWsaEk0iHMM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the X-axis labels mentioned in the coordinates [0.044, 0.952, 0.981, 0.975]?" }, { "from": "gpt", "value": "They represent the score labels from score_1 to score_4 [[0.044, 0.952, 0.111, 0.975], [0.335, 0.952, 0.401, 0.975], [0.625, 0.952, 0.691, 0.975], [0.915, 0.952, 0.981, 0.975]]." } ], "id_raw": 993 }, { "id": "BzrTJJBaU1CXROEu", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, does Facebook [0.730, 0.152, 0.759, 0.935] have a higher user engagement score than Twitter [0.759, 0.068, 0.788, 0.935] for score_4?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 993 }, { "id": "rir8zsEAG5TiH4b8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nWhich platform has a higher user engagement score for score_1, Instagram [-0.082, 0.852, -0.053, 0.935] or Snapchat [-0.024, 0.862, 0.005, 0.935]?" }, { "from": "gpt", "value": "Snapchat." } ], "id_raw": 993 }, { "id": "AV2H9BD2Kx6SBmEg", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nDoes Instagram|r>[0.208, 0.727, 0.237, 0.935] have a higher score_2 than LinkedIn [0.237, 0.779, 0.266, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 993 }, { "id": "B6d4niziLOcNu6i5", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nBetween Facebook [0.150, 0.779, 0.179, 0.935] and LinkedIn [0.237, 0.779, 0.266, 0.935], which has a higher score for score_2?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 993 }, { "id": "MKQTZbK5odS8ROKR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in the rectangle [0.043, 0.091, 0.065, 0.114]?" }, { "from": "gpt", "value": "It's a label on the Y-axis that represents the number \"90\"." } ], "id_raw": 993 }, { "id": "wmIN7vcBd7WUw2y8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.150, 0.779, 0.179, 0.935]?" }, { "from": "gpt", "value": "It contains a bar representing Facebook's score_2, with a user engagement score of 15." } ], "id_raw": 993 }, { "id": "gjU7mtpmyuwWVXGF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area given by the coordinates [-0.140, 0.883, 0.759, 0.935]." }, { "from": "gpt", "value": "It contains the bars for Facebook representing scores from score_1 to score_4 with user engagement scores 5, 15, 20, and 75, respectively." } ], "id_raw": 993 }, { "id": "VuO0FX7W6fw4qy5C", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the legend area [0.086, 0.037, 0.237, 0.218]." }, { "from": "gpt", "value": "It contains five social media platforms: Facebook [0.142, 0.046, 0.223, 0.069], Twitter [0.142, 0.081, 0.200, 0.104], Instagram [0.142, 0.116, 0.230, 0.139], LinkedIn [0.142, 0.151, 0.214, 0.174], and Snapchat [0.142, 0.185, 0.223, 0.209], each with their respective color labels." } ], "id_raw": 993 }, { "id": "Zm5xy6ZZfdnSWi3F", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.174, 0.055, 0.218, 0.083]?" }, { "from": "gpt", "value": "It is the text label of {the Coal Energy resource}[0.174, 0.055, 0.218, 0.083] in the legend." } ], "id_raw": 994 }, { "id": "VeDgiKk7gWTX03WW", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the Nuclear resource}[0.174, 0.181, 0.251, 0.209] correspond to in the chart?" }, { "from": "gpt", "value": "It corresponds to the color blue shown at the set of coordinates [0.119, 0.183, 0.158, 0.203]." } ], "id_raw": 994 }, { "id": "koFs1Qn0qowfjc9k", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the year on the x-axis that corresponds to the coordinate [0.644, 0.942, 0.694, 0.970]?" }, { "from": "gpt", "value": "The year is 2040." } ], "id_raw": 994 }, { "id": "4VrMZTwUzAhNRotA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nWhat energy resource's bar is located at [0.797, 0.073, 0.826, 0.923]?" }, { "from": "gpt", "value": "It's the bar for {Renewable Energy Resources}[0.797, 0.073, 0.826, 0.923]." } ], "id_raw": 994 }, { "id": "lyyHit9obxjwiPDs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar for {Renewable energy resources}[0.174, 0.139, 0.283, 0.167]?" }, { "from": "gpt", "value": "The color is beige [0.119, 0.141, 0.158, 0.161]." } ], "id_raw": 994 }, { "id": "UfwVbU7NVDNytBl1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nIs the energy consumption of {Natural Gas in 2030}[0.200, 0.328, 0.229, 0.923] higher than that of {Coal in 2050}[0.740, 0.710, 0.769, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 994 }, { "id": "kKxl5lL7KnZCef0G", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nIs the energy consumption of {Oil in 2020}[0.001, 0.540, 0.030, 0.923] less than that of {Nuclear in 2050}[0.826, 0.519, 0.854, 0.923]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 994 }, { "id": "rhyD8VyYSotYcovQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nFrom highest to lowest, how would you rank the energy sources for 2030: {Oil}[0.286, 0.561, 0.314, 0.923], {Natural Gas}[0.200, 0.328, 0.229, 0.923], and {Coal}[0.172, 0.540, 0.200, 0.923]?" }, { "from": "gpt", "value": "{Natural Gas}[0.200, 0.328, 0.229, 0.923], {Coal}[0.172, 0.540, 0.200, 0.923], {Oil}[0.286, 0.561, 0.314, 0.923]." } ], "id_raw": 994 }, { "id": "lGq0Fb70ZhgJOOVm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.111, 0.044, 0.300, 0.262]." }, { "from": "gpt", "value": "This region contains the legend with five entries. The first label is {Coal}[0.174, 0.055, 0.218, 0.083], represented with a dark blue color[0.119, 0.058, 0.158, 0.077]. The second label is {Natural Gas}[0.174, 0.097, 0.292, 0.125], represented with a lighter blue color[0.119, 0.099, 0.158, 0.119]. The third label is {Renewable}[0.174, 0.139, 0.283, 0.167], represented with a beige color[0.119, 0.141, 0.158, 0.161]. The fourth label is {Nuclear}[0.174, 0.181, 0.251, 0.209], represented with another shade of blue[0.119, 0.183, 0.158, 0.203]. The fifth and final label is {Oil}[0.174, 0.223, 0.201, 0.251], represented with a deep navy color[0.119, 0.225, 0.158, 0.245]." } ], "id_raw": 994 }, { "id": "wqjgE4I9pvQQCbjX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area [0.456, 0.604, 0.485, 0.923]?" }, { "from": "gpt", "value": "This region includes one bar representing the consumption of the {Coal Energy resource}[0.456, 0.604, 0.485, 0.923] in 2040." } ], "id_raw": 994 }, { "id": "3wWNc1Lk5ynTbMjQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nWhat elements are contained in the area [0.075, 0.912, 0.087, 0.940]?" }, { "from": "gpt", "value": "This area contains the y-axis label for the value [0.075, 0.912, 0.087, 0.940]." } ], "id_raw": 994 }, { "id": "yOGUquRlTtzwmJ7w", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the area [0.740, 0.710, 0.769, 0.923]." }, { "from": "gpt", "value": "This area includes the bar for the energy resource {Coal}[0.740, 0.710, 0.769, 0.923] in the year 2050. The bar represent the consumption level." } ], "id_raw": 994 }, { "id": "eZmaJyG9N1hZ3ZlL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object in the chart with the coordinates [0.015, 0.457, 0.029, 0.514]?" }, { "from": "gpt", "value": "The main object is the label on the y-axis, indicating \"Values\"." } ], "id_raw": 995 }, { "id": "6GjtvFMashsuOKsr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the first label text in the legend?" }, { "from": "gpt", "value": "The coordinates are [0.455, 0.035, 0.552, 0.053]." } ], "id_raw": 995 }, { "id": "YLs8dHwyrihLCddZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar with the coordinates [0.003, 0.891, 0.035, 0.952]?" }, { "from": "gpt", "value": "This bar represents the \"Inflation rates\" for the year 2020." } ], "id_raw": 995 }, { "id": "SOHAkFE5oBgLQN0A", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nWhat indicators or categories are shown in the legend with the coordinates [0.411, 0.028, 0.636, 0.164]?" }, { "from": "gpt", "value": "The indicators or categories shown are \"Inflation rates\", \"Poverty rates\", \"Population increase\", \"Carbon emission\", and \"Renewable energy usage\"." } ], "id_raw": 995 }, { "id": "bVjGSZoa5BSpeoF3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Carbon emission\" for the year 2040, with coordinates [0.743, 0.228, 0.776, 0.952], taller or shorter than the bar representing \"Population increase\" for the same year, with coordinates [0.711, 0.941, 0.743, 0.952]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 995 }, { "id": "bvOdICIUe9mBFC2P", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nDo the \"Inflation rates\" bars have a general upward or downward trend from 2020 to 2040?" }, { "from": "gpt", "value": "Downward." } ], "id_raw": 995 }, { "id": "enXm01Qv8f0Ka8He", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nIs the \"Renewable energy usage\" bar for 2030 at [0.561, 0.236, 0.593, 0.952] higher or lower than the \"Population increase\" bar for the same year at [0.497, 0.938, 0.529, 0.952]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 995 }, { "id": "dt5mZC6q9LJWFd76", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nAmong \"Poverty rates\" and \"Carbon emission\" in 2030, which one is bigger?" }, { "from": "gpt", "value": "\"Carbon emission\"." } ], "id_raw": 995 }, { "id": "U35ygzhL8rlOHi93", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.411, 0.028, 0.636, 0.164]." }, { "from": "gpt", "value": "This region contains a legend with different categories and their corresponding bar colors such as: {Inflation rates} and its color at [0.417, 0.037, 0.444, 0.049], {Poverty rates} and its color at [0.417, 0.063, 0.444, 0.075], {Population increase} and its color at [0.417, 0.089, 0.444, 0.101], {Carbon emission} and its color at [0.417, 0.115, 0.444, 0.127], and {Renewable energy usage} and its color at [0.417, 0.141, 0.444, 0.154]." } ], "id_raw": 995 }, { "id": "RBzG48i02NZAG4oP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.132, 0.489, 0.164, 0.952]?" }, { "from": "gpt", "value": "This region contains one {bar}[0.132, 0.489, 0.164, 0.952] for \"Renewable energy usage\" in 2020." } ], "id_raw": 995 }, { "id": "wnSSf7TPHB5SZIpU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the content in the area [0.003, 0.891, 0.035, 0.952] represent?" }, { "from": "gpt", "value": "This area contains a bar that represents the inflation rates in 2020." } ], "id_raw": 995 }, { "id": "F2bBnEky7T6mihWp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.043, 0.813, 0.052, 0.830]." }, { "from": "gpt", "value": "The area contains a {label}[0.043, 0.813, 0.052, 0.830] for y-axis that indicates the value of 5." } ], "id_raw": 995 }, { "id": "3b2ubQOMzpJ4anut", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.015, 0.450, 0.029, 0.516]?" }, { "from": "gpt", "value": "Scores." } ], "id_raw": 996 }, { "id": "YgbeZTFIAhhYbGrU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first item in the legend?" }, { "from": "gpt", "value": "[0.083, 0.041, 0.111, 0.055]." } ], "id_raw": 996 }, { "id": "Abs1pyNYvrypMrch", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nWhat item does {the legend} indicate shown in [0.122, 0.039, 0.252, 0.059]?" }, { "from": "gpt", "value": "Digital Diagnostics." } ], "id_raw": 996 }, { "id": "FML4RHG9b9Nn8LG4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the {text} shown at the coordinates in [0.315, 0.959, 0.350, 0.979]?" }, { "from": "gpt", "value": "2011." } ], "id_raw": 996 }, { "id": "5pzB6U3ddOzPvk5f", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for 2009, Digital Diagnostics}[0.183, 0.698, 0.211, 0.945] taller than the {bar for 2008, Telemedicine}[0.140, 0.681, 0.169, 0.945]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 996 }, { "id": "C2ijGWNAMPIoGtRS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for 2013, Telemedicine}[0.493, 0.171, 0.521, 0.945] to the left or right of {the bar for 2013, Digital Diagnostics}[0.464, 0.567, 0.493, 0.945]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 996 }, { "id": "qUWo0XGEeCyfZNwo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nConsidering the bars representing Telemedicine and Digital Diagnostics in 2015, which one is taller?" }, { "from": "gpt", "value": "Telemedicine." } ], "id_raw": 996 }, { "id": "MRP6UyYLyKGX9iAl", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nAre the {Digital Diagnostics bars} generally taller than the {Telemedicine bars}?" }, { "from": "gpt", "value": "No." } ], "id_raw": 996 }, { "id": "1om6saQ3WNbEOnMl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.078, 0.031, 0.258, 0.097]." }, { "from": "gpt", "value": "It contains the legend of the chart with two items. {The color label}[0.083, 0.041, 0.111, 0.055] is blue, and the text label is {Digital Diagnostics}[0.122, 0.039, 0.252, 0.059]; The other color label [0.083, 0.071, 0.111, 0.085] is orange, and the corresponding text label is {Telemedicine}[0.122, 0.069, 0.213, 0.089]." } ], "id_raw": 996 }, { "id": "OeoK9hzpYxYJXCfm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.112, 0.804, 0.140, 0.945]." }, { "from": "gpt", "value": "This area contains a blue bar, representing the score of Digital Diagnostics in 2008, with a range of 0.00 -> 16.00." } ], "id_raw": 996 }, { "id": "IuEQxgUFzjJKym4G", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located within [0.560, 0.959, 0.595, 0.979]?" }, { "from": "gpt", "value": "The year 2014 on the x-axis is located in this area." } ], "id_raw": 996 }, { "id": "u0OgClOsAsIRXShR", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.5, 0.593, 0.6, 0.945]?" }, { "from": "gpt", "value": "Two bars are present. A blue bar, representing the Digital Diagnostics score for 2012 which is 40, with coordinates [0.394, 0.593, 0.422, 0.945]. And another narrower orange bar, representing the Telemedicine score for 2013 which is 88 and its coordinates are [0.493, 0.171, 0.521, 0.945]." } ], "id_raw": 996 }, { "id": "Yo6j8hvwvQrSc7SV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of chart used?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 997 }, { "id": "O2jUNyEp7PH3H38t", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis represent in the chart?" }, { "from": "gpt", "value": "Age groups in years." } ], "id_raw": 997 }, { "id": "YOL1WcmtMUVcRpUU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label of Y-axis}[0.019, 0.442, 0.036, 0.519] represent?" }, { "from": "gpt", "value": "Scores." } ], "id_raw": 997 }, { "id": "jaWH0379sbGLRXmE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the group item labelled {Running} in the legends[0.440, 0.037, 0.613, 0.218] represent?" }, { "from": "gpt", "value": "Running bars." } ], "id_raw": 997 }, { "id": "VH3o5rpagr711P7o", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nIs the {Yoga bar}[0.447, 0.083, 0.482, 0.099] taller than the {Swimming bar}[0.447, 0.118, 0.482, 0.134]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 997 }, { "id": "Ap0prEGzCtKQEOcS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nIs the score of {Boxing}[0.831, 0.155, 0.871, 0.935] for age 60 higher than the score of {Weightlifting}[0.791, 0.242, 0.831, 0.935]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 997 }, { "id": "QvFgIV3AdTWH7iA0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nIs the height of the {Running bar}[0.672, 0.328, 0.712, 0.935] at age 60 more or less than half of the {Boxing bar}[0.831, 0.155, 0.871, 0.935]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 997 }, { "id": "Qmyby4Q5Qby1MNab", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nIs the {Boxing bar}[0.447, 0.188, 0.482, 0.204] in the legend to the right or left of the {Swimming bar}[0.447, 0.118, 0.482, 0.134]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 997 }, { "id": "er21IjShn84bErnU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.036, 0.415, 0.076, 0.935]?" }, { "from": "gpt", "value": "The \"Boxing\" score for age 0 is visible which is 0.60 and the bar is from 0.415 to 0.935 on the y-axis." } ], "id_raw": 997 }, { "id": "Tt3LZeoEa6LJthL5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.830, 0.150, 0.870, 0.935]." }, { "from": "gpt", "value": "This area contains the bar for \"Boxing\" for age 60, with a score of 0.90 increasing from 0.155 to 0.935 on the y-axis." } ], "id_raw": 997 }, { "id": "Pn3iohwDGAsPvSf0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the legend [0.440, 0.037, 0.613, 0.218]." }, { "from": "gpt", "value": "It contains labels for each group of bars. Representing scores in various categories like Running, Yoga, Swimming, Weightlifting and Boxing for different ages." } ], "id_raw": 997 }, { "id": "WOqniH7QIPbBhVZB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nIf possible, describe the visual elements in the region [0.142, 0.762, 0.182, 0.935]." }, { "from": "gpt", "value": "This region depicts the \"Running\" score at age 20, with its value at 0.20 and its bar reaching from 0.762 to 0.935 on the y-axis." } ], "id_raw": 997 }, { "id": "8pwgUa2nPgsbCXo3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.021, 0.430, 0.041, 0.522]?" }, { "from": "gpt", "value": "It is the y-axis label, titled \"Scores\"." } ], "id_raw": 998 }, { "id": "ATtLcQ3WO06GFivT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {BioNTech}[0.168, 0.097, 0.259, 0.125] according to the legend?" }, { "from": "gpt", "value": "The color is '#6d8abb'." } ], "id_raw": 998 }, { "id": "fWhyXz5k9Rgvdrlk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at the fourth x-axis coordinate [0.840, 0.942, 0.865, 0.970]?" }, { "from": "gpt", "value": "The value is '60'." } ], "id_raw": 998 }, { "id": "aN0LWdqYsdkK6Wjl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the bar at coordinates [0.284, 0.536, 0.309, 0.923]?" }, { "from": "gpt", "value": "It's the Pfizer score at '20' represented by the '#495c80' colored bar." } ], "id_raw": 998 }, { "id": "exfyKZz5qRH0sqCh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nIs the {Pfizer's bar} at '20' [0.284, 0.536, 0.309, 0.923] taller than the {Moderna's bar} at '20' [0.335, 0.459, 0.360, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 998 }, { "id": "K9mgRc2OF7EZYCb6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nIs {BioNTech's bar} at '40' [0.562, 0.536, 0.587, 0.923] to the left or right of {Moderna's bar} at '40' [0.587, 0.613, 0.612, 0.923]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 998 }, { "id": "YhPmUAutvkS1Qy6D", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nBetween {BioNTech}<[0.168, 0.097, 0.259, 0.125],[0.113, 0.099, 0.152, 0.119]> and {Johnson & Johnson}<[0.168, 0.223, 0.351, 0.251],[0.113, 0.225, 0.152, 0.245]>, which company scored a higher value at '60' [0.814, 0.073, 0.840, 0.923],[0.890, 0.343, 0.915, 0.923]?" }, { "from": "gpt", "value": "BioNTech." } ], "id_raw": 998 }, { "id": "6Ds7DEc5xSPn9eq4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.105, 0.044, 0.359, 0.262]." }, { "from": "gpt", "value": "This area includes the legend of the chart, showing the color and name representation for each Biotech firm. Pfizer is represented with color '#495c80', BioNTech with color '#6d8abb', Moderna with color '#eadcd1', Novavax with color '#5899b8', and Johnson & Johnson with color '#2b5a75'." } ], "id_raw": 998 }, { "id": "Jqwrvpa5U3wrogdH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region [0.032, 0.923, 0.133, 0.923]?" }, { "from": "gpt", "value": "It includes the bars for each company representing their scores at '0'." } ], "id_raw": 998 }, { "id": "hwymuHRySeBehg1k", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region [0.789, 0.227, 0.915, 0.923]?" }, { "from": "gpt", "value": "This region contains four bars representing scores of four companies (Pfizer, BioNTech, Moderna, and Johnson & Johnson) at '60'." } ], "id_raw": 998 }, { "id": "AZkPWaYmV0dbi8VO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe what can be seen in the area [0.049, -0.016, 0.081, 0.012]?" }, { "from": "gpt", "value": "This area contains the number '1.2', which is one of the y-tick labels." } ], "id_raw": 998 }, { "id": "lNjIseqEyPRRXtwF", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in this bar chart?" }, { "from": "gpt", "value": "The label is \"Scores\" and its coordinates are {the y-axis label}[0.021, 0.430, 0.041, 0.522]." } ], "id_raw": 999 }, { "id": "4HYTZ25E6P39PIOp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is represented by the color blue in the legend?" }, { "from": "gpt", "value": "The color blue in the legend represents the \"Community Development\", and its coordinates are {Community Development color label in the legend}[0.651, 0.058, 0.690, 0.077]." } ], "id_raw": 999 }, { "id": "Ls6fjmyDeGXwJXPQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nWhat text can be found at the coordinates [0.706, 0.055, 0.961, 0.083]?" }, { "from": "gpt", "value": "The text at the given coordinates refers to the legend label \"Community Development\"." } ], "id_raw": 999 }, { "id": "fI092rbM6JsIEznY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted on the x-axis at the coordinates [0.261, 0.942, 0.274, 0.970]?" }, { "from": "gpt", "value": "At these coordinates is an {x-axis label}[0.261, 0.942, 0.274, 0.970] displaying the value \"0\"." } ], "id_raw": 999 }, { "id": "vB9QiMaEB5GM1fKR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nIs the bar for \"Community Development\" at the x-axis value of \"60\" higher or lower than the bar for \"Urban Planning\" at that same value?" }, { "from": "gpt", "value": "The bar for \"Community Development\" is lower." } ], "id_raw": 999 }, { "id": "sMiVrlvNIg8mq4Rw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for community development at 20}[0.375, 0.333, 0.454, 0.923] taller than {the bar for urban planning at 20}[0.454, 0.246, 0.533, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 999 }, { "id": "mWgk7u7oFNFYL72A", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, is \"Community Development\" generally performing better or worse than \"Urban Planning\" over the time period observed?" }, { "from": "gpt", "value": "\"Community Development\" is generally performing worse." } ], "id_raw": 999 }, { "id": "nBm8WQC7HxH6XgK0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nWhich category has the highest score at the 40 mark: {Community Development}[0.601, 0.610, 0.680, 0.923] or {Urban Planning}[0.680, 0.489, 0.759, 0.923]?" }, { "from": "gpt", "value": "Urban Planning." } ], "id_raw": 999 }, { "id": "qIaN2PcsPAajEyJl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within the box [0.643, 0.044, 0.969, 0.136]?" }, { "from": "gpt", "value": "This area contains the legend of the chart. It illustrates two categories: \"Community Development\" with a blue color represented by {the box}[0.651, 0.058, 0.690, 0.077] and \"Urban Planning\" with a light blue color represented by {the other box}[0.651, 0.099, 0.690, 0.119]." } ], "id_raw": 999 }, { "id": "0TQYmct8JikRGRwk", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nWhat do I notice in the area [0.375, 0.333, 0.454, 0.923]?" }, { "from": "gpt", "value": "Within this area, there is a bar representing \"Community Development\" at the 20 mark on the x-axis. Its height corresponds to a score of 3.4." } ], "id_raw": 999 }, { "id": "bpH80C6qTTfkgWiB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nWhat is found within the coordinates [0.601, 0.610, 0.680, 0.923]?" }, { "from": "gpt", "value": "Within these coordinates is a bar representing the score of \"Community Development\" at the 40 mark on the x-axis. Its height corresponds to a score of 1.8." } ], "id_raw": 999 }, { "id": "csoplponOkgDIvcQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are present in the region [0.149, 0.125, 0.984, 0.923]?" }, { "from": "gpt", "value": "The region encompasses all the bars representing scores of \"Community Development\" and \"Urban Planning\" at different x-axis marks, which are 0, 20, 40, and 60. The heights of the bars reflect their respective scores." } ], "id_raw": 999 }, { "id": "gX12DHvyAqEaJnaL", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.021, 0.430, 0.041, 0.522] in the chart?" }, { "from": "gpt", "value": "Y-axis label, called Scores." } ], "id_raw": 1000 }, { "id": "8Y49xb91Xzndfqc8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nWhere is the legend for \"WhatsApp\" located?" }, { "from": "gpt", "value": "{the legend}[0.168, 0.223, 0.270, 0.251]." } ], "id_raw": 1000 }, { "id": "zb4JBwuJXLje1LGI", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at coordinates [0.167, 0.734, 0.196, 0.923]?" }, { "from": "gpt", "value": "20." } ], "id_raw": 1000 }, { "id": "IiOj24rs2vBMaQZN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nWhich social media platforms are indicated in the legend inside [0.105, 0.044, 0.277, 0.262]?" }, { "from": "gpt", "value": "{Facebook}[0.168, 0.055, 0.262, 0.083], {Instagram}[0.168, 0.097, 0.269, 0.125], {Twitter}[0.168, 0.139, 0.235, 0.167], {Snapchat}[0.168, 0.181, 0.262, 0.209], and {WhatsApp}[0.168, 0.223, 0.270, 0.251]." } ], "id_raw": 1000 }, { "id": "ElZscH3ppktWWaAW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Facebook at 60}[0.748, 0.261, 0.777, 0.923] taller than {the bar for WhatsApp at 60}[0.864, 0.073, 0.893, 0.923]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1000 }, { "id": "dLHgpLFIxmfh27I1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nAre {the bars for Instagram}[[0.196, 0.545, 0.225, 0.923], [0.487, 0.639, 0.516, 0.923], [0.777, 0.261, 0.806, 0.923]] generally taller than {the bars for Snapchat}[[0.254, 0.828, 0.283, 0.923], [0.545, 0.734, 0.574, 0.923], [0.835, 0.545, 0.864, 0.923]]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1000 }, { "id": "JGB67t8YstqOKPGb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 40 score bar for Twitter}[0.516, 0.167, 0.545, 0.923] higher than {the 40 score bar for WhatsApp}[0.574, 0.261, 0.603, 0.923]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1000 }, { "id": "Zsox49PsBm6dgT7v", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nBetween {the 60 score bar for Facebook}[0.748, 0.261, 0.777, 0.923] and {the 60 score bar for Instagram}[0.777, 0.261, 0.806, 0.923], which one is taller?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 1000 }, { "id": "pBaaGecviFmjlupe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.105, 0.044, 0.277, 0.262]." }, { "from": "gpt", "value": "The content is a legend that contains symbols and names of five different social media platforms, {Facebook} with coordinates [0.168, 0.055, 0.262, 0.083], {Instagram} with coordinates [0.168, 0.097, 0.269, 0.125], {Twitter} with coordinates [0.168, 0.139, 0.235, 0.167], {Snapchat} with coordinates [0.168, 0.181, 0.262, 0.209], and {WhatsApp} with coordinates [0.168, 0.223, 0.270, 0.251]." } ], "id_raw": 1000 }, { "id": "0pz9biOZ0w7LJhO4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.021, 0.430, 0.041, 0.522]." }, { "from": "gpt", "value": "This area contains the Y-axis label, named {Scores}[0.021, 0.430, 0.041, 0.522]." } ], "id_raw": 1000 }, { "id": "iBe1GrykcOtzt7iI", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.167, 0.734, 0.196, 0.923]?" }, { "from": "gpt", "value": "There's a bar that represents the 20 score for {Facebook}[0.167, 0.734, 0.196, 0.923]." } ], "id_raw": 1000 }, { "id": "TcDeUl44cMZ9rVXF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [-0.123, 0.923, -0.094, 0.923]?" }, { "from": "gpt", "value": "There is a bar representing a score of 0 for {Facebook} at location [-0.123, 0.923, -0.094, 0.923], which indicates that Facebook had a score of 0 in this measurement." } ], "id_raw": 1000 }, { "id": "Po4LA5HeK8eyXYyp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the labeling on the y-axis?" }, { "from": "gpt", "value": "The label is called 'Scores', found at {coordinates}[0.052, 0.430, 0.072, 0.522]." } ], "id_raw": 1001 }, { "id": "ye5yqJZY8VcWKTaq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nWhere is the legend located relative to the main chart?" }, { "from": "gpt", "value": "The legend is situated towards the lower left part of the main chart with its location at {coordinates}[0.136, 0.044, 0.343, 0.136]." } ], "id_raw": 1001 }, { "id": "mmwhMRulfTTVWydJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar with coordinates [0.820, 0.356, 0.845, 0.923] represent and what is its value?" }, { "from": "gpt", "value": "The bar represents the score of 'iPhone 12' for the 'Outliers' category with a value range of 0.00 to 0.80." } ], "id_raw": 1001 }, { "id": "ZSOTfuohWGajOO8c", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nComparing the scores of 'Samsung S20' and 'iPhone 12' in the 'Sudden increase' category, which one is higher?" }, { "from": "gpt", "value": "'Samsung S20' has a higher score." } ], "id_raw": 1001 }, { "id": "Kaim8FPfOGlhJLxV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing 'Samsung S20' in the 'Outliers' category [0.796, 0.073, 0.820, 0.923] taller or shorter than the same category bar representing 'iPhone 12' [0.820, 0.356, 0.845, 0.923]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 1001 }, { "id": "8wC8t1wrqSsuB4OE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nComparing the overall range of scores achieved by 'Samsung S20' and 'iPhone 12', which product has a wider range of scores?" }, { "from": "gpt", "value": "'Samsung S20' has a wider range of scores." } ], "id_raw": 1001 }, { "id": "TmxgggH05Vlx2upz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the coordinates [0.308, 0.498, 0.333, 0.923]." }, { "from": "gpt", "value": "The content within these coordinates corresponds to a bar in the 'Sudden increase' category for the product 'Samsung S20'. The bar represents a score range of 0.00 to 0.60." } ], "id_raw": 1001 }, { "id": "9FqMGFwmaPkdTs7R", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.136, 0.044, 0.343, 0.136]." }, { "from": "gpt", "value": "This region houses the chart legend. It comprises two items: 'Samsung S20', located at {coordinates}[0.199, 0.055, 0.335, 0.083] and displayed in color '#6d8abb', & 'iPhone 12', located at {coordinates}[0.199, 0.097, 0.297, 0.125] and displayed in color '#495c80'." } ], "id_raw": 1001 }, { "id": "avCqERA4jBZThLBJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nProvide a description of the area [0.065, 0.781, 0.089, 0.923]." }, { "from": "gpt", "value": "This area represents a bar for 'Samsung S20' in the 'Continuous decrease' category. The value for this category for 'Samsung S20' ranges from 0.00 to 0.20." } ], "id_raw": 1001 }, { "id": "VW8OcT1vfbQyvjqT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.338, 0.029, 0.502]?" }, { "from": "gpt", "value": "It's the y-axis label titled \"Parameter Value\"." } ], "id_raw": 1002 }, { "id": "uORgBAtb9tcPzFdX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the information at coordinates [0.749, 0.833, 0.839, 0.962]?" }, { "from": "gpt", "value": "It's the X-axis label for \"R&D Investment\"." } ], "id_raw": 1002 }, { "id": "fIzmmLGrVpncRdoh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first bar representing Region 1 in the chart?" }, { "from": "gpt", "value": "The first bar representing Region 1 has coordinates [0.071, 0.650, 0.133, 0.819]." } ], "id_raw": 1002 }, { "id": "Tp7cfYYtpIWfg5Gq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located in [0.873, 0.031, 0.978, 0.097] region?" }, { "from": "gpt", "value": "{The legend}[0.867, 0.031, 0.978, 0.097] is located in that region." } ], "id_raw": 1002 }, { "id": "2SXpk0JNoPBhINTj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for Income Equality from Region 2}[0.133, 0.228, 0.194, 0.819] higher than the {bar for Income Equality from Region 1}[0.071, 0.650, 0.133, 0.819]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1002 }, { "id": "2YSJtql2JOsUAp2P", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Healthcare Quality from Region 1}[0.247, 0.144, 0.309, 0.819] located to the right of {the bar for Income Equality from Region 2}[0.133, 0.228, 0.194, 0.819]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1002 }, { "id": "5HzXA19eX1WJVpE1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the bar for Climate Policy from Region 1}[0.424, 0.566, 0.486, 0.819] or {the bar for Climate Policy from Region 2}[0.486, 0.228, 0.548, 0.819]?" }, { "from": "gpt", "value": "Region 2." } ], "id_raw": 1002 }, { "id": "bSArxIr07mrjrsoN", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for Education Budget from Region 2}[0.663, 0.144, 0.724, 0.819] have a higher value than {the bar for R&D Investment from Region 1}[0.777, 0.735, 0.839, 0.819]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1002 }, { "id": "LZSAKPi5FmcGc8PB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nWhat content is in [0.071, 0.650, 0.133, 0.819]?" }, { "from": "gpt", "value": "This region contains the bar for \"Income Equality\" from Region 1, with a value of 0.2." } ], "id_raw": 1002 }, { "id": "g8nqlAQL5QIAnwOi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.867, 0.031, 0.978, 0.097]." }, { "from": "gpt", "value": "In this region, there is the legend for the chart. Within it, there are two color-text labels representing the two regions: Region 1, represented by the color blue at the coordinates [0.873, 0.041, 0.901, 0.055], and Region 2, represented by the color darker blue at the coordinates [0.873, 0.071, 0.901, 0.085]." } ], "id_raw": 1002 }, { "id": "jOE9GklG8hp2ghWd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.247, 0.144, 0.309, 0.819]?" }, { "from": "gpt", "value": "In this region, the bar for \"Healthcare Quality\" associated with Region 1 is present, with a value of 0.8." } ], "id_raw": 1002 }, { "id": "KaIHQNZNuXt9YWiJ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.424, 0.566, 0.486, 0.819]." }, { "from": "gpt", "value": "This area contains the bar for \"Climate Policy\" from Region 1, representing a value of 0.3." } ], "id_raw": 1002 }, { "id": "CBPMd6WpHHZyHuWT", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the coordinate [0.019, 0.349, 0.036, 0.611] refer to in the chart?" }, { "from": "gpt", "value": "It corresponds to the label on the Y-axis, which denotes 'Population (In Millions)'." } ], "id_raw": 1003 }, { "id": "jTPKoH1uP74ZvcOD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nWhat content does the coordinate [0.316, 0.952, 0.360, 0.975] mark?" }, { "from": "gpt", "value": "It indicates the X-axis label '2016'." } ], "id_raw": 1003 }, { "id": "dKeIsqQEXPsbJaeb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nWhat is within the bounding box [0.905, 0.046, 0.941, 0.069]?" }, { "from": "gpt", "value": "It's the label 'Asia' in the legend." } ], "id_raw": 1003 }, { "id": "cSIkAlg9lMHeMsMk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [-0.006, 0.321, 0.083, 0.935] in the bar chart?" }, { "from": "gpt", "value": "The bar chart element representing the Asia region for the year 2012." } ], "id_raw": 1003 }, { "id": "dZdu0UkXFL5hIuTi", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nIn the legend, is the label 'Asia'<{Asia label}[0.905, 0.046, 0.941, 0.069]> above or below the label 'Europe'<{Europe label}[0.905, 0.081, 0.966, 0.104]>?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1003 }, { "id": "nETC8VctIPp3dpBY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nIs the {2012 bar of Europe}[0.083, 0.068, 0.172, 0.935] taller than the {2012 bar of Asia}[-0.006, 0.321, 0.083, 0.935] in the chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1003 }, { "id": "JpfkOrXWf4Go8Zst", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nIs the {2016 bar of Asia}[0.249, 0.249, 0.338, 0.935] located to the left or right of the {2016 bar of Europe}[0.338, 0.104, 0.427, 0.935]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 1003 }, { "id": "0925ISrXEZv6nOok", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nComparing the {2024 bar of Europe}[0.847, 0.155, 0.937, 0.935] to the {2024 bar of Asia}[0.758, 0.104, 0.847, 0.935], which one is taller?" }, { "from": "gpt", "value": "The bar of Europe." } ], "id_raw": 1003 }, { "id": "9879ys615QBNEXGm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content at the coordinates [0.849, 0.037, 0.973, 0.113]?" }, { "from": "gpt", "value": "This region includes the legend of the chart. It contains two entries: 'Asia' with the color code at [0.856, 0.048, 0.891, 0.064] and 'Europe' with the color code at [0.856, 0.083, 0.891, 0.099]." } ], "id_raw": 1003 }, { "id": "7iX5sFhObXOzkAjd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the region [0.503, 0.177, 0.593, 0.935]?" }, { "from": "gpt", "value": "There are two bars in this region. The first one is {the 2020 bar of Asia}[0.503, 0.177, 0.593, 0.935] and the second is {the 2020 bar of Europe}[0.593, 0.126, 0.682, 0.935]." } ], "id_raw": 1003 }, { "id": "bK6ezYucBEbXuD6e", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object lying within [0.826, 0.952, 0.869, 0.975]?" }, { "from": "gpt", "value": "The object is the '2024' label on the X-axis." } ], "id_raw": 1003 }, { "id": "IJlV3KgiaRz1ULjM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the coordinates [0.758, 0.104, 0.847, 0.935]?" }, { "from": "gpt", "value": "Here, we have {the 2024 bar for Asia}[0.758, 0.104, 0.847, 0.935], representing the predicted population for Asia in the year 2024." } ], "id_raw": 1003 }, { "id": "CwzQ1MG9kU4j108y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nWhat's located at the coordinates [0.811, 0.055, 0.914, 0.083]?" }, { "from": "gpt", "value": "It's the label for the \"Initial Risk\" bar in the legend." } ], "id_raw": 1004 }, { "id": "aHeZ6gNgIAeHfWe0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nWhat is in the spot [0.050, 0.345, 0.081, 0.373]?" }, { "from": "gpt", "value": "It is a Y-axis label of \"0.4\"." } ], "id_raw": 1004 }, { "id": "cIhcRO1Qkq7OS66E", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nWhat is tagged at the location [0.164, 0.073, 0.248, 0.923]?" }, { "from": "gpt", "value": "It is the \"Initial Risk\" bar representing the category of \"High Risk\"." } ], "id_raw": 1004 }, { "id": "ZjVr5UrlTBge5ffD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Initial Risk for Low Risk\"}[0.723, 0.781, 0.807, 0.923] higher or lower than {the bar representing \"Residual Risk for Low Risk\"}[0.807, 0.498, 0.891, 0.923]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1004 }, { "id": "VIYdDkT8vyjroflx", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nWhich is taller: {the bar for \"Initial Risk in the High Risk category\"}[0.164, 0.073, 0.248, 0.923] or {the bar depicting \"Residual Risk in the Medium Risk category\"}[0.528, 0.214, 0.611, 0.923]?" }, { "from": "gpt", "value": "The bar for \"Initial Risk in the High Risk category\"." } ], "id_raw": 1004 }, { "id": "50JU4GJwHVtaPHWV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Residual Risk bar for the High Risk category}[0.248, 0.639, 0.332, 0.923] lower or higher than the {Initial Risk bar for the Medium Risk category}[0.444, 0.639, 0.528, 0.923]?" }, { "from": "gpt", "value": "The same height." } ], "id_raw": 1004 }, { "id": "j1Uk1uOTHucwHcV9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the legend located in [0.748, 0.044, 0.950, 0.136]?" }, { "from": "gpt", "value": "There are two entries in the legend. The \"Initial Risk\" label is a dark blue color at coordinates [0.756, 0.058, 0.796, 0.077] and has a label located at [0.811, 0.055, 0.914, 0.083]. The \"Residual Risk\" label is a light blue color at [0.756, 0.099, 0.796, 0.119], with its text located at [0.811, 0.097, 0.942, 0.125]." } ], "id_raw": 1004 }, { "id": "Sb1D4Omoucu7RIYl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region ([0.164, 0.073, 0.332, 0.923])." }, { "from": "gpt", "value": "This region contains two bars: the \"Initial Risk for the High Risk category\" bar, located at [0.164, 0.073, 0.248, 0.923], and the \"Residual Risk for the High Risk category\" bar, located at [0.248, 0.639, 0.332, 0.923]." } ], "id_raw": 1004 }, { "id": "H6PlJaMQqkRLgZmz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nCan you give some insight into the area [0.444, 0.214, 0.611, 0.923]?" }, { "from": "gpt", "value": "This area features two bars: the \"Initial Risk\" bar for the \"Medium Risk\" category and the \"Residual Risk\" bar for the \"Medium Risk\" category. Their coordinates are [0.444, 0.639, 0.528, 0.923], and [0.528, 0.214, 0.611, 0.923] respectively." } ], "id_raw": 1004 }, { "id": "p1htHSycxOt1DBTN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.159, 0.814, 0.733, 0.887]?" }, { "from": "gpt", "value": "It is a bar representing the growth percentage of {Electronics}[0.159, 0.814, 0.733, 0.887] market in Emerging Markets." } ], "id_raw": 1005 }, { "id": "A3ayda1YCxf7O0JY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinates [0.394, 0.019, 0.750, 0.041] represent?" }, { "from": "gpt", "value": "It is the subtitle of the chart, labeled {Emerging Markets and Consumer Behavior}[0.394, 0.019, 0.750, 0.041]." } ], "id_raw": 1005 }, { "id": "ts1gQF8ljNrAyYz5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the value represented by the bar at [0.159, 0.087, 0.449, 0.159]?" }, { "from": "gpt", "value": "This bar represents a 12.3% growth for the {Toys}[0.159, 0.087, 0.449, 0.159] market in Emerging Markets." } ], "id_raw": 1005 }, { "id": "tEHoWasaooHEFT0R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nBetween the two market categories {Beverages}[0.159, 0.451, 0.570, 0.523] and {Automobile}[0.159, 0.269, 0.527, 0.341], which one has a higher growth percentage?" }, { "from": "gpt", "value": "Beverages." } ], "id_raw": 1005 }, { "id": "9LLUsdfvXvNnG4LR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing the {Pharmaceuticals}[0.159, 0.632, 0.435, 0.705] market taller or shorter compared to the {Footwears}[0.159, 0.541, 0.511, 0.614] market?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 1005 }, { "id": "n0oj0d6FLlNulsSR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nWhich has more growth, the {Toys}[0.159, 0.087, 0.449, 0.159] market or the {Textiles}[0.159, 0.723, 0.596, 0.796] market?" }, { "from": "gpt", "value": "Textiles." } ], "id_raw": 1005 }, { "id": "FhuZqj3Z1Q65m4cX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area between coordinates [0.159, 0.360, 0.848, 0.432]." }, { "from": "gpt", "value": "The area contains a bar representing the growth percentage of the {Software}[0.159, 0.360, 0.848, 0.432] market, which is the highest among all the markets in Emerging Markets." } ], "id_raw": 1005 }, { "id": "WbFXBaPkt0Jb1S3c", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.074, 0.844, 0.149, 0.861]." }, { "from": "gpt", "value": "This region covers a label for the {Electronics}[0.074, 0.844, 0.149, 0.861] market on the Y-axis." } ], "id_raw": 1005 }, { "id": "VHMiaRRUR8p2Rzjl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between [0.155, 0.939, 0.164, 0.957]?" }, { "from": "gpt", "value": "The region includes the value {0}[0.155, 0.939, 0.164, 0.957] on the X-axis, representing growth percentage." } ], "id_raw": 1005 }, { "id": "MCebYGA7n7wnkQoj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the mentioned label \"Suburban\"?" }, { "from": "gpt", "value": "[0.043, 0.466, 0.127, 0.484]." } ], "id_raw": 1006 }, { "id": "2PPsE7TbYGxbl6zb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.019, 0.448, 0.036, 0.497]?" }, { "from": "gpt", "value": "Areas." } ], "id_raw": 1006 }, { "id": "1hD1iuP496yJLRde", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the specific object and its related quantities in these coordinates [0.139, 0.060, 0.738, 0.296]?" }, { "from": "gpt", "value": "{Urban}[0.074, 0.171, 0.127, 0.189] with a Development Index of 7.2." } ], "id_raw": 1006 }, { "id": "kctXE0rqLf6xYYOs", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nIs the bar of {Suburban}[0.139, 0.355, 0.455, 0.591] higher than the bar of {Urban}[0.139, 0.060, 0.738, 0.296]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1006 }, { "id": "tXhRbvNa4KJ2rtzG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nDoes the bar of {Rural}[0.139, 0.650, 0.264, 0.886] have a smaller or larger Development Index than that of {Urban}[0.139, 0.060, 0.738, 0.296]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 1006 }, { "id": "8lxUZwS8axDx7Poq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Urban bar}[0.139, 0.060, 0.738, 0.296] closer to the top or the bottom of the chart compared to the {Rural bar}[0.139, 0.650, 0.264, 0.886]?" }, { "from": "gpt", "value": "Bottom." } ], "id_raw": 1006 }, { "id": "UZFv51g6DV5YwHge", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.555, 0.008, 0.555, 0.008]." }, { "from": "gpt", "value": "The area point represents a subtitle exist, but unlabeled." } ], "id_raw": 1006 }, { "id": "oASb6dQPSM5IFEZO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.082, 0.761, 0.127, 0.779]." }, { "from": "gpt", "value": "The area includes the y-axis label for {Rural}[0.082, 0.761, 0.127, 0.779]." } ], "id_raw": 1006 }, { "id": "j2TjgfKLu1Fy8snq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.139, 0.060, 0.738, 0.296]?" }, { "from": "gpt", "value": "There is the {Urban bar}[0.139, 0.060, 0.738, 0.296] in the bar chart, which represents an Urban area with a Development Index of 7.2." } ], "id_raw": 1006 }, { "id": "tB5tKymH0t5hwNpa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.470, 0.964, 0.640, 0.981] contain?" }, { "from": "gpt", "value": "The region contains the label {Development Index}[0.470, 0.964, 0.640, 0.981], as it is the header of the x-axis." } ], "id_raw": 1006 }, { "id": "ESFtCUi6deae7qJ8", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text labeled \"Automation\"?" }, { "from": "gpt", "value": "[0.124, 0.684, 0.225, 0.702]" } ], "id_raw": 1007 }, { "id": "Bmxl4LbtqZ5aNbbK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar at coordinates [0.237, 0.495, 0.494, 0.560]?" }, { "from": "gpt", "value": "It represents the development trend score of {Blockchain}[0.131, 0.521, 0.225, 0.538]." } ], "id_raw": 1007 }, { "id": "XD3xUu4zO3MbhQv4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.524, 0.964, 0.694, 0.981]?" }, { "from": "gpt", "value": "It's the x-axis label, which is labeled as \"Development Trend\"." } ], "id_raw": 1007 }, { "id": "tKZM5bU9IWQCKIRM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the data indicated by the bar within [0.237, 0.250, 0.914, 0.315]?" }, { "from": "gpt", "value": "It represents the development trend score of 5.0 for the {Data Science}[0.111, 0.276, 0.225, 0.293] industry." } ], "id_raw": 1007 }, { "id": "hmQmEBkxcO0t6woF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Blockchain}[0.131, 0.521, 0.225, 0.538] taller or shorter than the bar representing {AR/VR}[0.171, 0.439, 0.225, 0.457]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 1007 }, { "id": "PTHQBPeY8HmKk7Os", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nIs the score for the industry {AI}[0.208, 0.848, 0.225, 0.865] higher than the score for {5G Technology}[0.098, 0.194, 0.225, 0.212]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1007 }, { "id": "QUkySiD2TJV0di0Q", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nIs the y-axis label positioned to the left or to the right of the bar representing the {Cloud Computing}[0.074, 0.603, 0.225, 0.620]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 1007 }, { "id": "REzuOjlz6E3oG48P", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhich has the highest development trend score: {IoT}[0.199, 0.766, 0.225, 0.783] or {Automation}[0.124, 0.684, 0.225, 0.702]?" }, { "from": "gpt", "value": "Automation." } ], "id_raw": 1007 }, { "id": "n5ctFOoOtR8szVgM", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.074, 0.603, 0.225, 0.620]." }, { "from": "gpt", "value": "This is a vertical text label, which is located to the left of the bars, marked as \"Cloud Computing\"." } ], "id_raw": 1007 }, { "id": "OtuyZlmMfJ4McoCm", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the layout and information in the area [0.237, 0.250, 0.914, 0.315]." }, { "from": "gpt", "value": "This area includes a bar representing the development trend score of Data Science. The value, represented by the bar length, is the highest, at 5.0." } ], "id_raw": 1007 }, { "id": "JxcfgWFC8Psk3NsT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the range [0.199, 0.740, 0.630, 0.805]?" }, { "from": "gpt", "value": "This range contains a single bar representing the \"IoT\" industry. The development trend score, represented by the length of the bar, is 2.90." } ], "id_raw": 1007 }, { "id": "Yn1BxXwopUk7jXl0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.019, 0.451, 0.036, 0.523] entail?" }, { "from": "gpt", "value": "These coordinates represent the section of the y-axis label that reads \"Industry\". It is a vertical label placed along the y-axis to the left of the bars." } ], "id_raw": 1007 }, { "id": "lHY3Tahp8BRcwLwN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.331, 0.769, 0.651, 0.863] represent?" }, { "from": "gpt", "value": "It represents the frequency of Hotel Bookings." } ], "id_raw": 1008 }, { "id": "4DnioRK6biWrnpNs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nCan you specify the coordinates of the text label \"Entertainment Ticket Purchases\"?" }, { "from": "gpt", "value": "Yes, it's [0.043, 0.220, 0.319, 0.243]." } ], "id_raw": 1008 }, { "id": "2m0gaty3uhJxPOfO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nWhat frequency is represented at [0.331, 0.417, 0.810, 0.511]?" }, { "from": "gpt", "value": "It shows the frequency of Airport Transfers." } ], "id_raw": 1008 }, { "id": "2a69S5I5EzVu44GM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nWhat categories can be seen in [0.331, 0.065, 0.384, 0.159]?" }, { "from": "gpt", "value": "It's the {Gym Memberships}[0.331, 0.065, 0.384, 0.159] category." } ], "id_raw": 1008 }, { "id": "Xfv0WKfjCaDUL2TS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nAre the frequency bars for {Gym Memberships}[0.331, 0.065, 0.384, 0.159] shorter than the frequency bars for {Entertainment Ticket Purchases}[0.331, 0.182, 0.864, 0.276]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1008 }, { "id": "o8VNj98s0DLO6dxI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nIs the {Tour Package Purchases}[0.331, 0.534, 0.544, 0.628] frequency lower than {Spa Appointments}[0.331, 0.300, 0.491, 0.394] frequency?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1008 }, { "id": "srmEQLaJnN3DJ3HU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Hotel Bookings}[0.331, 0.769, 0.651, 0.863] and {Restaurant Reservations}[0.331, 0.652, 0.731, 0.746], which one has a higher frequency?" }, { "from": "gpt", "value": "Restaurant Reservations." } ], "id_raw": 1008 }, { "id": "gRSk0rKls8g1hf1K", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nDoes {Airport Transfers}[0.331, 0.417, 0.810, 0.511] have a higher frequency compared to {Entertainment Ticket Purchases}[0.331, 0.182, 0.864, 0.276]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1008 }, { "id": "8Kbcf1qO57bvx46J", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the coordinates [0.331, 0.417, 0.810, 0.511]." }, { "from": "gpt", "value": "This area contains a frequency bar for Airport Transfers. It shows that the frequency is around 18." } ], "id_raw": 1008 }, { "id": "Piy05QYyvXntt3On", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the details within the area specified by [0.331, 0.065, 0.384, 0.159]?" }, { "from": "gpt", "value": "This area comprises a frequency bar for the category Gym Memberships. It signifies that the frequency of gym memberships is approximately 2." } ], "id_raw": 1008 }, { "id": "waDZpgrxKWqmMzE8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen at [0.331, 0.182, 0.864, 0.276]?" }, { "from": "gpt", "value": "This area encompasses a frequency bar for Entertainment Ticket Purchases that indicates a frequency of around 20." } ], "id_raw": 1008 }, { "id": "cvXe9bZRR4axFcQV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's contained within the area [0.331, 0.652, 0.731, 0.746]?" }, { "from": "gpt", "value": "This region includes a frequency bar representation of the category Restaurant Reservations, with a frequency of 15." } ], "id_raw": 1008 }, { "id": "19Pq37IVFyOXdl9e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel}[0.019, 0.383, 0.036, 0.591] of the chart?" }, { "from": "gpt", "value": "Machine Learning Areas." } ], "id_raw": 1009 }, { "id": "1jMrWtoFW06X2dMb", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar}[0.263, 0.332, 0.772, 0.397] referring to?" }, { "from": "gpt", "value": "The bar corresponds to the \"Classification\" data in the chart." } ], "id_raw": 1009 }, { "id": "bUv9cCfmL0G5Jmwx", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the area represented by {the coordinates}[0.124, 0.603, 0.251, 0.620]?" }, { "from": "gpt", "value": "The area represents the \"Deep Learning\" label on the y-axis." } ], "id_raw": 1009 }, { "id": "2I6oVnS2g12JL2fB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the {bar}[0.263, 0.658, 0.405, 0.724] represent in terms of data value and category?" }, { "from": "gpt", "value": "The bar represents the \"Reinforcement Learning\" area with a frequency of 5%." } ], "id_raw": 1009 }, { "id": "dqSZ51xL9IQyx4X1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nComparing the two bars {Bar 1}[0.263, 0.495, 0.885, 0.560] and {Bar 2}[0.263, 0.577, 0.829, 0.642], which one has a higher value?" }, { "from": "gpt", "value": "Bar 1." } ], "id_raw": 1009 }, { "id": "IuB9chs8i6l0SiSz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Deep Learning}[0.263, 0.577, 0.829, 0.642] longer than {the bar for Supervised Learning}[0.263, 0.822, 0.687, 0.887]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1009 }, { "id": "NfH3fZ7sUwv3bybL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nWhat is greater, the frequency of {Deep Learning}[0.263, 0.577, 0.829, 0.642] or {Neural Networks}[0.263, 0.495, 0.885, 0.560]?" }, { "from": "gpt", "value": "Neural Networks." } ], "id_raw": 1009 }, { "id": "jp7e65yvYdQjXqUH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nCompare {Clustering bar}[0.263, 0.168, 0.574, 0.234] and {Regression bar}[0.263, 0.250, 0.716, 0.315] in terms of their length." }, { "from": "gpt", "value": "Regression bar is longer." } ], "id_raw": 1009 }, { "id": "Q6qf19L0VJkiVv1P", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the coordinates [0.074, 0.848, 0.251, 0.865]." }, { "from": "gpt", "value": "It is a y-axis label representing the \"Supervised Learning\" area in machine learning." } ], "id_raw": 1009 }, { "id": "252pf5M0wdqQlS5U", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the region [0.263, 0.577, 0.829, 0.642]." }, { "from": "gpt", "value": "The region contains a purple bar representing the popularity of the data \"Deep Learning\". The data range that the bar corresponds to is from 0% to 20%." } ], "id_raw": 1009 }, { "id": "NYxxD9OWgoFUSEio", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is within the region specified by these coordinates [0.052, 0.766, 0.546, 0.783]?" }, { "from": "gpt", "value": "The region contains a y-axis label \"Unsupervised Learning\" and its corresponding bar for the corresponding frequency percentage." } ], "id_raw": 1009 }, { "id": "IeOw04vmpERmZcSb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nDescribe all the components within {this region}[0.019, 0.383, 0.036, 0.591]." }, { "from": "gpt", "value": "The given region includes a label of the Y-axis titled \"Machine Learning Areas\"." } ], "id_raw": 1009 }, { "id": "x9sa4MQIQpWpHu9L", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nWhat type of technology is represented by this bar chart at coordinates [0.247, 0.818, 0.428, 0.886]?" }, { "from": "gpt", "value": "Solar Energy." } ], "id_raw": 1010 }, { "id": "0y7rLhrkFpOMt7Cg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.542, 0.964, 0.686, 0.981]?" }, { "from": "gpt", "value": "Growth Rate (%)." } ], "id_raw": 1010 }, { "id": "CarSWVfgNgRZ0SVJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {percentage indicator} at [0.327, 0.939, 0.339, 0.957]?" }, { "from": "gpt", "value": "1." } ], "id_raw": 1010 }, { "id": "cWNJcjoJRpuCDtiX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects or information is present in [0.066, 0.255, 0.234, 0.273]?" }, { "from": "gpt", "value": "{Geothermal Energy Label}[0.066, 0.255, 0.234, 0.273]." } ], "id_raw": 1010 }, { "id": "g0CuUFGtq1PrO5f0", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Biomass\"}[0.247, 0.650, 0.506, 0.717] shorter or longer than {the bar representing \"Hydro Power\"}[0.247, 0.566, 0.549, 0.633]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 1010 }, { "id": "qauzVmCjpsPlaweV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nAre bars representing {Energy Storage}[0.247, 0.397, 0.653, 0.465] and {Smart Grid}[0.247, 0.313, 0.679, 0.380] of the same length?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1010 }, { "id": "BckGh3iJ52o1UFQO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nBetween {Carbon Capture}[0.247, 0.481, 0.575, 0.549] and {Energy Storage}[0.247, 0.397, 0.653, 0.465], which bar is longer?" }, { "from": "gpt", "value": "Energy Storage." } ], "id_raw": 1010 }, { "id": "ysz4QEkzPH01M8oB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nAre bars representing {Wind Energy}[0.247, 0.734, 0.480, 0.802] and {Biomass}[0.247, 0.650, 0.506, 0.717] of the same color?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1010 }, { "id": "wtsk8wJ4Mn1mxUu2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.247, 0.019, 0.981, 0.927]." }, { "from": "gpt", "value": "It embeds the main plot of the visualization. This plot is drawn in the figure, it represents different technologies on the vertical axis and their corresponding growth rates on the horizontal axis. The heights of the bars denote the growth rate of each technology." } ], "id_raw": 1010 }, { "id": "pZhLx4ZMppUSvUGj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.247, 0.060, 0.912, 0.127]." }, { "from": "gpt", "value": "This area includes a bar representing the technology 'Fusion Energy'. The length of the bar indicates a growth rate of 7.7%" } ], "id_raw": 1010 }, { "id": "IF76sWmXoXhgmMDO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.247, 0.144, 0.765, 0.212]?" }, { "from": "gpt", "value": "This region contains a bar which represents the technology 'Wave and Tidal Power'. The length of the bar suggests a growth rate of 6.0% for this technology." } ], "id_raw": 1010 }, { "id": "3ia7jKRcBavc4Ql8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred from the area [0.161, 0.677, 0.234, 0.694]?" }, { "from": "gpt", "value": "This area corresponds to the label 'Biomass' in the y-axis which indicates that the bars within this scale on the y-axis correspond to growth rates for the technology 'Biomass'." } ], "id_raw": 1010 }, { "id": "TpbpM8sWWRwlja18", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text title}[0.322, 0.019, 0.795, 0.041]?" }, { "from": "gpt", "value": "\"Market Competition Analysis and Comparison\"" } ], "id_raw": 1011 }, { "id": "23VR2mA8i8QWxq3G", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented at {this part of the bar}[0.150, 0.189, 0.271, 0.271]?" }, { "from": "gpt", "value": "It represents the market share for \"Motorola\"." } ], "id_raw": 1011 }, { "id": "eIQq7Z5KQhGe0rSO", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis scale} at [0.019, 0.446, 0.036, 0.528]?" }, { "from": "gpt", "value": "It represents the y-axis label, called Company." } ], "id_raw": 1011 }, { "id": "ZPC3hBE3roEqO8L4", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar representing \"Xiaomi\"}[0.136, 0.497, 0.326, 0.579] represent a larger market share than {the bar representing \"Huawei\"}[0.136, 0.600, 0.559, 0.682]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1011 }, { "id": "nEkzykx9mTOUiMKC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nIs {the market share of \"Motorola\"}[0.136, 0.189, 0.150, 0.271] smaller than {the market share of \"OPPO\"}[0.136, 0.395, 0.220, 0.477]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1011 }, { "id": "SBdT1qGwgvez59Qm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nIs {the market share of \"Samsung\"}[0.136, 0.702, 0.749, 0.785] closer to {the market share of \"Apple\"}[0.136, 0.805, 0.854, 0.887] than to {the market share of \"Huawei\"}[0.136, 0.600, 0.559, 0.682]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1011 }, { "id": "oM9zvX58GN43hp6k", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within this area [0.136, 0.805, 0.854, 0.887]?" }, { "from": "gpt", "value": "This area contains a blue bar representing the market share of Apple, which is approximately 34%." } ], "id_raw": 1011 }, { "id": "9qbwOq9MvtDuIa64", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.136, 0.087, 0.141, 0.169]?" }, { "from": "gpt", "value": "There is a blue bar within this region representing the market share of Google, which is approximately 0.25%." } ], "id_raw": 1011 }, { "id": "bKiPtWS1CNPmR2iX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects and their properties within the bounding box [0.136, 0.702, 0.749, 0.785]." }, { "from": "gpt", "value": "Within this bounding box, there is a blue bar representing the market share of Samsung, which is approximately 29%." } ], "id_raw": 1011 }, { "id": "zZsJpQNrQwfLnoSV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.319, 0.019, 0.911, 0.041]?" }, { "from": "gpt", "value": "The subtitle, named \"Social Welfare and Charity Activities Funding Distribution\"." } ], "id_raw": 1012 }, { "id": "4ihk01CrBem5cX0D", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nWhat is at the location [0.260, 0.189, 0.271, 0.271]?" }, { "from": "gpt", "value": "A bar that represents \"Animal Welfare\" with a percentage of 0.6." } ], "id_raw": 1012 }, { "id": "NFAzGGJkp7n4mawi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented by the coordinates [0.104, 0.223, 0.236, 0.241]?" }, { "from": "gpt", "value": "{Y-axis label \"Animal Welfare\"}[0.104, 0.223, 0.236, 0.241]." } ], "id_raw": 1012 }, { "id": "NALHWDMCdwI4ynpV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.043, 0.634, 0.236, 0.651]?" }, { "from": "gpt", "value": "{The label for \"Healthcare Assistance\"}[0.043, 0.634, 0.236, 0.651]." } ], "id_raw": 1012 }, { "id": "gxfXTC83gz2XUTIP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nHow does the length of {the bar for \"Educational Aid\"}[0.248, 0.805, 0.585, 0.887] compare to {that of \"Food Distribution\"}[0.248, 0.702, 0.680, 0.785]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 1012 }, { "id": "fECL1aETBpSW9mka", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nIs the graph at {Housing Aid}[0.248, 0.497, 0.514, 0.579] longer than the bar at {Disaster Relief}[0.248, 0.395, 0.455, 0.477]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1012 }, { "id": "egiMNRcRnoWyA9GQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nAre the bars corresponding to {Educational Aid}[0.248, 0.805, 0.585, 0.887] and {Animal Welfare}[0.248, 0.189, 0.260, 0.271] of the same length?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1012 }, { "id": "lQOrZUOSU4Sx5nT9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nDoes {Food Distribution}[0.248, 0.702, 0.680, 0.785] have a larger value than {Research & Advocacy}[0.248, 0.292, 0.422, 0.374]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1012 }, { "id": "L85ow2T6YqqXbSLH", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.248, 0.805, 0.585, 0.887]?" }, { "from": "gpt", "value": "It is a blue bar representing the funding distribution percentage 16.1 for \"Educational Aid\"." } ], "id_raw": 1012 }, { "id": "BKXsbcBhKV3KJJL8", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the graphic detail in [0.248, 0.805, 0.585, 0.887] and [0.100, 0.839, 0.236, 0.857]?" }, { "from": "gpt", "value": "It shows a blue bar, labeled \"Educational Aid\", that corresponds to a percentage of 16.1." } ], "id_raw": 1012 }, { "id": "DF0nfYEKzKkiTRRV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.043, 0.634, 0.236, 0.651] contain?" }, { "from": "gpt", "value": "It contains the y-axis label \"Healthcare Assistance\"." } ], "id_raw": 1012 }, { "id": "fv3XDHVQERshOkmg", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.132, 0.531, 0.236, 0.549] and [0.248, 0.497, 0.514, 0.579]?" }, { "from": "gpt", "value": "\"Housing Aid\" is written at [0.132, 0.531, 0.236, 0.549] on the y-axis and corresponding to it on the chart is a blue bar graph of range 12.7 at [0.248, 0.497, 0.514, 0.579]." } ], "id_raw": 1012 }, { "id": "zJV2WpOKz9ufgBHv", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of internet usage has the coordinates [0.066, 0.711, 0.243, 0.729]?" }, { "from": "gpt", "value": "E-commerce growth." } ], "id_raw": 1013 }, { "id": "v9Ailk0fucc6euAy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of Internet of Things represented by the bar at the coordinates [0.255, 0.130, 0.657, 0.186]?" }, { "from": "gpt", "value": "4.7" } ], "id_raw": 1013 }, { "id": "z7G3FpqGbF4RYxBH", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nWhat axis label is located at [0.573, 0.964, 0.663, 0.981]?" }, { "from": "gpt", "value": "Frequency." } ], "id_raw": 1013 }, { "id": "GW9FDQ6VPRPzo0QG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of internet usage is associated with the label at the coordinates [0.135, 0.641, 0.243, 0.659]?" }, { "from": "gpt", "value": "Email Usage." } ], "id_raw": 1013 }, { "id": "ZLSCJAsa7KrR3ueZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, the one associated with Video Conferencing at [0.255, 0.340, 0.870, 0.396] or the one for Online Gaming at [0.255, 0.410, 0.622, 0.466]?" }, { "from": "gpt", "value": "Video Conferencing." } ], "id_raw": 1013 }, { "id": "BtBPd34iIszxklNA", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing Virtual Reality at [0.255, 0.200, 0.494, 0.256] taller or shorter than the bar representing Cryptocurrency at [0.255, 0.060, 0.614, 0.116]?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 1013 }, { "id": "w7WQC0ejQlXrmMme", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nDoes the bar representing Mobile Internet Users at [0.255, 0.830, 0.554, 0.886] extend further out along the x-axis than the bar representing Social Media Usage at [0.255, 0.550, 0.819, 0.606]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1013 }, { "id": "tSyl5v0wuG4HlCJj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nBetween the bars representing Video Streaming at [0.255, 0.480, 0.930, 0.536] and Video Conferencing at [0.255, 0.340, 0.870, 0.396], which one extends further along the x-axis?" }, { "from": "gpt", "value": "Video Streaming." } ], "id_raw": 1013 }, { "id": "stIZGxJu17n3XQ0I", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is present in the region [0.255, 0.410, 0.622, 0.466]?" }, { "from": "gpt", "value": "This area contains a single bar that corresponds to the frequency of Online Gaming, which is 4.3." } ], "id_raw": 1013 }, { "id": "0wYLEXJl9gP6p5uT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nWhat information is provided in the region from [0.255, 0.270, 0.776, 0.326]?" }, { "from": "gpt", "value": "In this region, there's a bar that represents the frequency of Online Education, which is 6.1." } ], "id_raw": 1013 }, { "id": "kY2V1URntaGYOjDx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.255, 0.130, 0.657, 0.186]?" }, { "from": "gpt", "value": "This region contains a single blue bar representing the frequency of Internet of Things usage, which is 4.7." } ], "id_raw": 1013 }, { "id": "fifSWEBnOyyjDpn7", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the area between the coordinates [0.109, 0.081, 0.243, 0.099] convey?" }, { "from": "gpt", "value": "This area includes the y-axis label for \"Cryptocurrency\"." } ], "id_raw": 1013 }, { "id": "SMU5tpjhVQblJRKk", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nThere is a bar starting from the x-axis at 0 and ending at around 11.2, can you identify the item it corresponds to?" }, { "from": "gpt", "value": "The bar corresponds to the item: {Mid-Quality Electronics}[0.215, 0.452, 0.722, 0.527]." } ], "id_raw": 1014 }, { "id": "EmzvhNjQdXhhOBgl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the text title of the graph located at approximately [0.495, 0.015, 0.705, 0.033]?" }, { "from": "gpt", "value": "The title of the graph is: {Statistical Data Overview}[0.495, 0.015, 0.705, 0.033]." } ], "id_raw": 1014 }, { "id": "3ykWeAWKlnfINh17", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis scale}[0.256, 0.951, 0.265, 0.965]?" }, { "from": "gpt", "value": "The x-axis scale at these coordinates is: 1." } ], "id_raw": 1014 }, { "id": "zqHi7Ov36PSFaXXy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nCan you compare the lengths of {High-Quality Automobile}[0.215, 0.826, 0.903, 0.901] and {High-Quality Clothing}[0.215, 0.265, 0.867, 0.340] bars?" }, { "from": "gpt", "value": "The {High-Quality Automobile}[0.215, 0.826, 0.903, 0.901] bar is shorter than the {High-Quality Clothing}[0.215, 0.265, 0.867, 0.340] bar." } ], "id_raw": 1014 }, { "id": "6IoEM7sbsUhHvwQO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nIs the {High-Quality Electronics}[0.215, 0.546, 0.876, 0.620] bar shorter than the {High-Quality Automobile}[0.215, 0.826, 0.903, 0.901] bar?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1014 }, { "id": "aJvpQaGhYJlx8p5G", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nDoes {Low-Quality Clothing}[0.215, 0.078, 0.682, 0.153] represent a lower quantity than {Low-Quality Automobile}[0.215, 0.639, 0.691, 0.714]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1014 }, { "id": "vWtGvlme81p3kVoS", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nBetween {Mid-Quality Automobile}[0.215, 0.732, 0.777, 0.807] and {Low-Quality Electronics}[0.215, 0.359, 0.659, 0.433], which item has a lower quantity?" }, { "from": "gpt", "value": "{Low-Quality Electronics}[0.215, 0.359, 0.659, 0.433]." } ], "id_raw": 1014 }, { "id": "98pl8YsZdaSVrF4j", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what you see from the coordinates [0.215, 0.452, 0.722, 0.527]." }, { "from": "gpt", "value": "These coordinates reveal a bar that represents the number of {Mid-Quality Electronics}[0.215, 0.452, 0.722, 0.527] which is around 11.2." } ], "id_raw": 1014 }, { "id": "6XGSdlCNfeg2MGXf", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.215, 0.826, 0.903, 0.901] contain?" }, { "from": "gpt", "value": "The area shows a bar that represents the quantity of {High-Quality Automobile}[0.215, 0.826, 0.903, 0.901]." } ], "id_raw": 1014 }, { "id": "IT5XD0LpxJJZwVR5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell us about the region [0.215, 0.172, 0.768, 0.247]?" }, { "from": "gpt", "value": "It contains a single bar that represents {Mid-Quality Clothing}[0.215, 0.172, 0.768, 0.247]." } ], "id_raw": 1014 }, { "id": "xCROAsZqWYf5mRGl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.215, 0.078, 0.682, 0.153] indicate?" }, { "from": "gpt", "value": "These coordinates represent a bar of {Low-Quality Clothing}[0.215, 0.078, 0.682, 0.153] with a quantity of around 10.3." } ], "id_raw": 1014 }, { "id": "cHr9S4xjKGfNTDQm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar chart refer to as the x-axis?" }, { "from": "gpt", "value": "The x-axis is referred to as {Trend}[0.584, 0.964, 0.631, 0.981]." } ], "id_raw": 1015 }, { "id": "8k6HmMUGzKbgeE0h", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nWhat is stated at the coordinates [0.159, 0.616, 0.221, 0.633] in the chart?" }, { "from": "gpt", "value": "{Fintech}[0.159, 0.616, 0.221, 0.633] is stated at these coordinates." } ], "id_raw": 1015 }, { "id": "HxQrtG0zUSOirCUE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented by the coordinates [0.233, 0.778, 0.840, 0.826] in the chart?" }, { "from": "gpt", "value": "The coordinates represent a bar that indicates the value of {Cryptocurrency}[0.233, 0.778, 0.840, 0.826] trend in the bar chart." } ], "id_raw": 1015 }, { "id": "pgk83eeQoCQfk0nE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nWhich bar has a higher value between {Data Science}[0.233, 0.539, 0.879, 0.587] and {Cybersecurity}[0.233, 0.180, 0.714, 0.228]?" }, { "from": "gpt", "value": "Data Science." } ], "id_raw": 1015 }, { "id": "Y4aKteqzT3Ut14hL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nIs {Big Data}[0.233, 0.240, 0.903, 0.287] placed higher than {Cloud Computing}[0.233, 0.359, 0.714, 0.407] on the Y-axis?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1015 }, { "id": "uvktMulhUxOi3KvD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nIs the trend of {Blockchain}[0.233, 0.479, 0.619, 0.527] higher or lower than the trend of {Artificial Intelligence}[0.233, 0.718, 0.777, 0.766]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1015 }, { "id": "vM1utpPHSiJ5HaLa", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents from [0.233, 0.718, 0.777, 0.766] to [0.233, 0.838, 0.643, 0.886] on the chart?" }, { "from": "gpt", "value": "On this range, two bars are represented. {Artificial Intelligence}[0.233, 0.718, 0.777, 0.766] and {Biotechnology}[0.233, 0.838, 0.643, 0.886]. Both are demonstrating their respective trend values." } ], "id_raw": 1015 }, { "id": "BHK50rfziNMLh6qX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.233, 0.239, 0.880, 0.287] represent on the chart?" }, { "from": "gpt", "value": "This area represents the bar for {Big Data}[0.233, 0.240, 0.903, 0.287] showing its trend value on the x-axis." } ], "id_raw": 1015 }, { "id": "jukpeCZLYhTuvsiq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are displayed in the region [0.465, 0.939, 0.494, 0.957]?" }, { "from": "gpt", "value": "This region displays the x-axis tick mark label {4}'s coordinates [0.465, 0.939, 0.494, 0.957]." } ], "id_raw": 1015 }, { "id": "C0TivNjPm127hgbr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the value visualized by the rectangle at [0.256, 0.825, 0.691, 0.886]?" }, { "from": "gpt", "value": "The rectangle represents the frequency of \"Increasing Partisanship\", which is 30." } ], "id_raw": 1016 }, { "id": "vujx4xHwJg7kWLBp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the name associated with the coordinate [0.166, 0.237, 0.244, 0.254]?" }, { "from": "gpt", "value": "It's labeled as \"Populism\"." } ], "id_raw": 1016 }, { "id": "AC7Bib0VNCycudAh", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at [0.019, 0.408, 0.036, 0.537] represent?" }, { "from": "gpt", "value": "It represents \"Political Trends\"." } ], "id_raw": 1016 }, { "id": "PRUQfMtStnPCLmlo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the frequency at x-axis coordinate [0.848, 0.939, 0.920, 0.957]?" }, { "from": "gpt", "value": "It is 45." } ], "id_raw": 1016 }, { "id": "Am9NC9qGV6oEwdSt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Increasing Partisanship}[0.256, 0.825, 0.691, 0.886] shorter or longer than the one for {Alternative Media}[0.256, 0.137, 0.764, 0.198]?" }, { "from": "gpt", "value": "Longer." } ], "id_raw": 1016 }, { "id": "y7dbUw3LDo3THlIZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nDoes {Electoral Volatility}[0.256, 0.672, 0.459, 0.733] has a higher frequency than {Political Scandal}[0.256, 0.366, 0.720, 0.427]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1016 }, { "id": "pT7ZDaL27ZdsUQK2", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nIs the frequency for {Political Satire}[0.256, 0.060, 0.532, 0.121] more than the one for {Political Disaffection}[0.256, 0.748, 0.604, 0.809]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1016 }, { "id": "2BILdiaMpwofJZwU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nBetween {Digital Politics}[0.256, 0.442, 0.851, 0.504] and {Identity Politics}[0.256, 0.289, 0.662, 0.351], which one has a higher occurence?" }, { "from": "gpt", "value": "Digital Politics." } ], "id_raw": 1016 }, { "id": "HXUdxE4MLdFpbkpc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the y-axis scale in the area [0.043, 0.084, 0.244, 0.866]." }, { "from": "gpt", "value": "The area represents the y-axis labels which indicate political trends, ranging from \"Increasing Partisanship\" at the topmost [0.043, 0.848, 0.244, 0.866] to \"Political Satire\" at the bottom [0.121, 0.084, 0.244, 0.101]." } ], "id_raw": 1016 }, { "id": "XYxI3BxV24j6A8lW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the structure of the bar chart in the region [0.256, 0.060, 0.851, 0.886]." }, { "from": "gpt", "value": "The region comprises a series of bars denoting frequencies of various political trends. The bars range from \"Increasing Partisanship\"[0.256, 0.825, 0.691, 0.886] at the top to \"Political Satire\"[0.256, 0.060, 0.532, 0.121] at the bottom. The length of each bar corresponds to the frequency of each political trend." } ], "id_raw": 1016 }, { "id": "kBaAp4B9Sjeq90Ib", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.556, 0.964, 0.681, 0.981] represent?" }, { "from": "gpt", "value": "This region contains the x-axis label, which is \"Frequency (%)\"." } ], "id_raw": 1016 }, { "id": "p1OGc3lQXlGQRZxl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.251, 0.939, 0.920, 0.957]." }, { "from": "gpt", "value": "This area has a series oflabels representing the x-axis scale of frequency starting from '0'[0.251, 0.939, 0.262, 0.957] to '45'[0.898, 0.939, 0.920, 0.957]." } ], "id_raw": 1016 }, { "id": "ITZjWbipKnemKiYQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the first bar in the chart with coordinates represent [0.264, 0.856, 0.312, 0.901]?" }, { "from": "gpt", "value": "Augmented Reality, with a frequency of 0.6." } ], "id_raw": 1017 }, { "id": "cygJDTJ30SeRQGgs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what technology category the y-label at [0.194, 0.151, 0.254, 0.165] corresponds to?" }, { "from": "gpt", "value": "Big Data." } ], "id_raw": 1017 }, { "id": "wBQ9EkARNkY9KpHU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis represented at [0.572, 0.971, 0.672, 0.985]?" }, { "from": "gpt", "value": "Frequency (%)." } ], "id_raw": 1017 }, { "id": "zvMjp1SHMAPGG4pj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nWhat technology does the bar displayed at [0.264, 0.134, 0.901, 0.178] represent?" }, { "from": "gpt", "value": "Big Data." } ], "id_raw": 1017 }, { "id": "xnSXo7qFrhazcppy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nIn the bar chart, is the technology category represented by [0.126, 0.706, 0.254, 0.720] more frequent than the one in [0.106, 0.540, 0.254, 0.554]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1017 }, { "id": "YuCqlsijFzdLcIUC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nWhen comparing the height of bars [0.264, 0.601, 0.431, 0.623] and [0.264, 0.523, 0.479, 0.567], which bar is taller?" }, { "from": "gpt", "value": "The bar at [0.264, 0.523, 0.479, 0.567]." } ], "id_raw": 1017 }, { "id": "9vlaUgwihgmEwd2G", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nBetween {\"Flipped Classroom\"}[0.264, 0.634, 0.423, 0.678] and {\"Bring Your Own Device\"}[0.264, 0.578, 0.431, 0.623], which technology is more common based on their corresponding frequency?" }, { "from": "gpt", "value": "Flipped Classroom." } ], "id_raw": 1017 }, { "id": "808eYRqePVHomZbL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nIs {Artificial Intelligence}[0.264, 0.745, 0.367, 0.790] more frequent than {Game-Based Learning}[0.264, 0.690, 0.399, 0.734]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1017 }, { "id": "kDJOZjXAPKJGhsEj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the bars in the [0.264, 0.645, 0.646, 0.745] section." }, { "from": "gpt", "value": "This region contains the bars for \"Flipped Classroom\" and \"Bring Your Own Device\". The \"Flipped Classroom\" bar spans [0.264, 0.634, 0.423, 0.678] and the \"Bring Your Own Device\" bar spans [0.264, 0.578, 0.431, 0.623]." } ], "id_raw": 1017 }, { "id": "DfjrmFpuPIv7LJkU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area of the chart [0.015, 0.440, 0.029, 0.530]?" }, { "from": "gpt", "value": "This region contains the y-label, which is the word \"Technology\"." } ], "id_raw": 1017 }, { "id": "SNWgh2rLKr8P0gE2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the area in [0.259, 0.951, 0.268, 0.965]." }, { "from": "gpt", "value": "There is a label of \"0\" in the x-axis' scale at [0.259, 0.951, 0.268, 0.965]." } ], "id_raw": 1017 }, { "id": "wpQ1bF36tI8JqR94", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nPlease run through the details of the top third section of the chart from [0.264, 0.745, 0.917, 0.123]." }, { "from": "gpt", "value": "In this top third section, there are 7 bars representing different technologies. These are (from top to bottom): \n1. Artificial Intelligence: [0.264, 0.745, 0.367, 0.790]\n2. Game-Based Learning: [0.264, 0.690, 0.399, 0.734]\n3. Flipped Classroom: [0.264, 0.634, 0.423, 0.678]\n4. Bring Your Own Device: [0.264, 0.578, 0.431, 0.623]\n5. Wearable Technology: [0.264, 0.523, 0.479, 0.567]\n6. Mobile Browsers: [0.264, 0.467, 0.543, 0.512]\n7. Learning Management Systems: [0.264, 0.412, 0.550, 0.456]" } ], "id_raw": 1017 }, { "id": "M1HLLJDoaJ6BcwF2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of chart being represented?" }, { "from": "gpt", "value": "Bar chart" } ], "id_raw": 1018 }, { "id": "R9W7AGjPUkMajxCU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis in the chart?" }, { "from": "gpt", "value": "Networks and Relationships" } ], "id_raw": 1018 }, { "id": "6GkL6CYY2CJ4TkFi", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the {label}[0.087, 0.822, 0.217, 0.840] on the far left of the bar chart?" }, { "from": "gpt", "value": "Social Network" } ], "id_raw": 1018 }, { "id": "4P6t2XiVU5JZa7eo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nWhat value does {bar}[0.229, 0.772, 0.921, 0.886] for the \"Social Network\" represent?" }, { "from": "gpt", "value": "9.2" } ], "id_raw": 1018 }, { "id": "N54EQKvMnSK0guO6", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nWhich is longer between the bar for {Social Network}[0.229, 0.772, 0.921, 0.886] and the bar for {Personal Interaction}[0.229, 0.630, 0.740, 0.743]?" }, { "from": "gpt", "value": "Social Network." } ], "id_raw": 1018 }, { "id": "VBiHBRDCqF3V4c28", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nIs the {bar}[0.229, 0.345, 0.537, 0.459] for \"Work Relations\" longer than the {bar}[0.229, 0.487, 0.628, 0.601] for \"Family Ties\u201d?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1018 }, { "id": "7oCryxMfZJhhdYPI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nDoes the {bar}[0.229, 0.060, 0.304, 0.174] for \"Communities\" have a smaller value than the {bar}[0.229, 0.202, 0.402, 0.316] for \"Online Friendship\u201d?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1018 }, { "id": "UPd1Q7fhSbmCUH1Y", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y axis in the chart?" }, { "from": "gpt", "value": "The label is \"Category\" and its coordinates are [0.015, 0.458, 0.029, 0.521]." } ], "id_raw": 1019 }, { "id": "euBOMDF4lUZNG9W9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at the coordinates [0.187, 0.246, 0.857, 0.313]?" }, { "from": "gpt", "value": "It is a bar representing the category \"Artificial Intelligence\" with a frequency of 8.4%." } ], "id_raw": 1019 }, { "id": "jKvJV8qEwesiKJF0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the bar located between [0.187, 0.833, 0.785, 0.901]?" }, { "from": "gpt", "value": "The bar represents the \"Biotechnology\" category with a value of 7.5%." } ], "id_raw": 1019 }, { "id": "b9sGh8OTQpB19oO9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nDoes the rectangle at [0.187, 0.498, 0.442, 0.565] represent a higher value than the rectangle at coordinates [0.187, 0.246, 0.857, 0.313]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1019 }, { "id": "n1Tyrw6wp4RBrqZO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Digital media\"}[0.187, 0.414, 0.650, 0.481] larger than {the bar representing \"Software\"}[0.187, 0.582, 0.722, 0.649]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1019 }, { "id": "BEpSs0QJAJd12HJV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nAmong all the bars, does {the bar representing \"Artificial Intelligence\"}[0.187, 0.246, 0.857, 0.313] have the highest value?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1019 }, { "id": "DaJo1VZoNdRQofba", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what is located at the area [0.187, 0.162, 0.554, 0.229]." }, { "from": "gpt", "value": "This area includes a bar representing the category \"Telecommunications\" with a frequency of 4.6%." } ], "id_raw": 1019 }, { "id": "TIIZi82lxUnGQ0GV", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the area [0.187, 0.498, 0.442, 0.565] contains?" }, { "from": "gpt", "value": "This area contains a bar representing the \"IT service\" category, with a frequency of 3.2%." } ], "id_raw": 1019 }, { "id": "WzeFATN8iWPyWkRT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nTell me more about what's inside [0.187, 0.750, 0.594, 0.817]." }, { "from": "gpt", "value": "It contains one bar which indicates the frequency percentage of the \"Semiconductors\" category, which is 5.1%." } ], "id_raw": 1019 }, { "id": "ShboXId3PlyXZZa3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nWhat label represents the main object with coordinates [0.025, 0.107, 0.295, 0.131] in the chart?" }, { "from": "gpt", "value": "The y-axis label, called \"reinforcement_learning\"." } ], "id_raw": 1020 }, { "id": "W1b3GxOF4Zu11sL3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the last bar of the second group in the subplot_0?" }, { "from": "gpt", "value": "[0.598, 0.808, 0.720, 0.906]" } ], "id_raw": 1020 }, { "id": "5tADWFFIXY73J74P", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the synthetic_data}[0.557, 0.067, 0.598, 0.166] in the bar chart?" }, { "from": "gpt", "value": "It represents the measurement for reinforcement_learning." } ], "id_raw": 1020 }, { "id": "ibJx8R6zBv2V74EM", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nWhat categories are marked with these coordinates [0.174, 0.354, 0.295, 0.377] and [0.244, 0.724, 0.295, 0.748] on the y-axis?" }, { "from": "gpt", "value": "It indicates the {regression}[0.174, 0.354, 0.295, 0.377] and {SVM}[0.244, 0.724, 0.295, 0.748] respectively." } ], "id_raw": 1020 }, { "id": "J6jmQ2jeATEtgoiC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nIs {the synthetic data bar for RNN}[0.434, 0.808, 0.598, 0.906] wider than {the human-authored data bar for RNN}[0.598, 0.808, 0.720, 0.906]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1020 }, { "id": "sL6eUFtudbkZp5S3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nIs {the synthetic data bar for SVM}[0.499, 0.684, 0.598, 0.783] taller than {the human-authored data bar for SVM}[0.598, 0.684, 0.843, 0.783]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1020 }, { "id": "M0ZDldIbib2wjG3l", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nDo {synthetic data bars}[0.557, 0.067, 0.598, 0.166], [0.393, 0.190, 0.598, 0.289], [0.475, 0.314, 0.598, 0.413], [0.516, 0.437, 0.598, 0.536], [0.450, 0.561, 0.598, 0.659], [0.499, 0.684, 0.598, 0.783], [0.434, 0.808, 0.598, 0.906] have greater values compared to {human-authored data bars}[0.598, 0.067, 0.745, 0.166], [0.598, 0.190, 0.868, 0.289], [0.598, 0.314, 0.761, 0.413], [0.598, 0.437, 0.778, 0.536], [0.598, 0.561, 0.688, 0.659], [0.598, 0.684, 0.843, 0.783], [0.598, 0.808, 0.720, 0.906]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1020 }, { "id": "STjjlHQmwacBEjdH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nBetween {synthetic_data bar}[0.557, 0.067, 0.598, 0.166] and {human-authored data bar}[0.598, 0.067, 0.745, 0.166] for {reinforcement_learning}[0.025, 0.107, 0.295, 0.131], which one is larger?" }, { "from": "gpt", "value": "Human-authored data." } ], "id_raw": 1020 }, { "id": "YxPESDrowghvdoQx", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.311, 0.025, 0.950, 0.948]." }, { "from": "gpt", "value": "This area contains several grouped bar elements representing synthetic and human-authored data for different categories such as reinforcement_learning, neural_networks, regression, unsupervised_learning, convolutions, SVM, RNN. The x-axis starts at about -25% extending to 30%, and the y-axis, from bottom to top, shows reinforcement_learning, neural_networks, regression, unsupervised_learning, convolutions, SVM, and RNN." } ], "id_raw": 1020 }, { "id": "3yctrDtruJym13R1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.598, 0.067, 0.745, 0.906]." }, { "from": "gpt", "value": "This area primarily contains all the human-authored data bars. From there, it ranges from reinforcement_learning with percentage 18, neural_networks with percentage 33, regression with percentage 20, unsupervised_learning with percentage 22, convolutions with percentage 11, SVM with percentage 30, to RNN with percentage 15." } ], "id_raw": 1020 }, { "id": "Ka5kOfX79QffnnWP", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found within [0.598, 0.067, 0.598, 0.166]?" }, { "from": "gpt", "value": "The parameters within this coordinate contain just one object - a blue bar representing the measurement of reinforcement_learning category for synthetic data." } ], "id_raw": 1020 }, { "id": "OEla1ApTzMREvYf4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.311, 0.025, 0.598, 0.906]?" }, { "from": "gpt", "value": "It contains all the bars representing synthetic data for different categories such as reinforcement_learning, neural_networks, regression, unsupervised_learning, convolutions, SVM, and RNN." } ], "id_raw": 1020 }, { "id": "2gj5vVEqbN9yBugo", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the object within the coordinates [0.348, 0.066, 0.544, 0.202] represent?" }, { "from": "gpt", "value": "It is a bar in the bar chart representing the \"Dramatic surge\" category for \"Political Trends\" with a value of 35%." } ], "id_raw": 1021 }, { "id": "IV0bHmQS3yT8KrXE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is at the coordinates [0.767, 0.122, 0.818, 0.145]?" }, { "from": "gpt", "value": "It is the value label for the \"Dramatic surge\" Political Trends, and the label reads \"35%\"." } ], "id_raw": 1021 }, { "id": "sWaAkkRMIaoISh8H", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nWhat can I find at [0.544, 0.236, 0.661, 0.372]?" }, { "from": "gpt", "value": "The bar represents the \"Subdued expansion\" of \"Social Trends\" with a value of 15%." } ], "id_raw": 1021 }, { "id": "Vnsszovz1EhQIk6E", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing {Political Trends for Sluggish Growth}[0.426, 0.746, 0.544, 0.882] bigger or smaller than the bar representing {Social Trends for Subdued Expansion}[0.544, 0.236, 0.661, 0.372]?" }, { "from": "gpt", "value": "Bigger." } ], "id_raw": 1021 }, { "id": "mgzBwCUm383NyQR3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nIs {the x tick labeled \"10\"}[0.607, 0.939, 0.637, 0.962] to the left or right of {the x tick labeled \"-10\"}[0.441, 0.939, 0.490, 0.962]?" }, { "from": "gpt", "value": "Right" } ], "id_raw": 1021 }, { "id": "Tcag72HrxsUdnloj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nWhich is taller between {the bar for Rapid Recovery in Political Trends}[0.387, 0.406, 0.544, 0.542] and {the bar for Dramatic Surge in Social Trends}[0.544, 0.066, 0.818, 0.202]?" }, { "from": "gpt", "value": "Bar for Rapid Recovery in Political Trends." } ], "id_raw": 1021 }, { "id": "IOPKTZ2EjtMHp4Li", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.544, 0.236, 0.818, 0.202]?" }, { "from": "gpt", "value": "There is a purple bar representing the \"Dramatic surge\" category for \"Social Trends\" with a value of 35%." } ], "id_raw": 1021 }, { "id": "ZM7y3sbWKPIVbMIl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.348, 0.066, 0.544, 0.202]." }, { "from": "gpt", "value": "This area contains a purple bar which represents the category \"Dramatic surge\" of \"Political Trends\" with a labelled value of 35%." } ], "id_raw": 1021 }, { "id": "zxAa6wQwefBwySYp", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.544, 0.406, 0.897, 0.542]?" }, { "from": "gpt", "value": "There is a purple bar in the region representing the \"Rapid Recovery\" category of \"Social Trends\", and it has a value of 45%." } ], "id_raw": 1021 }, { "id": "tjwLuVg5yA5FtNjf", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the top bar labeled \"Rapid recovery\"?" }, { "from": "gpt", "value": "[0.589, 0.684, 0.705, 0.783]." } ], "id_raw": 1022 }, { "id": "8Ww8PIOppOTu6YgJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.025, 0.354, 0.274, 0.377]?" }, { "from": "gpt", "value": "Pronounced decrease" } ], "id_raw": 1022 }, { "id": "1aoeZvYs89ZWsqLs", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar at coordinates [0.589, 0.561, 0.698, 0.659]?" }, { "from": "gpt", "value": "#d4a3da" } ], "id_raw": 1022 }, { "id": "r24Er3yiS2GEY1qj", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWhere are the \"Private Equity Investments Return Rate\" bars located, particularly for \"Upward trajectory\"?" }, { "from": "gpt", "value": "[0.516, 0.561, 0.589, 0.659]." } ], "id_raw": 1022 }, { "id": "5bjkMpSjr0v98dzZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWhich group represented by the bottom bar in {the area}[0.589, 0.067, 0.705, 0.166] had better performance in \"Dramatic surge\"?" }, { "from": "gpt", "value": "Public Equity Investments" } ], "id_raw": 1022 }, { "id": "g3TQTKBKz6WA1hlf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWas the return rate of the {Private Equity Investments}[0.530, 0.067, 0.589, 0.166] larger or smaller than {Public Equity Investments}[0.589, 0.067, 0.705, 0.166] in \"Dramatic surge\"?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 1022 }, { "id": "1jui3Z5cwjeYymLY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nAre {Public Equity Investment bars}[0.589, 0.067, 0.705, 0.906] generally higher than {Private Equity Investment bars}[0.530, 0.067, 0.589, 0.906]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1022 }, { "id": "7KlK5s3HTMbTAiwB", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nComparing the bars of {the Public Equity Investments sector}[0.589, 0.067, 0.705, 0.906] and {the Private Equity Investments sector}[0.530, 0.067, 0.589, 0.906], which sector has a higher return rate for \"Rapid decline\"?" }, { "from": "gpt", "value": "Public Equity Investments." } ], "id_raw": 1022 }, { "id": "7xuMNgAb9dz0EiQr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.589, 0.067, 0.705, 0.906]." }, { "from": "gpt", "value": "The area includes seven pairs of bars. Each pair corresponds to a different investment trend as labeled in the y-axis, which from bottom to top are: \"Dramatic surge\", \"Rapid decline\", \"Pronounced decrease\", \"Consistent expansion\", \"Upward trajectory\", \"Rapid recovery\", \"Persistent growth\". On the left of each pair is a purple bar representing Private Equity Investments Return Rate, and on the right is a lighter violet bar representing Public Equity Investments Return Rate." } ], "id_raw": 1022 }, { "id": "R2nweiTNUkpmzVD0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.363, 0.190, 0.792, 0.289]?" }, { "from": "gpt", "value": "This region contains two bars representing the return rate for the investment trend \"Rapid decline\". The left bar represents the Private Equity Investment with a return rate of -31% and the right bar represents the Public Equity Investment with a return rate of +28%." } ], "id_raw": 1022 }, { "id": "aRWRuvwfGxPpn8HY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the structure within the coordinates [0.589, 0.808, 0.654, 0.906]?" }, { "from": "gpt", "value": "In this region, there is a single light violet bar, which represents the Public Equity Investments Return Rate for the investment trend \"Persistent growth\", with a return value labeled as 9%." } ], "id_raw": 1022 }, { "id": "ZoIXRYvULqba3FyO", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are positioned within the area defined by the coordinates [0.589, 0.314, 0.669, 0.413]?" }, { "from": "gpt", "value": "There is a light violet bar located in this area. It corresponds to the Public Equity Investments Return Rate for the \"Pronounced decrease\" trend, with a value labeled as +11%." } ], "id_raw": 1022 }, { "id": "GUpq9dMbliopSfMr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the policy that corresponds to the coordinates [0.216, 0.067, 0.420, 0.166]?" }, { "from": "gpt", "value": "Public Health." } ], "id_raw": 1023 }, { "id": "qVOFEV0KZHE60LFJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the \"Government Ethics\" bar?" }, { "from": "gpt", "value": "[0.297, 0.808, 0.420, 0.906]." } ], "id_raw": 1023 }, { "id": "v9Avx5pSio7iClUy", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the effectiveness evaluation data for {the Infrastructure bar}[0.420, 0.314, 0.810, 0.413]?" }, { "from": "gpt", "value": "67%." } ], "id_raw": 1023 }, { "id": "mTMoQyk6VRt6EE7l", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the policy related to {the bar at}[0.320, 0.684, 0.420, 0.783]?" }, { "from": "gpt", "value": "Energy." } ], "id_raw": 1023 }, { "id": "SIde6Aku4PgpFyzm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nIs {the bar indicating \"Government Ethics\"}[0.297, 0.808, 0.420, 0.906] higher than {the bar indicating \"Public Health\"}[0.216, 0.067, 0.420, 0.166]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1023 }, { "id": "SHR6STdLHDf5nzm8", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the Energy bar}[0.420, 0.684, 0.682, 0.783] represent a higher value in the effectiveness evaluation data than {the Defense bar}[0.420, 0.561, 0.740, 0.659]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1023 }, { "id": "bnvoGl29cm2FL0MD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {the Infrastructure bar}[0.420, 0.314, 0.810, 0.413] or {the Education bar}[0.420, 0.437, 0.857, 0.536] under the effectiveness evaluation data?" }, { "from": "gpt", "value": "Education." } ], "id_raw": 1023 }, { "id": "EMKU9YX46TmIJIxt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Public Health}[0.216, 0.067, 0.420, 0.166] and {Government Ethics}[0.297, 0.808, 0.420, 0.906], which policy data has a higher value?" }, { "from": "gpt", "value": "Government Ethics." } ], "id_raw": 1023 }, { "id": "wFGpqGELl5Uk1Ct4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.216, 0.067, 0.420, 0.166]?" }, { "from": "gpt", "value": "This area contains the bar representing policy data for Public Health. The bar is filled with the color #6e9d9f, indicating a value of -35 (or -35% when presented as a percentage)." } ], "id_raw": 1023 }, { "id": "SsAVicR2RdnQrAL2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nGive a description of the region [0.420, 0.314, 0.810, 0.413]." }, { "from": "gpt", "value": "The rectangular area represents Infrastructure in the effectiveness evaluation data. It's colored in #c89c4c, suggesting a positive value of 67 (67%)." } ], "id_raw": 1023 }, { "id": "GfN4smhUxkwn1USv", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.420, 0.560, 0.740, 0.660]?" }, { "from": "gpt", "value": "There is one bar in this region representing Defense with an effectiveness evaluation value of 55, colored in #c89c4c." } ], "id_raw": 1023 }, { "id": "HtQe8tXSVAlsAqRW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.291, 0.190, 0.420, 0.289]?" }, { "from": "gpt", "value": "This region includes a bar representing the policy data of Economic Policy. It is colored #6e9d9f and stands for a value of -22." } ], "id_raw": 1023 }, { "id": "aFUcNjRInrnZo30r", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}(1) at [0.052,0.231,0.289,0.254] (2), [0.080, 0.314, 0.289, 0.413](3) and [0.136, 0.477, 0.289, 0.501] (4)?" }, { "from": "gpt", "value": "(1) Autonomous Driving, (2) Medical Diagnosis, (3) Supply Chain" } ], "id_raw": 1025 }, { "id": "SAjKuHw65chkCTH2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the names of the bars located at [0.338, 0.067, 0.612, 0.166](1), [0.402, 0.190, 0.612, 0.289](2) and [0.425, 0.314, 0.612, 0.413] (3) respectively?" }, { "from": "gpt", "value": "(1) Speech Recognition, (2) Autonomous Driving, (3) Medical Diagnosis." } ], "id_raw": 1025 }, { "id": "lkPKcOfnuWGWj0lV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage value of the bar located at [0.368, 0.437, 0.612, 0.536](1) and [0.422, 0.684, 0.612, 0.783](2)?" }, { "from": "gpt", "value": "According to the text annotations, (1) 73%, (2) 57%." } ], "id_raw": 1025 }, { "id": "unu5vwiRaEXWh3mq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nConcerning the two bars representing Autonomous Driving, is {the synthetic data bar} at [0.402, 0.190, 0.612, 0.289] higher or lower than {the real use case bar} at [0.612, 0.190, 0.873, 0.289]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 1025 }, { "id": "NJN8QXDZIURJyImG", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the synthetic data bar} at [0.425, 0.314, 0.612, 0.413] for Medical Diagnosis surpass {the real use case bar} at [0.612, 0.314, 0.836, 0.413]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 1025 }, { "id": "yevlYPEYxLAzWAXh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nBetween {the speech recognition category} at [0.338, 0.067, 0.612, 0.166] for synthetic data and [0.612, 0.067, 0.930, 0.166] for real use case, which one has a higher value?" }, { "from": "gpt", "value": "Real use case" } ], "id_raw": 1025 }, { "id": "zE8NYGAfOMb0VLPF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.338, 0.067, 0.930, 0.906]." }, { "from": "gpt", "value": "This region includes the bars representing the values for all the AI applications categories. For both synthetic data and real use cases, the categories include Speech Recognition, Autonomous Driving, Medical Diagnosis, Supply Chain, Advertisement, Smart Homes, and Customer Service Bots." } ], "id_raw": 1025 }, { "id": "8hYm2Dupmkifnzjl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.024, 0.848, 0.289, 0.871]." }, { "from": "gpt", "value": "This region includes the label for the AI application category - Customer Service Bots." } ], "id_raw": 1025 }, { "id": "BwuAZz77j8jkIwRT", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.338, 0.068, 0.930, 0.166]?" }, { "from": "gpt", "value": "There are 2 bars: The purple bar represents synthetic data for the Speech Recognition category and the lilac bar represents the real use case for the same category. Their respective values are annotated with text within them - 82% for synthetic data and 95% for real use cases." } ], "id_raw": 1025 }, { "id": "mLah0uPJICAa9hxm", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar with coordinates [0.556, 0.068, 0.806, 0.145] represent?" }, { "from": "gpt", "value": "It represents the Gym participation rate for Running." } ], "id_raw": 1026 }, { "id": "W475gUR2kKKAttKX", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the name of the category with coordinates [0.087, 0.291, 0.172, 0.314]?" }, { "from": "gpt", "value": "Cycling." } ], "id_raw": 1026 }, { "id": "SPxMu0XuBAI1S4lc", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the activity and participation type that corresponds to the coordinates [0.556, 0.649, 0.886, 0.726]?" }, { "from": "gpt", "value": "It is Boxing with Gym participation." } ], "id_raw": 1026 }, { "id": "vICaBeWQLSDuKzi7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nIs the traditional participation bar for Weightlifting at [0.305, 0.455, 0.556, 0.532] higher or lower than the Gym participation bar at [0.556, 0.455, 0.869, 0.532]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1026 }, { "id": "UbbkzpKJIcGmXnch", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nCompare the heights of the Gym participation bars for Cycling at [0.556, 0.261, 0.765, 0.339] and Boxing at [0.556, 0.649, 0.886, 0.726]. Which one is taller?" }, { "from": "gpt", "value": "Boxing." } ], "id_raw": 1026 }, { "id": "uDxPJPDv11V712Cy", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nAre the bars for Traditional participation generally shorter than those for Gym participation?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1026 }, { "id": "DedahcnPk9TCKbeW", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.556, 0.068, 0.923, 0.920]?" }, { "from": "gpt", "value": "This region contains all the bars representing Gym participation percentages for various sports such as Running, Swimming, Cycling, Yoga, Weightlifting, Crossfit, Boxing, Golf, and Basketball. The sizes of the bars vary, with sports like Crossfit having a higher percentage and others like Golf having a lower percentage." } ], "id_raw": 1026 }, { "id": "kxQ6USh54wX0mBn5", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nWhat appears within the region [0.051, 0.164, 0.172, 0.242]?" }, { "from": "gpt", "value": "There's a y-axis label within this region, indicating 'Swimming'." } ], "id_raw": 1026 }, { "id": "JJ7AMsoNJgcmnIRj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the region from [0.347, 0.068, 0.556, 0.145]." }, { "from": "gpt", "value": "This region contains a bar and a text label. The bar, colored in '#d4a3da', represents the Traditional participation rate for Running. The text label, written in black and located to the left of the bar, indicates a percentage of 50%." } ], "id_raw": 1026 }, { "id": "1iMJDTWK0hw9OHab", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nWhat value does the {Investment Strategy bar}[0.583, 0.561, 0.783, 0.659] correspond to in the Human-Investment group?" }, { "from": "gpt", "value": "22%" } ], "id_raw": 1027 }, { "id": "AMInx0gC6oFavCYE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the item represented by the coordinates [0.030, 0.848, 0.265, 0.871] in the y-axis?" }, { "from": "gpt", "value": "Retirement Planning" } ], "id_raw": 1027 }, { "id": "u8Nh4Ndezzw7pTLK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar located at [0.418, 0.808, 0.583, 0.906] representing in the SynRobo-Investment group?" }, { "from": "gpt", "value": "Retirement Planning" } ], "id_raw": 1027 }, { "id": "PZVoefky8pnSfMHm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for Financial Planning in SynRobo-Investment}[0.473, 0.190, 0.583, 0.289] larger or smaller than the {bar for Financial Planning in Human-Investment}[0.583, 0.190, 0.774, 0.289]?" }, { "from": "gpt", "value": "Smaller" } ], "id_raw": 1027 }, { "id": "VF0YRbxw0o1vqd8s", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nDoes the {Asset Allocation bar in SynRobo-Investment}[0.409, 0.437, 0.583, 0.536] have a higher or lower position than the {Risk Management bar in Human-Investment}[0.583, 0.067, 0.802, 0.166]?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 1027 }, { "id": "c4U4SPXoyMQUXedQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for Cash Flow in Human-Investment}[0.583, 0.684, 0.884, 0.783] larger or smaller than the {bar for Cash Flow in SynRobo-Investment}[0.400, 0.684, 0.583, 0.783]?" }, { "from": "gpt", "value": "Larger" } ], "id_raw": 1027 }, { "id": "Xxl8sjqMb7YHdb1y", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements inside [0.282, 0.025, 0.975, 0.948]?" }, { "from": "gpt", "value": "This region includes a combination of labels and bars representing different categories of investment strategies (Risk Management, Financial Planning, Wealth Management, Asset Allocation, Investment Strategy, Cash Flow, Retirement Planning) from SynRobo-Investment and Human-Investment visualized as a horizontal bar chart." } ], "id_raw": 1027 }, { "id": "4ukF3cxcTMX3UFFu", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the structures found at [0.400, 0.684, 0.884, 0.783]." }, { "from": "gpt", "value": "This region contains two bars. The bar located at [0.400, 0.684, 0.583, 0.783] represents the performance of Cash Flow in SynRobo-Investment, with value -20%. Another bar located at [0.583, 0.684, 0.884, 0.783] represents the performance of Cash Flow in Human-Investment, with value 33%." } ], "id_raw": 1027 }, { "id": "s7ZT0rjwIHHLt67j", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nWhat do objects within the region [0.473, 0.190, 0.774, 0.289] tell us?" }, { "from": "gpt", "value": "Within this region, there are two bars. The bar located at [0.473, 0.190, 0.583, 0.289] represents Financial Planning with SynRobo-Investment marked at -12%, and another bar ([0.583, 0.190, 0.774, 0.289]) represents Financial Planning with Human-Investment marked at 21%." } ], "id_raw": 1027 }, { "id": "SmzhKMWbJe9xc3J2", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.056, 0.753, 0.292, 0.776] in the graph?" }, { "from": "gpt", "value": "Y-axis label, referred to as Genetic Engineering." } ], "id_raw": 1028 }, { "id": "o780KeIfhJWeLH6b", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth text in the y-axis label?" }, { "from": "gpt", "value": "[0.134, 0.427, 0.292, 0.450]" } ], "id_raw": 1028 }, { "id": "yIkguJ90Ma7tz9ip", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the bar}[0.252, 0.176, 0.530, 0.263]?" }, { "from": "gpt", "value": "Climate Change for the human-authored data." } ], "id_raw": 1028 }, { "id": "EJPyO9KyboBv4VgD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nWhich technology type do the coordinates [0.074, 0.861, 0.292, 0.885] refer to?" }, { "from": "gpt", "value": "VR - Virtual Reality." } ], "id_raw": 1028 }, { "id": "wxjBcHJNlxKPK0eZ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for self-driving cars in human-authored data}[0.252, 0.284, 0.808, 0.371] longer than {the bar for smart homes in human-authored data}[0.252, 0.393, 0.697, 0.480]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1028 }, { "id": "1a92DdssYgezoQKP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for cryptocurrencies in synthetic data}[0.252, 0.501, 0.586, 0.588] shorter than {the bar for artificial intelligence in synthetic data}[0.252, 0.610, 0.697, 0.697]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1028 }, { "id": "EpX6b0HYQxsgR64p", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the bar for VR - Virtual Reality in synthetic data}[0.252, 0.827, 0.419, 0.914] and {the bar for VR - Virtual Reality in human-authored data}[0.252, 0.827, 0.641, 0.914], which one is longer?" }, { "from": "gpt", "value": "The bar for VR - Virtual Reality in human-authored data." } ], "id_raw": 1028 }, { "id": "R7lIeUKmvoHUaFvb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for genetic engineering in human-authored data}[0.252, 0.718, 0.363, 0.805] shorter than {the bar for genetic engineering in synthetic data}[0.252, 0.718, 0.808, 0.805]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1028 }, { "id": "wneKSiTC8oA6ozzt", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.252, 0.067, 0.808, 0.914]." }, { "from": "gpt", "value": "This area corresponds to all the bars in the graph. It contains two sets of bars, representing synthetic data and human-authored data. Each set of bars consists of 8 bars, each representing a different technology type: Domestic Robots, Climate Change, Self-driving Cars, Smart Homes, Cryptocurrencies, Artificial Intelligence, Genetic Engineering, and VR - Virtual Reality." } ], "id_raw": 1028 }, { "id": "lozc7HfRO9CMVLp4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.096, 0.102, 0.292, 0.885]." }, { "from": "gpt", "value": "This area corresponds to all the y-axis labels, which are the names of the technology types: Domestic Robots, Climate Change, Self-driving Cars, Smart Homes, Cryptocurrencies, Artificial Intelligence, Genetic Engineering, VR - Virtual Reality." } ], "id_raw": 1028 }, { "id": "ll5bqLCu4XKkkrsU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.252, 0.501, 0.586, 0.588]?" }, { "from": "gpt", "value": "There is 1 object:\nThe lavender-colored bar represents the value for Cryptocurrencies in synthetic data, and it spans from 0% to 30% on the x-axis." } ], "id_raw": 1028 }, { "id": "UHAuff4wlCLPYR6W", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.252, 0.501, 0.864, 0.697]?" }, { "from": "gpt", "value": "This area contains two bars:\n1. The lavender-colored bar that corresponds to Cryptocurrencies under synthetic data, the percentage value is 30%, ranging from 0% to 30% on the x-axis.\n2. The deep violet-colored bar that corresponds to Artificial Intelligence under human-authored data, the percentage value is 55%, ranging from 0% to 55% on the x-axis." } ], "id_raw": 1028 }, { "id": "Ks9562HaHoiRhppd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label at [0.019, 0.209, 0.266, 0.226]?" }, { "from": "gpt", "value": "Authentic Leadership." } ], "id_raw": 1029 }, { "id": "TElhBybdStSMuE85", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text that indicates the value of the bar titled \"Charismatic Leadership\"?" }, { "from": "gpt", "value": "[0.607, 0.647, 0.646, 0.664]" } ], "id_raw": 1029 }, { "id": "jPHzPCkFGdyxG2D0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the bar at [0.710, 0.392, 0.901, 0.480] representing?" }, { "from": "gpt", "value": "Laissez-faire Leadership with a decline of 12%." } ], "id_raw": 1029 }, { "id": "8DxdIckoM0EEIZA6", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value indicated at [0.543, 0.867, 0.582, 0.884]?" }, { "from": "gpt", "value": "20%" } ], "id_raw": 1029 }, { "id": "fw1C5CPGVZqwHCyI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Service Leadership\"}[0.550, 0.722, 0.901, 0.810] shorter or taller than {the bar representing \"Authentic Leadership\"}[0.518, 0.172, 0.901, 0.260]?" }, { "from": "gpt", "value": "The same." } ], "id_raw": 1029 }, { "id": "xkH5p0y0ziuZecMI", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nWhich is greater, {the decline in \"Charismatic Leadership\"}[0.646, 0.612, 0.901, 0.700] or {the decline in \"Transactional Leadership\"}[0.758, 0.502, 0.901, 0.590]?" }, { "from": "gpt", "value": "The decline in Charismatic Leadership." } ], "id_raw": 1029 }, { "id": "INZJVBrkDIZWsjdE", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nIs {the Service Leadership bar}[0.550, 0.722, 0.901, 0.810] longer than {the Laissez-faire Leadership bar}[0.710, 0.392, 0.901, 0.480]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1029 }, { "id": "5WyeKkE3GYDwMF1y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nBetween {Transformational Leadership Point}[0.614, 0.062, 0.901, 0.150] and {Participative Leadership Point}[0.566, 0.282, 0.901, 0.370] which point represents a higher value?" }, { "from": "gpt", "value": "Transformational Leadership Point." } ], "id_raw": 1029 }, { "id": "1uLlAY2tCFKn1UP2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.278, 0.062, 0.901, 0.920]." }, { "from": "gpt", "value": "It represents the decline of various leadership styles in business teams, with data displayed as bars. From top to bottom, the leadership styles are Coaching Leadership, Service Leadership, Charismatic Leadership, Transactional Leadership, Laissez-faire Leadership, Participative Leadership, Authentic Leadership, and Transformational Leadership, labels are positioned to the left of each bar." } ], "id_raw": 1029 }, { "id": "GxhgDdR2xUVv0L4t", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.518, 0.172, 0.901, 0.260] contain?" }, { "from": "gpt", "value": "It contains a bar representing the decline in Authentic Leadership, which is reported as 24% and labeled to the left of the bar." } ], "id_raw": 1029 }, { "id": "4ZyfZ6NUUi8Ybh7x", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the data in [0.710, 0.392, 0.901, 0.480]." }, { "from": "gpt", "value": "This region contains a single data visualization bar. It represents the Laissez-faire Leadership style, showing a decline of 12% in Business Teams." } ], "id_raw": 1029 }, { "id": "96RvWDPUvSM4F053", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region [0.582, 0.832, 0.901, 0.920]?" }, { "from": "gpt", "value": "This area contains a bar representing Coaching Leadership, which shows a decline of 20% in Business Teams. The decline is labeled to the left of the bar." } ], "id_raw": 1029 }, { "id": "cds4hRehVYEOpBHA", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart with coordinates [0.408, 0.019, 0.866, 0.041]?" }, { "from": "gpt", "value": "The title is \"Transportation and Logistics Industry Trends\"." } ], "id_raw": 1030 }, { "id": "cS0RN5SDRpwKidFl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the activity trend at coordinate [0.805, 0.088, 0.949, 0.106] in the legend?" }, { "from": "gpt", "value": "This activity trend is named \"Surveyed Trends\"." } ], "id_raw": 1030 }, { "id": "wuJJMf68pKEf5quV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nWhat label is located at [0.126, 0.634, 0.281, 0.651]?" }, { "from": "gpt", "value": "The label is \"Last Mile Delivery\"." } ], "id_raw": 1030 }, { "id": "kVONRCTKb5B2KVgY", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the x-axis located at [0.556, 0.964, 0.719, 0.981]?" }, { "from": "gpt", "value": "The label is \"Activity Trends (%)\"." } ], "id_raw": 1030 }, { "id": "vJEsBGfaSQplLyFF", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {Air Freight Volume}[0.518, 0.805, 0.669, 0.887] higher or lower than for {Ocean Freight Volume}[0.356, 0.702, 0.669, 0.785]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1030 }, { "id": "pw1pkNz86j86Y4Hz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nDoes the {Wholesale and Distribution}[0.669, 0.497, 0.756, 0.579] bar stand to the left or right of the {Retail and ECommerce}[0.669, 0.395, 0.794, 0.477] bar?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 1030 }, { "id": "ZMTj42bQK1kRMHbj", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nBetween the {Benchmark Trends}[0.805, 0.062, 0.966, 0.080] and the {Surveyed Trends}[0.805, 0.088, 0.949, 0.106], which one has a green marker?" }, { "from": "gpt", "value": "Surveyed Trends." } ], "id_raw": 1030 }, { "id": "HNZ94INzJkWz4aFt", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nWhich is higher? {Manufacturing and Production Surveyed Trends}[0.669, 0.292, 0.844, 0.374] or {3PL/4PL Services Surveyed Trends}[0.669, 0.189, 0.781, 0.271]?" }, { "from": "gpt", "value": "Manufacturing and Production Surveyed Trends." } ], "id_raw": 1030 }, { "id": "8g02ChL1xmoVK6uY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within coordinates [0.293, 0.087, 0.981, 0.927]" }, { "from": "gpt", "value": "This area contains the chart axes, legend and the bars representing the Benchmark Trends and Surveyed Trends for different categories. The Benchmark Trends bars are red and the Surveyed Trends bars are green. The y-axis labels range from \"Air Freight Volume\" at the top to \"Transportation Management\" at the bottom. The x-axis represents the Activity Trends (%) ranging from -30 to 30." } ], "id_raw": 1030 }, { "id": "bJTQ6yLo95twA82i", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the coordinates [0.749, 0.055, 0.973, 0.113]." }, { "from": "gpt", "value": "This region contains a legend with two entries. The first entry is the Benchmark Trends with a red square at [0.756, 0.064, 0.791, 0.076] and its label at [0.805, 0.062, 0.966, 0.080]. The second one is the Surveyed Trends with a green square at [0.756, 0.090, 0.791, 0.102] and its label [0.805, 0.088, 0.949, 0.106]." } ], "id_raw": 1030 }, { "id": "qtf8I27Q9nYGn5hc", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area [0.275, 0.939, 1.055, 0.957]?" }, { "from": "gpt", "value": "This area contains labels for the x-axis, which are the values of Activity Trends (%). The values are -30, -20, -10, 0, 10, 20, 30 from left to right." } ], "id_raw": 1030 }, { "id": "D0g4rHIpgB4CN0MD", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.518, 0.805, 0.669, 0.887]?" }, { "from": "gpt", "value": "This area contains a red bar that is representative of the \"Air Freight Volume\" category for the Benchmark Trends. In addition, an accompanying text annotation indicating the value \"12%\" is present within this area." } ], "id_raw": 1030 }, { "id": "I0LEiLDV7DI5Hwof", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.107, 0.477, 0.171, 0.501] represent on the Y-axis?" }, { "from": "gpt", "value": "The label 'Blogs'." } ], "id_raw": 1031 }, { "id": "seti8lFdBAOD8I1b", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first bar in the 'synthetic data' group?" }, { "from": "gpt", "value": "[0.370, 0.067, 0.514, 0.166]" } ], "id_raw": 1031 }, { "id": "W4HhuuvTo5xoSY7v", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.754, 0.105, 0.805, 0.128] signify?" }, { "from": "gpt", "value": "It signifies the percentage value 25% associated with the TV bar." } ], "id_raw": 1031 }, { "id": "ptCXsWsMHmqP3Gv9", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the text represented by the coordinates [0.069, 0.601, 0.171, 0.624]?" }, { "from": "gpt", "value": "The label 'Podcasts'." } ], "id_raw": 1031 }, { "id": "bJdOlWKhQ8nzoW29", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"Memes\" in synthetic data}[0.456, 0.808, 0.514, 0.906] shorter or taller than {the bar representing \"Forums\" in synthetic data}[0.427, 0.684, 0.514, 0.783]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 1031 }, { "id": "uNPFSWgxmdTQtqKH", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing \"TV\" in human-authored data}[0.514, 0.190, 0.879, 0.289] wider than {the bar representing \"Podcasts\" in human-authored data}[0.514, 0.561, 0.629, 0.659]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1031 }, { "id": "E4wEcioD2ZkwLcqD", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {the bar representing \"Newspapers\" in synthetic data}[0.370, 0.067, 0.514, 0.166] compared to {the bar representing \"Newspapers\" in human-authored data}[0.514, 0.067, 0.754, 0.166]?" }, { "from": "gpt", "value": "To the left." } ], "id_raw": 1031 }, { "id": "FrYr7MNONl7M2GBo", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nComparing {the bar representing \"Blogs\" in synthetic data}[0.418, 0.437, 0.514, 0.536] and {the bar representing \"Blogs\" in human-authored data}[0.514, 0.437, 0.562, 0.536], which group shows a larger values?" }, { "from": "gpt", "value": "Human-authored data." } ], "id_raw": 1031 }, { "id": "4tdRSLW34aTwIdDQ", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.370, 0.067, 0.514, 0.906]." }, { "from": "gpt", "value": "The region contains the complete set of bars labelled as 'synthetic data'. They each represent a percentage value for different media categories: \n1. Newspapers at coordinates [0.370, 0.067, 0.514, 0.166] with a negative representation of -15%.\n2. TV at coordinates [0.283, 0.190, 0.514, 0.289] with a negative representation of -24%.\n3. Social Media at coordinates [0.312, 0.314, 0.514, 0.413] with a negative representation of -21%.\n4. Blogs at coordinates [0.418, 0.437, 0.514, 0.536] with a negative representation of -10%.\n5. Podcasts at coordinates [0.466, 0.561, 0.514, 0.659] with a negative representation of -5%.\n6. Forums at coordinates [0.427, 0.684, 0.514, 0.783] with a negative representation of -9%.\n7. Memes at coordinates [0.456, 0.808, 0.514, 0.906] with a negative representation of -6%." } ], "id_raw": 1031 }, { "id": "A4OsLkKkWYM0EepU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the coordinates [0.514, 0.067, 0.879, 0.906]?" }, { "from": "gpt", "value": "It is the region containing all the bars representing 'human-authored data' each signifying a percentage of different media categories in positive values:\n1. Newspapers: [0.514, 0.067, 0.754, 0.166], with a representation of 25%\n2. TV: [0.514, 0.190, 0.879, 0.289], with a representation of 38%\n3. Social Media: [0.514, 0.314, 0.668, 0.413], with a representation of 16%\n4. Blogs: [0.514, 0.437, 0.562, 0.536], with a representation of 5%\n5. Podcasts: [0.514, 0.561, 0.629, 0.659], with a representation of 12%\n6. Forums: [0.514, 0.684, 0.706, 0.783], with a representation of 20%\n7. Memes: [0.514, 0.808, 0.591, 0.906], with a representation of 8%." } ], "id_raw": 1031 }, { "id": "ib9lCN6YfQwrow4s", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area represented by the coordinates [0.0, 0.0, 0.171, 0.871]?" }, { "from": "gpt", "value": "The area contains the labels of different media categories on the y-axis. From top to bottom, the labels are 'Memes', 'Forums', 'Podcasts', 'Blogs', 'Social Media', 'TV' and 'Newspapers'. The corresponding coordinates for each label are '[0.088, 0.848, 0.171, 0.871]', '[0.086, 0.724, 0.171, 0.748]', '[0.069, 0.601, 0.171, 0.624]', '[0.107, 0.477, 0.171, 0.501]', '[0.025, 0.354, 0.171, 0.377]', '[0.141, 0.231, 0.171, 0.254]' and '[0.029, 0.107, 0.171, 0.131]' respectively." } ], "id_raw": 1031 }, { "id": "pvIYxCSUgJrGBYPq", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the plotting library used to generate the chart?" }, { "from": "gpt", "value": "matplotlib" } ], "id_raw": 1032 }, { "id": "I9j7LmXWHq8qnpoE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the process represented by the bar with the coordinates [0.556, 0.061, 0.707, 0.161]?" }, { "from": "gpt", "value": "Customer inquiries" } ], "id_raw": 1032 }, { "id": "589sIiFXCTMTjKLz", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the bar at the coordinates [0.462, 0.061, 0.556, 0.161]?" }, { "from": "gpt", "value": "Outsourced" } ], "id_raw": 1032 }, { "id": "SxWKcTQh2tIuxTww", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nIs the outsourced bar {bar}[0.462, 0.061, 0.556, 0.161] taller than the bar representing internal handling {bar}[0.556, 0.061, 0.707, 0.161] for customer enquiries?" }, { "from": "gpt", "value": "No" } ], "id_raw": 1032 }, { "id": "2h3pjMMvElZKF0VO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nComparing the internal handling sales {bar}[0.556, 0.687, 0.895, 0.787] and after-sales service {bar}[0.556, 0.812, 0.745, 0.912], which is higher?" }, { "from": "gpt", "value": "Sales" } ], "id_raw": 1032 }, { "id": "2uljnNhPlAYENSwh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nDoes the outsourced manufacturing {bar}[0.255, 0.437, 0.556, 0.537] have a higher value than internal handling manufacturing {bar}[0.556, 0.437, 0.933, 0.537]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 1032 }, { "id": "u6RCX1tntnRySCbl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the area with coordinates [0.556, 0.061, 0.707, 0.161]." }, { "from": "gpt", "value": "This area contains a blue bar that represents the number of customer inquiries that have been handled internally. The volume of internal handling customer inquiries is 40, indicated by a text label at [0.933, 0.478, 0.982, 0.495] marked as 100%." } ], "id_raw": 1032 }, { "id": "zT7pH8ByAq3XynbF", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area with coordinates [0.462, 0.061, 0.556, 0.161] refer to?" }, { "from": "gpt", "value": "This area contains the bar representing the outsourced Customer inquiries having a volume of -25 as indicated by a text label at [0.424, 0.103, 0.462, 0.120] marked as 25%." } ], "id_raw": 1032 }, { "id": "5NMZsgJn1RedzPXB", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the coordinates [0.556, 0.812, 0.745, 0.912] region?" }, { "from": "gpt", "value": "The region contains a blue bar that represents the number of after-sales service processes handled internally with a value of 50 as denoted by a text label at [0.745, 0.853, 0.783, 0.871] with a percentage of 50%." } ], "id_raw": 1032 }, { "id": "gQCvbS00jvzZ3ogg", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nWhat's the color of the bar representing \"Testing\" in synthetic data?" }, { "from": "gpt", "value": "The color of the bar representing \"Testing\" in synthetic data is {cyan}[0.298, 0.560, 0.488, 0.660]." } ], "id_raw": 1033 }, { "id": "02ZOpZozPU7fkY3E", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nWhat's the label of the y-axis tick at coordinates [0.022, 0.852, 0.190, 0.870]?" }, { "from": "gpt", "value": "The label of the y-axis tick at the given coordinates is \"Review and Control\"." } ], "id_raw": 1033 }, { "id": "XBF4BtX7sVQPHfR3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nWhat's located at the coordinates [0.274, 0.186, 0.488, 0.286]?" }, { "from": "gpt", "value": "The cyan bar representing synthetic data for the \"Ideation\" process with its value as 45% located at {coordinates}[0.274, 0.186, 0.488, 0.286]." } ], "id_raw": 1033 }, { "id": "nie8tQnKifcqjS2e", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nWhat's labeled at coordinates [0.355, 0.102, 0.393, 0.120]?" }, { "from": "gpt", "value": "The text '20%' is labeled at the given {coordinates}[0.355, 0.102, 0.393, 0.120]." } ], "id_raw": 1033 }, { "id": "tmXU0CvCtMWfkm0R", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nIs the value of synthetic data for the \"Ideation\" process (cyan bar) at [0.274, 0.186, 0.488, 0.286] higher than its corresponding human-authored data (purple bar) at [0.488, 0.186, 0.631, 0.286]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1033 }, { "id": "AAwxnXDJ9O6Xyg1Y", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nWhere is the cyan bar for \"Design\" [0.441, 0.311, 0.488, 0.410] located in relation to the purple bar for \"Design\" [0.488, 0.311, 0.726, 0.410]?" }, { "from": "gpt", "value": "To the Left." } ], "id_raw": 1033 }, { "id": "qYvDQxARMV2nLzOP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nIs the synthetic data bar for \"Review and Control\" located at [0.321, 0.809, 0.488, 0.909] shorter than the human-authored data bar for \"Review and Control\" at [0.488, 0.809, 0.774, 0.909]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1033 }, { "id": "92Krs5okeJbUXvHf", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nConsidering the x-axis labels, is the value at \"40\" [0.668, 0.964, 0.690, 0.981] located to the right of the value \"0\" [0.483, 0.964, 0.494, 0.981]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1033 }, { "id": "j5EbAYLngRH07wjq", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.202, 0.019, 0.965, 0.952]?" }, { "from": "gpt", "value": "The region [0.202, 0.019, 0.965, 0.952] represents the entire chart area, including all synthetic and human-authored data bars, x-axis and y-axis, labels for all bars, and all their corresponding values." } ], "id_raw": 1033 }, { "id": "Xla5prwUfrfbFx5P", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nCan you detail what's within the region [0.441, 0.311, 0.488, 0.410]?" }, { "from": "gpt", "value": "Within the region [0.441, 0.311, 0.488, 0.410] is the cyan bar marking synthetic data for the \"Design\" stage, with a value of 10%." } ], "id_raw": 1033 }, { "id": "cod32pz50Tcb7rda", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the coordinates [0.321, 0.809, 0.488, 0.909]." }, { "from": "gpt", "value": "The object within these coordinates [0.321, 0.809, 0.488, 0.909] is a cyan bar that stands for the synthetic data of the \"Review and Control\" stage, with its value as 35%." } ], "id_raw": 1033 }, { "id": "5rFtI5SbIYAHsTfU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.274, 0.186, 0.631, 0.286]." }, { "from": "gpt", "value": "The area contains two horizontal bars. The cyan bar at {coordinates}[0.274, 0.186, 0.488, 0.286] represents synthetic data for \"Ideation\" at 45%. The purple bar at {coordinates}[0.488, 0.186, 0.631, 0.286] represents human-authored data for \"Ideation\" at 30%." } ], "id_raw": 1033 }, { "id": "bP4IvXVd1dRnDorp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the bar related to \"Transportation\" in the group \"Urban Growth\"?" }, { "from": "gpt", "value": "The color of the category \"Transportation\" in the \"Urban Growth\" group is #20639b, referring to the bar located at {the bar}[0.653, 0.814, 0.885, 0.915]." } ], "id_raw": 1034 }, { "id": "PjrFzQCKjPRqgSA3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label aligned to the coordinates [0.015, 0.103, 0.212, 0.117]?" }, { "from": "gpt", "value": "The y-axis label at {this position}[0.015, 0.103, 0.212, 0.117] is \"Infrastructural Development\"." } ], "id_raw": 1034 }, { "id": "iDnBZnDrwLYvxnRr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar that represents \"Technological Advancement\" in the \"Urban Growth\" category?" }, { "from": "gpt", "value": "The \"Technological Advancement\" bar in the \"Urban Growth\" category is located at {this position}[0.653, 0.562, 0.852, 0.663]." } ], "id_raw": 1034 }, { "id": "L4BTI9l3TMWhAZxS", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage value associated with {the bar}[0.653, 0.184, 0.753, 0.285] in the \"Urban Growth\" group?" }, { "from": "gpt", "value": "The value is 15%, referring to the text located at [0.905, 0.101, 0.936, 0.115]." } ], "id_raw": 1034 }, { "id": "uD8kReQ9fISyagwz", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the \"Rural Growth\" bar corresponding to \"Agriculture\"}[0.388, 0.436, 0.653, 0.537] reach further on the x-axis than {the \"Urban Growth\" bar corresponding to \"Agriculture\"}[0.653, 0.436, 0.819, 0.537]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1034 }, { "id": "mvKIfwdX5ybzRQCh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Employment Opportunities\" in the \"Rural Growth\" category taller or shorter than the corresponding bar in the \"Urban Growth\" category?" }, { "from": "gpt", "value": "Shorter." } ], "id_raw": 1034 }, { "id": "7b5pgemh90hYBspn", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nBetween \"Educational Facilities\" and \"Healthcare Facilities\", which one has a higher growth in the Urban area, according to the bar chart?" }, { "from": "gpt", "value": "\"Educational Facilities\"." } ], "id_raw": 1034 }, { "id": "IUClRP8T3VQruWIQ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, is \"Rural Growth\" in \"Transportation\" larger or smaller than \"Urban Growth\"?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 1034 }, { "id": "mbh6hOhoXdmCF4Yn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the information represented in the area [0.653, 0.058, 0.905, 0.159]." }, { "from": "gpt", "value": "This area contains a bar chart representing the \"Urban Growth\" of \"Infrastructural Development\", with a growth percentage of 38%, as can be seen from the bar at {the bar}[0.653, 0.058, 0.905, 0.159] and the text at {the bar text}[0.819, 0.480, 0.850, 0.494]." } ], "id_raw": 1034 }, { "id": "ykmsOlUfmhq4HTUd", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the distribution along the y-axis from [0.018, 0.607, 0.212, 0.621] to [0.112, 0.859, 0.212, 0.873]." }, { "from": "gpt", "value": "These labels represent the growth of different areas from \"Technological Advancement\" at {this position}[0.018, 0.607, 0.212, 0.621] to \"Transportation\" at {this position}[0.112, 0.859, 0.212, 0.873] on the y-axis." } ], "id_raw": 1034 }, { "id": "zovMTD3IvHRMpLc4", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained within the coordinates [0.487, 0.184, 0.653, 0.285]?" }, { "from": "gpt", "value": "Within these coordinated exists a bar, representing \"Rural Growth\" for the category \"Educational Facilities\". It has a negative growth of 25% as indicated by the text at {this location}[0.905, 0.101, 0.936, 0.115]." } ], "id_raw": 1034 }, { "id": "wACkyM0qLUOfm7Ub", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nCan you discuss the data depicted from [0.308, 0.058, 0.653, 0.159] to [0.355, 0.814, 0.653, 0.915]?" }, { "from": "gpt", "value": "This range includes the representation of \"Rural Growth\" in all categories from \"Infrastructural Development\" at {the first bar}[0.308, 0.058, 0.653, 0.159] to \"Transportation\" at {the last bar}[0.355, 0.814, 0.653, 0.915]. It shows a variety of growth percentages for different categories." } ], "id_raw": 1034 }, { "id": "7DKMI6BbApjOMAFw", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nWhat color is used to represent the label \"7 Constraint Words\" in the legend?" }, { "from": "gpt", "value": "Pink." } ], "id_raw": 1051 }, { "id": "BJyxwkrmekTG2MRJ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the tick marking on the x-axis for the label \"-1.0\"?" }, { "from": "gpt", "value": "[0.542, 0.475]" } ], "id_raw": 1051 }, { "id": "MEtuLCbx1Yp1UMcK", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the specialty label} at [0.031, 0.426, 0.105, 0.440]?" }, { "from": "gpt", "value": "Cardiology." } ], "id_raw": 1051 }, { "id": "DZEb41ryhvztfg3X", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for \"Cardiology\"}[0.571, 0.408, 0.830, 0.455] taller than {the bar for \"Dermatology\"}[0.225, 0.174, 0.830, 0.221]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1051 }, { "id": "Jq36zkydXkv1NjUO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the blue bar for \"Neurology\"}[0.686, 0.291, 0.830, 0.338] start at a higher value than {the blue bar for \"Urology\"}[0.629, 0.057, 0.830, 0.104]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1051 }, { "id": "eRBQ6s0wpLntwJPm", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nAre {the green bars for \"Dermatology\"}[0.341, 0.675, 0.830, 0.696] shorter in length than {the blue bars for \"Dermatology\"}[0.225, 0.174, 0.830, 0.221]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1051 }, { "id": "3J0wQtFpMjwlHcvC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects and their positions within the region [0.110, 0.037, 0.974, 0.475]." }, { "from": "gpt", "value": "This region contains the main chart with bars representing medical specialties against different constraint words. Each medical specialty, like Cardiology, Neurology, Dermatology, and Urology, has different colored bars located at different heights, indicating the improvement in symptom scores." } ], "id_raw": 1051 }, { "id": "66Pn4JZxaqvjpH8V", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nCan you give details about the region defined by the coordinates [0.117, 0.516, 0.309, 0.604]?" }, { "from": "gpt", "value": "This section is the legend area of {the second subplot}[0.110, 0.509, 0.974, 0.947]. It includes color handles and text labels, identifying four different groups: '3 Constraint Words' with a pink handle, '5 Constraint Words' with a green handle, '7 Constraint Words' with a magenta handle, and '10 Constraint Words' with a blue handle." } ], "id_raw": 1051 }, { "id": "OYHrICy3VeBCbeUr", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements within [0.000, 0.000, 0.100, 0.056]?" }, { "from": "gpt", "value": "This specific region is outside the main chart area and does not contain any visible elements related to the chart." } ], "id_raw": 1051 }, { "id": "1TIoBQ5kKoJ8xkyR", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the handle color} at [0.169, 0.142, 0.197, 0.155]?" }, { "from": "gpt", "value": "'#05b9e2'" } ], "id_raw": 1054 }, { "id": "6ohJYSLo7mbEHKB4", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nWhat's located at [0.647, 0.402, 0.974, 0.450] on the chart figure?" }, { "from": "gpt", "value": "It's a bar depicting the 3-year average for the research field 'Graphene Research'." } ], "id_raw": 1054 }, { "id": "rfKJMM0CJ4WkckTU", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the content positioned at [0.413, 0.066, 0.974, 0.114]?" }, { "from": "gpt", "value": "There's a bar signifying the 'AI Technology' category's 10-Year Average." } ], "id_raw": 1054 }, { "id": "PzVGWZg0uC8Cx4yb", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nIs {the bar representing 'Graphene Research'}[0.343, 0.722, 0.974, 0.770] longer or shorter than {the bar representing 'AI Technology'}[0.413, 0.530, 0.974, 0.578]?" }, { "from": "gpt", "value": "Longer." } ], "id_raw": 1054 }, { "id": "ieQAq1iC8gZfHKRR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nBetween {3-Year Average} [0.647, 0.866, 0.974, 0.914] and {5-Year Average} [0.764, 0.818, 0.974, 0.866] on 'Graphene Research' category, which one has the higher value?" }, { "from": "gpt", "value": "3-Year Average." } ], "id_raw": 1054 }, { "id": "WiduEgtUfop1tSWO", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nIs {AI Technology's 10-Year Average bar}[0.413, 0.530, 0.974, 0.578] longer than its {7-Year Average bar}[0.507, 0.578, 0.974, 0.626]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1054 }, { "id": "H9L6fEF1aQVXuNfS", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nWhat's the description of the region [0.163, 0.520, 0.325, 0.630]?" }, { "from": "gpt", "value": "The specified region contains a legend with four entries. Each entry consists of a color handle and a label. They are: {3-Year Average}[0.208, 0.527, 0.311, 0.544] with handle color #f27970, {5-Year Average}[0.208, 0.553, 0.311, 0.570] with handle color #54b345, {7-Year Average}[0.208, 0.579, 0.311, 0.597] with handle color #c76da2 and {10-Year Average}[0.208, 0.605, 0.320, 0.623] with handle color #05b9e2." } ], "id_raw": 1054 }, { "id": "fxkLUvsRxZtLCnH2", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.647, 0.402, 0.974, 0.450]?" }, { "from": "gpt", "value": "It is a bar representing the 3-Year Average for the research field 'Graphene Research', showing the decrease in the budget." } ], "id_raw": 1054 }, { "id": "vnJLHMWkqYgST6Xn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside the coordinates [0.505, 0.964, 0.626, 0.981]." }, { "from": "gpt", "value": "The content represents the x-label, which is the 'Budget ($ Billion)'." } ], "id_raw": 1054 }, { "id": "0MXFk9VrjDvc3pxr", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart element represented by the coordinates [0.797, 0.520, 0.978, 0.630]?" }, { "from": "gpt", "value": "It's a legend which contains labels for 'Gradual Recovery', 'Sporadic Growth', 'Marginal Decrease', and 'Gradual Downturn'." } ], "id_raw": 1055 }, { "id": "aNoxxFYeOmECTSs5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nWhich axis label is positioned at coordinates [0.504, 0.964, 0.543, 0.981]?" }, { "from": "gpt", "value": "It's the x-axis label, named 'Score'." } ], "id_raw": 1055 }, { "id": "YVTez4Dqamh8387q", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the color marker at [0.804, 0.607, 0.832, 0.619] represents?" }, { "from": "gpt", "value": "This color marker corresponds to the 'Gradual Downturn' trend in the legend." } ], "id_raw": 1055 }, { "id": "GajdQDdU05ClmtNJ", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nAre the values of 'Sporadic Growth' trend for 'Urban' and 'Rural' regions located at [0.523, 0.829, 0.782, 0.871] and [0.523, 0.616, 0.763, 0.658] respectively, similar or different?" }, { "from": "gpt", "value": "Different." } ], "id_raw": 1055 }, { "id": "KrNn8MwS3bKZv1nq", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nIs the 'Gradual Downturn' bar for 'Urban' located at [0.154, 0.743, 0.523, 0.786] shorter or taller than the 'Gradual Downturn' bar for 'Rural' at [0.191, 0.530, 0.523, 0.573]?" }, { "from": "gpt", "value": "Taller." } ], "id_raw": 1055 }, { "id": "IVGBuLKrDAuIaHqU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the two bars at coordinates [0.523, 0.871, 0.690, 0.914] and [0.523, 0.658, 0.653, 0.701], which represents 'Gradual Recovery' trend's value for 'Urban' and 'Rural' respectively, which one is larger?" }, { "from": "gpt", "value": "Urban." } ], "id_raw": 1055 }, { "id": "Qyp4UEUHigMXqWkn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nCould you please describe what is found in the region [0.797, 0.520, 0.978, 0.630]?" }, { "from": "gpt", "value": "This area contains the legend of the chart. The legend consists of four labels, each paired with a color marker. The four labels represent different trends: 'Gradual Recovery', 'Sporadic Growth', 'Marginal Decrease', and 'Gradual Downturn'." } ], "id_raw": 1055 }, { "id": "odcZlQ6QMzLa0a3L", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.523, 0.658, 0.782, 0.871]?" }, { "from": "gpt", "value": "This area contains two bars from the bar chart. The first bar at [0.523, 0.829, 0.782, 0.871] represents the 'Sporadic Growth' trend value for 'Urban'. The second bar at [0.523, 0.658, 0.763, 0.658] is the 'Sporadic Growth' trend value for 'Rural'." } ], "id_raw": 1055 }, { "id": "SE7cDd6T5mdCPA1h", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the detail within the region with coordinates [0.154, 0.530, 0.523, 0.786]?" }, { "from": "gpt", "value": "This region contains two bars. The first bar at [0.154, 0.743, 0.523, 0.786] corresponds to the 'Gradual Downturn' trend for the 'Urban' region. The second bar at [0.191, 0.530, 0.523, 0.573] represents the 'Gradual Downturn' trend for the 'Rural' region." } ], "id_raw": 1055 }, { "id": "vAHeUCkU04AYRElB", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label, provided coordinates [0.122, 0.462, 0.136, 0.479], represent in the chart?" }, { "from": "gpt", "value": "The Y-axis label{AI}[0.122, 0.462, 0.136, 0.479] refers to the technology AI." } ], "id_raw": 1056 }, { "id": "nBRQEsZYCfebOIcd", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the X-axis tick, located at [0.921, 0.939, 0.943, 0.957], mean?" }, { "from": "gpt", "value": "The X-axis tick at this location{tick}[0.921, 0.939, 0.943, 0.957] represents a Score of 3.5." } ], "id_raw": 1056 }, { "id": "3X9LYzRhncCjauoD", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nWhat kind of trend does the red bar located at [0.563, 0.885, 0.932, 0.885] represent?" }, { "from": "gpt", "value": "This bar {Trend 2021 AI}[0.563, 0.885, 0.932, 0.885] represents the prediction trend for the technology 'AI' in '2021." } ], "id_raw": 1056 }, { "id": "TaVGWpZDgVJOtd01", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nHow does the height of the blue bar, identified at [0.563, 0.885, 0.647, 0.914], compare to the red bar at [0.563, 0.857, 0.932, 0.885]?" }, { "from": "gpt", "value": "The red bar (indicating the 'AI' trend for '2021) is taller than the blue one (indicating the 'AI' trend for '2020)." } ], "id_raw": 1056 }, { "id": "mKVGWkNxpmVkbZsL", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nWhen considering the bars indicating 'Blockchain' technology, as represented by blue at [0.299, 0.804, 0.563, 0.832] and red at [0.563, 0.775, 0.710, 0.804], is the red bar taller than the blue one?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1056 }, { "id": "vzm3tLAa2vP6xHrV", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nBetween the 'Blockchain' and 'IoT', whose red bar{Blockchain red bar}[0.563, 0.775, 0.710, 0.804],{IoT red bar}[0.563, 0.530, 0.647, 0.559], has a higher score for the trend in 2021?" }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 1056 }, { "id": "h4kh9VmbJhLIYpe9", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nWhat do the provided coordinates of [0.140, 0.047, 0.985, 0.469] refer to in this chart?" }, { "from": "gpt", "value": "The chart region [0.140, 0.047, 0.985, 0.469] represents the entire lower subplot of the graph, where the bars representing the technology trends for '2020 are displayed. Interestingly, all bars start from the point '0.00' on the X-axis and extend to the right based on the respective score." } ], "id_raw": 1056 }, { "id": "V1xGxORRU3hnFUrY", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.848, 0.520, 0.978, 0.578]?" }, { "from": "gpt", "value": "The region [0.848, 0.520, 0.978, 0.578] contains small rectangles and the respective text labels serving as the legend for the upper subplot. It includes 'Trend_2020' label with blue color at [0.892, 0.527, 0.972, 0.544] and 'Trend_2021' label marked with red color at [0.892, 0.553, 0.973, 0.571], corresponding to the colors of the bars in the graph." } ], "id_raw": 1056 }, { "id": "1YnTBzzKz8pIS2xN", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nPlease explain the content represented by the region [0.563, 0.530, 0.647, 0.559] on this graph." }, { "from": "gpt", "value": "The region [0.563, 0.530, 0.647, 0.559] consists of a single red bar which indicates the predicted 2021 trend of the 'IoT' technology." } ], "id_raw": 1056 }, { "id": "0zDgonPvZlbRqJvN", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the information located at coordinates [0.392, 0.019, 0.749, 0.041]?" }, { "from": "gpt", "value": "It's the subtitle, reading: \"Rural and Urban Development Comparison\"." } ], "id_raw": 1057 }, { "id": "uK1fQWK0raiuDRh3", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the object present at the coordinates [0.180, 0.541, 0.208, 0.553]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1057 }, { "id": "xrYV0HCAstkN6E1O", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the title at coordinates [0.392, 0.019, 0.749, 0.041]?" }, { "from": "gpt", "value": "Rural and Urban Development Comparison." } ], "id_raw": 1057 }, { "id": "4NIFYffMkUM91Ew5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nWhat is referred by the coordinates in the area [0.066, 0.899, 0.163, 0.917]?" }, { "from": "gpt", "value": "The y-axis label on the second plot, \"Steep Decline\"." } ], "id_raw": 1057 }, { "id": "of32SBxR7VIDB4E1", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nWhich bar is taller, the one located at [0.510, 0.898, 0.974, 0.915] or the one at [0.571, 0.881, 0.974, 0.898]?" }, { "from": "gpt", "value": "The bar at [0.510, 0.898, 0.974, 0.915]." } ], "id_raw": 1057 }, { "id": "uam3hCgXtpdUKzov", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nIs the bar for {\"Urban Development\"}[0.510, 0.898, 0.974, 0.915] longer than the bar for {\"Rural Development\"}[0.571, 0.881, 0.974, 0.898]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1057 }, { "id": "YnIWCG6BD0aHSBzw", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nIs {\"Steep Decline\"}[0.066, 0.899, 0.163, 0.917] higher on the graph than {\"Insignificant Increase\"}[0.015, 0.786, 0.163, 0.804]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1057 }, { "id": "ZOJyBbubjiXF0zNY", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nComparing the bars from the categories {\"Steep Decline\"}[0.510, 0.898, 0.974, 0.915] and {\"Insignificant Increase\"}[0.631, 0.785, 0.974, 0.802], which one has higher scores for the Urban Development?" }, { "from": "gpt", "value": "Steep Decline." } ], "id_raw": 1057 }, { "id": "Jd9Ye6Si9J0msipz", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found within the region [0.167, 0.047, 0.974, 0.457]." }, { "from": "gpt", "value": "This region includes the first subplot of the chart. It contains the bars representing the categories of \"Steep Decline\", \"Insignificant Increase\", \"Sudden Decrease\", and \"Persistent Decline\" for both \"Urban Development\" and \"Rural Development\". It also contains x-axis labels ranging from -3.5 to 0.0." } ], "id_raw": 1057 }, { "id": "p0kptqthbSn8Wfil", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area marked as [0.571, 0.513, 0.571, 0.513]?" }, { "from": "gpt", "value": "There is no visible content in the specified region. It's an empty space in between the two charts." } ], "id_raw": 1057 }, { "id": "oxCqGq1EgYssVFIj", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.254, 0.055, 0.364, 0.113]?" }, { "from": "gpt", "value": "This area contains the legend, which details the color coding of the bars. There are two labels here: a blue label indicating \"Urban Development\" and a green label indicating \"Rural Development\"." } ], "id_raw": 1057 }, { "id": "F00NglrlYQ7uOFFn", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in [0.167, 0.523, 0.974, 0.933]?" }, { "from": "gpt", "value": "It features the second subplot of the chart, similar to the first but shifted upwards. It, too, contains bars representing \"Steep Decline\", \"Insignificant Increase\", \"Sudden Decrease\", and \"Persistent Decline\" for both \"Urban Development\" and \"Rural Development\", alongside x-axis labels from -3.5 to 0.0." } ], "id_raw": 1057 }, { "id": "aTcBo7nOati9tCtE", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the text labeled in the x-axis?" }, { "from": "gpt", "value": "Impact." } ], "id_raw": 1058 }, { "id": "pbXEgIOFkkz3OZ7j", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the located at [0.383, 0.662, 0.410, 0.680]?" }, { "from": "gpt", "value": "The text \"-2.9\" associated with the Moderate Decline of the category \"Social Cultural Change\"." } ], "id_raw": 1058 }, { "id": "5GdxVw5Bm9fFljUp", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nWhat color does the legend item in coordinates [0.185, 0.506, 0.357, 0.616] associate with the label \"Erratic Growth\"?" }, { "from": "gpt", "value": "#f27970" } ], "id_raw": 1058 }, { "id": "TgPUBWZqoAVIYpau", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the label say at the y-tick with coordinates [0.015, 0.395, 0.173, 0.413]?" }, { "from": "gpt", "value": "\"Social Cultural Change\"." } ], "id_raw": 1058 }, { "id": "91DXtLmfcVB7uNZX", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater impact, \"Social Cultural Change\" as depicted by {the Erratic Growth bar}[0.337, 0.869, 0.974, 0.913] or \"Value Change\" as illustrated by {the Marginal Growth bar}[0.556, 0.605, 0.974, 0.649]?" }, { "from": "gpt", "value": "\"Social Cultural Change\"." } ], "id_raw": 1058 }, { "id": "Adutzpc5TGVHZrQ9", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nFor \"Value Change\", is {the impact of Sporadic Growth}[0.516, 0.517, 0.974, 0.561] greater or less than {the impact of Moderate Decline}[0.656, 0.561, 0.974, 0.605]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 1058 }, { "id": "URAWFgTNgH7Kug5Z", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nAmong the four groups displaying different impacts, is the {group representing Moderate Decline}\"Moderate Decline\" coordinates in legend: [0.229, 0.565, 0.352, 0.583] generally larger or smaller than the {group representing Sporadic Growth}\"Sporadic Growth\" coordinates in legend: [0.229, 0.591, 0.345, 0.609]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 1058 }, { "id": "ZqALBs46veDaFE0F", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nIs the bar representing \"Value Change\" under {Erratic Growth}[0.397, 0.039, 0.974, 0.105] taller than the same category under {Marginal Growth}[0.556, 0.605, 0.974, 0.649]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1058 }, { "id": "dGG1niKFvn7TT67G", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the pattern of bars within [0.178, 0.497, 0.974, 0.933]." }, { "from": "gpt", "value": "This region contains two sets of bars representing \"Social Cultural Change\" and \"Value Change\" across four different impacts: \"Erratic Growth\", \"Marginal Growth\", \"Moderate Decline\", and \"Sporadic Growth\". Each set contains four bars in the sequence of the impacts described. The bars appear to be generally decreasing in length from left to right, indicating a decrease in negative impact." } ], "id_raw": 1058 }, { "id": "R0J38YgPD1KFphWl", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between [0.185, 0.506, 0.357, 0.616]?" }, { "from": "gpt", "value": "This region contains the legend of the bar chart. It lists four types of impacts: \"Erratic Growth\" with color code #f27970, \"Marginal Growth\" with color code #54b345, \"Moderate Decline\" with color code #c76da2, and \"Sporadic Growth\" with color code #05b9e2." } ], "id_raw": 1058 }, { "id": "2mtRYvVTkM91oEK1", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the main components within the area [0.178, 0.019, 0.974, 0.455]?" }, { "from": "gpt", "value": "The area comprises bars representing \"Social Cultural Change\" and \"Value Change\" under the impact of \"Erratic Growth\" with x-axis labels showing different scale degrees of impact from \"-3.5\" to \"0.0\"." } ], "id_raw": 1058 }, { "id": "RQYl8ZVxIesgwegi", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.337, 0.369, 0.974, 0.435] illustrate?" }, { "from": "gpt", "value": "This region encompasses one bar representing \"Social Cultural Change\" with an impact of \"Erratic Growth\", measuring at -3.2." } ], "id_raw": 1058 }, { "id": "999uGopvKSJOT6FC", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart type of this visual?" }, { "from": "gpt", "value": "Bar chart." } ], "id_raw": 1060 }, { "id": "nTAKHku97Vv8XtUV", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nWhat disease is associated with the coordinates [0.015, 0.409, 0.102, 0.427]?" }, { "from": "gpt", "value": "Heart Stroke." } ], "id_raw": 1060 }, { "id": "tLaoHTHh0ifezj47", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at coordinates [0.333, 0.487, 0.372, 0.505]?" }, { "from": "gpt", "value": "Score." } ], "id_raw": 1060 }, { "id": "CmRDBNS19kkfp9A1", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.814, 0.062, 0.908, 0.087]?" }, { "from": "gpt", "value": "{the chart subtitle}[0.144, 0.019, 0.560, 0.041]." } ], "id_raw": 1060 }, { "id": "fpgHrZEv9JEeKU6j", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for Influenza for Critical Cases}[0.597, 0.284, 0.843, 0.328] longer than {the bar for Diabetes for Critical Cases}[0.597, 0.065, 0.781, 0.109]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1060 }, { "id": "1lwwo1SfbSvmXslU", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the bar for Diabetes for Recovered Cases} have its starting point to the left of {the x-tick for -1.0}[0.458, 0.939, 0.492, 0.957]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1060 }, { "id": "lQwwIVyCUES6jpXW", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nIs the {Recovered Cases bar for Heart Stroke}[0.536, 0.871, 0.597, 0.915] shorter than the {Critical Cases bar for Heart Stroke}[0.377, 0.394, 0.597, 0.438]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1060 }, { "id": "XEAnutDQhhbxxxLh", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nIs {the bar for COPD for Recovered Cases}[0.450, 0.652, 0.597, 0.695] closer to the {x-tick for -1.0}[0.458, 0.463, 0.492, 0.480] or the {x-tick for -1.5}[0.397, 0.463, 0.431, 0.480]?" }, { "from": "gpt", "value": "The x-tick for -1.5." } ], "id_raw": 1060 }, { "id": "HA9iBpctZH2c6VoX", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.107, 0.523, 0.597, 0.933]?" }, { "from": "gpt", "value": "This region contains the second plot area of the chart, specifically showing bars for Recovered Cases for various diseases. The bars and their labels are laid out along the x-axis, showing the scores associated with each disease's Recovered Cases." } ], "id_raw": 1060 }, { "id": "0TVKxRPrNjlNzB1o", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region [0.107, 0.047, 0.597, 0.457]?" }, { "from": "gpt", "value": "This region features the first plot area of the chart, focusing on the bar representations of Critical Cases for different diseases. The bars and their corresponding labels and values are positioned on the x-axis, depicting the scores associated with each disease's Critical Cases." } ], "id_raw": 1060 }, { "id": "cVAthPeiWrVLkvLb", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.114, 0.532, 0.283, 0.563] contain?" }, { "from": "gpt", "value": "This region involves the label for Recovered Cases with its corresponding color representation in the legend. The label and the color patch are positioned towards the left side of the plot." } ], "id_raw": 1060 }, { "id": "6oqWMUK3G7NiCH6M", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area [0.446, 0.055, 0.591, 0.087]?" }, { "from": "gpt", "value": "This area includes a legend entry for Critical Cases with its corresponding color representation. Both the color block and the label \"Critical Cases\" are positioned on the lower-right portion of the first plot area." } ], "id_raw": 1060 }, { "id": "x9zttzWhl58HERmZ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.498, 0.019, 0.561, 0.041] in the chart?" }, { "from": "gpt", "value": "Ratings." } ], "id_raw": 1063 }, { "id": "A0CTTFvrmIzp00VG", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the bar representing the score of Italian food in the Ratings chart?" }, { "from": "gpt", "value": "[0.083, 0.065, 0.815, 0.225]." } ], "id_raw": 1063 }, { "id": "XvarqnUYDVSUm0hQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nWhat is presented at [0.510, 0.964, 0.549, 0.981]?" }, { "from": "gpt", "value": "X-axis label, called Score." } ], "id_raw": 1063 }, { "id": "9saGcPfIJfUNdBfl", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nWhat information can we gather from [0.083, 0.755, 0.637, 0.915]?" }, { "from": "gpt", "value": "The score for Japanese food's interest, which is 6.2." } ], "id_raw": 1063 }, { "id": "oGqz0j8Zx5A7d98g", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Japanese bar}[0.083, 0.265, 0.932, 0.425] higher than {the Italian bar}[0.083, 0.065, 0.815, 0.225] in the Ratings chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1063 }, { "id": "87wdpDDPjiov9lyM", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nAre the scores for {Japanese cuisine}[0.083, 0.265, 0.932, 0.425 , 0.083, 0.755, 0.637, 0.915] higher than for {Italian cuisine}[0.083, 0.065, 0.815, 0.225 , 0.083, 0.555, 0.521, 0.715] in both the Ratings and Interest charts?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1063 }, { "id": "AeIb7sXJFV6hPJOR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Interest of Japanese cuisine}<[0.083, 0.755, 0.637, 0.915]> greater than {Ratings of Italian cuisine}<[0.083, 0.065, 0.815, 0.225]>?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1063 }, { "id": "8ca6MP9LHrCwFXx7", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nBetween {Scores in Ratings}<[{Ratings of Japanese cuisine}[0.083, 0.265, 0.932, 0.425]], [{Ratings of Italian cuisine}[0.083, 0.065, 0.815, 0.225]]> and {Scores in Interest}<[{Interest of Japanese cuisine}[0.083, 0.755, 0.637, 0.915]], [{Interest of Italian cuisine}[0.083, 0.555, 0.521, 0.715]]>, which category has higher values overall?" }, { "from": "gpt", "value": "Ratings." } ], "id_raw": 1063 }, { "id": "as0uSCor8mf9XPXe", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.083, 0.537, 0.976, 0.933]." }, { "from": "gpt", "value": "This area includes the \"Interest\" subplot. It contains the x-axis label \"Score\", y-axis' tick marks and their labels, and bars representing the respective scores of Japanese and Italian cuisine. The Japanese Cuisine Bar can be found at [0.083, 0.755, 0.637, 0.915], and the Italian Cuisine Bar at [0.083, 0.555, 0.521, 0.715]." } ], "id_raw": 1063 }, { "id": "qK6Ai86bPpdDQ1uy", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.083, 0.047, 0.976, 0.443]." }, { "from": "gpt", "value": "This area consists of the x-axis label \"Score\", y-axis' tick marks and their labels, and horizontal bars representing the 'Ratings' for Japanese and Italian cuisine. The bar for Japanese cuisine is located at [0.083, 0.265, 0.932, 0.425] and for Italian cuisine is at [0.083, 0.065, 0.815, 0.225]." } ], "id_raw": 1063 }, { "id": "nfTWJ3p51jFTmXyC", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.083, 0.755, 0.637, 0.915]?" }, { "from": "gpt", "value": "There is one object which is the bar representing the Interest of Japanese cuisine, which has a score of 6.2." } ], "id_raw": 1063 }, { "id": "Y12H0bGgHLHbV97J", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area [0.083, 0.065, 0.932, 0.425]?" }, { "from": "gpt", "value": "There is a bar representing the rating of Italian cuisine, which has a score of 8.2." } ], "id_raw": 1063 }, { "id": "rwIsnOWnEsJ53u7m", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis?" }, { "from": "gpt", "value": "There is no label in the y-axis." } ], "id_raw": 1064 }, { "id": "y1clyPVWQpfapTrQ", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content mapped at the coordinates [0.043, 0.210, 0.206, 0.228]?" }, { "from": "gpt", "value": "The string \"Public Service Provision\"." } ], "id_raw": 1064 }, { "id": "xtAvr6v5JQ2ycrW5", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the chunk of data located at (x1, y1, x2, y2) = [0.015, 0.618, 0.206, 0.635] refer to?" }, { "from": "gpt", "value": "It refers to the y-axis label \"Local Government Stronger\"." } ], "id_raw": 1064 }, { "id": "DLu8qgOA6mt8EFy0", "Chart_type": "bar", "QA_type": "Referring QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nCan you identify the primary object at location [0.043, 0.687, 0.206, 0.704]?" }, { "from": "gpt", "value": "It is a y-axis label, \"Public Service Provision\"." } ], "id_raw": 1064 }, { "id": "gY4e4me0B8QttNd3", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nWhats the comparison between the lengths of {bar for category 'Population Engagement'}[0.593, 0.832, 0.812, 0.846] and {bar for category 'Decentralization Measures'}[0.593, 0.749, 0.745, 0.763]?" }, { "from": "gpt", "value": "The bar for 'Population Engagement' is longer than the bar for 'Decentralization Measures'." } ], "id_raw": 1064 }, { "id": "ApFcuHn6U1PDKpVC", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nIs the {bar for category 'Governance Structure'}[0.974, 0.410, 1.241, 0.438] longer than the {bar for category 'Community Insights Better'}[0.488, 0.556, 0.593, 0.570]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1064 }, { "id": "igKUIpvj6eZqp5bR", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {the bar representing \"Community Insights Better\" for Row1}[0.764, 0.065, 0.974, 0.093] and {the bar representing \"Community Insights Better\" for Row2}[0.469, 0.542, 0.593, 0.556]?" }, { "from": "gpt", "value": "The bar for Row1 is longer than the bar for Row2." } ], "id_raw": 1064 }, { "id": "ysutTgdy20VdKHTP", "Chart_type": "bar", "QA_type": "Relationship QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nCompare the heights of {the bar for category 'Local Government Stronger' for Row1}[0.898, 0.134, 0.974, 0.162] and {the bar for category 'Local Government Stronger' for Row2}[0.497, 0.611, 0.593, 0.625]." }, { "from": "gpt", "value": "The bar for Row2 is taller than the bar for Row1." } ], "id_raw": 1064 }, { "id": "N7IVCk8T8vcGAai6", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents in the area {area}[0.211, 0.523, 0.974, 0.933]." }, { "from": "gpt", "value": "The area contains the second subplot of a bar chart, which includes bars representing the scores for different rows in categories such as \"Governance Structure\", \"Population Engagement\", \"Decentralization Measures\", \"Public Service Provision\", \"Local Government Stronger\", and \"Community Insights Better\". The legend for this chart is located at [0.879, 0.532, 0.967, 0.590], showing two rows: Row1 colored in '#f27970' and Row2 colored in '#54b345'." } ], "id_raw": 1064 }, { "id": "VlU8uwLVfiewLgZ0", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me about the objects within the area defined by [0.973, 0.272, 1.069, 0.300]?" }, { "from": "gpt", "value": "This area contains a bar representing the value of \"Decentralization Measures\" in Row1." } ], "id_raw": 1064 }, { "id": "SubUISvTuvtn7XWU", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the box from [0.497, 0.611, 0.593, 0.625]?" }, { "from": "gpt", "value": "Within this box, there is a horizontal bar that represents the score of the 'Local Government Stronger' category for Row2." } ], "id_raw": 1064 }, { "id": "OamJFtsZkFogV5GK", "Chart_type": "bar", "QA_type": "Regional Description QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found between coordinates [0.211, 0.047, 0.974, 0.457]." }, { "from": "gpt", "value": "The coordinates contain the first subplot of a bar chart including bars of 'Governance Structure', 'Population Engagement', 'Decentralization Measures', 'Public Service Provision', 'Local Government Stronger', 'Community Insights Better', and a subtitle \"Scores by category for different rows\"." } ], "id_raw": 1064 }, { "id": "O2XYCKFFe0mHlrAQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nCould you turn the data from the bar chart into a structured table?" }, { "from": "gpt", "value": "From the {chart's subtitle}[0.401, 0.019, 0.642, 0.041], it's evident that the chart is showing a comparison of market shares across different industry sectors for the years 2020 and 2021. The x-axis, represented by {x-axis labels}[0.126, 0.738, 0.138, 0.799]; [0.213, 0.738, 0.225, 0.835]; [0.299, 0.738, 0.311, 0.805]; [0.386, 0.738, 0.397, 0.833]; [0.472, 0.738, 0.484, 0.899]; [0.559, 0.738, 0.570, 0.951]; [0.645, 0.738, 0.657, 0.829]; [0.732, 0.738, 0.743, 0.818]; [0.818, 0.738, 0.830, 0.839]; [0.905, 0.738, 0.916, 0.957], corresponds to different industry sectors, while the y-axis, with labels from {0.0}[0.029, 0.719, 0.047, 0.737] to {0.7}[0.029, 0.022, 0.047, 0.040], represents the percentage of market share. According to {the legend}[0.911, 0.055, 0.982, 0.113], the blue bars correspond to the year 2020, and the orange bars represent the year 2021. Each sector has a pair of bars, with each bar's height representing the percentage share for that year. For example, {the blue and orange bars for \"Energy\"}[0.098, 0.427, 0.167, 0.726], [0.098, 0.079, 0.167, 0.427] represent market shares of 0.3 and 0.35 for 2020 and 2021, respectively.\nBased on the above thought, the answer is:\nSector,2020,2021\nEnergy,0.3,0.35\nTechnology,0.2,0.225\nFinance,0.15,0.2\nHealthcare,0.1,0.15\nConsumer_Staples,0.05,0.05\nConsumer_Discretionary,0.05,0.025\nIndustrials,0.1,0.1\nMaterials,0.05,0.025\nReal_Estate,0.025,0.025\nCommunication_Services,0.025,0.05" } ], "id_raw": 0 }, { "id": "rVPd7XDboA1ft92k", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nBased on the market share comparison chart you observed, which sectors show positive growth from 2020 to 2021 and why?" }, { "from": "gpt", "value": "I can answer this question through a detailed analysis of the respective bar heights for 2020 and 2021. If the 2021 bar is taller than the 2020 bar, it means that sector showed growth. From left to right, {Energy bars}[0.098, 0.427, 0.167, 0.726], [0.098, 0.079, 0.167, 0.427], {Technology bars}[0.184, 0.527, 0.253, 0.726], [0.184, 0.303, 0.253, 0.527], and {Finance bars}[0.271, 0.577, 0.340, 0.726], [0.271, 0.378, 0.340, 0.577]all show positive growth. This is because their 2021 bars have greater heights compared to their 2020 ones.\nBased on the above thought, the answer is:\nEnergy, Technology and Finance sectors show positive growth from 2020 to 2021 because their market shares have increased from 2020 to 2021." } ], "id_raw": 0 }, { "id": "AUU3kkKRgGZ3GuVa", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_0.png", "conversations": [ { "from": "human", "value": "\nWhich sector experienced the highest growth in market share from 2020 to 2021?" }, { "from": "gpt", "value": "To find out which sector grew the most, I need to analyze the height difference between the 2020 and 2021 bars for each sector. Comparing the differences, {The Energy sector bars}[0.098, 0.427, 0.167, 0.726], [0.098, 0.079, 0.167, 0.427] have a 0.05 difference in height, {Technology}[0.184, 0.527, 0.253, 0.726], [0.184, 0.303, 0.253, 0.527] and {Finance}[0.271, 0.577, 0.340, 0.726], [0.271, 0.378, 0.340, 0.577] have 0.025 difference, and many other sectors have less or no difference. Thus, the Energy sector experienced the highest growth in market share from 2020 to 2021.\nBased on the above thought, the answer is:\nEnergy." } ], "id_raw": 0 }, { "id": "HtsJKFAmZbyYQ65b", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nCould you please provide the emission level of each category for the years 2020 and 2021 as observed in the chart, in CSV format?" }, { "from": "gpt", "value": "I deduce the values of equivalent CO2 emissions from the height of the bars in the graph. The bars that are colored light blue {bars representing 2020}[0.140, 0.062, 0.309, 0.329; 0.352, 0.062, 0.521, 0.443; 0.564, 0.062, 0.733, 0.291; 0.775, 0.062, 0.945, 0.367] stand for the data in the year 2020 and the bars in orange {bars representing 2021}[0.140, 0.329, 0.309, 0.634; 0.352, 0.443, 0.521, 0.863; 0.564, 0.291, 0.733, 0.634; 0.775, 0.367, 0.945, 0.748] are for the data in 2021. By measuring the height of these bars in reference to the Y-axis scale ranging from 0 to -12000, I can extract the values of CO2 emissions for each category in both years.\nBased on the above thought, the answer is:\nCategories,2020,2021\nUrban Greening,-3500,-7500\nRoad Traffic,-5000,-10500\nWaste Management,-3000,-7500\nAir Condition Usage,-4000,-9000" } ], "id_raw": 1 }, { "id": "oHe34lmd2fNLSIzr", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nFrom the perspective of carbon emission reduction, could you please give suggestions based on the bar chart?" }, { "from": "gpt", "value": "The goal of carbon emission reduction would be to find categories where emissions values have increased over time and aim to bring them down. Analyzing the emissions of various categories from {2020's bars}[0.140, 0.062, 0.309, 0.329; 0.352, 0.062, 0.521, 0.443; 0.564, 0.062, 0.733, 0.291; 0.775, 0.062, 0.945, 0.367] to {2021's bars}[0.140, 0.329, 0.309, 0.634; 0.352, 0.443, 0.521, 0.863; 0.564, 0.291, 0.733, 0.634; 0.775, 0.367, 0.945, 0.748], all categories namely Urban Greening, Road Traffic, Waste Management and Air Condition Usage have increased emissions. However, Road Traffic and Air Condition Usage are the main contributors showing a higher increase in emissions compared to other categories. Thus, efforts should be made to control and reduce emissions in all categories with a particular emphasis on controlling Road Traffic and Air Condition Usage.\nBased on the above thought, the answer is:\nControlling and reducing emissions in all categories is crucial. However, based on the chart, particular emphasis should be placed on controlling Road Traffic and Air Condition Usage as they showed a significant increase from 2020 to 2021." } ], "id_raw": 1 }, { "id": "c2192cP1jIAjSW0T", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_1.png", "conversations": [ { "from": "human", "value": "\nWhich category experienced the greatest increase in CO2 emissions from 2020 to 2021?" }, { "from": "gpt", "value": "To determine which category experienced the greatest increase relative to the 2020 emissions, I need to subtract the value of the emissions in 2020 from the value of the emissions in 2021 for each category. The value of each category's emissions is represented by the height of their corresponding bars. From left to right, the categories are Urban Greening: {blue bar for 2020}[0.140, 0.062, 0.309, 0.329] and {orange bar for 2021}[0.140, 0.329, 0.309, 0.634] with an increase of 4000; Road Traffic: {blue bar for 2020}[0.352, 0.062, 0.521, 0.443] and {orange bar for 2021}[0.352, 0.443, 0.521, 0.863] with an increase of 5500; etc. Among all categories, the bar of Road Traffic has the greatest increase from 2020 to 2021.\nBased on the above thought, the answer is:\nRoad Traffic" } ], "id_raw": 1 }, { "id": "ZM19WhfV6p4q7B5V", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart of investments in Education Technology and Online Learning in two consecutive years, could you please organize the data into a CSV format?" }, { "from": "gpt", "value": "In this chart, {the x-axis}[0.221, 0.919, 0.813, 0.942] lists the two sectors in the focus - 'Education Technology' and 'Online Learning'. The y-axis indicates the investments, ranging from {0 to 140 million dollars}[0.052, 0.894, 0.061, 0.020]. According to {the legend}[0.078, 0.074, 0.212, 0.151], {the blue bars}<{\"first year\"}[0.122, 0.083, 0.185, 0.106]> for each sector denote the investment for the first year and {the orange bars}<{\"second year\"}[0.122, 0.118, 0.207, 0.141]> represent the additional investment in the following year. For example, the {blue bar for \"Education Technology\"}[0.112, 0.615, 0.482, 0.903] ends at roughly the 45 points mark on the y-axis, indicating an investment of 45 million in the first year. The {top of the orange bar for \"Education Technology\"}[0.112, 0.230, 0.482, 0.615] corresponds to 60, so the total investment for the second year is 60 million dollars.\nBased on the above thought, the answer is:\nSector,First Year,Second Year\nEducation Technology,45,60\nOnline Learning,55,70" } ], "id_raw": 2 }, { "id": "DtnT347rtXNvJwCm", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhich sector received a higher increase in investment in the second year, based on the chart?" }, { "from": "gpt", "value": "To answer the question, I have to compare the lengths of {the orange bars} in both sectors, which represent the increased investments for the second year. The {orange bar for \"Education Technology\"}<[0.112, 0.230, 0.482, 0.615]> extends from around the 45 mark to the 60 mark on the y-axis, indicating a 15 million dollars increase. On the other hand, the {orange bar for \"Online Learning\"}<[0.574, 0.102, 0.943, 0.551]> extends from approximately the 55 mark to the 70 mark, representing a 15 million dollars increase as well. It seems the increase in investment for the second year is the same in both sectors.\nBased on the above thought, the answer is:\nThe increase in investment is identical in both sectors, with a raise of 15 million dollars each." } ], "id_raw": 2 }, { "id": "z2siRlpQosXtQSBD", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat\u2019s the total amount of investment for each sector for both years?" }, { "from": "gpt", "value": "Looking at the chart, the total investment for each sector can be calculated by adding the investment of the first year to the second year. The {blue bar for \"Education Technology\"}[0.112, 0.615, 0.482, 0.903] indicates an investment of 45 million dollars and the {orange bar for \"Education Technology\"}[0.112, 0.230, 0.482, 0.615] indicates an additional 15 million dollars for the second year. Therefore, the total investment for the Education Technology sector is 45 million + 15 million = 60 million dollars. As for the Online Learning sector, the {blue bar}[0.574, 0.551, 0.943, 0.903] signifies an investment of 55 million dollars whereas the {orange bar}[0.574, 0.102, 0.943, 0.551] represents an additional investment of 15 million dollars, bringing the total investment to 70 million dollars.\nBased on the above thought, the answer is:\nEducation Technology: 60 Million dollars\nOnline Learning: 70 Million dollars" } ], "id_raw": 2 }, { "id": "FTOxL8ByqMUWyMQe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nCould you convert the chart data into a CSV table format including time spent on each sector for Current and Previous year?" }, { "from": "gpt", "value": "Looking at the chart, I can identify two sectors from {the x-axis labels}<{\"Social Media\"}[0.269, 0.919, 0.356, 0.942]; {\"Digital Communication\"}[0.684, 0.919, 0.843, 0.942]>. Each sector has two bars, with the {blue bars}[{\"Current Year\"}[0.880, 0.824, 0.966, 0.847]] representing the current year and the {brown bars}[{\"Previous Year\"}[0.880, 0.859, 0.972, 0.882]] representing the previous year. By comparing the height of these bars with {the y-axis tick labels}[{\"-1600\"}[0.034, 0.987, 0.081, 1.010]; {\"-1400\"}[0.035, 0.870, 0.081, 0.893]; {\"-1200\"}[0.035, 0.753, 0.081, 0.777]; {\"-1000\"}[0.035, 0.637, 0.081, 0.660]; {\"-800\"}[0.043, 0.520, 0.081, 0.543]; {\"-600\"}[0.043, 0.403, 0.081, 0.427]; {\"-400\"}[0.043, 0.286, 0.081, 0.310]; {\"-200\"}[0.043, 0.170, 0.081, 0.193]; {\"0\"}[0.073, 0.053, 0.081, 0.076]], I can work out the extent of the decrease in time spent on each sector before and after. To create a CSV table, I can align sector names in the leading column, followed by each year's decrease in time spent.\nBased on the above thought, the answer is:\nSectors,Current_Year,Previous_Year\nSocial Media,-922,-1372\nDigital Communication,-310,-450" } ], "id_raw": 3 }, { "id": "OzmTrWvIbnHPjG70", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nInterpret the change in time spent on Digital Communication based on the chart." }, { "from": "gpt", "value": "Drawing conclusions from the chart, we can compare the {brown bar for the previous year}[0.583, 0.243, 0.944, 0.646] and the {blue bar for the current year}[0.583, 0.062, 0.944, 0.243] which refer to Digital Communication. It is evident that the time spent has decreased from the previous year to the current year. While the decrease is noticeable, it appears steady, when compared to the significant drop seen in Social Media.\nBased on the above thought, the answer is:\nThe time spent on Digital Communication has decreased from the previous year to the current year, although the decrease is not as dramatic as seen for Social Media." } ], "id_raw": 3 }, { "id": "LXDaM04tFmluxtpc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_3.png", "conversations": [ { "from": "human", "value": "\nWhich sector has shown the larger decrease in time, as per the chart?" }, { "from": "gpt", "value": "To find out which sector has seen the larger decrease in time, I can compare the heights of the {brown bars representing the previous year}[{\"Social Media\"}[0.132, 0.600, 0.493, 0.863]; {\"Digital Communication\"}[0.583, 0.243, 0.944, 0.646]] with the {blue bars representing the current year}[{\"Social Media\"}[0.132, 0.062, 0.493, 0.600]; {\"Digital Communication\"}[0.583, 0.062, 0.944, 0.243]]. By comparing these differences, the {Social Media}[0.269, 0.919, 0.356, 0.942] sector appears to have a larger decrease in time spent as the change from the previous to the current year is more significant.\nBased on the above thought, the answer is:\nSocial Media." } ], "id_raw": 3 }, { "id": "l0WuIBTLtaFgFudK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart figure, please extract the data and organize it into a CSV format table." }, { "from": "gpt", "value": "The chart contains four categories on the x-axis: {Human Rights}[0.168, 0.919, 0.266, 0.942], {Income Inequality}[0.369, 0.919, 0.493, 0.942], {Child Development}[0.578, 0.919, 0.711, 0.942], and {Healthcare}[0.821, 0.919, 0.897, 0.942]. Each category has two bars representing values for the Current Year and Previous Year based on the chart legend {Current Year}[0.143, 0.824, 0.229, 0.847] and {Previous Year}[0.143, 0.859, 0.235, 0.882] respectively. The y-values can be inferred from the length of the bars and correspond to the number of issues (x 100) in each sector for each year. For instance, in the Human Rights category, the {Current Year bar}[0.132, 0.062, 0.303, 0.298] corresponds to a value of -500, and the {Previous Year bar}[0.132, 0.298, 0.303, 0.618] corresponds to a value of -680. The same procedure is applied to the remaining categories. Thus, the CSV format data can be organized as follows:\nBased on the above thought, the answer is:\nSector, Current Year, Previous Year\nHuman Rights, -500, -680\nIncome Inequality, -800, -900\nChild Development, -350, -480\nHealthcare, -300, -500" } ], "id_raw": 5 }, { "id": "R2CXMEfmWnzVVhph", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector needs the most immediate attention and why?" }, { "from": "gpt", "value": "The bar chart indicates that the sectors with the highest negative values have the most pressing issues. By comparing the heights of the bars in the {Current Year}[0.143, 0.824, 0.229, 0.847], it appears that {Income Inequality}[0.346, 0.062, 0.517, 0.439] has the tallest bar, representing -800 issues, which is more than any other sector. This means that the Income Inequality sector currently has the most issues, hence needs urgent attention.\nBased on the above thought, the answer is:\nThe Income Inequality sector needs the most immediate attention because it currently has the highest number of issues." } ], "id_raw": 5 }, { "id": "ijnSFoUdlQzlkqw0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_5.png", "conversations": [ { "from": "human", "value": "\nWhich sector had the largest improvement from the previous year to the current year?" }, { "from": "gpt", "value": "Improvement in this context is interpreted as the biggest decrease in the number of issues. I calculate the improvement by subtracting the value of {Current Year}[0.143, 0.824, 0.229, 0.847] from {Previous Year}[0.143, 0.859, 0.235, 0.882] for each sector. The sector with the largest positive difference was {Income Inequality}[0.346, 0.439, 0.517, 0.863], whose value improved from -900 to -800, resulting in an improvement of 100 issues.\nBased on the above thought, the answer is:\nIncome Inequality" } ], "id_raw": 5 }, { "id": "JD9ZPC1OvTZfHi1N", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nCan you organize the raw department performance data depicted in the chart into a structured table?" }, { "from": "gpt", "value": "The chart displays performance data for five departments across two years, as per the bar plots and their color coding found in {the legend}[0.893, 0.815, 0.978, 0.891]. Each department forms a category on the x-axis, as specified by {the x-axis tick labels}[{\"Marketing\"}[0.165, 0.919, 0.234, 0.942]; {\"Sales\"}[0.350, 0.919, 0.387, 0.942]; {\"Development\"}[0.491, 0.919, 0.585, 0.942]; {\"Operations\"}[0.669, 0.919, 0.746, 0.942]; {\"HR\"}[0.867, 0.919, 0.887, 0.942]]. The y-axis is a representation of the performance values, running from -2500 to 0. To formulate the CSV table, I'll extract the negative performance values for each department in both years. In the \"Marketing\" category, {the blue bar}[0.132, 0.062, 0.267, 0.523] represents -1325 for 2020 and {the orange bar}[0.132, 0.523, 0.267, 0.863] represents -975.736 for 2019, and likewise for \"Sales\", \"Development\", \"Operations\", and \"HR\".\nBased on the above thought, the answer is:\nDepartment,2020,2019\nMarketing,-1325,-975.736\nSales,-525.819,-729.07\nDevelopment,-785.384,-899.372\nOperations,-953.243,-350.341\nHR,-399.653,-875.85" } ], "id_raw": 6 }, { "id": "99dt1AFx4KxgErqe", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat key insights can be drawn from the chart regarding the performance shift across the departments over the two years?" }, { "from": "gpt", "value": "The chart compares the performance shift of five departments between 2019 and 2020. There's an evident sustained decline in 'Marketing' visible in {the bars for Marketing}[{2020}[0.132, 0.062, 0.267, 0.523]; {2019}[0.132, 0.523, 0.267, 0.863]]; with an inconsistent decrease for 'Sales' and 'Development' shown in {their bars}[{Sales 2020}[0.301, 0.062, 0.436, 0.245]; {Sales 2019}[0.301, 0.245, 0.436, 0.499]; {Development 2020}[0.470, 0.062, 0.606, 0.336]; {Development 2019}[0.470, 0.336, 0.606, 0.648]]. In contrast, there's a slight rise in the 'Operations' department, noticeable in the {Operations bars}[{2020}[0.640, 0.062, 0.775, 0.394]; {2019}[0.640, 0.394, 0.775, 0.516]], while the 'Human Resources (HR)' department faced a sharp performance decline, apparent in {HR bars}[{2020}[0.809, 0.062, 0.944, 0.201]; {2019}[0.809, 0.201, 0.944, 0.506]]. Comparing the performance changes, the 'Marketing' and 'Operations' departments experienced the most significant shifts, with the latter showing a noticeable upswing compared to the rest.\nBased on the above thought, the answer is:\nKey insights include a sustained decline in Marketing, fluctuating performance in Sales and Development, a slight increase in Operations, and a pronounced decline in HR. The Operations department stands out with a performance shift opposite to the general trend, while Marketing faced a consistent decrease over the two years." } ], "id_raw": 6 }, { "id": "WW4UiNA6eObMkhFs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_6.png", "conversations": [ { "from": "human", "value": "\nWhich department experienced the least decline in performance from 2019 to 2020?" }, { "from": "gpt", "value": "The chart shows the fall in performance for five departments - Marketing, Sales, Development, Operations, and HR. To find out which department experienced the least decline, the difference between the performance scores of 2019 and 2020 for each department needs to be calculated. These differences can be found by comparing the heights of the colored bars for each department in two sets\u2014 {2020}[{Marketing}[0.132, 0.062, 0.267, 0.523]; {Sales}[0.301, 0.062, 0.436, 0.245]; {Development}[0.470, 0.062, 0.606, 0.336]; {Operations}[0.640, 0.062, 0.775, 0.394]; {HR}[0.809, 0.062, 0.944, 0.201]] and {2019}[{Marketing}[0.132, 0.523, 0.267, 0.863]; {Sales}[0.301, 0.245, 0.436, 0.499]; {Development}[0.470, 0.336, 0.606, 0.648]; {Operations}[0.640, 0.394, 0.775, 0.516]; {HR}[0.809, 0.201, 0.944, 0.506]]. Analyzing the bars, it can be observed that the HR department had the smallest difference between the two years.\nBased on the above thought, the answer is:\nHR" } ], "id_raw": 6 }, { "id": "Uzj3GRaTMhLTVKbc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nCould you please generate a structured CSV table from the data visualized in the bar chart?" }, { "from": "gpt", "value": "The bar chart presents data for different eras in human history, labeled along {the x-axis}[{\"Stone Age\"}[0.164, 0.919, 0.235, 0.942]; {\"Bronze Age\"}[0.329, 0.919, 0.408, 0.942]; {\"Iron Age\"}[0.509, 0.919, 0.567, 0.942]; {\"Middle Ages\"}[0.665, 0.919, 0.750, 0.942]; {\"Modern Era\"}[0.837, 0.919, 0.916, 0.942]]. The y-axis, ranging from {0}[0.073, 0.053, 0.081, 0.076] to {-5000}[0.035, 0.905, 0.081, 0.928], represents the advancements in civilization with arbitrary units. The chart displays two sets of bars, one for the {Current Century}[{\"bar set\"}[0.132, 0.062, 0.944, 0.488]] identified by a light blue color, and the other for the {Previous Century}[{\"bar set\"}[0.132, 0.216, 0.944, 0.863]] represented in a light orange color. This is also clarified by {the legend}[0.098, 0.815, 0.266, 0.891]. A structured CSV table can be generated by treating each era as a category, and the values of the Current and Previous Century bars as values for these categories. The corresponding advancement in arbitrary units is indicated by the bar\u2019s location along the y-axis. For example, the {Current Century bar for the Stone Age}[0.132, 0.062, 0.267, 0.216] is at around -900, and the {Previous Century bar for the Stone Age}[0.132, 0.216, 0.267, 0.386] is at around -1000.\nBased on the above thought, the answer is:\nTime Periods,Current Century,Previous Century\nStone Age,-900,-1000\nBronze Age,-500,-800\nIron Age,-800,-1300\nMiddle Ages,-1500,-2000\nModern Era,-2500,-2200" } ], "id_raw": 7 }, { "id": "uS7Cs0BwK6VMDJA0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which era has seen the least progress from the previous century to the current century and why do you think so?" }, { "from": "gpt", "value": "We're trying to find where the least improvement occurred between two centuries. This is evident by finding which time period saw the smallest difference in progression between the Previous Century to the Current Century. By comparing the heights of the two bars representing {the Stone Age}[{\"Previous Century\"}[0.132, 0.216, 0.267, 0.386]; {\"Current Century\"}[0.132, 0.062, 0.267, 0.216]], {the Bronze Age}[{\"Previous Century\"}[0.301, 0.147, 0.436, 0.284]; {\"Current Century\"}[0.301, 0.062, 0.436, 0.147]], {the Iron Age}[{\"Previous Century\"}[0.470, 0.198, 0.606, 0.420]; {\"Current Century\"}[0.470, 0.062, 0.606, 0.198]], {the Middle Ages}[{\"Previous Century\"}[0.640, 0.318, 0.775, 0.658]; {\"Current Century\"}[0.640, 0.062, 0.775, 0.318]], and {the Modern Era}[{\"Previous Century\"}[0.809, 0.488, 0.944, 0.863]; {\"Current Century\"}[0.809, 0.062, 0.944, 0.488]];\nBased on the above thought, the answer is:\nThe Bronze Age. The bars representing the Bronze Age for both centuries [Current Century: (-500); Previous Century (-800)] show a minimal change compared to other eras. This minimal difference in arbitrary units of advancement could imply that civilization's development was somewhat stagnant during this period." } ], "id_raw": 7 }, { "id": "EPgX9kPk6h8WtFi6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_7.png", "conversations": [ { "from": "human", "value": "\nIn which Time Period does the most significant improvement happen from the Previous Century to the Current Century?" }, { "from": "gpt", "value": "To identify where the most considerable improvement occurs from the Previous Century to the Current Century, I would compare the differences between the corresponding bars in both centuries for each era. For example, for the {Stone Age bars}[{\"Previous Century\"}[0.132, 0.216, 0.267, 0.386]; {\"Current Century\"}[0.132, 0.062, 0.267, 0.216]], the difference is 1000 - 900 = 100. I would do the same for the {Bronze Age bars}[{\"Previous Century\"}[0.301, 0.147, 0.436, 0.284]; {\"Current Century\"}[0.301, 0.062, 0.436, 0.147]], {Iron Age bars}[{\"Previous Century\"}[0.470, 0.198, 0.606, 0.420]; {\"Current Century\"}[0.470, 0.062, 0.606, 0.198]], {Middle Ages bars}[{\"Previous Century\"}[0.640, 0.318, 0.775, 0.658]; {\"Current Century\"}[0.640, 0.062, 0.775, 0.318]], and {Modern Era bars}[{\"Previous Century\"}[0.809, 0.488, 0.944, 0.863]; {\"Current Century\"}[0.809, 0.062, 0.944, 0.488]]. The bar with the largest difference would represent the Time Period with the most significant improvement from the Previous Century to the Current Century.\nBased on the above thought, the answer is:\nModern Era." } ], "id_raw": 7 }, { "id": "f4bdpMo8w1dQ27Gl", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, distinguish the policy impacts in terms of exact data for each social-economic area for years 2020 and 2021? Please present the data in CSV format." }, { "from": "gpt", "value": "To convert the data from the bar chart into a CSV format, I have to identify values for each category for years 2021 and 2020. Here \"socio-economic areas\" are represented by the x-axis and \"policy impact\" by the y-axis. The x-axis categories can be seen by referring to the x-axis labels {Housing}[0.181, 0.919, 0.238, 0.942], {Education}[0.391, 0.919, 0.460, 0.942], {Healthcare}[0.604, 0.919, 0.680, 0.942], and {Transportation}[0.807, 0.919, 0.908, 0.942]. The y-axis values go from -400 to 1400 as indicated by {y-axis tick labels}[0.035, 0.929, 0.072, 0.952] to [0.037, 0.028, 0.072, 0.051].\nUsing these, I can interpret the bars representing the years 2021 and 2020 for each socio-economic area. Looking at the {bars representing Housing}[0.123, 0.187, 0.296, 0.738] for 2021 and [0.123, 0.187, 0.296, 0.638] for 2020, I can determine the policy impact values for the respective years.\nGoing over each category in a similar way, I can construct a CSV table.\nBased on the above thought, the answer is:\n\"Socioeconomic Areas\",\"Policy Impact (2021)\",\"Policy Impact (2020)\"\n\"Housing\",1100,-900\n\"Education\",850,-650\n\"Healthcare\",950,-1200\n\"Transportation\",1350,-1000" } ], "id_raw": 8 }, { "id": "wr2Sush6EikdEcPi", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nBased on the information from the bar chart, which area has seen the most improvement in policy impact from 2020 to 2021, and why?" }, { "from": "gpt", "value": "To answer this question, I need to calculate the difference in policy impact from 2020 to 2021 for each area and compare them. For instance, the {bars representing Housing}[0.123, 0.187, 0.296, 0.738] for 2021 and [0.123, 0.187, 0.296, 0.638] for 2020 shows an improvement of 2000. I then compare this with the improvements in {Education}[0.339, 0.312, 0.512, 0.738] for 2021 and [0.339, 0.312, 0.512, 0.638] for 2020, {Healthcare}[0.555, 0.262, 0.728, 0.738] for 2021 and [0.555, 0.262, 0.728, 0.863] for 2020, and {Transportation}[0.771, 0.062, 0.944, 0.738] for 2021 and [0.771, 0.062, 0.944, 0.563] for 2020. Comparing these values, I see that Healthcare has seen the most improvement.\nBased on the above thought, the answer is:\nHealthcare has seen the most improvement in terms of policy impact from 2020 to 2021. It has increased by 2150, which is the most considerable increase among all the areas. This might be because healthcare has become a topmost priority due to the pandemic situation worldwide." } ], "id_raw": 8 }, { "id": "X6lu9biQuTAo5ryh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in policy impact from 2020 to 2021 in the Healthcare sector?" }, { "from": "gpt", "value": "To find the difference in policy impact from 2020 to 2021, I would need to subtract the policy impact in 2020 from that of 2021. Looking at the {bar graph of Healthcare}[0.555, 0.262, 0.728, 0.738] for 2021 and [0.555, 0.262, 0.728, 0.863] for 2020, the policy's impact was 950 in 2021 and -1200 in 2020. By subtracting -1200 from 950, I can find the difference.\nBased on the above thought, the answer is:\n2150" } ], "id_raw": 8 }, { "id": "ciOFsVXka3FjtuB4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nExtract the data from the bar chart and present it in a CSV table form according to the different historical aspects and eras." }, { "from": "gpt", "value": "In order to convert the information from the chart into a tabulated form, the x-axis labels tell us that we have to list the different historical aspects: {the x-axis labels}[\"Feudal System\": [0.166, 0.919, 0.268, 0.942], \"Birth of Nations\": [0.377, 0.919, 0.485, 0.942], \"Technology Advancements\": [0.552, 0.919, 0.738, 0.942], \"Global Cooperation\": [0.792, 0.919, 0.926, 0.942]]. Then using {the legend}[0.098, 0.074, 0.232, 0.151], I know that the red bars represent the Middle Ages and the blue bars represent the Modern Age. Therefore, we will have to compare and record the length of the red and blue bars for each historical aspect to determine their respective impact. Let's take the Feudal System as an example, the {red bar for the Feudal System}[0.132, 0.062, 0.303, 0.436] extends roughly to the -700 mark on the y-axis while the {blue bar for the Feudal System}[0.132, 0.436, 0.303, 0.863] extends to the -800 mark on the y-axis. Combining all the data will yield the following table.\nBased on the above thought, the answer is:\nHistorical Aspects,Middle Ages,Modern Age\nFeudal System,-700,-800\nBirth of Nations,-500,-200\nTechnology Advancements,-100,-350\nGlobal Cooperation,-200,-100" } ], "id_raw": 9 }, { "id": "PNuF3Bz42u4eyY36", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat insights do you derive from the progression of the \"Feudal System\" and \"Birth of Nations\" from the Middle Ages to the Modern Age?" }, { "from": "gpt", "value": "For the \"Feudal System\" aspect, the height of {the red bar}[0.132, 0.062, 0.303, 0.436] representing the Middle Ages is lower than {the blue bar}[0.132, 0.436, 0.303, 0.863] representing the Modern Age. This implies that the intensity or impact of the Feudal System has increased from the Middle Ages to the Modern Age; which could suggest that elements of the Feudal System have become more prevalent in the Modern Age. Simultaneously, for the \"Birth of Nations\" aspect, {the red bar}[0.346, 0.062, 0.517, 0.329] for the Middle Ages is higher than {the blue bar}[0.346, 0.329, 0.517, 0.436] for the Modern Age. This indicates that the creation of new nations or states has decreased as we transitioned from the Middle Ages to the Modern Age. As a deduction, this could suggest that the world has become more politically stable or less expansionist in nature during the Modern Age compared to the Middle Ages.\nBased on the above thought, the answer is:\nThe intensity of the Feudal System has increased from the Middle Ages to the Modern Age suggesting its elements have become more prevalent in the modern society. On the contrary, the creation of new nations or states has declined as we moved to the Modern Age from the Middle Ages suggesting more political stability or lower expansionist tendencies." } ], "id_raw": 9 }, { "id": "ch4o9ZLJIHSeF5Wm", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_9.png", "conversations": [ { "from": "human", "value": "\nIn which historical aspect, the impact difference between the Middle Ages and the Modern Age is the largest?" }, { "from": "gpt", "value": "To determine the historical aspect with the largest impact difference, I need to compare the relative lengths of the red and blue bars in each category. Let's take the Feudal System as an example, the {red bar for the Feudal System}[0.132, 0.062, 0.303, 0.436] extends roughly to -700 on the y-axis, while the {blue bar for the Feudal System}[0.132, 0.436, 0.303, 0.863] extends roughly to -800 on the y-axis, giving us a difference of 100. Continuing with the similar comparison for the remaining historical sectors namely Birth of Nations, Technology Advancements and Global Cooperation, it's observed that {Feudal System}[0.166, 0.919, 0.268, 0.942] has the largest difference among all the sectors.\nBased on the above thought, the answer is:\nFeudal System." } ], "id_raw": 9 }, { "id": "scClxnEbAHC9xIA8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, please prepare a CSV format table for different healthcare fields with the corresponding medical expenses in current and previous year." }, { "from": "gpt", "value": "The x-axis represents various healthcare fields such as {Emergency Services}[0.119, 0.919, 0.260, 0.942], {Primary Care}[0.315, 0.919, 0.406, 0.942], {Pediatric Care}[0.484, 0.919, 0.581, 0.942], {Geriatric Care}[0.655, 0.919, 0.752, 0.942], and {Public Health}[0.830, 0.919, 0.921, 0.942]. The y-axis illustrates medical expenses, and the values can be deduced from {y-axis tick labels}[0.061, 0.894, 0.070, 0.917] {0},[0.035, 0.743, 0.070, 0.766] {1000} to [0.035, -0.013, 0.070, 0.011] {6000}. The {legend}[0.086, 0.074, 0.229, 0.151] indicates {red bars}[0.131, 0.083, 0.217, 0.106] correspond to \"Current Year\" and {blue bars}[0.131, 0.118, 0.223, 0.141] correspond to \"Previous Year\". Looking at {the red bar for Emergency Services}[0.121, 0.676, 0.258, 0.903] and {the blue bar for the same}[0.121, 0.435, 0.258, 0.676], they are approximately on 1500 and 3100 of the y-axis respectively, thus the medical expenses of Current Year and Previous Year for Emergency Services are 1500 and 3100. By applying the same method, values for all fields can be inferred.\nBased on the above thought, the answer is:\nHealthcare Fields,Current Year,Previous Year\nEmergency Services,1500,3100\nPrimary Care,2000,3900\nPediatric Care,2300,4550\nGeriatric Care,2100,4150\nPublic Health,2700,5300" } ], "id_raw": 11 }, { "id": "jPfmmz49okwhQfh4", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat could be interpreted about the trends of medical expenses in different healthcare fields based on the chart?" }, { "from": "gpt", "value": "The heights of the {red bars}[0.131, 0.083, 0.217, 0.106] representing \"Current Year\" and the {blue bars}[0.131, 0.118, 0.223, 0.141] representing \"Previous Year\" for each healthcare field indicate the trend. The higher the bar, the greater the medical expense for the corresponding year. The bar heights suggest an upward trend in expenses from the previous year to the current year in most healthcare fields. For example, in the {Public Health}[0.830, 0.919, 0.921, 0.942] field, the red bar is higher than the corresponding blue bar indicating an increase in this field's expenses. However, there is an exception in the {Emergency Services}[0.119, 0.919, 0.260, 0.942] field where the blue bar is higher than the red one, indicating a decrease in expenses from the previous year to the current year.\nBased on the above thought, the answer is:\nMost healthcare fields have increased their medical expenses from the previous year to the current year, signifying more investment in healthcare. However, there is a decrease in expenses in the Emergency Services field, possibly indicating improved public health measures resulting in fewer emergencies." } ], "id_raw": 11 }, { "id": "io3n9M58kMB7GB4A", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_11.png", "conversations": [ { "from": "human", "value": "\nWhich healthcare field saw the smallest rise in medical expenses from the previous year to the current year?" }, { "from": "gpt", "value": "To calculate the change in medical expenses, the height difference between the bars representing \"Current Year\" and \"Previous Year\" for each healthcare field needs to be compared. For the {Emergency Services}[0.119, 0.919, 0.260, 0.942] field, the {red bar}[0.121, 0.676, 0.258, 0.903] and {blue bar}[0.121, 0.435, 0.258, 0.676] indicate a decrease in medical expenses. After similarly calculating the differences for other healthcare fields like {Primary Care}[0.315, 0.919, 0.406, 0.942], {Pediatric Care}[0.484, 0.919, 0.581, 0.942], {Geriatric Care}[0.655, 0.919, 0.752, 0.942], and {Public Health}[0.830, 0.919, 0.921, 0.942], it can be inferred that the Emergency Services field has seen the smallest rise, in fact, it shows a decrease.\nBased on the above thought, the answer is:\nEmergency Services." } ], "id_raw": 11 }, { "id": "nwMcLbnafsVuGHu8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nCan you summarize the expenditures distribution among the categories including 'Education', 'Healthcare', 'Transportation', and 'Welfare' across 'Current Year' and 'Previous Year' as a CSV data?" }, { "from": "gpt", "value": "The stacked bar graph depicts the expenditures for the 'Education', 'Healthcare', 'Transportation', and 'Welfare' sectors for 'Current Year' and 'Previous Year'. According to {the legend}[0.098, 0.815, 0.240, 0.891], the color '#87ceeb' corresponds to 'Current Year', and '#f4a460' corresponds to 'Previous Year'. I can extract the expenditure values by visually observing the heights of the bars. For example, in the 'Education' category, {the bar representing 'Current Year'}[0.132, 0.062, 0.303, 0.405] suggests an expenditure of -800(thousand dollars), and {the bar representing 'Previous Year'}[0.132, 0.405, 0.303, 0.713] indicates an expenditure of -720(thousand dollars). By performing similar observations and calculations for the other categories, I can create the following CSV data.\nBased on the above thought, the answer is:\nServices,Current Year,Previous Year\nEducation,-800,-720\nHealthcare,-950,-920\nTransportation,-700,-770\nWelfare,-850,-920" } ], "id_raw": 12 }, { "id": "wOmU4chojgW0LBxY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar graph, can you suggest which among the 'Education', 'Healthcare', 'Transportation', and 'Welfare' sectors showed an overall increase in spendings over the previous year?" }, { "from": "gpt", "value": "To answer this question, we need to compare the expenditure distributions under 'Current Year' and 'Previous Year' for each category. For instance, the 'Education' category, represented by bars with coordinates {{\"Current Year\"}[0.132, 0.062, 0.303, 0.405] and {\"Previous Year\"}[0.132, 0.405, 0.303, 0.713]}, indicates a small increase in expenditure. Similar comparisons can be made across all the categories to find the ones that have shown an overall increase.\nBased on the above thought, the answer is:\nThe 'Education' sector has shown an overall increase in expenditures over the 'Previous Year'." } ], "id_raw": 12 }, { "id": "MPABjXKSWiHAmE05", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_12.png", "conversations": [ { "from": "human", "value": "\nWhich social welfare category experienced the largest difference in expenditures between the 'Current Year' and 'Previous Year'?" }, { "from": "gpt", "value": "To answer this question, I first need to observe the area sizes of each of the category bars across 'Current Year' and 'Previous Year'. The differences between the heights of the bars indicate the expenditure changes. For example, in the 'Education' category, {the bar representing 'Current Year'}[0.132, 0.062, 0.303, 0.405] suggests an expenditure of -800(thousand dollars), and {the bar representing 'Previous Year'}[0.132, 0.405, 0.303, 0.713] indicates an expenditure of -720(thousand dollars), giving a difference of 80 (thousand dollars). By comparing these differences across all categories, I can identify which category had the largest difference.\nBased on the above thought, the answer is:\nWelfare." } ], "id_raw": 12 }, { "id": "QDzpl9x6XgW01h71", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nBased on the information from this bar chart, could you please convert the spending in different education and training technology innovation sectors into a structured table? Exclude the minus sign and use positive numbers." }, { "from": "gpt", "value": "The information in this chart consists of two main parts: {Category}[0.144, 0.919, 0.911, 0.942] and {Spending}[0.015, 0.336, 0.029, 0.629]. There are four categories on the x-axis, including \"Mobile Learning Tech\", \"Gamification\", \"E-Learning\", and \"Virtual Training\". The y-axis represents spending in millions. For \"Mobile Learning Tech\", for example, we see that the value of spending in the current year is about 432.43 million, indicated by {the first skyblue bar}[0.132, 0.062, 0.303, 0.313], and in the previous year, the spending was about 576.57 million, shown by {the first sandybrown bar}[0.132, 0.313, 0.303, 0.646]. Hence we can organize the data accordingly for the other categories.\nBased on the above thought, the answer is:\nSector,Spending Current Year (Million dollars),Spending Previous Year (Million dollars)\nMobile Learning Tech,432.43,576.57\nGamification,256.12,749.72\nE-Learning,658.38,724.26\nVirtual Training,800.98,345.40" } ], "id_raw": 13 }, { "id": "cWxIumtTaospq50Q", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, what trend can we observe regarding the spending in 'Virtual Training' and how does it compare to the other sectors?" }, { "from": "gpt", "value": "To figure out the trend in 'Virtual Training', we need to look at {the last pair of bars on the right}[0.773, 0.062, 0.944, 0.726], which represent the spending in the 'Virtual Training' sector for the current year and previous year. Compared to the previous year, the spending in the current year significantly increased, as indicated by the length of the skyblue bar in this sector. When compared to the other sectors, {the bars representing 'Mobile Learning Tech'}[0.132, 0.062, 0.303, 0.646], {the bars for 'Gamification'}[0.346, 0.062, 0.517, 0.645], and {those for 'E-Learning'}[0.559, 0.062, 0.730, 0.863], we can notice that the spending in the 'Virtual Training' sector is the only one that shows a noticeable increase from the previous year to the current year. The remaining sectors exhibit a decrease in spending from the previous year to the current year.\nBased on the above thought, the answer is:\nThe spending in the 'Virtual Training' sector has increased significantly from the previous year to the current year, which contrasts with the other sectors where the spending decreased over the same period. Therefore, it seems that 'Virtual Training' is gaining more attention and investment compared to the other sectors in education and training technology innovation." } ], "id_raw": 13 }, { "id": "QcUn0hFZDmoMDTpe", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_13.png", "conversations": [ { "from": "human", "value": "\nIn which sector the difference in spending between the current year and the previous year is the smallest?" }, { "from": "gpt", "value": "To identify the sector with the smallest difference, it is essential to visually estimate the length difference between the skyblue and the sandybrown bars in each pair of bars representing each sector. In {the first pair of bars}[0.132, 0.062, 0.303, 0.646] representing the 'Mobile Learning Tech' sector, the difference between the lengths is relatively large. Similar observations can be noted for the 'E-Learning' and 'Virtual Training' sectors - {the third pair of bars}[0.559, 0.062, 0.730, 0.863] and {the fourth pair of bars}[0.773, 0.062, 0.944, 0.726]. Compared to these, we have a smaller length difference between {the second pair of bars}[0.346, 0.062, 0.517, 0.645] representing the 'Gamification' sector.\nBased on the above thought, the answer is:\nGamification" } ], "id_raw": 13 }, { "id": "f2k3xdoxKUT1QdNG", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nPlease provide the information in the chart in tabular form." }, { "from": "gpt", "value": "The chart illustrates the number of participants for the fitness activities - Yoga, Weightlifting, Running, and Biking, in the years 2019 and 2020, with their respective coordinates: {2019 bars}<{\"Yoga\"} [0.129, 0.593, 0.301, 0.903]; {\"Weightlifting\"} [0.344, 0.515, 0.515, 0.903]; {\"Running\"} [0.558, 0.645, 0.730, 0.903]; {\"Biking\"} [0.773, 0.670, 0.944, 0.903]>, and {2020 bars}<{\"Yoga\"}[0.129, 0.231, 0.301, 0.593]; {\"Weightlifting\"} [0.344, 0.102, 0.515, 0.515]; {\"Running\"} [0.558, 0.335, 0.730, 0.645]; {\"Biking\"} [0.773, 0.412, 0.944, 0.670]> . Each pair of bars indicate the annual participation in each activity. The Y-axis marks it as the number of individuals in thousands from \"{0}\"[0.070, 0.894, 0.079, 0.917] to \"{16000}\"[0.035, 0.067, 0.079, 0.091]. Therefore, by analysing the height of each bar and comparing it with the Y-axis, I can determine the number of participants each year.\nBased on the above thought, the answer is:\nActivity, 2019, 2020\nYoga,6000,13000\nWeightlifting,7500,15500\nRunning,5000,11000\nBiking,4500,9500" } ], "id_raw": 14 }, { "id": "4oc0s2eOw9fr4rQ0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nBased on the trends visible in the chart, which fitness activity do you think is gaining more popularity, and why?" }, { "from": "gpt", "value": "From the chart, it's clear that all four activities - Yoga, Weightlifting, Running, and Biking - have shown an increase in participants from 2019 to 2020. These are represented by {2019 bars}<{\"Yoga\"} [0.129, 0.593, 0.301, 0.903]; {\"Weightlifting\"} [0.344, 0.515, 0.515, 0.903]; {\"Running\"} [0.558, 0.645, 0.730, 0.903]; {\"Biking\"} [0.773, 0.670, 0.944, 0.903]> and {2020 bars}<{\"Yoga\"} [0.129, 0.231, 0.301, 0.593]; {\"Weightlifting\"} [0.344, 0.102, 0.515, 0.515]; {\"Running\"} [0.558, 0.335, 0.730, 0.645]; {\"Biking\"} [0.773, 0.412, 0.944, 0.670]>. However, in terms of growth, {Yoga}[0.129, 0.231, 0.301, 0.593] saw the highest increase in the number of participants, making it the most rapidly growing and therefore, potentially the most popular among these fitness activities\nBased on the above thought, the answer is:\nAccording to the chart, Yoga seems to be gaining the most popularity due to its highest increase in participants from 2019 to 2020." } ], "id_raw": 14 }, { "id": "MYrKSyO6lejHO1hG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_1_14.png", "conversations": [ { "from": "human", "value": "\nWhich activity showed the highest growth from 2019 to 2020?" }, { "from": "gpt", "value": "To determine the fitness activity that showed the highest growth from 2019 to 2020, I am comparing the differences in height between the 2019 and 2020 bars for each activity: Yoga {2019 Yoga bar}[0.129, 0.593, 0.301, 0.903] and {2020 Yoga bar}[0.129, 0.231, 0.301, 0.593], Weightlifting {2019 Weightlifting bar}[0.344, 0.515, 0.515, 0.903] and {2020 Weightlifting bar}[0.344, 0.102, 0.515, 0.515], Running {2019 Running bar}[0.558, 0.645, 0.730, 0.903] and {2020 Running bar}[0.558, 0.335, 0.730, 0.645], and Biking {2019 Biking bar}[0.773, 0.670, 0.944, 0.903] and {2020 Biking bar}[0.773, 0.412, 0.944, 0.670]. After comparing these differences, I can conclude that the fitness activity with the largest increase in participation from 2019 to 2020 is Yoga since it demonstrates the biggest increase in bar height.\nBased on the above thought, the answer is:\nYoga" } ], "id_raw": 14 }, { "id": "70sNiu1L1lI6aDnv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nPlease convert the bar chart data into a CSV formatted table." }, { "from": "gpt", "value": "I can see from the {x-axis label}[0.504, 0.964, 0.555, 0.981] that the policies are on the x-axis. In particular, individual policies are represented by the {x-axis ticks}[{\"Heavy Taxation\"}[0.099, 0.939, 0.206, 0.957]; {\"Reduced Tariffs\"}[0.250, 0.939, 0.356, 0.957]; {\"Subsidies for SMEs\"}[0.389, 0.939, 0.519, 0.957]; {\"Worker Training Programs\"}[0.515, 0.939, 0.694, 0.957]; {\"Unemployment Benefits\"}[0.671, 0.939, 0.839, 0.957]; {\"Debt Relief\"}[0.868, 0.939, 0.944, 0.957]]. The y-axis represents effectiveness from the {y-axis label}[0.015, 0.419, 0.029, 0.534].The actual values for each policy is represented as height of bars and we can see their values from the {annotations}[{\"50\"}[0.144, 0.340, 0.161, 0.358]; {\"65\"}[0.144, 0.703, 0.161, 0.721]; {\"75\"}[0.294, 0.251, 0.312, 0.269]; {\"55\"}[0.295, 0.792, 0.312, 0.810]; {\"90\"}[0.445,0.192,0.463,0.209]; {\"80\"}[0.445, 0.733, 0.463, 0.750];]. Using this information, I can generate the CSV table.\nBased on the above thought, the answer is:\nPolicy,Effectiveness\nHeavy Taxation,50\nReduced Tariffs,65\nSubsidies for SMEs,75\nWorker Training Programs,55\nUnemployment Benefits,90\nDebt Relief,80" } ], "id_raw": 28 }, { "id": "uoqQqKJL27kKXmbH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nIf the government wants to prioritize policymaking based on the effectiveness demonstrated by this chart, in what order would you recommend they concentrate their efforts?" }, { "from": "gpt", "value": "To answer this question, I need to sort the policies according to the corresponding bar heights from highest to lowest. From my observation, the bar heights for {Unemployment Benefits}[0.718, 0.085, 0.793, 0.620], {Debt Relief}[0.868, 0.144, 0.944, 0.620], {Subsidies for SMEs}[0.416, 0.174, 0.492, 0.620], {Reduced Tariffs}[0.266, 0.234, 0.341, 0.620], {Worker Training Programs}[0.567, 0.293, 0.642, 0.620], and {Heavy Taxation}[0.115, 0.323, 0.190, 0.620] indicate that Unemployment Benefits is the most effective, followed by Debt Relief, Subsidies for SMEs, Reduced Tariffs, Worker Training Programs, and then Heavy Taxation.\nBased on the above thought, the answer is:\nThe government might want to prioritize their policymaking efforts in the following order: Unemployment Benefits, Debt Relief, Subsidies for SMEs, Reduced Tariffs, Worker Training Programs, Heavy Taxation." } ], "id_raw": 28 }, { "id": "nw5pgStmbElcpPtf", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_2_13.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which policy has the highest effectiveness?" }, { "from": "gpt", "value": "To find out which policy has the highest effectiveness, I need to look at the height of the bars and corresponding values. By looking at the highest bar and value, it shows that {Unemployment Benefits}[0.718, 0.085, 0.793, 0.620] with a corresponding value of {\"90\"}[0.445,0.192,0.463,0.209] exhibits the highest effectiveness among all policies.\nBased on the above thought, the answer is:\nUnemployment Benefits" } ], "id_raw": 28 }, { "id": "yBOCmTdfA6PXHBPX", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nLooking at the bar chart you analyzed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "According to the chart title {\"Career and Workplace Development Trends over 2.5 Years\"}[0.277, 0.025, 0.761, 0.055], and x-axis labels representing quarters {Q1 - Q2 over 2.5 Years}[{\"Q1\"}[0.116, 0.919, 0.136, 0.942] - {\"Q2\"}[0.902, 0.919, 0.922, 0.942]], the x-axis indicates the passage of time in quarters. The ratings on the y-axis range {from 0 to 10}[{0}[0.035, 0.894, 0.043, 0.917] - {10}[0.026, 0.051, 0.043, 0.074]]. For each quarter, there are two bars indicating the ratings of \"Career Development\" and \"Workplace Trends\" respectively. According to the legend {\"Career Development and Workplace Trends\"}[{label: 'Career Development'}[0.466, 0.083, 0.611, 0.106] - {label: 'Workplace Trends'}[0.466, 0.118, 0.589, 0.141]], ratings for both categories can be derived from the relative height of the respective bars. For example, the height of the {bar for \"Career Development in Q1\"}[0.096, 0.195, 0.126, 0.903] matches with the 8.4 on the y-axis, thus the rating is 8.4 for this category in Q1. Similarly, the height of the {bar for \"Workplace Trends in Q1\"}[0.126, 0.102, 0.157, 0.903] matches with 9.5 on the y-axis, determining the rating of 9.5 for this category in Q1. This process is repeated for each quarter to construct the CSV table.\nBased on the above thought, the answer is:\nQuarters,Career Development,Workplace Trends\nQ1,8.4,9.5\nQ2,7.9,8.5\nQ3,6.8,7.8\nQ4,7.2,7.0\nQ1,8.3,8.0\nQ2,8.7,8.6\nQ3,8.5,9.1\nQ4,7.8,8.4\nQ1,6.0,7.2\nQ2,9.0,8.8" } ], "id_raw": 30 }, { "id": "Zsx4wmISLnP5Plt9", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what conclusions can you draw about the development trends of Career Development and Workplace Trends over 2.5 years?" }, { "from": "gpt", "value": "To draw conclusions about the development trends of \"Career Development\" and \"Workplace Trends\" over 2.5 years, we need to look at the trend of bar heights over time. Starting with {\"Career Development\"}[0.466, 0.083, 0.611, 0.106], we can see that it begins high at 8.4 in Q1 of the first year, dips to a low of 6.0 in Q1 of the third year, and then rebounds to a high of 9.0 in Q2 of the third year. This indicates some oscillations, but also a general upward trend, especially in the latter period. In contrast, for {\"Workplace Trends\"}[0.466, 0.118, 0.589, 0.141] the trend begins at a height of 9.5 in Q1 of the first year, with minor fluctuations. It drops to 7.0 in Q4 of the first year, but then shows a general upward trend, reaching a height of 9.1 in Q3 of the second year. This suggests that while there are fluctuations throughout the period, both trend lines generally show an upward trajectory, with \"Career Development\" showing larger fluctuations and more dynamic changes.\nBased on the above thought, the answer is:\nBoth \"Career Development\" and \"Workplace Trends\" show a generally upward trend over the 2.5 years. However, \"Career Development\" shows larger fluctuations and more dynamic changes, while \"Workplace Trends\" appears more stable and consistent, despite minor fluctuations." } ], "id_raw": 30 }, { "id": "hyDgyMPo993Xtunh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_0.png", "conversations": [ { "from": "human", "value": "\nWhich quarter has the highest difference in trend ratings between Career Development and Workplace Trends?" }, { "from": "gpt", "value": "To identify the quarter with the biggest difference between the ratings of \"Career Development\" and \"Workplace Trends\", I have to compare the differences in the heights of the two bars of every quarter. Starting with Q1 in the first year, the difference between {\"Career Development\"} bar[0.096, 0.195, 0.126, 0.903] and {\"Workplace Trends\"} bar[0.126, 0.102, 0.157, 0.903] is 1.1. Moving sequentially to Q2, Q3, and other quarters, we make similar comparisons. Eventually, we find the largest difference in Q2 of the third year, where the heights of {the \"Career Development\"} bar[0.882, 0.144, 0.912, 0.903] and {the \"Workplace Trends\"} bar[0.912, 0.161, 0.943, 0.903] denote ratings of 9.0 and 8.8 respectively, resulting in a difference of 0.2.\nBased on the above thought, the answer is:\nQ2 of the third year." } ], "id_raw": 30 }, { "id": "0azxNbVoQ4q8gKzL", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart showing New Energy and Clean Technology Development into a CSV format table?" }, { "from": "gpt", "value": "From the title {\"New Energy and Clean Technology Development\"}[0.304, 0.030, 0.753, 0.066], I can identify that the chart displays data about clean energy technologies. The X-axis which represents the tech types covers {\"Solar Panels\"}[0.125, 0.903, 0.219, 0.931], {\"Wind Turbines\"}[0.261, 0.903, 0.369, 0.931], {\"Tidal Power\"}[0.413, 0.903, 0.502, 0.931], {\"Geothermal Plants\"}[0.528, 0.903, 0.671, 0.931], {\"Electric Vehicles\"}[0.680, 0.903, 0.805, 0.931], and {\"Hydrogen Fuel Cells\"}[0.808, 0.903, 0.962, 0.931]. The Y-axis represents the corresponding ratings, ranging from {0.0}[0.039, 0.872, 0.063, 0.900] to {4.5}[0.039, 0.047, 0.063, 0.075]. Each technology has four bars with different colors, and by referring to the {*}legend[0.772, 0.089, 0.976, 0.264], I can identify these as Efficiency, Stability, Development Rate, and Market Acceptance. To generate the CSV table, I need to extract the values for each of these metrics for each technology. I can use the height of the bars to determine the values. For instance, the first technology, Solar Panels, has an Efficiency bar {here}[0.115, 0.187, 0.144, 0.883] with a height that corresponds to approx 3.8 on the ongoing scale.\nBased on the above thought, the answer is:\nTechnologies,Efficiency,Stability,Development Rate,Market Acceptance\nSolar Panels,3.8,3.2,4.0,2.5\nWind Turbines,3.0,3.8,3.5,3.0\nTidal Power Generators,2.0,2.8,3.0,1.5\nGeothermal Plants,3.5,4.0,3.2,2.8\nElectric Vehicles,4.0,3.2,4.2,3.5\nHydrogen Fuel Cells,2.5,2.0,3.5,2.2" } ], "id_raw": 31 }, { "id": "5D9x7F4lQAvw2QaB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which clean energy technology would you recommend for investment? And why?" }, { "from": "gpt", "value": "This question asks about recommendations for investment which involves considering not just the efficiency of a technology, but also is developmental rate and market acceptance. Comparing all the clean energy technologies, we see that {\"Electric Vehicles\"}[0.680, 0.903, 0.805, 0.931] show a higher rating in all the factors. The bars representing Efficiency[0.685, 0.150, 0.714, 0.883], Stability[0.714, 0.297, 0.742, 0.883], Development Rate[0.742, 0.113, 0.771, 0.883], and Market Acceptance[0.771, 0.242, 0.799, 0.883] show values consistent and above 3.2 indicating Robustness in all the aspects. Additionally, the bar for Development Rate is the highest among all indicating a greater scope of future growth.\nBased on the above thought, the answer is:\nElectric Vehicles. Their high ratings across efficiency, stability, development rate, and market acceptance indicate that they are a solid investment. Their Development Rate is also the highest, indicating potential for future growth." } ], "id_raw": 31 }, { "id": "OGV8potI8GdUN1X6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_1.png", "conversations": [ { "from": "human", "value": "\nWhich technology has the highest average rating based on the four factors: Efficiency, Stability, Development Rate, and Market Acceptance?" }, { "from": "gpt", "value": "To identify the highest average rating overall, I'll need to analyze the bars under every technology. For each technology, there are four bars for each of the four factors. I'll need to calculate the average of the values represented by these bars and compare them to identify the highest. For example, for {\"Solar Panels\"}[0.125, 0.903, 0.219, 0.931], the bars representing, Efficiency[0.115, 0.187, 0.144, 0.883], Stability[0.144, 0.297, 0.172, 0.883], Development Rate[0.172, 0.150, 0.201, 0.883] and Market Acceptance[0.201, 0.425, 0.229, 0.883], these four bars give the average value as 3.375. In the similar way we calculate for other technologies as well and see that {\"Electric Vehicles\"}[0.680, 0.903, 0.805, 0.931] having the bars at, Efficiency[0.685, 0.150, 0.714, 0.883], Stability[0.714, 0.297, 0.742, 0.883], Development Rate[0.742, 0.113, 0.771, 0.883], and Market Acceptance[0.771, 0.242, 0.799, 0.883] show the highest average value of 3.725 among all the technologies.\nBased on the above thought, the answer is:\nElectric Vehicles." } ], "id_raw": 31 }, { "id": "jJPvkapu2RtEVTkt", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nCould you transform the chart's data representing the projects evaluation into a CSV format table?" }, { "from": "gpt", "value": "The main title {\"Innovation Project Evaluation\"}[0.386, 0.030, 0.656, 0.066] of the chart indicates the overall theme. The x-axis represents the project names and include {\"Slow Expansion\"}[0.251, 0.903, 0.371, 0.931] and {\"Rapid Decline\"}[0.678, 0.903, 0.785, 0.931]. The y-axis {\"Ratings\"}[0.017, 0.427, 0.032, 0.531] indicates the scores achieved by each project in various aspects. The group of elements represented in {the legend}[0.814, 0.089, 0.976, 0.306] represent different aspects evaluated for each project including \"Life\", \"Infrastructure\", \"IP\", \"Ecosystem\", and \"Commercial\". Each aspect is associated with a particular color which matches with the color of the bars on the graph corresponding to their score for each project. The score is determined by the height of each bar which scales from 0 to 6 as per {the y-tick labels}[0.00, 0.883] to [6.00, -0.041]. To transform this data into a table, I can simply match scores from the height of the bars to the corresponding aspect for each project.\nBased on the above thought, the answer is:\nProject,Life,Infrastructure,IP,Ecosystem,Commercial\nSlow Expansion,4.0,3.0,3.5,5.0,4.0\nRapid Decline,1.0,2.0,1.5,1.0,2.0" } ], "id_raw": 32 }, { "id": "6mIRApBVjAof9tYT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nGiven the evaluation of the two innovation projects, which project seems to have been more successfully managed and why?" }, { "from": "gpt", "value": "According to {the chart}[0.059, 0.075, 0.983, 0.883], it's clear that the {\"Slow Expansion\"}[0.251, 0.903, 0.371, 0.931] project scored considerably higher in each evaluated aspect compared to the {\"Rapid Decline\"}[0.678, 0.903, 0.785, 0.931] project as the height of each bar for 'Slow Expansion' is higher. For instance, for the aspect 'Life', the blue bar for 'Slow Expansion' [0.101, 0.267, 0.185, 0.883] is longer than the equivalent bar for 'Rapid Decline' [0.521, 0.729, 0.605, 0.883], indicating a higher score. This same pattern is observed for all other aspects including 'Infrastructure', 'IP', 'Ecosystem', and 'Commercial'.\nBased on the above thought, the answer is:\nThe 'Slow Expansion' project seems to have been more successfully managed. This is because it scored consistently higher in all evaluated aspects compared to the 'Rapid Decline' project." } ], "id_raw": 32 }, { "id": "TkPdSIlapEWy2CXt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_2.png", "conversations": [ { "from": "human", "value": "\nWhich aspect scored the lowest in both projects, 'Slow Expansion' and 'Rapid Decline'?" }, { "from": "gpt", "value": "To determine which aspect scored lowest, I'll look at the shortest bar for each project and match that color with an aspect in {the legend}[0.814, 0.089, 0.976, 0.306]. For the {\"Slow Expansion\"}[0.101, 0.267, 0.185, 0.883] to [0.437, 0.267, 0.521, 0.883] project, the shortest bar is yellow, representing 'Infrastructure'. For the {\"Rapid Decline\"}[0.521, 0.729, 0.605, 0.883] to [0.857, 0.575, 0.941, 0.883] project, the shortest bars represent 'Life' and 'Ecosystem'. Therefore, no single aspect scored the lowest in both projects.\nBased on the above thought, the answer is:\nNo single aspect scored the lowest in both projects." } ], "id_raw": 32 }, { "id": "L5bZAg6LVzFq2QSK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nCould you convert the key performance data for each given company represented in the bar chart into a structured data table, preferably CSV format?" }, { "from": "gpt", "value": "The bar chart represents four key metrics for six different companies within the transportation and logistics industry. The x-axis represents each firm {x-axis labels}[{\"Imperial Solutions\"}[0.107, 0.903, 0.246, 0.931]; {\"Quick Transfer\"}[0.263, 0.903, 0.374, 0.931]; {\"Solid Transit\"}[0.413, 0.903, 0.507, 0.931]; {\"Direct Deliver\"}[0.549, 0.903, 0.655, 0.931]; {\"Momentum Movers\"}[0.669, 0.903, 0.818, 0.931]; {\"Titan's Transport\"}[0.821, 0.903, 0.950, 0.931]], while the metrics are encoded as characteristics of the bars corresponding to each company. Each group of bars for a company represents Sales Growth (%), Market Share (%), Investment in R&D ($ M), and Employee Performance in respective order. The color of the bars is mapped to each metric according to the {legend}[0.730, 0.089, 0.976, 0.264]. To extract the data into a CSV format, I would need to determine the height of each bar, corresponding to each company and each metric, and convert this height to the value on the y-axis.\nBased on the above thought, the answer is:\nCompanies,Sales Growth (%),Market Share (%),Investment in R&D ($ M),Employee Performance\nImperial Solutions,8.6,24.3,100.2,3.3\nQuick Transfer,12.2,19.1,90.3,3.6\nSolid Transit,-3.2,14.7,60.5,3.0\nDirect Deliver,9.1,18.4,80.0,3.4\nMomentum Movers,7.3,17.8,85.6,3.2\nTitan's Transport,-6.5,11.8,45.2,2.7" } ], "id_raw": 33 }, { "id": "5HiVLLWkLcBMfFA7", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you suggest which company is the best in terms of both performance and market leadership?" }, { "from": "gpt", "value": "To choose the best company in terms of both performance and market leadership, I need to evaluate companies both on \u201cMarket Share (%)\u201d and \u201cEmployee Performance.\u201d The second bar in each group represents market share and the fourth bar represents employee performance. As per the {legend}[0.730, 0.089, 0.976, 0.264], the 'Market Share (%)' metric is represented by {orange-color bars}[0.779, 0.142, 0.913, 0.170] and the 'Employee Performance' metric is represented by {-green color bars}[0.779, 0.225, 0.958, 0.253]. So, I need to evaluate both the second and fourth bars of each group to find the company which performs the best in both dimensions. By comparing the heights of the second and fourth bars, 'Imperial Solutions' has the highest market share bar {[0.148, 0.634, 0.176, 0.802]} and 'Quick Transfer' has the highest employee performance bar {[0.376, 0.778, 0.405, 0.802]}. However, 'Direct Deliver' has a fairly high market share bar {[0.573, 0.675, 0.602, 0.802]} and also the second highest employee performance bar {[0.659, 0.778, 0.687, 0.802]} which makes it the best performer in terms of both market leadership and performance.\nBased on the above thought, the answer is:\nDirect Deliver. Because it strikes a balance between having a high market share and having high employee performance." } ], "id_raw": 33 }, { "id": "hJD5KwJO4vEqF36g", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_3.png", "conversations": [ { "from": "human", "value": "\nWhich company has the highest investment in Research and Development?" }, { "from": "gpt", "value": "The investment in Research and Development for each company is represented by the third bar in each group. To determine the company with the highest R&D investment, I need to find the tallest third bar in each group. By comparing the heights of the {third bars}[{\"Imperial Solutions\"}[0.176, 0.111, 0.205, 0.802]; {\"Quick Transfer\"}[0.318, 0.180, 0.347, 0.802]; {\"Solid Transit\"}[0.460, 0.385, 0.488, 0.802]; {\"Direct Deliver\"}[0.602, 0.251, 0.630, 0.802]; {\"Momentum Movers\"}[0.744, 0.212, 0.772, 0.802]; {\"Titan's Transport\"}[0.885, 0.490, 0.914, 0.802]], the highest third bar represents 'Imperial Solutions', suggesting that this company has the highest investment in R&D.\nBased on the above thought, the answer is:\nImperial Solutions" } ], "id_raw": 33 }, { "id": "F3VRYp2zTze8LBuY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data you're viewing into a CSV format table?" }, { "from": "gpt", "value": "Based on the bar chart's subtitle {\"Acceptance Rate of Academic Papers in 2020 and 2021\"}[0.293, 0.025, 0.754, 0.055] and {the x-axis tick labels}[{\"Physics\"}[0.130, 0.919, 0.181, 0.942]; {\"Chemistry\"}[0.267, 0.919, 0.338, 0.942]; {\"Biology\"}[0.424, 0.919, 0.476, 0.942]; {\"Mathematics\"}[0.552, 0.919, 0.642, 0.942]; {\"Psychology\"}[0.705, 0.919, 0.783, 0.942]; {\"Sociology\"}[0.858, 0.919, 0.925, 0.942]],I understand that the x-axis represents various research fields. The y-axis {tick labels}[{\"0\"}[0.043, 0.894, 0.052, 0.917]; {\"20\"}[0.035, 0.725, 0.052, 0.748]; {\"40\"}[0.035, 0.557, 0.052, 0.580]; {\"60\"}[0.035, 0.388, 0.052, 0.411]; {\"80\"}[0.035, 0.219, 0.052, 0.243]; {\"100\"}[0.026, 0.051, 0.052, 0.074]] demonstrate various acceptance rates from 0% to 100%. Two data series are represented in this chart, one for 2020 and the other for 2021, distinguished by {the legend}[0.893, 0.074, 0.978, 0.151] with blue bars indicating 2020 and red bars indicating 2021. I'll then compare the height of the bars and extract their values to construct a CSV table using each research field as a category and inserting the acceptance rates for 2020 and 2021 in each field.\nBased on the above thought, the answer is:\nFields,2020,2021\nPhysics,90,84\nChemistry,83,82\nBiology,79,76\nMathematics,95,93\nPsychology,88,89\nSociology,80,79" } ], "id_raw": 34 }, { "id": "zjg35LWYQWzdy5aa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nFrom the chart you're seeing, which research field had the most consistent acceptance rate between 2020 and 2021?" }, { "from": "gpt", "value": "The consistency of the acceptance rate in a research field can be determined by observing the difference in the height of the 2020 and 2021 bars. A smaller difference in height represents a more consistent acceptance rate. To do this, comparing the {2020 bar for Physics}[0.104, 0.144, 0.155, 0.903] with the {2021 bar for Physics}[0.155, 0.195, 0.207, 0.903], for instance, I can see there's a noticeable difference. Similarly, observing the {year 2021's bar for Biology}[0.450, 0.262, 0.501, 0.903] with respect to {2020 bar for Biology}[0.398, 0.237, 0.450, 0.903], I can see some evident difference. Conducting this comparison for each field and judging by the minimal difference between the two years, it is evident that Chemistry had the most consistent acceptance rate, where the two bars are almost at the same level. Shadows of {2020's bar}[0.251, 0.203, 0.303, 0.903] and {2021's bar}[0.303, 0.212, 0.354, 0.903] are almost equal.\nBased on the above thought, the answer is:\nChemistry" } ], "id_raw": 34 }, { "id": "25ZIIdaTWqGMiA8j", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what was the greatest decline in the acceptance rate from 2020 to 2021 in any of the research fields?" }, { "from": "gpt", "value": "To answer this question, I need to evaluate the decrease in the acceptance rates by comparing the heights of the bars representing 2020 and 2021 for each research field. Starting from Physics, I can see that the {bar for 2020}[0.104, 0.144, 0.155, 0.903] is slightly taller than the {bar for 2021}[0.155, 0.195, 0.207, 0.903]. I can do this successively for each field until I find the field where the acceptance rate decreases the most. After analyzing all the fields, I can see that the largest decline happens in Biology where the {bar for 2020}[0.398, 0.237, 0.450, 0.903] has a higher value compared to the {bar for 2021}[0.450, 0.262, 0.501, 0.903]. This drop represents a decrease in acceptance rate from 79% in 2020 to 76% in 2021, a decline of 3%.\nBased on the above thought, the answer is:\nBiology" } ], "id_raw": 34 }, { "id": "vfLKUgYyFi5w1qVI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart provided, convert all the values for each risk assessment into a structured table." }, { "from": "gpt", "value": "The bar chart with title {Risk Assessment and Management}[0.356, 0.030, 0.680, 0.066] contains six groups of bars. Each group has three bars representing the probability, impact, and risk score of each risk factor. The x-axis labels at[0.077, 0.903, 0.217, 0.931], [0.221, 0.903, 0.320, 0.931], [0.342, 0.903, 0.447, 0.931], [0.471, 0.903, 0.565, 0.931], [0.570, 0.903, 0.714, 0.931], [0.703, 0.903, 0.829, 0.931] and [0.806, 0.903, 0.973, 0.931] represent these risk factors. The legend at [0.822, 0.089, 0.960, 0.223] tells that the blue, orange, and red bars correspond to the probability, impact, and risk score respectively. Therefore, by calculating the height of each bar, I can convert the chart into a table form.\nBased on the above thought, the answer is:\nRisk Factors, Probability, Impact, Risk Score\nEquipment Failure, 3.1, 4.9, 15.19\nHuman Error, 4.2, 4.5, 18.9\nFinancial Risk, 2.5, 4.8, 12\nLegal Issues, 4.6, 5.0, 23\nMarket Fluctuation, 2.7, 3.5, 9.45\nNatural Disaster, 1.5, 5.0, 7.5\nCybersecurity Breach, 3.8, 4.7, 17.86" } ], "id_raw": 35 }, { "id": "dnQAvZBptstqlzo0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nBased on the Risk Assessment and Management chart, which risk factor should the company prioritize and why?" }, { "from": "gpt", "value": "In order to identify the risk factor that should be prioritized, I have to compare the risk scores of each risk factor. These are represented by the {red bars}[red bar groups][0.159, 0.375, 0.184, 0.883], [0.283, 0.250, 0.308, 0.883], [0.407, 0.482, 0.431, 0.883], [0.531, 0.113, 0.555, 0.883], [0.654, 0.567, 0.679, 0.883], [0.778, 0.632, 0.803, 0.883], [0.902, 0.285, 0.927, 0.883]. From the visual assessment, I can see that the red bar for the {Legal Issues}[0.471, 0.903, 0.565, 0.931] is the tallest among the red bars of all the risk factors, indicating it has the highest risk score. Therefore, the company should prioritize addressing Legal Issues first.\nBased on the above thought, the answer is:\nThe company should prioritize addressing the Legal Issues as they have the highest risk score." } ], "id_raw": 35 }, { "id": "wx9bP38VKgZ0FBpn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the risk analysis, which risk factor has the least risk score?" }, { "from": "gpt", "value": "To find the risk factor with the least risk score, I need to locate the red bars for each risk. I can determine the value by examining the height of the bar. Starting from left to right, the first risk factor is Equipment Failure with {risk score bar}[0.159, 0.375, 0.184, 0.883], which has a risk score of about 15.19; the second is Human Error with {risk score bar}[0.283, 0.250, 0.308, 0.883], which has a risk score of about 18.90 and so on. After comparing all the risk factors, I can see that the risk score of {Natural Disaster}[0.703, 0.903, 0.829, 0.931] is the least as the {red bar}[0.778, 0.632, 0.803, 0.883] is the shortest.\nBased on the above thought, the answer is:\nNatural Disaster" } ], "id_raw": 35 }, { "id": "XFKAwHo6gsdHG4n6", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you see, please convert the fitness trends data into a CSV format table." }, { "from": "gpt", "value": "The chart title {\"Trends in Fitness and Sports in 2021 and 2022\"}[0.308, 0.030, 0.735, 0.066]indicates that the chart is about different fitness trends in 2021 and 2022. The x-axis represents the years, which can be found from the {x-axis tick labels}[{\"2022\"}[0.268, 0.903, 0.307, 0.931]; {\"2021\"}[0.735, 0.903, 0.774, 0.931]]. The y-axis represents ratings scale, which ranges from {0}[0.039, 0.872, 0.048, 0.900] to {5}[0.039, 0.017, 0.048, 0.045]. The four different colored bars indicate four sport categories, based on {the legend}[0.828, 0.089, 0.976, 0.264]. Each of these categories has two bars corresponding to two years, respectively. By analyzing the heights of these bars and the corresponding mid-point coordinates on the y-axis, the annual ratings for each category in these two years can be estimated.\nBased on the above thought, the answer is:\nSport,2022,2021\nCardio,4.5,3.5\nStrength,4.5,4.0\nMindfulness,2.5,3.0\nFlexibility,3.0,3.5" } ], "id_raw": 41 }, { "id": "uvv2I3IIHGXhUljl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which category has been faring the best during these two years and why do you think so?" }, { "from": "gpt", "value": "To answer this, I need to observe the height of the bars representing each category in the two years. For Cardio and Strength, it can be seen that both their rating increased from 2021 to 2022. However, the Cardio category, indicated by the {yellow bars}[{2021 bar}[0.568, 0.284, 0.661, 0.883]; {2022 bar}[0.101, 0.113, 0.194, 0.883]], reaches to the height of approximately 4.5 in 2022, which is the highest rating compared to the other categories. Therefore, I think Cardio has been faring the best during these two years.\nBased on the above thought, the answer is:\nCardio. The bar representing Cardio has the highest height, which indicates it has the highest rating. Moreover, its rating actually increased from 2021 to 2022. Therefore, based on the chart, Cardio seems to have fared the best during these two years." } ], "id_raw": 41 }, { "id": "Uxngp61ooMdkfsFc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_11.png", "conversations": [ { "from": "human", "value": "\nWhich sport category has seen the least change in ratings from 2021 to 2022?" }, { "from": "gpt", "value": "To answer this, I need to compare the difference in ratings for each sport category from 2021 to 2022. For the Cardio category, the 2021 rating is represented by the {yellow bar}[0.568, 0.284, 0.661, 0.883] and the 2022 rating is represented by the {yellow bar}[0.101, 0.113, 0.194, 0.883]. The difference between the two ratings is approximately 1. Similarly, the difference in the Strength category can be calculated from the {blue bars}[{2021 bar}[0.661, 0.199, 0.755, 0.883]; {2022 bar}[0.194, 0.113, 0.288, 0.883]], which is also approximately 0.5. By continuing to observe the difference in rating for Mindfulness and Flexibility categories, it can be found that the Flexibility category has the smallest change, which is approximately 0.5.\nBased on the above thought, the answer is:\nFlexibility" } ], "id_raw": 41 }, { "id": "M9g9uoHSJWtNBtRK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nCould you generate a CSV format table according to the visible data from the bar chart?" }, { "from": "gpt", "value": "From the x-axis with labels that include {\"Public_Figure1\"}[0.154, 0.909, 0.264, 0.938], {\"Public_Figure2\"}[0.275, 0.909, 0.385, 0.938], {\"Public_Figure3\"}[0.397, 0.909, 0.507, 0.938], {\"Public_Figure4\"}[0.518, 0.909, 0.628, 0.938], {\"Public_Figure5\"}[0.640, 0.909, 0.750, 0.938], {\"Public_Figure6\"}[0.761, 0.909, 0.871, 0.938], it can be told that each tick represents an individual public figure. The y-axis represents ratings, ranging from {0.00}[0.080, 0.879, 0.114, 0.907] to {2.00}[0.080, 0.064, 0.114, 0.092]. The legend includes four categories, {\"Liberal Stance\"}[0.727, 0.145, 0.837, 0.173], {\"Conservative Stance\"}[0.727, 0.187, 0.886, 0.215], {\"Neutral Stance\"}[0.727, 0.229, 0.842, 0.257], and {\"No Stance\"}[0.727, 0.271, 0.806, 0.299], each represented by a uniquely colored bar. To extract the data, I will need to refer to the height of the bars associated with each category and public figure.\nBased on the above thought, the answer is:\nIndividual,Liberal_Stance,Conservative_Stance,Neutral_Stance,No_Stance\nPublic_Figure1,1.8,1.3,0.9,1.0\nPublic_Figure2,1.2,1.7,0.6,1.5\nPublic_Figure3,1.4,1.2,0.8,1.6\nPublic_Figure4,1.1,1.6,0.9,1.4\nPublic_Figure5,1.6,1.1,0.7,1.3\nPublic_Figure6,1.3,1.5,0.8,1.2" } ], "id_raw": 42 }, { "id": "nwGXOggdvbRe9kB9", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which political stance is predominant among the public figures?" }, { "from": "gpt", "value": "To identify the predominant stance, we need to look at the overall height of the corresponding bars for each political stance among all public figures. Reviewing the heights of the bars corresponding to each category - {\"Liberal Stance\"}[0.727, 0.145, 0.837, 0.173], {\"Conservative Stance\"}[0.727, 0.187, 0.886, 0.215], {\"Neutral Stance\"}[0.727, 0.229, 0.842, 0.257], and{\"No Stance\"}[0.727, 0.271, 0.806, 0.299] - it seems that the \"Liberal Stance\" and the \"Conservative Stance\" bars are generally taller than the \"Neutral Stance\" and \"No Stance\" across most public figures. This suggests that most public figures have a more defined political stance, either Liberal or Conservative, rather than a neutral or no stance.\nBased on the above thought, the answer is:\nThe Liberal and Conservative stances are the predominant political inclinations among the displayed public figures." } ], "id_raw": 42 }, { "id": "AcU6yaWSLbn8cZMF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_12.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, who is the public figure that has the highest rating for the conservative stance?" }, { "from": "gpt", "value": "To determine which public figure has the highest rating for the \"Conservative Stance\", I need to identify the highest bar color as indicated in the legend {\"Conservative Stance\"}[0.727, 0.187, 0.886, 0.215]. Looking at the heights of the bars representing this stance, I see that the {\"Public_Figure2\"}[0.275, 0.909, 0.385, 0.938] has the highest bar with a conservative stance, located at [0.306, 0.197, 0.330, 0.890] that corresponds to a rating of 1.7.\nBased on the above thought, the answer is:\nPublic_Figure2" } ], "id_raw": 42 }, { "id": "wL1q5EbcUphiTjTq", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nCan you construct a structured table in CSV format based on the data from the bar chart?" }, { "from": "gpt", "value": "From the title {\"Key Economic Indicators for 2021\"}[0.386, 0.025, 0.664, 0.055] and the x-axis labels [{label: 'YE2020'}[0.150, 0.919, 0.202, 0.942]; {label: 'Q1_2021'}[0.320, 0.919, 0.381, 0.942]; {label: 'Q2_2021'}[0.494, 0.919, 0.556, 0.942]; {label: 'Q3_2021'}[0.668, 0.919, 0.730, 0.942]; {label: 'Q4_2021'}[0.843, 0.919, 0.904, 0.942]], this bar chart compares the changes of four economic indicators over five periods. With the legend located at [0.072, 0.745, 0.265, 0.891], I can identify that these four different colored bars represent Unemployment Rate, GDP Growth Rate, Inflation Rate, and Interest Rate respectively. For constructing the table, I have to extract the value for each indicator at each period using the height of the bar like for {Unemployment Rate in YE2020}[0.106, 0.116, 0.141, 0.635] or {GDP Growth Rate in Q1_2021}[0.316, 0.635, 0.351, 0.865].\nBased on the above thought, the answer is:\nPeriods,Unemployment Rate,GDP Growth Rate,Inflation Rate,Interest Rate\nYE2020,6.8,4.3,1.4,0.2\nQ1_2021,6.1,-3.0,2.5,0.1\nQ2_2021,5.4,6.0,4.2,0.1\nQ3_2021,4.8,6.2,5.3,0.2\nQ4_2021,4.2,7.0,6.1,0.25" } ], "id_raw": 43 }, { "id": "Mpow0o8c8R2jCWjQ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what could be the probable cause for the drop in the Unemployment Rate in 2021?" }, { "from": "gpt", "value": "This question requires understanding the relationships among the data points. Firstly, I notice that the {Unemployment Rate bars}[0.106, 0.116, 0.141, 0.635];[0.281, 0.169, 0.316, 0.635];[0.455, 0.223, 0.490, 0.635];[0.629, 0.269, 0.664, 0.635];[0.804, 0.314, 0.839, 0.635] decrease gradually from about 6.8% to 4.2% through the year. In parallel, I notice that the {GDP Growth Rate bars}[0.141, 0.307, 0.176, 0.635];[0.316, 0.635, 0.351, 0.865];[0.490, 0.177, 0.525, 0.635];[0.664, 0.162, 0.699, 0.635];[0.839, 0.100, 0.873, 0.635] show a significant increase from -3.0% to 7.0% from Q1_2021 to Q4_2021. This parallel trend can suggest that the improving economy, indicated by increases in the GDP Growth rate, could be a possible reason for the decrease in the unemployment rate as more jobs become available.\nBased on the above thought, the answer is:\nThe probable cause could be the economic improvements witnessed in 2021, as evidenced by the increase in the GDP Growth Rate. The recovery phase of the economy usually translates to creation of more jobs and thus can lead to a drop in the Unemployment Rate." } ], "id_raw": 43 }, { "id": "QVUpQYrYEPWl6qzx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_13.png", "conversations": [ { "from": "human", "value": "\nDuring which period did the inflation rate rise the most?" }, { "from": "gpt", "value": "To answer this, I need to compare the heights of the bars representing the inflation rate for each period. The inflation rate is represented by the {red bars}[0.176, 0.528, 0.211, 0.635];[0.351, 0.444, 0.385, 0.635];[0.525, 0.314, 0.560, 0.635];[0.699, 0.230, 0.734, 0.635];[0.873, 0.169, 0.908, 0.635]. In Q1_2021, the inflation rate was around 2.5%, in Q2_2021 it was around 4.2%, in Q3_2021 it was around 5.3% and finally in Q4_2021, it was around 6.1%. By comparing these, the largest increase is between Q1_2021 and Q2_2021.\nBased on the above thought, the answer is:\nQ2_2021." } ], "id_raw": 43 }, { "id": "OXRwuzRmPHgbaY9K", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis of the bar chart, can you provide the rating data for Facebook and Instagram across all features in a structured table format?" }, { "from": "gpt", "value": "From the analysis of the chart, I note that there are 4 bars for each platform in the chart. The legends {'Interactions'}[0.873, 0.100, 0.964, 0.128], {'Connectivity'}[0.873, 0.142, 0.969, 0.170], {'Popularity'}[0.873, 0.183, 0.950, 0.211], {'Trust'}[0.873, 0.225, 0.910, 0.253] represent these categories. For Facebook, the bars and corresponding ratings {3.5 for Interactions}[0.115, 0.209, 0.207, 0.883], {3.5 for Connectivity}[0.207, 0.209, 0.299, 0.883], {2.8 for Popularity}[0.299, 0.344, 0.391, 0.883], {3.0 for Trust}[0.391, 0.306, 0.483, 0.883] can be derived from the respective heights each bar reaches on the y-axis. Similarly, for Instagram, the bars and corresponding ratings {4.0 for Interactions}[0.574, 0.113, 0.666, 0.883], {3.5 for Connectivity}[0.666, 0.209, 0.758, 0.883], {3.5 for Popularity}[0.758, 0.209, 0.850, 0.883], and {2.5 for Trust}[0.850, 0.402, 0.942, 0.883] can be extracted in the same way. All these data points can be translated into a structured table.\nBased on the above thought, the answer is:\nSocial Networks,Interactions,Connectivity,Popularity,Trust\nFacebook,3.5,3.5,2.8,3.0\nInstagram,4.0,3.5,3.5,2.5" } ], "id_raw": 44 }, { "id": "Zh6E5KmHA58HmV5W", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nWhat does this chart portray about Facebook and Instagram's networking experience regarding interactions and trust?" }, { "from": "gpt", "value": "The rating for Interactions and Trust on Facebook and Instagram can be derived from the bars for both aspects and both platforms on the chart. In terms of interactions, Instagram's Interactions bar< r>[0.574, 0.113, 0.666, 0.883]< /r> shows a higher rating than Facebook's Interactions bar< r>[0.115, 0.209, 0.207, 0.883]< /r>, indicating that the users felt the interactions were more substantial on Instagram. However, in terms of Trust, Facebook's Trust bar< r>[0.391, 0.306, 0.483, 0.883]< /r> indicates a slightly better rating than Instagram's Trust bar< r>[0.850, 0.402, 0.942, 0.883]< /r>. This might suggest that despite Instagram having more user interaction, Facebook is seen as a more reliable social media platform by users.\nBased on the above thought, the answer is:\nAccording to the chart, Instagram has higher user interactions while Facebook was rated higher on trust. This depicts that whilst Instagram's engagement levels are superior, Facebook is more trusted by its users." } ], "id_raw": 44 }, { "id": "1z5WLxAuYbzg9bvK", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_3_14.png", "conversations": [ { "from": "human", "value": "\nBased on the data in the chart, on which aspect does Instagram outperform Facebook the most?" }, { "from": "gpt", "value": "If you look at the difference in heights between the bars of Instagram and Facebook for each aspect, you'll see that Instagram has a higher rating in Interactions, Popularity, and Connectivity. While Instagram's bar for Interactions< r>[0.574, 0.113, 0.666, 0.883]< /r> shows a rating of 4.0 and Facebook's Interactions< r>[0.115, 0.209, 0.207, 0.883]< /r> shows a rating of 3.5, the difference is 0.5. Popularity and Connectivity, on the other hand, both show the same rating difference of 0.7 with Instagram's Popularity< r>[0.758, 0.209, 0.850, 0.883]< /r> and Connectivity< r>[0.666, 0.209, 0.758, 0.883]< /r> bars indicating ratings of 3.5 and Facebook's Popularity< r>[0.299, 0.344, 0.391, 0.883]< /r> and Connectivity< r>[0.207, 0.209, 0.299, 0.883]< /r> bars indicating ratings of 2.8 and 3.5 respectively. In conclusion, Instagram outperforms Facebook most in Popularity and Connectivity.\nBased on the above thought, the answer is:\nPopularity and Connectivity." } ], "id_raw": 44 }, { "id": "XLfAvSaoktUgpl88", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information on the bar chart into a table format?" }, { "from": "gpt", "value": "From the x-axis and the y-axis labels, I know that the x-axis represents the volatility of the entrepreneurial skills in percentage terms, and the y-axis represents different entrepreneurial skills. Thus, I can form a table with two columns - 'Entrepreneurial Skills' and 'Volatility'. For each skill, I can refer to the corresponding bar and the text above to obtain the data. For example, the bar for 'Marketing' {bar}[0.576, 0.777, 0.910, 0.887] ends at around 16.7% according to {the text}[0.920, 0.823, 0.964, 0.841]. Hence, 'Marketing' has a volatility of '16.7'. If I perform the same steps for all the bars, I can generate the complete table.\nBased on the above thought, the answer is:\nEntrepreneurial Skills,Volatility\nMarketing,16.7\nPublic Speaking,12.9\nRisk Taking,-8.3\nProduct Development,1.8\nNetworking,6.5\nStrategic Planning,-3.7" } ], "id_raw": 45 }, { "id": "or6szcZc8PtDrUA6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which entrepreneurial skill needs improvement the most?" }, { "from": "gpt", "value": "To determine the entrepreneurial skill that needs the most improvement, I need to look for the skill that has experienced the greatest negative volatility. This is represented by the bar that extends the furthest to the left of the 0 line on the x-axis. The extent of the bars can be determined by the values provided in the texts above the bars. For example, the {bar for 'Risk Taking'}[0.410, 0.501, 0.576, 0.611] extends to -8.3% as per the {text}[0.360, 0.547, 0.400, 0.565], and the {bar for 'Strategic Planning'}[0.502, 0.087, 0.576, 0.197] extends to -3.7% as per the {text}[0.452, 0.133, 0.492, 0.151]. Comparing these, it can be seen that 'Risk Taking' had the greatest negative volatility and hence, needs the most improvement.\nBased on the above thought, the answer is:\nRisk Taking" } ], "id_raw": 45 }, { "id": "pZafYntYaikRe2wf", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_0.png", "conversations": [ { "from": "human", "value": "\nWhich entrepreneurial skill has shown the highest growth?" }, { "from": "gpt", "value": "To find the skill that has the highest growth, I need to compare the extent of the bars on the right side of the 0 line for each skill. The extent of the bars can be determined by the values provided in the texts above the bars. For example, the {bar for 'Marketing'}[0.576, 0.777, 0.910, 0.887] ends at around 16.7% as per the {text}[0.920, 0.823, 0.964, 0.841]. Similarly, the {bar for 'Public Speaking'}[0.576, 0.639, 0.834, 0.749] shows a growth of 12.9% as indicated by the {text}[0.844, 0.685, 0.888, 0.703]. After comparing all the bars, it can be seen that the bar for 'Marketing' goes the furthest on the chart, thereby indicating that 'Marketing' had the highest growth.\nBased on the above thought, the answer is:\nMarketing" } ], "id_raw": 45 }, { "id": "iqDYYNtpI7rBdzPa", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from this chart and present it in a CSV table format?" }, { "from": "gpt", "value": "The bar chart presents data for 5 social media platforms. Starting from the top of the y-axis {\"Facebook\"}[0.020, 0.792, 0.086, 0.815], {\"Twitter\"}[0.039, 0.633, 0.086, 0.656], {\"Instagram\"}[0.015, 0.473, 0.086, 0.497], {\"Snapchat\"}[0.020, 0.314, 0.086, 0.337], and {\"TikTok\"}[0.043, 0.155, 0.086, 0.178]. Each platform has a corresponding bar indicating its impact level. By comparing the span of each horizontal bar on the x-axis and the impact percentage at the end of each bar, the impact levels can be determined. For example, the bar {\"for Facebook\"}[0.536, 0.737, 0.756, 0.865] extends to just past the 10 point on the x-axis, and the {}ending impact percentage \"<12.5%>\"[0.774, 0.789, 0.818, 0.813] confirms this value. Following the same approach for the remaining platforms will provide the complete data to be tabulated.\nBased on the above thought, the answer is:\nModel,Impact(%)\nFacebook,12.5\nTwitter,-19.8\nInstagram,7.3\nSnapchat,-2.4\nTikTok,18.3" } ], "id_raw": 46 }, { "id": "0xAxxJV2NlHDWiT2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nBased on observation of the chart, can we infer which social media platform had the most significant decline in impact and why?" }, { "from": "gpt", "value": "The negative impact or decline in engagement on social media platforms is represented by bars extending to the left of the x-axis. From this chart figure, I need to find the bar that extends furthest to the left. In this case, the bar {\"representing Twitter\"}[0.187, 0.578, 0.536, 0.705] extends the furthest to the left with a {}negative impact value of \"-19.8%\"[0.120, 0.630, 0.169, 0.653]. This indicates that Twitter had the most significant decline in impact.\nBased on the above thought, the answer is:\nYes, Twitter had the most significant decline in impact based on this chart. It shows a noticeable decrease in engagement, as indicated by the length of its bar on the negative side of the x-axis and its impact value of -19.8%." } ], "id_raw": 46 }, { "id": "5YfOy4gJgn0apznx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_1.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform shown in the chart had the highest positive impact?" }, { "from": "gpt", "value": "To identify the social media platform with the highest positive impact, I need to locate the bars that extend the furthest to the right of the x-axis. By scanning the length of the horizontal bars, I see that the longest bar is the one {\"representing TikTok\"}[0.536, 0.100, 0.858, 0.228]. The {}impact percentage \"18.3%\"[0.876, 0.152, 0.920, 0.176] at the end of the bar confirms this observation. Hence, TikTok had the highest positive impact.\nBased on the above thought, the answer is:\nTikTok." } ], "id_raw": 46 }, { "id": "BPUriWLYff5Tx5I3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nCan you present the age distribution trends across various demographic groups from the horinzontal bar chart in a CSV format?" }, { "from": "gpt", "value": "I can derive this information from the lengths and colors of the horizontal bars in the chart. The bar lengths extend from the zero baseline, which I've found at [0.536, 0.927]. The range of change is denoted from '\u221210' to '10'. On finding the bars, {'Youths'}[0.536, 0.777, 0.906, 0.887], {'Adults'}[0.360, 0.639, 0.536, 0.749], {'Seniors'}[0.536, 0.501, 0.642, 0.611], {'Veterans'}[0.184, 0.363, 0.536, 0.473], {'Children'}[0.536, 0.225, 0.686, 0.335], and {'Teenagers'}[0.272, 0.087, 0.536, 0.197], I can see that each bar's left or right edge is the point of the change value. Based on the colors of the bar, I can determine whether the change is negative or positive. Therefore, by comparing all these values, I can convert them into a CSV format.\nBased on the above thought, the answer is:\nDemographic Group, Age Distribution\nYouths,8.4\nAdults,-4\nSeniors,2.4\nVeterans,-8.0\nChildren,3.4\nTeenagers,-6.0" } ], "id_raw": 47 }, { "id": "D4c9Pu87qsuAQFCp", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which demographic group witnessed a decline in age distribution, and according to you, what could be the potential implications of this situation?" }, { "from": "gpt", "value": "Upon careful observation, I note that three demographic groups {'Adults'}[0.360, 0.639, 0.536, 0.749], {'Veterans'}[0.184, 0.363, 0.536, 0.473] and {'Teenagers'}[0.272, 0.087, 0.536, 0.197] have red bars which denote a decline in age distribution. As a visual AI assistant, while I am not privy to specific implications, from a generic standpoint, a lower age distribution could potentially mean a smaller working age population, which might impact the economy. Furthermore, a declining age distribution among 'Teenagers' might imply reduced consumption of goods and services specifically tailored for that age group and a decrease in the number of eligible recruits for military service in the 'Veterans' demographic group.\nBased on the above thought, the answer is:\nThe 'Adults', 'Veterans' and 'Teenagers' demographic groups witnessed a decline in age distribution. A smaller working-age population could impact the economy while a lower 'Teenagers' age distribution might imply reduced consumption of teenager-specific goods and services and a decrease in the number of eligible recruits for military service in the 'Veterans' demographic group." } ], "id_raw": 47 }, { "id": "sAFkJaaAFQMVMx9i", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat was the highest percentage increase in age distribution amongst all the demographic groups outlined in the bar chart?" }, { "from": "gpt", "value": "The highest increase would be represented by the longest bar extending to the right from the zero baseline [0.536, 0.927]. By comparing the lengths of the white bars for {'Youths'}[0.536, 0.777, 0.906, 0.887], {'Seniors'}[0.536, 0.501, 0.642, 0.611] and {'Children'}[0.536, 0.225, 0.686, 0.335], I find that the bar corresponding to 'Youths' extends the longest. Therefore, 'Youths' had the highest percentage increase in age distribution.\nBased on the above thought, the answer is:\nYouths" } ], "id_raw": 47 }, { "id": "utXgOGKQ7aszdYlS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nConvert the data displayed in the barchart into a structured table in CSV format." }, { "from": "gpt", "value": "From the title at the top of the chart, located at the suspension section '{\"Rural and Urban Development Comparison\"}[0.376, 0.019, 0.733, 0.041]'. The x-axis labels represent the percentage change for both Rural and Urban areas. The y-axis labels list the type of development - 'Stable Growth', 'Sustained Growth', 'Abrupt Increase', 'Upward Trajectory', 'Stable Decline' at various locations {'Stable Growth'}[0.043, 0.814, 0.142, 0.831]; {'Sustained Growth'}[0.018, 0.647, 0.142, 0.664]; {'Abrupt Increase'}[0.032, 0.480, 0.142, 0.498]; {'Upward Trajectory'}[0.015, 0.313, 0.142, 0.331]; {'Stable Decline'}[0.041, 0.147, 0.142, 0.164]. By observing the length of each bar graph corresponding to these development types, I can determine that the magnitude for each type of area development - Rural and Urban. So, I'll arrange all the data collected into a structured table.\nBased on the above thought, the answer is:\nDevelopment Type,Rural,Urban\nStable Growth,13.2,-6.5\nSustained Growth,-10.4,4.8\nAbrupt Increase,7.3,-5.6\nUpward Trajectory,-15.2,18.3\nStable Decline,0.9,-20.4" } ], "id_raw": 48 }, { "id": "2B1Gj1OeinlqyHKu", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nFrom your analysis of the bar chart, which type of Development (Rural or Urban) has experienced more growth and which has seen more decline?" }, { "from": "gpt", "value": "From my analysis of the bar chart, I need to consider all bars that represent growth (the bars extending east from the center point) for both Rural and Urban development. For Rural, we see growth in 'Stable Growth', 'Abrupt Increase' and 'Stable Decline' with the bars located at {bar of 'Stable Growth'} [0.575, 0.754, 0.825, 0.887], {bar of 'Abrupt Increase'} [0.575, 0.420, 0.713, 0.554] and {bar of 'Stable Decline'} [0.575, 0.087, 0.592, 0.220] . For Urban, we only see growth in 'Sustained Growth' and 'Upward Trajectory' as can be seen by their respective bars at {bar of 'Sustained Growth'} [0.575, 0.587, 0.666, 0.720] and {bar of 'Upward Trajectory'} [0.575, 0.253, 0.921, 0.387]. For decline, or bars heading west from center, we see the opposite trend. Urban development has declinations in 'Stable Growth', 'Abrupt Increase' and 'Stable Decline', while Rural development has declination in 'Sustained Growth' and 'Upward Trajectory'\nBased on the above thought, the answer is:\nFrom the chart, Rural development has seen more growth, while Urban development has seen more decline." } ], "id_raw": 48 }, { "id": "wlVkHeTHSktgmUA4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_3.png", "conversations": [ { "from": "human", "value": "\nConsidering the given chart, where is the highest decrease observed, in Rural or Urban area?" }, { "from": "gpt", "value": "To locate the highest decrease, let's start to analyze the bar graph lengths indicating decrease (represented by the negative numbers or bars going west from the centre). For Rural development, the maximum decrease is observed in 'Upward Trajectory' {-bar of 'Upward Trajectory'}[0.287, 0.253, 0.575, 0.387] which is about -15.2%. On the other hand, for Urban development, the maximum decrease is seen in 'Stable Decline' {-bar of 'Stable Decline'}[0.188, 0.087, 0.575, 0.220], which is about -20.4%. Hence, the highest decrease observed is greater in Urban development compared to Rural development.\nBased on the above thought, the answer is:\nUrban" } ], "id_raw": 48 }, { "id": "mKX7PNIoFZOGezhV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nExtract the data from organizational performance chart and provide it in CSV format?" }, { "from": "gpt", "value": "The chart graphic contains data about the performance of different organizational strategies. This data can be extracted from the {bars}[0.497, 0.777, 0.581, 0.887; 0.529, 0.639, 0.581, 0.749; 0.581, 0.501, 0.873, 0.611; 0.561, 0.363, 0.581, 0.473; 0.355, 0.225, 0.581, 0.335; 0.581, 0.087, 0.909, 0.197] and the corresponding labels for each bar on the y-axis {\"label\"}[0.022, 0.825, 0.175, 0.843; 0.048, 0.687, 0.175, 0.705; 0.015, 0.549, 0.175, 0.567; 0.048, 0.411, 0.175, 0.429; 0.016, 0.273, 0.175, 0.291; 0.060, 0.135, 0.175, 0.153]. The position of the bar on the x-axis marks the performance percent for each concept. For example, the category {\"Customer Satisfaction\"}[0.022, 0.825, 0.175, 0.843] has a bar extending to the left from the midpoint, indicating a decrease in performance. The exact figure can be determined from the text {\"-2.1%\"}[0.438, 0.823, 0.478, 0.841] at the end of the bar. By examining each concept and its corresponding bar in the chart, I can compile the CSV table.\nBased on the above thought, the answer is:\nConcepts,Performance\nCustomer Satisfaction,-2.1\nStrategic Planning,-1.3\nOperational Excellence,7.4\nPeople Leadership,-0.5\nResource Management,-5.7\nRevenue Growth,8.3" } ], "id_raw": 49 }, { "id": "02L2IVHi6jrgySYQ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart's data, which area should the organization focus on improving next?" }, { "from": "gpt", "value": "To answer this question, I need to identify the area with the lowest performance based on the chart. By observing the lengths and directions of the bars and the corresponding performance percentages, it's clear that {\"Resource Management\"}[0.016, 0.273, 0.175, 0.291] has the lowest performance, as indicated by the length of the bar {\"Resource Management Bar\"}[0.355, 0.225, 0.581, 0.335] extending to the left and the associated performance value of {\"-5.7%\"}[0.295, 0.271, 0.335, 0.289]. Since low or negative performance indicates areas that need improvement, the organization should focus on improving Resource Management next.\nBased on the above thought, the answer is:\nThe organization should focus on improving Resource Management as it currently has the lowest performance." } ], "id_raw": 49 }, { "id": "OafzbyqrwFsVHfRq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_4.png", "conversations": [ { "from": "human", "value": "\nWhich organizational concept experienced the highest growth in performance?" }, { "from": "gpt", "value": "For this question, I'll focus on the length and direction of the bars to evaluate growth in performance. The bars to the right of the y-axis indicate growth, and the length indicates the size of this growth. By comparing the length of the bars, it seems that the longest bar corresponds to the {\"Revenue Growth\"}[0.060, 0.135, 0.175, 0.153] concept. This observation is also supported by the value {\"8.3%\"}[0.929, 0.133, 0.964, 0.151] present at the end of the bar. Hence, the concept with the highest growth in performance is Revenue Growth.\nBased on the above thought, the answer is:\nRevenue Growth" } ], "id_raw": 49 }, { "id": "U9kY0UY4lBZWOeGK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "The title of the plot located at {\"Market Trends in the Real Estate Sector\"}[0.375, 0.019, 0.703, 0.041] suggests that it describes the market trends in the real estate sector. The y-axis displays the types of buildings in the sector - {Residential}[0.102, 0.754, 0.393, 0.887], {Retail}[0.393, 0.587, 0.460, 0.720], {Industrial}[0.393, 0.420, 0.589, 0.554], {Warehouse}[0.393, 0.253, 0.845, 0.387], and {Office}[0.373, 0.087, 0.393, 0.220]. The x-axis shows the percentage change from {\"\u221210\"}[0.087, 0.939, 0.116, 0.957] to {\"20\"}[0.968, 0.939, 0.985, 0.957], with {\"0\"}[0.389, 0.939, 0.398, 0.957] being the no-change reference line. Each category has one bar representing a specific trend. The length of these bars indicates the percentage change in value they represent. For instance, the trend for {Residential}[0.248, 0.754, 0.393, 0.887] can be calculated by the labeled value, {\"\u22125.0%\"}[0.193, 0.812, 0.233, 0.829] shown at the end of each bar.\nBased on the above thought, the answer is:\nBuilding_Type,Market_Trend\nResidential,-5.0\nRetail,2.3\nIndustrial,6.7\nWarehouse,15.5\nOffice,-0.7" } ], "id_raw": 50 }, { "id": "ZzxJnDgB689CZXMP", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nWhich building type seems to be a risky investment based on the chart trends and why?" }, { "from": "gpt", "value": "The riskiness of an investment in a building type can be inferred from the trends shown on the chart. Specifically, a negative trend would suggest a higher risk as it indicates a decrease in value. According to the chart, {Residential}[0.102, 0.754, 0.393, 0.887] and {Office}[0.373, 0.087, 0.393, 0.220] show negative trends as their bars are oriented left of the {\"0\"}[0.389, 0.939, 0.398, 0.957] line, indicating a decrease in value. Residential has a trend value of {\"-5.0%\"}[0.193, 0.812, 0.233, 0.829], and Office has a trend value of {\"-0.7%\"}[0.318, 0.145, 0.358, 0.162]. Although both types have shown negative trends, Residential properties have experienced a larger decrease making them potentially more risky.\nBased on the above thought, the answer is:\nResidential. It shows a greater negative trend with a decrease of 5.0% in value." } ], "id_raw": 50 }, { "id": "xKWnJaIs36xc7eEO", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_5.png", "conversations": [ { "from": "human", "value": "\nWhich building type has shown the greatest positive change?" }, { "from": "gpt", "value": "To find out which building type has shown the greatest positive change, I need to look at the bars on the chart that extend to the right of the {\"0\"}[0.389, 0.939, 0.398, 0.957] line on the x-axis and compare their lengths. The longest of these represents the building type with the greatest positive trend. It appears that the {Warehouse}[0.393, 0.253, 0.845, 0.387] mark shows the largest increase with a value of {\"15.5%\"}[0.860, 0.311, 0.904, 0.329].\nBased on the above thought, the answer is:\nWarehouse." } ], "id_raw": 50 }, { "id": "mx19yDYszO1zS8G3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data represented in the chart in CSV format?" }, { "from": "gpt", "value": "To convert the chart data into CSV format, I need to extract the y-axis labels, which are the categories, and x-axis values, which are the percentage of perception change. The categories include {Freedom of Speech}[0.057, 0.814, 0.191, 0.831], {Family Values}[0.096, 0.647, 0.191, 0.664], {Religious Freedom}[0.064, 0.480, 0.191, 0.498], {Environmental Protection}[0.015, 0.313, 0.191, 0.331], and {Gender Equality}[0.079, 0.147, 0.191, 0.164]. For the change percentages, I can extract the values from the annotations above each bar. For example, for Freedom of Speech, the bar is located at [0.478, 0.754, 0.573, 0.887], and the corresponding value is -5.1%, as indicated by the value annotation [0.429, 0.812, 0.469, 0.829].\nBased on the above thought, the answer is:\nSocietal Aspects, Change in Perception(%)\nFreedom of Speech, -5.1\nFamily Values, 13.3\nReligious Freedom, -4.8\nEnvironmental Protection, 20\nGender Equality, -0.5" } ], "id_raw": 51 }, { "id": "MZIhPRATjkLnVyBt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which societal aspect has shown improvement in perception? Provide your analysis." }, { "from": "gpt", "value": "We can understand the improvement or deterioration in the perception of societal aspects by judging the direction and the value of each bar in the chart. A positive value on the x-axis indicates an improvement whereas a negative value indicates a deterioration. For instance, {Family Values}[0.573, 0.587, 0.820, 0.720] bar is towards the positive side of the x-axis with a value of 13.3%, indicating an improvement. Similarly, {Environmental Protection}[0.573, 0.253, 0.945, 0.387] bar is also on the positive side with a value of 20%, indicating an improvement. Therefore, Family Values and Environmental Protection have seen an improvement in perception.\nBased on the above thought, the answer is:\nFamily Values and Environmental Protection have shown an improvement in perception." } ], "id_raw": 51 }, { "id": "vFmcwW8Npe0pQ3MF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_6.png", "conversations": [ { "from": "human", "value": "\nWhich societal aspect shows the greatest change in perception according to the chart?" }, { "from": "gpt", "value": "I can determine the societal aspect with the greatest change in perception by looking at the width of the bars in the graph. The larger the absolute value of the change in perception, the wider the distance between the start of the bar and the centerline at x=0. For example, the 'Environmental Protection' bar starts at [0.573, 0.253] and goes up to [0.945, 0.387]. Measuring this distance, I can infer that 'Environmental Protection' has the greatest absolute change.\nBased on the above thought, the answer is:\nEnvironmental Protection." } ], "id_raw": 51 }, { "id": "dm0E9XqheZ1QUzRN", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nConsidering the bar chart presented, please take the data and convert it into a table in CSV format." }, { "from": "gpt", "value": "Analyzing the chart, the Y-axis represents different geographic regions {South America}[0.127, 0.820], {Asia}[0.127, 0.654], {Africa}[0.127, 0.487], {Europe}[0.127, 0.320], and {Australia}[0.127, 0.153]. The X-axis represents the percent change in tourism and hospitality value. For each region, there is a horizontal bar, where the length and direction of the bar depicts the value. The lengths of the bars represent the specific values for each region's change, for example, the bar for {South America}[0.452, 0.754, 0.551, 0.887] extends a distance of about -4.7% on the x-axis, which is confirmed by the {text annotation \"-4.7%\"}[0.391, 0.812, 0.430, 0.829] while the bar for {Asia}[0.551, 0.587, 0.711, 0.720] extends a distance of about 7.5% on the X-axis, also confirmed by the {text annotation \"7.5%\"}[0.732, 0.645, 0.767, 0.662]. To convert to a CSV format, the geographic region names are placed in the first column 'Region', and the corresponding values from text annotations are placed in the second column 'Value'.\nBased on the above thought, the answer is:\nRegion,Value\nSouth America,-4.7\nAsia,7.5\nAfrica,-11.4\nEurope,10.2\nAustralia,0.3" } ], "id_raw": 52 }, { "id": "6S8aNHkGVLtmoa5W", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nFrom the data depicted in the chart, can you infer which region's tourism and hospitality industry is recovering the fastest? And explain the reasons." }, { "from": "gpt", "value": "To infer which region's industry is recovering the fastest we need to analyze the value the bars are extending to on the X-axis. Positive growth is indicated by bars extending to the right of the Y-axis in black, while decline is shown by bars extending to the left of the Y-axis in red. Comparing the black bars, {Europe}[0.551, 0.253, 0.768, 0.387] extends the furthest to the right at around 10.2%, verified by the {text annotation \"10.2%\"}[0.789, 0.311, 0.833, 0.329], indicating the fastest recovery rate.\nBased on the above thought, the answer is:\nEurope is recovering the fastest in the tourism and hospitality industry, with a growth of 10.2%." } ], "id_raw": 52 }, { "id": "9uzqERSUsPAcrhZM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_7.png", "conversations": [ { "from": "human", "value": "\nWhich region experienced the least change in the tourism and hospitality industry?" }, { "from": "gpt", "value": "To discover which region experienced the least change, it's necessary to observe the length of each bar representing the region. By comparing each of these, I can see that the bar representing {Australia}[0.551, 0.087, 0.558, 0.220] extends the smallest distance along the X-axis from the central Y-axis. This is verified by the accompanying text annotation {0.3%}[0.579, 0.145, 0.614, 0.162], suggesting a minor change in value.\nBased on the above thought, the answer is:\nAustralia." } ], "id_raw": 52 }, { "id": "i0UtnWJTxqU7Kti3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nGiven the bar chart and its data, could you create a table in csv format listing the Resources and their Percentage changes?" }, { "from": "gpt", "value": "The necessary data is provided by the x-axis labeled {\"\u0394%\"}[0.541, 0.964, 0.564, 0.981] and by six horizontal bars, signifying different resources. Their labels are {\"Students\"}[0.070, 0.825, 0.131, 0.843], {\"Professors\"}[0.060, 0.687, 0.131, 0.705], {\"Expenditure\"}[0.047, 0.549, 0.131, 0.567], {\"Research Output\"}[0.015, 0.411, 0.131, 0.429], {\"Facilities\"}[0.072, 0.273, 0.131, 0.291], and {\"Scholarships\"}[0.044, 0.135, 0.131, 0.153]. Percentage changes occur along the horizontal, or x-axis. X-axis begins at zero and spans both negative and positive values. Each bar's length or position on this axis indicates the percentage change for the resource it corresponds to. For instance, the \"Expenditure\" bar stretches from the zero mark located at [0.455, 0.927] to the left, making it a negative change; the coordinate of its end is [0.231, 0.501, 0.455, 0.611]. I can look at the x-axis labels and estimate the value as approximately -10.7%. Therefore, I'll follow this method to determine the corresponding percentage changes for the rest of the resources.\nBased on the above thought, the answer is:\nResource,Percentage change\nStudents,5.6\nProfessors,2.3\nExpenditure,-10.7\nResearch Output,8.5\nFacilities,-1.3\nScholarships,22.7" } ], "id_raw": 54 }, { "id": "WjC42zFAbXXQcOYI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be concluded about the overall resource management based on displayed changes in educational resource distribution?" }, { "from": "gpt", "value": "The displayed data in the form of a horizontal bar chart with changes in various educational resources shows a clear distribution pattern of resource utilization in education. For important resources like {\"Students\"}[0.070, 0.825, 0.131, 0.843], {\"Professors\"}[0.060, 0.687, 0.131, 0.705] and {\"Research Output\"}[0.015, 0.411, 0.131, 0.429], there are positive changes. This suggests that the investment and efforts in these sectors have resulted in an increase in numbers. However, the negative figure in {\"Expenditure\"}[0.047, 0.549, 0.131, 0.567] might suggest savings or reduced allocations. Also, notable is the significant increase in {\"Scholarships\"}[0.044, 0.135, 0.131, 0.153]. This could be an initiative to attract more students and promote their enrollment and retention in the universities.\nBased on the above thought, the answer is:\nThe data indicates efficient and strategic resource utilization across the board. Notably, the rise in investments in personnel and research output coupled with a decline in expenditure suggests better financial efficiency. The significant increase in scholarships could be a tactical measure to attract more students and boost enrollment numbers." } ], "id_raw": 54 }, { "id": "XA0P0girVyJeJ9x0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat has been the largest positive change among the resources?" }, { "from": "gpt", "value": "To find the resource with the largest positive change, I should look at the length of the bars on the positive side of the x-axis {\"\u0394%\"}[0.541, 0.964, 0.564, 0.981]. Scanning through the bars from top to bottom: \"Students\" at {\"5.6%\"}[0.582, 0.823, 0.617, 0.841], \"Professors\" at {\"2.3%\"}[0.513, 0.685, 0.548, 0.703], \"Research Output\" at {\"8.5%\"}[0.643, 0.409, 0.678, 0.427], and \"Scholarships\" at {\"22.7%\"}[0.940, 0.133, 0.984, 0.151]. Among these, the bar for \"Scholarships\" is the longest.\nBased on the above thought, the answer is:\nScholarships." } ], "id_raw": 54 }, { "id": "aBm7VSnKesrLpYLJ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the details from the bar chart into a tabular representation?" }, { "from": "gpt", "value": "The chart titled {\"Tourism and Hospitality Industry Performance Analysis\"}[0.326, 0.019, 0.779, 0.041] depicts the performance of different tourism types. Each label on the y-axis {\"yticks\"}[{\"Mass Tourism\"}[0.025, 0.814, 0.118, 0.831]; {\"Dark Tourism\"}[0.027, 0.647, 0.118, 0.664]; {\"Eco-Tourism\"}[0.036, 0.480, 0.118, 0.498]; {\"Sports Tourism\"}[0.016, 0.313, 0.118, 0.331]; {\"Digital Tourism\"}[0.015, 0.147, 0.118, 0.164]] represents a different type of tourism. For each of these, there is a colored bar indicating the performance increase percentage. The x-axis {\"Performance Increase \u25b2%\"}[0.463, 0.964, 0.642, 0.981] shows this range, from negative to positive. I will analyze each bar and their corresponding text to determine the performance increase percentage number. This data can then be organized into a CSV table with 'Tourism Type' and 'Performance Increase (%)' as the headers. Let's begin by using the first bar {\"Mass Tourism\"}[0.340, 0.754, 0.555, 0.887] along with its percentage {\"-10.2%\"}[0.270, 0.812, 0.319, 0.829].\nBased on the above thought, the answer is:\nTourism Type,Performance Increase (%)\nMass Tourism,-10.2\nDark Tourism,5.8\nEco-Tourism,12.6\nSports Tourism,-18.7\nDigital Tourism,10.8" } ], "id_raw": 55 }, { "id": "psuUx28muKd7riIB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, do you see more positive or negative performance trends in the tourism and hospitality industry?" }, { "from": "gpt", "value": "Analysis of the question would require a comparative review of the performances in terms of positive and negative increases. Observing closely, the {\"Performance Increase \u25b2%\"}[0.463, 0.964, 0.642, 0.981] on the X-axis shows that the zero point is centred with negative and positive sides. Evaluating the bars on both sides: firstly, on the negative side we have {\"Mass Tourism\"}[0.340, 0.754, 0.555, 0.887] with {\"-10.2%\"}[0.270, 0.812, 0.319, 0.829] and {\"Sports Tourism\"}[0.161, 0.253, 0.555, 0.387] with {\"-18.7%\"}[0.091, 0.311, 0.140, 0.329], which amounts to 2 categories. On the positive side we have {\"Dark Tourism\"}[0.555, 0.587, 0.677, 0.720] with {\"5.8%\"}[0.698, 0.645, 0.733, 0.662], {\"Eco-Tourism\"}[0.555, 0.420, 0.820, 0.554] with {\"12.6%\"}[0.841, 0.478, 0.885, 0.496], and {\"Digital Tourism\"}[0.555, 0.087, 0.782, 0.220] with {\"10.8%\"}[0.804, 0.145, 0.848, 0.162], which also amounts to 3 categories.\nBased on the above thought, the answer is:\nThere are more positive performance trends. There are 3 tourism types with positive performance increase and 2 with a decrease." } ], "id_raw": 55 }, { "id": "1KGIRa2unVXs8HgP", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_10.png", "conversations": [ { "from": "human", "value": "\nWhat was the highest performance increase percentage in the tourism and hospitality industry categories according to the chart?" }, { "from": "gpt", "value": "In order to find the highest performance increase, I need to examine the length of each bar and their corresponding percentage increases for each category. Here are the bars with their associated labels: {\"Mass Tourism bar\"}[0.340, 0.754, 0.555, 0.887] at {\"-10.2%\"}[0.270, 0.812, 0.319, 0.829], {\"Dark Tourism bar\"}[0.555, 0.587, 0.677, 0.720] at {\"5.8%\"}[0.698, 0.645, 0.733, 0.662], {\"Eco-Tourism bar\"}[0.555, 0.420, 0.820, 0.554] at {\"12.6%\"}[0.841, 0.478, 0.885, 0.496], {\"Sports Tourism bar\"}[0.161, 0.253, 0.555, 0.387] at {\"-18.7%\"}[0.091, 0.311, 0.140, 0.329], and {\"Digital Tourism bar\"}[0.555, 0.087, 0.782, 0.220] at {\"10.8%\"}[0.804, 0.145, 0.848, 0.162]. From these, I can ascertain that the highest performance increase percentage is {\"12.6%\"}[0.841, 0.478, 0.885, 0.496] for {\"Eco-Tourism\"}[0.036, 0.480, 0.118, 0.498].\nBased on the above thought, the answer is:\n12.6%" } ], "id_raw": 55 }, { "id": "bjtdZIMjiAu0dvIA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data shown in the bar chart into a CSV format table?" }, { "from": "gpt", "value": "To convert the bar chart into a CSV table, I need to identify the platform name from the y-axis and the corresponding percentage change from the length and label of the bars. For example, the label at the tick on the y-axis {Khan Academy}[0.015, 0.814, 0.119, 0.831] corresponds with the bar [0.394, 0.754, 0.553, 0.887] with the label {-7.5%}[0.343, 0.812, 0.383, 0.829] which signifies a -7.5% change. Therefore, I can conclude that for Khan Academy, the percentage change in users from 2019 to 2020 was -7.5%. I can continue this process for the other platforms to complete the CSV table.\nBased on the above thought, the answer is:\nPlatform,Increase_Percentage\nKhan Academy,-7.5\nCoursera,13.8\nEdX,-16.2\nUdacity,6.0\nCareerFoundry,-0.9" } ], "id_raw": 56 }, { "id": "5ykS0pKqvv8z0CrP", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat conclusions can we draw about the EdTech sector from this chart?" }, { "from": "gpt", "value": "The chart provides valuable information on the growth trends of various online learning platforms. We can observe some platforms such as {Coursera}[0.553, 0.587, 0.845, 0.720] saw positive growth, while others like {EdX}[0.209, 0.420, 0.553, 0.554] and {Khan Academy}[0.394, 0.754, 0.553, 0.887] saw a decline. The success of platforms varies significantly, suggesting the competitiveness and volatility in the EdTech sector. Additionally, significant outliers like EdX's sharp decline and Coursera's noticeable increase suggest unpredictable events that sway user preferences drastically in this field. The varying growth rates across the platforms indicate that their performance in 2020 might have been influenced by individual factors unique to each platform, such as the quality of the courses offered, the platform's user interface, marketing strategies, or the effects of the COVID-19 pandemic.\nBased on the above thought, the answer is:\nThe EdTech sector is competitive and volatile with inconsistent growth trends among different platforms. The performance, in terms of user growth, of each platform appears to be influenced by various factors possibly unique to each platform." } ], "id_raw": 56 }, { "id": "AY9WQgXrRluZ36ee", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which platform experienced the least user growth in 2020?" }, { "from": "gpt", "value": "According to this chart, the platform that experienced the least growth in user numbers in 2020 is the one with the bar extending farthest to the left. We can see that the bar for {EdX}[0.209, 0.420, 0.553, 0.554] is the longest among the negative bars and extended to the leftmost part of the chart. Hence, EdX is the platform that experienced the least user growth in 2020.\nBased on the above thought, the answer is:\nEdX" } ], "id_raw": 56 }, { "id": "DvNRD03bkmpeGIkf", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the industry change percentages from the bar chart into a structured table?" }, { "from": "gpt", "value": "Starting from the chart title, {\"Culture and Arts Industry\"}[0.428, 0.019, 0.637, 0.041], it's clear that this graph is showing the change in different areas within the Culture and Arts Industry. Looking at {the y-axis}[0.049, 0.814, 0.089, 0.831] it's clear that these bars represent different areas within the industry ranging from Dance to Film. The {x-axis}[0.464, 0.964, 0.600, 0.981] parameters show the percentage change. I can use the length of the bars and the accompanying text to figure out the percentage change for each area. For instance, for dance, the bar starts at 0 and ends around -13.7% {according to the text}[0.078, 0.812, 0.126, 0.829]. Therefore, I can surmise that the change in Dance is -13.7% incrementally applying this methodology, we can generate the table.\nBased on the above thought, the answer is:\nIndustry,Percentage Change\nDance,-13.7\nTheatre,20.4\nMusic,-5.4\nVisual Arts,9.7\nFilm,-4.2" } ], "id_raw": 57 }, { "id": "Pds6V0u9OxUmKWz1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about the overall culture and arts industry based on this chart?" }, { "from": "gpt", "value": "Since this {graph} [0.098, 0.047, 0.966, 0.927] is about the Culture and Arts Industry, one can make inferences about the overall industry based on the graph. Starting from the top, {Dance}[0.138, 0.754, 0.455, 0.887] shows a significant decrease, indicating some troubles in this field. Alternatively, both {Theatre}[0.455, 0.587, 0.927, 0.720] and {Visual Arts}[0.455, 0.253, 0.679, 0.387] are trending in black, indicating positive growth. However, both {Music}[0.330, 0.420, 0.455, 0.554] and {Film}[0.358, 0.087, 0.455, 0.220] are represented in red, showing a decrease, while {Music} is showing a considerable downturn than {Film}. Thus it seems that the performance of different sectors of Arts and Culture industry is inconsistent, with two industries showing significant growth, two industries contracting, and one showing a severe contraction.\nBased on the above thought, the answer is:\nThe overall Culture and Arts Industry is mixed, with two branches (Theatre and Visual Arts) showing growth, while other areas (Dance, Music, and Film) are contracting, notably within the Dance and Music sectors." } ], "id_raw": 57 }, { "id": "ByMfsDtfIALaYsB9", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_12.png", "conversations": [ { "from": "human", "value": "\nWhich industry has the largest percentage increase?" }, { "from": "gpt", "value": "To identify which industry has seen the largest percentage increase, I need to locate the longest black bar on the graph. Starting from the left, the {Dance}[0.138, 0.754, 0.455, 0.887] bar is red and represents a decrease. Let's move on. The next, {Theatre}[0.455, 0.587, 0.927, 0.720], is black and quite long. Taking note of it and moving on. The {Music}[0.330, 0.420, 0.455, 0.554] again is a decline. {Visual Arts}[0.455, 0.253, 0.679, 0.387] is an increase but isn't as long as the Theatre one. Finally, {Film}[0.358, 0.087, 0.455, 0.220] again shows a decline. Therefore, Theatre is the industry with the highest percentage increase.\nBased on the above thought, the answer is:\nTheatre." } ], "id_raw": 57 }, { "id": "WBs43Fl9akRV4mPO", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nUse the plot data and draw this information into a CSV table." }, { "from": "gpt", "value": "The Y-axis contains the names of companies, which can be extracted from the labels of each bar: {Tesla}[0.026, 0.814, 0.061, 0.831], {Uber}[0.027, 0.647, 0.061, 0.664], {Lyft}[0.036, 0.480, 0.061, 0.498], {Toyota}[0.015, 0.313, 0.061, 0.331] and {Ford}[0.030, 0.147, 0.061, 0.164]. The X-axis holds the Market Research results represented as a percentage displayed at the right end of each bar. For positive results, the bars and corresponding percentages are located at {Tesla}[0.504, 0.754, 0.937, 0.887>, {Lyft}[0.504, 0.420, 0.576, 0.554], {Ford}[0.504, 0.087, 0.720, 0.220]. For negative results, the bars and corresponding percentages are located at {Uber}[0.215, 0.587, 0.504, 0.720], {Toyota}[0.359, 0.253, 0.504, 0.387]. It is necessary to clarify that the sign is the opposite when calculating negative results. The data drawn from the chart and the CSV output is as follows.\nBased on the above thought, the answer is:\n\"Companies\", \"Market Research\"\n\"Tesla\", 30\n\"Uber\", -20\n\"Lyft\", 5\n\"Toyota\", -10\n\"Ford\", 15" } ], "id_raw": 58 }, { "id": "hm6VRx3c4dMRglnu", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how would you generally describe the Automotive and Transportation market's performance?" }, { "from": "gpt", "value": "The chart shows the market performance of five automotive and transportation companies. By looking at the bars, I can see that for {Tesla}[0.504, 0.754, 0.937, 0.887] and {Ford}[0.504, 0.087, 0.720, 0.220], the performance is positive, while for {Uber}[0.215, 0.587, 0.504, 0.720} and {Toyota}[0.359, 0.253, 0.504, 0.387], the performance is negative with a decline in market. For {Lyft}[0.504, 0.420, 0.576, 0.554], they have a moderate positive performance. The company with the most noticeable increase in market performance is Tesla, while Uber faces the most considerable decline. Therefore, the market performance of these companies is mixed, with some showing growth and others showing a decrease.\nBased on the above thought, the answer is:\nThe Automotive and Transportation market's performance is mixed, with some companies like Tesla and Ford showing growth, while others like Uber and Toyota showing a decline. Lyft has moderate growth." } ], "id_raw": 58 }, { "id": "s6jXaGh4nBsCzTe3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_13.png", "conversations": [ { "from": "human", "value": "\nWhich company has the largest growth in market performance?" }, { "from": "gpt", "value": "I need to compare the values of market performance across all companies to answer this question. Starting with {Tesla}[0.504, 0.754, 0.937, 0.887], which has a value of \"30%\", then moving to {Uber}[0.215, 0.587, 0.504, 0.720] with a value of \"-20%\", followed by {Lyft}[0.504, 0.420, 0.576, 0.554] with a value of \"5%\", afterward {Toyota}[0.359, 0.253, 0.504, 0.387] with a value of \"-10%\", and finally {Ford}[0.504, 0.087, 0.720, 0.220] with a value of \"15%\". From these comparisons, it's clear that Tesla has the highest market performance growth ratio.\nBased on the above thought, the answer is:\nTesla" } ], "id_raw": 58 }, { "id": "8pY0reO4hpgB8ve4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the chart data and generate a structured data table?" }, { "from": "gpt", "value": "From the title at the top of the chart {\"Local Government Expenditure Changes\"}[0.408, 0.019, 0.743, 0.041], I can understand that this chart is about the change percentages of various department expenditures of a local government. There is a separate horizontal bar for each department with positive or negative values dictated by their position relative to the center line {\"0\"}[0.519, 0.939, 0.527, 0.957]. The actual change percentage is shown at the end of each bar in number annotations. I will use the department names on the y-axis and the percentage changes annotated at the end of each bar for creating the structured data table. For example, the first department is {\"Health & Sanitation\"}[0.025, 0.825, 0.161, 0.843] , and the value annotation at the end of its corresponding bar is {\"15%\"}[0.742, 0.823, 0.772, 0.841] so the value associated with \"Health & Sanitation\" is 15%.\nBased on the above thought, the answer is:\nDepartment,Change%\nHealth & Sanitation,15%\nEducation,-20%\nPublic Works,10%\nCommunity Service,-5%\nEnvironment & Parks,30%\nPublic Safety,-7.5%" } ], "id_raw": 59 }, { "id": "6A4KRfLviA7UTNeu", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nConsidering the information presented in the chart, which department seems to be more prioritized in term of budgetary allocations and why do you think so?" }, { "from": "gpt", "value": "To answer this question, I need to assess which department saw a significant increase in expenditure. An increase in expenditure could be interpreted as the department being prioritized in terms of budget allocations. As I mentioned earlier, the {\"Environment & Parks\"}[0.523, 0.225, 0.946, 0.335] department bar extends further to the right of the middle axis {\"0\"}[0.519, 0.939, 0.527, 0.957], this implies that \"Environment & Parks\" has the highest budget increase. Their budgetary allocation has increased by a whopping {\"30%\"}[0.953, 0.271, 0.984, 0.289], which is the highest among all departments. This could indicate that prioritization has skewed towards the \"Environment & Parks\" department, possibly due to increased awareness of environmental protection or new projects concerning parks and the environment.\nBased on the above thought, the answer is:\n\"Environment & Parks\" department. This is because it witnessed the highest increase, 30%, in budget allocations compared to other departments, indicating higher priority." } ], "id_raw": 59 }, { "id": "7L81vPC2i0yryU4R", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_4_14.png", "conversations": [ { "from": "human", "value": "\nWhich department saw the largest increase in expenditure, according to the bar chart?" }, { "from": "gpt", "value": "To identify the department that had the biggest increase in expenditure, I must examine the horizontal bars on the right side of the middle axis {\"0\"}[0.519, 0.939, 0.527, 0.957] because positive changes are represented by bars extending to the right. I'll compare the lengths of each of this set of bars. For instance, the bars for {\"Health & Sanitation\"}[0.523, 0.777, 0.734, 0.887] and {\"Public Works\"}[0.523, 0.501, 0.664, 0.611] both extend to the right of the middle axis. By looking at every bar that extends right from the middle axis and comparing their lengths, I can find that the longest bar represents the {\"Environment & Parks\"}[0.523, 0.225, 0.946, 0.335] department. Therefore, \"Environment & Parks\" saw the largest increase in expenditure.\nBased on the above thought, the answer is:\nEnvironment & Parks." } ], "id_raw": 59 }, { "id": "wHVZxCP10oIFcQOK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nHow can the change in influence for each cultural perspective from Influence Metric 1 to Influence Metric 2 be represented in a table format?" }, { "from": "gpt", "value": "Observing the two subplots, I place my focus on the bars representing each cultural perspective in {subplot_0}[0.174, 0.025, 0.562, 0.903] for Influence Metric 1 and {subplot_1}[0.587, 0.025, 0.975, 0.903] for Influence Metric 2. By comparing the end of each bar on the x-axis, I can note the value change they represent. {The x-axis labels}[0.583, 0.919, 1.002, 0.942] and {the y-axis labels}[0.174, 0.065, 0.164, 0.845] denote the scale and cultural perspectives, respectively. For instance, for the cultural perspective of {Traditionalism}[0.067, 0.821, 0.164, 0.845], I see that in Influence Metric 1, the value is {5.5}[0.377, 0.819, 0.404, 0.842], and in Influence Metric 2, the value becomes {8.1}[0.957, 0.819, 0.983, 0.842]. This process can be repeated for each cultural perspective to obtain a complete table of values from both Influence Metrics.\nBased on the above thought, the answer is:\nCultural Perspective,Influence Metric 1,Influence Metric 2\nTraditionalism,5.5,8.1\nModernism,7,5\nGlobalism,9,2\nConservatism,2,8.1\nLiberalism,6.5,7.1\nCollectivism,3.5,4.2\nIndividualism,8.5,3.2\nCommercialization,10,1\nSecularism,4.5,6.4\nSpirituality,2,7" } ], "id_raw": 76 }, { "id": "mylDEka7MHDc43Vd", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nWhich cultural perspectives have shown a decline in influence and might require attention to reinforce their values?" }, { "from": "gpt", "value": "To determine which cultural perspectives have declined, I must evaluate the difference in bar length for each perspective between {subplot_0}[0.174, 0.025, 0.562, 0.903] and {subplot_1}[0.587, 0.025, 0.975, 0.903]. By examining each pair of bars, I observe a decrease in value when the bar in {subplot_1}[0.587, 0.025, 0.975, 0.903] is shorter than its counterpart in {subplot_0}[0.174, 0.025, 0.562, 0.903]. {Commercialization}[0.035, 0.251, 0.164, 0.275], for instance, shows a significant decrease, going from {10}[0.544, 0.249, 0.566, 0.272] to {1}[0.633, 0.249, 0.646, 0.272], which is a 9.0 change. Similarly, {Globalism}[0.095, 0.658, 0.164, 0.682] and {Modernism}[0.088, 0.740, 0.164, 0.763] also show substantial decreases.\nBased on the above thought, the answer is:\nCommercialization, Globalism, and Modernism have shown a decline in influence. Addressing this issue could involve reassessing the values associated with these perspectives, highlighting their importance and relevance to society, and potentially developing programs or initiatives that would reinforce the values and principles tied to these perspectives." } ], "id_raw": 76 }, { "id": "mifQ7gXcE5Vrn93b", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_6_1.png", "conversations": [ { "from": "human", "value": "\nWhich cultural perspective shows the greatest increase in influence from Metric 1 to Metric 2?" }, { "from": "gpt", "value": "I must compare the increase in the value of bars for each cultural perspective between the two subplots. By reviewing each bar's end coordinate on the x-axis in {subplot_0}[0.174, 0.025, 0.562, 0.903] and {subplot_1}[0.587, 0.025, 0.975, 0.903], I can calculate the change in value. After analyzing all the cultural perspectives, it is evident that {Conservatism}[0.069, 0.577, 0.164, 0.600] has the highest increase from {2}[0.248, 0.574, 0.261, 0.598] in Metric 1 to {8.1}[0.957, 0.574, 0.983, 0.598] in Metric 2, which is a 6.1 increase.\nBased on the above thought, the answer is:\nConservatism" } ], "id_raw": 76 }, { "id": "3WoF4oLKhm0OUByI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the given chart data regarding tourism and hospitality industry trends into a CSV formatted table?" }, { "from": "gpt", "value": "Observing the structuring of {the first subplot's bars}[0.203, 0.810, 0.459, 0.876] and the corresponding {labels}[0.459, 0.833, 0.481, 0.853] for Season 1, as well as {the second subplot's bars}[0.604, 0.810, 0.790, 0.876] and {labels}[0.790, 0.833, 0.812, 0.853] for Season 2, I can determine the frequency for each type in the tourism and hospitality industry. Along the y-axis, there are {tick labels}[0.093, 0.835, 0.193, 0.855] to [0.094, 0.087, 0.193, 0.107] which list the types to be included in the table. This information will be tabulated in accordance with the labels associated with each bar. For example, 'Resort Rentals' has a frequency of 15 for Season 1 and 13 for Season 2.\nBased on the above thought, the answer is:\nType,Season 1,Season 2\nResort Rentals,15,13\nHotel Bookings,12,14\nAdventure Tours,8,10\nCamping Ground Visits,10,9\nCity Tours,15,17\nCruise Bookings,18,15\nRestaurant Revenue,20,23\nEvent Ticket Sales,21,25\nSpa Services,19,21\nMuseum Visits,14,16" } ], "id_raw": 77 }, { "id": "cAJw4X64SIq0gZgq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nAre there any trends that have reversed from Season 1 to Season 2, and can you identify the types for which this is the case?" }, { "from": "gpt", "value": "A reversal of trends would be indicated by bars in Season 2 having lengths greater or smaller than their counterparts in Season 1 for the same type. By comparing {the pairs of bars for each type}[0.203, 0.810, 0.459, 0.876] and [0.604, 0.810, 0.790, 0.876] for 'Resort Rentals', [0.203, 0.727, 0.408, 0.793] and [0.604, 0.727, 0.805, 0.793] for 'Hotel Bookings' and so on, we can see which pairs show this trend reversal by examining the textual data at the end of each bar to determine the related frequencies. For example, a reversal is seen for 'Resort Rentals' as the bar for Season 2 [0.604, 0.810, 0.790, 0.876] is shorter than the bar for Season 1 [0.203, 0.810, 0.459, 0.876], indicating a decrease in frequency from 15 to 13.\nBased on the above thought, the answer is:\nYes, 'Resort Rentals', 'Camping Ground Visits', and 'Cruise Bookings' show reversed trends with decreased frequencies from Season 1 to Season 2." } ], "id_raw": 77 }, { "id": "ph8jLe2nN9q9ghWR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of bookings and visits for Season 2 across all types?" }, { "from": "gpt", "value": "To calculate the total for Season 2, I look at the labeled frequencies on each {bar of Season 2}[0.604, 0.810, 0.790, 0.876] to [0.604, 0.062, 0.833, 0.129] and sum up the numerical values that are presented above each bar, such as {the label '13'}[0.790, 0.833, 0.812, 0.853] for 'Resort Rentals'. Adding each of these numbers yields the total cumulative frequency for Season 2.\nBased on the above thought, the answer is:\n149" } ], "id_raw": 77 }, { "id": "A7A7pD0YwiqXHqYP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nCould you create a table showing both Frequency Metric 1 and Frequency Metric 2 for each cybersecurity and data privacy issue?" }, { "from": "gpt", "value": "By examining the two horizontal bar charts, I can discern the relationship between the issue types and their corresponding frequency metrics. Starting with {Frequency Metric 1}[0.168, 0.037, 0.535, 0.854], it features horizontal bars, each beginning at the y-axis and extending rightward to encapsulate the respective percentage frequency. I can locate the start of each bar at {y-axis tick marks}[0.168, 0.787] and follow the length of the bar to the endpoint, such as for {Password Attacks}[0.168, 0.757, 0.437, 0.817] which concludes at 37.5%. The same process can be applied to the second bar chart corresponding to {Frequency Metric 2}[0.586, 0.757, 0.761, 0.817], where I observe that the bar for {Password Attacks}[0.586, 0.757, 0.761, 0.817] reaches 12.2%. I'll continue this method for all ten issues and collect the data for both frequency metrics.\nBased on the above thought, the answer is:\nIssue,Frequency Metric 1 (%),Frequency Metric 2 (%)\nPassword Attacks,37.5,12.2\nPhishing,45.2,16.8\nMalware,32.1,20.7\nRansomware,48.7,9.5\nData Breach,44.1,18.0\nIdentity Theft,31.3,14.8\nInsider Threats,39.4,21.2\nIoT Vulnerabilities,47.1,8.7\nDeepFakes,30.4,24.4\nCloud Security,35.8,19.3" } ], "id_raw": 80 }, { "id": "0LqTti1dknBbwRJM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nBetween Frequency Metric 1 and Frequency Metric 2, which metric shows a greater concern across all cybersecurity issues?" }, { "from": "gpt", "value": "To determine which frequency metric indicates greater concern across all cybersecurity issues, I must compare the lengths of the bars corresponding to each metric. I start with {Password Attacks}[0.168, 0.757, 0.437, 0.817] using comparison, I note that the bar for {Frequency Metric 1}[0.168, 0.757, 0.437, 0.817] is longer than for {Frequency Metric 2}[0.586, 0.757, 0.761, 0.817]. Progressing methodically through the bars representing Phishing, Malware, and other issues, I recognize a consistent pattern: bars representing {Frequency Metric 1}[0.168, 0.037, 0.535, 0.854] are consistently longer than bars for {Frequency Metric 2}[0.586, 0.037, 0.954, 0.854], indicating that the issues are more prevalent in Frequency Metric 1. Thus concluding that Frequency Metric 1 showcases a higher level of concern for cybersecurity issues.\nBased on the above thought, the answer is:\nFrequency Metric 1 shows greater concern across all cybersecurity issues because the corresponding bars are longer than those of Frequency Metric 2, signifying a higher percentage frequency of incidents for each issue." } ], "id_raw": 80 }, { "id": "WOIhzVI3Kidhbigl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the average frequency percentage for Identity Theft across both frequency metrics?" }, { "from": "gpt", "value": "To calculate the average frequency percentage for {Identity Theft}[0.168, 0.378, 0.393, 0.438] across both frequency metrics, I first identify the specific data points. The frequency percentage from {Frequency Metric 1}[0.168, 0.378, 0.393, 0.438] for Identity Theft is 31.3%, confirmed by its data label. The frequency percentage from {Frequency Metric 2}[0.586, 0.378, 0.799, 0.438] is found to be 14.8%. Now, I add these two percentages and divide by the number of metrics, which is 2, to derive the average.\nBased on the above thought, the answer is:\n23.05" } ], "id_raw": 80 }, { "id": "oY1KXHHg0oZJu0kD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nPlease convert the data depicted on the chart into a structured CSV format table." }, { "from": "gpt", "value": "To do this, I will consider the categories listed on {the y-axis}[0.015, 0.352, 0.029, 0.540] and the respective frequencies for each category as represented by the bar lengths on {the x-axis metric 1}[0.323, 0.927, 0.484, 0.963] and {x-axis metric 2}[0.710, 0.927, 0.871, 0.963]. Following the bar locations and their annotated frequencies, I can determine the exact frequency of each category from the labels at the end of the bars, such as {Example bar metric 1}[0.232, 0.757, 0.559, 0.817] and {Example bar metric 2}[0.618, 0.757, 0.796, 0.817]. Therefore, I can allot each category with its respective values in the table.\nBased on the above thought, the answer is:\nCategory,Frequency Metric 1 (%),Frequency Metric 2 (%)\nInvestment,8.2,4.5\nWaste Management,6.5,5.8\nEmployee Welfare,6.2,6.5\nCarbon Footprint,5.9,7.2\nSupply Chain Management,5.0,4.5\nClean Energy,4.5,8.3\nEqual Opportunities,4.0,3.5\nEthical Business,3.6,6.0\nCommunity Development,3.4,5.2\nSustainable Raw Materials,2.8,5.7" } ], "id_raw": 83 }, { "id": "GXEC3WTpLNyOUzNC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nWhich area of social responsibility experiences a greater emphasis shift from Frequency Metric 1 to Frequency Metric 2?" }, { "from": "gpt", "value": "The shift from Frequency Metric 1 to Frequency Metric 2 can be determined by comparing the length of the bars linked to a single category across the two plots. The difference between the lengths of the bars represents the shift in emphasis for each category. For instance, 'Clean Energy' in {Frequency Metric 1's plot}[0.618, 0.378, 0.946, 0.438] shows lower emphasis than in {Frequency Metric 2's plot}[0.232, 0.378, 0.412, 0.438]. By going through each category in a similar manner, we can determine which category experiences the greatest emphasis shift.\nBased on the above thought, the answer is:\nThe 'Clean Energy' category experiences the greatest emphasis shift from Frequency Metric 1 to Frequency Metric 2." } ], "id_raw": 83 }, { "id": "kIVgxg2PBLr9OAS0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_6_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of percentages as depicted on the frequency axis for both metrics?" }, { "from": "gpt", "value": "On the chart, the frequency percentages for both metrics are represented on the x-axes. The smallest and largest numbers on {the x-axis for Frequency Metric 1}[0.323, 0.927, 0.484, 0.963] are '0%' and '10%' respectively and similarly on {the x-axis for Frequency Metric 2}[0.710, 0.927, 0.871, 0.963]. Therefore, the range of frequencies for both metrics is from 0% to 10%.\nBased on the above thought, the answer is:\n0% to 10%" } ], "id_raw": 83 }, { "id": "p1b9U1KnGPnkbYOH", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nCould you create a structured table from the grouped bar chart that shows the market growth rates for the innovation ecosystems?" }, { "from": "gpt", "value": "The {chart}[0.187, 0.030, 0.962, 0.883] illustrates two trends in the innovation ecosystems. The left section corresponds to {Rate1}%[0.187, 0.030, 0.553, 0.883] while the right section corresponds to {Rate2}%[0.596, 0.030, 0.962, 0.883]. Starting with {Artificial Intelligence}[0.187, 0.781, 0.536, 0.845], the bar reaches up to 8.5% for the first rate and 7.6% for the second rate. Moving down, {Blockchain}[0.187, 0.702, 0.511, 0.765] has a rate of 7.9% for the first and 6.3% for the second. This pattern continues with each row representing a different sector. I will structure the table using the ecosystem names as rows and two columns for each rate, marking the values for each as per the length of the bars and corresponding labels.\nBased on the above thought, the answer is:\nEcosystem,Rate1 (%),Rate2 (%)\nArtificial Intelligence,8.5,7.6\nBlockchain,7.9,6.3\nBig Data,6.2,4.9\nCloud Computing,6.0,5.1\nGreen Tech,5.9,4.6\nDrones Tech,4.5,3.7\nFintech,4.0,2.8\nHealthcare Tech,3.5,3.9\nEdtech,2.9,2.3\nSpace Tech,1.8,3.2" } ], "id_raw": 84 }, { "id": "ULusKbgHhNntFu78", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nWhich sector has experienced the highest overall growth rate when combining the rates from both Rate1 and Rate2?" }, { "from": "gpt", "value": "To find the sector with the highest overall growth, it is necessary to observe the {length of the bars}[0.187, 0.781, 0.536, 0.845];[0.596, 0.781, 0.945, 0.845] corresponding to both rates for all sectors and calculate their combined values. The sector {Artificial Intelligence}[0.035, 0.802, 0.177, 0.830] starts with the longest bars, giving an early indication of it having high rates. The bar lengths from {Rate1 bar}[0.187, 0.781, 0.536, 0.845] and {Rate2 bar}[0.596, 0.781, 0.945, 0.845] are added to calculate the total rate. This process is repeated for every sector, comparing the sums to identify the highest overall growth rate.\nBased on the above thought, the answer is:\nArtificial Intelligence. Because when adding the growth rates of Rate1 (8.5%) and Rate2 (7.6%), it totals 16.1%, which is higher than the combined rates for any other sector as indicated by the total length of {their bars}[0.187, 0.781, 0.536, 0.845];[0.596, 0.781, 0.945, 0.845]." } ], "id_raw": 84 }, { "id": "HFUGs4kBW6oyJNWF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_6_9.png", "conversations": [ { "from": "human", "value": "\nIs the growth rate for Big Data greater in Rate1 than in Rate2?" }, { "from": "gpt", "value": "To ascertain the growth rates for {Big Data}[0.117, 0.644, 0.177, 0.672], I analyze the corresponding bars in each rate section. The {Rate1 bar for Big Data}[0.187, 0.623, 0.441, 0.686] extends to approximately 6.2%, while the {Rate2 bar}[0.596, 0.623, 0.821, 0.686] has a length indicating a rate of approximately 4.9%. Therefore, the observed length provides the necessary numerical data to compare.\nBased on the above thought, the answer is:\nYes." } ], "id_raw": 84 }, { "id": "xGdYYdPhY2xgWvpY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nCould you please create a CSV table that represents the data from the bar chart?" }, { "from": "gpt", "value": "Based on the structure of the provided bar chart, it is apparent that the X-axis represents different types of emerging technology {\"Emerging Tech\"}[{\"VR\"}[0.075, 0.952, 0.091, 0.975]; {\"Blockchain\"}[0.102, 0.952, 0.165, 0.975}; {\"AI\"}[0.179, 0.952, 0.190, 0.975]; {\"5G\"}[0.228, 0.952, 0.244, 0.975]; {\"Quantum Computing\"}[0.226, 0.952, 0.347, 0.975]; {\"IoT\"}[0.329, 0.952, 0.346, 0.975]; {\"Big Data\"}[0.363, 0.952, 0.414, 0.975]; {\"Cloud Computing\"}[0.389, 0.952, 0.490, 0.975]], and the Y-axis represents the \"Outlook Score\" {\"Outlook Score\"}[0.013, 0.418, 0.024, 0.580] scaled from 0 to 1. I can extract the necessary information by analyzing the height of the bars in correlation with the labeled Y-axis values. For example, the height of the {bar for \"VR\"}[0.074, 0.237, 0.092, 0.935] extends to a point that corresponds to a little below 1 on the Y-axis, which is approximated as 0.8.\nBased on the above thought, the answer is:\nEmerging Tech,Outlook Score\nVR,0.8\nBlockchain,0.7\nAI,0.6\n5G,0.5\nQuantum Computing,0.4\nIoT,0.35\nBig Data,0.3\nCloud Computing,0.25" } ], "id_raw": 96 }, { "id": "xvHulC3viY6PVvAn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall trend of the outlook score in the emerging technology market according to the bar chart?" }, { "from": "gpt", "value": "By observing the graph, I can analyze the overall trend in the outlook of emerging technologies. Starting from {VR}[0.074, 0.237, 0.092, 0.935], which has a relatively high outlook score of about 0.8, the outlook score seems to show a consistent downward trend as we move towards {Cloud Computing}[0.431, 0.717, 0.448, 0.935] on the X-axis, which only has an outlook score of about 0.25. Therefore, the general trend in emerging technologies' outlook scores is decreasing.\nBased on the above thought, the answer is:\nThe overall trend in the outlook of emerging technologies is decreasing." } ], "id_raw": 96 }, { "id": "rPy8Eq7BTrcUJAYb", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_7_6.png", "conversations": [ { "from": "human", "value": "\nWhich emerging technology has the highest outlook score according to the bar chart?" }, { "from": "gpt", "value": "To determine which emerging technology has the highest outlook score, I need to compare the height of all the bars on the chart. Looking at the bar for {\"VR\"}[0.074, 0.237, 0.092, 0.935], I can see that it extends to the highest position on the Y-axis (close to 1.0), which represents the outlook score. Therefore, VR is the emerging technology with the highest outlook score.\nBased on the above thought, the answer is:\nVR" } ], "id_raw": 96 }, { "id": "xdr8V6JxxNAc8uh8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, could you please convert the E-commerce sales data from January to May into a CSV format table?" }, { "from": "gpt", "value": "From the title {\"E-commerce and Retail Industry Analysis\"}[0.330, 0.020, 0.670, 0.056], it is clear that this chart is showing sales data for the E-commerce and Retail sectors. Looking at the sub-chart labeled as {E-commerce}[0.224, 0.146, 0.328, 0.182], the x-axis represents different months, and the y-axis represents sales. The blue bars represent sales data for each month. For instance, {the bar for \"January\"}[0.079, 0.398, 0.145, 1.594]indicates the sales is approximately at the 4260 level, and from the raw data provided, it is confirmed that the January sales for E-commerce is 4260.3k. Similar process applies to all other bars.\nBased on the above thought, the answer is:\nMonths,E-commerce Sales\nJanuary,4260.3\nFebruary,3927.1\nMarch,4320.7\nApril,4543.5\nMay,4697.5" } ], "id_raw": 105 }, { "id": "jBr6ojWQt6g12MeM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, could you please compare the sales trends of E-commerce and Traditional Retail from January to May?" }, { "from": "gpt", "value": "From the chart marked as {E-commerce}[0.224, 0.146, 0.328, 0.182] and {Traditional Retail}[0.699, 0.146, 0.838, 0.182], we can see two different sub-charts. For E-commerce, there is a clear upward trend in sales from January to May. However, for Traditional Retail, the trend is slower with a slight dip in February and March, followed by a slow recovery. This indicates that E-commerce has done far better than Traditional Retail during these months.\nBased on the above thought, the answer is:\nE-commerce experienced a consistent increase in sales from January to May with a slight dip in February. In contrast, Traditional Retail showed a slow recovery in sales with a slight decline in February and March. Overall, E-commerce had a more robust growth trend compared to Traditional Retail." } ], "id_raw": 105 }, { "id": "ru0fauhdHzvA7TqA", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, in which month did Traditional Retail have the lowest sales?" }, { "from": "gpt", "value": "Looking at the sub-chart marked as {Traditional Retail}[0.699, 0.146, 0.838, 0.182], I can see that the blue bars represent sales for each month. To determine the month with the lowest sales, I would need to identify the shortest bar. It appears that {the bar representing March}[0.736, 0.786, 0.801, 1.594] is the shortest among the five bars. So, from the chart, the lowest sales occurred in March.\nBased on the above thought, the answer is:\nMarch" } ], "id_raw": 105 }, { "id": "pB9hEeRrj94TSp4Y", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nCan you help me transform data for the Annual Energy Use (GW) and Resource Consumption (TJ) from the bar charts into CSV format?" }, { "from": "gpt", "value": "Yes, from the two bar charts that I visualize, one denotes {Annual Energy Use}[0.211, 0.146, 0.348, 0.182] and the other holds the data for {Resource Consumption}[0.700, 0.146, 0.850, 0.182]. The first chart's bars labeled with {Coal, Natural Gas, Nuclear, Hydro, and Other Renewables}[0.107, 0.912, 0.127, 0.940];[0.171, 0.912, 0.226, 0.940];[0.262, 0.912, 0.298, 0.940];[0.347, 0.912, 0.375, 0.940];[0.401, 0.912, 0.485, 0.940] represent the annual energy use in GW. The correlating heights of these bars showcase the magnitude of energy usage for these resources. Accordingly, for the second chart, the resource consumption data in TJ is represented by the different heights of the bars labeled with the same resources names.\nBased on the above thought, the answer is:\nResource,Annual Energy Use(GW),Resource Consumption(TJ)\nCoal,40000,160000\nNatural Gas,25000,90000\nNuclear,9500,45000\nHydro,6000,12000\nOther Renewables,4500,22500" } ], "id_raw": 106 }, { "id": "Uy52ld348qiyQuFx", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat can we infer about the country's resource utilization strategy based on the two charts provided?" }, { "from": "gpt", "value": "From the chart on {Annual Energy Use}[0.211, 0.146, 0.348, 0.182], I observe that the resource with the maximum usage is {Coal}[0.084, 0.752, 0.149, 0.893], while the least utilized resource from an energy use perspective is {Other Renewables}[0.410, 0.877, 0.475, 0.893]. Similarly, for {Resource Consumption}[0.700, 0.146, 0.850, 0.182], the resource that stands out the most in terms of consumption is again {Coal}[0.579, 0.331, 0.644, 0.893]. The resource with the least consumption is {Hydro}[0.824, 0.850, 0.889, 0.893]. It seems that fossil fuels, especially coal, are the primary contributors to the country's energy, which may have environmental implications. The renewable resources, while underutilized, show positive signs of usage, suggesting a slow progression towards sustainable and environment-friendly resources.\nBased on the above thought, the answer is:\nThis country might primarily be dependent on non-renewable resources like Coal for its energy needs, which may lead to environmental issues. However, the usage of renewables, though currently limited, indicates a positive inclination towards clean and sustainable power sources." } ], "id_raw": 106 }, { "id": "QqRYYhN68G3LhFur", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat resource has the lowest usage in terms of Annual Energy Use (GW) from the bar chart I gave you?" }, { "from": "gpt", "value": "To answer this question, I need to compare the bars' heights from the bar chart representing {Annual Energy Use}[0.211, 0.146, 0.348, 0.182]. After careful analysis, we see that the bar labelled {Other Renewables}[0.410, 0.877, 0.475, 0.893] has the shortest height, indicating that it has the lowest usage.\nBased on the above thought, the answer is:\nOther Renewables" } ], "id_raw": 106 }, { "id": "Jan7B1EKndkwauXW", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nCan you translate the information from the first bar chart showing Disinfectants sales into a structured table outlining the sales for each month?" }, { "from": "gpt", "value": "To convert the chart data for Disinfectants into a table, I must look at each bar and its corresponding month. For instance, the bar for February is defined by coordinates {the bar for \"Feb\"}[0.042, 0.519, 0.062, 0.893], which after analyzing the y-axis ticks, indicates a sales figure of approximately 37221. Similarly, I go through each bar for the different months {bar coordinates for \"Apr\"}[0.067, 0.362, 0.088, 0.893], {bar coordinates for \"Jun\"}[0.093, 0.420, 0.114, 0.893], {bar coordinates for \"Aug\"}[0.119, 0.429, 0.140, 0.893], {bar coordinates for \"Oct\"}[0.145, 0.397, 0.165, 0.893], and {bar coordinates for \"Dec\"}[0.170, 0.314, 0.191, 0.893] and note down their corresponding sales figures.\nBased on the above thought, the answer is:\nMonth,Sales of Disinfectants (in thousands)\nFeb,37221\nApr,52874\nJun,47092\nAug,46215\nOct,49423\nDec,57735" } ], "id_raw": 107 }, { "id": "cpmabN67eArf3jRG", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nWhich product category experienced the highest growth in sales from February to December based on the chart?" }, { "from": "gpt", "value": "To determine the highest growth, I will compare the height difference between the bars in February and December for each product category. Starting with Disinfectants, the February {bar}[0.042, 0.519, 0.062, 0.893] and the December {bar}[0.170, 0.314, 0.191, 0.893] exhibit a growth. I will perform this comparative analysis for each category, continuing with Medical Masks from the {February bar}[0.240, 0.680, 0.261, 0.893] and {December bar}[0.369, 0.371, 0.390, 0.893], Testing Kits from {February bar}[0.439, 0.573, 0.459, 0.893] to {December bar}[0.567, 0.229, 0.588, 0.893], Ventilators from {February bar}[0.637, 0.652, 0.658, 0.893] to {December bar}[0.766, 0.472, 0.787, 0.893], and finally Medical Gloves from {February bar}[0.836, 0.623, 0.856, 0.893] to {December bar}[0.964, 0.309, 0.985, 0.893]. After calculating the growth figures for each, I determine which category had the highest increase in sales.\nBased on the above thought, the answer is:\nTesting Kits. Comparing the February to December bars, the category of Testing Kits shows the most significant increase in height, meaning it experienced the highest growth in sales throughout the year." } ], "id_raw": 107 }, { "id": "1vOFmYFoHP4RVxcC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_2.png", "conversations": [ { "from": "human", "value": "\nHow much did the sales of Testing Kits increase from February to December?" }, { "from": "gpt", "value": "To calculate the increase in sales for Testing Kits, I have to analyze the height of {the bar for \"Feb\"}[0.439, 0.573, 0.459, 0.893] and {the bar for \"Dec\"}[0.567, 0.229, 0.588, 0.893]. The February sales start from a height correlating to 31847 and the December sales reach a height correlating to 66148 when comparing with the y-axis ticks. I subtract the former from the latter to find the increase.\nBased on the above thought, the answer is:\n34301" } ], "id_raw": 107 }, { "id": "G5q64MS9wVht1Y53", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nBased on this bar chart, representing risk assessment data, can you create a CSV structured table to describe the metrics better?" }, { "from": "gpt", "value": "The CSV table formation requires to segregate the data following the chart pattern. It begins with the classification of {the bar chart title as \"Risk Assessment Across Areas\"}[0.416, 0.020, 0.584, 0.056]. The x-labels represent different categories, with {Risk Area 1, 2, and 3}[0.151, 0.146, 0.213, 0.182]; [0.481, 0.146, 0.543, 0.182]; [0.811, 0.146, 0.873, 0.182]. The x-axis here proves to be our categories, being Threat Level, Vulnerability Level, and Risk Rating, identified in {Risk Area 1's x labels}[0.057, 0.912, 0.115, 0.940]; [0.139, 0.912, 0.225, 0.940]; [0.252, 0.912, 0.304, 0.940]. The y-axis replicates the corresponding values, ranging from {0}[0.022, 0.882, 0.028, 0.910] to {140}[0.010, 0.126, 0.028, 0.154]. The height of the bars and the color diffrenciation for different risk areas ({Risk Area 1}[0.048, 0.731, 0.124, 0.893]; {Risk Area 2}[0.378, 0.639, 0.454, 0.893]; {Risk Area 3}[0.708, 0.552, 0.784, 0.893]) signify the value for each category. Hence using these entities, the CSV table can be synthesized.\nBased on the above thought, the answer is:\nRisk_Factor,Risk_Area_1,Risk_Area_2,Risk_Area_3\nThreat_Level,30,47,63\nVulnerability_Level,25,45,58\nRisk_Rating,55,92,121" } ], "id_raw": 108 }, { "id": "gf1q3jUfk0Yhvvdy", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, is there an increasing trend in the overall risks of the three areas?" }, { "from": "gpt", "value": "Analyzing the chart, it can be seen that there is an upward progression in overall risks from {Risk Area 1}[0.048, 0.731, 0.124, 0.893] to {Risk Area 3}[0.900, 0.239, 0.977, 0.893], notwithstanding the outlier evident in {Risk Area 2}[0.570, 0.396, 0.647, 0.893]. This is visible when comparing the 'Risk Rating' for each risk area, as it increases from the first to the third area. Therefore, it can be concluded that there is an increasing trend in the overall risks of the three areas.\nBased on the above thought, the answer is:\nYes, there is an increasing trend in the overall risks of the three areas." } ], "id_raw": 108 }, { "id": "KfNLgEsZtLZpOko5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_3.png", "conversations": [ { "from": "human", "value": "\nWhich Risk Area has the maximum Risk Rating?" }, { "from": "gpt", "value": "Comparing the three Risk Area's Risk Rating values is necessary, whose bars are depicted by {Risk Area 1's Risk Rating}[0.240, 0.596, 0.317, 0.893], {Risk Area 2's Risk Rating}[0.570, 0.396, 0.647, 0.893], {Risk Area 3's Risk Rating}[0.900, 0.239, 0.977, 0.893], respectively. Visually analyzing the chart, Risk Area 3's Risk Rating bar's height is the largest, signifying the highest Risk Rating value.\nBased on the above thought, the answer is:\nRisk Area 3." } ], "id_raw": 108 }, { "id": "PbZvEeMZ3QwwbCOL", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data presented in the two bar charts into two CSV format tables?" }, { "from": "gpt", "value": "Sure, the two charts present two different trends in Entrepreneurship Ecosystem. The left chart represents {\"Erratic Fluctuations\"}[0.213, 0.146, 0.320, 0.182], while the right chart represents {\"Continuous Decrease\"}[0.702, 0.146, 0.820, 0.182]. Each chart has five bars representing five key indicators, and the vertical axis represents their values. From the bar heights and y-axis, I can see that these key indicators are: Startup Formation, Patent Applications, VC Funding, Acquired Startups, and Top-Tier Tech Talent. Now, Let's process each bar to get its corresponding indicator's value.\nBased on the above thought, the answer is:\n\"Erratic Fluctuations\"\nStartup Formation, 15.00\nPatent Applications, 227.00\nVC Funding, 3345.00\nAcquired Startups, 4.00\nTop-Tier Tech Talent, 675.00\n\n\"Continuous Decrease\"\nStartup Formation, 20.00\nPatent Applications, 315.00\nVC Funding, 4578.00\nAcquired Startups, 7.00\nTop-Tier Tech Talent, 829.00" } ], "id_raw": 109 }, { "id": "lZw8HAGrUcd6IXFt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat was the overall trend of the Entrepreneurship Ecosystem in terms of VC funding and why?" }, { "from": "gpt", "value": "To analyze the overall trend in terms of VC funding, we can inspect the bars corresponding to the VC Funding in the Erratic Fluctuations and Continuous Decrease charts. The VC Funding in the Erratic Fluctuations chart[0.232, 0.423, 0.301, 0.893] and the VC Funding in the Continuous Decrease chart[0.727, 0.250, 0.796, 0.893] represent the funding under the two trends. From the heights, it is quite clear that there was a significant increase in VC funding during the period of continuous decrease trend compared to the erratic fluctuations trend.\nBased on the above thought, the answer is:\nThere was an increasing trend in the VC funding throughout, despite the overall Ecosystem showed a continuous decrease. The specific increase in VC funding might be attributed to more tech startups attracting venture capital funding due to their innovative products or solutions." } ], "id_raw": 109 }, { "id": "eq5cZOs6QRtHWbZC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_4.png", "conversations": [ { "from": "human", "value": "\nIn both the trends, which key indicator showed the most decrease?" }, { "from": "gpt", "value": "To find out which key indicator showed the most decrease, I need to compare the difference in values of the same key indicator for the two trends. For example, for the Startup Formation indicator, the bars are located at {Erratic Fluctuations}[0.060, 0.890, 0.129, 0.893] and {Continuous Decrease}[0.555, 0.890, 0.624, 0.893]. By comparing the heights, I can find out the difference. By doing this for all the indicators, we will be able to find out which indicator showed the most decrease.\nBased on the above thought, the answer is:\nVC Funding" } ], "id_raw": 109 }, { "id": "ADqhsNwZNUyYhcdg", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nCould you convert the migration data for the given regions from the bar chart into a CSV-formatted table?" }, { "from": "gpt", "value": "By examining {the chart title \"Population Migration and Mobility Trends\"}[0.416, 0.020, 0.584, 0.065], I can see which chart represents different regions. For each region, there are two bars that represent migration to {Developed and Developing}[0.047, 0.897, 0.175, 0.932] nations. I will convert each of the subplot bars into table rows. For example, in the {Europe subplot}[0.023, 0.226, 0.199, 0.873], there is {a bar for \"Developed\" migration}[0.031, 0.575, 0.102, 0.679] with value 1.6 and {a bar for \"Developing\" migration}[0.119, 0.679, 0.191, 0.808] with value -2. I will continue this process for all the regions covered in the chart: Asia, Africa, America, and Oceania.\nBased on the above thought, the answer is:\nRegion,Developed,Developing\nEurope,1.6,-2\nAsia,1.2,-1.5\nAfrica,-0.5,6.3\nAmerica,0.7,-1\nOceania,0.2,4" } ], "id_raw": 110 }, { "id": "xO4RcZpaXCt8YuL4", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nWhich region has witnessed the highest migration rate to developing countries, based on the chart?" }, { "from": "gpt", "value": "The chart presents different regions with two bars each for Developed and Developing migrations. By looking at the height of each bar, I can deduce which region has the highest rate. For {Developing migration}, I notice that the {Africa region bar}[0.516, 0.271, 0.588, 0.679] has the longest length, extending from 0 to 6.3, which overtakes the other bars, such as the ones in {Asia}[0.318, 0.679, 0.389, 0.776], {America}[0.715, 0.679, 0.786, 0.744], and {Oceania}[0.913, 0.420, 0.985, 0.679]. The height of the bars is directly proportional to the value of the migration rate.\nBased on the above thought, the answer is:\nAfrica. Due to the longest bar length indicating the migration rate to developing countries, Africa has witnessed the highest migration rate." } ], "id_raw": 110 }, { "id": "j0Y6vEsvtAjwfKuf", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total migration rate to Developed nations across all regions as observed in the bar chart?" }, { "from": "gpt", "value": "To calculate the total migration rate to Developed regions, I will sum up the values associated with Developed migrations across all subsections of the bar chart. Starting with {Europe's Developed bar}[0.031, 0.575, 0.102, 0.679], the value is 1.6; in {Asia's Developed bar}[0.229, 0.601, 0.300, 0.679], the value is 1.2; in {Africa's Developed bar}[0.428, 0.679, 0.499, 0.711], the value is -0.5; in {America's Developed bar}[0.626, 0.634, 0.697, 0.679], the value is 0.7; and finally, in {Oceania's Developed bar}[0.825, 0.666, 0.896, 0.679], the value is 0.2. By adding all these values, I can find the total migration rate to Developed nations.\nBased on the above thought, the answer is:\n3.2" } ], "id_raw": 110 }, { "id": "LkYSwME8TxNOA213", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nCould you rearrange the data in the chart into a CSV table?" }, { "from": "gpt", "value": "From the {title \"Environmental and Climate Change Impact\"}[0.321, 0.020, 0.679, 0.056] and the numbers on the y-axes for each graph: {left graph 'CO2 Emissions'}[0.042, 0.191, 0.492, 0.893] and {right graph 'Ocean Acidification'}[0.535, 0.191, 0.985, 0.893], it is clear that the y-axis represents the values for CO2 Emissions and Ocean Acidification. The x-axis for both graphs represent the years {1975, 1985, 1995, 2005, 2015, 2025}[0.073, 0.912, 0.954, 0.940]. Hence, by recording the values from each bar in the two bar graphs, we can convert these into a CSV format. For instance, the first data point reviewed is {the bar representing 1975}[0.063, 0.682, 0.119, 0.893] in the first bar chart, indicating a CO2 emission of 15 in 1975, and the corresponding bar in the second bar graph indicates {an Ocean Acidification of 7.4}[0.555, 0.789, 0.612, 0.893] for the same year. This process will be used for the subsequent years to gather data for each category.\nBased on the above thought, the answer is:\nYear, CO2 Emissions, Ocean Acidification\n1975, 15, 7.4\n1985, 17, 7.3\n1995, 19, 7.1\n2005, 25, 7\n2015, 35, 6.8\n2025, 40, 6.6" } ], "id_raw": 112 }, { "id": "stxZryKRvta4Xc2Q", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat can we infer regarding the rate of environmental change based on the trends observable in the charts?" }, { "from": "gpt", "value": "We can get the trends by comparing the heights of the bars in both bar graphs. Starting from the left, {the first CO2 Emission bar}[0.063, 0.682, 0.119, 0.893] corresponding to the year 1975 is the shortest among all other bars for the years leading up to 2025, with {the tallest CO2 Emission bar}[0.416, 0.331, 0.472, 0.893] indicating an increase in CO2 emissions over the years. Similarly, for the second category, {the highest Ocean Acidification bar}[0.555, 0.789, 0.612, 0.893] corresponds to the year 1975 and {the lowest Ocean Acidification bar}[0.908, 0.800, 0.965, 0.893] corresponds to 2025, thus indicating a decline in Ocean Acidification.\nBased on the above thought, the answer is:\nThe continual increase in CO2 emissions and decrease in Ocean Acidification from 1975 to 2025 suggests an accelerating rate of environmental change. This trend indicates that our environment is deteriorating rapidly due to increasing emissions, which in turn intensifies the acidity of oceans." } ], "id_raw": 112 }, { "id": "V8yVn29fjsoTjnei", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_7.png", "conversations": [ { "from": "human", "value": "\nIn which year was the highest CO2 emission observed?" }, { "from": "gpt", "value": "To identify the year with the highest CO2 emission, I need to observe and compare the heights of the bars in the first bar graph {right graph 'CO2 Emissions'}[0.042, 0.191, 0.492, 0.893]. Consider that the bar symbolizing 2025 {year 2025}[0.416, 0.331, 0.472, 0.893] appears the tallest of all, therefore it means that the most CO2 emission occurred in this year.\nBased on the above thought, the answer is:\n2025" } ], "id_raw": 112 }, { "id": "WuZU8YaGt6qDHhcf", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the data from the bar charts in a CSV format?" }, { "from": "gpt", "value": "The chart includes three subplots, titled 'Region 1 Evaluation'[0.127, 0.146, 0.237, 0.182], 'Region 2 Evaluation'[0.457, 0.146, 0.567, 0.182], and 'Region 3 Evaluation'[0.787, 0.146, 0.897, 0.182]. Each subplot has two bars representing 'PolicyEffectiveness' and 'PolicyDuration'. To translate the data into a CSV format, we need to record the region, the policy, and the corresponding value. For example, the PolicyEffectiveness bar for Region 1 is located at coordinates [0.048, 0.370, 0.167, 0.893], reaching a height corresponding roughly to 74.5 on the graph. Similarly, the PolicyDuration bar for Region 1 is located at [0.197, 0.871, 0.317, 0.893], corresponding to a value of 3.1. The same process must be followed for the bars in the other subplots.\nBased on the above thought, the answer is:\nRegion,PolicyEffectiveness,PolicyDuration\nRegion 1,74.5,3.1\nRegion 2,63.2,1.8\nRegion 3,84.1,2.5" } ], "id_raw": 113 }, { "id": "7g3aydA6iOQqAyyC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nBased on the observed data, which region's policies seem to be the most effective?" }, { "from": "gpt", "value": "To determine which region's policies are most effective, we'll compare the heights of the 'PolicyEffectiveness' bars in each subplot. The coordinates of these bars are as follows: 'Region 1'[0.048, 0.370, 0.167, 0.893], 'Region 2'[0.378, 0.449, 0.497, 0.893], and 'Region 3'[0.708, 0.302, 0.827, 0.893]. The highest bar corresponds to 'Region 3', which suggests that this region's policies are the most effective.\nBased on the above thought, the answer is:\nRegion 3, as it has the highest 'PolicyEffectiveness' value based on the observed data." } ], "id_raw": 113 }, { "id": "aO0c8h8XawA07ehN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_8.png", "conversations": [ { "from": "human", "value": "\nWhich region has the longest duration of implemented policies?" }, { "from": "gpt", "value": "For this, we need to compare the heights of the 'PolicyDuration' bars across the three subplots. The coordinates of these bars in the subplots are as follows: 'Region 1'[0.197, 0.871, 0.317, 0.893], 'Region 2'[0.527, 0.880, 0.647, 0.893], and 'Region 3'[0.857, 0.875, 0.977, 0.893]. After comparing these, the bar corresponding to 'Region 1' is the tallest, indicating that Region 1 has the longest duration of implemented policies.\nBased on the above thought, the answer is:\nRegion 1" } ], "id_raw": 113 }, { "id": "C30r6gDPHD2FrmPg", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data observed from the bar chart into a CSV table?" }, { "from": "gpt", "value": "Reviewing the visual data, it's clear the chart is divided into three separate bar charts. Each chart represents a specific month with different processes noted on the x-axis. The y-axis scales show efficiency scores from {50}[0.016, 0.882, 0.028, 0.910] to {110}[0.010, 0.180, 0.028, 0.208].\nIn the first chart for {December}[0.153, 0.146, 0.211, 0.182], the height represents efficiency scores for each process, such as {Threading}[0.048, 0.460, 0.092, 1.477] at 87 and {Drilling}[0.104, 0.553, 0.148, 1.477] at 79. Similarly, data from the chart titled {January}[0.491, 0.146, 0.533, 0.182] and {February}[0.818, 0.146, 0.867, 0.182] are also observed. The entire process will be applied to the other charts in the same way to form a CSV table.\nBased on the above thought, the answer is:\nProcess, December, January, February\nThreading, 87, 75, 73\nDrilling, 79, 62, 65\nSawing, 95, 87, 85\nAssembly, 88, 92, 95\nFinishing, 90, 78, 81" } ], "id_raw": 114 }, { "id": "ZGV4aLpqQtNqxBqH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nFrom your observation of the chart, which process experienced the most stable production efficiency across the three months?" }, { "from": "gpt", "value": "Reviewing the chart, the stability of production efficiency can be determined by how similar the bar heights are across the three months for each manufacturing process. The {Threading bars}[0.048, 0.460, 0.092, 1.477; 0.378, 0.600, 0.422, 1.477; 0.708, 0.623, 0.752, 1.477] indicated that the efficiency dropped from 87 to 73, demonstrating significant fluctuation.\nThe process showing the least fluctuation or the most stable production efficiency is {Sawing}[0.160, 0.366, 0.204, 1.477; 0.490, 0.460, 0.534, 1.477; 0.820, 0.483, 0.864, 1.477]. This process saw efficiency levels move from 95 in December, to 87 in January and 85 in February, a relatively smaller shift compared with other processes.\nBased on the above thought, the answer is:\nSawing" } ], "id_raw": 114 }, { "id": "8jsCYd9fZVvPRmvS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_9.png", "conversations": [ { "from": "human", "value": "\nFrom December to February, which process showed an increase in production efficiency?" }, { "from": "gpt", "value": "The data for each process across the three months should be observed to determine which process showed an increase in production efficiency. For example, in the case of the {Threading process bars}[0.048, 0.460, 0.092, 1.477; 0.378, 0.600, 0.422, 1.477; 0.708, 0.623, 0.752, 1.477], the efficiency in December was 87, 75 in January, and 73 in February, indicating a decrease in efficiency over those months.\nBy comparing these observations across all processes, it can be seen that the {Assembly process bars}[0.216, 0.448, 0.261, 1.477; 0.546, 0.401, 0.591, 1.477; 0.876, 0.366, 0.921, 1.477] display increasing efficiency from December to February, from 88 to 92 and finally to 95.\nBased on the above thought, the answer is:\nAssembly" } ], "id_raw": 114 }, { "id": "QLQ3oLgzeLvNkEDW", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nGiven the bar charts you observed, can you generate a structured table from the data collected in CSV format?" }, { "from": "gpt", "value": "In order to generate a table in CSV format, I have to process the chart into structured data. The title encoded in { \"Ecological Environment Sustainability Assessment Across Regions\"}[0.317, 0.020, 0.683, 0.056] and {the x-axis tick labels}[{ \"Air Quality\"}[0.040, 0.912, 0.089, 0.940]; {\"Water Quality\"}[0.076, 0.912, 0.140, 0.940]; {\"Soil Health\"}[0.127, 0.912, 0.177, 0.940]; {\"Biodiversity\"}[0.168, 0.912, 0.223, 0.940]; {\"Carbon Footprint\"}[0.200, 0.912, 0.278, 0.940]; {\"Forestation Levels\"}[0.241, 0.912, 0.325, 0.940]]; indicate the categories. The three regions - Region 1, Region 2 and Region 3 can be recognized from the subtitles in { \"Region 1\"}[0.149, 0.146, 0.198, 0.182], { \"Region 2\"}[0.479, 0.146, 0.528, 0.182] and { \"Region 3\"}[0.809, 0.146, 0.858, 0.182]. The y-axis tick labels offer a scale from {0}[0.022, 0.882, 0.028, 0.910] to {100}[0.010, 0.180, 0.028, 0.208], so I can get the values by comparing the heights of the bars in each region. For instance, in Region 1, the height of {\"Air Quality\"}[0.047, 0.436, 0.082, 0.893] represents the value of 65. These processes will guide me to create the required table.\nBased on the above thought, the answer is:\nEco Factors,Region1,Region2,Region3\nAir Quality,65,70,85\nWater Quality,55,68,49\nSoil Health,70,82,76\nBiodiversity,76,72,90\nCarbon Footprint,82,74,61\nForestation Levels,88,86,91" } ], "id_raw": 115 }, { "id": "5qYXtdlTDx9BmW6O", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nBased on the graphs, can you analyze the sustainability status of these three regions?" }, { "from": "gpt", "value": "The sustainability status in the three regions presented in this graph can be deduced by looking at the ecological factors for each region. For example, in {\"Region 1\"}[0.034, 0.191, 0.313, 0.893], the heights of the bars for each eco factor from left to right reflect their sustainability status. This region seems to have performed better overall in terms of forestation level but lags behind in terms of water quality. A similar analysis can be done for {\"Region 2\"}[0.364, 0.191, 0.643, 0.893] and {\"Region 3\"}[0.694, 0.191, 0.973, 0.893], taking into account the bar heights for each factor in these regions. Region 2 has an evenly distributed profile with no major outliers. Region 3, however, has water quality quite lagging behind while standing out in terms of forestation levels. In summary, the sustainability status can be deduced based on these eco factors where the taller bars indicate better performance in that specific factor and vice versa.\nBased on the above thought, the answer is:\nRegion 1 performed well in terms of forestation levels but was lacking in water quality. Region 2 has a balanced profile across all ecological factors with no major outliers. Meanwhile, Region 3 stands out in terms of forestation levels but lags significantly behind in water quality." } ], "id_raw": 115 }, { "id": "1AVaHkRVMQu58gWh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_10.png", "conversations": [ { "from": "human", "value": "\nWhich region has the least ecological impact in terms of carbon footprint, according to the bar charts?" }, { "from": "gpt", "value": "An ecological footprint with a smaller size generally implies a lower ecological impact. The bar charts of the \"Carbon footprint\" factor for the three regions are denoted by the rectangles in-{ \"Region 1\"}[0.221, 0.317, 0.256, 0.893], { \"Region 2\"}[0.551, 0.373, 0.586, 0.893], and { \"Region 3\"}[0.881, 0.464, 0.916, 0.893]. I need to compare the heights of the three bars representing each region. According to the coordinates, the bar for \"Region 3\" is the shortest, suggesting that region has the smallest carbon footprint.\nBased on the above thought, the answer is:\nRegion 3." } ], "id_raw": 115 }, { "id": "yZDVLNuzkUfscG2g", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nBased on the observed chart, could you please transform the graphical data into a CSV format table?" }, { "from": "gpt", "value": "From the main title of the {chart}[0.268, 0.020, 0.732, 0.056] as well as the subtitiles of the {first}[0.226, 0.146, 0.309, 0.182] and {second} subcharts[0.719, 0.146, 0.801, 0.182], the data represented is the comparison of certain global trends during the years 2010 and 2020. There are three categories depicted both in the {first}[0.042, 0.191, 0.492, 0.893] and {second} subcharts[0.535, 0.191, 0.985, 0.893]: CO2 Emissions, Sea Level, and Forest Cover. Based on the height of the bars and the y-axis labels, the values for each category and year can be estimated. For example, in the {first} subchart, the {CO2 Emissions bar}[0.063, 0.277, 0.180, 0.893] is about approximately at the y-axis label of 35, indicating that the CO2 Emissions were 35.1 billion tons in 2010.\nBased on the above thought, the answer is:\nYear,CO2 Emissions,Sea Level,Forest Cover\n2010,35.1,3.3,31.6\n2020,36.4,8.4,30.8" } ], "id_raw": 116 }, { "id": "tFT6v1mREzlrpVv8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat insights can you draw about global environmental conditions based on the observed chart?" }, { "from": "gpt", "value": "The chart provides a comparison of three significant global environmental indicators over a 10 year period: CO2 Emissions, Sea Level and Forest Cover. Observing the heights of the bars in the chart, I can draw several conclusions. {CO2 Emissions}[0.063, 0.277, 0.180, 0.893] increased over this period, suggesting a rise in activities contributing to global warming. The increase in {Sea Level}[0.209, 0.835, 0.326, 0.893] could be indicative of the impacts of global warming such as melting ice caps and rising ocean temperatures. The decrease in {Forest Cover}[0.355, 0.338, 0.472, 0.893] over the same period could imply increased deforestation activities which further exacerbate global warming by reducing carbon sequestration capabilities. These trends are concerning for the global environment and could signal a need for more sustainable practices.\nBased on the above thought, the answer is:\nThe chart suggests a deterioration in global environmental conditions over the 10 year period due to the increase in CO2 Emissions and Sea Level alongside the reduction in Forest Cover, indicating the impacts of global warming and deforestation. This implies a need for action towards more sustainable practices and climate change mitigation." } ], "id_raw": 116 }, { "id": "hLLFeERKCpqUSe3q", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_11.png", "conversations": [ { "from": "human", "value": "\nBetween 2010 and 2020, by what percent did the global Forest Cover decrease?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height of the Forest Cover bars for the two years. The {2010 Forest Cover bar}[0.355, 0.338, 0.472, 0.893] is approximately at the mid-30 range on the y-axis labels, indicating the forest cover percentage in 2010 was 31.6. The {2020 Forest Cover bar}[0.848, 0.352, 0.965, 0.893] is at the mid-30 range as well on the y-axis labels, indicating the forest cover percentage in 2020 was 30.8. Hence, the decrease has been from 31.6% to 30.8%.\nBased on the above thought, the answer is:\n2.53%" } ], "id_raw": 116 }, { "id": "8W4NQulekBIxcDeu", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data observed in these 3 bar charts for the different trends related to AI and Automation across the 3 regions into a CSV format table?" }, { "from": "gpt", "value": "Looking at the charts, I can see that each of them represents a region. For Region 1 {SUBPLOT FOR REGION 1}[0.028, 0.191, 0.323, 0.893], Region 2 {SUBPLOT FOR REGION 2}[0.358, 0.191, 0.653, 0.893] and Region 3 {SUBPLOT FOR REGION 3}[0.688, 0.191, 0.983, 0.893]. The categories plotted on x-axis tags like {AI Research}[0.037, 0.912, 0.091, 0.940], {Automation Use}[0.083, 0.912, 0.157, 0.940], {AI Patents}[0.152, 0.912, 0.199, 0.940], {AI Education}[0.203, 0.912, 0.261, 0.940], {AI investments}[0.253, 0.912, 0.322, 0.940] and the y-axis labels range from {0}[0.016, 0.882, 0.022, 0.910] to {10}[0.010, 0.180, 0.022, 0.208]. The data from the bars in each chart are used to determine each numerical value. For example, in the category of \"AI Research\" for \"Region 1\", the green bar's top {BAR FOR AI RESEARCH IN REGION 1}[0.042, 0.738, 0.086, 0.893] aligns approximately with the 2 on the y-axis, thus the value should be 2.2. The same process was repeated for all regions and categories.\nBased on the above thought, the answer is:\nTrends,Region 1,Region 2,Region 3\nAI Research,2.2,5.4,3.9\nAutomation Use,5.5,4.2,6.3\nAI Patents,3.5,7.6,4.7\nAI Education,4.4,6.1,5.5\nAI Investments,6.0,8.4,7.1" } ], "id_raw": 117 }, { "id": "VvGaoQOnRuQL8hLC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nBased on the charts, which region seems to give the highest priority to Artificial Intelligence and Automation? Provide explanations for your answer." }, { "from": "gpt", "value": "This question requires an overall comparison of all the bars in all three regions. Region 1's {total bars}[0.042, 0.738, 0.310, 0.893] are generally shorter than those of Region 2 {total bars}[0.372, 0.514, 0.640, 0.893] and Region 3 {total bars}[0.702, 0.619, 0.970, 0.893]. Region 2, has the highest bars among the three in the categories of \"AI Research\", \"AI Patents\", and \"AI Investments\", indicating a higher trend in those categories which entails higher priority. Even though Region 3 has the highest bar in the \"Automation Use\" category, Region 2 tends to have the most consistently high bars across all categories.\nBased on the above thought, the answer is:\nRegion 2, because it has the highest trends in \"AI Research\", \"AI Patents\", and \"AI Investments\", and consistently high trends across all categories." } ], "id_raw": 117 }, { "id": "YrIaXV6cpnxtKqbF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_12.png", "conversations": [ { "from": "human", "value": "\nWhich category has the highest average value across all three regions?" }, { "from": "gpt", "value": "To make this calculation, for each category, I must identify and add up the values for all three regions then divide by 3. For example, for \"AI Research\", in Region 1 the bar {BAR FOR AI RESEARCH IN REGION 1}[0.042, 0.738, 0.086, 0.893] yields a value of 2.2, in Region 2 the bar {BAR FOR AI RESEARCH IN REGION 2}[0.372, 0.514, 0.416, 0.893] yields a value of 5.4, and in Region 3 the bar {BAR FOR AI RESEARCH IN REGION 3}[0.702, 0.619, 0.746, 0.893] yields a value of 3.9. So, the average for \"AI Research\" is (2.2+5.4+3.9)/3 = 3.83. I should repeat this process for other categories: \"Automation use\", \"AI Patents\", \"AI Education\", and \"AI Investments\". Finally, the category with the highest average value must be recognized.\nBased on the above thought, the answer is:\nAI Investments." } ], "id_raw": 117 }, { "id": "Nqcb4TqtRAgXb4hr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the monthly performance of the financial assets into a formatted CSV table as depicted on the chart?" }, { "from": "gpt", "value": "Upon examining {the chart}[0.428, 0.020, 0.572, 0.056], I can see there are three bar graphs corresponding to {Equity}[0.034, 0.191, 0.330, 0.893], {Bonds}[0.364, 0.191, 0.660, 0.893], and {Commodities}[0.694, 0.191, 0.990, 0.893], each presented monthly from January to December. By using the x-axis and y-axis coordinates to read the values for each month, and then capturing and converting these observed measurements directly into a CSV format.\nBased on the above thought, the answer is:\nMonth,Equity,Bonds,Commodities\nJanuary,100.0,100.0,100.0\nFebruary,96.3,100.6,102.3\nMarch,94.5,100.8,100.4\nApril,92.5,101.5,99.6\nMay,89.6,101.9,99.1\nJune,85.7,104.5,96.3\nJuly,81.5,105.3,94.8\nAugust,80.0,105.9,92.3\nSeptember,75.9,109.8,90.9\nOctober,72.3,112.3,88.6\nNovember,71.5,117.8,87.2\nDecember,76.5,121.5,85.8" } ], "id_raw": 118 }, { "id": "yUrKfxEUcJWxKXHJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the overall performance trend of Equities over the year?" }, { "from": "gpt", "value": "By analyzing {Equity}'s performance [0.034, 0.191, 0.330, 0.893], it can be noted that the bar heights for {Equity} generally decrease from January to November, with a slight increase in December. This indicates a mostly declining trend with values starting from high and gradually dropping except for a modest rebound in the final month.\nBased on the above thought, the answer is:\nEquities generally exhibited a declining trend throughout the year, starting from 100 in January and decreasing to as low as 71.5 in November, before slightly increasing to 76.5 in December." } ], "id_raw": 118 }, { "id": "QGi0XBaHT35m46YZ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest index value reached by Bonds during the observed months?" }, { "from": "gpt", "value": "To find the highest index value reached by {Bonds}[0.364, 0.191, 0.660, 0.893], I need to examine the bar heights depicted in the Bonds bar graph. By observing, December shows the highest bar position at [0.628, 0.235, 0.647, 1.786], indicating the highest value.\nBased on the above thought, the answer is:\n121.5" } ], "id_raw": 118 }, { "id": "RBuOg1fkirKZinW3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you create a CSV table which includes all the data represented by the Previous Year and Current Year bars for each category?" }, { "from": "gpt", "value": "From observing the chart, it has 2 subplots which display a horizontal bar chart with 2 group's data provided by Series1 and Series2. The first subplot represents the previous year, with the green bars labelled as {Previous Year}[0.522, 0.035, 0.657, 0.075], and the second subplot represents the current year, with the orange bars labelled as {Current Year}[0.522, 0.901, 0.792, 0.941]. Each group contains 8 categories from top to bottom: {Demand}[0.061, 0.050, 0.134, 0.064], {Supply}[0.075, 0.100, 0.134, 0.114], {Surplus}[0.070, 0.151, 0.134, 0.165], {Deficit}[0.078, 0.202, 0.134, 0.216], {Market Share}[0.019, 0.253, 0.134, 0.267], {Competition}[0.028, 0.303, 0.134, 0.317], {Opportunity}[0.030, 0.354, 0.134, 0.368], and {Risk}[0.098, 0.405, 0.134, 0.419]. For each category, the length of the bar corresponds to the respective values. For instance, for Demand in Previous Year, the width of the bar corresponds to the value 8, while for Demand in Current Year, the width of the bar corresponds to the value 16. Therefore, I am able to create a table containing these categories and their corresponding values.\nBased on the above thought, the answer is:\nCategory, Previous Year, Current Year\nDemand, 8, 16\nSupply, -5, 14\nSurplus, 10, 20\nDeficit, -20, 10\nMarket Share, 5, 25\nCompetition, -2, 12\nOpportunity, 12, 22\nRisk, -5, 15" } ], "id_raw": 120 }, { "id": "N5fnc9w9DXuW6mSa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which region shows a trend of improvement from Previous Year to Current Year, and why would you say so?" }, { "from": "gpt", "value": "To determine the improvement trend, I need to compare the Previous Year data with the Current Year data for each category. From the chart, I conclude that the {Supply}[0.075, 0.100, 0.134, 0.114] category shows an improvement trend. This is because the {Previous Year}[0.437, 0.086, 0.522, 0.126] data for Supply shows a negative value whereas the {Current Year}[0.522, 0.086, 0.758, 0.126] data shows a positive value. A shift from previously negative to a positive value indicates an improvement trend.\nBased on the above thought, the answer is:\nThe Supply category shows an improvement trend from Previous Year to Current Year since it moved from a negative to a positive value." } ], "id_raw": 120 }, { "id": "xepK4aRiegW2dQWo", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_9_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which category has the highest Current Year value?" }, { "from": "gpt", "value": "To determine the highest Current Year value, I need to check the length of the bars on the second subplot where orange bars represent current year values. Comparing the lengths of each bar in {Current Year}[0.522, 0.901, 0.792, 0.941], the {Market Share}[0.522, 0.238, 0.943, 0.278] bar has the highest length, thus indicating the highest Current Year value.\nBased on the above thought, the answer is:\nMarket Share." } ], "id_raw": 120 }, { "id": "ovnJLn9Ew34OAOny", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nUsing the information from the bar chart, extract and compile the raw data in the CSV format for easier analysis." }, { "from": "gpt", "value": "The Chart contains two subplots each representing a different perspective of the companies' performance. From the first subplot titled {\"Innovation of Companies\"}[0.201, 0.025, 0.408, 0.055], I can see each bar with text annotations indicating the company\u2019s innovation score. For example, the bar for Google has {\"7 points\"}[0.340, 0.856, 0.396, 0.880] written above it. Similarly, the second subplot titled {\"Technology of Companies\"}[0.656, 0.025, 0.868, 0.055] shows each company's technology score in a similar way. For instance, Google has {\"9 points\"}[0.869, 0.856, 0.925, 0.880] in its technology score. This needs to be replicated for each of the companies listed vertically on the y-axis in the form of y-label like {\"Google\"}[0.031, 0.859, 0.080, 0.882] and so on.\nBased on the above thought, the answer is:\nCompany,Innovation Score,Technology Score\nGoogle,7,9\nApple,5,4\nMicrosoft,2,3\nAmazon,8,7\nTesla,5,1\nFacebook,6,3\nNetflix,3,1\nTwitter,2,1\nSpaceX,10,2\nUber,3,1" } ], "id_raw": 127 }, { "id": "bQ7vjhh9q8M31Ewt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which company appear to focus most on technological innovation?" }, { "from": "gpt", "value": "This question requires a composite judgment considering both the \"Innovation of Companies\" and \"Technology of Companies\" values together. In order to find the qualified company, I need to numerically compare the values from both charts for each company. For example, {Google's innovation score}[0.340, 0.856, 0.396, 0.880] and {Google's technology score}[0.869, 0.856, 0.925, 0.880] suggests a total score of 16 (7 from innovation, and 9 from technology) which is significantly higher compared to other companies' total scores, this suggests that Google appears to focus most on technological innovation.\nBased on the above thought, the answer is:\nGoogle, as it has both high scores in innovation and technology, suggesting a strong focus on technological innovation." } ], "id_raw": 127 }, { "id": "0XyKr6sbHcdCAS6t", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_9_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which company has the highest Technology score?" }, { "from": "gpt", "value": "If we observe the subplot {\"Technology of Companies\"}[0.656, 0.025, 0.868, 0.055], we can compare the lengths of each bar to determine which one has the longest bar, hence the highest technology score. Visually scanning through the list of companies and their respective bars like {Google's bar}[0.548, 0.838, 0.869, 0.898] and others, we could see that {Google}'s bar seems to be the longest.\nBased on the above thought, the answer is:\nGoogle" } ], "id_raw": 127 }, { "id": "yhWANPLa7tZPcyyy", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, can you extract and organize the evaluations of various policies by synthetic data and human-authored data into a CSV formatted table?" }, { "from": "gpt", "value": "From the labels on the y-axis of the chart, I can see the names of the different policies implemented by the government. These include {Guaranteed Income}[0.121, 0.843, 0.259, 0.866], {Renewable Energy Encouragement}[0.015, 0.731, 0.259, 0.754], {Immigration Control}[0.119, 0.618, 0.259, 0.642], {Public Health}[0.168, 0.506, 0.259, 0.529], {Free Education}[0.156, 0.393, 0.259, 0.417], {Early Retirement}[0.142, 0.281, 0.259, 0.304], and {Tax Hike}[0.201, 0.169, 0.259, 0.192]. The x-axis of the subplot represents the evaluation score ranges from synthetic data and human-authored data. By observing each bar length and comparing with the coordinates on the x-axis, I can calculate their value and create a CSV table to represent the data more effectively.\nBased on the above thought, the answer is:\nPolicy Name,Synthetic Data,Human-authored Data\nGuaranteed Income,-10,20\nRenewable Energy Encouragement,-20,25\nImmigration Control,-11,30\nPublic Health,-13,33\nFree Education,-14,28\nEarly Retirement,-22,34\nTax Hike,-16,32" } ], "id_raw": 128 }, { "id": "bORUybXsYGaYEZlh", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nJudging from this chart, what can you tell about the difference in evaluating policies between synthetic data and human-authored data?" }, { "from": "gpt", "value": "This chart presents the evaluation of various government policies by synthetic data and human-authored data. Each bar represents an evaluation of a policy by the data set it represents. For example, in the case of {Guaranteed Income}[0.121, 0.843, 0.259, 0.866], the synthetic data, represented by {the left bar}[0.369, 0.807, 0.415, 0.897], gives a negative evaluation (-10), while human-authored data, represented by {the right bar}[0.415, 0.807, 0.507, 0.897], gives a positive evaluation (20). This disparity is consistent across all policies, with synthetic data generally evaluating policies negatively and human-authored data giving them positive evaluations. There appears to be a considerable difference between the two types of data, suggesting divergences in judgment or perception.\nBased on the above thought, the answer is:\nThe synthetic data generally gives negative evaluations, suggesting dissatisfaction or demerits of these policies, while the human-authored data provides positive evaluations, indicating benefits or successes of these policies. This could be due to differences in the traits or biases of the data source, methods of analysis, or other factors." } ], "id_raw": 128 }, { "id": "U5eYQC1lpL9BVYSi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_9_8.png", "conversations": [ { "from": "human", "value": "\nAs per the chart, which policy had the greatest evaluation difference between synthetic data and human-authored data?" }, { "from": "gpt", "value": "The evaluation difference can be found by subtracting synthetic data from human-authored data for each policy. That means I need to measure the length of each bar from the center to either end, for both types of data, for each policy. Looking from top to bottom, the first pair of bars represent evaluations for Guaranteed Income, the second pair for Renewable Energy Encouragement, and so on up to Tax Hike. It appears that {Early Retirement}[0.142, 0.281, 0.259, 0.304], with {synthetic data}[0.683, 0.245, 0.783, 0.335] at -22 and {human-authored data}[0.783, 0.245, 0.939, 0.335] at 34, has the greatest difference, which is 56.\nBased on the above thought, the answer is:\nEarly Retirement" } ], "id_raw": 128 }, { "id": "gGFJB7s7v0P536xT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nCould you transcribe the data from the bar chart into a .CSV file?" }, { "from": "gpt", "value": "The chart title as denoted by its header, {Two plots representing '2017' and '2018'}[0.399, 0.025, 0.824, 0.055], definitely suggests that the X-Axis denotes the timeline of the activities, which are {2017}[0.399, 0.025, 0.441, 0.055] and {2018}[0.782, 0.025, 0.824, 0.055]. The Y-Axis title is not provided, but the {Y-Axis labels}[0.044, 0.838, 0.228, 0.861, 0.056, 0.710, 0.228, 0.733, 0.102, 0.582, 0.228, 0.605, 0.015, 0.453, 0.228, 0.477, 0.065, 0.325, 0.228, 0.349, 0.091, 0.197, 0.228, 0.220] are the specific categories of the projects such as 'Public space improvement', 'Residential development', 'Transport systems', 'Environment and conservation', 'Economic development', and 'Social development'. The {values of each bar}[0.278, 0.796, 0.420, 0.898, \n0.339, 0.668, 0.420, 0.770, 0.307, 0.539, 0.420, 0.642, 0.331, 0.411, 0.420, 0.514, 0.359, 0.283, 0.420, 0.386, 0.347, 0.155, 0.420, 0.257, 0.803, 0.796, 0.924, 0.898, 0.803, 0.668, 0.904, 0.770, 0.803, 0.539, 0.945, 0.642, 0.803, 0.411, 0.916, 0.514, 0.803, 0.283, 0.884, 0.386, 0.803, 0.155, 0.908, 0.257] are calculated with respect to the {X-axis labels}[0.546, 0.952, 0.576, 0.975, 0.627, 0.952, 0.656, 0.975, 0.708, 0.952, 0.737, 0.975, 0.799, 0.952, 0.808, 0.975, 0.875, 0.952, 0.893, 0.975, 0.956, 0.952, 0.974, 0.975, 1.037, 0.952, 1.054, 0.975]. Hence, with the X-Axis labels ranging from -60 to 60, the length of the bars can be treated as the exact values of the projects. I will now arrange this data into a .CSV format.\nBased on the above thought, the answer is:\nYear,Public space improvement,Residential development,Transport systems,Environment and conservation,Economic development,Social development\n2017,-35,-20,-28,-22,-15,-18\n2018,30,25,35,28,20,26" } ], "id_raw": 129 }, { "id": "vhCpRE1nYyHSlfZF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nConsidering the year-over-year changes, in which sector do you suggest the city invest more resources and why?" }, { "from": "gpt", "value": "Determining where to invest more resources depends not only on the observed year-on-year changes, but also on the overall levels of investment across different sectors. From the chart, the areas with the least attention are {Economic development}[0.065, 0.325, 0.228, 0.349] and {Social development}[0.091, 0.197, 0.228, 0.220] based on their bar lengths {in 2017}[0.359, 0.283, 0.420, 0.386, 0.347, 0.155, 0.420, 0.257] and {2018}[0.803, 0.283, 0.884, 0.386, 0.803, 0.155, 0.908, 0.257]. They lack relative growth, indicating that more resources and focus should be given to these sectors. Given that these areas play a crucial role in the long-term sustainability and quality of life for the residents, it is a good idea to invest more in these sectors.\nBased on the above thought, the answer is:\nThe city should invest more resources in Economic development and Social development, as these sectors have seen minimal changes and require more attention due to their importance in long-term sustainability and quality of life." } ], "id_raw": 129 }, { "id": "FGI9JEkerPmk2p8S", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_9_9.png", "conversations": [ { "from": "human", "value": "\nWhich community development category has seen the most increase in focus and resource allocation from 2017 to 2018?" }, { "from": "gpt", "value": "To answer this question, the bar lengths in 2017 and 2018 need to be compared, for each category on the y-axis. For instance, the 'Public space improvement' category has bars at coordinates {2017}[0.278, 0.796, 0.420, 0.898] and {2018}[0.803, 0.796, 0.924, 0.898] indicating a substantial increase. Completing the same procedure for 'Residential development', 'Transport systems', 'Environment and conservation', 'Economic development', and 'Social development' I've found that {the 'Public space improvement' bars}[0.278, 0.796, 0.420, 0.898, 0.803, 0.796, 0.924, 0.898] show the greatest difference in length. Therefore, 'Public space improvement' has seen the most increase.\nBased on the above thought, the answer is:\nPublic space improvement" } ], "id_raw": 129 }, { "id": "Q23Cm3glkg0y0xzD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nCould you please translate the information shown in the chart into a tabular format?" }, { "from": "gpt", "value": "The chart has two subplots, both representing bar charts across several leadership traits. In the top subplot, bars represent the score values for {'Team Approach'}<{\"Subtitle\"}[0.449, 0.025, 0.591, 0.055]>, and in the bottom subplot, bars represent the score values for {'Process Oriented'}<{\"Subtitle\"}[0.442, 0.512, 0.598, 0.542]>. Each subplot has five bars each depicting a leadership trait \u2014 'Communication', 'Planning', 'Leadership', 'Decision Making', 'Strategic Thinking'. As per the information given in the text labels of each bar, each bar's height corresponds to the respective score for that trait in each management approach. The resulting table will follow the structure: Method, Communication, Planning, Leadership, Decision Making, Strategic Thinking.\nBased on the above thought, the answer is:\nMethod,Communication,Planning,Leadership,Decision Making,Strategic Thinking\nTeam Approach, 30, 40, 25, 45, 35\nProcess Oriented, 5, 15, 35, 50, 70" } ], "id_raw": 135 }, { "id": "HgzwubbJnFjrYknA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which management approach seems to have a more balanced leadership score?" }, { "from": "gpt", "value": "To answer this question, I will carefully examine the bar heights for the 'Team Approach' and 'Process Oriented'. For the 'Team Approach', the bar scores from left to right are for {'Communication'}<{\"bar\"}[0.099, 0.332, 0.192, 0.448]>, {'Planning'}<{\"bar\"}[0.286, 0.294, 0.380, 0.448]>, {'Leadership'}<{\"bar\"}[0.473, 0.352, 0.567, 0.448]>, {'Decision Making'}<{\"bar\"}[0.660, 0.274, 0.754, 0.448]>, and {'Strategic Thinking'}<{\"bar\"}[0.848, 0.313, 0.941, 0.448]>. The 'Process Oriented' bars are {'Communication'}<{\"bar\"}[0.099, 0.916, 0.192, 0.935]>, {'Planning'}<{\"bar\"}[0.286, 0.878, 0.380, 0.935]>, {'Leadership'}<{\"bar\"}[0.473, 0.800, 0.567, 0.935]>, {'Decision Making'}<{\"bar\"}[0.660, 0.743, 0.754, 0.935]>, and {'Strategic Thinking'}<{\"bar\"}[0.848, 0.665, 0.941, 0.935]>. All of 'Team Approach''s bar heights are rather similar, between 25 and 45, whereas 'Process Oriented' varies massively from 5 to 70. Hence, the 'Team approach' has a more balanced leadership score.\nBased on the above thought, the answer is:\n'Team Approach' has a more balanced leadership score." } ], "id_raw": 135 }, { "id": "K66gxtyJlcBDB3TU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_0.png", "conversations": [ { "from": "human", "value": "\nUnder which leadership trait does 'Team Approach' have the highest score?" }, { "from": "gpt", "value": "I am referring to the top subplot where the 'Team Approach' is indicated by {'Team Approach'}<{\"Subtitle\"}[0.449, 0.025, 0.591, 0.055]>. I need to compare the five bars in terms of their heights. Looking from left to right, they are {'Communication'}<{\"bar\"}[0.099, 0.332, 0.192, 0.448]>, {'Planning'}<{\"bar\"}[0.286, 0.294, 0.380, 0.448]>, {'Leadership'}<{\"bar\"}[0.473, 0.352, 0.567, 0.448]>, {'Decision Making'}<{\"bar\"}[0.660, 0.274, 0.754, 0.448]>, and {'Strategic Thinking'}<{\"bar\"}[0.848, 0.313, 0.941, 0.448]>. It seems the 'Decision Making' bar is the tallest, indicating that 'Team Approach' has the highest score in the 'Decision Making'.\nBased on the above thought, the answer is:\nDecision Making." } ], "id_raw": 135 }, { "id": "6pBGFxmHZhtFlNG3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the two bar charts you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "The chart with the title {\"Performance Scores of Tech Companies\"}[0.000, 0.227, 0.020, 0.773] is comprised of two sections for \"Innovative Businesses\" {upper plot}[0.057, 0.062, 0.983, 0.448] and \"Traditional Businesses\" {lower plot}[0.057, 0.550, 0.983, 0.935]. Each section represents the performance scores for the three different tech companies signified by the x-axis labels {\"Microsoft\"}[0.134, 0.464, 0.204, 0.487], {\"Apple\"}[0.498, 0.464, 0.542, 0.487], and {\"Facebook\"}[0.835, 0.464, 0.907, 0.487]. I would create a table from these data points by extracting the bar heights corresponding to the scores for each company in each category. For instance, the score for Microsoft in Innovative Businesses ({the blue bar}[0.099, 0.101, 0.239, 0.448]) is 90 and for Traditional Businesses ({the red bar}[0.099, 0.665, 0.239, 0.935]) is 70.\nBased on the above thought, the answer is:\nType,Microsoft,Apple,Facebook\nInnovative Businesses,90,85,88\nTraditional Businesses,70,55,66" } ], "id_raw": 136 }, { "id": "ZYTaVvlmXIMGkKzx", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which company do you think performs a better in maintaining a balance between traditional businesses and innovative businesses? And explain the reasons." }, { "from": "gpt", "value": "This question requires a comparison of the performance scores of the three companies in both the \"Innovative Businesses\" and \"Traditional Businesses\" categories. For {\"Microsoft\"}[0.099, 0.101, 0.239, 0.448] in lower plot[0.057, 0.550, 0.983, 0.935]), the score difference between these two categories is 20 (90 -70). For {\"Apple\"}[0.450, 0.120, 0.590, 0.448] in lower plot[0.057, 0.550, 0.983, 0.935]), the score difference is 30 (85-55), and for {\"Facebook\"}[0.801, 0.109, 0.941, 0.448] in lower plot[0.057, 0.550, 0.983, 0.935]), the score difference is 22 (88-66). The company with the smallest difference is considered the best at balancing between traditional and innovative businesses.\nBased on the above thought, the answer is:\nFacebook. This is because the difference between Facebook's Innovative Business and Traditional Business scores is the smallest among the three companies, indicating that it has a better balance between the two business types." } ], "id_raw": 136 }, { "id": "qiPErXx3LeNrfq5X", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_1.png", "conversations": [ { "from": "human", "value": "\nWhich company has a better performance in Innovative Businesses in terms of the score?" }, { "from": "gpt", "value": "To answer this question, I need to compare the heights of the bars in the {\"Innovative Businesses\"}[0.057, 0.062, 0.983, 0.448] subplot. Looking at the bars, {\"Microsoft\"}[0.099, 0.101, 0.239, 0.448] is approximately at position 90, {\"Apple\"}[0.450, 0.120, 0.590, 0.448] is at approximately position 85, and {\"Facebook\"}[0.801, 0.109, 0.941, 0.448] is at approximately position 88. The highest bar represents the company with the best performance score in Innovative Businesses.\nBased on the above thought, the answer is:\nMicrosoft" } ], "id_raw": 136 }, { "id": "zmDBK8ksTFZIlpel", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you tabulate the percentage achieved for Cultural Heritage and Cultural Integration for each strategy?" }, { "from": "gpt", "value": "Yes, based on the subplots I observed, each strategy has two corresponding percentage values: one for Cultural Heritage {\"subplot_0\"}[0.188, 0.062, 0.969, 0.415] and another for Cultural Integration {\"subplot_1\"}[0.188, 0.550, 0.969, 0.903]. For example, in Cultural Heritage, the bar for Manual Integration {\"Manual Integration bar\"}[0.188, 0.332, 0.383, 0.399] is ending at around 30 on the x-axis, therefore we can say it has a Cultural Heritage percentage of 30. Similarly, analyzing the plot for Cultural Integration, we can say that Manual Integration has a percentage of 60{\"Manual Integration bar\"}[0.188, 0.819, 0.578, 0.887]. We can use the same process to get the similar values for all the four strategies.\nBased on the above thought, the answer is:\nStrategy,Cultural Heritage(%),Cultural Integration(%)\nManual Integration,30,60\nProgressive Heritage,70,50\nEvolving Integration,90,40\nRegulative Heritage,100,30" } ], "id_raw": 137 }, { "id": "WMjdWf0FECosNi6X", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat type of strategy would you recommend if priority is given to Cultural Heritage?" }, { "from": "gpt", "value": "If the priority is given to Cultural Heritage, then, according to the subplot representing Cultural Heritage {\"subplot_0\"}[0.188, 0.062, 0.969, 0.415], the highest percentage of Cultural Heritage was achieved by the 'Regulative Heritage' strategy. Its corresponding bar {\"Regulative Heritage bar\"}[0.188, 0.078, 0.839, 0.146] extends to 100 on the x-axis, indicating that it achieved the highest percentage.\nBased on the above thought, the answer is:\nI would recommend the 'Regulative Heritage' strategy as it achieved the highest percentage in Cultural Heritage." } ], "id_raw": 137 }, { "id": "I1U97zeeu0CkrGD8", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the Cultural Heritage percentage for the strategy 'Evolving Integration'?" }, { "from": "gpt", "value": "To get the Cultural Heritage percentage for 'Evolving Integration', I need to locate its respective bar in the subplot representing Cultural Heritage {\"subplot_0\"}[0.188, 0.062, 0.969, 0.415]. The bar corresponding to 'Evolving Integration'{\"Evolving Integration bar\"}[0.188, 0.163, 0.773, 0.230] extends to about 90 on the x-axis of the subplot.\nBased on the above thought, the answer is:\n90%" } ], "id_raw": 137 }, { "id": "TplhkbqH5ED2MhV9", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, could you extract the data and organize it into a table?" }, { "from": "gpt", "value": "Sure. From the bar chart, I can observe two subplots titled {Social Change}[0.455, 0.025, 0.585, 0.055] and {Cultural Change}[0.446, 0.512, 0.594, 0.542]. Each subplot has four categories on the x-axis, namely {Upward trend}[0.135, 0.464, 0.240, 0.487], {Erratic behavior}[0.347, 0.464, 0.471, 0.487], {Marginal growth}[0.568, 0.464, 0.694, 0.487], and {Irregular variations}[0.779, 0.464, 0.926, 0.487]. The respective degrees (in percentages) for each category can be calculated from the height of the bars in the chart, with the y-coordinate {100}[0.017, 0.541, 0.046, 0.564] represents 100%.\nBased on the above thought, the answer is:\nChange Type, Upward trend, Erratic behavior, Marginal growth, Irregular variations\nSocial Change, 30, 40, 70, 45\nCultural Change, 50, 35, 60, 50" } ], "id_raw": 140 }, { "id": "Cxa3WVQZVp2KLWzw", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhich category shows the greatest degree of change for Cultural Change? Explain your observations." }, { "from": "gpt", "value": "To find the category that shows the greatest degree of Cultural Change, I need to compare the heights of the bars in the subplot titled {Cultural Change}[0.446, 0.512, 0.594, 0.542]. The bar corresponding to {Upward trend}[0.099, 0.743, 0.276, 0.935] has a y-coordinate of {50}[0.027, 0.772, 0.046, 0.795], {Erratic behavior}[0.321, 0.800, 0.498, 0.935] has a y-coordinate of {35}[0.027, 0.849, 0.046, 0.872], {Marginal growth}[0.542, 0.704, 0.720, 0.935] has a y-coordinate of {60}[0.026, 0.695, 0.046, 0.718], and {Irregular variations}[0.764, 0.743, 0.941, 0.935] has a y-coordinate of {50}[0.027, 0.772, 0.046, 0.795]. Comparing these y-coordinate values, I can infer that the greatest degree of Cultural Change is associated with the Marginal growth category.\nBased on the above thought, the answer is:\nMarginal growth shows the greatest degree of Cultural Change based on it having the highest value among all categories." } ], "id_raw": 140 }, { "id": "MHYMRC03MpIGyswr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the degree of Irregular variations in Cultural Change?" }, { "from": "gpt", "value": "To find the degree of Irregular variations in Cultural Change, I need to look at the second subplot titled {Cultural Change}[0.446, 0.512, 0.594, 0.542] and find the corresponding bar. The bar for {Irregular variations}[0.764, 0.743, 0.941, 0.935] in Cultural Change has a height that corresponds to the y-coordinate of {50}[0.027, 0.772, 0.046, 0.795]. Therefore, the degree of Irregular variations in Cultural Change is 50%.\nBased on the above thought, the answer is:\n50%" } ], "id_raw": 140 }, { "id": "Ej64C7IVQc4bXspD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart observed, could you transcribe the popularity data of the two subcultures - \"Hippie Bohemian\" and \"Punk Rocker\" for the five different fashion trends into CSV data?" }, { "from": "gpt", "value": "The chart consists of {two sub-charts}[0.064, 0.075, 0.981, 0.923], each representing one of the two different subcultures. The details of the subcultures are found in the subplots \u2019subtitles,{the caption of \"hippie bohemian\"}[0.434, 0.030, 0.611, 0.066] and {the caption of \"punk rocker\"}[0.459, 0.515, 0.586, 0.551]. Also, there are {five labeled X-ticks}[0.088, 0.457, 0.957, 0.970] corresponding to the five fashion trends. I'll extract the specific values from the length of the bars for each subculture using their color as a key based on each subplot's bars. For example, in the \"Hippie Bohemian\" subplot, {the bar for \"60s Fashion\"}[0.106, 0.220, 0.173, 0.438] represents a popularity of 60%.\nBased on the above thought, the answer is:\nSubculture,60s Fashion,80s Fashion,Graveyard Chic,Soaring Eagle Style,Gothic Frills\nHippie Bohemian,60,20,10,50,90\nPunk Rocker,40,10,0,70,80" } ], "id_raw": 143 }, { "id": "SCOJigg9Fr1VOI7q", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which fashion trend shows a noticeable difference in popularity between Hippie Bohemians and Punk Rockers? Explain your reasoning." }, { "from": "gpt", "value": "This question requires a comparison between the bar heights in two different sub-charts that represent Hippie Bohemians and Punk rockers. A considerable gap can be noticed in \"Soaring Eagle Style\". For Hippie Bohemians, {the bar for \"Soaring Eagle Style\"}[0.681, 0.256, 0.748, 0.438] is significantly smaller than the corresponding bar in the Punk Rockers sub-chart {the corresponding bar for \"Soaring Eagle Style\"}[0.681, 0.669, 0.748, 0.923].\nBased on the above thought, the answer is:\nSoaring Eagle Style. This trend is significantly more popular with the Punk Rocker subculture when contrasted to its popularity among Hippie Bohemians as displayed by the marked difference in bar heights in the chart." } ], "id_raw": 143 }, { "id": "j1VmzlUyuBEnZvAx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_8.png", "conversations": [ { "from": "human", "value": "\nFor Punk Rockers, which fashion trend observed in the bar chart displays the lowest popularity?" }, { "from": "gpt", "value": "I'll identify the sub-chart representing the \"Punk Rocker\" subculture. That's the second sub-chart[0.064, 0.560, 0.981, 0.923] based on its title {caption}[0.459, 0.515, 0.586, 0.551]. I see five bars here, each representing a trend, and I need to locate the shortest bar. The bar for { \"Graveyard Chic\"}[0.489, 0.923, 0.556, 0.923] has no height indicating the popularity value to be zero. Therefore, this is the trend with the lowest popularity among Punk Rockers.\nBased on the above thought, the answer is:\nGraveyard Chic." } ], "id_raw": 143 }, { "id": "j5gxcdg8FLYlAmIb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nCan you please transform the data visualized in the bar chart into a structured CSV table for me?" }, { "from": "gpt", "value": "Looking at the chart, I observe that there are two bars, one for each category {\"Erratic decline\"}[0.454, 0.025, 0.586, 0.055] and {\"Gradual increase\"}[0.442, 0.512, 0.598, 0.542]. Each bar is grouped by energy type on the x-axis, with {\"Solar Energy\"}[0.094, 0.464, 0.192, 0.487], {\"Hydro Energy\"}[0.342, 0.464, 0.447, 0.487], {\"Wind Energy\"}[0.597, 0.464, 0.695, 0.487], and {\"Nuclear Energy\"}[0.838, 0.464, 0.956, 0.487] as the categories. The height of each bar and the y-axis tick labels indicate the production values. For instance, under Solar Energy, the bar coordinate [0.099, 0.139, 0.187, 0.448] shows that the production value for Erratic decline is approximately 80, and the bar coordinate [0.099, 0.781, 0.187, 0.935] shows that the production value for Gradual increase is 40. I will repeat this pattern for the other types of energy to create my table.\nBased on the above thought, the answer is:\nTypes of energy, Erratic decline, Gradual increase\nSolar Energy, 80, 40\nHydro Energy, 30, 20\nWind Energy, 50, 50\nNuclear Energy, 10, 70" } ], "id_raw": 144 }, { "id": "7iw5sv2PrYlqqZ9I", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what did you infer about the changes in energy production?" }, { "from": "gpt", "value": "Based on the visual representation, I infer significant changes in energy production over time. Comparing the bar heights under both conditions of {\"Erratic decline\"}[0.454, 0.025, 0.586, 0.055] and {\"Gradual increase\"}[0.442, 0.512, 0.598, 0.542] in the subcharts, it's clear that Solar and Hydro energy production has seen a drop, while Wind Energy production has remained the same. On the other hand, Nuclear Energy production showed a drastic increase from 10 to 70. This shows a transition from erratic decline where the energy production was less and more volatile to a gradual increase where the production was more stable and tended towards growth, especially in the case of Nuclear Energy.\nBased on the above thought, the answer is:\nThe production of Solar and Hydro energies decreased, Wind Energy production remained the same, while Nuclear Energy saw a substantial increase during the transition from Erratic decline to Gradual increase. Hence, it indicates a phase of stabilization and growth particularly in the case of Nuclear Energy." } ], "id_raw": 144 }, { "id": "ufxB0JFXcpQgAfie", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_9.png", "conversations": [ { "from": "human", "value": "\nFrom the data in the chart, which energy type experienced the most growth from 'Erratic decline' to 'Gradual increase'?" }, { "from": "gpt", "value": "Comparing the bars for each energy type under Erratic decline and Gradual increase, like Solar Energy which has bar coordinates [0.099, 0.139, 0.187, 0.448] under Erratic decline and [0.099, 0.781, 0.187, 0.935] under Gradual increase, we can determine the growth by subtracting the bar heights in Erratic decline from those in Gradual increase. Taking into account each energy type {\"Solar Energy\"}[0.094, 0.464, 0.192, 0.487], {\"Hydro Energy\"}[0.342, 0.464, 0.447, 0.487], {\"Wind Energy\"}[0.597, 0.464, 0.695, 0.487], and {\"Nuclear Energy\"}[0.838, 0.464, 0.956, 0.487], I see that Nuclear Energy saw the most growth from 10 to 70, yielding a net increase of 60.\nBased on the above thought, the answer is:\nNuclear Energy" } ], "id_raw": 144 }, { "id": "s5tZGWpIGn7egQBA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart being represented, could you convert the chart into a structured CSV table?" }, { "from": "gpt", "value": "As per the visualization, there are two primary subplots {\"Infrastructure\"}[0.057, 0.062, 0.492, 0.735] and {\"Economy\"}[0.548, 0.062, 0.983, 0.735], denoting two different areas of focus. Each subplot has bars representing various planning methods and their respective roles. The x-axis represents different planning methods including {\"Manual Planning\"}[0.059, 0.752, 0.160, 0.904], {\"Technology Assisted\"}[0.131, 0.752, 0.252, 0.933], {\"Automated Planning\"}[0.213, 0.752, 0.335, 0.935], {\"Government Intervention\"}[0.282, 0.752, 0.431, 0.975], and the {\"Total\"}[0.421, 0.752, 0.457, 0.807] progress rate of each category. The y-axis in each subplot represents the progress rate percentage in the respective category, ranging from {0}[0.036, 0.726, 0.046, 0.750] to {100}[0.017, 0.053, 0.046, 0.076]. To generate the CSV table, I evaluate the height of each bar in both subplots and map them to the corresponding y-axis value, percentage. Combining this process for both subplots, it creates the structured CSV data.\nBased on the above thought, the answer is:\nArea,Manual Planning,Technology Assisted,Automated Planning,Government Intervention,Total\nInfrastructure,60,75,90,40,90\nEconomy,70,80,60,80,60" } ], "id_raw": 145 }, { "id": "ssErhUGBwXqjBEaC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nIn light of the chart, can we conclude that with the implementation of government intervention, the progress trend in infrastructure is lesser than economy?" }, { "from": "gpt", "value": "The question aims at comparing the performance of Government Intervention in two areas - Infrastructure and Economy. In order to comprehend that, we ought to evaluate the heights of bars corresponding to Government Intervention in both subplots. Accordingly, the bar representing the Government Intervention in {\"Infrastructure\"}[0.324, 0.466, 0.390, 0.735] layers at an approximate progress rate of 40% whilst the bar under {\"Economy\"}[0.815, 0.197, 0.881, 0.735] layers at around 80% - the higher part of the y-axis. Consequently, it's evident that the Government Intervention implemented in an economy area portrays a larger progress trend than infrastructure.\nBased on the above thought, the answer is:\nYes, the progress trend of Government Intervention in Economy is greater than in Infrastructure. This can be inferred from the significantly higher bar height in the Economy area compared to Infrastructure. Hence, it indicates better performance of government intervention in the Economy realm." } ], "id_raw": 145 }, { "id": "MyCkLatao3oZ2fWx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_10.png", "conversations": [ { "from": "human", "value": "\nBy comparing the development planning methods, which method has the maximum utilization in the economy area?" }, { "from": "gpt", "value": "To determine the method that has the maximum utilization in the economy area, we need to evaluate the height of the bars in {\"Economy\"}[0.548, 0.062, 0.983, 0.735] subplot. Each bar represents each of the planning methods. The bar heights essentially show the progress percentages, which would infer utilization. Analogously, reviewing -> {\"Manual Planning\"}[0.568, 0.264, 0.634, 0.735], {\"Technology Assisted\"}[0.651, 0.197, 0.716, 0.735], {\"Automated Planning\"}[0.733, 0.332, 0.799, 0.735], and {\"Government Intervention\"}[0.815, 0.197, 0.881, 0.735], one can acknowledge that Technology Assisted possesses the maximum height amongst the mentioned bars thereby being the optimal choice.\nBased on the above thought, the answer is:\nTechnology Assisted" } ], "id_raw": 145 }, { "id": "SLibVb9fh3bfg4Va", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "From the title at the top of the subcharts {\"USA\"}[0.501, 0.025, 0.539, 0.055] and {\"Canada\"}[0.485, 0.512, 0.555, 0.542], each geometrically defined by the x-axis and y-axis, representing different countries. The y-axis tick labels represent the corresponding values, ranging from {0}[0.036, 0.439, 0.046, 0.462] to {350}[0.017, 0.053, 0.046, 0.076]. The x-axis display categories are {Sightseeing}[0.088, 0.464, 0.178, 0.487], {Gastronomy}[0.279, 0.464, 0.374, 0.487], {Adventure}[0.480, 0.464, 0.560, 0.487], {Nature}[0.687, 0.464, 0.740, 0.487], and {Total}[0.889, 0.464, 0.925, 0.487]. Due to the labeling colors and sub-legend, unidentified bars can be connected to their corresponding countries. The blue bars for {USA}[0.938, 0.083, 0.969, 0.106], and orange bars for {Canada}[0.910, 0.571, 0.969, 0.594], each value can be extracted using the height of the bar in comparison to the y-axis.\nBased on the above thought, the answer is:\nCountry,Sightseeing,Gastronomy,Adventure,Nature,Total\nUSA,70,80,75,90,315\nCanada,60,85,70,95,310" } ], "id_raw": 146 }, { "id": "0MSuSKi4LtKesz2Y", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which country has greater tourism diversity and explain the reasons." }, { "from": "gpt", "value": "This question requires an overall comparison of the {blue bars} and the {orange bars} which represent the diversity of each country in the aspects of tourism given in the x-axis categories. From the {Sightseeing bars}[USA {bar}[0.099, 0.371, 0.167, 0.448], Canada {bar}[0.099, 0.869, 0.167, 0.935]], Canada's bar is lower than USA's bar, showing that USA has more sightseeing attractions. A similar pattern is seen for {Adventure bars}[USA {bar}[0.486, 0.365, 0.554, 0.448], Canada {bar}[0.486, 0.858, 0.554, 0.935]], indicating the US is more adventurous too. Though for {Gastronomy bars}[USA {bar}[0.293, 0.360, 0.360, 0.448], Canada {bar}[0.293, 0.842, 0.360, 0.935]], and {Nature bars}[USA {bar}[0.680, 0.349, 0.748, 0.448], Canada {bar}[0.680, 0.831, 0.748, 0.935]] Canada is higher, showing that it has a lead in Gastronomy and Nature. Since both countries lead in two aspects each, they are equally diverse in terms of tourism.\nBased on the above thought, the answer is:\nBoth countries are equally diverse in terms of tourism." } ], "id_raw": 146 }, { "id": "0d48RHKGLATPrPq7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total tourist attractiveness score difference between USA and Canada?" }, { "from": "gpt", "value": "To find the total tourist attractiveness score difference, I need to focus on the {Total bars}[USA {bar}[0.873, 0.101, 0.941, 0.448], Canada {bar}[0.873, 0.594, 0.941, 0.935]]. From the Total bars, the {height of USA's bar}[0.873, 0.101, 0.941, 0.448] is corresponding to an approximate 315 and the {height of Canada's bar}[0.873, 0.594, 0.941, 0.935] is corresponding to 310 on the Y-axes scale, the difference is 5 which means the Total tourist attractiveness score difference between USA and Canada is 5 points.\nBased on the above thought, the answer is:\n5." } ], "id_raw": 146 }, { "id": "czUVNqg69tgj2SqN", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nCould you interpret the data in the bar chart and convert into a table format represented in CSV?" }, { "from": "gpt", "value": "The bar chart includes two trends under separate subplots: {AI Technology}[0.057, 0.062, 0.983, 0.448] and {FinTech}[0.057, 0.550, 0.983, 0.935]. For each technology trend, there are 4 bars representing different measurement tools: {Manual Mapping}, {LLM-revision}, {LLM-revision w/CoT}, {VerifNER}, placed respectively at positions {Manual Mapping}[0.099, 0.197, 0.187, 0.448 & 0.099, 0.627, 0.187, 0.935], {LLM-revision}[0.350, 0.170, 0.438, 0.448 & 0.350, 0.704, 0.438, 0.935], {LLM-revision w/CoT}[0.602, 0.255, 0.690, 0.448 & 0.602, 0.800, 0.690, 0.935] and {VerifNER}[0.853, 0.224, 0.941, 0.448 & 0.853, 0.773, 0.941, 0.935] for AI Technology and FinTech respectively. The value for each bar can be obtained by comparing its height to the y-axis. For example, the {Manual Mapping bar of the AI Technology}[0.099, 0.197, 0.187, 0.448] is approximately at the y-axis' 65 label, thus, its value is 65.\nBased on the above thought, the answer is:\nTechnology,Manual Mapping,LLM-revision,LLM-revision w/CoT,VerifNER\nAI Technology,65,72,50,58\nFinTech,80,60,35,42" } ], "id_raw": 148 }, { "id": "B5UdKXD5ky8JAw0V", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how do the technologies' development trends differ, and what might you infer from this?" }, { "from": "gpt", "value": "The bars in the AI Technology subplot and FinTech subplot are at positions: {AI Technology}[0.057, 0.062, 0.983, 0.448] and {FinTech}[0.057, 0.550, 0.983, 0.935] respectively. For AI Technology, the bars showing development trends show a fluctuating course, reaching a peak at {LLM-revision}[0.350, 0.170, 0.438, 0.448] and the slightest fall at {LLM-revision w/CoT}[0.602, 0.255, 0.690, 0.448]. In contrast, the bars in the FinTech subplot show a steady decline, from {Manual Mapping}[0.099, 0.627, 0.187, 0.935] to {VerifNER}[0.853, 0.773, 0.941, 0.935]. The development of AI Technology seems to oscillate, perhaps due to the rapid evolution and abundance of methods that optimize its processes, while FinTech shows a constant decline, suggesting the area's challenges need escalating attention to keep up to pace.\nBased on the above thought, the answer is:\nAI Technology shows a fluctuating development trend, suggesting a constant evolution and optimization in the field. FinTech, contrastingly, illustrates a downward trend, indicating the need for more significant improvements or solutions to meet the industry's challenges." } ], "id_raw": 148 }, { "id": "ShypNpv1fKfc6ggl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_13.png", "conversations": [ { "from": "human", "value": "\nWhich tool shows the maximum development in AI technology according to the data on the plotted chart?" }, { "from": "gpt", "value": "Here, I need to compare the bar heights for AI Technology. On observing, the bars in the AI technology graph are posed at the following positions: {Manual Mapping}[0.099, 0.197, 0.187, 0.448], {LLM-revision}[0.350, 0.170, 0.438, 0.448], {LLM-revision w/CoT}[0.602, 0.255, 0.690, 0.448] and {VerifNER}[0.853, 0.224, 0.941, 0.448]. The bar at {LLM-revision}[0.350, 0.170, 0.438, 0.448] has the tallest height, which corresponds to the highest development trend in the AI technology sector.\nBased on the above thought, the answer is:\nLLM-revision" } ], "id_raw": 148 }, { "id": "j9spzuarWXFNXjcc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data for the subject of Mathematics into a structured table format?" }, { "from": "gpt", "value": "To compile the Mathematics chart data into a table, I'll first observe the subject title {Mathematics}[0.461, 0.025, 0.580, 0.055]. Next, I notice four different learning method categories represented in the x-axis labels: {Traditional Learning}[0.083, 0.464, 0.235, 0.487], {E-Learning}[0.359, 0.464, 0.441, 0.487], {Blended Learning}[0.573, 0.464, 0.708, 0.487], and {MOOCs}[0.853, 0.464, 0.909, 0.487]. The y-axis is labeled with success rate percentages from 0 to 100 as shown in {the y-axis tick labels}[0.036, 0.439, 0.046, 0.462]; [0.027, 0.362, 0.046, 0.385]; [0.027, 0.284, 0.046, 0.308]; [0.026, 0.207, 0.046, 0.231]; [0.026, 0.130, 0.046, 0.154]; [0.017, 0.053, 0.046, 0.076]. The success rate data for each learning method category in Mathematics is indicated by the height of the bars and can be extracted as follows: {Traditional Learning has a success rate of about 60%}[0.099, 0.217, 0.219, 0.448], {E-Learning is approximately 80%}[0.340, 0.217, 0.460, 0.448], {Blended Learning appears to be around 70%}[0.580, 0.217, 0.701, 0.448], and {MOOCs show a rate near 90%}[0.821, 0.217, 0.941, 0.448].\nBased on the above thought, the answer is:\nMode of Learning,Success Rate (%)\nTraditional Learning,60\nE-Learning,80\nBlended Learning,70\nMOOCs,90" } ], "id_raw": 149 }, { "id": "2gppUWiC9WvDeO2g", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nWhy might MOOCs have a noticeably higher success rate compared to other learning methods according to the bar chart?" }, { "from": "gpt", "value": "Observing the chart for trends, I see higher bars for {MOOCs in Mathematics}[0.821, 0.217, 0.941, 0.448] and {MOOCs in Science}[0.821, 0.569, 0.941, 0.935] compared to the other learning methods in their respective categories. This implies MOOCs are associated with higher success rates, which could be due to a variety of reasons such as the flexibility they offer, allowing students to learn at their own pace, as well as the wider reach, enabling access to quality education for people who may not have such opportunities otherwise. The success rates for MOOCs are near 90% for Mathematics and 95% for Science, significantly higher than other methods exhibited in the chart.\nBased on the above thought, the answer is:\nMOOCs may have a higher success rate because they offer greater flexibility and accessibility, enabling students to learn at their own pace and gain access to quality education, which can be particularly beneficial for those with limited opportunities. This notion is visually supported by the prominently taller bars for MOOCs, indicating higher success rates in both Mathematics and Science subjects." } ], "id_raw": 149 }, { "id": "Nonps14huBqKft0w", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the average success rate for E-Learning across both subjects?" }, { "from": "gpt", "value": "To calculate the average success rate for E-Learning, I must locate the bars corresponding to {E-Learning for Mathematics}[0.340, 0.217, 0.460, 0.448] and {E-Learning for Science}[0.340, 0.608, 0.460, 0.935]. I see that the success rate for E-Learning in Mathematics is 80%, and for Science, it's 85%. I will calculate the average by adding these two percentages and dividing by two, which is the number of subjects.\nBased on the above thought, the answer is:\n82.5" } ], "id_raw": 149 }, { "id": "PIk2vLM0jSPJaRKB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, could you provide the data in a structured table (CSV format)?" }, { "from": "gpt", "value": "This bar chart has stages of Big Data integration as the X-axis and the logarithmically scaled figures as the Y-axis. From the {x-axis}[0.490, 0.952, 0.565, 0.975], we can determine that there are six stages i.e., 'Rapid Expansion', 'Gradual Improvement', 'Erratic Growth', 'Inconsistent Decline', 'Pronounced Decline', and 'Rapid Expansion' again.\nFrom the {bar coordinates}[0.112, 0.525, 0.148, 184.387; 0.257, 0.341, 0.293, 184.387; 0.401, 0.157, 0.437, 184.387; 0.546, 0.341, 0.582, 184.387; 0.690, 0.341, 0.727, 184.387; 0.835, 0.157, 0.871, 184.387], I can conclude that the stages correspond to different integration strategies: 'Early integration', 'Further integration' and 'System integration'.\nUsing these legend labels and by comparing the three bars for each stage, I can record the data points for each category and for each strategy from their respective y-coordinates.\nBased on the above thought, the answer is:\nCategories,Early integration, Further integration, System integration\nRapid Expansion,0.001,0.01,0.001\nGradual Improvement,0.01,0.01,0.01\nErratic Growth,0.1,0.001,0.1\nInconsistent Decline,0.01,0.1,0.01\nPronounced Decline,0.01,0.0001,0.1\nRapid Expansion,0.1,0.01,0.001" } ], "id_raw": 150 }, { "id": "Pew6LIFPnb1mCq0P", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat conclusions can you draw about the data integration experience from this bar chart?" }, { "from": "gpt", "value": "This bar chart illustrates the processes of Early, Further, and System integration for six categories of strategies {x-axis}[0.490, 0.952, 0.565, 0.975]. We can conclude the following:\n1. The 'Gradual Improvement' category has equal performance across all three types of integration, with the height of these bars[0.257, 0.341, 0.293, 184.387; 0.293, 0.341, 0.329, 184.387; 0.329, 0.341, 0.365, 184.387] nearly identical.\n2. The 'Erratic Growth' category sees wide-ranging performance variance, as can be seen from the significantly different bar heights here[0.401, 0.157, 0.437, 184.387; 0.437, 0.525, 0.474, 184.387; 0.474, 0.157, 0.510, 184.387].\n3. The initial and final category, 'Rapid Expansion', shows a decrease in intensity from 'Early integration' to 'System integration'. One can see this from the coordinates of the corresponding bars[0.112, 0.525, 0.148, 184.387; 0.148, 0.341, 0.184, 184.387; 0.184, 0.341, 0.221, 184.387].\nOverall, the performance across different stages of integration varies depending on the category, showing improvements in some and declinations in others.\nBased on the above thought, the answer is:\nThe bar chart reveals that the performance throughout the three stages of data integration process: 'Early integration', 'Further integration', and 'System integration' can fluctually change, improve equally, or decrease for different strategies. This shows that the effectiveness of a strategy is dependent on its type and the stage of the process." } ], "id_raw": 150 }, { "id": "PKWLwfHhhPVVZf3Z", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which category experienced the greatest change when moving from early integration to system integration?" }, { "from": "gpt", "value": "To identify the category with the largest difference in the value from Early integration to System integration, I need to compare the height of the bars representing each category for the two strategies.\nLooking at the chart, the heights of the bars representing 'Early integration' and 'System integration' are found at [0.112, 0.525, 0.148, 184.387] and [0.184, 0.341, 0.221, 184.387], [0.257, 0.341, 0.293, 184.387] and [0.329, 0.341, 0.365, 184.387] and so on. The categories represented by these bars are 'Rapid Expansion', 'Gradual Improvement', 'Erratic Growth', 'Inconsistent Decline', 'Pronounced Decline', and 'Rapid Expansion' again. I can determine that the {Erratic Growth}[0.415, 0.746, 0.496, 0.880] has the largest difference between the heights of the two bars. This differential suggests that the Erratic Growth category underwent the greatest change when moving from Early to System integration.\nBased on the above thought, the answer is:\nErratic Growth." } ], "id_raw": 150 }, { "id": "OwfNSwRiRSrrzAam", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nCan you process the information from the bar graph into a well-structured CSV format?" }, { "from": "gpt", "value": "Yes, indeed. As can be deduced from the given data, there are three bars per category on the x-axis. The x-axis represents different economic indicators with labels {GDP}[0.180, 0.919, 0.210, 0.942], {Consumer_Price_Index}[0.288, 0.919, 0.445, 0.942], {Unemployment_Rate}[0.465, 0.919, 0.610, 0.942], {Interest_Rate}[0.663, 0.919, 0.755, 0.942] and {Stock_Market}[0.833, 0.919, 0.927, 0.942]. The y-axis represents the corresponding values, represented in logarithmic scale. The legend at {the bottom}[0.353, 0.020, 0.722, 0.062] identifies the three different measures: {RBF_INT}[0.398, 0.029, 0.455, 0.053], {RBF_POL}[0.522, 0.029, 0.583, 0.053], and {RBF_COM}[0.650, 0.029, 0.716, 0.053]. These three measures each have bars corresponding to each economic indicator. The data to be extracted from each economic indicator should therefore be classified according to these three measures.\nBased on the above thought, the answer is:\nEconomic_Indicators,RBF_INT,RBF_POL,RBF_COM\nGDP,3247,4334,2923\nConsumer_Price_Index,1344,1459,1224\nUnemployment_Rate,979,895,1050\nInterest_Rate,1123,900,1340\nStock_Market,3640,2799,3050" } ], "id_raw": 151 }, { "id": "H67kIJ7evV5eceMl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how is the performance of RBF_INT in relation to the other two measures (RBF_POL and RBF_COM) as far as the economic indicators are concerned?" }, { "from": "gpt", "value": "To answer this question, one needs to analyze and compare the heights of the bars for RBF_INT, RBF_POL, and RBF_COM across the different economic indicators. Starting with the GDP bars at {coordinates}[0.131, 0.264, 0.174, 1092.651], [0.174, 0.128, 0.217, 1092.651] and [0.217, 0.314, 0.259, 1092.651], it's observed that RBF_INT has a relatively lower value but not the lowest. For the Consumer Price Index at {coordinates}[0.302, 0.681, 0.345, 1092.651], [0.345, 0.642, 0.388, 1092.651] and [0.388, 0.725, 0.431, 1092.651], RBF_INT has the lowest value. Similarly, for the Unemployment Rate and Interest Rate at coordinates at [{Unemployment Rate}[0.473, 0.831, 0.516, 1092.651], [0.516, 0.873, 0.559, 1092.651], [0.559, 0.798, 0.602, 1092.651]] and [{Interest Rate}[0.645, 0.766, 0.687, 1092.651], [0.687, 0.871, 0.730, 1092.651], [0.730, 0.683, 0.773, 1092.651]], RBF_INT does consistently better than RBF_COM but not RBF_POL. Lastly, in the Stock Market category at {coordinates}[0.816, 0.210, 0.859, 1092.651], [0.859, 0.334, 0.902, 1092.651] and [0.902, 0.294, 0.944, 1092.651], RBF_INT performs better than both RBF_POL and RBF_COM.\nBased on the above thought, the answer is:\nThe performance of RBF_INT is inconsistent. It does not consistently perform better or worse across all economic indicators when compared to RBF_POL and RBF_COM. While it performs better in the categories of Unemployment Rate, Interest Rate, and Stock Market, it lags in the GDP and Consumer Price Index categories." } ], "id_raw": 151 }, { "id": "pqV0CcucvsG0sJ0e", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the lowest value of the Unemployment rate across all the measures (RBF_INT, RBF_POL, RBF_COM)?" }, { "from": "gpt", "value": "To ascertain the lowest unemployment rate value, the heights of the bars corresponding to the \"Unemployment Rate\" must be compared. Let's start by analyzing the height denoting RBF_INT which is at {coordinates}[0.473, 0.831, 0.516, 1092.651] representing the unemployment rate value of 979. Then RBF_POL coordinates at [0.516, 0.873, 0.559, 1092.651] representing value 895, and finally RBF_COM coordinates at [0.559, 0.798, 0.602, 1092.651] representing the value 1050. Among all these values, the lowest is 895 for RBF_POL.\nBased on the above thought, the answer is:\n895" } ], "id_raw": 151 }, { "id": "gb7lohyMM0uKtC57", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart details you've analyzed, can you generate a structured table in csv format that displays the investment return rates for the three different investment classes across the seven scenarios?" }, { "from": "gpt", "value": "Sure, from the title {Scenarios on the x-axis}[0.494, 0.952, 0.561, 0.975], I notice that the x-axis represents different scenarios. The y-axis, as suggested by its label {Return Rate}[0.015, 0.432, 0.029, 0.569], represents the return rates of investment. The data for each investment class is represented by a different colored bar as per the {legend}[0.296, 0.020, 0.759, 0.062]: {Tech Startups with purple bars}[0.341, 0.029, 0.428, 0.053], {Real Estate with red bars}[0.495, 0.029, 0.573, 0.053], and {Crypto Currency with orange bars}[0.640, 0.029, 0.753, 0.053]. Next, to create the csv table, I need to look at the height of each bar under each scenario to determine the return rate. For instance, under the \"Sharp rise\" scenario, the {purple bar}[0.112, 0.356, 0.142, 573.254] is approximately at the 0.16 position, the {red bar}[0.142, 0.818, 0.173, 573.254] is near the 0.03 spot, and the {orange bar}[0.173, 0.408, 0.204, 573.254] is around the 0.13 line. Doing the same for the rest of the scenarios would give us the entire data.\nBased on the above thought, the answer is:\nScenarios,Tech Startup,Real Estate,Crypto Currency\nSharp rise,0.16,0.03,0.13\nSubstantial decrease,0.1,0.19,0.4\nSlow expansion,0.04,0.075,0.023\nUpward trend,0.15,0.18,0.3\nGradual recovery,0.05,0.16,0.11\nSudden decrease,0.035,0.082,0.4\nInexorable decline,0.02,0.06,0.29" } ], "id_raw": 152 }, { "id": "HeWW7CAqDPdBA3Va", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nFrom a comparative point of view, across different scenarios, which type of investment might be the most volatile based on the chart?" }, { "from": "gpt", "value": "The volatility of an investment class can be determined by observing the fluctuations in return rates across different scenarios. Looking at the chart, we can see that the {purple bars indicating Tech Startups' return rates}<[0.112, 0.356, 0.142, 573.254]> have clearly noticeable changes in their heights, indicating that their return rates vary quite significantly across different scenarios. However, the {orange bars representing Crypto Currency return rates}<[0.173, 0.408, 0.204, 573.254]> appear to exhibit larger fluctuations. They range from very low in the 'Slow expansion' scenario to very high in the 'Substantial decrease' and 'Sudden decrease' scenarios. This suggests that Crypto Currency may be the most volatile investment choice.\nBased on the above thought, the answer is:\nFrom the chart, the Crypto Currency seems to be the most volatile investment due to its larger fluctuations in return rates across various scenarios." } ], "id_raw": 152 }, { "id": "b3IiBIe2L4IXzKQq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_2.png", "conversations": [ { "from": "human", "value": "\nIn which scenario does the Crypto Currency have the largest return rate?" }, { "from": "gpt", "value": "To find when the Crypto Currency has the largest return rate, I need to look at the height of the {orange bars}[0.601, 0.031, 0.628, 0.048] under each scenario. From my analysis, the highest {orange bars} on the y-axis corresponds to the 'Substantial decrease'<[0.208, 0.919, 0.354, 0.942]> and 'Sudden decrease'<[0.714, 0.919, 0.834, 0.942]> scenarios, they both reach approximately the 0.4 mark on the y-axis. Hence, the Crypto Currency has the largest return rate during these scenarios.\nBased on the above thought, the answer is:\nSubstantial decrease & Sudden decrease." } ], "id_raw": 152 }, { "id": "nq7xeuKpEPfqZQdR", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nBased on the grouped bar chart that you see, could you generate a CSV format table that corresponds to the visible data?" }, { "from": "gpt", "value": "Observing the chart, I see that the x-axis represents different continents as denoted by {x-axis labels}[{'North America'}[0.116, 0.919, 0.217, 0.942]; {'South America'}[0.260, 0.919, 0.362, 0.942]; {'Africa'}[0.436, 0.919, 0.475, 0.942]; {'Asia'}[0.585, 0.919, 0.615, 0.942]; {'Europe'}[0.720, 0.919, 0.769, 0.942]; {'Australia'}[0.858, 0.919, 0.920, 0.942]]. The y-axis represents the logarithmic scale values represented by {y-axis labels}[{'1e-4'}[0.035, 0.902, 0.066, 0.925]; {'1e-3'}[0.035, 0.661, 0.066, 0.684]; {'1e-2'}[0.035, 0.420, 0.066, 0.444]; {'1e-1'}[0.035, 0.180, 0.066, 0.203]]. Each continent category has three bars standing for Spatial Expansion, Spatial Decrease and Spatial Improvement as shown by {}[{'Spatial Expansion'}[0.287, 0.029, 0.411, 0.053]; {'Spatial Decrease'}[0.477, 0.029, 0.595, 0.053]; {'Spatial Improvement'}[0.662, 0.029, 0.808, 0.053]]. To generate the CSV table, I need to extract the values represented by the heights of the bars. For instance, for the North America category, the height of the {bar for \"Spatial Expansion\"}[0.112, 0.116, 0.148, 240.664] extends nearly halfway up the log scale, implying a value of 0.2. Similarly, the {bar for \"Spatial Decrease\"}[0.148, 0.615, 0.184, 240.664] and {\"Spatial Improvement\"}[0.184, 0.430, 0.221, 240.664] can be interpreted as 1.7e-3 and 1e-2 respectively.\nBased on the above thought, the answer is:\nContinent,Spatial Expansion,Spatial Decrease,Spatial Improvement\nNorth America,2e-1,1.7e-3,1e-2\nSouth America,1e-1,2.1e-3,9e-3\nAfrica,1.7e-1,1.9e-3,8e-3 \nAsia,1.9e-1,1.5e-3,7e-3\nEurope,2e-1,1.6e-3,6e-3\nAustralia,1.8e-1,1.4e-3,5e-3" } ], "id_raw": 153 }, { "id": "Wu1sprZ6KVCznl3W", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which continent needs most attention to mitigate spatial expansion?" }, { "from": "gpt", "value": "To determine the continent that requires the most attention regarding Spatial Expansion, one needs to identify the tallest bar within the first subset of bars representing Spatial Expansion {Spatial Expansion}[0.287, 0.029, 0.411, 0.053]. The bars for this component across continents are {North America}[0.112, 0.116, 0.148, 240.664], {South America}[0.257, 0.189, 0.293, 240.664], {Africa}[0.401, 0.133, 0.437, 240.664], {Asia}[0.546, 0.122, 0.582, 240.664], {Europe}[0.690, 0.116, 0.727, 240.664], and {Australia}[0.835, 0.127, 0.871, 240.664]. Among these, the height of the bar for Europe and North America appears to be the tallest, denoting the highest Spatial Expansion which indicates these continents need the most attention to mitigate Spatial Expansion.\nBased on the above thought, the answer is:\nEurope and North America, as they have the highest Spatial Expansion." } ], "id_raw": 153 }, { "id": "PEdhWQYdosYRdt1h", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_3.png", "conversations": [ { "from": "human", "value": "\nWhich continent shows the highest spatial improvement?" }, { "from": "gpt", "value": "To identify the continent with the greatest Spatial Improvement, I have to look into the third bar in every continent's category, which is color-coded for Spatial Improvement by {Spatial Improvement}[0.662, 0.029, 0.808, 0.053]. Identifying the bars relating to Spatial Improvement for every continent such as {North America}[0.184, 0.430, 0.221, 240.664],{South America}[0.329, 0.441, 0.365, 240.664],{Africa}[0.474, 0.453, 0.510, 240.664],{Asia}[0.618, 0.467, 0.654, 240.664],{Europe}[0.763, 0.483, 0.799, 240.664], and {Australia}[0.907, 0.502, 0.943, 240.664]; I notice that the highest bar represents North America.\nBased on the above thought, the answer is:\nNorth America." } ], "id_raw": 153 }, { "id": "kpZnkfBUU6b0m8Pc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data displayed on the bar chart into a CSV format?" }, { "from": "gpt", "value": "This is a bar chart with 4 types of trends represented on the x-axis, listed from left to right as {Minimal Decline}[0.132, 0.919, 0.243, 0.942], {Continuous Decline}[0.344, 0.919, 0.479, 0.942], {Continous Rise}[0.584, 0.919, 0.687, 0.942], {Sustained Decline}[0.797, 0.919, 0.922, 0.942]. The values of these trends are represented on the y-axis as percentages. Based on colors of {the legend}[0.251, 0.020, 0.795, 0.062], the purple bars represent the Tech Industry, the red bars represent the Fast Food Industry, and the orange bars represent the Automobile Industry. I can determine the value of the bars based on their height and the y-axis labeling. For example, the value of Tech Industry in Minimal Decline can be approximated as {4%}[0.103, 0.376, 0.159, 825.220]. By following these steps for each bar, I can assemble the data in a table.\nBased on the above thought, the answer is:\nTrends, Tech Industry, Fast Food Industry, Automobile Industry\nMinimal Decline, 0.04, 0.06, 0.05\nContinuous Decline, 0.02, 0.01, 0.03\nContinuous Rise, 0.07, 0.08, 0.06\nSustained Decline, 0.01, 0.02, 0.02" } ], "id_raw": 154 }, { "id": "lHQDBgcH0jVVHGkD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nIf the higher the value indicates higher competitive intensity, which industry do you perceive as the most competitive?" }, { "from": "gpt", "value": "To perceive the most competitive industry, I need to aggregate the sums of the values expressed by the bars in each industry group. In the Minimal Decline trend, {the bar}[0.103, 0.376, 0.159, 825.220] corresponding to the Tech Industry shows a value of 4%. Adding this to the values shown by the Tech Industry bars in Continuous Decline, Continuous Rise, and Sustained Decline yields the overall competition intensity for the Tech Industry. I'll need to do the same for the Fast Food Industry and the Automobile Industry. After adding up all these values, it appears that the Fast Food Industry has the highest cumulative value, which means it would be the most competitive.\nBased on the above thought, the answer is:\nThe Fast Food Industry. It has the highest cumulative value of competition trends which infers it to be the most competitive among the three industries." } ], "id_raw": 154 }, { "id": "KUMnMEtWmkzCrEE4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_4.png", "conversations": [ { "from": "human", "value": "\nIn which trend, the difference between the highest and lowest percentages among the three industries is the largest?" }, { "from": "gpt", "value": "I'll have to compare the heights of the bars in each trend group. For instance, in the Minimal Decline trend, the tallest bar is from the {Fast Food Industry}[0.159, 0.231, 0.215, 825.220], showing about 6%, and the shortest is from the {Tech Industry}[0.103, 0.376, 0.159, 825.220], showing about 4%. The difference is 2%. I have to replicate the comparison for the Continuous Decline, Continuous Rise, and Sustained Decline trend groups. Finally, it seems the highest difference registered at 4% can be found in Continous Rise with the highest bar showing 8% from {Fast Food Industry}[0.607, 0.128, 0.663, 825.220] and lowest showing 4% from {Tech Industry}[0.551, 0.176, 0.607, 825.220].\nBased on the above thought, the answer is:\nContinuous Rise." } ], "id_raw": 154 }, { "id": "WCOl1K9uCroywObh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nConvert the data from the bar chart in a structured CSV format." }, { "from": "gpt", "value": "The title of the chart can be observed inside the label parameters for each sales category, which include: {\"Electronics\"} [0.146, 0.919, 0.222, 0.942], {\"Fashion\"} [0.335, 0.919, 0.387, 0.942], {\"Grocery\"} [0.510, 0.919, 0.565, 0.942], {\"Health & Beauty\"} [0.657, 0.919, 0.772, 0.942], {\"Sports & Outdoors\"} [0.827, 0.919, 0.955, 0.942]. The y-axis represents the sales revenue. For each sales category, there are three bars showing sales revenue for Q1, Q2, and Q3 2021, as indicated in the legend [0.357, 0.020, 0.718, 0.062]. To form the CSV table, I will extract the sales revenue for each quarter for each sales category. The bar height and the y-axis scale are used to determine these values. For instance, for the sales category of electronics in Q1 2021, the corresponding purple bar [0.131, 0.501, 0.166, 2130.183] can be scaled to the actual sales revenue value of 300000.\nBased on the above thought, the answer is:\nSales Category, Q1 2021, Q2 2021, Q3 2021\nElectronics, 300000, 325000, 290000\nFashion, 225000, 245000, 225000\nGrocery, 400000, 450000, 410000\nHealth & Beauty, 200000, 230000, 220000\nSports & Outdoors, 260000, 275000, 285000" } ], "id_raw": 155 }, { "id": "igYQ4YwKuubRCq9o", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what could you infer about the business performance over the three quarters of 2021?" }, { "from": "gpt", "value": "Analyzing the chart bars, I can infer that the performance of the business has been mixed over the three quarters, with some categories showing growth, and others remaining stable or declining. For instance, the {Electronics} category [0.146, 0.919, 0.222, 0.942] showed a slight increase in Q2, followed by a decrease in sales revenue in Q3, implying a volatile sales trend. The {Fashion} category [0.335, 0.919, 0.387, 0.942] showed a bit of growth over this period. On the other hand, the {Grocery category} [0.510, 0.919, 0.565, 0.942] demonstrates a dramatic increase in the first two quarters, implying high customer demand during this period. {Health & Beauty} [0.657, 0.919, 0.772, 0.942] shows a moderate growth, while the {Sports & Outdoors} [0.827, 0.919, 0.955, 0.942] shows a steady increase, implying steady demand in these items. Therefore, a strong performance has been observed in certain categories over time, indicating a possible trend towards consumers' preferences.\nBased on the above thought, the answer is:\nThere has been a mixed performance in the business over the three quarters of 2021. Some categories like Grocery and Fashion experienced growth, while others like Electronics have been volatile. The steady increase in Sports & Outdoors could hint at a consistent demand for those items." } ], "id_raw": 155 }, { "id": "lswbS9Py09upAjMS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_5.png", "conversations": [ { "from": "human", "value": "\nWhich sales category demonstrated the highest growth from Q1 to Q3 2021?" }, { "from": "gpt", "value": "To identify the sales category with the highest growth from Q1 to Q3 2021, I need to calculate the difference between the bar heights for Q1 and Q3 for each category. For instance, for the \"Electronics\" category, the purple bar for Q1 [0.131, 0.501, 0.166, 2130.183] relates to a value of 300000, while the orange bar for Q3 [0.202, 0.532, 0.237, 2130.183] relates to a value of 290000. Therefore, the difference for the \"Electronics\" category is -10000. I will perform similar calculations for the other sales categories. It can be observed from this analysis that the {Grocery} category [0.510, 0.919, 0.565, 0.942], with bars for Q1 [0.485, 0.236, 0.520, 2130.183] and Q3 [0.555, 0.213, 0.591, 2130.183] shows the highest growth with a difference of 10000.\nBased on the above thought, the answer is:\nGrocery." } ], "id_raw": 155 }, { "id": "LN7wnfTACG0UF6yo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, could you please create a CSV formatted table presenting the data?" }, { "from": "gpt", "value": "I can see that there are two groups of bars in the chart, each group contains five bars. According to {the x-axis labels}[{\"Urban\"}[0.269, 0.919, 0.311, 0.942]; {\"Rural\"}[0.747, 0.919, 0.783, 0.942]], the clusters of bars represent impact data on Urban and Rural areas respectively. Different indicators are represented by different colors, these are: {Greenhouse Gases}[0.112, 0.065, 0.183, 804.689], {Emissions Deforestation}[0.183, 0.143, 0.254, 804.689], {Emissions Agriculture}[0.254, 0.628, 0.326, 804.689], {Emissions Industrial Processes}[0.326, 0.308, 0.397, 804.689] and {Change in forests}[0.397, 0.807, 0.468, 804.689] in Urban areas, and the same indicators with same colors are present for Rural areas, i.e., {Greenhouse Gases}[0.587, 0.308, 0.658, 804.689], {Emissions Deforestation}[0.658, 0.486, 0.730, 804.689], {Emissions Agriculture}[0.730, 0.871, 0.801, 804.689], {Emissions Industrial Processes}[0.801, 0.486, 0.872, 804.689] and {Change in forests}[0.872, 0.871, 0.943, 804.689].To convert these observations into a CSV table, I would plot the indicators against the respective Urban and Rural values.\nBased on the above thought, the answer is:\nIndicator,Urban,Rural\nGreenhouse Gases,0.1,0.05\nEmissions Deforestation,0.08,0.03\nEmissions Agriculture,0.02,0.01\nEmissions Industrial Processes,0.05,0.03\nChange in forests,0.012,0.01" } ], "id_raw": 156 }, { "id": "3JnQUJ6jEkD2eLgS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nBased on the comparison shown in the chart, what conclusions could you draw regarding these environmental indicators?" }, { "from": "gpt", "value": "By looking at the bar chart, certain conclusions could be drawn. For most of the indicators, both {Urban}[0.269, 0.919, 0.311, 0.942] and {Rural}[0.747, 0.919, 0.783, 0.942] areas produce emissions from different sources. However, there are differences in the impact each indicator has between the regions. {Greenhouse Gases}[{urban}[0.112, 0.065, 0.183, 804.689]; {rural}[0.587, 0.308, 0.658, 804.689]] and {Emissions from Industrial Processes}[{urban}[0.326, 0.308, 0.397, 804.689]; {rural}[0.801, 0.486, 0.872, 804.689]] demonstrate a remarkably higher impact in Urban areas than in the Rural ones. {Emissions from Deforestation}[{urban}[0.183, 0.143, 0.254, 804.689]; {rural}[0.658, 0.486, 0.730, 804.689]] and {Emissions from Agriculture}[{urban}[0.254, 0.628, 0.326, 804.689]; {rural}[0.730, 0.871, 0.801, 804.689]] also show a higher impact for Urban areas, albeit to a lesser extent. These differences could be due to the increased activity in Urban areas compared to Rural ones, thus often resulting in higher emissions. However, for the 'Change in forests' indicator, the difference between Urban and Rural areas is smaller. This could suggest that change in forest areas due to factors such as deforestation, logging or wildfire, affects both Urban and Rural areas almost equally, suggesting the widespread nature of this problem.\nBased on the above thought, the answer is:\nThe chart suggests that for most indicators, Urban areas have a higher environmental impact than Rural areas, potentially due to increased activities such as industrial processes and higher greenhouse gas emissions. However, changes in forest areas appear to affect both Urban and Rural areas almost equally, suggesting that the issue of deforestation is widespread." } ], "id_raw": 156 }, { "id": "MMPNcH9c6UGkbwLL", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_6.png", "conversations": [ { "from": "human", "value": "\nWhich indicator shows the largest impact difference between the Urban and Rural areas?" }, { "from": "gpt", "value": "In order to find out the indicator that has the largest impact difference between Urban and Rural areas, it's required to compare the height of each pair of bars. Let's start with {Greenhouse Gases}[{urban}[0.112, 0.065, 0.183, 804.689]; {rural}[0.587, 0.308, 0.658, 804.689]], which shows a difference of 0.05; going on to {Emissions Deforestation}[{urban}[0.183, 0.143, 0.254, 804.689]; {rural}[0.658, 0.486, 0.730, 804.689]] indicating a difference of 0.05; then {Emissions Agriculture}[{urban}[0.254, 0.628, 0.326, 804.689]; {rural}[0.730, 0.871, 0.801, 804.689]] displays a difference of 0.01; next, {Emissions Industrial Processes}[{rural}[0.326, 0.308, 0.397, 804.689]; {rural}[0.801, 0.486, 0.872, 804.689]] indicates a difference of 0.02 and finally, {Change in forests}[{urban}[0.397, 0.807, 0.468, 804.689]; {rural}[0.872, 0.871, 0.943, 804.689]] yields a difference of 0.002. After making these comparisons, it seems that {Greenhouse Gases} and {Emissions Deforestation} have the most significant impact difference between Urban and Rural areas.\nBased on the above thought, the answer is:\nGreenhouse Gases and Emissions from Deforestation." } ], "id_raw": 156 }, { "id": "ky121jNTVYubXHa0", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nCould you turn the depicted chart data into a structured table?" }, { "from": "gpt", "value": "The x-axis of the chart comprises different financial indices such as {Equity}[0.177, 0.919, 0.221, 0.942], {Bond}[0.352, 0.919, 0.388, 0.942], {Forex}[0.523, 0.919, 0.561, 0.942], {Commodity}[0.673, 0.919, 0.753, 0.942], and {Futures}[0.859, 0.919, 0.910, 0.942]. On the y-axis, log value changes in these indices are plotted, as stated in the {ylabel}[0.015, 0.443, 0.029, 0.574]. There are two types of bars for each index, representing different trends. According to the chart legend, the {blue bars}[0.492, 0.030, 0.531, 0.053] represent initial trends, and the {yellow bars}[0.598, 0.030, 0.630, 0.053] represent final trends. For instance, in the {Equity} sector, the initial trend's value is about 0.30, visible from the height of {the first blue bar}[0.139, 0.755, 0.199, 885.059], and the final trend's value is 1.50, figured from the position of {the first yellow bar}[0.199, 0.137, 0.259, 885.059]. Following a similar pattern, I can analyze all the bars to extract the data needed for the table.\nBased on the above thought, the answer is:\nIndices, Initial, Final\nEquity, 0.30, 1.50\nBond, 0.40, 1.30\nForex, 0.50, 1.40\nCommodity, 0.60, 1.50\nFutures, 0.70, 1.40" } ], "id_raw": 157 }, { "id": "aqhUkiYkHvFQIAzA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nLooking at the graph, which index would you consider as the most unpredictable and why?" }, { "from": "gpt", "value": "By observing the differences between the initial and final trends across the indices, I can make an assessment. The blue bars represent the initial trends and the yellow bars represent the final trends. A maximum difference between the initial and final trends indicates high volatility and thus high unpredictability. Let's inspect each index. For {Equity}[0.177, 0.919, 0.221, 0.942], the change from {the blue bar}[0.139, 0.755, 0.199, 885.059] to {the yellow bar}[0.199, 0.137, 0.259, 885.059] is large, suggesting high unpredictability. However, if compare this change with all the other bars like {Bond}[0.352, 0.919, 0.388, 0.942]'s {blue bar}[0.310, 0.645, 0.370, 885.059] and {yellow bar}[0.370, 0.192, 0.430, 885.059], {Forex}[0.523, 0.919, 0.561, 0.942]'s {blue bar}[0.482, 0.559, 0.542, 885.059] and {yellow bar}[0.542, 0.163, 0.602, 885.059], {Commodity}[0.673, 0.919, 0.753, 0.942]'s {blue bar}[0.653, 0.489, 0.713, 885.059] and {yellow bar}[0.713, 0.137, 0.773, 885.059] and {Futures}[0.859, 0.919, 0.910, 0.942]'s {blue bar}[0.825, 0.430, 0.885, 885.059] and {yellow bar}[0.885, 0.163, 0.945, 885.059], we can see the change in Equity is the most number.\nBased on the above thought, the answer is:\nThe Equity index is the most unpredictable. This is because it shows the largest difference between the initial and final trends, indicating high volatility." } ], "id_raw": 157 }, { "id": "aKz38iiDTgXQkdH3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_7.png", "conversations": [ { "from": "human", "value": "\nWhich index shows the most significant increase from the initial to the final trend?" }, { "from": "gpt", "value": "The change for each index can be computed as the difference between the final trend (yellow bar) and the initial trend (blue bar). By comparing the height of these bars for each index, I can determine where the greatest increase occurs. For instance, {the Equity bar}[0.199, 0.137, 0.259, 885.059] shows a change from 0.30 to 1.50, which is an increase of 1.20 units. By comparing all the other bars such as {the Bond bar}[0.370, 0.192, 0.430, 885.059], {the Forex bar}[0.542, 0.163, 0.602, 885.059], {the Commodity bar}[0.713, 0.137, 0.773, 885.059] and {the Futures bar}[0.885, 0.163, 0.945, 885.059], I can come to a conclusion about which index has the largest increase from the initial trend to the final trend.\nBased on the above thought, the answer is:\nEquity" } ], "id_raw": 157 }, { "id": "qx6gpdHAL4clewGE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nAfter analyzing the chart, could you tabulate the occurrences of different trends based on forecast and prediction numbers?" }, { "from": "gpt", "value": "The chart analyses four different trends, {Continuous decrease}[0.114, 0.919, 0.260, 0.942], {Upward trajectory}[0.350, 0.919, 0.475, 0.942], {Pronounced decrease}[0.564, 0.919, 0.713, 0.942], and {Erratic decline}[0.814, 0.919, 0.914, 0.942]. The trends data represented by forecast and prediction are shown through bars of different colors. The forecasts are depicted with the color blue {representative sample forecase bar}[0.108, 0.361, 0.187, 0.911] and the predictions are illustrated with the color red {representative sample prediction bar}[0.187, 0.196, 0.266, 0.911]. For the values of these occurrences, the y-axis is marked from '0' to '800' with each tick representing a difference of 100. Therefore, considering the height and base of each bar for different trends, and the y-axis ticks, the data table can be generated.\nBased on the above thought, the answer is:\nTrends,Forecast,Prediction\nContinuous decrease,500,650\nUpward trajectory,680,710\nPronounced decrease,280,200\nErratic decline,330,360" } ], "id_raw": 158 }, { "id": "lphYNruosCYQqiX6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nBased on analysis of the chart, which method of data processing (forecasts or predictions) generally registers a higher number of occurrences for the trend phenomena?" }, { "from": "gpt", "value": "To answer this question, I need to compare the data of the forecast model and the machine learning model for each trend. I can see that the blue bars represent the forecast data {representative sample forecase bar}[0.108, 0.361, 0.187, 0.911], and the red bars represent the predicted data {representative sample prediction bar}[0.187, 0.196, 0.266, 0.911]. By comparing the heights of the corresponding pairs, it is found that for 'Continuous decrease' and 'Upward trajectory', the prediction data show higher occurrences; for 'Pronounced decrease' and 'Erratic decline', the forecast data show higher occurrences. Therefore, this question cannot have a single definitive answer, as the results are equally divided between the forecast model and the machine learning model regarding their achievements in the number of occurrences of the trend phenomena.\nBased on the above thought, the answer is:\nBoth Forecast and Machine learning models have higher recorded occurrences in equal number of trends." } ], "id_raw": 158 }, { "id": "Ds8a7zNgoqpZhzbs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_8.png", "conversations": [ { "from": "human", "value": "\nWhich trend shows the highest number of occurrences in the forecast data?" }, { "from": "gpt", "value": "In order to answer this question, I will look at the heights of the bars for 'Forecast' values in each trend category. The blue bars represent forecast values. So, I examine these bars: {Continuous decrease forecast}[0.108, 0.361, 0.187, 0.911], {Upward trajectory forecast}[0.333, 0.163, 0.412, 0.911], {Pronounced decrease forecast}[0.559, 0.603, 0.638, 0.911], and {Erratic decline forecast}[0.785, 0.548, 0.864, 0.911]. By comparing heights of these bars, it is observable that 'Upward trajectory' has the tallest bar.\nBased on the above thought, the answer is:\nUpward trajectory." } ], "id_raw": 158 }, { "id": "4dg6gjBZDN5X6VT7", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nCould you read the bar chart you've observed and convert the information into a table?" }, { "from": "gpt", "value": "To convert the information into a structured table, I first take a look at the x-axis where {the subculture labels}[0.11, 0.939, 0.132, 0.957];[0.263, 0.939, 0.293, 0.957];[0.423, 0.939, 0.448, 0.957];[0.574, 0.939, 0.611, 0.957];[0.725, 0.939, 0.775, 0.957];[0.890, 0.939, 0.925, 0.957] are located, including 'Punk', 'Gothic', 'Lolita', 'Hip-Hop', 'CyberPunk', and 'Hippies'. It's clear that these are my column headers. I then locate the y-axis where value labels are arranged, and by observing {the scale}[0.023, 0.765, 0.035, 0.782];[0.023, 0.603, 0.035, 0.621];[0.023, 0.442, 0.035, 0.459];[0.023, 0.280, 0.035, 0.298];[0.023, 0.119, 0.035, 0.136], I see that it is measured in thousands of people. Reading the individual bar positions and using the scale, I can determine the individual values represented by {the Punk bars}[0.081, 0.368, 0.121, 0.933];[0.121, 0.287, 0.160, 0.933] and {the Gothic bars}[0.239, 0.529, 0.278, 0.933];[0.278, 0.327, 0.317, 0.933] for the different subcultures. For instance, the first Punk bar is around the 3.5K line and the first Gothic bar is around the 4K line, so these would be their respective values in the 'Punk' category. Using the same method, I can deduce the values for each category and convert them into a structured table.\nBased on the above thought, the answer is:\nSubculture, Punk, Gothic\nPunk, 3500, 4000\nGothic, 2500, 3750\nLolita, 3000, 2800\nHip-Hop, 2100, 2000\nCyberPunk, 1900, 3650\nHippies, 4200, 3400" } ], "id_raw": 160 }, { "id": "xsolhK7EbsPgV1U0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, between Punk and Gothic, which fashion trend has more overall followers across all subcultures and why?" }, { "from": "gpt", "value": "To answer this question, I would add up the values represented by {the Punk bars}[0.081, 0.368, 0.121, 0.933];[0.239, 0.529, 0.278, 0.933];[0.396, 0.449, 0.435, 0.933];[0.553, 0.594, 0.593, 0.933];[0.711, 0.626, 0.750, 0.933];[0.868, 0.255, 0.907, 0.933] and {the Gothic bars}[0.121, 0.287, 0.160, 0.933];[0.278, 0.327, 0.317, 0.933];[0.435, 0.481, 0.475, 0.933];[0.593, 0.610, 0.632, 0.933];[0.750, 0.344, 0.789, 0.933];[0.907, 0.384, 0.947, 0.933] from all subcultures. Based on my observation, it's quite clear that the sum of the lengths of the bars representing the Gothic fashion trend is greater than the sum of the lengths of the bars representing the Punk fashion trend. Therefore, the Gothic fashion trend has more overall followers.\nBased on the above thought, the answer is:\nGothic, because the sum of the bar lengths representing the Gothic fashion trend is greater than that of the Punk trend, indicating it has a higher overall number of followers." } ], "id_raw": 160 }, { "id": "QWXUYU4Iv8O1IPpN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which subculture has the maximum number of people following the Punk fashion trend?" }, { "from": "gpt", "value": "To ascertain which subculture has the maximum followers for the Punk fashion trend, I should scan through the bar group representing Punk which is color-coded in purple. This includes {all bars marked as Punk}[0.081, 0.368, 0.121, 0.933];[0.239, 0.529, 0.278, 0.933];[0.396, 0.449, 0.435, 0.933];[0.553, 0.594, 0.593, 0.933];[0.711, 0.626, 0.750, 0.933];[0.868, 0.255, 0.907, 0.933]. Comparing the heights of these bars, it's noticeable that the last bar, representing the 'Hippies' subculture, is the tallest. It reaches a mark well above 4K indicating that \u2018Hippies' has the maximum number of people following the Punk trend.\nBased on the above thought, the answer is:\nHippies" } ], "id_raw": 160 }, { "id": "qB2y9CBNEc2x2sol", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nBased on your understanding of the bar chart, please extract the data and convert it into a CSV format table." }, { "from": "gpt", "value": "The chart's title {\"Cuisine & Effort/Popularity\"}[0.413, 0.941, 0.533, 1.0] gives us an indication that the x-axis represents different cuisines, while the two sets of bars {Effort level}[0.742, 0.046, 0.818, 0.069] and {Popular index}[0.885, 0.046, 0.979, 0.069] represent data of Effort level and Popular index respectively. Each component of the x-axis represents a cuisine such as {Italian}[0.112, 0.919, 0.155, 0.942], {Chinese}[0.205, 0.919, 0.260, 0.942], {Indian}[0.310, 0.919, 0.353, 0.942], {Mexican}[0.401, 0.919, 0.459, 0.942], {Vietnamese}[0.488, 0.919, 0.570, 0.942], and {Thailand}[0.598, 0.919, 0.658, 0.942]. For each cuisine, there are two column bars, the first signifies effort level and the second signifies popular index. By observing each bar's height relative to the y-axis, I can determine the respective values for the Effort level and Popular index.\nBased on the above thought, the answer is:\nCuisine,Effort level,Popular index\nItalian,1e-1,1e-2\nChinese,1e-2,1e-2\nIndian,1e-3,1e-2\nMexican,1e-2,1e-1\nVietnamese,1e-3,1e-1\nThailand,1e-1,1e-3" } ], "id_raw": 161 }, { "id": "JbYmDA1ooPHvTBPy", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nObserving the bar chart, which cuisine requires the least effort and is most popular?" }, { "from": "gpt", "value": "This question requires me to analyze the data from two perspectives - Effort level and Popular index of a cuisine. The height of the green bars signifies the effort level and the yellow bars signify the popular index. So, I am looking for the shortest green bar and tallest yellow bar for each cuisine. After observing the bars of {Italian}[0.099, 0.065, 0.133, 268.273], {Chinese}[0.198, 0.334, 0.232, 268.273], {Indian}[0.297, 0.602, 0.331, 268.273], {Mexican}[0.396, 0.334, 0.430, 268.273], {Vietnamese}[0.494, 0.602, 0.529, 268.273], and {Thailand}[0.593, 0.065, 0.628, 268.273], we can conclude that {Vietnamese}[0.494, 0.602, 0.529, 268.273] has the lowest effort level (green bar), and that {Mexican}[0.430, 0.065, 0.465, 268.273] cuisine has the highest popular index (yellow bar).\nBased on the above thought, the answer is:\nBased on the chart, Vietnamese cuisine requires the minimum amount of effort and Mexican cuisine is most popular." } ], "id_raw": 161 }, { "id": "RKm9CV7IDo6mB8sm", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which cuisine requires the highest effort level?" }, { "from": "gpt", "value": "To answer this question, I am focusing on the green bars representing effort level in the chart. I need to identify which bar has the highest height. After visually examining the bars from left to right for each cuisine - starting from {Italian}[0.099, 0.065, 0.133, 268.273], we can see that {Italian}[0.099, 0.065, 0.133, 268.273] and {Thailand}[0.593, 0.065, 0.628, 268.273] cuisines have similar maximum heights which indicate the highest effort levels.\nBased on the above thought, the answer is:\nItalian and Thailand." } ], "id_raw": 161 }, { "id": "OAADyq4jp3VsmHWu", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the chart into a CSV format table?" }, { "from": "gpt", "value": "Sure, based on the x-axis labels {\"Contemporary Art\"}[0.102, 0.919, 0.228, 0.942], {\"Classical Music\"}[0.258, 0.919, 0.363, 0.942], {\"Modern Dance\"}[0.406, 0.919, 0.506, 0.942], {\"Theatre\"}[0.575, 0.919, 0.628, 0.942], {\"Literature\"}[0.713, 0.919, 0.781, 0.942] and {\"Film\"}[0.878, 0.919, 0.907, 0.942], these correspond to different art forms. The Y-axis represents the relative scale of each bar in the chart. Given the legend {\"Relative Growth\"}[0.395, 0.029, 0.506, 0.053] and {\"Engagement Level\"}[0.572, 0.029, 0.702, 0.053], it's clear that the two different colored bars under each art form represent these two measures. Therefore, by defining the height of each bar, I can extract their corresponding values.\nBased on the above thought, the answer is:\nArt Forms,Relative Growth, Engagement Level\nContemporary Art,0.08, 0.22\nClassical Music,0.07, 0.13\nModern Dance,0.15, 0.09\nTheatre,0.05, 0.15\nLiterature,0.02, 0.10\nFilm,0.01, 0.05" } ], "id_raw": 162 }, { "id": "dTHOExQMu4Jq8GDw", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the chart, what can be inferred about the engagement levels of different art forms over time?" }, { "from": "gpt", "value": "To answer this question, I need to evaluate the bar heights of the \"Engagement Level\" series as indicated by the{\"Engagement Level\"}[0.572, 0.029, 0.702, 0.053]. Each bar in this series, for instance {\"Contemporary Art\"}[0.165, 0.128, 0.216, 555.438], represents the engagement level of a particular art form. Comparing these, it can be noticed that Contemporary Art has the highest level of engagement, followed by Theatre and Modern Dance, based on their respective heights. On the lower scale, Literature and Film seem to have significantly less engagement.\nBased on the above thought, the answer is:\nEngagement levels vary across different art forms. Contemporary Art and Theatre have higher engagement levels, while Literature and Film are less engaged." } ], "id_raw": 162 }, { "id": "uSoxtRUBiuKzFmKh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_12.png", "conversations": [ { "from": "human", "value": "\nWhich art form has the highest relative growth?" }, { "from": "gpt", "value": "To determine the art form with the highest relative growth, I need to compare bar heights within the {\"Relative Growth\"} series[0.395, 0.029, 0.506, 0.053]. For instance, the height of Contemporary Art is located at [0.114, 0.372, 0.165, 555.438]. Comparing each bar height in this series, I see that the highest is {\"Modern Dance\"}[0.405, 0.220, 0.456, 555.438].\nBased on the above thought, the answer is:\nModern Dance." } ], "id_raw": 162 }, { "id": "Sw1koRGBjJT6r1rz", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into a CSV table?" }, { "from": "gpt", "value": "From the labels at the bottom of the chart, I know the unique features are {IoT}[0.152, 0.919, 0.173, 0.942], {Cybersecurity}[0.347, 0.919, 0.443, 0.942], {AI}[0.621, 0.919, 0.634, 0.942] and {Blockchain}[0.823, 0.919, 0.898, 0.942]. The heights of the bars represent the measure of startups and R&D investments for each feature. Based on the {sublegend}[0.363, 0.020, 0.660, 0.062], I can tell that the {\"Startups\"}[0.407, 0.029, 0.466, 0.053] are represented by the blue bars, while the {\"R&D Investments\"}[0.533, 0.029, 0.654, 0.053] are shown by the orange bars. So, to generate a CSV file I will read the approximate value of each bar, for example the blue bar at {\"IoT\"}[0.081, 0.833, 0.162, 0.911] appears to start just below the 1 mark on the y-axis and the orange bar at {\"IoT\"}[0.162, 0.755, 0.244, 0.911] appears to start at the 2 mark on the y-axis. I will then match these values with their respective labels.\nBased on the above thought, the answer is:\nFeatures,Startups,R&D Investments\nIoT,1,2\nCybersecurity,3,7\nAI,6,10\nBlockchain,8,6" } ], "id_raw": 163 }, { "id": "JQXJ1g7TmjXw57DF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nBased on the data presented in the chart, how is R&D Investments distributed across different sectors of technological innovation?" }, { "from": "gpt", "value": "This question requires an analysis of the value of the orange bars \"\" in every feature. The orange bar at \"\"[0.162, 0.755, 0.244, 0.911] has a value of 2, indicating that IoT receives fewer investments in R&D. The orange bar in \"\"[0.395, 0.364, 0.476, 0.911] has a value of 7, showing a moderate investment level. The orange bar at \"\"[0.628, 0.129, 0.709, 0.911] has a value of 10, showing that AI receives the highest R&D Investments. The orange bar at \"\"[0.860, 0.442, 0.942, 0.911] has a value of 6, indicating less investment in this sector than in AI and Cybersecurity.\nBased on the above thought, the answer is:\nR&D Investments are the highest in AI, followed by Cybersecurity and Blockchain, and least in IoT. This suggests that the technological innovation ecosystem is heavily leaning towards AI due to its widespread applications with moderate interest in Cybersecurity and Blockchain and relatively lower investment appetite for IoT." } ], "id_raw": 163 }, { "id": "F3B44gDUz9EKWab1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_13.png", "conversations": [ { "from": "human", "value": "\nWhich feature has the highest combined score of startups and R&D Investments?" }, { "from": "gpt", "value": "To find out which feature has the highest combined score, I will add the values of the blue and orange bars for each feature. Let's start with IoT, {the combined score}[0.081, 0.833, 0.244, 0.911] is 3; for {Cybersecurity}[0.313, 0.677, 0.476, 0.911] the combined score is 10; for {AI}[0.546, 0.442, 0.709, 0.911] it has the highest combined score of 16; lastly for {Blockchain}[0.779, 0.286, 0.942, 0.911] the combined score is 14. Hence, AI has the highest combined score.\nBased on the above thought, the answer is:\nAI" } ], "id_raw": 163 }, { "id": "Ns1xNIEhja83xDEW", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nCan you transcribe the data in the bar chart into a CSV format?" }, { "from": "gpt", "value": "Sure. First, I can see from the chart's x-axis labels that there are six different scientific research fields represented: {AI Research}[0.115, 0.919, 0.183, 0.942], {Quantum Computing}[0.240, 0.919, 0.361, 0.942], {Autonomous Vehicles}[0.390, 0.919, 0.514, 0.942], {Genomic Research}[0.550, 0.919, 0.658, 0.942], {Nanotechnology}[0.709, 0.919, 0.803, 0.942], and {Space Exploration}[0.855, 0.919, 0.960, 0.942]. The y values involve both amounts of investments and research publications. By examining the individual bars for each field, like {the AI Research bars'[0.111, 0.169, 0.149, 301.558]; [0.149, 0.797, 0.187, 301.558] for investments and research publications respectively, we can generate the corresponding values for each category. This process continues for each category to create a structured table derived from the chart's information.\nBased on the above thought, the answer is:\nFields,Investments,Research Publications\nAI Research,1500000,12000\nQuantum Computing,2100000,15000\nAutonomous Vehicles,1800000,10000\nGenomic Research,1400000,16000\nNanotechnology,1900000,11000\nSpace Exploration,2000000,13000" } ], "id_raw": 164 }, { "id": "VF8F84sLTemCLPvT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nIn terms of research intensity, which field seems to be the most active based on the number of research publications?" }, { "from": "gpt", "value": "Research intensity can be reflected by the number of research publications. Thus, we can identify the most active field by locating which {red bar}Research Publications is the tallest among all fields. After visually checking, it's clear that the bar for {Genomic Research}[0.604, 0.760, 0.642, 301.558] is the tallest one in the red bars group, suggesting that Genomic Research has the highest number of research publications and thus is possibly the most research-active field among those given.\nBased on the above thought, the answer is:\nGenomic Research." } ], "id_raw": 164 }, { "id": "9qHeAf4yAGvATY3E", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_11_14.png", "conversations": [ { "from": "human", "value": "\nWhich scientific research field has the most investments?" }, { "from": "gpt", "value": "To identify the field with the most investments, I'm going to focus on the {blue bars}Investments that signify investment amounts. By examining the heights of these bars, such as the one for {Quantum Computing}[0.262, 0.125, 0.300, 301.558], it's evident that the highest bar corresponds to Quantum Computing. This suggests that Quantum Computing receives the largest amount of investment among the fields presented.\nBased on the above thought, the answer is:\nQuantum Computing" } ], "id_raw": 164 }, { "id": "oJqfaf1vDSZKskA1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nCan you detail the data on participant numbers for each method in the category of Food Drives, in a structured table format?" }, { "from": "gpt", "value": "To construct the table for Food Drives, I need to analyze the bars in the corresponding subchart. For the category of Food Drives, {the subtitle label}[0.216, 0.509, 0.339, 0.532] identifies the activity. There are three bars in this subplot, representative of the methods 'Random', 'Ours', and 'SOTA'. Examining {the 'Random' bar}[0.083, 0.828, 0.161, 0.952] with its top label showing {245.4}[0.097, 0.814, 0.147, 0.832], {the 'Ours' bar}[0.238, 0.856, 0.316, 0.952] with the top label {92.5}[0.258, 0.842, 0.296, 0.860], and {the 'SOTA' bar}[0.394, 0.913, 0.471, 0.952] with the top label {37.2}[0.413, 0.899, 0.452, 0.917], I can convert these details into a table format reflecting participant numbers in thousands for each method.\nBased on the above thought, the answer is:\nMethod,Participants (in thousands)\nRandom,119.5\nOurs,92.5\nSOTA,37.2" } ], "id_raw": 165 }, { "id": "9Sh3cFQBuQCUi4n1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nWhich method shows the most consistency in terms of participant numbers across all categories?" }, { "from": "gpt", "value": "Consistency can be assessed by examining the differences in participant numbers for each method across the four categories. I'll look at the bars for each method within the subplots and compare their values visually and numerically. Starting with 'Random', the {bars}[0.083, 0.207, 0.161, 0.461];[0.574, 0.125, 0.651, 0.461];[0.083, 0.828, 0.161, 0.952];[0.574, 0.884, 0.651, 0.952] show variations from 245.4 to 65.0. The 'Ours' method, with {bars}[0.238, 0.218, 0.316, 0.461];[0.729, 0.110, 0.807, 0.461];[0.238, 0.856, 0.316, 0.952];[0.729, 0.873, 0.807, 0.952], also displays a considerable range of values. Finally, analyzing 'SOTA', the {bars}[0.394, 0.227, 0.471, 0.461];[0.884, 0.098, 0.962, 0.461];[0.394, 0.913, 0.471, 0.952];[0.884, 0.848, 0.962, 0.952] show values that also vary greatly due to the outlier value in Food Drives. Upon inspection, 'Ours' seems to have the least variability overall, with no extreme outliers.\nBased on the above thought, the answer is:\n'Ours'. This method has participant numbers that vary the least across the four categories, with no extreme high or low outliers, indicating a more consistent level of participation." } ], "id_raw": 165 }, { "id": "qJTp3T0pKPNL4PMe", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the average number of participants in the 'Ours' method across all categories?" }, { "from": "gpt", "value": "To calculate the average, I must locate the bars representing the 'Ours' method in each category and analyze their values. {The Healthcare Campaigns 'Ours' bar}[0.238, 0.218, 0.316, 0.461] has a value of 234.7; {The Environment Protection 'Ours' bar}[0.729, 0.110, 0.807, 0.461] shows 338.9; {The Food Drives 'Ours' bar}[0.238, 0.856, 0.316, 0.952] is marked at 92.5; {The Educational Funds 'Ours' bar}[0.729, 0.873, 0.807, 0.952] indicates 75.6. Summing these values yields 741.7. There are four categories in total, so the average would be this sum divided by four.\nBased on the above thought, the answer is:\n185.425" } ], "id_raw": 165 }, { "id": "6LnImDswiQtz6YXp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nCould you convert the chart data into a structured table?" }, { "from": "gpt", "value": "By analyzing both bar charts, I see three stages of trends: T1, T2, and T3 represented on {the x-axis}[0.191, 0.964, 0.212, 0.981][0.519, 0.964, 0.541, 0.981][0.847, 0.964, 0.869, 0.981]. For each trend stage, there are two types of democracy levels shown for Erratic Decline and Steady Increase. For Erratic Decline, the bars are represented as {\"[0.119, 0.212, 0.284, 0.254]\"}[0.202, 0.213] with value 20.3 for T1, {\"[0.448, 0.221, 0.612, 0.254]\"}[0.511, 0.222] with value 15.9 for T2 and {\"[0.776, 0.236, 0.940, 0.254]\"}[0.844, 0.236] with value 8.8 for T3. While for Steady Increase, the bars are represented as {\"[0.119, 0.579, 0.284, 0.744]\"}[0.202, 0.580] with value 79.7 for T1, {\"[0.448, 0.570, 0.612, 0.744]\"}[0.511, 0.571] with value 84.1 for T2 and {\"[0.776, 0.556, 0.940, 0.744]\"}[0.839, 0.556] with value 91.2 for T3. Hence, I have structured the data as shown.\nBased on the above thought, the answer is:\nTrends,Erratic Decline,Steady Increase\nT1,20.3,79.7\nT2,15.9,84.1\nT3,8.8,91.2" } ], "id_raw": 166 }, { "id": "nxaECHlW3U9k5zO1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nBased on the data, can you make a general comment about democracy levels with respect to 'Erratic Decline' and 'Steady Increase'?" }, { "from": "gpt", "value": "The chart represents the democracy level under two circumstances: Erratic Decline and Steady Increase. Starting with the bars at {\"Erratic Decline\"}[0.119, 0.212, 0.284, 0.254] at T1 and {\"[0.776, 0.236, 0.940, 0.254]\"}[0.844, 0.236] at T3, we can observe a downward trend from 20.3 to 8.8 in the democracy level. This indicates that the democracy level suffers significantly with erratic declines, showcasing reduced stability and increased fragility in the system. On the other hand, the bars at {\"Steady Increase\"}[0.119, 0.579, 0.284, 0.744] at T1 and {\"[0.776, 0.556, 0.940, 0.744]\"}[0.839, 0.556] at T3, we see a continual improvement in democracy levels from 79.7 to 91.2. This suggests that a stable and steady increase leads to improvements and progress in the level of democracy, indicating resilience in the system.\nBased on the above thought, the answer is:\nDemocracy appears to be quite resilient to steady improvements and increases, with levels generally serving to improve under such circumstances. However, it tends to suffer quite dramatically with abrupt declines, showcasing a markedly noticeable drop in levels and thus indicating a possible fragility in the democratic setup to sudden and severe changes." } ], "id_raw": 166 }, { "id": "SdNc4KPdkLSyysKF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_12_1.png", "conversations": [ { "from": "human", "value": "\nAt which interval T1, T2, or T3, the Democracy Level difference between Erratic Decline and Steady Increase is the greatest?" }, { "from": "gpt", "value": "To find the interval with the largest difference of Democracy Level under two trends, I must compare the bar heights under each interval. At interval T1 the bars are {\"[0.119, 0.212, 0.284, 0.254]\"}[0.202, 0.213] and {\"[0.119, 0.579, 0.284, 0.744]\"}[0.202, 0.580] with the heights differentiates by their raw data 20.3 and 79.7 respectively. Similarly, for T2 and T3, the bars at {\"[0.448, 0.221, 0.612, 0.254]\"}[0.511, 0.222] and {\"[0.448, 0.570, 0.612, 0.744]\"}[0.511, 0.571] have values like 15.9 and 84.1, hence the difference; and at {\"[0.776, 0.236, 0.940, 0.254]\"}[0.844, 0.236] and {\"[0.776, 0.556, 0.940, 0.744]\"}[0.839, 0.556] with values of 8.8 and 91.2. The difference at both intervals are calculated and found that T3 has the biggest difference.\nBased on the above thought, the answer is:\nT3" } ], "id_raw": 166 }, { "id": "cdvfSuV7np4kZqwj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data for apple and orange prices for Year 2 and convert it into a CSV table based on the information in the subplots?" }, { "from": "gpt", "value": "Analyzing the bar heights and text annotations in the subplots for Year 2, I can see the {apple prices}[0.241, 0.117, 0.318, 0.254] with a text annotation {\u201c52.7\u201d}[0.260, 0.122, 0.298, 0.139] and the {orange prices}[0.241, 0.254, 0.318, 0.294] with a text annotation {\u201c15.6\u201d}[0.260, 0.268, 0.298, 0.285]. The corresponding data for the second subplot can be similarly extracted from the {apple prices}[0.731, 0.164, 0.808, 0.254] with a text annotation {\u201c34.8\u201d}[0.596, 0.283, 0.635, 0.301] and the {orange prices}[0.731, 0.254, 0.808, 0.330] with a text annotation {\u201c29.3\u201d}[0.750, 0.303, 0.789, 0.320], and so forth for subplots three and four. Collating this information into a CSV format table:\nBased on the above thought, the answer is:\nYear,Apple Prices,Orange Prices\nYear 2,52.7,15.6\nYear 2,34.8,29.3" } ], "id_raw": 168 }, { "id": "XUiKXzHCQ8Osvp9d", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nConsidering the data, how is the market trend for oranges different from that of apples?" }, { "from": "gpt", "value": "Observing the bar heights for the oranges and apples, I notice that {apple prices}[0.087, 0.098, 0.164, 0.254] are represented by a descending trend from the highest point like in {Year 1}[0.087, 0.098, 0.164, 0.254] to the lowest in {Year 3}[0.394, 0.718, 0.471, 0.744], indicating a decrease over the years. In contrast, the {orange prices}[0.087, 0.254, 0.164, 0.280] show a bar that is increasing in height from {Year 1}[0.087, 0.254, 0.164, 0.280] to {Year 3}[0.394, 0.744, 0.471, 0.863], which reflects an increasing trend over the same period.\nBased on the above thought, the answer is:\nThe market trend for oranges is characterized by an increasing price over the years, while the market trend for apples shows a decrease in price. This is depicted by the lengthening bars over time for oranges and shortening bars for apples, indicating opposite market behaviors for the two fruits." } ], "id_raw": 168 }, { "id": "bXRgUjO4xio89Sed", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease in the price of apples from Year 1 to Year 3?" }, { "from": "gpt", "value": "To find the percentage decrease, I must first determine the apple prices for Year 1 and Year 3. The {text for Year 1 apples}[0.106, 0.102, 0.144, 0.120] states \"60.3\" and the {text for Year 3 apples}[0.260, 0.707, 0.298, 0.724] shows \"10.4\". Using the formula for percentage decrease, [(initial - final) / initial] * 100, I calculate [(60.3 - 10.4) / 60.3] * 100.\nBased on the above thought, the answer is:\n82.75%" } ], "id_raw": 168 }, { "id": "7kzTjv4hoasgGpOv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nCan you structure the technology trend data you see into a CSV format focusing on the years 2021 to 2023?" }, { "from": "gpt", "value": "Observing the bar charts, I will organize the data as follows: Each subplot represents a different technology trend with {the axis}[0.053, 0.047, 0.491, 0.461] for the first subplot, indicating the technology \"Artificial Intelligence\". The bar charts display values for each year, indicated by the x-axis {tick labels}[\"2021\"][0.091, 0.473, 0.135, 0.491]; [\"2022\"][0.250, 0.473, 0.294, 0.491]; [\"2023\"][0.409, 0.473, 0.453, 0.491]. The y-axis {tick labels}[\"0\"][0.030, 0.454, 0.041, 0.472]; [\"45\"][0.019, 0.011, 0.041, 0.029] provide the range of values. The value annotations on {each bar}[0.073, 0.160, 0.152, 0.461]; [0.232, 0.115, 0.312, 0.461]; [0.391, 0.066, 0.471, 0.461] provide the actual figures to be tabulated. By analyzing each of these elements across all subplots, I can extract the required data for the table.\nBased on the above thought, the answer is:\nTechnology,2021,2022,2023\nArtificial Intelligence,30.6,35.2,40.1\nBlockchain,26.1,29.3,20.5\nCloud Computing,23.4,19.1,17.9\nMachine Learning,42.9,39.6,36.2\nInternet of Things,18.7,23.5,28.8" } ], "id_raw": 169 }, { "id": "rgUBpeUVpwX5Vj2s", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nWhich technology demonstrates the most consistent growth over the three years?" }, { "from": "gpt", "value": "To determine which technology shows the most consistent growth, I examine the trend indicated by the height of the bars in each technology's subplot. I focus on {Artificial Intelligence}[0.053, 0.047, 0.491, 0.461], {Blockchain}[0.544, 0.047, 0.981, 0.461], {Cloud Computing}[0.053, 0.537, 0.491, 0.952], {Machine Learning}[0.544, 0.537, 0.981, 0.952], and {Internet of Things}[not provided]. For instance, the bar heights in the {AI subplot}[0.073, 0.160, 0.152, 0.461]; [0.232, 0.115, 0.312, 0.461]; [0.391, 0.066, 0.471, 0.461] clearly show an upward trend. Comparing this consistent increase across all technologies, it's evident that both AI and IoT demonstrate a growth pattern, while others show a decline or inconsistent trends.\nBased on the above thought, the answer is:\nArtificial Intelligence and Internet of Things both demonstrate consistent growth over the three years." } ], "id_raw": 169 }, { "id": "xrVJpzHv6DrVzGyX", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage decrease in Blockchain from 2022 to 2023?" }, { "from": "gpt", "value": "To calculate the percentage decrease for Blockchain from 2022 to 2023, I first locate {the Blockchain subplot}[0.544, 0.047, 0.981, 0.461] and then identify {the bars}[0.723, 0.066, 0.802, 0.461] and [0.882, 0.185, 0.961, 0.461] corresponding to the years 2022 and 2023, respectively. The annotations above {the bars}[0.743, 0.093, 0.782, 0.111] and [0.902, 0.212, 0.941, 0.229] reveal the values of 29.3 for 2022 and 20.5 for 2023. The percentage decrease is calculated by subtracting the 2023 value from the 2022 value, dividing by the 2022 value, and then multiplying by 100.\nBased on the above thought, the answer is:\n30%" } ], "id_raw": 169 }, { "id": "XCKpOlZPx71rIzL3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the investment data for the category of Architecture into a structured CSV table based on your observation of the chart?" }, { "from": "gpt", "value": "Observing the Architecture subchart, I see bars representing investment values for different strategies. The investment for \"Random\" is denoted by {a blue bar}[0.799, 0.131, 0.828, 0.352] with its value indicated at the top as \"70.1\". For \"Ours\", {an orange bar}[0.873, 0.133, 0.902, 0.352] shows a value of \"69.7\". Lastly, \"SOTA\" is represented by {a green bar}[0.947, 0.141, 0.976, 0.352], with the value \"67.1\" at the top of the bar. These values allow me to construct the CSV table.\nBased on the above thought, the answer is:\nStrategy,Investment\nRandom,70.1\nOurs,69.7\nSOTA,67.1" } ], "id_raw": 170 }, { "id": "CpZ5iVrlxULnC5qk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nWhich strategy shows the highest total investment and consistency across all categories in the chart?" }, { "from": "gpt", "value": "In order to determine the strategy with the highest total investment and consistency, I must analyse each category within the chart. Comparing the height of {the blue bars}[different coordinates for each category] which represent \"Random\" with {the orange bars}[coordinates vary per category] for \"Ours\" and {the green bars}[coordinates vary per category] for \"SOTA\", I can see that the blue bars consistently reach higher up in the chart across most categories. The values atop the blue bars, when totalled, would also likely be the highest, indicating that the \"Random\" strategy has both the highest total investment and shows consistency across the categories provided.\nBased on the above thought, the answer is:\nThe \"Random\" strategy shows the highest total investment and consistency across all categories in the chart." } ], "id_raw": 170 }, { "id": "rx6FxXIRZ2LiiMQb", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total research investment for the category of Film across all strategies?" }, { "from": "gpt", "value": "To calculate the total investment for Film, I need to sum the values of each strategy's bar within that category. For \"Random\", {the blue bar}[0.060, 0.572, 0.090, 0.680] indicates an investment of \"34.4\". The \"Ours\" strategy is represented by {an orange bar}[0.134, 0.562, 0.163, 0.680] with an investment of \"37.6\". Lastly, \"SOTA\" has {a green bar}[0.208, 0.574, 0.237, 0.680] representing an investment of \"33.8\". The total investment is the sum of these three values.\nBased on the above thought, the answer is:\n105.8" } ], "id_raw": 170 }, { "id": "HPgCWR9xLL88Z94b", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data from the bar chart that presents the relationship between 'Funds received by LGCs' and 'Capacity Development Attendance' into a CSV format?" }, { "from": "gpt", "value": "Observing the bar chart, I note four subplots corresponding to different ratios of 'Funds received by LGCs' to 'Capacity Development Attendance'. Each subplot contains bars, with {blue bars}[0.087, 0.183, 0.164, 0.254] representing 'Funds received' and {red bars}[0.087, 0.254, 0.164, 0.372] for 'Attendance', across {three years}[{\"2018\"}[0.103, 0.473, 0.147, 0.491]; {\"2019\"}[0.257, 0.473, 0.301, 0.491]; {\"2020\"}[0.411, 0.473, 0.455, 0.491]]. The numerical values atop each bar, such as {27.3}[0.106, 0.188, 0.144, 0.205] for the first blue bar in 2018, indicate the percentage of total metrics. To structure this into CSV, I match the corresponding value and color annotation to the year and category.\nBased on the above thought, the answer is:\nYear,Funds Received by LGCs (%),Capacity Development Attendance (%)\n2018,27.3,45.7\n2019,34.3,35.9\n2020,41.6,25.1" } ], "id_raw": 171 }, { "id": "V4H7yCddHdjsP6Q7", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends in 'Funds received by LGCs' over the 3-year period across all ratios, how would you describe the general funding pattern?" }, { "from": "gpt", "value": "To gauge the general trend in 'Funds received by LGCs', I'll look at the {blue bars}[0.087, 0.183, 0.164, 0.254] for each year across all subplots. In the first subplot for 2018, the value is {27.3}[0.106, 0.188, 0.144, 0.205], for 2019, it's {34.3}[0.260, 0.169, 0.298, 0.187], and for 2020, {41.6}[0.414, 0.150, 0.452, 0.168]. Repeating this examination for all subplots reveals a rise and fall pattern: initial values are lower, showing an increase in the second year, followed by a decrease or a plateau in the third year, depending on the ratio considered. This pattern suggests an instability in funding over the examined period, influenced perhaps by periodic factors that might include policy changes or budget reallocations.\nBased on the above thought, the answer is:\nThe funding pattern for 'Funds received by LGCs' over the three years is characterized by initial lower values, an increase in the second year, and a fall or plateau in the third year. This indicates a roller-coaster pattern signifying instability in the funding received by LGCs over the given period." } ], "id_raw": 171 }, { "id": "x9COjZe1RVTZToBX", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_12_6.png", "conversations": [ { "from": "human", "value": "\nAcross all subplots, which year had the highest average 'Funds received by LGCs' percentage?" }, { "from": "gpt", "value": "To calculate the highest average 'Funds received by LGCs' percentage, I'll consider the blue bars across all subplots. In the first subplot, the 'Funds received' values for 2018, 2019, and 2020 are {27.3}[0.106, 0.188, 0.144, 0.205], {34.3}[0.260, 0.169, 0.298, 0.187], and {41.6}[0.414, 0.150, 0.452, 0.168] respectively. In the second subplot, the values are {25.5}[0.597, 0.192, 0.635, 0.210], {35.3}[0.751, 0.167, 0.789, 0.184], and {35.3}[0.904, 0.319, 0.943, 0.336]. For the third subplot, they are {36.4}[0.106, 0.655, 0.144, 0.672], {31.2}[0.260, 0.668, 0.298, 0.686], and {29.5}[0.414, 0.783, 0.452, 0.801]. Lastly, the fourth subplot shows {32.8}[0.597, 0.664, 0.635, 0.681], {38.5}[0.751, 0.655, 0.789, 0.673], and {36.2}[0.904, 0.794, 0.943, 0.811]. Calculating each year's average across subplots gives us the result after a simple arithmetic mean calculation for the three years.\nBased on the above thought, the answer is:\n2020" } ], "id_raw": 171 }, { "id": "6T3bOqVYz8kNL9ct", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the efficiency comparison of the four categories between Trend_1 and Trend_2 into a structured CSV table, as seen on the chart?" }, { "from": "gpt", "value": "Observing the structure of the bar chart with four subplots for each category, I will extract their values by pinpointing {the text annotations above the bars}[0.113, 0.199, 0.135, 0.216]; [0.327, 0.116, 0.349, 0.133]; [0.220, 0.157, 0.242, 0.175]; [0.220, 0.292, 0.242, 0.309] for Trend_1 and [0.113, 0.344, 0.135, 0.361]; [0.327, 0.261, 0.349, 0.278]; [0.434, 0.074, 0.456, 0.092]; [0.437, 0.219, 0.454, 0.237] for Trend_2. The respective trend values are labeled next to each bar on the chart across the categories respectively. Based on the data, each subplot's title [0.206, 0.019, 0.363, 0.041]; [0.672, 0.019, 0.878, 0.041]; [0.211, 0.509, 0.358, 0.532]; [0.740, 0.509, 0.810, 0.532] denotes the category for which the data is presented. Integrating these elements, I am able to accurately compile the CSV table.\nBased on the above thought, the answer is:\nCategory,Trend_1,Trend_2\nHighly_Efficient,25,55\nModerate_Efficiency,45,30\nLow_Efficiency,65,15\nOutlier,85,-5" } ], "id_raw": 172 }, { "id": "Y1ZHfNoHV8o8sHk1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nObserving the trends on the bar chart, what can be inferred about the production efficiency in the category labeled as an 'Outlier'?" }, { "from": "gpt", "value": "To answer this, I will look at the {Outlier category}[0.740, 0.509, 0.810, 0.532] and the corresponding bars for {Trend_1}[0.909, 0.568, 0.963, 0.744] and {Trend_2}[0.695, 0.744, 0.748, 0.807]. {Trend_1}[0.909, 0.568, 0.963, 0.744] has a value annotation of \"85\" suggesting a high level of efficiency, while {Trend_2}[0.695, 0.744, 0.748, 0.807] has a negative value annotation of \"-5\", indicating a loss or significant inefficiency. These observations indicate that while Trend_1 maintains very high efficiency even in the Outlier category, Trend_2 experiences a detrimental drop, signifying potential problems or deviations from efficient production.\nBased on the above thought, the answer is:\nIn the 'Outlier' category, Trend_1 maintains high efficiency, while Trend_2 indicates losses or significant inefficiencies." } ], "id_raw": 172 }, { "id": "3RhqTLDCRosfwjxW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_12_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall numerical difference in production values when comparing Trend_1 and Trend_2 across all categories?" }, { "from": "gpt", "value": "I will sum the differences by locating the value annotations for Trend_1 at [0.113, 0.199, 0.135, 0.216]; [0.327, 0.116, 0.349, 0.133]; [0.220, 0.157, 0.242, 0.175]; [0.220, 0.292, 0.242, 0.309] and for Trend_2 at [0.113, 0.344, 0.135, 0.361]; [0.327, 0.261, 0.349, 0.278]; [0.434, 0.074, 0.456, 0.092]; [0.437, 0.219, 0.454, 0.237]. I will calculate the difference between corresponding values of Trend_1 and Trend_2 for all the categories and then sum these differences to find the overall numerical value.\nBased on the above thought, the answer is:\n100" } ], "id_raw": 172 }, { "id": "60SJqjx1P4ju9XBp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nCan you create a table summarizing the SOTA method returns for each investment strategy?" }, { "from": "gpt", "value": "I'm assessing the bar charts, focusing on the bars that correspond to the SOTA method in each subplot. I see that there are seven subplots each representing a different investment strategy {High Risk}[0.051, 0.034, 0.487, 0.225], {Balanced}[0.538, 0.034, 0.974, 0.225], {Low Risk}[0.051, 0.281, 0.487, 0.472], {Stable Growth}[0.538, 0.281, 0.974, 0.472], {Secure Bonds}[0.051, 0.527, 0.487, 0.718], {Diversified Portfolio Tactics}[0.538, 0.527, 0.974, 0.718], and {Ultimate Safe}[0.051, 0.774, 0.487, 0.965]. By extracting the value annotations on each bar for SOTA within these subplots, such as {the SOTA bar in the High Risk subplot}[0.388, 0.062, 0.467, 0.225] with a value of 85.1 annotated right above it [0.412, 0.062, 0.443, 0.075], I can formulate the table.\nBased on the above thought, the answer is:\nStrategy,SOTA Returns\nHigh Risk,85.1\nBalanced,57.4\nLow Risk,40.6\nStable Growth,30.2\nSecure Bonds,16.4\nDiversified Portfolio Tactics,33.9\nUltimate Safe,11.6" } ], "id_raw": 179 }, { "id": "YstZWuNS8jxqVQe2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nWhich investment strategy is the most consistent across all methods?" }, { "from": "gpt", "value": "To answer this question, I must examine the variability in returns for each strategy across the three different methods\u2014Random, Ours, and SOTA. I'm looking for the strategy with the least variation in bar height which indicates consistency. Looking at the bar heights within each strategy subplot {Secure Bonds}[0.051, 0.527, 0.487, 0.718] for example, the height of the bars for Random [0.071, 0.679, 0.150, 0.718], Ours [0.229, 0.682, 0.309, 0.718], and SOTA [0.388, 0.687, 0.467, 0.718], I observe that the Secure Bonds returns show minimal variance between methods. Though {the 'Ours' method}[0.229, 0.682, 0.309, 0.718] is slightly lower, the overall range is narrower compared with other strategies. Therefore, based on the bar chart's visual comparison, Secure Bonds exhibit the most consistency across methods.\nBased on the above thought, the answer is:\nSecure Bonds. The bar heights for the Secure Bonds strategy in the Random, Ours, and SOTA methods show minimal variance, indicating that it's the most consistent across all methods compared to the other investment strategies." } ], "id_raw": 179 }, { "id": "QLqtdE9oFAmMaMPW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the average return for the 'Ours' method across all investment strategies?" }, { "from": "gpt", "value": "To calculate the average return for the 'Ours' method, I'm locating the bars representing this method in each strategy subplot. Once located, {the values on top}[0.254, 0.055, 0.285, 0.068], [0.741, 0.110, 0.771, 0.122], [0.254, 0.388, 0.284, 0.400], [0.741, 0.406, 0.771, 0.419], [0.254, 0.682, 0.284, 0.695], [0.741, 0.649, 0.771, 0.661], [0.254, 0.938, 0.284, 0.951] for these bars are added together and divided by the number of strategies, which is seven. Adding up the returns 88.6, 60.2, 43.7, 34.1, 18.7, 36.2, and 13.7 gives a total, which is then divided by seven to get the average.\nBased on the above thought, the answer is:\n42.0" } ], "id_raw": 179 }, { "id": "81xEteOx9T6pfhhB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you're seeing, could you convert the observed dataset into a structured table format?" }, { "from": "gpt", "value": "From the bar chart, the x-axis represents different companies as labeled by the x-axis ticks, from {ABC Corporation}[0.092, 0.952, 0.208, 0.975] to {YZA Limited}[0.850, 0.952, 0.934, 0.975]. The y-axis denotes the percentage increase in value, ranging from {0}[0.043, 0.934, 0.052, 0.958] to {30}[0.035, 0.053, 0.052, 0.076]. There are two bar groups for each company, with their meanings denoted in {the legend}[0.064, 0.074, 0.254, 0.185] - {green bars}[0.108, 0.118, 0.248, 0.141] represent 'Fund A Performance', and {orange bars}[0.108, 0.153, 0.249, 0.176] represent 'Fund B Performance'. The performance values can be inferred from the height and the annotated text on top of the bars. For instance, the green bar for 'ABC Corporation'[0.117, 0.650, 0.150, 0.944] with the text annotation '10'[0.118, 0.620, 0.149, 0.643] indicates that the value for 'Fund A Performance' in 'ABC Corporation' is 10, which can be arranged to form the desired structured table.\nBased on the above thought, the answer is:\n```csv\nCompany, Fund A Performance (%),Fund B Performance (%)\nABC Corporation,10,8\nDEF Enterprises,12,15\nGHI Industries,14,10\nJKL Limited,18,14\nMNO Corporation,20,12\nPQR Company,15,14\nSTU Enterprises,22,20\nVWX Industries,24,18\nYZA Limited,26,25\n```" } ], "id_raw": 180 }, { "id": "t5AY1zrAX3RKtJiX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nBased on the data, which fund, A or B, has a more consistent performance across the companies?" }, { "from": "gpt", "value": "For this question, consistent performance would mean that the fund's percentage increase in value does not fluctuate significantly across different companies. Comparing the bars of {Fund A}<0.108, 0.118, 0.248, 0.141> and {Fund B}<0.108, 0.153, 0.249, 0.176> from one company to the next, we can see that the changes in the heights of the Fund A green bars appear relatively steady, such as the bars for {ABC Corporation and DEF Enterprises}<0.117, 0.650, 0.150, 0.944> and <0.210, 0.591, 0.243, 0.944>, both showing a 2% increase. However, there's more variation in the heights of the Fund B orange bars, as seen in {DEF Enterprises and GHI Industries}<0.243, 0.503, 0.275, 0.944> and <0.335, 0.650, 0.368, 0.944>, where there's a 5% decrease.\nBased on the above thought, the answer is:\nFund A seems to have a more consistent performance because it shows less fluctuation in the percentage increase in value across different companies." } ], "id_raw": 180 }, { "id": "KIhlGHU6LtE56qAV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_0.png", "conversations": [ { "from": "human", "value": "\nIn which company, Fund B has the highest increase in value?" }, { "from": "gpt", "value": "To identify the company where Fund B has the highest increase in value, I need to compare the heights of the {orange bars}<0.108, 0.153, 0.249, 0.176>. For instance, {the orange bar for ABC Corporation}<0.150, 0.709, 0.182, 0.944> represents an 8% increase in value as confirmed by {the annotation} <0.155, 0.678, 0.177, 0.702>. Following a similar approach for each company, I eventually find that {the orange bar for YZA Limited}<0.892, 0.209, 0.925, 0.944> represents a 25% increase, which is the highest among all companies.\nBased on the above thought, the answer is:\nYZA Limited" } ], "id_raw": 180 }, { "id": "lBCjVcRbx5OOYwOD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you're observing, could you transcribe the respective 'Development Score' for 'Hardware Developments' and 'Software Developments' in each time frame into a CSV table?" }, { "from": "gpt", "value": "From the chart title {Future Technological Development Prospects}[0.339, 0.025, 0.712, 0.055] and the x-axis lgends[{'10 years ago'}[0.250, 0.952, 0.341, 0.975]; {'Now'}[0.510, 0.952, 0.541, 0.975]; {'Future Predictions'}[0.693, 0.952, 0.818, 0.975]], it indicates the X-axis corresponds to three different time periods. The y-axis range varies between 0[0.052, 0.934, 0.061, 0.958] and 120[0.035, 0.053, 0.061, 0.076], representing Development Scores. Using these score axis, I can establish the bars' numerical values. There are two sets of bars corresponding to Hardware and Software Developments for each timestamp as per the legend[0.861, 0.074, 0.978, 0.185]. By examining each bar, I can obtain numerical value from the values annotated at the top of the bar. For Hardware, the bars correspond to 45.7[0.215, 0.608, 0.296, 0.944], 70.5[0.445, 0.426, 0.525, 0.944], and 90.7[0.675, 0.277, 0.755, 0.944] respectively, for each time period, and for Software it's 37.4[0.296, 0.669, 0.376, 0.944], 65.3[0.525, 0.464, 0.606, 0.944], and 85.3[0.755, 0.317, 0.836, 0.944] respectively.\nBased on the above thought, the answer is:\nTime,Hardware Developments,Software Developments\n10 years ago,45.7,37.4\nNow,70.5,65.3\nFuture Predictions,90.7,85.3" } ], "id_raw": 181 }, { "id": "hg4qkGREuwlJYBKa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis of the chart, in comparison to the past, which type of technology development has shown more improvement and why do you believe so?" }, { "from": "gpt", "value": "The question requires a comparison between the improvements in 'Hardware Developments' and 'Software Developments' over time. By comparing the bars at two timepoints '10 years ago' and 'Now', we can see for 'Hardware Developments', the scores increased from 45.7[0.215, 0.608, 0.296, 0.944] to 70.5[0.445, 0.426, 0.525, 0.944], an increase of approximately 24.8 points. In contrast, 'Software Developments' saw a rise from 37.4[0.296, 0.669, 0.376, 0.944] to 65.3[0.525, 0.464, 0.606, 0.944], an increase of approximately 27.9 points. Thus, even though both technologies have made substantial developments, 'Software Developments' witnessed a slightly higher improvement.\nBased on the above thought, the answer is:\nSoftware Developments. This is because they had a higher increase in their Development Score when compared to Hardware Developments in the period considered." } ], "id_raw": 181 }, { "id": "69EnGRnK6WetYuHo", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the average Development Score between 'Hardware Developments' and 'Software Developments'?" }, { "from": "gpt", "value": "To calculate this, I need to take the average of the 'Development Scores' for both 'Hardware Developments' and 'Software Developments'. The three scores for Hardware Developments are 45.7[0.215, 0.608, 0.296, 0.944], 70.5[0.445, 0.426, 0.525, 0.944], 90.7[0.675, 0.277, 0.755, 0.944]. The average for these can be calculated as (45.7 + 70.5 + 90.7) / 3 = 68.97 approximately. The three scores for 'Software Developments' are 37.4[0.296, 0.669, 0.376, 0.944], 65.3[0.525, 0.464, 0.606, 0.944], 85.3[0.755, 0.317, 0.836, 0.944]. The average for these can be calculated as (37.4 + 65.3 + 85.3) / 3 = 62.67 approximately. The difference between the averages of these two is thus 68.97 - 62.67 = 6.3.\nBased on the above thought, the answer is:\n6.3" } ], "id_raw": 181 }, { "id": "zMVijp23RUfANKGd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, please create a structured table reflecting the scores of both Risk Assessment and Risk Management for the different scenarios." }, { "from": "gpt", "value": "The bar chart corresponds to 9 risk scenarios from {Scenario A}[0.121, 0.952, 0.195, 0.975] to {Scenario I}[0.859, 0.952, 0.927, 0.975]. Each scenario has 2 bars, one corresponds to {Risk Assessment}[0.848, 0.118, 0.964, 0.141] and the other corresponds to {Risk Management}[0.848, 0.153, 0.973, 0.176]. The height of the bars represents the Scores as labelled on the y-axis which goes from {0}[0.052, 0.934, 0.061, 0.958] to {120}[0.035, 0.053, 0.061, 0.076]. The scores associated with each bar can be determined by its height and also the label above each. For instance, in {Scenario A}[0.121, 0.952, 0.195, 0.975], the scores for Risk Assessment and Risk Management are approximately at 74.1 and 48.7 respectively. Considering these, I can create the table for all scenarios and both categories.\nBased on the above thought, the answer is:\nScenario, Risk Assessment, Risk Management\nScenario A, 74.1, 48.7\nScenario B, 85.6, 78.5\nScenario C, 39.5, 52.1\nScenario D, 95.3, 90.2\nScenario E, 45.2, 62.3\nScenario F, 57.6, 49.1\nScenario G, 41.1, 33.4\nScenario H, 87.9, 78.9\nScenario I, 62.7, 65.5" } ], "id_raw": 182 }, { "id": "ileBJn8R0OI85sSF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, can you identify and explain if there are any scenarios where Risk Management scores are higher than Risk Assessment scores?" }, { "from": "gpt", "value": "To find scenarios where Risk Management scores are higher than Risk Assessment scores, I need to analyze each scenario from {Scenario A}[0.121, 0.952, 0.195, 0.975] to {Scenario I}[0.859, 0.952, 0.927, 0.975] and compare the two scorebars for each. From left to right, it can be seen that in {Scenario C}[0.304, 0.952, 0.379, 0.975], {Scenario E}[0.489, 0.952, 0.562, 0.975], and {Scenario I}[0.859, 0.952, 0.927, 0.975], the Risk Management bars are higher than the Risk Assessment bars. This means that in these scenarios, the Risk Management scores are higher than the Risk Assessment scores, indicating a proactive management strategy in these situations.\nBased on the above thought, the answer is:\nYes, Scenarios C, E, and I have higher Risk Management scores than Risk Assessment scores. This suggests that in these scenarios, the risks have been better managed compared to their initial assessments." } ], "id_raw": 182 }, { "id": "1eSz9HRyiKlbqTk4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_2.png", "conversations": [ { "from": "human", "value": "\nWhich scenario has the highest Risk Management score?" }, { "from": "gpt", "value": "To find the scenario with the highest Risk Management score, I need to look at the height of {Risk Management bars}[0.848, 0.153, 0.973, 0.176] for each scenario from {Scenario A}[0.121, 0.952, 0.195, 0.975] to {Scenario I}[0.859, 0.952, 0.927, 0.975]. It can be observed that {the bar for scenario D in Risk Management}[0.433, 0.281, 0.466, 0.944] is the highest among all, reaching very close to a score of 100 on the y-axis, and thus this scenario should have the highest score in Risk Management as per the chart.\nBased on the above thought, the answer is:\nScenario D" } ], "id_raw": 182 }, { "id": "gMeuHxVGUrAQLV9R", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nAccording to your analysis of this chart, can you provide a structured table displaying GDP growth and inflation rates for each month of the year?" }, { "from": "gpt", "value": "I can see that the chart's x-axis represents the months of the year by looking at the {x-axis labels}[{\"January\"}[0.107, 0.868, 0.155, 0.947]; {\"February\"}[0.175, 0.868, 0.229, 0.957]; {\"March\"}[0.253, 0.868, 0.293, 0.935]; {\"April\"}[0.328, 0.868, 0.361, 0.922]; {\"May\"}[0.400, 0.868, 0.431, 0.919]; {\"June\"}[0.471, 0.868, 0.502, 0.920]; {\"July\"}[0.544, 0.868, 0.572, 0.914]; {\"August\"}[0.607, 0.868, 0.651, 0.942]; {\"September\"}[0.668, 0.868, 0.732, 0.975]; {\"October\"}[0.747, 0.868, 0.796, 0.950]; {\"November\"}[0.812, 0.868, 0.873, 0.970]; {\"December\"}[0.883, 0.868, 0.944, 0.969]]. The y-axis is labeled as \"{Percentages (%)}[0.015, 0.366, 0.029, 0.556]\", and the data is represented by two colored bars for each month. The green bars represent \"{GDP Growth Rate (%)\"}[0.111, 0.781, 0.261, 0.804], and the brown bars represent \"{Inflation Rate (%)}\"[0.111, 0.816, 0.233, 0.839]. The value of each indicator for a month is marked at the top of its corresponding bar. Therefore, I can create a table in CSV format to represent this data.\nBased on the above thought, the answer is:\nMonth,GDP Growth Rate(%),Inflation Rate(%)\nJanuary,3.7,1.6\nFebruary,2.9,1.8\nMarch,1.5,1.9\nApril,0.3,2.1\nMay,-1.6,2.3\nJune,-3.2,2.5\nJuly,-2.8,2.7\nAugust,-1.5,2.9\nSeptember,0.5,3.2\nOctober,2.1,3.5\nNovember,2.8,3.9\nDecember,3.4,4.1" } ], "id_raw": 183 }, { "id": "MIJpWXhGPf8bWjLA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nJudging from the chart, what economic trends can you observe during the evaluated year?" }, { "from": "gpt", "value": "By analyzing the green bars which represent the {GDP Growth Rate}[0.111, 0.781, 0.261, 0.804] and the brown bars which represent the {Inflation Rate}[0.111, 0.816, 0.233, 0.839], I find different trends. For instance, the GDP growth rate, represented by the green bars, saw a decline starting from {January}[0.107, 0.868, 0.155, 0.947] reaching its lowest point in {June}[0.471, 0.868, 0.502, 0.920], and then gradually recovering, whereas the inflation rate - represented by the brown bars - displays a steady increasing trend throughout the year. The main economic trend visible on this chart, therefore, is that while GDP growth slumped and then rebounded during the year, inflation steadily increased, suggesting a challenging economic environment especially in middle of the year when GDP growth was negative and inflation was on the rise.\nBased on the above thought, the answer is:\nThe year appears to have seen a sharp decline in GDP growth during the first half, reaching negative figures by June, followed by a gradual recovery in the latter part of the year. However, inflation has seen a steady growth throughout the year, indicating increasing costs of goods and services." } ], "id_raw": 183 }, { "id": "IaXrWBsxxMRGJt0v", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which month experienced the biggest difference between GDP growth rate and inflation rate?" }, { "from": "gpt", "value": "To answer this question, I should calculate the absolute difference between the GDP growth rate and the inflation rate for each month, which can be obtained from the top of each pair of bars in the chart. For example, in {January}[0.107, 0.868, 0.155, 0.947], the {GDP Growth Rate}[0.106, 0.166, 0.131, 0.461] is 3.7 and the {Inflation Rate}[0.131, 0.333, 0.156, 0.461] is 1.6, resulting in a difference of 2.1. After comparing all the months, I can see that the largest difference appears in {June}[0.471, 0.868, 0.502, 0.920], where the {GDP Growth Rate}[0.462, 0.461, 0.487, 0.716] is -3.2 and the {Inflation Rate}[0.487, 0.262, 0.512, 0.461] is 2.5, resulting in a difference of 5.7.\nBased on the above thought, the answer is:\nJune" } ], "id_raw": 183 }, { "id": "u2b4UlCV8feYfnMv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, can you create a structured table representing the international trade trends?" }, { "from": "gpt", "value": "Yes, based on the chart, I can create a structured table by analyzing information from various parts. The countries are represented on the x-axis specified by {the x-axis tick labels}<{\"USA\"}[0.151, 0.883, 0.181, 0.933]; {\"Germany\"}[0.229, 0.883, 0.284, 0.975]; {\"China\"}[0.329, 0.883, 0.367, 0.947]; {\"Canada\"}[0.415, 0.883, 0.463, 0.962]; {\"Australia\"}[0.503, 0.883, 0.557, 0.972]; {\"Japan\"}[0.602, 0.883, 0.640, 0.946]; {\"UK\"}[0.700, 0.883, 0.724, 0.922]; {\"India\"}[0.786, 0.883, 0.820, 0.940]; {\"Mexico\"}[0.872, 0.883, 0.916, 0.957]>. The y-axis represents the trade value in billions. By observing the bar heights, I can determine the corresponding values for export and import for each country - for example, for \"USA\", the Export value can be found from the green bar coordinates <[0.134, 0.367, 0.166, 0.875]> and Import value from the orange bar coordinates <[0.166, 0.333, 0.197, 0.875]>. It is also confirmed by the specific annotation {\"750\"}[0.137, 0.337, 0.163, 0.360] and {\"800\"}[0.168, 0.303, 0.195, 0.326].\nBased on the above thought, the answer is:\nCountry,Export,Import\nUSA,750,800\nGermany,900,790\nChina,1100,920\nCanada,680,710\nAustralia,620,640\nJapan,800,850\nUK,780,700\nIndia,850,810\nMexico,680,660" } ], "id_raw": 184 }, { "id": "JeLipiKipkbR167u", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trade balances, could you mention which countries show healthy trade scenario?" }, { "from": "gpt", "value": "The trade balance is indicated by the difference in the height of the Export and Import bars for each country. A closer height suggests a trade equilibrium, which is considered healthy. From the charts, we can see that Canada's Export {bar}[0.407, 0.414, 0.439, 0.875] and Import {bar}[0.439, 0.394, 0.471, 0.875] are quite close indicating a nearly equal export and import values which are considered healthy. Similarly, for Australia's Export {bar}[0.498, 0.455, 0.530, 0.875] and Import {bar}[0.530, 0.442, 0.562, 0.875] and Mexico's Export {bar}[0.862, 0.414, 0.894, 0.875] and Import {bar}[0.894, 0.428, 0.926, 0.875] also indicate equal export and import values.\nBased on the above thought, the answer is:\nThe countries showing a healthy trade scenario are Canada, Australia, and Mexico as their exports and imports figures show near equilibrium, indicating a balanced trade scenario." } ], "id_raw": 184 }, { "id": "uFlm9QopyTmHt2B6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_4.png", "conversations": [ { "from": "human", "value": "\nWhich country has the highest export value?" }, { "from": "gpt", "value": "The export value can be determined by the green bars represented by {the green bars coordinates}<[0.134, 0.367, 0.166, 0.875]; [0.225, 0.265, 0.257, 0.875]; [0.316, 0.130, 0.348, 0.875]; [0.407, 0.414, 0.439, 0.875]; [0.498, 0.455, 0.530, 0.875]; [0.589, 0.333, 0.621, 0.875]; [0.680, 0.347, 0.712, 0.875]; [0.771, 0.299, 0.803, 0.875]; [0.862, 0.414, 0.894, 0.875]>. The bar with the highest Y coordinate represents the highest export value. Observing these, the bar for China [0.316, 0.130, 0.348, 0.875] extends the furthest, representing the highest figure. The value is also confirmed by the associated annotation \"1100\" [0.314, 0.100, 0.349, 0.123].\nBased on the above thought, the answer is:\nChina" } ], "id_raw": 184 }, { "id": "5zh9dtsGOI8ArlGA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nCould you arrange the data from the graph into a CSV format table for easier understanding?" }, { "from": "gpt", "value": "In order to arrange the data into a CSV format table, we need to identify the years representing the x-axis, whose coordinates include the labels {\"2017\"} [0.209, 0.952, 0.244, 0.975], {\"2018\"} [0.360, 0.952, 0.396, 0.975], {\"2019\"} [0.512, 0.952, 0.547, 0.975], {\"2020\"} [0.664, 0.952, 0.699, 0.975], and {\"2021\"} [0.816, 0.952, 0.851, 0.975]. The y-axis represents the price with ticks ranging from {0}[0.061, 0.934, 0.070, 0.958] to {9000}[0.035, 0.053, 0.070, 0.076]. The bar values represent Bitcoin and Ethereum prices for each year. Looking at the bar labels and heights, {the Bitcoin bars}[0.173, 0.748, 0.226, 0.944] in 2017 and [0.325, 0.552, 0.378, 0.944] in 2018 represent the price of Bitcoin in those years. {The Ethereum bar}[0.226, 0.929, 0.279, 0.944] represents the price of Ethereum in 2017 and so on. By examining all these bars, I'm able to construct the CSV table.\nBased on the above thought, the answer is:\nDate,Bitcoin,Ethereum\n2017,2000,150\n2018,4000,500\n2019,8000,1000\n2020,6000,2500\n2021,5000,3000" } ], "id_raw": 185 }, { "id": "CLt1upAO693pNJyw", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which cryptocurrency appears more stable over this five-year period?" }, { "from": "gpt", "value": "To determine which cryptocurrency appears more stable, it is important to examine the variation in prices over the years for both Bitcoin and Ethereum. Starting with Bitcoin, its price doubles from 2000 in 2017 [0.176, 0.717, 0.224, 0.741] to 4000 in 2018 [0.327, 0.522, 0.376, 0.545], then doubles again to 8000 in 2019 [0.479, 0.130, 0.527, 0.153] before falling to 6000 in 2020 [0.631, 0.326, 0.679, 0.349] and decreasing further to 5000 in 2021 [0.783, 0.424, 0.831, 0.447]. In comparison, Ethereum gradually increases its prices from 150 in 2017 [0.233, 0.899, 0.273, 0.922] to 500 in 2018 [0.385, 0.864, 0.424, 0.888], then doubles to 1000 in 2019 [0.532, 0.815, 0.580, 0.839] and continues to rise to 2500 in 2020 [0.684, 0.668, 0.732, 0.692] and 3000 in 2021 [0.836, 0.620, 0.884, 0.643]. Ethereum's price progression displays a smoother increase over the years, demonstrating less volatility when compared to Bitcoin. Therefore, Ethereum appears more stable over this period.\nBased on the above thought, the answer is:\nEthereum. Because its price progression displays a smoother and gradual increase over the years, demonstrating less volatility compared to Bitcoin." } ], "id_raw": 185 }, { "id": "HVsOc7Z0hiOzoI5s", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_5.png", "conversations": [ { "from": "human", "value": "\nDuring which year did Bitcoin experience the most significant price decrease?" }, { "from": "gpt", "value": "For identifying the year with the most significant price decrease for Bitcoin, I need to compare differences between the bar heights for consecutive years. The bars representing Bitcoin prices are annotated with their respective values [0.176, 0.717, 0.224, 0.741] in 2017, [0.327, 0.522, 0.376, 0.545] in 2018, [0.479, 0.130, 0.527, 0.153] in 2019, [0.631, 0.326, 0.679, 0.349] in 2020, and [0.783, 0.424, 0.831, 0.447] in 2021. From these, we can see that the drop from 8000 in 2019 to 6000 in 2020 is the most substantial decrease in price for Bitcoin.\nBased on the above thought, the answer is:\n2020." } ], "id_raw": 185 }, { "id": "H9JiuM3UQToetQXk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nConvert the bar chart data on global trade and supply chain metrics into a structured data table." }, { "from": "gpt", "value": "From the chart's X-Axis labels, representing 10 different countries, I gather the different categories that attribute to the data. These countries are {\"US\"}[0.142, 0.869, 0.156, 0.900],\n{\"China\"}[0.226, 0.869, 0.240, 0.935], {\"Germany\"}[0.309, 0.869, 0.323, 0.975], {\"India\"}[0.393, 0.869, 0.407, 0.925], {\"Brazil\"}[0.477, 0.869, 0.491, 0.934], {\"Australia\"}[0.560, 0.869, 0.574, 0.971], {\"France\"}[0.644, 0.869, 0.658, 0.946], {\"Japan\"}[0.727, 0.869, 0.741, 0.933], {\"Canada\"}[0.811, 0.869, 0.825, 0.957], {\"Mexico\"}[0.894, 0.869, 0.908, 0.949].\nThe Y-Axis contains the scale of the metrics measured, ranging between {0}[0.052, 0.851, 0.061, 0.875] to {100}[0.035, 0.053, 0.061, 0.076].\nThe actual data points are obtained from the blue and orange bars with their respective top annotations for Free Trade and Global Sourcing that correspond to each country on the x axis. These data points can be retrieved from their respective height in relation to Y-axis.\nBased on the above thought, the answer is:\nCountry, Free Trade, Global Sourcing\nUS, 80, 70\nChina, 90, 85\nGermany, 84, 80\nIndia, 66, 75\nBrazil, 77, 65\nAustralia, 80, 70\nFrance, 70, 64\nJapan, 88, 81\nCanada, 87, 73\nMexico, 68, 69" } ], "id_raw": 186 }, { "id": "742KeDGfXRUtmP62", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nReferring to the chart, which country seems to be the best choice for setting up a multinational company considering both Free Trade and Global Sourcing metrics?" }, { "from": "gpt", "value": "To answer this question, I need to consider both blue bar {\"Free Trade\"}[0.863, 0.118, 0.935, 0.141] and orange bar {\"Global Sourcing\"}[0.863, 0.153, 0.973, 0.176] simultaneously for each country. To be specific, a suitable country should have both a high Free Trade score and Global Sourcing score. I can see that {\"China\"}[0.226, 0.869, 0.240, 0.935] has high values in both the blue bars with co-ordinates [0.204, 0.142, 0.233, 0.860] and the orange bars [0.233, 0.182, 0.262, 0.860], suggesting it is a potential choice. Going through all the countries, no other country's collective scores for both categories surpass China's.\nBased on the above thought, the answer is:\nChina seems to be the best choice considering it has high scores in both Free Trade and Global Sourcing according to the chart." } ], "id_raw": 186 }, { "id": "QXyUDpRk2bonexMt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_6.png", "conversations": [ { "from": "human", "value": "\nWhich country has the lowest Free Trade score demonstrated in the chart?" }, { "from": "gpt", "value": "To find out which country has the lowest Free Trade score, I need to check the height of each of the blue bars. Find the shortest blue bar amongst {\"US\"}[0.120, 0.222, 0.149, 0.860], {\"China\"}[0.204, 0.142, 0.233, 0.860], {\"Germany\"}[0.287, 0.190, 0.316, 0.860], {\"India\"}[0.371, 0.334, 0.400, 0.860], {\"Brazil\"}[0.454, 0.246, 0.484, 0.860], {\"Australia\"}[0.538, 0.222, 0.567, 0.860], {\"France\"}[0.621, 0.302, 0.651, 0.860], {\"Japan\"}[0.705, 0.158, 0.734, 0.860], {\"Canada\"}[0.789, 0.166, 0.818, 0.860], {\"Mexico\"}[0.872, 0.318, 0.901, 0.860]. Reviewing these, India\u2019s Free Trade score bar was the shortest, and thus it had the lowest Free Trade score.\nBased on the above thought, the answer is:\nIndia" } ], "id_raw": 186 }, { "id": "NyEpyJEoPt0lykBl", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV formatted table from the chart for me?" }, { "from": "gpt", "value": "From the title of the chart {'Building and Real Estate Market Trends'}[0.386, 0.019, 0.656, 0.041], we can determine that we are observing trends in the building and real estate market. The x-axis of the chart displays various property types, ranging from {Single Family Home}[0.153, 0.801, 0.164, 0.973] to {Recreational Land}[0.878, 0.801, 0.890, 0.958]. The y-axis, labelled {Market Trends}[0.012, 0.360, 0.024, 0.482], represents the strength or level of these trends on a numeric scale. The plot contains two sets of bars. The first set labeled as {Rental Market Trend}[0.849, 0.088, 0.965, 0.106] in the legend, identified by their color as #69b3a2. The second set labeled as {Property Market Trend}[0.849, 0.115, 0.977, 0.132], identified by their color as #d98763. I can create a table by cross-referencing the property types with the height of the corresponding bars in each trend group.\nBased on the above thought, the answer is:\nProperty Type,Rental Market Trend,Property Market Trend\nSingle Family Home,100,123\nApartment Building,90,80\nCommercial Building,80,110\nIndustrial Property,70,90\nVacant Land,60,83\nFarm/ Ranch,50,75\nHotel/ Motel,40,120\nRecreational Land,30,90" } ], "id_raw": 187 }, { "id": "h8RG4fBLtb37V2x0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart tell us about the market trends for \"Single Family Home\" properties?" }, { "from": "gpt", "value": "The category \"Single Family Home\" can be found in the x-axis labels, specifically located at {these coordinates} [0.153, 0.801, 0.164, 0.973]. The two bars representing market trends are located at {[0.122, 0.296, 0.158, 0.795] and [0.158, 0.181, 0.195, 0.795]}. For the Rental Market Trend colored in #69b3a2 as indicated in the legend {[0.849, 0.088, 0.965, 0.106]}, the value is at the height of around 100 as displayed by the top of the respective bar at these coordinates [0.122, 0.296, 0.158, 0.795]. For the Property Market Trend colored in #d98763 as indicated in the legend {[0.849, 0.115, 0.977, 0.132]}, the value of the height is about 123 as displayed by the top of the respective bar at coordinates [0.158, 0.181, 0.195, 0.795]. In deciphering these values, it seems that the values of Property Market Trend are slightly higher than those of the Rental Market Trend.\nBased on the above thought, the answer is:\nThe chart tells us that Single Family Homes have a comparably strong strong market trend with rental market trend valued at 100 and property market trend valued higher at 123. However, the property market trend for this property type is slightly stronger than the rental market." } ], "id_raw": 187 }, { "id": "MmiFEyGzAI6IJCkM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_7.png", "conversations": [ { "from": "human", "value": "\nFor which type of property is the discrepancy between the rental market trend and the property market trend the largest?" }, { "from": "gpt", "value": "To determine where the discrepancy between the rental market trend and the property market trend is largest, I need to compare the heights of the two bars for each type of property. The x-coordinate, determining the property type, stays the same while the y-coordinate, representing the trend, varies. This can be observed in all property types from the {Single Family Home}[0.153, 0.801, 0.164, 0.973] on the left to the {Recreational Land}[0.878, 0.801, 0.890, 0.958] on the right. The property type with the largest discrepancy is identified as the type where the difference in y-coordinates is the largest. On assessing the bar heights, it is observed that the {Hotel/ Motel}[0.774, 0.801, 0.786, 0.906] category presents the largest divergence. The rental market trend's bar {40.0}[0.749, 0.573, 0.775, 0.590] is significantly shorter than the property market trend's bar {120.0}[0.782, 0.174, 0.815, 0.191]. Hence, the difference between these two trends is the most considerable for this category.\nBased on the above thought, the answer is:\nHotel/ Motel" } ], "id_raw": 187 }, { "id": "HpswDJ0Jemt4lyre", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nReferring to the bar chart titled {\"Entrepreneurship and Innovation Ecosystem across Major cities\"}[0.301, 0.019, 0.741, 0.041], can you convert the data into a structured CSV format table?" }, { "from": "gpt", "value": "The x-axis shows the names of different cities, according to the {x-axis tick labels}[{\"San Francisco\"}[0.107, 0.885, 0.172, 0.981]; {\"New York\"}[0.202, 0.885, 0.247, 0.953]; {\"London\"}[0.290, 0.885, 0.328, 0.942]; {\"Beijing\"}[0.376, 0.885, 0.412, 0.939]; {\"Bangalore\"}[0.454, 0.885, 0.504, 0.960]; {\"Berlin\"}[0.548, 0.885, 0.580, 0.933]; {\"Paris\"}[0.634, 0.885, 0.662, 0.926]; {\"Tel Aviv\"}[0.714, 0.885, 0.753, 0.943]; {\"Shanghai\"}[0.795, 0.885, 0.841, 0.954]; {\"Seattle\"}[0.884, 0.885, 0.921, 0.940]]. The y-axis measures the counts of Startups and Hubs, with the scale in thousands, as marked by the {y-axis label \"Count (in thousands)\"}[0.012, 0.372, 0.024, 0.554]. Each city has two corresponding bars representing two measurements - Startups and Hubs, as indicated by {the legend}[0.891, 0.055, 0.982, 0.139]. The green bars represent the number of Startups, and the red bars represent the number of Hubs. I can extract the values from the top annotations of each bar.\nBased on the above thought, the answer is:\nCity,Startups,Hubs\nSan Francisco,95.5,108.6\nNew York,80.7,95.4\nLondon,55.2,77.0\nBeijing,70.5,66.9\nBangalore,30.7,50.3\nBerlin,45.5,60.7\nParis,40.7,55.3\nTel Aviv,38.7,45.3\nShanghai,60.8,75.9\nSeattle,65.9,85.7" } ], "id_raw": 188 }, { "id": "mF0moBAycHLBmJvN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat city seems most prepared for development, based on the bar chart of Entrepreneurship and Innovation Ecosystem across Major cities? And why?" }, { "from": "gpt", "value": "This question requires a comprehensive comparison between the numbers of Startups and Hubs across different cities. By looking at the {green bars}<{\"Startups\"}[0.928, 0.088, 0.977, 0.106]> and {red bars}<{\"Hubs\"}[0.928, 0.115, 0.957, 0.132]>, I can see that cities like San Francisco, New York, and Seattle are showing large numbers in both sectors. However, {San Francisco}[0.107, 0.885, 0.172, 0.981] has the highest heights in both Startups and Hubs, which means it has the most substantial ecosystem for entrepreneurship and innovation development.\nBased on the above thought, the answer is:\nSan Francisco. Because it has the highest number of both Startups and Hubs, which indicates a vibrant ecosystem for entrepreneurship and innovation." } ], "id_raw": 188 }, { "id": "bjMh4qxqrz1oPCcr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_8.png", "conversations": [ { "from": "human", "value": "\nWhich city has the fewest Startups?" }, { "from": "gpt", "value": "The heights of green bars represent the counts of Startups for each city. By observing and comparing these bars, I find that {the green bar for \"Bangalore\"}[0.449, 0.682, 0.479, 0.878] is the shortest. Also, the value annotation \"30.7\" at the top of this bar confirms this finding.\nBased on the above thought, the answer is:\nBangalore." } ], "id_raw": 188 }, { "id": "Ln9KuqewanvyXJp4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a CSV structured table?" }, { "from": "gpt", "value": "From the title {\"Healthcare Performance in Human Health & Disease Management\"}[0.250, 0.025, 0.800, 0.055] and the x-axis labels including the fields {Depression Recovery}[0.085, 0.952, 0.231, 0.975], {Alzheimer Management}[0.167, 0.952, 0.332, 0.975], {Oncology}[0.309, 0.952, 0.374, 0.975], {Cardiology}[0.396, 0.952, 0.471, 0.975], {Orthopedics}[0.483, 0.952, 0.568, 0.975], {Rheumatology}[0.567, 0.952, 0.668, 0.975], {Vision}[0.689, 0.952, 0.730, 0.975], {Nephrology}[0.761, 0.952, 0.841, 0.975], and {Immunology}[0.850, 0.952, 0.936, 0.975], we know that x-axis represents different healthcare fields. The y-axis from {0}[0.052, 0.934, 0.061, 0.958] to {140}[0.035, -0.015, 0.061, 0.009] represents the performance scores. We have two groups of bars representing two different methods which are labeled in the legend below - {Non-Aggregation}[0.117, 0.118, 0.235, 0.141] represented by a light green color, and {Aggregation}[0.117, 0.153, 0.203, 0.176] represented by an orange color. To represent the data in CSV format, we take the height of each bar corresponding to the label on the top, as the value of dataset. For example, {the bar for Non-Aggregation at Depression Recovery}[0.126, 0.279, 0.158, 0.944] has a value of 98 and similarly for Aggregation it is 60.\nBased on the above thought, the answer is:\nThemes,Non-Aggregation,Aggregation\nDepression Recovery,98,60\nAlzheimer Management,57,50\nOncology,90,80\nCardiology,30,100\nOrthopedics,80,85\nRheumatology,118,90\nVision,109,107\nNephrology,52,50\nImmunology,40,120" } ], "id_raw": 190 }, { "id": "6Jb7s4vhTFE4X2Wf", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, can we conclude that the Aggregation method performs better than the Non-Aggregation method in most of the healthcare fields? Explain your reasoning." }, { "from": "gpt", "value": "To answer this question, we have to compare the pair of bars at each healthcare field and determine which method has the higher score. For example, in {Depression Recovery}[0.085, 0.952, 0.231, 0.975], {the Non-Aggregation bar}[0.126, 0.279, 0.158, 0.944] is seen to be larger than {the Aggregation bar}[0.158, 0.537, 0.190, 0.944], meaning that the Non-Aggregation method performs better in this field. After examining all pairs of bars, I find that {Non-Aggregation}[0.117, 0.118, 0.235, 0.141] outperforms {Aggregation}[0.117, 0.153, 0.203, 0.176] in the fields of Depression Recovery, Alzheimer Management, Oncology, Orthopedics, and Rheumatology. However, {Aggregation}[0.117, 0.153, 0.203, 0.176] performs better than {Non-Aggregation}[0.117, 0.118, 0.235, 0.141] in Cardiology, Vision, and Immunology. In the Nephrology field, both methods have the same performance. Overall, the Non-aggregation method performs better in a greater number of healthcare fields.\nBased on the above thought, the answer is:\nNo, we cannot conclude that the Aggregation method performs better than the Non-Aggregation method in most of the healthcare fields. Instead, the Non-Aggregation method performs better in the majority of the fields according to the chart." } ], "id_raw": 190 }, { "id": "TchFb1j1DpN43lKx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_10.png", "conversations": [ { "from": "human", "value": "\nIn which healthcare field does the Aggregation method have the highest performance score?" }, { "from": "gpt", "value": "Starting from the left most {bars pair of Depression Recovery}[0.126, 0.279, 0.158, 0.944] to {the right most pair of Immunology}[0.861, 0.672, 0.893, 0.944], by comparing the value on top of each of the orange bars corresponding to Aggregation method, we can find the highest performance score. By observing, it can be seen that the {Immunology}[0.850, 0.952, 0.936, 0.975] field has the {highest score bar}[0.893, 0.130, 0.925, 0.944] with the value of 120, which is the highest among all the fields.\nBased on the above thought, the answer is:\nImmunology." } ], "id_raw": 190 }, { "id": "7Or0cvT30TjruLx1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nCan you please extract the data from the bar chart you're seeing and present it in a CSV format table?" }, { "from": "gpt", "value": "Sure. I see that this bar chart is related to population migration trends over the past decade. The x-axis {\"Years\"}[{\"2010\"}[0.120, 0.952, 0.155, 0.975] to {\"2019\"}[0.895, 0.952, 0.931, 0.975]] represents different years, and the y axis ranging from {0}[0.035, 0.053, 0.061, 0.076] to {110}[0.035, 0.053, 0.061, 0.076] represents the number of people migrating. Each year has two bars, indicating two different migration trends. According to {the legend}[0.841, 0.074, 0.978, 0.186], {the bar in green color}<{\"In_Country\"}[0.885, 0.118, 0.960, 0.142]> represents the count of people migrating into the country, and {the bar in orange color}<{\"Out_Country\"}[0.885, 0.153, 0.973, 0.177]> represents the count of people migrating out of the country. To generate a CSV table, I will extract the values of each year for both trends based on the bars and their heights. For example, the height of {the green bar for \"2010\"}[0.108, 0.820, 0.138, 2.001] is extended around the point marked 67, and there's also {a value annotation \"67\"}[0.114, 0.790, 0.132, 0.813] on it, which means that the In_Country migration count for 2010 was 67.\nBased on the above thought, the answer is:\nYear,In_Country,Out_Country\n2010,67,90\n2011,75,89\n2012,82,85\n2013,83,84\n2014,78,88\n2015,81,88\n2016,90,91\n2017,98,94\n2018,97,95\n2019,100,97" } ], "id_raw": 191 }, { "id": "jlQohUdgpCVifzDi", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nBased on the information in the graph, can you describe the trend of migration in and out of the country over this decade?" }, { "from": "gpt", "value": "The group bar chart illustrates two clear trends of migration. From the green bars representing the {In_Country} migration<{\"Trends\"}[0.841, 0.074, 0.978, 0.186]>, it looks like there's been a steady increase in the numbers from {2010}<{\"67\"}[0.114, 0.790, 0.132, 0.813]> to {2019}<{\"100\"}[0.885, 0.208, 0.911, 0.232]>, with some mild fluctuations. There's a slight decline in the year {2014}<{\"78\"}[0.459, 0.596, 0.476, 0.619]>. The numbers plateaued a bit towards the end of the decade.\n\nConversely, looking at the orange bars which represent {Out_Country}<{\"Trends\"}[0.841, 0.074, 0.978, 0.186]> migration, it starts high at {\"90\"}<{\"2010\"}[0.144, 0.384, 0.162, 0.408]> but has a few dips notably in the years {2012}<{\"85\"}[0.316, 0.473, 0.334, 0.496]> and {2013}<{\"84\"}[0.403, 0.490, 0.420, 0.514]>. It then sees a slight increase again in the years {2014}<{\"88\"}[0.489, 0.420, 0.506, 0.443]> and {2015}<{\"88\"}[0.575, 0.420, 0.592, 0.443]>. Thereafter, It keeps a constant slight ascend onwards to reach a count of {\"97\"}<{\"2019\"}[0.919, 0.261, 0.937, 0.284]> by the end of the decade. All in all, the Out_Country migration numbers remain higher than In_Country for the entire decade.\nBased on the above thought, the answer is:\nThe trend of In_Country migration has been generally increasing over the decade, despite a few minor fluctuations. On the other hand, the Out_Country migration started high at the beginning of the decade and saw a few dips around 2012 and 2013 before resuming a slight incremental trend till the end of the decade. Throughout the decade, Out_Country migration has been consistently higher than In_Country migration." } ], "id_raw": 191 }, { "id": "2PKtx9R5HY1OUDwp", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_11.png", "conversations": [ { "from": "human", "value": "\nFrom the data presented in the bar graph, in which year was the in-country migration at its peak?" }, { "from": "gpt", "value": "To locate the year with the highest in-country migration, I need to look for the highest bar among the green colored bars {In_Country}<{\"Trends\"}[0.841, 0.074, 0.978, 0.186]>. It starts with the bar representing 2010 located at [0.108, 0.820, 0.138, 2.001] and ends with the bar for the year 2019 at [0.883, 0.238, 0.913, 2.001]. After comparing each, it's clear that the highest green bar is at {\"2019\"}[0.883, 0.238, 0.913, 2.001], and its value is {100}[0.885, 0.208, 0.911, 0.232], marking it as the year with peak In_Country migration.\nBased on the above thought, the answer is:\n2019." } ], "id_raw": 191 }, { "id": "78FPE9iwINlXI9ko", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nCan you formulate this data from the bar chart into a CSV formatted table?" }, { "from": "gpt", "value": "To generate a CSV table from this bar chart, I need to identify the x-axis which here denotes the portfolios, {portfolio labels}[0.114, 0.952, 0.185, 0.975]; [0.198, 0.952, 0.268, 0.975]; [0.281, 0.952, 0.352, 0.975]; [0.365, 0.952, 0.435, 0.975]; [0.448, 0.952, 0.519, 0.975]; [0.532, 0.952, 0.602, 0.975]; [0.615, 0.952, 0.686, 0.975]; [0.699, 0.952, 0.770, 0.975]; [0.783, 0.952, 0.853, 0.975]; [0.862, 0.952, 0.941, 0.975]. The two types of investments indicated by the legend, {risky}[0.117, 0.118, 0.244, 0.141] and {stable}[0.117, 0.153, 0.251, 0.176] investments are our two Y-columns. Now by assessing the height of each bar for every portfolio under risky investments and stable investments respectively, I can fill in the investment values. This can be achieved by referencing the value annotated at the top of each bar. For instance, the first value for risky investment corresponds to the {blue bar for \"Portfolio 1\"}[0.120, 0.463, 0.149, 0.944] with a value annotation of {\"120.0\"}[0.115, 0.433, 0.154, 0.456]. This gives me a value of 120. I would repeat this process for every bar in risky and stable investments.\nBased on the above thought, the answer is:\nPortfolio, Risky Investments, Stable Investments\nPortfolio 1, 120, 50\nPortfolio 2, 130, 75\nPortfolio 3, 160, 85\nPortfolio 4, 150, 110\nPortfolio 5, 175, 100\nPortfolio 6, 180, 120\nPortfolio 7, 190, 145\nPortfolio 8, 185, 150\nPortfolio 9, 190, 160\nPortfolio 10, 200, 170" } ], "id_raw": 192 }, { "id": "SqfPznSlij9MsyTP", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nJudging from the chart, what can we say about the growth trend of the two investment types over the portfolios?" }, { "from": "gpt", "value": "Based on the data from the bar chart, I observe that both {risky}[0.117, 0.118, 0.244, 0.141] and {stable}[0.117, 0.153, 0.251, 0.176] investments have an upward trend over the portfolios. Starting from {Portfolio 1}[0.114, 0.952, 0.185, 0.975] to {Portfolio 10}[0.862, 0.952, 0.941, 0.975], both investment types show increasing values. However, it can be seen that the risky investments are more subject to fluctuations and have a sharper growth contrast, as seen in {Portfolio 4}[0.365, 0.952, 0.435, 0.975] and {Portfolio 5}[0.448, 0.952, 0.519, 0.975] for instance. On the other hand, stable investments show a smoother and steadier growth trend.\nBased on the above thought, the answer is:\nBoth risky and stable investments show an upward growth trend over the portfolios, with risky investments having sharper increments and drops, while stable investments exhibit a smoother and more steady upward trend." } ], "id_raw": 192 }, { "id": "916aicBU26RoMhs1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_12.png", "conversations": [ { "from": "human", "value": "\nWhich portfolio have the highest value of stable investment?" }, { "from": "gpt", "value": "To find the portfolio with the highest value in stable investments, I need to compare the height of the orange bars. By doing so, I notice that the tallest {orange bar}[0.901, 0.263, 0.931, 0.944] corresponding to {Portfolio 10}[0.862, 0.952, 0.941, 0.975] is the highest with an investment value of {\"170.0\"}[0.896, 0.232, 0.936, 0.256].\nBased on the above thought, the answer is:\nPortfolio 10." } ], "id_raw": 192 }, { "id": "jyrMfSE0svYX7iwo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from this bar chart and construct a table in a CSV format?" }, { "from": "gpt", "value": "Based on the chart figure, the x-axis represents two categories, which are {Wealthiest 10%}[0.318, 0.952, 0.426, 0.975] and {Poorest 10%}[0.635, 0.952, 0.722, 0.975]. The y-axis shows the percentile scores representing values for each category from {0}[0.052, 0.934, 0.061, 0.958] to {100}[0.035, 0.053, 0.061, 0.076]. There are two bars for each category, with different colors. According to {the legend}[0.804, 0.074, 0.978, 0.185], the category colors are specified. The {green bars}<{\"Income Inequality\"}[0.848, 0.118, 0.973, 0.141]> indicate income inequality, while the {orange bars}<{\"Rights Protection\"}[0.848, 0.153, 0.967, 0.176]> indicate rights protection. Now, for constructing the table, I can extract the values represented by the height of the bars and their annotated values. For example, under {Wealthiest 10%}[0.318, 0.952, 0.426, 0.975] category, the value for {Income Inequality}[0.265, 0.071, 0.372, 0.944] is 99 and for {Rights Protection}[0.372, 0.864, 0.479, 0.944] is 9.\nBased on the above thought, the answer is:\nCategory,Income Inequality,Rights Protection\nWealthiest 10%,99,9\nPoorest 10%,1,91" } ], "id_raw": 193 }, { "id": "XExuEbuyUDfjGcjb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart reveal about the relation between income inequality and rights protection?" }, { "from": "gpt", "value": "From the chart, it's clear that there's a marked disparity between income inequality and rights protection within the two demographics \"Wealthiest 10%\" and \"Poorest 10%\". The {Income Inequality bar}[0.265, 0.071, 0.372, 0.944] in the Wealthiest 10% section is distinctly higher than the {Rights Protection bar}[0.372, 0.864, 0.479, 0.944] in the same section, showing that there's a significant disparity in income in favor of the wealthier population. Conversely, the {Rights Protection bar}[0.679, 0.142, 0.786, 0.944] in the Poorest 10% section is noticeably higher than the {Income Inequality bar}[0.571, 0.935, 0.679, 0.944] in the same section, which shows that despite significant income inequality, rights protection mechanisms seem to be stronger within the less affluent demographic. Hence, there appears to be an inverse relationship between income inequality and rights protection.\nBased on the above thought, the answer is:\nThe chart suggests an inverse relationship between income inequality and rights protection. It indicates that the wealthier populace sees more income disparity but lesser rights protection, whereas the less affluent section experiences prominent rights protection despite considerable income inequality." } ], "id_raw": 193 }, { "id": "YKoPD9QzLHXgOJMe", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_13.png", "conversations": [ { "from": "human", "value": "\nFor which segment, 'Wealthiest 10%' or 'Poorest 10%', is the rights protection more inequality?" }, { "from": "gpt", "value": "By inspection of the bar heights and their annotated values, I can solve this question. First, I check the {orange bar for \"Wealthiest 10%\"}[0.372, 0.864, 0.479, 0.944], and see that it's annotated with {\"9\"}[0.421, 0.834, 0.430, 0.857]. Then I examine the {orange bar for \"Poorest 10%\"}[0.679, 0.142, 0.786, 0.944], and find that it's annotated with {\"91\"}[0.723, 0.111, 0.741, 0.135]. The value of Rights Protection is higher for the Poorest 10%, indicating that this group experiences more inequality in rights protection.\nBased on the above thought, the answer is:\nPoorest 10%" } ], "id_raw": 193 }, { "id": "YbjLCjpHwX0DQPcj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a CSV format table?" }, { "from": "gpt", "value": "By interpreting the bar chart, I can catch that the x-axis contains names of different energy resources - {the x-axis tick labels}[{\"Solar\"}[0.150, 0.952, 0.186, 0.975]; {\"Wind\"}[0.253, 0.952, 0.288, 0.975]; {\"Hydroelectric\"}[0.326, 0.952, 0.419, 0.975]; {\"Geothermal\"}[0.433, 0.952, 0.515, 0.975]; {\"Nuclear\"}[0.550, 0.952, 0.603, 0.975]; {\"Coal\"}[0.663, 0.952, 0.694, 0.975]; {\"Gas\"}[0.767, 0.952, 0.794, 0.975]; {\"Oil\"}[0.874, 0.952, 0.892, 0.975]]. The y-axis provides the corresponding percentage usage for these resources. Each of the resources has two bars - the first bar, encoded green, stands for Renewable Energy while the second one, colored orange, represents Nonrenewable Energy. I'll transform this data into a CSV format by extracting the resource, renewable energy and nonrenewable energy for each bar, example - for {Solar's green bar}[0.132, 0.389, 0.168, 0.944] which represents renewable energy the value is {75.5}[0.135, 0.359, 0.165, 0.382] and for {Solar's orange bar}[0.168, 0.758, 0.204, 0.944] which stands for nonrenewable energy, the value is {25.2}[0.170, 0.728, 0.201, 0.752].\nBased on the above thought, the answer is:\nResource, Renewable Energy, Nonrenewable Energy\nSolar, 75.5, 25.2\nWind, 68.3, 32.1\nHydroelectric, 60.6, 35.3\nGeothermal, 55.1, 40.6\nNuclear, 52.3, 83.9\nCoal, 40.2, 92.3\nGas, 38.1, 95.5\nOil, 32.8, 98.4" } ], "id_raw": 194 }, { "id": "E3fkfwhDMLCzk8jF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nObserving the chart, what trend can be inferred regarding the future of energy resource allocation in terms of renewable and nonrenewable energy usage?" }, { "from": "gpt", "value": "Looking over the bar chart, there is a clear trend. Specifically, the green bars represent renewable energy usage, while the orange bars stand for nonrenewable energy usage. For instance, when observing the {Solar energy bars}<{\"Renewable\"}[0.132, 0.389, 0.168, 0.944]; {\"Nonrenewable\"}[0.168, 0.758, 0.204, 0.944]>, we can see that renewable energy usage significantly exceeds nonrenewable energy. However, as we move towards the right of the graph, we could see that the height of the orange bars for each energy resource gradually rises while the green bars indicating renewable energy usage drop. This trend reaches its peak at {Oil}<{\"Renewable\"}[0.847, 0.703, 0.883, 0.944]; {\"Nonrenewable\"}[0.883, 0.221, 0.919, 0.944]>, where usage of nonrenewable energy significantly outweighs renewable energy consumption.\nBased on the above thought, the answer is:\nThe chart demonstrates an evident trend of increasing reliance on nonrenewable energy sources and decreasing use of renewable energy sources moving from Solar energy to Oil. This trend suggests that in terms of future energy resource allocation, there exists a rising preference for nonrenewable energy sources." } ], "id_raw": 194 }, { "id": "X72sy0k94PLb1jXX", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_13_14.png", "conversations": [ { "from": "human", "value": "\nWhich energy resource has the lowest consumption of nonrenewable energy?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height of the orange bars representing the nonrenewable energy consumption for each energy resource. Looking from left to right, I can see that the orange bar under the category {Solar}[0.150, 0.952, 0.186, 0.975] with height & value information at {[0.168, 0.758, 0.204, 0.944], \"25.2\"}[0.170, 0.728, 0.201, 0.752] is the shortest, indicating the lowest consumption of nonrenewable energy.\nBased on the above thought, the answer is:\nSolar" } ], "id_raw": 194 }, { "id": "8T8TfZK8WOTIEay6", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data given in the bar chart into a CSV format table?" }, { "from": "gpt", "value": "Sure, the chart is a bar chart with multiple groups, representing {Ecosystem Sustainability over Time}[0.431, 0.019, 0.727, 0.041]. The categories on the y-axis include 5 different ecosystems: {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831], {Ocean}[0.119, 0.647, 0.163, 0.664], {Desert}[0.117, 0.480, 0.163, 0.498], {Arctic Tundra}[0.072, 0.313, 0.163, 0.331], and {Urban City}[0.089, 0.147, 0.163, 0.164]; the x-axis shows the {Sustainability Score}[0.510, 0.964, 0.648, 0.981]. Each group of bars corresponds to a different time category identified in the {legend}[0.819, 0.055, 0.978, 0.165], including {Initial Condition}[0.863, 0.062, 0.972, 0.080], {Time 20}[0.863, 0.088, 0.919, 0.106], {Time 40}[0.863, 0.115, 0.919, 0.132], and {Time 60}[0.863, 0.141, 0.919, 0.158]. The sustainability scores for each ecosystem at each time can be calculated by measuring the bar lengths relative to the x-axis scale, while paying attention to the limits of the bar for each time.\nBased on the above thought, the answer is:\nEcosystem,Initial Condition,Time 20,Time 40,Time 60\nTropical Rainforest,0.9,0.85,0.82,0.81\nOcean,0.92,0.9,0.86,0.83\nDesert,0.5,0.48,0.47,0.45\nArctic Tundra,0.82,0.8,0.78,0.75\nUrban City,0.35,0.37,0.39,0.40" } ], "id_raw": 199 }, { "id": "5Wca1WgB6KAhP2su", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis of the visualization, which ecosystem seems to be the most resilient over time in terms of sustainability?" }, { "from": "gpt", "value": "To answer this question, I should consider the ecosystems with the least decrease in sustainability scores over time. For {Ocean}[0.119, 0.647, 0.163, 0.664] and {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831] sustainability scores generally decrease over time. The same applies to {Desert}[0.117, 0.480, 0.163, 0.498] and {Arctic Tundra}[0.072, 0.313, 0.163, 0.331] as demonstrated by the bars {[0.173, 0.253, 0.353, 0.387], [0.702, 0.253, 0.867, 0.387]}, from 0.82 to 0.75. {Urban City}[0.089, 0.147, 0.163, 0.164] alone exhibits improvement in sustainability, however it starts from a low base. The {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831] ecosystem has only a slight decrease from 0.9 to 0.81. So in terms of overall resilience and high sustainability, I would say the Tropical Rainforest ecosystem seems to be the most resilient.\nBased on the above thought, the answer is:\nThe most resilient ecosystem in terms of sustainability appears to be the Tropical Rainforest." } ], "id_raw": 199 }, { "id": "mU7IYbSfwA6FsiRU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_14_4.png", "conversations": [ { "from": "human", "value": "\nWhich ecosystem shows the largest decrease in sustainability score from initial condition to time 60?" }, { "from": "gpt", "value": "To determine this, I need to subtract the sustainability scores at Time 60 from the initial condition for each ecosystem. I will do this by comparing the lengths of the corresponding bars for each ecosystem in the initial condition group and the Time 60 group. The biggest difference will indicate the largest decrease. Looking at each ecosystem in turn, {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831] has bars at {[0.173, 0.754, 0.371, 0.887], [0.739, 0.754, 0.918, 0.887]} indicating a decrease from 0.9 to 0.81; {Ocean}[0.119, 0.647, 0.163, 0.664] has bars at {[0.173, 0.587, 0.376, 0.720], [0.763, 0.587, 0.946, 0.720]} indicating a decrease from 0.92 to 0.83; {Desert}[0.117, 0.480, 0.163, 0.498] has bars at {[0.173, 0.420, 0.283, 0.554], [0.492, 0.420, 0.592, 0.554]} indicating a decrease from 0.5 to 0.45; {Arctic Tundra}[0.072, 0.313, 0.163, 0.331] has bars at {[0.173, 0.253, 0.353, 0.387], [0.702, 0.253, 0.867, 0.387]} indicating a decrease from 0.82 to 0.75; and finally, {Urban City}[0.089, 0.147, 0.163, 0.164] has bars at {[0.173, 0.087, 0.250, 0.220], [0.417, 0.087, 0.506, 0.220]}, but it hasn't a decrease.\nBased on the above thought, the answer is:\nArctic Tundra." } ], "id_raw": 199 }, { "id": "HMgee7dQHIoQtcbb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nCan you transform the bar chart data representing the development indicators for urban and rural services into a CSV format, highlighting the changes over time?" }, { "from": "gpt", "value": "Observing {the subtitle}[0.345, 0.025, 0.702, 0.055] 'Rural and Urban Development Comparison', it indicates the content of the chart. The {x-axis}[0.474, 0.952, 0.573, 0.975] reads 'Time (Periods)', with period labels from 0 to 14, represented by {xtick labels}[0.117, 0.905, 0.133, 0.932]; [0.174, 0.905, 0.190, 0.932]; [0.231, 0.905, 0.247, 0.932]; ... [0.911, 0.905, 0.933, 0.942]. The {y-axis}[0.015, 0.337, 0.029, 0.614] is labeled 'Development Indicators' with values from 0 to 12. The {legend}[0.886, 0.074, 0.978, 0.185] explains that {blue bars}[0.104, 0.179, 0.147, 0.889] represent 'Urban Services', while {orange bars}[0.104, 0.427, 0.147, 0.889] depict 'Rural Services'. Using the height of bars and the text values associated with each, I can extract the values across different time periods. For instance, during time period 0, the 'Urban Services' value labeled 10.3 coincides with the bar height that starts at the lower y-coordinate of 0.179 and stretches up to 0.889, as shown in [0.104, 0.179, 0.147, 0.889].\nBased on the above thought, the answer is:\nTime Period,Urban Services,Rural Services\n0,10.3,6.7\n1,9.6,5.2\n2,9.2,4.0\n3,8.8,5.1\n4,8.4,5.8\n5,8.1,3.9\n6,7.8,4.3\n7,7.5,4.9\n8,6.9,5.0\n9,6.4,5.1\n10,6.2,5.0\n11,6.1,4.8\n12,5.9,4.7\n13,5.5,5.0\n14,5.2,5.4" } ], "id_raw": 211 }, { "id": "02A2nqJRGSs1qaWa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, can we deduce whether the rural development indicator improved or declined over the 14 periods?" }, { "from": "gpt", "value": "To answer this, I'll analyze the bar heights for 'Rural Services'. The initial value is located at {6.7}[0.114, 0.402, 0.136, 0.425] for period 0. Examining the sequence of bars, I notice fluctuations such as the decrease at {4.0}[0.228, 0.588, 0.250, 0.611] at period 2 and then the increase at period 4 to {5.8}[0.342, 0.464, 0.364, 0.487]. Lastly, I compare the final value at period 14, which is {5.4}[0.911, 0.492, 0.933, 0.515], against the initial value.\nBased on the above thought, the answer is:\nThe rural development indicator showed fluctuations but ultimately improved from 4.0 to 5.4 indicating a positive trend towards improvement by the end of the 14 periods." } ], "id_raw": 211 }, { "id": "6aqd1GlvjM9pGI3t", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_15_1.png", "conversations": [ { "from": "human", "value": "\nHow much did urban development indicators decrease over the entire observed period?" }, { "from": "gpt", "value": "Initially, I notice the value for urban development in the first period, stated as {10.3}[0.110, 0.154, 0.141, 0.177]. Then I locate the last period's urban development value, listed as {5.2}[0.911, 0.505, 0.933, 0.529]. To calculate the total decrease, I subtract the final period's value from the initial period's value.\nBased on the above thought, the answer is:\n5.1" } ], "id_raw": 211 }, { "id": "Ufcr46ukM2AWoWYe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nBased on the bars you observe in the chart, can you form a CSV table containing Stable growth and Sharp rise percentages of all companies?" }, { "from": "gpt", "value": "By investigating the {chart title}[0.345, 0.025, 0.702, 0.055] i.e., \"Medical and Biotechnology Industry Trends\", I understand this chart presents the growth trends of different companies. The x-axis represents different companies named as {Biomedica Inc.}[0.132, 0.919, 0.234, 0.942], {Medicon Solutions}[0.347, 0.919, 0.473, 0.942], {Biotec Corp.}[0.594, 0.919, 0.679, 0.942] and {GeneWorks}[0.824, 0.919, 0.903, 0.942]. The y-axis stands for the trend percentages that range from 0 to 10 as indicated by {y-axis labels}[0.035, 0.053, 0.052, 0.076]. There are two bars for each company, and according to {legend for the trends}[0.831, 0.074, 0.978, 0.185], one represents 'Stable growth' and the other represents 'Sharp rise'. To form the CSV table, I can estimate the trends values for each company by reading the y-coordinate of the bar top and also from the values stated next to the top of each bar.\nBased on the above thought, the answer is:\nCompany, Stabile growth (%), Sharp rise (%)\nBiomedica Inc., 2.5, 7.35\nMedicon Solutions, 4.3, 3.98\nBiotec Corp., 5.2, 2.07\nGeneWorks, 3.5, 6.21" } ], "id_raw": 213 }, { "id": "moJcgTlkedurDpgI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the graph, which company would you recommend for long-term investment and why?" }, { "from": "gpt", "value": "This question asks for an opinion based on analyzing the data. Long-term investment would favor stable growth, as represented by the {greyish bars}[0.875, 0.118, 0.972, 0.141] in the graph. The company with the highest value in Stable growth is {Biotec Corp.}[0.594, 0.919, 0.679, 0.942], which scores 5.20 in stable growth as identified by the value next to the top of the bar. Although it has less value in Sharp rise as reflected by the {purple bars}[0.875, 0.153, 0.946, 0.176], for long-term investment, it is typically wise to avoid fluctuations which are represented here by Sharp rise.\nBased on the above thought, the answer is:\nBiotec Corp. would be recommended for long-term investment due to its high stable growth and low sharp rise trend, which suggests steadiness and less likelihood of risk in the long-term." } ], "id_raw": 213 }, { "id": "4I7lOeC1tVHf3d8Y", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_15_3.png", "conversations": [ { "from": "human", "value": "\nWhich company has the highest total percentage value when we combine both Stable growth and Sharp rise trends?" }, { "from": "gpt", "value": "To figure out which company has the highest total percentage value, I need to add the values of Stable growth and Sharp rise for each company. I look at the bars and the text labels at the top of each bar to determine the values. After adding the values for each company, I note that {Biomedica Inc. }[0.132, 0.919, 0.234, 0.942] has a total of 9.85, {Medicon Solutions}[0.347, 0.919, 0.473, 0.942] has a total of 8.28, {Biotec Corp.}[0.594, 0.919, 0.679, 0.942] has a total of 7.27, and {GeneWorks}[0.824, 0.919, 0.903, 0.942] has a total of 9.71. It is clear that Biomedica Inc. has the highest total percentage value.\nBased on the above thought, the answer is:\nBiomedica Inc." } ], "id_raw": 213 }, { "id": "YJ3h8qpDvFl1ZyQX", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nFrom the data presented on this bar chart, could you please generate a table in CSV format?" }, { "from": "gpt", "value": "The bar chart plots data on percentage of graduates and non-graduates across different age groups. The age groups are indicated on {the x-axis tick labels}<{\"Young_Adults\"}[0.185, 0.818, 0.260, 0.942], {\"Middle_Aged\"}[0.493, 0.818, 0.566, 0.939], {\"Seniors\"}[0.813, 0.818, 0.859, 0.895]>. The y-axis indicates the percentage, as denoted by its label {Percentage [%]}[0.015, 0.343, 0.029, 0.521]. In this chart, two kinds of bars are marked: {Graduate Bars}<{handle blue color}[0.835, 0.120, 0.863, 0.136]> , {Non-Graduate Bars}<{handle brown color}[0.835, 0.155, 0.863, 0.171]>. Each bar resides on an age group, the index of the bar corresponds to the percentage. By matching the height of the bar and the percentage indicator, the numerical value of each bar can be obtained and thus the CSV table can be generated.\nBased on the above thought, the answer is:\nAge_Group,Graduate,Non-Graduate\nYoung_Adults,6.2,-1.4\nMiddle_Aged,12.5,3.7\nSeniors,-5.3,1.1" } ], "id_raw": 216 }, { "id": "6YeSLDsf1UIEx3XN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nBased on the data on the bar chart, how would you explain the pattern of education levels across different age groups?" }, { "from": "gpt", "value": "The bar chart shows the percentage of graduates and non-graduates across three age groups: Young_Adults[0.185,0.818,0.260,0.942], Middle_Aged[0.493,0.818,0.566,0.939] and Seniors[0.813,0.818,0.859,0.895]. The {Graduate Bars}{blue color}[0.835, 0.120, 0.863, 0.136] show that the percentage of graduates is highest in the 'Middle-Aged' group, somewhat lower in the 'Young Adults' group and negative in the 'Seniors' group. The {Non-Graduate Bars}{brown color}[0.835, 0.155, 0.863, 0.171] show sporadic growth with small negative value for young adults, somewhat high positive for middle-aged and marginal positive for seniors.\nBased on the above thought, the answer is:\nThe data indicates that middle-aged people have the highest percentage of graduates, followed by young adults. The data for seniors shows a decrease in graduation percentage. For non-graduates, the data shows a small decrease for young adults, an increase for middle-aged adults, and a marginal increase for seniors. It suggests that more middle-aged individuals have pursued an education to graduation compared to other age groups, while the graduation rate for seniors is negatively affected. This could potentially be due to historical trends, with more educational opportunities having been available to the younger and middle-aged groups. The non-graduates show sporadic growth across all age groups." } ], "id_raw": 216 }, { "id": "4Ikxt5OLzJd736c7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_15_6.png", "conversations": [ { "from": "human", "value": "\nIn which age group can one find the highest education attainment level among all, whether being a graduate or not?" }, { "from": "gpt", "value": "To determine the highest education attainment level among all the age groups, I should compare the height of the bars representing different age groups for both graduates and non-graduates. The {Graduate Bars} for {Young_Adults}[0.115, 0.322, 0.222, 0.506], {Middle_Aged}[0.422, 0.136, 0.529, 0.506], and {Seniors}[0.729, 0.506, 0.836, 0.662] as well as the {Non-graduate Bars} for {Young_Adults}[0.222, 0.506, 0.330, 0.547], {Middle_Aged}[0.529, 0.396, 0.637, 0.506], {Seniors}[0.836, 0.473, 0.944, 0.506] are compared in this case. Observing the bar heights, the highest bar is {Graduate Bar} in the \"Middle_Aged\" category [0.422, 0.136, 0.529, 0.506]. Thus the highest education attainment level among all age groups is in the Middle-Aged group among Graduates.\nBased on the above thought, the answer is:\nMiddle_Aged." } ], "id_raw": 216 }, { "id": "5HrCDvfcVlCYd2Xo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nCan you transform the information from the bar charts into a structured CSV table comparing regions and their corresponding variables for the year 2020?" }, { "from": "gpt", "value": "To create a CSV table, I must first recognize the {regions}[0.596, 0.449, 0.637, 0.466]; [0.695, 0.449, 0.734, 0.466]; [0.797, 0.449, 0.827, 0.466]; [0.893, 0.449, 0.927, 0.466] from the x-axis across the four bar charts. Then, I need to identify the types of data each color represents by referring to the {legend}[0.157, -0.041, 0.843, 0.016]. Each bar's height correlates to a specific value on the y-axis. By examining each bar within the year 2020 charts for {South}[0.577, 0.437, 0.656, 0.437], {North}[0.675, 0.436, 0.754, 0.437], {East}[0.773, 0.437, 0.851, 0.437], and {West}[0.871, 0.436, 0.949, 0.437] across the color categories, I can record the bars' heights as values by cross-referencing with the y-axis.\nBased on the above thought, the answer is:\nRegion,Elevation,Average Temperature,Average Precipitation,Population\nSouth,1000,22,1250,12000000\nNorth,1500,10,950,6000000\nEast,800,26,1150,9000000\nWest,1200,19.5,1050,6500000" } ], "id_raw": 227 }, { "id": "hGEbOZnxJOmTwuXd", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nWhich region showed the least growth in population from 2015 to 2020 according to the chart?" }, { "from": "gpt", "value": "To ascertain which region had the least growth in population, I analyze the {purple bars}[0.163, 0.437, 0.241, 0.437]; [0.261, 0.438, 0.339, 0.438]; [0.359, 0.438, 0.437, 0.438]; [0.457, 0.437, 0.535, 0.438] for 2015 and {purple bars}[0.656, 0.437, 0.734, 0.437]; [0.754, 0.438, 0.832, 0.438]; [0.851, 0.438, 0.930, 0.438]; [0.949, 0.437, 1.027, 0.438] for 2020, cross-referencing with the {y-axis}[0.527, 0.430, 0.549, 0.447] labels. Each bar pair corresponds to a region and depicts its population for given years. By comparing the differences in population between the years 2015 and 2020 for South, North, East, and West, it becomes apparent that the North region's bars show the smallest increase.\nBased on the above thought, the answer is:\nNorth. Upon examining the bars corresponding to the population for each region, the North region depicted by {purple bars}[0.261, 0.438, 0.339, 0.438]; [0.754, 0.438, 0.832, 0.438] demonstrates the least growth, indicating a smaller increase in population compared to the other regions." } ], "id_raw": 227 }, { "id": "YSp5lsC18VhkwcNi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_16_2.png", "conversations": [ { "from": "human", "value": "\nHow much did the temperature rise in the East region from 2015 to 2020?" }, { "from": "gpt", "value": "To determine the rise in temperature, I compare the {blue bars}[0.281, 0.436, 0.359, 0.437]; [0.773, 0.437, 0.851, 0.437] of the East region in the 2015 and 2020 charts. The y-values on the bar charts will provide the temperatures for both years. On the 2015 chart, the {blue bar}[0.281, 0.436, 0.359, 0.437] represents a temperature of 24 degrees Celsius, and on the 2020 chart, the {blue bar}[0.773, 0.437, 0.851, 0.437] shows a temperature of 26 degrees Celsius.\nBased on the above thought, the answer is:\n2 degrees Celsius." } ], "id_raw": 227 }, { "id": "PwVP1qqx94SP9TLq", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nCan you present the direct and indirect export values in a structured table?" }, { "from": "gpt", "value": "To create the table, let's look at the bar segments across each country's stacked bar. {The USA's bar}[0.092, 0.228, 0.173, 0.325] for direct exports shows a range of \"0.00 -> 35.00\" billion dollars. For indirect exports, I analyze the segment above direct exports, which extends the bar until it reaches the 40 billion mark {above the direct export segment}[0.092, 0.325, 0.173, 0.373]. Similarly, {China's bar}[0.193, 0.214, 0.273, 0.325] has a direct export value of 40 billion dollars and an indirect export value represented by the additional segment {above China's direct export segment}[0.193, 0.325, 0.273, 0.356] which is 30 billion dollars. Germany and Japan's figures are then sequentially determined in the same manner using their respective bars: {Germany's direct exports}[0.293, 0.256, 0.373, 0.325] and {Japan's direct exports}[0.393, 0.325, 0.473, 0.409], as well as the segments representing their indirect export values above those levels.\nBased on the above thought, the answer is:\nCountry,Direct Export,Indirect Export\nUSA,35,40\nChina,40,30\nGermany,25,20\nJapan,30,35" } ], "id_raw": 229 }, { "id": "mcc71rfNKibSKJf1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the chart, what can you tell me about the import trends for these four countries?" }, { "from": "gpt", "value": "To assess the import trends, I will examine the segments representing imports for each country. {The USA's import segment}[0.173, 0.325, 0.193, 0.373] indicates a value of 25 billion dollars. {China's import section}[0.273, 0.325, 0.293, 0.356] shows 20 billion dollars. When inspecting {Germany's and Japan's import segments}[0.373, 0.325, 0.393, 0.381] and [0.473, 0.325, 0.493, 0.409] respectively, their values can be assessed as 30 and 50 billion dollars. Notably, Japan has the largest import value, which is also the highest among all trade values represented on the chart for that country, signifying that it is the most import-dependent economy among the four.\nBased on the above thought, the answer is:\nJapan shows the highest level of imports, with a value of 50 billion dollars, which highlights its import dependency compared to the other three countries that show lower import values. Germany, with 30 billion dollars in imports, also has substantial import activity. The USA and China have comparably lower import trends at 25 and 20 billion dollars respectively." } ], "id_raw": 229 }, { "id": "i1frbzpIFRtskkp2", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_16_4.png", "conversations": [ { "from": "human", "value": "\nWhich country has the highest value for net exports?" }, { "from": "gpt", "value": "To determine the highest value for net exports, I will examine the topmost segments of each country's bar that represent net export data. I see that {China's net export segment}[0.685, 0.242, 0.765, 0.325] extends from 0 to 5 billion dollars, which appears to be the highest end point compared to other countries. By looking at the {USA's net export segment}[0.092, 0.228, 0.173, 0.254], {Germany's segment}[0.293, 0.256, 0.373, 0.284], and {Japan's segment}[0.393, 0.409, 0.473, 0.437], I can see they end at -30, -5, and -15 billion dollars respectively, which are all lower than China's figure. Therefore, China has the highest net export value among the displayed countries.\nBased on the above thought, the answer is:\nChina" } ], "id_raw": 229 }, { "id": "fVsQwybNzc9S4heo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nCould you list the GNP for each continent across the years 2000, 2010, and 2020 in a table format?" }, { "from": "gpt", "value": "Observing the bar chart, the graph for Africa shows {three bars}[0.086, 0.326, 0.196, 0.437];[0.224, 0.325, 0.335, 0.437];[0.362, 0.313, 0.473, 0.437] corresponding to the years 2000, 2010, and 2020 respectively, with the GNP values labeled on the x-axis beneath each bar. Similarly, the chart for Asia presents {three bars}[0.578, 0.344, 0.689, 0.437];[0.717, 0.315, 0.827, 0.437];[0.855, 0.280, 0.966, 0.437] for the same sequence of years. This pattern is repeated for {Europe}[0.066, 0.556, 0.492, 0.927], {North-America}[0.559, 0.556, 0.985, 0.927], {South-America}, and {Oceania} across their respective subplots and bars. By carefully examining each group of bars, I can list the GNP values for each continent from the chart.\nBased on the above thought, the answer is:\nContinent,GNP (2000),GNP (2010),GNP (2020)\nAfrica,890012.6,900315.7,1002146.8\nAsia,750021.4,980015.5,1269002.3\nEurope,1325100.6,1467801.2,1658001.4\nNorth-America,1801002.8,2105123.5,2390145.9\nSouth-America,510783.4,615796.4,792108.1\nOceania,410698.2,500912.8,625942.5" } ], "id_raw": 235 }, { "id": "UaPZkCC25IZRXuso", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nWhich continent exhibited the greatest growth in GNP from 2000 to 2020?" }, { "from": "gpt", "value": "To determine the continent with the greatest growth in GNP between 2000 and 2020, I must subtract the GNP of 2000 from the GNP of 2020 for each continent. Reviewing the bar charts, I take note of the height of the first and last bars within each group. Starting with {Africa\u2019s bars}[0.086, 0.326, 0.196, 0.437];[0.362, 0.313, 0.473, 0.437], the difference in GNP is substantial. The process is repeated to find the differences for {Asia}[0.578, 0.344, 0.689, 0.437];[0.855, 0.280, 0.966, 0.437], {Europe}[0.066, 0.556, 0.492, 0.927];[0.362, 0.722, 0.473, 0.927], {North-America}[0.559, 0.556, 0.985, 0.927];[0.855, 0.632, 0.966, 0.927], {South-America}, and {Oceania}. The continent with the largest numerical difference between 2000 and 2020 GNP is the one that experienced the greatest growth.\nBased on the above thought, the answer is:\nAsia. The difference in GNP between the year 2000 and 2020 for Asia is the highest, growing from 750021.4 to 1269002.3, indicating the most significant growth amongst all continents." } ], "id_raw": 235 }, { "id": "Lu8HSQOsJbjNWRdv", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the average GNP of all continents combined for the year 2020?" }, { "from": "gpt", "value": "To calculate the average GNP for the year 2020, I need to sum the GNP values for each continent in 2020 and divide by the number of continents. By identifying {the bar for Africa in 2020}[0.362, 0.313, 0.473, 0.437], {Asia's}[0.855, 0.280, 0.966, 0.437], {Europe's}[0.362, 0.722, 0.473, 0.927], {North-America's}[0.855, 0.632, 0.966, 0.927], {South-America's}, and {Oceania's} GNP bars, I glean the respective values. The sum of these values is 1002146.8 (Africa) + 1269002.3 (Asia) + 1658001.4 (Europe) + 2390145.9 (North-America) + 792108.1 (South-America) + 625942.5 (Oceania), which equals 7763246. After adding these, I divide by 6 (the number of continents) to find the average.\nBased on the above thought, the answer is:\n1293874.3" } ], "id_raw": 235 }, { "id": "hvqE5nLUkHp1vRSM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nCan you create a table from the bar chart data showing the trade volumes of United States from 2005 to 2020?" }, { "from": "gpt", "value": "Observing the first subplot titled {Global Trade Volumes Over the Years - United States}[0.035, 0.019, 0.511, 0.041], I see that the x-axis, labeled with {Year}[0.258, 0.473, 0.288, 0.491], has four tick marks corresponding to {2005}[0.098, 0.449, 0.133, 0.466], {2010}[0.203, 0.449, 0.238, 0.466], {2015}[0.308, 0.449, 0.343, 0.466], and {2020}[0.413, 0.449, 0.448, 0.466]. The y-axis is labeled with {Trade Volume (Trillion dollars)}[0.015, 0.113, 0.029, 0.370], with five ticks from {0}[0.035, 0.430, 0.043, 0.447] to {5}[0.035, 0.040, 0.043, 0.057], each representing an incremental trillion dollars. For each year, there is a bar representing the trade volume of the United States, such as the bar for {2005}[0.073, 0.271, 0.157, 0.437] is hovering just above the 2 tick, indicating a trade volume of approximately $2.12 trillion. The exact values can be garnered from the raw data provided, and so the CSV table reflecting the United States trade volume data can be presented.\nBased on the above thought, the answer is:\nYear,Trade Volume (Trillion dollars)\n2005,2.12\n2010,2.20\n2015,2.25\n2020,2.20" } ], "id_raw": 238 }, { "id": "RKgV9RLqrI9z5Bt0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nWhich country depicted in the chart has demonstrated the most significant growth in trade volume from 2005 to 2020?" }, { "from": "gpt", "value": "To evaluate the most significant growth, I need to observe the increase in bar heights for each country. First, I will compare the bars for {United States}[0.073, 0.271, 0.157, 0.437] showing minimal growth. Then I'll check {China}[0.566, 0.400, 0.650, 0.437] with a starting point near the 0 tick and ending much higher near the 4 tick. This indicates a significant increase. Continuing this method, I analyze {Germany}[0.073, 0.812, 0.157, 0.927], which shows a slight increase, similar to the {USA}[0.073, 0.812, 0.157, 0.927]. For {India}[0.566, 0.913, 0.650, 0.927], the growth looks impressive, starting just above 0 and ending near 1.2. And, {Japan}[not visible] is not depicted in the provided subplots and cannot be analyzed based on the provided chart. Comparing the starting and ending points of each, it's apparent that China has demonstrated the most significant growth.\nBased on the above thought, the answer is:\nChina. It started at approximately 0.47 trillion dollars in 2005 and increased to 4 trillion dollars in 2020, showing the most substantial growth among the depicted countries." } ], "id_raw": 238 }, { "id": "TnADlNYW6hd82Mow", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the average trade volume of China between 2005 and 2020 according to the data shown on the chart?" }, { "from": "gpt", "value": "To calculate the average trade volume of China between 2005 and 2020, I must locate the bars corresponding to China in the second subplot titled {Global Trade Volumes Over the Years - China}[0.560, 0.019, 0.971, 0.041]. The relevant bars are at coordinates {2005}[0.566, 0.400, 0.650, 0.437], {2010}[0.671, 0.298, 0.755, 0.437], {2015}[0.776, 0.224, 0.860, 0.437], and {2020}[0.881, 0.125, 0.965, 0.437]. By adding the trade volumes from the raw data for the given years: 0.47, 1.78, 2.72, and 4.00 respectively, and dividing by the number of years, which is 4, I can calculate the average.\nBased on the above thought, the answer is:\n2.2425" } ], "id_raw": 238 }, { "id": "NmXcWZASWx83irWo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nBased on the bar graph you observe, please structure the data into a table in CSV format." }, { "from": "gpt", "value": "The bar graph displays different industries that are being evaluated for investment distribution in software development and technology & innovation. From {the x-axis labels}[\"Healthcare\"[0.166, 0.710, 0.183, 0.900]; \"Education\"[0.285, 0.710, 0.302, 0.883]; \"Finance\"[0.403, 0.710, 0.421, 0.843]; \"Manufacturing\"[0.522, 0.710, 0.540, 0.963]; \"Energy\"[0.641, 0.710, 0.658, 0.832]; \"Transport\"[0.759, 0.710, 0.777, 0.874]; \"Agriculture\"[0.878, 0.710, 0.896, 0.902]] we can identify that there are seven industries. We distinguish two types of investments from {the legend}[0.347, 0.070, 0.714, 0.133], namely 'Software' represented by {green bar}[\"Software\"[0.403, 0.084, 0.480, 0.119]] and 'Tech & Innovation' represented by {yellow bar}[\"Tech&Innovation\"[0.563, 0.084, 0.707, 0.119]]. To get the percentage of investment in different industries, I need to calculate the ratios of the heights of the bars. By comparing the {heights of the green bars}['Healthcare'[0.121, 0.447, 0.228, 0.686]; 'Education'[0.240, 0.328, 0.347, 0.686]; 'Finance'[0.359, 0.251, 0.465, 0.686]; 'Manufacturing'[0.477, 0.341, 0.584, 0.686]; 'Energy'[0.596, 0.261, 0.703, 0.686]; 'Transport'[0.715, 0.290, 0.822, 0.686]; 'Agriculture'[0.833, 0.218, 0.940, 0.686]] and the {heights of the yellow bars}['Healthcare'[0.121, 0.136, 0.228, 0.447]; 'Education'[0.240, 0.136, 0.347, 0.328]; 'Finance'[0.359, 0.136, 0.465, 0.251]; 'Manufacturing'[0.477, 0.136, 0.584, 0.341]; 'Energy'[0.596, 0.136, 0.703, 0.261]; 'Transport'[0.715, 0.136, 0.822, 0.290]; 'Agriculture'[0.833, 0.136, 0.940, 0.218]].\nBased on the above thought, the answer is:\nIndustry,Software,Tech&Innovation\nHealthcare,43.5,56.5\nEducation,65,35\nFinance,79,21\nManufacturing,62.7,37.3\nEnergy,77.3,22.7\nTransport,72,28\nAgriculture,85,15" } ], "id_raw": 240 }, { "id": "n5HnNBhEtCCdviSC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what can you infer about investment trends in the Agriculture industry?" }, { "from": "gpt", "value": "There are two main observations for the {bar for \"Agriculture\"}[0.833, 0.218, 0.940, 0.686]. The first part of the bar, {green bar}[0.833, 0.218, 0.940, 0.686] indicating the software development investment, is significantly larger, taking up about 85% of the total investment. The second part, {yellow bar}[0.833, 0.136, 0.940, 0.218] representing the Technology & Innovation investment, is quite small, showing about 15% investment allocation. This clearly shows that investment in Agriculture is heavily shifted towards software development, with relatively lesser emphasis on Technology & Innovation.\nBased on the above thought, the answer is:\nThe trends indicate that the Agriculture industry receives heavy investment in software development compared to Technology & Innovation. The Software Development sector is allocated about 85% of the total investment, whereas Technology & Innovation is allocated about 15%." } ], "id_raw": 240 }, { "id": "q71GwVqqcjYbZKBG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_0.png", "conversations": [ { "from": "human", "value": "\nWhich industry is invested the smallest amount in Tech & Innovation according to the chart?" }, { "from": "gpt", "value": "To find the industry with the smallest investment in Technology & Innovation, I need to compare the heights of the {yellow bars}['Healthcare'[0.121, 0.136, 0.228, 0.447]; 'Education'[0.240, 0.136, 0.347, 0.328]; 'Finance'[0.359, 0.136, 0.465, 0.251]; 'Manufacturing'[0.477, 0.136, 0.584, 0.341]; 'Energy'[0.596, 0.136, 0.703, 0.261]; 'Transport'[0.715, 0.136, 0.822, 0.290]; 'Agriculture'[0.833, 0.136, 0.940, 0.218]] one by one. From the observation, the {bar for \"Agriculture\"}[0.833, 0.136, 0.940, 0.218] seems to have the smallest height, indicating the smallest investment in Technology & Innovation.\nBased on the above thought, the answer is:\nAgriculture." } ], "id_raw": 240 }, { "id": "Rrf9qLO1eJ2OVO3D", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the chart, could you please convert the given data into a CSV format table?" }, { "from": "gpt", "value": "The data provided are summarized in a bar chart, which includes the titles {\"Slow Growth\"}[0.261, 0.927, 0.370, 0.962] and {\"Accelerated Decline\"}[0.660, 0.927, 0.833, 0.962] on the x-axis. Three data sets are given, represented by three vertical bar sets, which include {\"Mobile Internet\"}[0.229, 0.052, 0.361, 0.087], {\"Optical Network\"}[0.444, 0.052, 0.584, 0.087] and {\"Satellite Communication\"}[0.667, 0.052, 0.881, 0.087] according to the legend. The y-axis ranges from {0%}[0.041, 0.889, 0.068, 0.924] to {100%}[0.019, 0.122, 0.068, 0.157]. The height of the bars describes the data for each set under each condition. The precise data can be determined by the intersection of the top of each bar with the y-axis gridlines. For example, the Mobile Internet under Slow Growth, the height of its {bar}[0.121, 0.635, 0.509, 0.903], intersects with the y-gridline at approximately 35%, so its value is 35.\nBased on the above thought, the answer is:\nStage,Mobile Internet,Optical Network,Satellite Communication\nSlow Growth,35,25,40\nAccelerated Decline,70,10,20" } ], "id_raw": 241 }, { "id": "bSBa1PUFjgBG4Swn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector you think has the most stable development? And explain your reasoning." }, { "from": "gpt", "value": "Stability can be judged primarily by the smallest fluctuation in development rate from the 'Slow Growth' stage to the 'Accelerated Decline' stage. At the 'Slow Growth' stage, the {Mobile Internet bar}[0.121, 0.635, 0.509, 0.903], {Optical Network bar}[0.121, 0.443, 0.509, 0.635] and the {Satellite Communication bar}[0.121, 0.136, 0.509, 0.443], the heights indicate that the data values are 35, 25, and 40 respectively. While at the 'Accelerated Decline' stage, the heights of the {Mobile Internet bar}[0.552, 0.366, 0.940, 0.903], {Optical Network bar}[0.552, 0.289, 0.940, 0.366], and the {Satellite Communication bar}[0.552, 0.136, 0.940, 0.289] suggest the values are 70, 10, and 20 respectively. Comparing the differences between the two stages for each industry, it is evident that the Optical Network sector has the smallest difference, meaning it has the most stable development.\nBased on the above thought, the answer is:\nThe Optical Network sector. Since the difference (absolute value of the contrast) between the 'Slow Growth' phase and the 'Accelerated Decline' phase is the smallest among all sectors, it can be said that the Optical Network sector has the most stable development." } ], "id_raw": 241 }, { "id": "JUTZ1aHxrVbfGcx1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_1.png", "conversations": [ { "from": "human", "value": "\nIn which category does the 'Accelerated Decline' stage decline the most?" }, { "from": "gpt", "value": "To find which category has the most decline in the 'Accelerated Decline' stage, I need to observe the height of the bars under {'Accelerated Decline'}[0.660, 0.927, 0.833, 0.962] for each category. The height of the {Mobile Internet bar}[0.552, 0.366, 0.940, 0.903] is approximately at 70% on the y-axis. The height of the {Optical Network bar}[0.552, 0.289, 0.940, 0.366] is around 10%. And the height of the {Satellite Communication bar}[0.552, 0.136, 0.940, 0.289] is close to 20% on the y-axis. Comparing these heights, it is clear that the Mobile Internet category shows the most rapid decline during the 'Accelerated Decline' stage.\nBased on the above thought, the answer is:\nMobile Internet." } ], "id_raw": 241 }, { "id": "eStnlWlLtS0ncG84", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into CSV format?" }, { "from": "gpt", "value": "The bar chart shows different categories of Innovation Projects on the x-axis and their respective performances on the y-axis. As given by the {x-axis labels}[0.166, 0.499, 0.183, 0.791] and so on, the categories include 'SecurityUpgrade', 'BackendRebuild', 'FrontendRevamp', 'MarketExpansion', 'LegacySystemRetirement', 'PartnershipDeal', and 'CustomerFeedbackSystem'. The y-axis ranges from {0%}[0.041, 0.461, 0.068, 0.496] to {100%}[0.019, 0.037, 0.068, 0.073]. In the {legend}[0.810, 0.069, 0.973, 0.236], we have three stages: 'RampingUP', 'ScalingUP', and 'CashCow'. The data for each category and each stage can be extracted from the heights of the bars. For example, for the 'SecurityUpgrade' project, the height of the {bar for 'RampingUP'}[0.121, 0.318, 0.228, 0.475] corresponds to 36.96%, the height of the {bar for 'ScalingUP'}[0.121, 0.188, 0.228, 0.318] corresponds to 30.74%, and the height of the {bar for 'CashCow'}[0.121, 0.051, 0.228, 0.188] corresponds to 32.29%. Hence, the CSV table can be generated based on these data.\nBased on the above thought, the answer is:\nProject,RampingUP,ScalingUP,CashCow\nSecurityUpgrade,36.96,30.74,32.29\nBackendRebuild,15.20,56.80,28.00\nFrontendRevamp,25.64,49.25,25.11\nMarketExpansion,32.15,45.34,22.51\nLegacySystemRetirment,38.11,43.89,18.00\nPartnershipDeal,30.93,29.18,39.89\nCustomerFeedbackSystem,40.11,49.89,10.00" } ], "id_raw": 242 }, { "id": "18qJ6pgGUXUWhl5K", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which project performed consistently across all three stages?" }, { "from": "gpt", "value": "The consistency of a project across all stages can be judged by observing the small variance in the height of the bars for each stage. The first project 'SecurityUpgrade' has bars at the 'RampingUP'[0.121, 0.318, 0.228, 0.475], 'ScalingUP'[0.121, 0.188, 0.228, 0.318], and 'CashCow' stages[0.121, 0.051, 0.228, 0.188]. The heights of these bars correspond to 36.96%, 30.74%, and 32.29% respectively. This means there is a moderate fluctuation across the stages. Now, I will similarly analyze the variance in bar heights for all other projects.\nBased on the above thought, the answer is:\nSecurityUpgrade. This project has the least variance in success rate across different stages compared to other projects." } ], "id_raw": 242 }, { "id": "Kxz7DRc10EBiPnwl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_2.png", "conversations": [ { "from": "human", "value": "\nWhich Innovation Project showed the most improvement from the RampingUP stage to the ScalingUP stage?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height difference in the bars for each project from the 'RampingUP' stage to the 'ScalingUP' stage. In the 'BackendRebuild' project, for example, the height of the {bar at 'RampingUP' stage}[0.240, 0.410, 0.347, 0.475] corresponds to 15.20%, while the height of the {bar at 'ScalingUP' stage}[0.240, 0.170, 0.347, 0.410] corresponds to 56.80%. So, the 'BackendRebuild' project saw an improvement of 41.60% points. Similarly, I will compare heights for all other projects.\nBased on the above thought, the answer is:\nBackendRebuild" } ], "id_raw": 242 }, { "id": "eiT10FVrqOTCx324", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nCan you transform the displayed bar chart data into a CSV format table?" }, { "from": "gpt", "value": "Observing the {x-ticks labels}[0.188, 0.609, 0.205, 0.898];[0.355, 0.609, 0.372, 0.963];[0.522, 0.609, 0.540, 0.845];[0.689, 0.609, 0.707, 0.933];[0.856, 0.609, 0.874, 0.874], the x-axis represents different activity growth trends. The y-axis, with its range from {0%}[0.041, 0.571, 0.068, 0.606] to {100%}[0.019, 0.122, 0.068, 0.157], represents the contributions in percentage. There are three color-coded segments for each activity growth trend, which represent the contributions of three types of aid: {Medical Assistance}[0.206, 0.099, 0.371, 0.134], {Education Aid}[0.454, 0.099, 0.574, 0.134], and {Environmental Conservation}[0.657, 0.099, 0.904, 0.134], as indicated in the legend. To construct the CSV table, I need to calculate the percentage contribution of each type of aid in each activity growth trend. I can use the relative height of each segment in a bar to do this. For example, in the \"Rapid Expansion\" trend, the {Medical Assistance segment}[0.121, 0.270, 0.272, 0.585] extends almost to 70%, the {Education Aid segment}[0.121, 0.177, 0.272, 0.270] reaches approximately to 20%, and the {Environmental Conservation segment}[0.121, 0.136, 0.272, 0.177] extends to about 10%.\nBased on the above thought, the answer is:\nActivity,Medical Assistance,Education Aid,Environmental Conservation\nRapid Expansion,70,20,10\nSubstantial Increase,10,30,60\nRapid Growth,15,70,15\nMarginal Decrease,25,20,55\nVolatile Growth,30,45,25" } ], "id_raw": 243 }, { "id": "dgBXDIGglr9sQJqb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which growth trend seems to be the most balanced in term of types of aid distribution?" }, { "from": "gpt", "value": "To answer this question, I need to observe the height variations in different sectors of each trend. The trend {Volatile Growth}[0.856, 0.609, 0.874, 0.874] seems to have the {Medical Assistance segment}[0.790, 0.444, 0.940, 0.585] running from 0% to 30%, {Education Aid segment}[0.790, 0.250, 0.940, 0.444] from 30% to 75%, and {Environmental Conservation segment}[0.790, 0.136, 0.940, 0.250] from 75% to 100%. The distribution across the three types of aid is the most even among all the trends.\nBased on the above thought, the answer is:\nVolatile Growth. It has a more evenly distributed contribution towards all three types of aid: Medical Assistance, Education Aid, Environmental Conservation as compared to other trends." } ], "id_raw": 243 }, { "id": "2CzOWRQmzPCmMvX5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat trend has the largest percentage of Education Aid?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height of the {Education Aid segment}[0.454, 0.099, 0.574, 0.134] in each trend. The trend named {Rapid Growth}[0.522, 0.609, 0.540, 0.845] has a Education Aid segment ranging from roughly 15% to 85%, meaning it has the greatest height among every trend.\nBased on the above thought, the answer is:\nRapid Growth" } ], "id_raw": 243 }, { "id": "n6XucRt1EVrJeANI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nCan you summarise the data behind the bar chart you're analysing in a CSV format table?" }, { "from": "gpt", "value": "To create the CSV table, I need to extract the values associated with each bar representing an industry sector (x-axis) and the social responsibility measure it signifies (color). As per {the legend}[0.232, 0.054, 0.817, 0.104], the green bars represent 'Environmental Impact', the gold bars represent 'Social Initiatives', and the blue bars represent 'Profit Sustainability'. I'll use these colors to differentiate between the measures. Then, for each industry, namely {'Technology'}[0.096, 0.759, 0.161, 0.889], {'E-commerce'}[0.211, 0.759, 0.282, 0.902], {'Energy'}[0.359, 0.759, 0.403, 0.848], {'Transportation'}[0.444, 0.759, 0.525, 0.921], {'Food and Beverage'}[0.541, 0.759, 0.646, 0.970], {'Financial Services'}[0.670, 0.759, 0.767, 0.954], and {'Healthcare'}[0.825, 0.759, 0.889, 0.886], I will identify the corresponding values for 'Environmental Impact', 'Social Initiatives', and 'Profit Sustainability' from the heights of the respective bars.\nBased on the above thought, the answer is:\nCategories,Environmental Impact,Social Initiatives,Profit Sustainability\nTechnology,35,45,20\nE-commerce,40,35,25\nEnergy,50,35,15\nTransportation,35,40,25\nFood and Beverage,40,25,35\nFinancial Services,50,35,15\nHealthcare,60,30,10" } ], "id_raw": 244 }, { "id": "2zqnRvJCkVQZ5ubI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nWhich industry is the most oriented towards sustainable business practices and why?" }, { "from": "gpt", "value": "By assessing each industry's bars, I can see the total height represents the industry's overall commitment to sustainable business practices. This total height is the sum of the values for 'Environmental Impact'{\"Environmental Impact\"}[0.237, 0.068, 0.265, 0.087], 'Social Initiatives'{\"Social Initiatives\"}[0.458, 0.068, 0.485, 0.087] and 'Profit Sustainability'{\"Profit Sustainability\"}[0.638, 0.068, 0.666, 0.087]. Here all bars reach the same height, indicating that all industries have a 100% commitment to sustainable business practices. However, if we consider the distribution of focus, 'Healthcare'[0.825, 0.759, 0.889, 0.886] has the largest focus on 'Environmental Impact' and lower focus on 'Profit Sustainability', suggesting a strong focus on environmental sustainability over profit.\nBased on the above thought, the answer is:\nAll industries exhibit the same level of total commitment to sustainable business practices. However, 'Healthcare' stands out with its significant focus on 'Environmental Impact', indicating a strong emphasis on environmental sustainability over profit optimization." } ], "id_raw": 244 }, { "id": "qnAvbryBpwFVwb8Z", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_4.png", "conversations": [ { "from": "human", "value": "\nIn which industry does 'Profit Sustainability' have the smallest percentage share?" }, { "from": "gpt", "value": "To find out the industry where 'Profit Sustainability' is the least, I need to look at the {blue bars}{\"Profit Sustainability\"}[0.638, 0.068, 0.666, 0.087] associated with each category. Scanning from left to right, I find 'Profit Sustainability' for 'Technology'[0.106, 0.132, 0.215, 0.253] at 20%, 'E-commerce'[0.227, 0.132, 0.337, 0.284] at 25%, 'Energy'[0.349, 0.132, 0.458, 0.223] at 15%, 'Transportation'[0.470, 0.132, 0.579, 0.284] at 25%, 'Food and Beverage'[0.591, 0.132, 0.701, 0.344] at 35%, 'Financial Services'[0.713, 0.132, 0.822, 0.223] at 15%, and 'Healthcare'[0.834, 0.132, 0.943, 0.192] at 10%. Therefore, 'Healthcare' demonstrates the lowest 'Profit Sustainability'.\nBased on the above thought, the answer is:\nHealthcare" } ], "id_raw": 244 }, { "id": "1zUx82BZkVvaqaBz", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nAs per the bar chart you see, gather the required data from the GDP growth, Unemployment rate and Inflation rate and convert it into CSV format." }, { "from": "gpt", "value": "Based on the color code of the chart, there are three types of data represented by different colors according to their corresponding legends. The {green bars}[0.374, 0.67, 0.425, 0.68] signify the GDP growth. The {yellow bars}[0.374, 0.67, 0.425, 0.68] indicate the unemployment rate and the {blue bars}[0.374, 0.67, 0.425, 0.68] show the inflation rate. The years are denoted at the base of these bars {Year1}[0.150, 0.845, 0.167, 0.941], {Year2}[0.232, 0.845, 0.250, 0.940] and so on. My task is to form a CSV table by recording the heights of the bars which represent the respective percentages in each category and for each year.\nBased on the above thought, the answer is:\nYear,GDP Growth,Unemployment Rate,Inflation Rate\nYear1,4.3,5.6,1.3\nYear2,3.9,6.1,1.6\nYear3,3.7,6.4,1.9\nYear4,3.1,6.9,2.1\nYear5,2.8,7.2,2.4\nYear6,2.3,7.7,2.8\nYear7,1.9,8.3,3.1\nYear8,1.5,8.7,3.5\nYear9,1.1,9.4,3.9\nYear10,0.7,9.9,4.2" } ], "id_raw": 245 }, { "id": "52gEzh1Mpv2nGyYF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what trends can you notice in terms of macroeconomic indicators over the given period of time." }, { "from": "gpt", "value": "According to {the green bars}[0.121, 0.791, 0.196, 0.821] representing GDP growth consistently descending, it shows that the economy of the country has slowed down over this period. At the same time, as per the {yellow bars}[0.204, 0.752, 0.278, 0.794] or unemployment rate indicated by yellow color is on the rise which shows that job creation has not been able to keep up with the increase in the labor force. Last but not least, the {blue bars}[0.674, 0.067, 0.708, 0.092] reflecting the inflation rate have been increasing at an accelerated rate which indicates there has been an increase in the overall price levels.\nBased on the above thought, the answer is:\nThe trends observed from the chart indicates a slowing down of the economy as GDP growth is declining, the unemployment rate is increasing and inflation is on the rise." } ], "id_raw": 245 }, { "id": "pvp92iU4ylQmXauT", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_5.png", "conversations": [ { "from": "human", "value": "\nIn which year the unemployment rate rose more than 1%?" }, { "from": "gpt", "value": "I need to compare the heights of yellow bars representing the unemployment rate year by year. For example, comparing the height of the yellow bar for {Year1}[0.121, 0.753, 0.196, 0.791] to the height of the yellow bar for {Year2}[[0.204, 0.752, 0.278, 0.794]. Keep doing this until I find the first year where the difference in heights represents a rise of more than 1%.\nBased on the above thought, the answer is:\nYear2." } ], "id_raw": 245 }, { "id": "g8wYYnXpEIG07Wbv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into a CSV format?" }, { "from": "gpt", "value": "By scanning the x-axis tick labels, I see that they represent the categories: {Asian Villages}[0.166, 0.555, 0.183, 0.796], {European Architectures}[0.285, 0.555, 0.302, 0.963], {African Traditions}[0.403, 0.555, 0.421, 0.856], {Indigenous Cultures}[0.522, 0.555, 0.540, 0.901], {Global Expo}[0.641, 0.555, 0.658, 0.763], {Street Festivals}[0.759, 0.555, 0.777, 0.822], {World Heritage Sites}[0.878, 0.555, 0.896, 0.912]. The percentages labeled on y-axis tick labels range from {0%}[0.041, 0.517, 0.068, 0.552] to {100%}[0.019, 0.122, 0.068, 0.157]. Through the {legend}[0.322, 0.094, 0.739, 0.156] above the chart, I can find the percentages of {Local Culture}[0.378, 0.108, 0.492, 0.143] and {External Influence}[0.526, 0.111, 0.561, 0.135] in each category. Therefore, to generate the CSV table, I should consider the height of the bars and the percentage on the y-axis. For example, in Asian Villages category, the Local Culture bar[0.121, 0.238, 0.228, 0.531] reaches about the 74 position and the External Influence bar[0.121, 0.136, 0.228, 0.238] starts at about 74 and ends at 100. Thus, it is safe to say that the percentages of Local Culture and External Influence in Asian Villages are 74% and 26% respectively.\nBased on the above thought, the answer is:\ncultural_heritage,local_culture,external_influence\nAsian Villages,74,26\nEuropean Architectures,80,20\nAfrican Traditions,40,60\nIndigenous Cultures,60,40\nGlobal Expo,70,30\nStreet Festivals,65,35\nWorld Heritage Sites,85,15" } ], "id_raw": 246 }, { "id": "0GuTmxHcdywYMi7a", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nCan you identify a trend in the cultural integration across the different types of cultural heritages based on the chart?" }, { "from": "gpt", "value": "To spot a trend, I need to compare the colored bars across all seven categories of cultural heritages. The {green bars}Local Culture: [0.121, 0.238, 0.228, 0.531; 0.240, 0.215, 0.347, 0.531; 0.359, 0.373, 0.465, 0.531; 0.477, 0.294, 0.584, 0.531; 0.596, 0.254, 0.703, 0.531; 0.715, 0.274, 0.822, 0.531; 0.833, 0.195, 0.940, 0.531] indicate the percentage of local culture, and the {yellow bars}External Influence: [0.121, 0.136, 0.228, 0.238; 0.240, 0.136, 0.347, 0.215; 0.359, 0.136, 0.465, 0.373; 0.477, 0.136, 0.584, 0.294; 0.596, 0.136, 0.703, 0.254; 0.715, 0.136, 0.822, 0.274; 0.833, 0.136, 0.940, 0.195] represent the percentage of external influences. I observe that the global heritages like World Heritage Sites and European Architectures have larger percentages of local culture, suggesting a tendency of more local cultures being preserved in these heritages. On the other hand, the region-specific heritages such as African Traditions and Indigenous Cultures have lesser percentages of local culture but higher percentages of external influences, indicating more exchanges and interactions between different cultures.\nBased on the above thought, the answer is:\nYes, there seems to be a tendency that more global heritages have higher percentages of local culture while more region-specific heritages have higher percentages of external influences, suggesting great culture integrations in these heritages." } ], "id_raw": 246 }, { "id": "Igb6kdqnarbks0z6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_6.png", "conversations": [ { "from": "human", "value": "\nWhich type of cultural heritage has the least local culture?" }, { "from": "gpt", "value": "To find the type of cultural heritage with the least local culture, I should analyze the height of the green bars representing the local culture for each type. For instance, the green bar for {Asian Villages}[0.121, 0.238, 0.228, 0.531] reaches almost to the 74% position on the y-axis, indicating that 74% of the culture in Asian Villages is local. Analyzing the other bars in the same manner, I find that the green bar for {World Heritage Sites}[0.833, 0.195, 0.940, 0.531] is the smallest, with its top at the 85% position on the y-axis. This indicates that only 85% of the culture in World Heritage Sites is local.\nBased on the above thought, the answer is:\nWorld Heritage Sites." } ], "id_raw": 246 }, { "id": "ob4cv94LQoP12XpY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the gig company's contributions regarding environmental\n protection, charity events and social initiatives into a structured csv\n table?" }, { "from": "gpt", "value": "The chart's arrangement shows that the x-axis represents companies such as {Apple}[0.175, 0.799, 0.192, 0.898], \n {Microsoft}[0.314, 0.799, 0.331, 0.957], {IBM}[0.453, 0.799, 0.470, 0.863], {Google}[0.591, 0.799, 0.609, 0.921], {Facebook}[0.730, 0.799, 0.748, 0.963], and {Amazon}[0.869, 0.799, 0.887, 0.939]\n while the y-axis indicates the percentage of contributions, ranging from {0%}[0.041, 0.761, 0.068, 0.796] to {100%}[0.019, 0.122, 0.068, 0.157].\n The different colored bars within each company represent the three different aspects of contribution. According to the {legend}[0.168, 0.057, 0.894, 0.120],\n the green color represents {Environment Protection}[0.223, 0.071, 0.427, 0.106],\n the yellow represents {Charity Programs}[0.511, 0.071, 0.662, 0.106] and the blue represents {Social Initiatives}[0.745, 0.071, 0.887, 0.106].\n Reading from the chart: {Apple}'s in the environmental protection is approximately {60%}[0.121, 0.391, 0.246, 0.774], \n charity programs is {30%}[0.121, 0.200, 0.246, 0.391] and social initiatives is {10%}[0.121, 0.136, 0.246, 0.200]. \n The same method can be applied to make sense of data from the other companies.\nBased on the above thought, the answer is:\nCompanyName,Environment Protection,Charity Programs,Social Initiatives\nApple,60,30,10\nMicrosoft,50,25,25\nIBM,40,40,20\nGoogle,70,20,10\nFacebook,60,25,15\nAmazon,30,50,20" } ], "id_raw": 247 }, { "id": "8NMJlN4nUfn5yw27", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which company appears to prioritize environmental protection over other areas? And what can you infer about their social responsibility approach?" }, { "from": "gpt", "value": "Looking at the green bars, which represent environmental protection in the {legend}[0.168, 0.057, 0.894, 0.120], {Google}'s green section [0.538, 0.327, 0.663, 0.774] is visually the tallest. This indicates that Google contributes the most to environmental protection. It filled around 70% of the total bar, far more than the other areas indicated by the yellow section for charity programs [0.538, 0.200, 0.663, 0.327] and blue section for social initiatives [0.538, 0.136, 0.663, 0.200].\nBased on the above thought, the answer is:\nGoogle appears to prioritize environmental protection over charity programs and social interventions. This could suggest an organization strategy that places a high value on sustainability and environmental stewardship as a part of its social responsibility." } ], "id_raw": 247 }, { "id": "u2qYqtIeE4ZNIDzu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_7.png", "conversations": [ { "from": "human", "value": "\nWhich company has the lowest involvement in social initiatives according to the chart?" }, { "from": "gpt", "value": "The blue sections of the bars represent social initiatives performance, and we need to look at the height of the blue sections for each company. As per the data represented by the bars; {Apple}'s blue section is approximately {10%}[0.121, 0.136, 0.246, 0.200], {Microsoft}'s blue section is about {25%}[0.260, 0.136, 0.385, 0.295], {IBM}'s blue section is about {20%}[0.399, 0.136, 0.524, 0.263], {Google}'s blue section is about {10%}[0.538, 0.136, 0.663, 0.200], {Facebook}'s blue section is about {15%}[0.677, 0.136, 0.801, 0.231], and {Amazon}'s blue section is about {20%}[0.815, 0.136, 0.940, 0.263]. From this analysis, Google and Apple both have the lowest involvement in social initiatives, each with a value of 10%.\nBased on the above thought, the answer is:\nGoogle and Apple." } ], "id_raw": 247 }, { "id": "I5WR1P09VrC5K169", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart, can you convert the data into a CSV formatted table?" }, { "from": "gpt", "value": "The chart contains two categories, represented on the x-axis: {Market Share}[0.306, 0.731, 0.324, 0.963] and {Projection}[0.738, 0.731, 0.755, 0.903], which correspond to current and future projected market shares of three companies. For each category, three different bars are stacked on top of each other, representing the three companies: {Company A}[0.320, 0.082, 0.421, 0.117], {Company B}[0.504, 0.082, 0.605, 0.117], and {Company C}[0.689, 0.082, 0.790, 0.117]. Taking the height of these bars relative to y-axis percentages and considering they are stacked on top of each other, it can give the market share data. The y-axis percentage labels range from {0%}[0.041, 0.693, 0.068, 0.728] at the bottom to {100%}[0.019, 0.122, 0.068, 0.157] at the top. By observing the stacks, the top of the Company A bar for Market Share reaches about 30%, for Company B it reaches around 56%, and for Company C, it reaches 100%. Similarly, for projections, the height of each bar signifies the respective percentages.\nBased on the above thought, the answer is:\nCategories,Company A,Company B,Company C\nMarket Share,30,26,44\nProjection,29,30,41" } ], "id_raw": 248 }, { "id": "ZdDz8dTPISfDF0J9", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which company do you think can potentially dominate the market in the future, and why?" }, { "from": "gpt", "value": "It is apparent from comparing {Market Share}[0.306, 0.731, 0.324, 0.963] and {Projection}[0.738, 0.731, 0.755, 0.903] stacks that all three companies \u2013 {Company A}[0.320, 0.082, 0.421, 0.117], {Company B}[0.504, 0.082, 0.605, 0.117], and {Company C}[0.689, 0.082, 0.790, 0.117] \u2013 are projecting changes in their market shares. However, it is only {Company B}[0.504, 0.082, 0.605, 0.117] that is expecting an increase in its market share from 26% to 30%, while both {Company A}[0.320, 0.082, 0.421, 0.117] and {Company C}[0.689, 0.082, 0.790, 0.117] predict a decrease. Although Company B will not attain the highest market share, its upward trend might imply that it has a stronger potential for market growth and domination.\nBased on the above thought, the answer is:\nCompany B. While it may not hold the highest share in the projection, it is the only one projecting a market share increase, suggesting a potential for future growth and possible market domination." } ], "id_raw": 248 }, { "id": "YOWBAMSPuOnZhfjE", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_8.png", "conversations": [ { "from": "human", "value": "\nWho among the three companies will see the biggest alteration in their market share according to the projection scenario?" }, { "from": "gpt", "value": "In order to answer this question, I have to calculate the difference between each company's current market share and their projection. This can be achieved by comparing the height of the bars in the {Market Share}[0.306, 0.731, 0.324, 0.963] stack and the {Projection}[0.738, 0.731, 0.755, 0.903] stack for each company. For {Company A}[0.320, 0.082, 0.421, 0.117], the difference is 1%; for {Company B}[0.504, 0.082, 0.605, 0.117], the difference is 4%, and for {Company C}[0.689, 0.082, 0.790, 0.117], the difference is 3%. Therefore, the company with the biggest change in market share will be the one with the highest difference.\nBased on the above thought, the answer is:\nCompany B" } ], "id_raw": 248 }, { "id": "tt7TSRjSjeC5kPxp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nCan you present the data in CSV format from the stacked bar chart that shows the progression of human civilization across Agriculture, Science, and Industrialization?" }, { "from": "gpt", "value": "The chart layout shows that the x-axis represents different stages of civilization from {the first stage being \"Primitive Stage\"}[0.082, 0.770, 0.167, 0.939] to {the last stage being \"Contemporary Era\"}[0.782, 0.770, 0.882, 0.970]. The y-axis has tick points going from {0%}[0.033, 0.739, 0.055, 0.767] to {max point is 100%}[0.015, 0.121, 0.055, 0.149]. Each stage of civilization has three stacked bars which represent three areas of development: {\"Agriculture\"}[0.357, 0.064, 0.433, 0.092], {\"Science\"}[0.500, 0.064, 0.553, 0.092], and {\"Industrialization\"}[0.620, 0.064, 0.732, 0.092]. One must observe the height of each segment in the stacked bars and compare it to the points on the y-axis to convert it into CSV data. For instance, for the \"Primitive Stage\", the green color bar annotated as \"Agriculture\" goes from 0% up to 20%, the yellow bar \"Science\" starts from 20% and ends at 25%, and the blue bar \"Industrialization\" indicates 0% change with no visible progression from the start point of the yellow bar section.\nBased on the above thought, the answer is:\nCivilization Stage,Agriculture,Science,Industrialization\nPrimitive Stage,20,5,0\nAncient Period,52,15,2\nClassical Period,70,35,5\nMiddle Ages,81,55,31\nModern History,89,83,72\nContemporary Era,90,96,93" } ], "id_raw": 249 }, { "id": "Gpn77EPzXrI6XQeB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat are some trends you can identify in human civilization progression across Agriculture, Science, and Industrialization as depicted in the chart?" }, { "from": "gpt", "value": "Based on the chart, it is evident that there are some clear trends in the progression of human civilization across all three dimensions - Agriculture, Science, and Industrialization. As civilization progresses, all three sectors have seen growth, but at different rates and in different stages. Specifically, the section for {\"Agriculture\"}[0.357, 0.064, 0.433, 0.092] shows an early focus and steady growth, suggesting that agriculture was a vital aspect of early civilization and continued to improve over time. As for {\"Science\"}[0.500, 0.064, 0.553, 0.092], it also exhibits a smooth upward trend, however, its acceleration appears later than Agriculture, indicating that the development of science might have taken off after securing agricultural stability. Lastly, {\"Industrialization\"}[0.620, 0.064, 0.732, 0.092] can be seen as stagnant over the early civilization stages, and only begins to show significant progress noticeably later - particularly booming from the Middle Ages onwards, which aligns with the historical period where industrial revolution took place.\nBased on the above thought, the answer is:\nThe trends suggest an early onset and steady rise in Agriculture, pointing to its importance in human survival and civilization. Science similarly grows but starts accelerating in later stages, particularly from the Classical Period onwards, suggesting a post agricultural stability phase of intellectual and scientific pursuits. Industrialization only ramps up in much later stages, most prominently between the Middle Ages and Modern History, reflecting the historical period where the industrial revolution famously took place." } ], "id_raw": 249 }, { "id": "nBAYJNWATSpDYkSF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_9.png", "conversations": [ { "from": "human", "value": "\nIn which stage of civilization does Industrialization shows the greatest improvement as compared to the previous stage?" }, { "from": "gpt", "value": "To answer this, I would first identify the height of the Industrialization section for each civilization stage. Then I would calculate the difference between each stage and the previous one. For example, the difference between the height of the Industrialization section in the Middle Ages {height of the Middle Ages Industrialization bar}[0.535, -0.283, 0.657, -0.091] and the one in Classical Period {height of the Classical Period Industrialization bar}[0.392, 0.070, 0.514, 0.101] is about 26% (31% in the Middle Ages - 5% in the Classical Period). By performing a similar comparison between all the stages, I can identify the stage in which Industrialization shows the greatest leap.\nBased on the above thought, the answer is:\nFrom Middle Ages to Modern History." } ], "id_raw": 249 }, { "id": "xyow9ZHslseYk51R", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the bar chart data into a structured table in CSV format?" }, { "from": "gpt", "value": "Sure, the bar chart appears to represent fitness and sports-related trends. The title {Fitness and Sports Health Trends}[0.355, 0.080, 0.695, 0.125] confirms this. The x-axis represents {the categories}[{\"Physical Activity\"}[0.149, 0.596, 0.167, 0.878]; {\"Strength Training\"}[0.254, 0.596, 0.272, 0.895]; {\"Yoga\"}[0.359, 0.596, 0.377, 0.677]; {\"Meditation\"}[0.464, 0.596, 0.482, 0.781]; {\"Healthy Eating\"}[0.569, 0.596, 0.587, 0.853]; {\"Dietary Supplements\"}[0.674, 0.596, 0.691, 0.963]; {\"Water Intake\"}[0.779, 0.596, 0.796, 0.818]; {\"Treadmill Run\"}[0.884, 0.596, 0.901, 0.831]], while the y-axis represents the corresponding values with a scale seen from the ticks from {0%}[0.030, 0.558, 0.057, 0.593] to {50%}[0.019, 0.122, 0.057, 0.157]. I notice that there are two bars for each category: one color-coded as {green}[handle][0.308, 0.105, 0.343, 0.129] representing Fitness Trends, and another as {blue}[handle][0.514, 0.105, 0.548, 0.129] representing Sports Health Trends as confirmed by the legend {Fitness Trends and Sports Health Trends}[0.301, 0.088, 0.749, 0.150]. By analyzing bar height and using the y-axis scale, I can determine the values for each trend in each category. As such, the bar for {Fitness Trends in Physical Activity}[0.111, 0.354, 0.205, 0.572] is around 25 as per the y-axis scale, and similarly for the other bars.\nBased on the above thought, the answer is:\nActivity,Fitness Trends,Sports Health Trends\nPhysical Activity,25,35\nStrength Training,28,27\nYoga,30,12\nMeditation,21,18\nHealthy Eating,38,24\nDietary Supplements,28,30\nWater Intake,35,28\nTreadmill Run,34,42" } ], "id_raw": 250 }, { "id": "10nkz3IANWmV6hcI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, could you argue which one between fitness and sports health trends is more balanced in terms of diverse activities?" }, { "from": "gpt", "value": "Looking at the two categories - {Fitness Trends}[handle][0.308, 0.105, 0.343, 0.129] and {Sports Health Trends}[handle][0.514, 0.105, 0.548, 0.129] across different activities, it's clear that they display different patterns of popularity. For {Fitness Trends}, the activities - {Physical Activity}[bar][0.111, 0.354, 0.205, 0.572], {Strength Training}[bar][0.216, 0.327, 0.310, 0.572], and {Yoga}[bar][0.321, 0.310, 0.415, 0.572], have similar popularity levels, while {Healthy Eating}[bar][0.531, 0.240, 0.625, 0.572] is significantly more popular. On the other hand, for {Sports Health Trends}, most activities exhibit a relatively balanced popularity, except for {Treadmill Run}[bar][0.845, -0.091, 0.940, 0.275], which significantly stands out. Therefore, by comparing the spread of popularity across diverse activities between both trends, it can be concluded that Sports Health Trends has a more balanced spread of popularity across different activities.\nBased on the above thought, the answer is:\nSports Health Trends. The popularity of different activities is more balanced in Sports Health Trends." } ], "id_raw": 250 }, { "id": "fakCtsOJRc61tur4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_10.png", "conversations": [ { "from": "human", "value": "\nWhich activity has the largest difference in popularity between Fitness Trends and Sports Health Trends?" }, { "from": "gpt", "value": "Comparing the bar heights between fitness and sports health trend for each category can answer this. Let's begin from the left side: the first pair of bars are for {Physical Activity}[0.111, 0.354, 0.205, 0.572] and [0.111, 0.048, 0.205, 0.354], and the difference between these bars is 10. Moving rightwards, the next pair of bars are for {Strength Training}[0.216, 0.327, 0.310, 0.572] and [0.216, 0.092, 0.310, 0.327], the difference here is 1. I will continue this analysis from {Yoga} through to {Treadmill Run}. On analysis, I see that the category with the largest difference in popularity between Fitness Trends and Sports Health Trends is found in {Yoga}[0.321, 0.310, 0.415, 0.572] and [0.321, 0.205, 0.415, 0.310], where the popularity ratings are starkly different, at 30 and 12 respectively, the difference adds up to be 18, which is the largest amongst all categories.\nBased on the above thought, the answer is:\nYoga." } ], "id_raw": 250 }, { "id": "oG5teR8UbTYeC4wE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nCan you turn the stack bars you see into a CSV format table?" }, { "from": "gpt", "value": "From the color segmentation in the bars, I can tell there are three different fitness activities - Cardio Exercise, Weight Lifting, and Yoga. Each fitness activity is represented by a different color in the stack. For example, {green part}[0.106, 0.800, 0.234, 0.935] of the bar represents Cardio Exercise. We can extract the values by examining the length (height) of each colored segment on the {y-scale}[0.033, 0.926, 0.055, 0.950], [0.024, 0.746, 0.055, 0.769], [0.024, 0.566, 0.055, 0.589], [0.024, 0.386, 0.055, 0.409] [0.024, 0.205, 0.055, 0.229] and [0.015, 0.025, 0.055, 0.048]. Each segment represents a \"block\" of fitness activity participation for that year. From the {legend}[0.821, 0.046, 0.978, 0.157], the green bar corresponds to cardio exercise, the orange bar corresponds to weight lifting and the blue bar corresponds to yoga. Going through the chart, slicing up each vertically stacked bar into their colored segments and extracting the lengths onto their corresponding activities will produce the CSV table.\nBased on the above thought, the answer is:\nYear, Cardio Exercise, Weight Lifting, Yoga\n2010, 15, 25, 60\n2011, 20, 30, 50\n2012, 25, 35, 40\n2013, 30, 40, 30\n2014, 35, 45, 20\n2015, 40, 50, 10" } ], "id_raw": 251 }, { "id": "pGDrbdZ4JaTfjraW", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the chart, what could we predict for the fitness preferences in 2016?" }, { "from": "gpt", "value": "Seeing as Cardio Exercise, represented by the {green}[0.106, 0.800, 0.234, 0.935] bars, and Weight Lifting, represented by the {orange}[0.106, 0.575, 0.234, 0.800] bars, have consistently increased over the years from 2010 to 2015, we can predict that their participation rates will continue to increase into 2016. Conversely, participation in Yoga, represented by the {blue}[0.106, 0.034, 0.234, 0.575] bars, has been decreasing consistently from 2010 to 2015. If these trends continue into 2016, we anticipate that the participation rate for Yoga will continue to decrease.\nBased on the above thought, the answer is:\nWe can predict that participation in Cardio Exercise and Weight Lifting will increase, while participation in Yoga will decrease in 2016." } ], "id_raw": 251 }, { "id": "RqcDb0RPaWGITNMn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_11.png", "conversations": [ { "from": "human", "value": "\nWhich year saw the most significant reduction in Yoga participation?" }, { "from": "gpt", "value": "By comparing the heights of the {blue bars}[0.106, 0.034, 0.234, 0.575], [0.248, 0.034, 0.376, 0.485], [0.390, 0.034, 0.518, 0.395], [0.532, 0.034, 0.659, 0.305], [0.674, 0.034, 0.801, 0.214], [0.815, 0.034, 0.943, 0.124] that represent Yoga participation from year to year, we can calculate the difference between adjacent years. For example, the length of the blue bar in 2011 [0.248, 0.034, 0.376, 0.485] is smaller than that in 2010 [0.106, 0.034, 0.234, 0.575], indicating a decrease in participation. Comparing these reductions year by year, it's clear that the most significant difference happened between 2012 and 2013.\nBased on the above thought, the answer is:\n2013." } ], "id_raw": 251 }, { "id": "cDVxzXGKNU61onIM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nCan you provide the percentages of effort mentioned in the chart in CSV format?" }, { "from": "gpt", "value": "To organize the chart data into a CSV table, I first identify the x-axis labels as the categories, which include {Environment}[0.207, 0.632, 0.224, 0.853], {Work Policies}[0.417, 0.632, 0.435, 0.859], {Education}[0.627, 0.632, 0.645, 0.805], and {Affordable Housing}[0.837, 0.632, 0.855, 0.963].\nNext, I see that each category has three bars representing {Social Awareness}[0.187, 0.099, 0.222, 0.124], {Sustainable Production}[0.420, 0.099, 0.455, 0.124], and {Human Rights}[0.703, 0.099, 0.738, 0.124] based on the legend. The heights of these bars can direct me to the corresponding y-coordinate percentages. Using this method, I can determine the percentages of effort for each category and compile them into CSV format.\nBased on the above thought, the answer is:\nCategory,Social Awareness,Sustainable Production,Human Rights\nEnvironment,43,23,34\nWork Policies,52,32,16\nEducation,3,55,42\nAffordable Housing,38,20,42" } ], "id_raw": 252 }, { "id": "DM6rFLLglBU5nKhT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which social responsibility category should companies focus more on for sustainable production and why?" }, { "from": "gpt", "value": "From the chart, it can be seen that sustainable production is denoted by the {second (yellow) bar}[0.420, 0.099, 0.455, 0.124] in each category. Checking each bar for {Environment}[0.121, 0.296, 0.310, 0.404], {Work Policies}[0.331, 0.211, 0.520, 0.362], {Education}[0.541, 0.334, 0.730, 0.593], and {Affordable Housing}[0.751, 0.334, 0.940, 0.428], I notice that for Education and Affordable Housing, the Sustainable Production bars are placed considerably higher, indicating a larger percentage focus on this factor. This is because sustainable production in these categories can lead to a significant positive impact on society, including benefits such as more affordable housing and better educational opportunities. Therefore, companies should focus more on Sustainable Production in the categories of Education and Affordable Housing.\nBased on the above thought, the answer is:\nEducation and Affordable Housing" } ], "id_raw": 252 }, { "id": "Fe2hx7QDJeas9GYg", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_12.png", "conversations": [ { "from": "human", "value": "\nIn which field of corporate responsibility, companies are focusing less on Social Awareness?" }, { "from": "gpt", "value": "To find the category with the least amount of focus on social awareness, I need to compare the heights of the {Social Awareness bars}[0.121, 0.404, 0.310, 0.607] in each category from left to right. The heights of Social Awareness bars corresponding to the categories of {Environment}[0.207, 0.632, 0.224, 0.853], {Work Policies}[0.417, 0.632, 0.435, 0.859], {Education}[0.627, 0.632, 0.645, 0.805], and {Affordable Housing}[0.837, 0.632, 0.855, 0.963] are approximately 43%, 52%, 3% and 38% respectively, as calculated from the y-axis. Thus, companies are focusing the least on social awareness in the category of Education.\nBased on the above thought, the answer is:\nEducation" } ], "id_raw": 252 }, { "id": "UNGZ07w2LR3KH0Ap", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nIn reference to the bar chart you examined, could you reproduce the data in a CSV structured table?" }, { "from": "gpt", "value": "After examining the diagram, I observe that there are two distinct groups of bars. These groups are labeled as {Community Development}[0.336, 0.072, 0.558, 0.107] and {Urban Planning}[0.642, 0.072, 0.775, 0.107] as per the legend. The height of the bars represents the data values. Checking the y-axis, the range of values seems to go from 0 to 100, with 20% intervals demarcated by {grid lines}[0.080, 0.769], [0.080, 0.642], [0.080, 0.516], [0.080, 0.389], [0.080, 0.262], [0.080, 0.136]. To construct the CSV, the height of each bar in both groups for each instance (from Instance 1 to Instance 7) needs to be evaluated in relation to these y-axis intervals. The Community Development and Urban Planning values under each instance are determined by matching the top edge of the respective bars with the y-ticks grid lines. For example, the \"Instance 1\" values are obtained from the height of the {Community Development}[0.121, 0.452, 0.228, 0.769] and {Urban Planning}[0.121, 0.098, 0.228, 0.452] bars, both appear to correspond to approximately 50 and 56 respectively.\nBased on the above thought, the answer is:\nInstance,Community Development,Urban Planning\nInstance1,50,56\nInstance2,67,70\nInstance3,58,80\nInstance4,34,40\nInstance5,76,68\nInstance6,59,61\nInstance7,70,65" } ], "id_raw": 253 }, { "id": "7e6jwR2QGndRn6Mg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nWhat insights does the graph provide regarding the trends followed in the allocation of funds for community development and urban planning?" }, { "from": "gpt", "value": "Studying the height of the {Community Development bars}[0.121, 0.452, 0.228, 0.769];[0.240, 0.345, 0.347, 0.769];[0.359, 0.402, 0.465, 0.769];[0.477, 0.554, 0.584, 0.769];[0.596, 0.288, 0.703, 0.769];[0.715, 0.395, 0.822, 0.769];[0.833, 0.326, 0.940, 0.769] and the {Urban Planning bars}[0.121, 0.098, 0.228, 0.452]; [0.240, -0.099, 0.347, 0.345]; [0.359, -0.105, 0.465, 0.402]; [0.477, 0.300, 0.584, 0.554]; [0.596, -0.143, 0.703, 0.288]; [0.715, 0.009, 0.822, 0.395]; [0.833, -0.086, 0.940, 0.326], there seems to be a general trend of slow increase in community development, despite a few dips and surges. This shows that the funds allocated to meet community development needs vary from instance to instance. In contrast, urban planning shows a fluctuating decline with a sudden surge in 'Instance4', indicating a significant unexpected influx of funds, possibly due to an emergent development initiative. It's interesting to note that the funding for urban planning is relatively volatile compared to community development.\nBased on the above thought, the answer is:\nThe funding for community development follows a slow and steady rise, with occasional dips and surges, indicating a consistent interest and investment in community development initiatives. Urban planning, however, shows a general downward trend with sudden surges, pointing to variability and reactive funding based on need or opportunity, rather than a steady investment as in community development. This could also indicate changing strategies and focus in city planning." } ], "id_raw": 253 }, { "id": "KbrQEcYbGtuiEsXy", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_13.png", "conversations": [ { "from": "human", "value": "\nIn terms of funding, which initiative, Community Development or Urban Planning, attracted the most funds across all instances?" }, { "from": "gpt", "value": "To determine which initiative garnered the most funds, I will have to evaluate the bars corresponding to each initiative in every instance. For example, in 'Instance1', the {Community Development bar}[0.121, 0.452, 0.228, 0.769] is higher than the {Urban Planning bar}[0.121, 0.098, 0.228, 0.452] indicating that Community Development was preferred in this instance. Similar comparisons can be made for all instances. However, to get a total estimate, the heights of all bars for that initiative should be added up, i.e. summing up all the values of Community Development and Urban Planning. The initiative with the larger sum is the one that attracted the most funding.\nBased on the above thought, the answer is:\nCommunity Development" } ], "id_raw": 253 }, { "id": "HQ7wvOaVGV0YaGRE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nBased on the given bar chart data, can you create a table showing the percentage of each media influence source for each category?" }, { "from": "gpt", "value": "Yes, the chart represents three different sources of media influence for \"News Coverage\", \"Social Media Influence\", \"Campaign promotions\", and \"Political debates\". The influence of each source is expressed as green, yellow, and blue bars. The green bars standing for {Traditional Media}[0.292, 0.105, 0.441, 0.140], yellow for {New Media}[0.524, 0.105, 0.620, 0.140], and blue for {Public Events}[0.703, 0.105, 0.818, 0.140]. We can determine the contribution of each influence source for a particular category by looking at the height of each colored bar segment within the total bar for that category. For example, in the \"News Coverage\" category, the green bar takes up about 55%, the yellow about 35%, and the blue about 10%. I can repeat this process to extract data for each category and represent it in a CSV format table.\nBased on the above thought, the answer is:\nCategories,Traditional Media,New Media,Public Events\nNews Coverage,55,35,10\nSocial Media Influence,20,70,10\nCampaign promotions,30,55,15\nPolitical debates,50,20,30" } ], "id_raw": 254 }, { "id": "P4f1PxzvA4wzZkGa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nWhich category does traditional media have the most influence on?" }, { "from": "gpt", "value": "From the chart, each category's bars' height that is colored green represents the influence of {Traditional Media}[0.292, 0.105, 0.441, 0.140] to a particular category, which includes four categories: \"News Coverage\", \"Social Media Influence\", \"Campaign promotions\", and \"Political debates\". To find out which category is most influenced by Traditional Media, I need to compare the height of the green bars in each category. From the chart, it can be seen that the green bar for {the \"News Coverage\" category}[0.121, 0.322, 0.310, 0.549] and {the \"Political debates\" category}[0.751, 0.342, 0.940, 0.549] are the tallest compared to the other two categories. The height of the bars indicates that Traditional Media influences both \"News Coverage\" and \"Political debates\" to 50%. As such, Traditional Media has the most significant influence on both the \"News Coverage\" and \"Political debates\" categories.\nBased on the above thought, the answer is:\nNews Coverage and Political debates." } ], "id_raw": 254 }, { "id": "UxWirH5yi1GZ16J6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_17_14.png", "conversations": [ { "from": "human", "value": "\nWhich source has the least influence on 'Campaign promotions' according to the chart?" }, { "from": "gpt", "value": "According to the bar chart, the 'Campaign promotions' category is represented by the third group of bars from the left with color details shown in {the legend}[0.236, 0.091, 0.825, 0.154]. Looking at the height of each color in the bar, it can be seen that the smallest segment is the blue one, {Public Events}[0.541, 0.136, 0.730, 0.198], which is around 15%, is less than the other two sources in this category.\nBased on the above thought, the answer is:\nPublic Events." } ], "id_raw": 254 }, { "id": "NWritbvaXbx9okhB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nPlease convert the data you see on the chart into a table in CSV format." }, { "from": "gpt", "value": "The chart is a bar chart showing three business processes - 'Call Resolution', 'Meeting Efficiency', and 'Budget Usage' for the years 2021 and 2022. The x-axis includes three categories marked as 'Call Resolution'[0.187, 0.919, 0.315, 0.942], 'Meeting Efficiency'[0.465, 0.919, 0.625, 0.942], and 'Budget Usage'[0.778, 0.919, 0.900, 0.942]. The y-axis has label 'Improvement (%)'[0.019, 0.367, 0.036, 0.570] and the values range below zero as these are improvements and hence can be negative. The legend contains two time periods'2021'[0.940, 0.021, 0.984, 0.044] and '2022'[0.940, 0.056, 0.984, 0.079]. Each category has two bars corresponding to these years. The data for each bar was found on it, like for instance the 'Call Resolution' for '2021'[0.148, 0.034, 0.251, 0.182] is '-5.12', and for '2022'[0.251, 0.034, 0.354, 0.220] is '-6.43'. Similar readings can be taken for 'Meeting Efficiency' and 'Budget Usage'.\nBased on the above thought, the answer is:\nProcesses,2021,2022\nCall Resolution,-5.12,-6.43\nMeeting Efficiency,-8.43,-12.12\nBudget Usage,-17.8,-23.2" } ], "id_raw": 256 }, { "id": "E3prxoNZT4sf5FAD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what conclusions can you draw about the company's business processes from 2021 to 2022?" }, { "from": "gpt", "value": "Based on the chart, it is evident that there has been a continual improvement in all three business processes from 2021 to 2022. This can be seen through the comparison of bars for 2021 and 2022 in 'Call Resolution'[from '2021'[0.148, 0.034, 0.251, 0.182] to '2022'[0.251, 0.034, 0.354, 0.220]], 'Meeting Efficiency'[from '2021'[0.442, 0.034, 0.545, 0.278] to '2022'[0.545, 0.034, 0.648, 0.385]], and 'Budget Usage'[from '2021'[0.736, 0.034, 0.839, 0.550] to '2022'[0.839, 0.034, 0.942, 0.706]]. Most importantly, the 'Budget Usage' process stands out with a significant increase in improvement, showing an extreme decrease in budget usage from 2021 to 2022. This means that spending has become much more efficient, which might be a result of cost-saving measures or more efficient management. However, all three processes could still see further improvement, as improvement figures remain negative.\nBased on the above thought, the answer is:\nThe company's business processes have shown improvement from 2021 to 2022 with a significant decrease in budget usage. Despite the gains made, there is still room for further improvement as all the improvements are expressed in negative percentages." } ], "id_raw": 256 }, { "id": "YLWI7q1FanPbnYOl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_1.png", "conversations": [ { "from": "human", "value": "\nWhich process saw the largest percentage improvement from 2021 to 2022?" }, { "from": "gpt", "value": "The chart provides data for three different process categories which are 'Call Resolution', 'Meeting Efficiency', and 'Budget Usage'. The improvement for each process can be calculated by subtracting the 2021 value from the 2022 value. From comparing the heights of the bars for each process for both the years example; 'Call Resolution' for '2021'[0.148, 0.034, 0.251, 0.182] and '2022'[0.251, 0.034, 0.354, 0.220], 'Meeting Efficiency' for '2021'[0.442, 0.034, 0.545, 0.278] and '2022'[0.545, 0.034, 0.648, 0.385], and 'Budget Usage' for '2021'[0.736, 0.034, 0.839, 0.550] and '2022'[0.839, 0.034, 0.942, 0.706], it can be noticed that the 'Budget Usage' process experienced the largest percentage improvement from 2021 to 2022.\nBased on the above thought, the answer is:\nBudget Usage." } ], "id_raw": 256 }, { "id": "ndWJ7orzAWbFQj4I", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data displayed in the bar chart into a CSV format?" }, { "from": "gpt", "value": "Sure. According to the bar chart presented, there are two trends identified on the x-axis, \"Steep increase\" and \"Inexorable decline\", with the label identified at the coordinates {xlabel}[0.472, 0.952, 0.528, 0.975]. These trends have three investment strategies associated with them namely, \"IPO\", \"RPI\" and \"Dividend\", indicated by different colored bars according to {the legend}[0.752, 0.777, 0.891, 0.888]. Now, observing the bars, the \"Steep increase\" trend has 30 for \"IPO\"{bar for Steep increase IPO}[0.126, 0.642, 0.233, 0.903] , 47 for \"RPI\"{bar for Steep increase RPI}[0.233, 0.495, 0.340, 0.903], and 50 for \"Dividend\"{bar for Steep increase Dividend}[0.340, 0.469, 0.447, 0.903]. Similarly, the values for the \"Inexorable decline\" trend are 90 for \"IPO\"{bar for Inexorable decline IPO}[0.553, 0.121, 0.660, 0.903], 70 for \"RPI\"{bar for Inexorable decline RPI}[0.660, 0.295, 0.767, 0.903], and 60 for \"Dividend\"{bar for Inexorable decline Dividend}[0.767, 0.382, 0.874, 0.903]. By compiling these, we can convert the bar chart data into a CSV format.\nBased on the above thought, the answer is:\nTrends,IPO,RPI,Dividend\nSteep increase,30,47,50\nInexorable decline,90,70,60" } ], "id_raw": 257 }, { "id": "clXX9D9XsKqFbC4r", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nBased on the observed trends, for which investment strategy was the decline in value most significant?" }, { "from": "gpt", "value": "Firstly, I will ascertain the difference between the values of each investment strategy for the two trends. For the \"IPO\" strategy, the values are displayed by the bars at {Steep increase IPO bar}[0.126, 0.642, 0.233, 0.903] and {Inexorable decline IPO bar}[0.553, 0.121, 0.660, 0.903], and the difference is 60. For the \"RPI\" strategy, the values are displayed at {Steep increase RPI bar}[0.233, 0.495, 0.340, 0.903] and {Inexorable decline RPI bar}[0.660, 0.295, 0.767, 0.903], and the difference is 23. Lastly, for the \"Dividend\" strategy, the values are shown at {Steep increase Dividend bar}[0.340, 0.469, 0.447, 0.903] and {Inexorable decline Dividend bar}[0.767, 0.382, 0.874, 0.903], and the difference is 10. Comparing these differences, it can be inferred that the decline was most significant for the \"IPO\" investment strategy.\nBased on the above thought, the answer is:\nThe decline in value was the most significant for \"IPO\"." } ], "id_raw": 257 }, { "id": "h25oezXpiA0M6ijZ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_2.png", "conversations": [ { "from": "human", "value": "\nWhich investment strategy has the least value in the \"Steep increase\" trend?" }, { "from": "gpt", "value": "To answer this, I will examine the three bars representing the different investment strategies during the \"Steep increase\" trend. The \"IPO\" value is indicated by the bar with coordinates {IPO bar}[0.126, 0.642, 0.233, 0.903], and it is 30. The \"RPI\" value is symbolised by the bar with coordinates {RPI bar}[0.233, 0.495, 0.340, 0.903], and it is 47. Lastly, the \"Dividend\" value is represented by the bar with coordinates {Dividend bar}[0.340, 0.469, 0.447, 0.903], and it is 50. By comparing the heights of these three bars, it can be deduced that the least value belongs to \"IPO\".\nBased on the above thought, the answer is:\nIPO" } ], "id_raw": 257 }, { "id": "vwZbZFjkuVP77R8E", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data into a structured CSV table?" }, { "from": "gpt", "value": "To create a CSV table, I need to extract the values distribution of 'Home Amenities', 'Mortgage Payment Percentage', and 'Food Shopping Average' across 'SEQ', 'REPLAY', and 'MTL'. Looking at the chart, the {legend shows}[0.561, 0.212, 0.891, 0.323] three colored groups each representing one of the categories 'Home Amenities', 'Mortgage Payment Percentage', or 'Food Shopping Average'. From the x-axis labels we can see the categories 'SEQ', 'REPLAY', 'MTL', and from the y-axis labels we can understand the percent of income spent for the first two categories, and an average monthly spend for the third category. I will obtain the values for each house condition category by examining the height of the bars and the numbers annotated to the top of each bar. For example, the height of {the bar for 'Home Amenities' in 'SEQ'}[0.131, 0.512, 0.197, 1.772] is annotated with the number {43.50}[0.139, 0.482, 0.189, 0.505]\nBased on the above thought, the answer is:\nFamily,Home_Amenities,Mortgage_Payment_Percentage,Food_Shopping_Average\nSEQ,43.5,35.8,-7.23\nREPLAY,53.2,45.7,-6.41\nMTL,58.9,49.1,4.35" } ], "id_raw": 258 }, { "id": "uVFuT5H03cQOleqW", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nWhich lifestyle seems to be the most economically sustainable according to the chart, and why?" }, { "from": "gpt", "value": "To define the economic sustainability of the lifestyles, it's key to analyze the distribution of expenses. {The blue bars} representing 'Home Amenities', {the orange bars} representing 'Mortgage Payment Percentage', and {the red bars} representing 'Food Shopping Average' expenses for each lifestyle should be considered. The 'REPLAY' lifestyle shows balanced expenses across all three categories, with the values mapped to bars located at {Home_Amenities_REPLAY}[0.398, 0.231, 0.465, 1.772], {Mortgage_Percentage_REPLAY}[0.465, 0.448, 0.532, 1.772], and {Food_Average_REPLAY}[0.532, 0.344, 0.598, 0.742], respectively. Although its 'Home Amenities' and 'Mortgage Payment Percentage' expenses are not the lowest compared to 'SEQ' and 'MTL', 'REPLAY' shows a smaller Food Shopping Average making it more balanced overall.\nBased on the above thought, the answer is:\nAccording to the chart, the most economically sustainable lifestyle appears to be REPLAY, as it displays a balanced spread of expenses across home amenities, mortgage payment percentage, and food shopping average." } ], "id_raw": 258 }, { "id": "EIyw6KRUiyFa3zSm", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_3.png", "conversations": [ { "from": "human", "value": "\nFor which lifestyle, the percentage of income spent on home amenities is the highest?" }, { "from": "gpt", "value": "To find out the lifestyle in which the highest percentage of income is spent on home amenities, I need to examine the height of {the blue bars} representing 'Home Amenities' for each lifestyle type. Bars located at {SEQ}[0.131, 0.512, 0.197, 1.772], {REPLAY}[0.398, 0.231, 0.465, 1.772], and {MTL}[0.665, 0.066, 0.732, 1.772] have heights corresponding to values '43.5', '53.2', '58.9', respectively. The highest value among these is 58.9, corresponding to the MTL lifestyle.\nBased on the above thought, the answer is:\nMTL" } ], "id_raw": 258 }, { "id": "VbYLqKEHyjojhgiE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nCan you present the varying trends in the Banking, Energy, Technology and Industrial sectors in a structured table format?" }, { "from": "gpt", "value": "Sure. I can create the CSV table based on the bars for each sector and each trend. The x-axis represents the sectors, which are the categories: {Banking Sector}[0.148, 0.127, 0.257, 0.153], {Energy Sector}[0.318, 0.127, 0.424, 0.153], {Technology Sector}[0.460, 0.127, 0.577, 0.153], {Industrial Sector}[0.617, 0.127, 0.735, 0.153]. The y-axis values represents the percentage change for each trend. Each sector has five bars indicating five different trends subjected to the four areas of market sectors. From {Subdued expansion}[0.161, 0.225, 0.264, 0.970] to {Slow recovery}[0.723, 0.225, 0.786, 0.970] trends, extracting individual estimates for each sector will help to tabulate the raw data.\nBased on the above thought, the answer is:\nSector,Subdued Expansion(In %),Inconsistent decline(In %), Significant decrease(In %), Abrupt increase(In %), Slow recovery(In %)\nBanking Sector,-0.4,-0.2,-5.5,1.7,0.2\nEnergy Sector,-0.6,-1.3,-4.2,0.8,0.1\nTechnology Sector,-0.3,-0.8,-3.1,1.1,0.1\nIndustrial Sector,-0.7,-1.5,-4.8,1.0,0.3" } ], "id_raw": 259 }, { "id": "P22yriUmVv98aLIT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector shows the least slowdown and quickest recovery?" }, { "from": "gpt", "value": "For the least slowdown, this involves {subdued expansion}[0.153, 0.906, 0.226, 0.930], {Inconsistent decline}[0.238, 0.906, 0.325, 0.930], and {Significant decrease}[0.298, 0.906, 0.393, 0.930] trends, we need to evaluate the lengths of {Banking Sector's first three bars}[0.151, 0.457, 0.296, 0.894], {Energy Sector's first three bars}[0.322, 0.424, 0.469, 0.894], {Technology Sector's first three bars}[0.468, 0.428, 0.615, 0.894] and {Industrial Sector's first three bars}[0.620, 0.409, 0.766, 0.894]. Visually, it seems the {Technology Sector's bars}[0.468, 0.428, 0.615, 0.894] appear to be least among all sectors. \n\nFor a quick recovery, it combines {Abrupt increase}[0.433, 0.906, 0.505, 0.930] and {Slow recovery}[0.550, 0.906, 0.623, 0.930]. From the plot, it is evident that {Banking sector's last two bars}[0.298, 0.457, 0.443, 0.894], {Energy sector's last two bars}[0.470, 0.424, 0.615, 0.894], {Technology sector's last two bars}[0.616, 0.428, 0.761, 0.894] and {Industrial sector's last two bars}[0.768, 0.409, 0.914, 0.894], where the {Technology sector's bars}[0.616, 0.428, 0.761, 0.894] still seem to be the largest. Hence, the Technology sector fulfilled both conditions.\nBased on the above thought, the answer is:\nThe Technology Sector. It underwent a relatively minor slowdown during the negative trends and displayed the quickest recovery in positive trends." } ], "id_raw": 259 }, { "id": "Yz5MHjHZSK1tsNow", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_4.png", "conversations": [ { "from": "human", "value": "\nWhich sector experienced the biggest negative change during the \"Significant Decrease\" trend?" }, { "from": "gpt", "value": "{Significant Decrease}[0.298, 0.906, 0.393, 0.930] is the third trend. For each sector, I need to compare the value of the third bar. The {Banking sector}[0.237, 0.645, 0.307, 0.894], {Energy Sector}[0.405, 0.614, 0.476, 0.894], {Technology Sector}[0.573, 0.601, 0.644, 0.894], and {Industrial Sector}[0.740, 0.622, 0.811, 0.894] has the bars corresponding to the trend. Visually comparing the heights of these bars, we can see the bar for {Industrial Sector}[0.742, 0.622, 0.791, 0.894] is the longest, leading to conclude that the Industrial Sector experienced the largest negative change.\nBased on the above thought, the answer is:\nIndustrial Sector" } ], "id_raw": 259 }, { "id": "e3T3ei8mVnxlzNOG", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nCan you summarize the bar chart data in a CSV format table?" }, { "from": "gpt", "value": "Certainly. The chart has two categories namely {City}[0.279, 0.919, 0.314, 0.942] and {Country}[0.669, 0.919, 0.738, 0.942]. In each category, three attributes are reported: Economic Growth, Population Change, and Land Use Change. The values for each attribute can be obtained from the height of the respective bars and their annotations. For example, the bar for Economic Growth in the City category is located at [0.144, 0.469, 0.246, 0.729] and has an annotation showing its value as {1.5}[0.181, 0.462, 0.209, 0.485]. I will follow the same method to extract all the values.\nBased on the above thought, the answer is:\nCategories,Economic Growth,Population Change,Land Use Change\nCity,1.5,-0.8,0.3\nCountry,3.2,-0.2,1.0" } ], "id_raw": 260 }, { "id": "i3Bg34V3VKhbJRtL", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nAccording to this chart, what is the trend in population change for both cities and countries?" }, { "from": "gpt", "value": "To answer this query, I need to observe the second set of bars (Population Change) in both the city and country categories. For the city category, the bar representing population change is located at {the bar}[0.246, 0.729, 0.347, 0.868]. The text annotation present indicate it to be {-0.8}[0.280, 0.861, 0.313, 0.885]. And, for the country category, the population change bar can be found at {the bar}[0.653, 0.729, 0.754, 0.764] and is indicated to be {-0.2}[0.687, 0.757, 0.720, 0.780].\nBased on the above thought, the answer is:\nBased on the chart, both cities and countries display a negative population change. However, the decline in population is significantly more severe in cities (-0.8) as compared to countries (-0.2)." } ], "id_raw": 260 }, { "id": "frMbO7GdbGZlnL2r", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_5.png", "conversations": [ { "from": "human", "value": "\nWhich category, City or Country, has witnessed higher economic growth?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height of the first bar in both categories. The height of the Economic Growth bar for the City category can be found at {the bar}[0.144, 0.469, 0.246, 0.729] and is indicated to be {1.5}[0.181, 0.462, 0.209, 0.485]. On the other hand, the corresponding bar for the Country category is located at {the bar}[0.551, 0.173, 0.653, 0.729] and has a value of {3.2}[0.588, 0.166, 0.615, 0.190]. Thus, by comparing these two values, it is seen that economic growth is higher in the Country category.\nBased on the above thought, the answer is:\nCountry" } ], "id_raw": 260 }, { "id": "R6l5MTCetXwxMNVH", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nTurn the data shown in the bar chart into a structured CSV table." }, { "from": "gpt", "value": "The chart describes three categories on the x-axis namely {Book Publishing}[0.159, 0.919, 0.296, 0.942], {Art Exhibitions}[0.429, 0.919, 0.555, 0.942] and {Opera Performances}[0.667, 0.919, 0.845, 0.942]. The bars for each category represent three different rates: Probing Growth Rate, Observed Growth Rate and Expanding Rate. I can confirm the corresponding value for each rate from the length of the bar and the annotations above the bar. For instance, {the bar for Probing Growth Rate for Book Publishing}[0.128, 0.534, 0.194, 0.555] is associated with a value annotation of 1.22[0.142, 0.504, 0.181, 0.527]. Therefore, the value of probing growth rate for book publishing is 1.22. Using this methodology, I can extract the values for all the categories and rates.\nBased on the above thought, the answer is:\nCategory, Probing Growth Rate, Observed Growth Rate, Expanding Rate\nBook Publishing, 1.22, 12.30, 0.16\nArt Exhibitions, 3.90, 25.90, 0.43\nOpera Performances, -2.13, -15.67, -0.11" } ], "id_raw": 261 }, { "id": "C07daaK9uYls3xut", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nFrom the perspective of cultural and artistic industry development, which category is theoretically expected to have future growth potential based on its expansion rate, and why?" }, { "from": "gpt", "value": "To analyze the potential growth of a category, we can refer to the {expanding rate}[0.687, 0.856, 0.825, 0.879]. If the expanding rate is positive, it may indicate future growth potential. I see that the bar representing the expanding rate of {Book Publishing}[0.260, 0.456, 0.327, 0.655] and {Art Exhibitions}[0.525, 0.121, 0.591, 0.655] are above the x-axis, meaning their expanding rates are positive. However, {Art Exhibitions}[0.429, 0.919, 0.555, 0.942] has a bar with much greater height which means a larger expanding rate, which means it's expanding at a faster pace compared to Book Publishing.\nBased on the above thought, the answer is:\nArt Exhibitions. Because it has a higher expanding rate compared to the other categories which suggests it is growing rapidly. This rapid growth indicates promising future growth potential." } ], "id_raw": 261 }, { "id": "vQLKoEOttTwDgC2r", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_6.png", "conversations": [ { "from": "human", "value": "\nIn which category does the observed growth rate exceed the probing growth rate the most?" }, { "from": "gpt", "value": "To determine which category has the maximum difference between observed growth rate and probing growth rate, I need to compare the height difference between {probing growth rate}[0.687, 0.786, 0.867, 0.809] and {observed growth rate}[0.687, 0.821, 0.884, 0.844] for each category. The difference can be determined by subtracting the value of the probing growth rate from the value of the observed growth rate, for all categories. After calculating all the differences, the category with the highest positive difference has the maximum difference between the observed growth rate and the probing growth rate. I can see that the category {Art Exhibitions}[0.429, 0.919, 0.555, 0.942] has a considerable height difference between {observed growth rate}[0.459, 0.105, 0.525, 0.555] and {probing growth rate}[0.393, 0.488, 0.459, 0.555] with respective values of 25.90 and 3.90. This difference of 22 is the largest among the three categories.\nBased on the above thought, the answer is:\nArt Exhibitions" } ], "id_raw": 261 }, { "id": "VJXdSoso3cI6ErnO", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nBased on the data in the chart, please format this information into a structured table for better visualization." }, { "from": "gpt", "value": "Looking at the chart, we can distinguish different categories from the X-axis labels that display the names of the sectors: \"Advanced Manufacturing\", \"Biotechnology\", and \"IT Services\". These labels can be seen at the coordinates {AdvancedManufacturing}[0.138, 0.919, 0.350, 0.942], {Biotechnology}[0.446, 0.919, 0.570, 0.942], and {ITServices}[0.728, 0.919, 0.817, 0.942]. While Y-axes at the right and left of the plot represent the different datasets of each category. Using the legend at the coordinates {Legend}[0.597, 0.777, 0.891, 0.888], I can identify the three different sets of data. They are: \"Intangible Asset Turnover\", \"Human Capital Index 2022\" and \"TechInvestmentIndex\". The values of these datasets can be determined by the height of the bars in each group and the annotations at the top of each bar.\nBased on the above thought, the answer is:\nCategories,Intangible Asset Turnover,Human Capital Index 2022,Tech Investment Index \nAdvanced Manufacturing, 0.67,93,-8.2\nBiotechnology,0.22,152,-9.1\nIT Services,1.14,62,0.33" } ], "id_raw": 262 }, { "id": "huRNfCVfoD0BaTWl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, which sector appears to be the most promising in terms of current trends?" }, { "from": "gpt", "value": "In order to identify the most promising sector, we need to compare the datasets across all three sectors. A high value of Intangible Asset Turnover implies that a company's non-physical assets are creating more revenue which would be a positive indicator. Also, a high Human Capital Index 2022 shows that there are highly skilled workforce in that sector, another positive sign. Finally, Tech Investment Index depicts the technological advancement or decline in the sector. For ITServices the TechInvestmentIndex shows a positive trend which is beneficial. Looking at all these datasets together, we can conclude based on the bars at these coordinates {Intangible Asset Turnover for IT Services}[0.673, 0.857, 0.740, 0.862], {Human Capital Index for IT Services}[0.740, 0.605, 0.806, 0.862], {TechInvestmentIndex for IT Services}[0.806, 0.155, 0.872, 0.179] that the IT Services sector appears to be the most promising.\nBased on the above thought, the answer is:\nIT Services, as it has highest Intangible Asset Turnover, considerable Human Capital Index and a positive Tech Investment Index." } ], "id_raw": 262 }, { "id": "RPHDdEeRM9052Xmc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_7.png", "conversations": [ { "from": "human", "value": "\nWhich sector has the highest value of Intangible Asset Turnover?" }, { "from": "gpt", "value": "To find out the sector with the highest Intangible Asset Turnover, I need to examine the heights of the bars that correspond to this dataset. Based on the legend at {Legend}[0.597, 0.777, 0.891, 0.888], the bars representing the Intangible Asset Turnover are identified by the color '#6e7a5f'. Now by comparing the heights of the three bars, {AdvancedManufacturing}[0.145, 0.859, 0.211, 0.862], {Biotechnology}[0.409, 0.861, 0.475, 0.862], and {ITServices}[0.673, 0.857, 0.740, 0.862] , I can see that the height of the bar corresponding to the IT Services sector is the highest, indicating it has the highest value for Intangible Asset Turnover.\nBased on the above thought, the answer is:\nIT Services." } ], "id_raw": 262 }, { "id": "lxrlRyqBGwSzVzIc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nCan you please extract the data that has been visually represented in the chart and convert it to a structured CSV format?" }, { "from": "gpt", "value": "From the x-axis, we can see that there are three categories: {CITY_A}[0.207, 0.919, 0.266, 0.942], {CITY_B}[0.478, 0.919, 0.537, 0.942], and {CITY_C}[0.749, 0.919, 0.808, 0.942]. Each of those categories has three bars representing three parameters: {Population Growth}[0.661, 0.788, 0.695, 0.804], {Urban Development}[0.661, 0.823, 0.695, 0.839], and {Green Space}[0.661, 0.858, 0.695, 0.874] respectively as identified from the legend. By measuring the height of the bars in relation to the y-axis scale, I can identify the values. For example, the height of the {Population Growth bar for CITY_A}[0.134, 0.269, 0.202, 0.469] corresponds to {2.3}[0.155, 0.238, 0.182, 0.262] on the y-axis, hence the Population Growth for CITY_A is 2.3 percent. Following the same methodology for all bars will allow me to construct the table.\nBased on the above thought, the answer is:\nCITY, POPULATION_GROWTH, URBAN_DEVELOPMENT, GREEN_SPACE\nCITY_A, 2.3, -4.1, 0.8\nCITY_B, -3.1, 2.2, 0.3\nCITY_C, 4.1, 0.1, 1.2" } ], "id_raw": 263 }, { "id": "zuiVGO1Xz7tXcB1F", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which city is experiencing balanced development in terms of both population growth and urban planning initiatives? Please elaborate." }, { "from": "gpt", "value": "To answer this question, I need to analyze both Population Growth and Urban Development in each city. Both these categories are evident from the legend, {Population Growth}[0.661, 0.788, 0.695, 0.804] and {Urban Development}[0.661, 0.823, 0.695, 0.839]. Under {CITY_A}[0.207, 0.919, 0.266, 0.942], while Population Growth is positive, Urban development is negative, suggesting rapid population growth but a reduction in urban space, which might negatively impact residents' quality of life. In the case of {CITY_B}[0.478, 0.919, 0.537, 0.942], Urban Development is positive and Population Growth is negative, suggesting that although the city is developing, it is not attracting or retaining residents. The most balanced seems to be {CITY_C}[0.749, 0.919, 0.808, 0.942] where both Population Growth and Urban Development are positive, albeit the latter is only marginal.\nBased on the above thought, the answer is:\nCITY_C. It's experiencing an increase in both population, urban development, and green spaces, albeit urban development is only marginal which could indicate balanced growth and better planning. However, further data and context are needed to conclude decisively." } ], "id_raw": 263 }, { "id": "z0Y4FcleNMAy7kbZ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_8.png", "conversations": [ { "from": "human", "value": "\nWhich city has the highest green space?" }, { "from": "gpt", "value": "Green Space is represened by bars in {color}[0.661, 0.858, 0.695, 0.874] denoted from the legend. We can compare the heights of the bars in this color group across all cities. By looking at the coordinates representing these bars we get {CITY_A's green space bar}[0.270, 0.555, 0.338, 0.903], {CITY_B's green space bar}[0.541, 0.773, 0.609, 0.903] and {CITY_C's green space bar}[0.812, 0.382, 0.880, 0.903]. The maximum height corresponds to CITY_C represented by the coordinates [0.812, 0.382, 0.880, 0.903].\nBased on the above thought, the answer is:\nCITY_C." } ], "id_raw": 263 }, { "id": "IBqDRMKNAuXfCw3H", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nBased on the visualized bar chart's data, can you generate a structured table in CSV format?" }, { "from": "gpt", "value": "Examination of the bar chart shows market share trends for three types of industries across three subsequent years: 2018, 2019, and 2020. To convert this information into a structured table, the figures corresponding to the representative bars for each year across each industry group must be extracted. For example, observing the {Electronics bar group}[0.155, 0.034, 0.317, 0.900] reveals a steady declining trend in its market share from -50.3% in 2018 to -100% by 2020. The other pairs of bars that need to be examined similarly are as follows: {Automobile bar group}[0.426, 0.034, 0.589, 0.903] and {Textile bar group}[0.697, 0.033, 0.860, 0.903].\nBased on the above thought, the answer is:\nIndustry,Market Share 2018(%),Market Share 2019(%),Market Share 2020(%)\nElectronics,-50.3,-75.0,-100.0\nAutomobile,-100.0,-50.0,-75.0\nTextile,0.2,0.15,0.3" } ], "id_raw": 264 }, { "id": "fCICJCSTNAsZnC8Q", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nGiven the trends shown in the bar chart, which industry shows the most unpredictable year-to-year changes in market share? Please elaborate." }, { "from": "gpt", "value": "For this question, the volatility of market share across the three years needs to be assessed for each industry. Looking at the {Electronics bar group}[0.155, 0.034, 0.317, 0.900], we see a consistent downward trend across the years. A similar pattern can be observed with the {Automobile bars}[0.426, 0.034, 0.589, 0.903], where there is a reduction in the market share each year, albeit at a slowing rate. In contrast, the {Textile bars}[0.697, 0.033, 0.860, 0.903] show an increase from 2018 to 2020, but with a dip in 2019. This inconsistency in the textile industry's trend suggests the highest unpredictability in market share year-to-year.\nBased on the above thought, the answer is:\nTextile industry. Contrary to the Electronics and Automobile industries that follow a steady decline, the Textile industry shows inconsistency with a rise in market share in 2018 and 2020, but a dip in 2019. This fluctuation indicates the Textile industry has the most unpredictable change in market share year-to-year." } ], "id_raw": 264 }, { "id": "SwrRc88tFi8RPSbc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_9.png", "conversations": [ { "from": "human", "value": "\nWhich industry has seen the least decline in the year 2019 compared to 2018?" }, { "from": "gpt", "value": "This question requires comparing the market share for each industry from 2018 to 2019. The difference between the bar heights for the years 2018 and 2019 within the same industry category will provide the answer. Focusing on the {Electronics bars}[0.155, 0.034, 0.263, 0.900], the difference between 2018 and 2019 is -75.0 - (-50.3) = -24.7. Following the same calculation for {Automobile bars}[0.426, 0.034, 0.534, 0.903], the difference is -50.0 - (-100.0) = 50.0. However, in the case of {Textile bars}[0.697, 0.033, 0.806, 0.903], the difference is 0.15 - 0.2 = -0.05. Therefore, the Textile industry has seen the least decline.\nBased on the above thought, the answer is:\nTextile" } ], "id_raw": 264 }, { "id": "zjMVmTptGRWUrpIy", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data represented in the bar chart into a structured table format?" }, { "from": "gpt", "value": "As an AI visual assistant, I can infer from the chart that it represents the performance of three different methods on three metrics. They are 'Lobbying', 'Protesting', and 'Litigation', located at {\"Electronics\"}[0.189, 0.919, 0.283, 0.942], {\"Automobile\"}[0.458, 0.919, 0.557, 0.942] and {\"Textile\"}[0.751, 0.919, 0.806, 0.942] respectively. The metrics are 'Rights Protection Score','Social Inequality Score', and 'Progress Score'. The bar heights at each of these positions represent the scores, which I can visually estimate by comparing the positions of the tops of the bars with the positions of the y-tick labels. For example, the 'Rights Protection Score' of 'Lobbying' can be estimated as '{-50.3}'[0.159, 0.353, 0.204, 0.377],and the 'Progress Score' as '{0.2}'[0.819, 0.219, 0.847, 0.243]. So, I will start from 'Lobbying', go through each score type and move on to the next method after that.\nBased on the above thought, the answer is:\nMethod,Rights Protection Score,Social Inequality Score,Progress Score\nLobbying,-50.3,-100.0,0.2\nProtesting,-75.0,-50.0,0.15\nLitigation,-100.0,-0.05,0.3" } ], "id_raw": 265 }, { "id": "ZJCVmzzihwkYebNY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nGiven the performance scores, which social justice method would you say is the most efficient and why?" }, { "from": "gpt", "value": "To answer the question, I can visually analyze the bar representations of each method's scores. The bar indicators for the 'Rights Protection Score' and 'Social Inequality Score' lie on the negative part of the Y-axis and are color coded. Good performance on these metrics would be indicated by smaller magnitudes, that is, bars closer to the 0 line. The 'Progress Score' lies on the positive part of the Y-axis and the color used is a lighter shade compared to the other two scores. Good performance on this metric is bigger magnitudes. By comparing the three methods, we see the 'Litigation' method, indicated by {\"Textile\"}[0.751, 0.919, 0.806, 0.942], has its 'Rights Protection Score' closer to 0 and the highest 'Progress Score' among the three methods, indicating overall better performance.\nBased on the above thought, the answer is:\nLitigation, because it has better Rights Protection and Progress Scores, and its Social Inequality Score is relatively less compared to the other methods hence depicting overall efficiency." } ], "id_raw": 265 }, { "id": "w8NrMxqL25CXYNcX", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_10.png", "conversations": [ { "from": "human", "value": "\nWhich method has the highest Social Inequality Score?" }, { "from": "gpt", "value": "To answer the question, I can inspect the bar chart and see the bar heights of 'Social Inequality Score' for each method. Considering the method 'Lobbying', the 'Social Inequality Score' can be found at the location of {\"Automobile\"}[0.426, 0.034, 0.480, 0.729]. Next, we see that the score for method 'Protesting' is located at {\"Automobile\"}[0.480, 0.034, 0.534, 0.382] and the score for 'Litigation'is located at {\"Textile\"}[0.751, 0.033, 0.806, 0.034]. By comparing these, it's clear that 'Lobbying' has the highest Social Inequality Score.\nBased on the above thought, the answer is:\nLobbying" } ], "id_raw": 265 }, { "id": "1t2rNngwFzuc0mLS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nCould you convert the bar graph data for each business process across different categories into a CSV formatted table?" }, { "from": "gpt", "value": "The bar graph shows 3 different business processes: {Productivity}[0.693, 0.786, 0.796, 0.809], {Innovation}[0.693, 0.821, 0.784, 0.844], and {Customer Satisfaction}[0.693, 0.856, 0.884, 0.879], which are color-coded and represented across 4 categories: {Steady Increase}[0.106, 0.919, 0.246, 0.942], {Moderate Growth}[0.307, 0.919, 0.456, 0.942], {Moderate Increase}[0.507, 0.919, 0.668, 0.942], and {Gradual Recovery}[0.718, 0.919, 0.868, 0.942]. The bar's height and the corresponding values annotated at the top are used to determine the respective value for each process in each category. For example, in the category Steady Increase, the value for Productivity is given by the height of its bar[0.114, 0.248, 0.156, 0.903] indicated by the annotation \"15.34\"[0.110, 0.217, 0.160, 0.241] above the bar.\nBased on the above thought, the answer is:\nCategories,Productivity,Innovation,Customer_Satisfaction\nSteady increase,15.34,13.42,-2.51\nModerate growth,14.20,15.60,1.67\nModerate increase,16.12,17.68,3.71\nGradual recovery,16.90,18.33,5.92" } ], "id_raw": 266 }, { "id": "dRWH6ooPaxTNihZz", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what are the trends you observe in the business processes?" }, { "from": "gpt", "value": "There are several trends that can be observed in the chart. For the {Productivity}[0.693, 0.786, 0.796, 0.809] and the {Innovation}[0.693, 0.821, 0.784, 0.844] processes, every consecutive bar increases in size, indicating a steady increase in {Productivity} and {Innovation} as we go from {Steady Increase}[0.106, 0.919, 0.246, 0.942] category to {Gradual Recovery}[0.718, 0.919, 0.868, 0.942] category. However, for the {Customer Satisfaction}[0.693, 0.856, 0.884, 0.879] process which starts off with a negative value in the {Steady Increase} category and then moves into the positive zone by the {Moderate Growth}[0.307, 0.919, 0.456, 0.942] category. This one increases at a faster pace than {Productivity} and {Innovation}, indicating a more substantial growth. The common trend among all three processes is that they all show their highest values in the {Gradual Recovery} category.\nBased on the above thought, the answer is:\nThe Productivity and Innovation processes gradually increase across all categories. Customer Satisfaction starts with a negative value, indicating a previous decline, but then recovers and grows more rapidly than the other two processes. All processes reach their highest values under the Gradual Recovery category." } ], "id_raw": 266 }, { "id": "J6kK4wKr2qpuVqYG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_11.png", "conversations": [ { "from": "human", "value": "\nWhich category has the highest Customer Satisfaction value?" }, { "from": "gpt", "value": "To find out the category with the highest Customer Satisfaction value, I need to look at the height of the third bar, the {Customer Satisfaction}[0.693, 0.856, 0.884, 0.879] bars, in each category. The third bar in the {Gradual Recovery}[0.718, 0.919, 0.868, 0.942] category can be seen at [0.814, 0.056, 0.855, 0.618] with the highest value annotated on top as \"5.92\"[0.815, 0.026, 0.853, 0.049].\nBased on the above thought, the answer is:\nGradual recovery" } ], "id_raw": 266 }, { "id": "URUqHo13sWcQlSVd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nPlease create a table in CSV format representing the data from the chart you analyzed, including the categories of Local Government and Community Development, budget allocation, and community satisfaction details." }, { "from": "gpt", "value": "The chart contains two sets of bars, representing 'Budget Allocation in million USD' and 'Community Satisfaction in %', respectively. Each category under 'Local Government and Community Development' which are {'Planning and Land Use'}[0.113, 0.919, 0.311, 0.942], {'Infrastructure and Municipal Services'}[0.338, 0.919, 0.662, 0.942] and {'Social and Recreational Services'}[0.647, 0.919, 0.929, 0.942] has a corresponding pair of bars. For instance, the 'Planning and Land Use' category has the budget allocation bar {'bar'}[0.126, 0.686, 0.212, 0.903] and the community satisfaction bar {'bar'}[0.212, 0.425, 0.298, 0.903] with the allocated budget of 30 million USD and a 55% satisfaction rate, as shown by the annotations {'30'}[0.158, 0.655, 0.180, 0.679] and {'55%'}[0.236, 0.395, 0.274, 0.418]. The same method is applied to each category to convert the chart into a table in CSV format.\nBased on the above thought, the answer is:\nCategory,Budget Allocation in million USD,Community Satisfaction in %\nPlanning and Land Use,30,55\nInfrastructure and Municipal Services,100,85\nSocial and Recreational Services,60,75" } ], "id_raw": 268 }, { "id": "prhCPOHb6EDWGSZu", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, should the local government prioritize more on Planning and Land Use or on Social and Recreational Services? Explain your answer." }, { "from": "gpt", "value": "The question needs a comparative analysis between the two categories 'Planning and Land Use' and 'Social and Recreational Services'. In the aspect of budget allocation, 'Social and Recreational Services' {'bar'}[0.702, 0.469, 0.788, 0.903] receives double the amount of 'Planning and Land Use' {'bar'}[0.126, 0.686, 0.212, 0.903], which can be seen from their respective annotations {'60'}[0.734, 0.438, 0.756, 0.462] and {'30'}[0.158, 0.655, 0.180, 0.679]. On the other hand, in terms of community satisfaction, 'Planning and Land Use' {'bar'}[0.212, 0.425, 0.298, 0.903] only received a 55% satisfied rate {'annotation'}[0.236, 0.395, 0.274, 0.418], whereas 'Social and Recreational Services' {'bar'}[0.788, 0.251, 0.874, 0.903] secured a 75% rating {'annotation'}[0.812, 0.221, 0.850, 0.244].\nBased on the above thought, the answer is:\nThe local government should prioritize more on Planning and Land Use. Although Social and Recreational Services are currently receiving a higher budget and have a higher satisfaction rate, Planning, and Land Use is an area that sorely needs improvement despite its fundamental importance. Hence, more resources should be directed towards this so as to enhance community satisfaction and overall development." } ], "id_raw": 268 }, { "id": "gLrLdYZpTtO7C5EG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_13.png", "conversations": [ { "from": "human", "value": "\nIn the given chart, which area of local government and community development received the highest budget allocation?" }, { "from": "gpt", "value": "To find out the area with the highest budget, I need to compare the heights of the first set of bars, corresponding to 'Budget Allocation in million USD'. The heights of these bars directly reflect the budget allocation amounts. Upon examination, the tallest bar in this set {'bar'}[0.414, 0.179, 0.500, 0.903] is found under the category {'Infrastructure and Municipal Services'}[0.338, 0.919, 0.662, 0.942]. This bar's value is annotated as '100' million USD {'annotation'}[0.440, 0.149, 0.473, 0.172], which is indeed the highest among the three categories.\nBased on the above thought, the answer is:\nInfrastructure and Municipal Services." } ], "id_raw": 268 }, { "id": "d90ERDK48g4qHpCo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nExtract the values from the bars in the chart and present them in a CSV format." }, { "from": "gpt", "value": "Given the positioning of {\"Category\"}[0.459, 0.952, 0.539, 0.975] and {\"Consumption (%)\"}[0.019, 0.368, 0.036, 0.569] labels, it's evident we are comparing consumption percentages for two sectors. As per {the legend}[0.656, 0.777, 0.891, 0.888], each bar color represents a different type of consumtion: {Power Consumption}[0.712, 0.786, 0.884, 0.809] as green, {Water Consumption}[0.712, 0.821, 0.883, 0.844] as yellow, and {C02 Emission}[0.712, 0.856, 0.828, 0.879] as white. For the {Residential} category[0.240, 0.919, 0.335, 0.942], identified by examining the x-axis labels, the power consumption is annotated as 125 on {the green bar}[0.129, 0.396, 0.235, 0.758], water consumption is 20 on {the yellow bar}[0.235, 0.700, 0.340, 0.758], and CO2 emission is -3.5 on {the white bar}[0.340, 0.469, 0.446, 0.621]. By using the same process for the {Industrial} category[0.670, 0.919, 0.751, 0.942], the respective values for power, water and CO2 are 200, -10 and 0.83.\nBased on the above thought, the answer is:\nCategory, Power Consumption(%), Water Consumption(%), CO2 Emission\nResidential, 125, 20, -3.5\nIndustrial, 200, -10, 0.83" } ], "id_raw": 269 }, { "id": "vVbQGR6uBMNemzh2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nCan you infer which sector contributes more to a potential environmental impact?" }, { "from": "gpt", "value": "Understanding environmental impact requires a combination of seeing the bars for carbon dioxide (CO2) emissions and water consumption. Here, the {yellow bar} representing water consumption[0.712, 0.821, 0.883, 0.844] and the {white bar} for CO2 emissions[0.712, 0.856, 0.828, 0.879] are key. For the {Residential} sector[0.240, 0.919, 0.335, 0.942], the CO2 emissions are marked as negative {-3.5}[0.376, 0.590, 0.410, 0.614] indicating lower emissions, while water consumption is {20}[0.276, 0.670, 0.298, 0.693]. For the {Industrial} sector[0.670, 0.919, 0.751, 0.942], the CO2 emissions are {0.83}[0.797, 0.402, 0.836, 0.426], and also shows a negative water consumption period at {-10}[0.696, 0.757, 0.725, 0.780]. This combination of higher CO2 emissions and water conservation efforts in the Industrial sector suggest a potential for environmental impact.\nBased on the above thought, the answer is:\nThe Industrial sector, due to higher CO2 emissions despite efforts for water conservation." } ], "id_raw": 269 }, { "id": "7hECyG0xGp0ZGaML", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_18_14.png", "conversations": [ { "from": "human", "value": "\nWhich category has the higher power consumption recorded?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height of the representative bar for power consumption, per the {legend}, the {green bar}[0.712, 0.786, 0.884, 0.809], in both the categories. The {Residential} category[0.240, 0.919, 0.335, 0.942] of power consumption is marked by {a green bar}[0.129, 0.396, 0.235, 0.758] with label \"125\" indicating the consumption level. The {Industrial} category[0.670, 0.919, 0.751, 0.942] of power consumption is indicated by {a green bar}[0.552, 0.179, 0.658, 0.758] with label \"200\" which is significantly taller. Hence, the category with the higher power consumption is clear.\nBased on the above thought, the answer is:\nIndustrial" } ], "id_raw": 269 }, { "id": "Q43t4qSpsx0kbw2z", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the data presented in the bar chart into a CSV format table?" }, { "from": "gpt", "value": "I noticed that the chart consists of 5 bars, each representing different suppliers' cost change in the Global Trade and Supply Chain Management. Starting from the left, the first bar represents {Wang Enterprises}[0.136, 0.245, 0.302, 0.560] with an increase of 0.45, followed by {Liu Corp}[0.302, 0.560, 0.468, 0.736] with a decrease of -0.25. Subsequent bars are for {Zhang Inc}[0.468, 0.490, 0.633, 0.560],\n{Li LLC}[0.633, 0.560, 0.799, 0.841] and {Chen Industries}[0.799, 0.174, 0.965, 0.560] with cost change values 0.10, -0.40 and 0.55 respectively.\nBased on the above thought, the answer is:\nSupplier,Cost Change\nWang Enterprises,0.45\nLiu Corp,-0.25\nZhang Inc,0.10\nLi LLC,-0.40\nChen Industries,0.55" } ], "id_raw": 270 }, { "id": "pwgXo7BpKQ3R48cf", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nBased on your visual analysis of the chart, which supplier should be considered if the aim is to cut costs and why?" }, { "from": "gpt", "value": "If the aim is to cut costs, I would suggest considering the suppliers who are shown to have decreased their costs. Looking at the chart, {Liu Corp}[0.302, 0.560, 0.468, 0.736] and {Li LLC}[0.633, 0.560, 0.799, 0.841] have their bars extending downwards from the {0 line}[0.136, 0.560, 0.965, 0.560], indicating a decrease in cost. Among these two, {Li LLC}[0.633, 0.560, 0.799, 0.841] has a longer bar in the negative direction, indicating it experienced a larger decrease in cost.\nBased on the above thought, the answer is:\nLi LLC should be considered if the aim is to cut costs because it saw a larger decrease in cost compared to the other suppliers." } ], "id_raw": 270 }, { "id": "MjvFMb5TjBZptVEt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_0.png", "conversations": [ { "from": "human", "value": "\nWhich supplier had the largest decrease in cost?" }, { "from": "gpt", "value": "To solve this, I'll look at which 'cost change' bar on the chart extends the furthest below the {0 line}[0.136, 0.560, 0.965, 0.560]. I see that the bar for {Li LLC}[0.633, 0.560, 0.799, 0.841] is longer in the negative direction than the bar for {Liu Corp}[0.302, 0.560, 0.468, 0.736] so Li LLC saw a larger decrease in cost.\nBased on the above thought, the answer is:\nLi LLC" } ], "id_raw": 270 }, { "id": "7O2zY83rDZweN1Az", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you transform the data on research field and their corresponding impacts into a structured table format?" }, { "from": "gpt", "value": "From {the x-axis labels}[{\"Computer Science\"}[0.086, 0.939, 0.245, 0.957]; {\"Biology\"}[0.282, 0.939, 0.346, 0.957]; {\"Physics\"}[0.430, 0.939, 0.494, 0.957]; {\"Chemistry\"}[0.566, 0.939, 0.655, 0.957]; {\"Mathematics\"}[0.703, 0.939, 0.815, 0.957]; {\"Philosophy\"}[0.860, 0.939, 0.954, 0.957]], I understand that these are the different research fields. The y-axis, labeled as {Impact}[0.019, 0.449, 0.036, 0.510], represents the impact of each research field, the positive numbers indicate expansion, and negative numbers represent a decline. I need to extract the impact value for each field from the bar heights. For example, in {Computer Science}[0.086, 0.939, 0.245, 0.957], the height of {the bar}[0.091, 0.139, 0.240, 0.593] reaches up to the value of 2 on the y-axis, hence the impact of Computer Science is 2. I will follow the same process for the rest of the fields.\nBased on the above thought, the answer is:\nField,Impact\nComputer Science,2\nBiology,-1.2\nPhysics,1.3\nChemistry,-0.8\nMathematics,2.1\nPhilosophy,0.5" } ], "id_raw": 272 }, { "id": "FBF1c0EGXOhhw0V5", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which academic fields are expanding and which are declining? Briefly explain your answer." }, { "from": "gpt", "value": "The academic fields are represented on the x-axis while their impact is depicted on the y-axis. Fields with a positive impact imply expansion, while those with a negative impact indicate decline. Looking at the bars, we can see that {Computer Science}[0.086, 0.939, 0.245, 0.957] and {Mathematics}[0.703, 0.939, 0.815, 0.957] depicted by their corresponding {bars}[{bar of CS}[0.091, 0.139, 0.240, 0.593]; {bar of Mathematics}[0.685, 0.116, 0.833, 0.593]] have positive heights and thus are expanding. On the other hand, {Biology}[0.282, 0.939, 0.346, 0.957] and {Chemistry}[0.566, 0.939, 0.655, 0.957] represented by their corresponding {bars}[{bar of Biology}[0.240, 0.593, 0.388, 0.865]; {bar of Chemistry}[0.536, 0.593, 0.685, 0.774]] show negative heights, suggesting these fields are declining.\nBased on the above thought, the answer is:\nAccording to the chart, the academic fields of Computer Science and Mathematics are expanding, while Biology and Chemistry are declining." } ], "id_raw": 272 }, { "id": "dm6bSvxGyuZ5Zq4x", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_2.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which academic field shows greatest decline?" }, { "from": "gpt", "value": "To find the research field showing the greatest decline, I need to look for the bar reaching the lowest point on the y-axis. Looking at the heights of the bars, it's clear that {Biology}[0.282, 0.939, 0.346, 0.957] represented by {the bar}[0.240, 0.593, 0.388, 0.865], has the maximum negative height, reaching -1.2 on the y-axis. Therefore, Biology is the academic field that shows the greatest decline.\nBased on the above thought, the answer is:\nBiology." } ], "id_raw": 272 }, { "id": "Y4t1wmghdS5oPP25", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nCan you structure the chart data in a CSV format?" }, { "from": "gpt", "value": "Observing the chart's title {\"Media and Entertainment Industry Trends\"}[0.136, 0.034, 0.0893, 0.911] and using the {x-axis and y-axis labels}[{\"Television\"}[0.163, 0.919, 0.275, 0.942]; {\"Movies\"}[0.344, 0.919, 0.425, 0.942]; {\"MobileGames\"}[0.471, 0.919, 0.628, 0.942]; {\"Music\"}[0.682, 0.919, 0.748, 0.942]; {\"OnlineShopping\"}[0.789, 0.919, 0.972, 0.942]] as the headers, I can build the CSV table. Looking at {each bar's height} and the {respective y-tick annotation}, I can get the corresponding data values. For example, the {Television's bar}[0.178, 0.144, 0.260, 0.473] extends midway between '0.50' and '1.00' on the y-axis, so its corresponding value is 0.75. Similarly, I can obtain all the values to construct the CSV table.\nBased on the above thought, the answer is:\nArea,Relevance\nTelevision,0.75\nMovies,-0.9\nMobileGames,0.50\nMusic,-0.65\nOnlineShopping,-0.45" } ], "id_raw": 274 }, { "id": "bfrLrM6fLfm1yBEX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the general trend in the Media and Entertainment Industry based on the chart?" }, { "from": "gpt", "value": "According to {the bars on the chart}, this question requires an analysis of the general trend in different areas. Here, positive areas such as {Television}[0.178, 0.144, 0.260, 0.473] and {MobileGames}[0.509, 0.253, 0.591, 0.473] are trending upwards as indicated by the bar heights being above the zero mark on the y-axis. On the contrary, areas such as {Movies}[0.343, 0.473, 0.426, 0.867], {Music}[0.674, 0.473, 0.757, 0.758], and {OnlineShopping}[0.839, 0.473, 0.922, 0.670] are trending downwards as their bars extend below the zero line. The overall trend seems to be a mix of upward and downward trends, indicating a dynamic and shifting industry landscape.\nBased on the above thought, the answer is:\nThe Media and Entertainment industry shows a mix of upward and downward trends. The Television and MobileGames domains are gaining in relevance, while the Movies, Music, and OnlineShopping domains are seeing a decrease in relevance, indicating a dynamic industry landscape." } ], "id_raw": 274 }, { "id": "BwlhSwwTE4r4Moqj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_4.png", "conversations": [ { "from": "human", "value": "\nWhich area experienced the greatest fluctuation in the trend?" }, { "from": "gpt", "value": "To determine the area with the greatest fluctuation, I will identify the bar with the greatest absolute height difference from the y-axis value of zero. Starting from the left, the first bar is {Television}[0.178, 0.144, 0.260, 0.473] with a distance of 0.75, the second is {Movies}[0.343, 0.473, 0.426, 0.867] with a distance of 0.9, and so on for the bars {MobileGames}, {Music}, and {OnlineShopping}. After comparison, I find the {Movies bar} extends the furthest from zero along the y-axis. Therefore, the Movies area experienced the greatest fluctuation.\nBased on the above thought, the answer is:\nMovies" } ], "id_raw": 274 }, { "id": "5u7wHiHae50CBDYF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart information into a CSV table?" }, { "from": "gpt", "value": "The title {\"Household Condition\"}[0.394, 0.952, 0.634, 0.975] at the bottom of the chart and the y-axis {\"% Family\"}[0.025, 0.421, 0.048, 0.525] specify the two categories for the table. The content of the table can be taken by looking at the labels of the x-axis and the height of each bar. For example, the first bar, which represents {\"Minimal Basic Amenities\"}[0.050, 0.919, 0.332, 0.942], extends {\"70%\"}[0.142, 0.297, 0.239, 0.911] up the y-axis. So, \"Minimal Basic Amenities, 70\" is the first data entry for the table.\nBased on the above thought, the answer is:\nHousehold Condition,% Family\nMinimal Basic Amenities,70\nInadequate Sanitation,55\nLack of Electricity,10\nNo Access to Clean Drinking Water,3\nChronic Food Insecurity,12" } ], "id_raw": 275 }, { "id": "bfWlRt2oAd3oLemN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nBased on this chart, which social issues need immediate improvement?" }, { "from": "gpt", "value": "Looking at the bar chart, the conditions which affect the most households can be considered as needing immediate improvement. The conditions which affect the most households will have the bars with the wider ranges. The widest range bar is {\"Minimal Basic Amenities\"}[0.050, 0.919, 0.332, 0.942], which extends up to {\"70%\"}[0.110, 0.297] of the y-axis, followed by the {\"Inadequate Sanitation\"}[0.224, 0.919, 0.481, 0.942] with its range extended upto {\"55%\"}[0.304, 0.429, 0.401, 0.911] on y axis.\nBased on the above thought, the answer is:\nThe provision of Minimal Basic Amenities and improving Sanitation need immediate attention as these conditions affect 70% and 55% of households respectively." } ], "id_raw": 275 }, { "id": "HrU7wVH2EEvNZCms", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_5.png", "conversations": [ { "from": "human", "value": "\nWhich household condition affects the least number of families according to the chart?" }, { "from": "gpt", "value": "From looking at the chart, it can be determined that condition affecting the least number of families would be represented by the shortest bar on the chart. Looking at all the bars, the shortest one appears to be {\"No Access to Clean Drinking Water\"}[0.628, 0.885, 0.725, 0.911] with a height only reaching {\"3%\"}[0.628, 0.885, 0.725, 0.911] on the y-axis.\nBased on the above thought, the answer is:\nNo Access to Clean Drinking Water" } ], "id_raw": 275 }, { "id": "tZINoXvHtDbzggNW", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart's depiction, can you please convert the data into a well-structured table format?" }, { "from": "gpt", "value": "Given that the x-axis represents different scientific fields as seen in {the x-axis tick labels}[{\"Artificial Intelligence\"}[0.082, 0.919, 0.224, 0.942]; {\"Robotics\"}[0.297, 0.919, 0.356, 0.942]; {\"Space Technology\"}[0.438, 0.919, 0.562, 0.942]; {\"Quantum Computing\"}[0.601, 0.919, 0.746, 0.942]; {\"Nanotechnology\"}[0.790, 0.919, 0.904, 0.942]], the y-axis, as mentioned in {the ylabel}[0.015, 0.314, 0.029, 0.614], denotes the Amount of New Discovery. Also, there are bars indicating the Pace of Innovation, as implied by {the ylabel}[0.971, 0.357, 0.985, 0.571]. To transform the chart into tabular format, we'll need to extract the values represented by each color. For instance, {the bar corresponding to Artificial Intelligence in the Amount of New Discovery category}[0.118, -86.884, 0.188, 0.903] shows a value of 100 and {the bar indicating the Pace of Innovation for Artificial Intelligence category}[0.118, 0.276, 0.188, 0.903] has the value of 0.45.\nBased on the above thought, the answer is:\nScientific Fields,Amount of New Discovery,Pace of Innovation\nArtificial Intelligence,100,0.45\nRobotics,80,0.35\nSpace Technology,120,0.60\nQuantum Computing,75,0.20\nNanotechnology,90,0.30" } ], "id_raw": 276 }, { "id": "wpqoqP6d3p4ooHdE", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nFrom your analysis of the chart, which scientific field would you say is at the forefront of research and technological development presently? Explain your choice." }, { "from": "gpt", "value": "To give an appropriate response to this question, I need to consider both the highest amounts of new discovery and the fastest pace of innovation depicted in the chart. Considering {the bar representing the Amount of New Discovery}[0.118, -86.884, 0.188, 0.903] and {the bar indicating the Pace of Innovation for the Space Technology field}[0.465, 0.067, 0.535, 0.903], which are both highest among their respective sets, I would conclude that Space Technology is currently leading. This is because it not only has the highest number of new discoveries but also demonstrates the quickest pace of innovation.\nBased on the above thought, the answer is:\nSpace Technology. It has the most new discoveries and the fastest pace of innovation." } ], "id_raw": 276 }, { "id": "mLQGP2z4zZl4TFfH", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_6.png", "conversations": [ { "from": "human", "value": "\nWhich field shows the smallest pace of innovation as reflected in the chart?" }, { "from": "gpt", "value": "To identify the field with the lowest pace of innovation, we have to assess the height of all the bars depicting the Pace of Innovation. Looking across the fields from left to right, starting with {the bar for Artificial Intelligence}[0.118, 0.276, 0.188, 0.903], {the bar for Robotics}[0.292, 0.415, 0.361, 0.903], {the bar for Space Technology}[0.465, 0.067, 0.535, 0.903], {the bar for Quantum Computing}[0.639, 0.624, 0.708, 0.903] and finally, {the bar for Nanotechnology}[0.812, 0.485, 0.882, 0.903]. By visually comparing these bars, we can ascertain that the bar for Quantum Computing is the shortest, indicating the least pace of innovation in this field.\nBased on the above thought, the answer is:\nQuantum Computing." } ], "id_raw": 276 }, { "id": "Eiq7RMHm9hU7zP8c", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "The chart is describing the number of patents across various technology sectors. The sectors are indicated by {x-axis tick labels}[{\"AI_and_Machine_Learning\"}[0.087, 0.714, 0.258, 0.942]; {\"Biotech\"}[0.323, 0.714, 0.381, 0.792]; {\"Renewable_Energy\"}[0.467, 0.714, 0.596, 0.886]; {\"Space_Technology\"}[0.649, 0.714, 0.774, 0.881]; {\"Telecommunications\"}[0.823, 0.714, 0.960, 0.898]]. The corresponding values are indicated by the height of the bars. Each bar's height corresponds to the count of patents, as indicated on the Y-axis which ranges from {0}[0.065, 0.697, 0.076, 0.720] to {250}[0.044, 0.025, 0.076, 0.048]. The CSV table will include the technology sectors as the first column and corresponding patent count as the other column.\nBased on the above thought, the answer is:\nTechnology_Sectors, Number_of_Patents\nAI_and_Machine_Learning, 134\nBiotech, 218\nRenewable_Energy, 189\nSpace_Technology, 134\nTelecommunications, 240" } ], "id_raw": 277 }, { "id": "UUzKDN47Kglu3pdT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about Technological innovation from the barchart?" }, { "from": "gpt", "value": "Observing the heights of the bars, I can infer the extent of scientific research and technological innovation across different sectors. From highest to lowest: {Telecommunications}[0.837, 0.061, 0.945, 0.706], {Biotech}[0.298, 0.120, 0.406, 0.706], {Renewable Energy}[0.478, 0.198, 0.586, 0.706], {AI and Machine Learning and Space Technology}[0.118, 0.346, 0.226, 0.706] and [0.658, 0.346, 0.766, 0.706]. From this, it's clear that the field of Telecommunications has shown the most pronounced increase in terms of the number of patents granted, pointing towards high innovation. Biotech and Renewable Energy fields have also witnessed considerable growth. AI, Machine Learning, and Space Technology have a moderate number of patents, indicating steady growth in these areas.\nBased on the above thought, the answer is:\nThe Telecommunications sector has shown the highest technological innovation followed by Biotech, and Renewable Energy. AI and Machine Learning along with Space Technology exhibit moderate growth. Hence, sectors such as Telecommunications, Biotech, and Renewable Energy are currently the leading areas of technological innovation." } ], "id_raw": 277 }, { "id": "rl2pczqYz0yUvp3w", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_7.png", "conversations": [ { "from": "human", "value": "\nWhich technology sector has the highest number of patents?" }, { "from": "gpt", "value": "To find the sector with the highest number of patents, I need to compare the heights of the bars. From the left to right, the bars are located at [0.118, 0.346, 0.226, 0.706] for AI and Machine Learning, [0.298, 0.120, 0.406, 0.706] for Biotech, [0.478, 0.198, 0.586, 0.706] for Renewable Energy, [0.658, 0.346, 0.766, 0.706] for Space Technology and [0.837, 0.061, 0.945, 0.706] for Telecommunications. By comparing their heights relative to the y-axis that ranges from 0 to 250, I find that the {bar for Telecommunications}[0.837, 0.061, 0.945, 0.706] is the highest one, thus indicating that Telecommunications have the highest number of patents.\nBased on the above thought, the answer is:\nTelecommunications." } ], "id_raw": 277 }, { "id": "mbzvB8PlM64l3AbD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the bar chart data into a table with two columns 'Charity Type' and 'Donation Amount'?" }, { "from": "gpt", "value": "Looking at the bar plot, I can see that each bar {\"rectangle\"} contains two pieces of information: the category, which is given by the {x-axis label}, and the value, which is the length of the bar and can be calculated as {y_range}. For example, for {the bar named 'Food Bank'}[0.113, 0.414, 0.204, 0.933], its label is 'Food Bank' and its value is the difference between 0 and 12500 which is 12500. Similarly, I can analyse the rest of the bars like {the bar named 'Animal Welfare'}[0.264, 0.601, 0.354, 0.933], {the bar named 'Environmental Conservation'}[0.414, 0.089, 0.504, 0.933], etc.to obtain the table.\nBased on the above thought, the answer is:\nCharity Type,Donation Amount\nFood Bank,12500\nAnimal Welfare,8000\nEnvironmental Conservation,20300\nEducational Support,15600\nHealthcare Services,9500\nDisaster Relief,18500" } ], "id_raw": 278 }, { "id": "1MHwSgH18hNiFDxt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which areas of charity do people pay more attention to and donate more to based on their concerns and interests?" }, { "from": "gpt", "value": "To answer this question, I need to evaluate which charity type has the largest bar on the plot, showing the highest donation amount. By checking each bar, I can see that {the bar named 'Environmental Conservation'}[0.414, 0.089, 0.504, 0.933] is the longest with the {y_range} from 0 to 20300 indicating that 'Environmental Conservation' has received the highest donation amount. Hence, people seems to pay more attention to and donate more towards 'Environmental Conservation'.\nBased on the above thought, the answer is:\nPeople pay more attention to and donate more to Environmental Conservation." } ], "id_raw": 278 }, { "id": "s1Y4o6h7Rlrdot2V", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_8.png", "conversations": [ { "from": "human", "value": "\nWhich charity type has received the least amount of donation according to the bar chart?" }, { "from": "gpt", "value": "To determine which charity type received the smallest amount of donations, I should analyse the length of the bars on the chart. By visually looking, {the bar named 'Animal Welfare'}[0.264, 0.601, 0.354, 0.933] appears to be the smallest. Further checking the {y_range} indicates that the donation amount is 8000 which is indeed the smallest among all the given bars.\nBased on the above thought, the answer is:\nAnimal Welfare." } ], "id_raw": 278 }, { "id": "YxRCJfs4QHdhyRx2", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nCould you summarize the data from the chart into a structured CSV format?" }, { "from": "gpt", "value": "Given the chart, I can see that the index \"Value Change\" is plotted over the years from 1960 to 2020. This is represented by the y-axis titled {Value Change}[0.019, 0.420, 0.036, 0.539] and the x-axis labeled as {Decades}[0.499, 0.964, 0.574, 0.981]. There are a total of 7 categories, represented by 7 bars in the chart. The position and height of these bars are displayed by their coordinates. For instance, the bar for 1960 has a height that reaches up to -0.12 on the y-axis, indicated by the bar coordinates {1960}[0.091, 0.438, 0.218, 0.537]. Similarly, each of the other bars represents the value of the index for their corresponding decade. The bars, along with their labels, translate to the value of \"Value Change\" index for their respective decades. Let me gather this information into a CSV format.\nBased on the above thought, the answer is:\nDecades,Value Change\n1960,-0.12\n1970,-0.36\n1980,-0.55\n1990,-0.45\n2000,-0.28\n2010,0.15\n2020,0.45" } ], "id_raw": 279 }, { "id": "7dZHRVbyS1gwHjI2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe the changing trend of 'Value Change' index from the year 1960 to 2020 according to the bar chart?" }, { "from": "gpt", "value": "Now, let us analyze the evolution of the 'Value Change' index as per the chart. From the start at {1960}[0.091, 0.438, 0.218, 0.537] to {1980}[0.346, 0.438, 0.473, 0.892], the bar heights decrease, implying a downward trend in the 'Value Change' index. Also, all the bars are below the x-axis, indicating that the 'Value Change' values are negative during these decades. \n\nHowever, there is something peculiar about the bar representing {1990}[0.473, 0.438, 0.600, 0.809]. Its position is higher than its preceding bar {1980}[0.346, 0.438, 0.473, 0.892], signaling a recovery. Then, from {2000}[0.600, 0.438, 0.727, 0.669] to {2020}[0.854, 0.067, 0.981, 0.438], there is a steady growth in the height of bars. The 'Value Change' moved from the negative region to the positive region, indicating a persistent growth in cultural values with positive values. So, in general, the trending of 'Value Change' index is that there was a decline from 1960 to 1980, followed by a recovery in 1990 and a steady growth from 2000 to 2020.\nBased on the above thought, the answer is:\nThe 'Value Change' index experienced a decline from 1960 to 1980. It began to recover in the 1990s, and then increased steadily from 2000 to 2020." } ], "id_raw": 279 }, { "id": "HVfPfbhPA0VwM6js", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_9.png", "conversations": [ { "from": "human", "value": "\nWhen did the 'Value Change' index start to increase, based on the bar chart data?" }, { "from": "gpt", "value": "The 'Value Change' index started to increase when the value of the index changed from negative to positive. From the chart, we can see that the bars representing the index for each decade gradually decrease and then start to increase. According to the coordinates of each bar and the value labeling, the bar {1990}[0.473, 0.438, 0.600, 0.809] shows a higher position than the bar {1980}[0.346, 0.438, 0.473, 0.892], signifying an increase in the index. Hence, 'Value Change' index started to increase from 1990 onwards.\nBased on the above thought, the answer is:\n1990" } ], "id_raw": 279 }, { "id": "yuzHp0gNDyNJlHDn", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract data from the bar chart and present it in a structured table?" }, { "from": "gpt", "value": "The chart shows five independent bars, each representing a quarter. From the location of {tick labels on the x-axis}[\"Q1\"[0.204, 0.919, 0.237, 0.942]], [\"Q2\"[0.372, 0.919, 0.404, 0.942]], [\"Q3\"[0.539, 0.919, 0.572, 0.942]], [\"Q4\"[0.707, 0.919, 0.740, 0.942]], [\"Q5\"[0.875, 0.919, 0.907, 0.942]] and by the height of the corresponding bars {\"Q1\"[0.136, 0.122, 0.304, 0.473], \"Q2\"[0.304, 0.210, 0.472, 0.473], \"Q3\"[0.472, 0.473, 0.640, 0.692], \"Q4\"[0.640, 0.473, 0.807, 0.845], \"Q5\"[0.807, 0.210, 0.975, 0.473]} relative to {the y-axis labels}[\"-1.00\"[0.058, 0.902, 0.128, 0.925]], [\"-0.75\"[0.058, 0.792, 0.128, 0.816]], [\"-0.50\"[0.058, 0.683, 0.128, 0.706]], [\"-0.25\"[0.058, 0.573, 0.128, 0.596]], [\"0.00\"[0.077, 0.463, 0.128, 0.487]], [\"0.25\"[0.077, 0.354, 0.128, 0.377]], [\"0.50\"[0.077, 0.244, 0.128, 0.268]], [\"0.75\"[0.077, 0.135, 0.128, 0.158]], [\"1.00\"[0.077, 0.025, 0.128, 0.048]], we can extract the values for each quarter. For instance, the bar for \"Q1\" is located at roughly the 0.80 mark on the y-axis, indicating a value of 0.80.\nBased on the above thought, the answer is:\nTime,Value\nQ1,0.80\nQ2,0.60\nQ3,-0.50\nQ4,-0.85\nQ5,0.60" } ], "id_raw": 280 }, { "id": "pr65l9yrHyx5Hpqa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, describe the overall trend observed over the five quarters." }, { "from": "gpt", "value": "To establish the overall trend, we can consider the height of the bars {\"Q1\"[0.136, 0.122, 0.304, 0.473], \"Q2\"[0.304, 0.210, 0.472, 0.473], \"Q3\"[0.472, 0.473, 0.640, 0.692], \"Q4\"[0.640, 0.473, 0.807, 0.845], \"Q5\"[0.807, 0.210, 0.975, 0.473]} for each quarter {on the x-axis}[\"Q1\"[0.204, 0.919, 0.237, 0.942]], [\"Q2\"[0.372, 0.919, 0.404, 0.942]], [\"Q3\"[0.539, 0.919, 0.572, 0.942]], [\"Q4\"[0.707, 0.919, 0.740, 0.942]], [\"Q5\"[0.875, 0.919, 0.907, 0.942]]. In the first two quarters, the bars are in the positive region, indicating growth. However, in the third and fourth quarters, the bars shift to the negative region, thus implying a decline. Then in the fifth quarter, the bar returns to the positive region, signifying a resurgence in growth. Hence, the overall trend is inconsistent, with periods of growth and decline.\nBased on the above thought, the answer is:\nThe trend is erratic and inconsistent. It starts with two quarters of growth, followed by two quarters of a sharp decline, and then a resurgence in the final quarter." } ], "id_raw": 280 }, { "id": "AvLzpSDDvDIQFHsR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_10.png", "conversations": [ { "from": "human", "value": "\nIn which quarter do you observe the greatest decline in the data?" }, { "from": "gpt", "value": "The most significant decline will correspond to the bar with the lowest height. After looking at the heights of all bars: {\"Q1\"[0.136, 0.122, 0.304, 0.473], \"Q2\"[0.304, 0.210, 0.472, 0.473], \"Q3\"[0.472, 0.473, 0.640, 0.692], \"Q4\"[0.640, 0.473, 0.807, 0.845], \"Q5\"[0.807, 0.210, 0.975, 0.473]}, the lowest one is the bar corresponding to {Q4}[0.640, 0.473, 0.807, 0.845] reaching down to about -0.85 on the {y-axis labels}[\"-1.00\"[0.058, 0.902, 0.128, 0.925]], [\"-0.75\"[0.058, 0.792, 0.128, 0.816]], [\"-0.50\"[0.058, 0.683, 0.128, 0.706]], [\"-0.25\"[0.058, 0.573, 0.128, 0.596]], [\"0.00\"[0.077, 0.463, 0.128, 0.487]], [\"0.25\"[0.077, 0.354, 0.128, 0.377]], [\"0.50\"[0.077, 0.244, 0.128, 0.268]], [\"0.75\"[0.077, 0.135, 0.128, 0.158]], [\"1.00\"[0.077, 0.025, 0.128, 0.048]]. Hence, the quarter with the greatest decline is Q4.\nBased on the above thought, the answer is:\nQ4" } ], "id_raw": 280 }, { "id": "wz1ELJtwmvHItS24", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nCan you extract the bar chart's information and present them in a structured table format?" }, { "from": "gpt", "value": "From the bar chart, I can see the x-axis represents different cuisine categories, labeled as {\"Italian Cuisine\"}[0.113, 0.939, 0.236, 0.957], {\"Chinese Cuisine\"}[0.251, 0.939, 0.389, 0.957], {\"Mexican Cuisine\"}[0.395, 0.939, 0.536, 0.957], {\"Indian Cuisine\"}[0.550, 0.939, 0.672, 0.957], {\"Thai Cuisine\"}[0.703, 0.939, 0.809, 0.957], {\"Japanese Cuisine\"}[0.828, 0.939, 0.975, 0.957]. The y-axis shows the growth in their popularity, ranging from {\"-0.50\"}[0.043, 0.926, 0.096, 0.944] to {\"1.00\"}[0.058, 0.019, 0.096, 0.036]. The bars represent the growth in popularity for each cuisine, such as {Italian's bar}[0.102, 0.147, 0.247, 0.631]. We can use the middle position of each bar on y-axis to find their corresponding popularity growth.\nBased on the above thought, the answer is:\nCuisine Category,Popularity Trend\nItalian Cuisine,0.80\nChinese Cuisine,0.60\nMexican Cuisine,0.30\nIndian Cuisine,0.10\nThai Cuisine,-0.10\nJapanese Cuisine,-0.25" } ], "id_raw": 281 }, { "id": "DolvoaBRhqxYR6x0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what assessment can be made about the trend of Asian cuisine popularity?" }, { "from": "gpt", "value": "To answer this question, I need to examine the bars representing Asian cuisines - {\"Chinese Cuisine\"}[0.251, 0.939, 0.389, 0.957], {\"Indian Cuisine\"}[0.550, 0.939, 0.672, 0.957], {\"Thai Cuisine\"}[0.703, 0.939, 0.809, 0.957], and {\"Japanese Cuisine\"}[0.828, 0.939, 0.975, 0.957]. These bars are present on the x-axis, and their length on the y-axis represents their popularity growth. Based on these four bars, it appears that Chinese cuisine is growing in popularity, while Japanese cuisine is on a decline, with Thai cuisine also seeing a slight decrease. However, Indian cuisine seems to be gaining popularity at a slow pace. From this, it would seem that the popularity of Asian cuisine is mixed, and it might also vary greatly depending on the specific cuisine.\nBased on the above thought, the answer is:\nThe trend of Asian cuisine popularity is mixed. While Chinese and Indian cuisines are seeing growth in popularity, Thai and Japanese cuisines are experiencing a decline. It suggests that preference for Asian cuisine varies greatly among different types." } ], "id_raw": 281 }, { "id": "8fZv319bvLWQPSB1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, what is the growth difference of popularity between the most popular and least popular cuisine?" }, { "from": "gpt", "value": "To get the growth difference of popularity between the most and least popular cuisines, I need to extract the highest and lowest bars in the chart. As per the chart, {Italian Cuisine's bar}[0.102, 0.147, 0.247, 0.631] is the highest and represents a popularity growth of 0.80. On the other hand, {Japanese Cuisine's bar}[0.829, 0.631, 0.974, 0.782] is the lowest, with a decline in popularity of 0.25. Now, I can calculate the difference by subtracting the lowest growth from the highest growth.\nBased on the above thought, the answer is:\n1.05" } ], "id_raw": 281 }, { "id": "V162Ldc46jZx3BC0", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you have observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "The x-axis of this chart denotes different innovation areas as observed by the x-axis labels La1: {\"Cloud_Based_Learning\"}[0.067, 0.919, 0.263, 0.942], La2: {\"MOOCs_Adoption\"}[0.239, 0.919, 0.388, 0.942], La3: {\"Gamified_Education\"}[0.374, 0.919, 0.548, 0.942], La4: {\"Augmented_Reality\"}[0.525, 0.919, 0.694, 0.942], La5: {\"Adaptive_Learning\"}[0.677, 0.919, 0.838, 0.942], and La6: {\"AI_in_Education\"}[0.837, 0.919, 0.974, 0.942]. Whereas the y-axis represents the innovation index quantified as seen by the labels ranging from Lb1: {-2.0}[0.043, 0.902, 0.085, 0.925] to Lb11: {3.0}[0.058, 0.025, 0.085, 0.048]. The associated innovation index for every innovation area is shown by the position of the bars on the y-axis, such as the bar of Cloud-Based Learning is B1 [0.121, 0.358, 0.210, 0.560] and it has an innovation index of 1.15 as the height reaches approximately to Lb9: {1.0}. Each of the components from La1 to La6 on the x-axis has an associated bar on the chart from which the corresponding values on the y-axis can be deduced.\nBased on the above thought, the answer is:\nInnovation_Area,Innovation_Index\nCloud_Based_Learning,1.15\nMOOCs_Adoption,-1.75\nGamified_Education,0.90\nAugmented_Reality,0\nAdaptive_Learning,-0.6\nAI_in_Education,2.2" } ], "id_raw": 282 }, { "id": "UUMKwfk7up9khWCK", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which area seems to be more innovative and why?" }, { "from": "gpt", "value": "Analyzing the bar chart, the heights of bars indicate innovation index. Higher the bar, higher is the innovation. The bar for {'AI_in_Education'}[0.861, 0.174, 0.950, 0.560] has the highest position on the y-axis from all the bars, reaching up to 2.2, which also corresponds to the highest y-axis label Lb11: {3.0}. It implies that 'AI_in_Education' has the highest innovation index among all the innovation areas listed on the x-axis, hence it is the most innovative.\nBased on the above thought, the answer is:\nAI_in_Education, because it has the highest innovation index among all areas." } ], "id_raw": 282 }, { "id": "0ibT9KPY5TPeotRj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of the innovation index according to the chart?" }, { "from": "gpt", "value": "The values of the innovation index are expressed on the y-axis, from Lb1: {-2.0}[0.043, 0.902, 0.085, 0.925] at the bottom to Lb11: {3.0}[0.058, 0.025, 0.085, 0.048] at the top. Therefore, the range of the innovation index is from -2.0 to 3.0.\nBased on the above thought, the answer is:\n-2.0 to 3.0" } ], "id_raw": 282 }, { "id": "wqteUQCyXOxWLcZ7", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nFrom the Bar Chart, can you please extract the data and structure it into a table format?" }, { "from": "gpt", "value": "This bar chart represents age groups on the x-axis and their earned income on the y-axis. By identifying each of the values in the chart, I can directly transpose this visual data into a structured table format. The x-axis labels can be found with the key {xticks} [{\"20-29\"}[0.183, 0.919, 0.249, 0.942], {\"30-39\"}[0.321, 0.919, 0.387, 0.942], {\"40-49\"}[0.459, 0.919, 0.525, 0.942], {\"50-59\"}[0.597, 0.919, 0.663, 0.942], {\"60-69\"}[0.734, 0.919, 0.802, 0.942], {\"70+\"}[0.882, 0.919, 0.930, 0.942]] and the corresponding y-axis values can be found by analyzing the bar heights. As examples, the bar representing the age group of 20-29 extends up to the 70,000 mark, while the bar for the 70+ age group extends up to the 30,000 mark.\nBased on the above thought, the answer is:\nAge Group,Earnings\n20-29,70000\n30-39,65000\n40-49,60000\n50-59,50000\n60-69,40000\n70+,30000" } ], "id_raw": 283 }, { "id": "oDjjUzY7bPAU14n6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, can you describe the trend in average income as age increases?" }, { "from": "gpt", "value": "Absolutely, in the observed bar chart, each bar corresponds to an age group according to the x-axis labels {\"20-29\"}[0.183, 0.919, 0.249, 0.942] - {\"70+\"}[0.882, 0.919, 0.930, 0.942]. From the left to the right (or from younger to older age groups), one can see a general decline in the height of the bars, which indicates a decrease in earnings. This downward trend suggests that as people's age increases, their average earnings have a tendency to decrease. This is also reflected in the actual values represented by each bar, such as the $70,000 earnings for the {\"20-29\"}[0.147, 0.143, 0.285, 0.903] age group decreasing to $30,000 for the {\"70+\"}[0.837, 0.577, 0.975, 0.903] age group. However, there is an exception in the {\"50-59\"}[0.561, 0.360, 0.699, 0.903] age group, where the earnings drop more than the general trend before rising again in the {\"60-69\"}[0.699, 0.469, 0.837, 0.903] age group.\nBased on the above thought, the answer is:\nAs age increases, average earnings generally decrease. Specifically, earnings are highest in the 20-29 age group and decrease progressively until the 70+ age group. However, the 50-59 age group has a distinctive drop which is not in line with the overall trend." } ], "id_raw": 283 }, { "id": "Ts6EzibXSsBuGJ1C", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_19_13.png", "conversations": [ { "from": "human", "value": "\nWhich age group had the highest average earnings according to the chart?" }, { "from": "gpt", "value": "To identify this, I need to look at the bars that reach the highest point on the chart. The height of the bars is a direct representation of earnings. Comparing all the bars, the tallest is the {\"20-29\"}[0.147, 0.143, 0.285, 0.903] bar. The height of this bar corresponds with the highest value on the y-axis, which is 70,000. So, the age group with the highest average earnings according to the chart is 20-29.\nBased on the above thought, the answer is:\n20-29" } ], "id_raw": 283 }, { "id": "GC4boQgurhtZOQU6", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured table from the bar chart data you observed?" }, { "from": "gpt", "value": "From the title at the top {\"Healthcare and Medical Technology Aspects\"}[0.380, 0.952, 0.685, 0.975] and {the x-axis tick labels}[{\"medicine\"}[0.132, 0.919, 0.194, 0.942]; {\"treatment\"}[0.233, 0.919, 0.304, 0.942]; {\"diagnosis\"}[0.340, 0.919, 0.408, 0.942]; {\"technology\"}[0.441, 0.919, 0.518, 0.942]; {\"R&D\"}[0.569, 0.919, 0.601, 0.942]; {\"telemedicine\"}[0.645, 0.919, 0.736, 0.942]; {\"AI\"}[0.789, 0.919, 0.803, 0.942]; {\"apps\"}[0.884, 0.919, 0.919, 0.942]], I can see that the x-axis represents different aspects of healthcare and medical technology. The y-axis shows the frequency of mentions for each aspect from {0}[0.061, 0.894, 0.070, 0.917] to {4500}[0.035, -0.047, 0.070, -0.024]. Now, if I refer to the {legend}[0.086, 0.037, 0.199, 0.183], I can identify the sentiment associated with each category: {negative}[0.092, 0.048, 0.120, 0.064] in red, {positive}[0.092, 0.083, 0.120, 0.099] in green, {none}[0.092, 0.118, 0.120, 0.134] in grey, {mixed}[0.092, 0.153, 0.120, 0.169] in orange. Each bar on the x-axis constitutes a stack of these four categories, like {the medicine bar}[0.121, 0.767, 0.205, 0.903]. The table needs to be generated in such a way that each row corresponds to the given aspect, and each column includes the frequency of sentiment category for that particular aspect, which can be determined from the segmented parts of each bar.\nBased on the above thought, the answer is:\nAspect,Negative,Positive,None,Mixed\nMedicine,650,0,0,40\nTreatment,520,200,0,75\nDiagnosis,400,0,0,0\nTechnology,980,0,0,0\nR&D,0,2010,0,10\nTelemedicine,0,0,3900,0\nAI,500,300,0,0\nApps,0,0,0,380" } ], "id_raw": 285 }, { "id": "DzK36nfWrPMWrRZg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which aspect of healthcare and medical technology is most positively discussed? Can you provide a brief explanation?" }, { "from": "gpt", "value": "This question requires a systematic analysis of each bar on the chart that represents an aspect of healthcare and medical technology along {the x-axis}[{\"medicine\"}[0.132, 0.919, 0.194, 0.942]; {\"treatment\"}[0.233, 0.919, 0.304, 0.942]; {\"diagnosis\"}[0.340, 0.919, 0.408, 0.942]; {\"technology\"}[0.441, 0.919, 0.518, 0.942]; {\"R&D\"}[0.569, 0.919, 0.601, 0.942]; {\"telemedicine\"}[0.645, 0.919, 0.736, 0.942]; {\"AI\"}[0.789, 0.919, 0.803, 0.942]; {\"apps\"}[0.884, 0.919, 0.919, 0.942]. The green segment in each bar on the chart represents positive mentions. A careful examination of all bars shows that the green segment of the {'R&D'}[0.543, 0.483, 0.627, 0.903] bar is the longest among all, indicating that the aspect of 'R&D' in healthcare and medical technology is most positively discussed.\nBased on the above thought, the answer is:\nR&D. This aspect has the longest green segment representing positive mentions, indicating that 'R&D' is the most positively discussed aspect of healthcare and medical technology." } ], "id_raw": 285 }, { "id": "IhkPXOJARhlqHlqm", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_0.png", "conversations": [ { "from": "human", "value": "\nWhich aspect of healthcare and medical technology has the highest number of mentions with mixed sentiment?" }, { "from": "gpt", "value": "To find the aspect with the highest number of mentions with mixed sentiment, I can compare the orange segments of the bars representing each aspect on the x-axis. {The four bars}[0.121, 0.767, 0.205, 0.903]; [0.226, 0.716, 0.311, 0.794]; [0.332, 0.819, 0.416, 0.903]; [0.437, 0.698, 0.522, 0.903]; [0.543, 0.483, 0.627, 0.903]; [0.648, 0.088, 0.733, 0.903]; [0.754, 0.736, 0.838, 0.903]; [0.859, 0.823, 0.944, 0.903] correspond to {medicine}[0.132, 0.919, 0.194, 0.942], {treatment}[0.233, 0.919, 0.304, 0.942], {diagnosis}[0.340, 0.919, 0.408, 0.942], {technology}[0.441, 0.919, 0.518, 0.942], {R&D}[0.569, 0.919, 0.601, 0.942], {telemedicine}[0.645, 0.919, 0.736, 0.942], {AI}[0.789, 0.919, 0.803, 0.942], and {apps}[0.884, 0.919, 0.919, 0.942] respectively. Observing the orange segments, it is clear that the segment for the Apps aspect is the longest, indicating that it has the highest number of mentions with mixed sentiment.\nBased on the above thought, the answer is:\nApps." } ], "id_raw": 285 }, { "id": "PPpk0z2KlXwJGO4h", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the representation of the chart data in a structured table format?" }, { "from": "gpt", "value": "The chart shows two trends: {Growth}[0.218, 0.083, 0.299, 0.118] and {Decline}[0.218, 0.135, 0.301, 0.170], which are represented by green and red colors respectively. Various stages of scientific research and innovation are listed on the x-axis. These stages include Experimentation : {Exp}[0.188, 0.879, 0.231, 0.914], Analysis : {Ana}[0.286, 0.879, 0.331, 0.914], Publication : {Pub}[0.385, 0.879, 0.428, 0.914], Peer Review : {Peer}[0.479, 0.879, 0.530, 0.914], Replication : {Rep}[0.581, 0.879, 0.625, 0.914], Development : {Dev}[0.678, 0.879, 0.724, 0.914], Implementation : {Imp}[0.777, 0.879, 0.821, 0.914] and Communication : {Com}[0.871, 0.879, 0.924, 0.914]. The frequencies for each stage are represented by the heights of the bars belonging to each trend. For each stage, I can observe the frequencies for both the Growth and Decline trends, which can be computed based on the y-axis labels and the proportion of the bar height. Considering all these, we can list the data in a structured format as follows.\nBased on the above thought, the answer is:\nFields,Growth,Decline\nExperimentation,2000,100\nAnalysis,1650,200\nPublication,2050,500\nPeer Review,2000,50\nReplication,1800,800\nDevelopment,1700,900\nImplementation,1300,300\nCommunication,1800,150" } ], "id_raw": 286 }, { "id": "AtQpVr1UZbwlebVq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nIn your observation, which field seems to have witnessed a significant shift from growth to decline?" }, { "from": "gpt", "value": "A significant shift from growth to decline in a field is indicated when the decline bar is significantly taller than the growth bar within the same field. I observe each pair of bars from left to right. When considering 'Communication' field, I note that the {decline red bar of Communication}[0.858, 0.463, 0.937, 0.493] is smaller than the {growth green bar of Communication}[0.858, 0.493, 0.937, 0.854]. This indicates that the growth in 'Communication' has not shifted significantly to decline. By applying the same process, I notice a significant contrast in the 'Publication' field, where the {decline red bar of Publication}[0.367, 0.342, 0.446, 0.443] is much taller than the {growth green bar of Publication}[0.367, 0.443, 0.446, 0.854]. This indicates a significant shift from growth to decline in the 'Publication' field.\nBased on the above thought, the answer is:\nThe 'Publication' field has witnessed a significant shift from growth to decline." } ], "id_raw": 286 }, { "id": "F9AFvY4CXvpFRtmY", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_1.png", "conversations": [ { "from": "human", "value": "\nWhich field has the least growth?" }, { "from": "gpt", "value": "To find out which field has the least growth, I need to investigate the green bars which represent the growth trend. The heights of these bars reflect the amount of growth in each respective field. By observing the green bars from left to right, I can notice that the bar for the 'Implementation' field {the green bar of Implementation}[0.760, 0.593, 0.838, 0.854] appears to be the shortest among all green bars.\nBased on the above thought, the answer is:\nImplementation." } ], "id_raw": 286 }, { "id": "c0IeeGwm6S0fcWBs", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the given visual data representation into a CSV formatted table?" }, { "from": "gpt", "value": "The bar chart comprises four different bars that represent two different categories, namely, {Social Services}[0.163, 0.046, 0.293, 0.069] and {Public services}[0.163, 0.081, 0.291, 0.104]. \nEach category comprises four sub categories namely: {Healthcare}[0.176, 0.919, 0.271, 0.942], {Education}[0.391, 0.919, 0.478, 0.942], {Infrastructure}[0.586, 0.919, 0.705, 0.942], and {Environmental Protection}[0.746, 0.919, 0.966, 0.942]. To construct the CSV table, we need to keep track of the height of the bars for each resource within their sectors. For example, in the Healthcare sector, we can see that the resource allocation for Social Services (Red bar)[0.139, 0.823, 0.308, 0.903] is almost in range of the 100 mark, and for Public Services (Green bar)[0.139, 0.823, 0.308, 0.823] it is same. We can proceed in a similar manner for all sectors under both categories.\nBased on the above thought, the answer is:\nSectors,Social Services,Public services\nHealthcare,100,100\nEducation,500,500\nInfrastructure,0,1000\nEnvironmental Protection,0,0" } ], "id_raw": 287 }, { "id": "aqKVxyWKMTSl3bfJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nFrom your observations of the chart, which category, Social Services or Public Services, had a more stable budget allocation across different sectors, and why?" }, { "from": "gpt", "value": "To determine stability of allocation, we need to analyse the variation in resource allocation for different sectors in both the categories. For example, in {Social Services}[0.163, 0.046, 0.293, 0.069], the allocation for {Healthcare}[0.139, 0.823, 0.308, 0.903] and {Education}[0.350, 0.504, 0.519, 0.903] shows quite a bit of variance, while in {Public Services}[0.163, 0.081, 0.291, 0.104] the allocation for sectors such as {Infrastructure}[0.561, 0.105, 0.730, 0.903] and {Environmental Protection}[0.772, 0.903, 0.941, 0.903] also shows a very high difference. On comparing both, we can see that Public Services shows a more uneven allocation as compared to Social Services, therefore Social Services had a more stable budget allocation.\nBased on the above thought, the answer is:\nSocial Services. Due to the lesser variance in allocation across sectors as compared to Public Services." } ], "id_raw": 287 }, { "id": "pODavUkAdf6K1P75", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_2.png", "conversations": [ { "from": "human", "value": "\nIn which sector is the difference in resource allocation between Social Services and Public Services the largest?" }, { "from": "gpt", "value": "The way to find the sector with the largest resource allocation difference is to compare the heights of bars under Social Services and Public services for each sector. The first pair for the Healthcare sector upon comparison provides us with no difference. Continuing in such a manner for every sector, we see that the {Infrastructure}[0.561, 0.105, 0.730, 0.903] has the largest difference with Public Services Resource Allocation surpassing by 1000 units.\nBased on the above thought, the answer is:\nInfrastructure." } ], "id_raw": 287 }, { "id": "UWs0h47cs1bKa5ae", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nCan you list down the total values across all trends for each category in a CSV table based on the information in the bar chart?" }, { "from": "gpt", "value": "In order to generate the CSV table, I can begin by collating the labels on the x-axis as my categories. These labels I have detected are {'Innovation Ecosystem'}[0.214, 0.879, 0.468, 0.914] and {'Technology Entrepreneurship'}[0.598, 0.879, 0.935, 0.914]. By examining the height of the bar stacks and their corresponding y-value labels I can calculate the values of each trend for a particular category. For instance, in 'Innovation Ecosystem', I find four different bars stacked with each representing a unique trend. The respective trends are {'Surging Growth'}[0.171, 0.754, 0.511, 0.854], {'Descending Crisis'}[0.171, 0.593, 0.511, 0.754], {'Stable Market'}[0.171, 0.192, 0.511, 0.593] and {'Emerging Trend'}[0.171, 0.172, 0.511, 0.192]. Reading the y-axis tick labels for each of these bars I can determine the individual values for each trend in the category. Using the same approach, I can gather the values for the 'Technology Entrepreneurship' category as well.\nBased on the above thought, the answer is:\nEcosystems,Surging Growth,Descending Crisis,Stable Market,Emerging Trend,Total Value\nInnovation Ecosystem,500,800,2000,100,3400\nTechnology Entrepreneurship,200,1500,700,450,2850" } ], "id_raw": 288 }, { "id": "gKjsLRvb2rKFhE4e", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nIf we were to base our decision on these trends, which ecosystem - Innovation Ecosystem or Technology Entrepreneurship - would you recommend for investment? Support your answer with reasons from the chart's data." }, { "from": "gpt", "value": "This question asks about choosing an investment based on performance across different trends. I need to look at the performance of each ecosystem in the four trends: {'Surging Growth'}, {'Descending Crisis'}, {'Stable Market'} and {'Emerging Trend'}. From the chart's data, I can see that the Innovation Ecosystem's values for {'Surging Growth'}[0.171, 0.754, 0.511, 0.854], {'Stable Market'}[0.171, 0.192, 0.511, 0.593] and {'Emerging Trend'}[0.171, 0.172, 0.511, 0.192] are higher than respective values for Technology Entrepreneurship's {'Surging Growth'}[0.596, 0.814, 0.937, 0.854], {'Stable Market'}[0.596, 0.372, 0.937, 0.513] and {'Emerging Trend'}[0.596, 0.282, 0.937, 0.372]. Conversely, Innovation Ecosystem's value for {'Descending Crisis'}[0.171, 0.593, 0.511, 0.754] is lower than the value of {'Descending Crisis'} for Technology Entrepreneurship[0.596, 0.513, 0.937, 0.814]. Investing in an ecosystem that has a down trend would not be ideal, hence having a lower value for 'Descending Crisis' is better.\nBased on the above thought, the answer is:\nInnovation Ecosystem. Because it has higher values in 'Surging Growth', 'Stable Market' and 'Emerging Trend' which are positive indicators for investment. It also has a lower value in 'Descending Crisis' which is a negative indicator. Thus, it presents a safer and more promising option for investment compared to Technology Entrepreneurship." } ], "id_raw": 288 }, { "id": "dwv3UYW3IztgATzp", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_3.png", "conversations": [ { "from": "human", "value": "\nHow much has the value for 'Stable Market' trend in the 'Technology Entrepreneurship' ecosystem changed compared to the 'Innovation Ecosystem'?" }, { "from": "gpt", "value": "To answer this, I need to compare the values of the 'Stable Market' trend in both ecosystems. Using the height of the green bars representing the 'Stable Market' trend in {'Innovation Ecosystem'}[0.171, 0.192, 0.511, 0.593] and in {'Technology Entrepreneurship'}[0.596, 0.372, 0.937, 0.513], I can determine the corresponding values from the y-axis. Once I have these values, I can subtract the 'Stable Market' value of 'Technology Entrepreneurship' from that of 'Innovation Ecosystem' to get the change.\nBased on the above thought, the answer is:\n600" } ], "id_raw": 288 }, { "id": "QyOcfvZgtK6TAAHx", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information revealed in the stacked bar chart into a structured CSV format table?" }, { "from": "gpt", "value": "Sure. The x-axis represents different types of investments labeled {\"Bond Investments\"}[0.114, 0.671, 0.307, 0.858], {\"Stock Investments\"}[0.246, 0.671, 0.443, 0.861], {\"Real Estate Investments\"}[0.352, 0.671, 0.606, 0.911], {\"Mutual Funds\"}[0.541, 0.671, 0.686, 0.817], {\"Commodity Investments\"}[0.619, 0.671, 0.877, 0.914], and {\"Cryptocurrency\"}[0.799, 0.671, 0.965, 0.835]. The Y-axis represents the return rate percentage ranging from 0 to 100 which can be inferred from the y-axis labels {0}[0.087, 0.633, 0.102, 0.668], {20}[0.072, 0.514, 0.102, 0.549], up to {100}[0.058, 0.037, 0.102, 0.073]. The bar chart is divided into different colored sections each indicating a different trend. According to {the legend}[0.129, 0.069, 0.432, 0.288], the red color represents return rates falling, the green represents return rates remaining the same, the gray represents return rates rising, and the orange signalizes an abrupt drop. Each segment's height in the bars represents the percentage rate for each investment type under different trends.\nBased on the above thought, the answer is:\nInvestment Types,Return Rate Falling,Return Rate Same,Return Rate Rising,Abrupt Drop\nBond Investments,50,40,10,0\nStock Investments,20,20,55,5\nReal Estate Investments,10,30,50,10\nMutual Funds,35,35,25,5\nCommodity Investments,5,15,60,20\nCryptocurrency,30,10,45,15" } ], "id_raw": 289 }, { "id": "aDb4aWWePrifBKn1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhich investment type has the most balanced growing possibility and risk according to the chart?" }, { "from": "gpt", "value": "A well-balanced investment would be one that has a high percentage chance for return rates to rise (grey colored in the bar in our context), and relatively low percentage chances of abrupt drops (orange colored in the bar). Looking over each investment type, the {bar for \"Real Estate Investments\"}[0.425, 0.051, 0.533, 0.646] stands as balanced because it shows a substantial grey segment implying a good possibility of increasing return rates, and the orange segment at the bottom is relatively small, signaling a lesser risk of abrupt drops compared to others. Thus, \"Real Estate Investments\" can be considered as having a more balanced growth possibility and risk.\nBased on the above thought, the answer is:\nReal Estate Investments." } ], "id_raw": 289 }, { "id": "6rwYz1kiKK2E0tgl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_4.png", "conversations": [ { "from": "human", "value": "\nWhich type of investment has the highest chance of seeing a return rate rise, according to the chart?" }, { "from": "gpt", "value": "To determine which investment type has the highest chance of a rising return rate, I need to look at the gray segments of each bar for each investment type. It can be seen that the grey segment is the highest in the {bar for \"Commodity Investments\"}[0.694, 0.051, 0.802, 0.646]. The height of the gray segment is much larger compared to the gray segments in other investment type bars, indicating that commodity investments have the highest percentage for a rising return rate.\nBased on the above thought, the answer is:\nCommodity Investments." } ], "id_raw": 289 }, { "id": "hkP4WdqJPgCEOTA6", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart you have, could you encode the data represented into a table in CSV format?" }, { "from": "gpt", "value": "To create a CSV table from the chart, I want to consider the information on both axes. The {'x-axis'}[0.479, 0.927, 0.628, 0.963] label is 'School Types', which refers to the two categories: {'Urban Schools'}[0.164, 0.879, 0.330, 0.914] and {'Rural Schools'}[0.782, 0.879, 0.938, 0.914]. The {'y-axis'}[0.025, 0.267, 0.048, 0.639] label is 'Number of Resources', indicating the quantity of each type of resource for each school category. Resource types can be found in the {'legend'}[0.144, 0.069, 0.368, 0.288], which are 'Textbooks'[0.218, 0.083, 0.333, 0.118], 'Pencils'[0.218, 0.135, 0.297, 0.170], 'School Bags'[0.218, 0.187, 0.359, 0.222] and 'Laptops'[0.218, 0.240, 0.310, 0.275]. The heights of each resource in each category represent the quantities. I'll assess the heights of each rectangle to determine the quantity. I pull the data together and build a CSV table.\nBased on the above thought, the answer is:\nSchool Types,Textbooks,Pencils,School Bags,Laptops\nUrban Schools,2750,3850,5100,1800\nRural Schools,1400,2550,860,700" } ], "id_raw": 290 }, { "id": "z3vrZPJAvpAl9HWv", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nDoes the chart suggest an equity issue between urban and rural schools in terms of resource allocation?" }, { "from": "gpt", "value": "To evaluate this, I would compare the resource allocation for {'Urban Schools'}[0.164, 0.879, 0.330, 0.914] and {'Rural Schools'}[0.782, 0.879, 0.938, 0.914]. From the stacked bars, it can be seen that the resources for Urban Schools reach approximately up to around {'7000'}[0.058, 0.138, 0.116, 0.173] on the y-axis. In contrast, the total resources for Rural Schools, as indicated by the bars, reach up to around {'2000'}[0.058, 0.640, 0.116, 0.675] on the y-axis. The bar for Urban Schools is notably taller than the bar for Rural Schools, suggesting a disparity in resource allocation. This implies that there could indeed be an equity issue between the schools in different areas, with Urban ones receiving more resources while rural ones receive less.\nBased on the above thought, the answer is:\nYes, the chart suggests a disparity in resource allocation between urban and rural schools, with urban schools receiving more resources, indicating an equity issue." } ], "id_raw": 290 }, { "id": "RHxdf6C9OZKj6hxM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what is the maximum number of resources provided by urban schools?" }, { "from": "gpt", "value": "To find this, I need to look at the rectangle representing Urban Schools. From the chart, I can see in {'Urban Schools'}[0.164, 0.879, 0.330, 0.914] that the bars within that rectangle represent different resources. These bars each represent a block or quantity of the resources. If I add all these quantities up by calculating the total height of the bar for Urban Schools, I can see that it reaches up to around {'7000'}[0.058, 0.138, 0.116, 0.173] on the y-axis. Therefore, the total resource provided by Urban Schools is 7000.\nBased on the above thought, the answer is:\n7000." } ], "id_raw": 290 }, { "id": "uZv9VVb3fFrtE5QX", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nPlease organize the stacked bar chart data into a CSV format table according to the trends you observed." }, { "from": "gpt", "value": "From the labels on the x-axis {\"2022\"}[0.194, 0.879, 0.253, 0.914], {\"2023\"}[0.326, 0.879, 0.385, 0.914], {\"2024\"}[0.458, 0.879, 0.517, 0.914], {\"2025\"}[0.591, 0.879, 0.649, 0.914], {\"2026\"}[0.723, 0.879, 0.781, 0.914], {\"2027\"}[0.855, 0.879, 0.913, 0.914], I can see that the x-axis corresponds to the years from 2022 to 2027. The y-axis label {\"Quantity\"}[0.025, 0.378, 0.048, 0.528] shows the quantity of each trend per year. Each bar represents the outcomes of the pessimistic, normal, and optimistic scenarios in each year. The pessimistic scenario is depicted in the {red bars}[0.171, 0.232, 0.276, 0.854], the normal scenario in the {green bars}[0.303, 0.292, 0.408, 0.854], and the optimistic scenario in the {grey bars}[0.435, 0.332, 0.540, 0.854]. I need to extract the length of each part of the bar using the associated y-axis value to determine the values for each trend in each year.\nBased on the above thought, the answer is:\nYear,Pessimistic,Normal,Optimistic\n2022,3100,2800,2200\n2023,2800,2700,2250\n2024,2600,2600,2300\n2025,2350,2550,2400\n2026,2100,2500,2500\n2027,1800,2450,2600" } ], "id_raw": 291 }, { "id": "pg7NpL04dQNpSBEC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nIn terms of the overall trend from 2022 to 2027, which scenario performed the best and why?" }, { "from": "gpt", "value": "To identify which scenario performed the best over the given period, I need to consider the direction of change. The {red bars}[0.171, 0.232, 0.276, 0.854] correspond to the pessimistic scenario, {green bars}[0.303, 0.292, 0.408, 0.854] to the normal scenario, and {grey bars}[0.435, 0.332, 0.540, 0.854] to the optimistic scenario. For the pessimistic scenario, the quantity is decreasing from 3100 in 2022 to 1800 in 2027, the normal scenario shows a slow but steady recovery, and optimistic scenario shows a gradual increase from 2200 in 2022 to 2600 in 2027. Additionally, the optimistic scenario has less variation compared to the other two scenarios.\nBased on the above thought, the answer is:\nThe Optimistic scenario performed the best. It shows an upward trend over time and less variation compared to the other scenarios." } ], "id_raw": 291 }, { "id": "uAUBj5oUzAmYMaXS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_6.png", "conversations": [ { "from": "human", "value": "\nFor the year 2025, what is the total quantity combining all trends in the forecast?" }, { "from": "gpt", "value": "To find the total quantity of the three trends in 2025, I need to add the quantities of the pessimistic, normal, and optimistic trends. In 2025, marked with {\"2025\"}[0.591, 0.879, 0.649, 0.914] on the x-axis, the length of each part of the stacked bar represents a specific trend. I can locate the pessimistic trend in a {red part}[0.567, 0.383, 0.673, 0.854], the normal trend in a {green part}[0.567, -0.129, 0.673, 0.383], and the optimistic trend in a {grey part}[0.567, -0.611, 0.673, -0.129] of the 2025 bar. I need to add the length of each part to get the total quantity.\nBased on the above thought, the answer is:\n7300." } ], "id_raw": 291 }, { "id": "UG11EEidm52GbZ93", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar graph, could you convert the frequency of sentiments within different policy areas as you see them into a meaningful CSV-formatted table?" }, { "from": "gpt", "value": "The x-asix is divided into eight different policy sectors, namely, {Children and Families}[0.104, 0.690, 0.209, 0.882], {Environmental Protection}[0.202, 0.690, 0.324, 0.914], {Economic Development}[0.312, 0.690, 0.427, 0.901], {Public Safety}[0.443, 0.690, 0.509, 0.813], {Health and Welfare}[0.535, 0.690, 0.630, 0.864], {Education and Skills}[0.640, 0.690, 0.739, 0.871], {Transportation Infrastructure}[0.727, 0.690, 0.864, 0.942], and {Public Finance}[0.866, 0.690, 0.938, 0.823]. The height of the different sections of each bar displays the sentiment frequency in terms of sentiments which are described in the legend as {negative}[0.119, 0.055, 0.174, 0.078], {positive}[0.119, 0.090, 0.168, 0.113], {none}[0.119, 0.125, 0.150, 0.148] and {mixed}[0.119, 0.160, 0.158, 0.183]. I evaluate the exact frequencies by compare the height of each colored segment with y-axis labels, from {0}[0.055, 0.665, 0.063, 0.688] to {4000}[0.032, 0.025, 0.063, 0.048]. Hence by going through each policy area and sentiments, a proper CSV table can be mapped out.\nBased on the above thought, the answer is:\nPolicy Areas,Negative,Positive,None,Mixed\nChildren and Families,400,10,300,290\nEnvironmental Protection,850,30,50,70\nEconomic Development,250,800,50,0\nPublic Safety,400,200,400,0\nHealth and Welfare,200,1800,0,0\nEducation and Skills,500,100,1400,0\nTransportation Infrastructure,600,350,50,0\nPublic Finance,550,50,400,0" } ], "id_raw": 292 }, { "id": "MlGMAuC6tmgHg4cd", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which policy area has received the most positive response?" }, { "from": "gpt", "value": "To determine this, I have to look at the green sections representing {positive sentiment}[0.119, 0.090, 0.168, 0.113] in each bar. These bars represent, from left to right, the policy areas of {Children and Families}[0.114, 0.608, 0.199, 0.610], {Environmental Protection}[0.220, 0.533, 0.306, 0.538], {Economic Development}[0.327, 0.506, 0.412, 0.634], {Public Safety}[0.433, 0.578, 0.519, 0.610], {Health and Welfare}[0.540, 0.354, 0.625, 0.642], {Education and Skills}[0.646, 0.578, 0.732, 0.594], {Transportation Infrastructure}[0.753, 0.522, 0.838, 0.578], and {Public Finance}[0.860, 0.578, 0.945, 0.586]. By comparing the heights, it's evident that {Health and Welfare}[0.540, 0.354, 0.625, 0.642] has the highest green segment, indicating that it has received the most-positive sentiments.\nBased on the above thought, the answer is:\nThe policy area 'Health and Welfare' has seen the most positive responses according to the chart." } ], "id_raw": 292 }, { "id": "Xtmo2g9D8avliD18", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_7.png", "conversations": [ { "from": "human", "value": "\nFor which policy area is there the highest occurrence of no particular sentiment?" }, { "from": "gpt", "value": "I investigate the gray areas denoting {the 'none' sentiment}[0.119, 0.125, 0.150, 0.148] of each bar on the chart to answer this question. Starting from the left, the first bar represents {Children and Families}[0.114, 0.560, 0.199, 0.608], followed by {Environmental Protection}[0.220, 0.525, 0.306, 0.533], {Economic Development}[0.327, 0.498, 0.412, 0.498], {Public Safety}[0.433, 0.514, 0.519, 0.514], {Health and Welfare}[0.540, 0.354, 0.625, 0.354], {Education and Skills}[0.646, 0.354, 0.732, 0.354], {Transportation Infrastructure}[0.753, 0.514, 0.838, 0.514], and finally, {Public Finance}[0.860, 0.514, 0.945, 0.514]. The gray segment for {Education and Skills}[0.646, 0.354, 0.732, 0.578] is visibly the largest, indicating the highest occurrence of no particular sentiment in this policy area.\nBased on the above thought, the answer is:\nEducation and Skills" } ], "id_raw": 292 }, { "id": "O11jN2aBABhY37oT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nKindly extract chart data and organize it into a structured table to better illustrate the migration trends for both Human and Industrial categories." }, { "from": "gpt", "value": "Based on the labels identified in the chart, my first step will be to list down the categories of trends that are marked by different colours as indicated {in the legend}[0.096, 0.055, 0.320, 0.356]. We have 'Erratic Decline', 'Volatile Growth', 'Rapid Decline', 'Sustained Growth', 'Sluggish Progress', 'Roller-Coaster Pattern' and 'Moderate Decline'. Next, I will note down the xaxis labels which classify migration data into 'Human' and 'Industrial' categories from {the xlabel coordinates}[0.472, 0.942, 0.600, 0.970]. The height of the segments of each bar corresponding to these categories are found from {the bars attribute}Bars information. The values for 'Human' migration will correspond to the 'y_range' property of the bars at 'x_label' 'Human' and the 'Industrial' values would similarly correspond to 'y_range' of bars at 'x_label' 'Industrial'. Matching each value range with the corresponding category from the legend would allow me to construct the table.\nBased on the above thought, the answer is:\nMigration Trends,Human,Industrial\nErratic Decline,3500,2200\nVolatile Growth,5200,7000\nRapid Decline,1100,1500\nSustained Growth,1200,2300\nSluggish Progress,1000,4000\nRoller-Coaster Pattern,4200,3500\nModerate Decline,2000,2700" } ], "id_raw": 293 }, { "id": "rPzIipASU1NR6dyM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which category appears to be more stable in migration trend and why?" }, { "from": "gpt", "value": "We will consider the category with smaller fluctuations or less extreme highs and lows as more stable. From the analysis of bars, the 'Human' migration observed from {Human x_label}[0.195, 0.903, 0.251, 0.931] begins at '0' and rises to its peak at 'Roller-Coaster Pattern' and then slightly declines at 'Moderate Decline', which means 'Human' migration has fluctuations. Similarly, for 'Industrial' migration marked by {Industrial x_label}[0.813, 0.903, 0.885, 0.931], it starts at '0', ascends to a high at 'Roller-Coaster Pattern' and then descends very slightly to 'Moderate Decline', indicating smaller fluctuations. Therefore, we can suggest that the 'Industrial' category is more stable in migration trends.\nBased on the above thought, the answer is:\nThe 'Industrial' category appears to be more stable, because it experiences smaller fluctuations as compared to 'Human' migration. It rises to a high peak and then descends very slightly, indicating relatively steady migration trends." } ], "id_raw": 293 }, { "id": "dX1yrX8lG9BotIuR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_8.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart, which category, Human or Industrial, has the highest overall migration trend?" }, { "from": "gpt", "value": "In order to determine which category has the maximum migration trend, I need to individually summarize the segment heights for each category. For 'Human', sum of all segments from {Erratic Decline}[0.129, 0.515, 0.317, 0.883] to {Moderate Decline}[0.129, -1.033, 0.317, -0.823] gives me the total migration trend. Similarly, for 'Industrial', I can sum up all segments from {Erratic Decline}[0.755, 0.652, 0.943, 0.883] to {Moderate Decline}[0.755, -1.560, 0.943, -1.275]. By comparing these two totals we can find the category with the highest overall migration trend.\nBased on the above thought, the answer is:\nIndustrial" } ], "id_raw": 293 }, { "id": "DwYx3xqs69fZql3k", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nHow would you reformat the bar chart's data into a csv?" }, { "from": "gpt", "value": "The title reveals that the bars in the chart represent the consumption and growth trends of different countries. The `x-label` of the chart {xlabel}[0.499, 0.959, 0.555, 0.979] indicates the countries. I noticed there are eight categories which are China, India, USA, Brazil, Russia, South Africa, Nigeria and Indonesia. The `ylabel` of the chart {ylabel}[0.013, 0.309, 0.024, 0.637] discloses the values in the billions USD for consumption and percentage for growth. For each country, there are two bars which represent Consumption (blue bar) and Growth (red bar). Every bar's vertical range corresponds to the data value, and the y-axis tick labels span from 0 to 5000 {yticks}{\"0\": [0.051, 0.909, 0.058, 0.929], \"5000\": [0.029, 0.021, 0.058, 0.041]}, which allows us to determine the values. For example, in China's case, its Consumption bar {China Consumption bar}[0.108, 0.207, 0.146, 0.917] is approximately at the 4000 position, and its Growth bar {China Growth bar}[0.146, 0.914, 0.184, 0.917] is near the 15 position, therefore, the values for China are 4000 and 15 for Consumption and Growth respectively. By similarly evaluating the bars for other countries, I can construct a csv.\nBased on the above thought, the answer is:\nCountry,Consumption,Growth\nChina,4000,15\nIndia,3200,12\nUSA,4500,2\nBrazil,1600,9\nRussia,1500,-5\nSouth Africa,800,-7\nNigeria,1000,20\nIndonesia,1500,6" } ], "id_raw": 294 }, { "id": "o1ZucHycdSILcJIk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nCan you comment on the performance of the United States in terms of consumer behavior based on the chart?" }, { "from": "gpt", "value": "Considering the bars on the chart referring to the United States, we have two indicators, Consumption in blue {USA Consumption bar}[0.326, 0.118, 0.364, 0.917] and Growth in red {USA Growth bar}[0.364, 0.916, 0.402, 0.917]. The Consumption bar is highest on the y-axis {yticks}{\"4500\": [0.029, 0.199, 0.058, 0.219]} indicating that the United States has the highest consumption. However, the Growth bar lies very close to zero on the y-axis indicating a much smaller growth rate. This tells us that while the consumption level is high, the growth in consumption is quite slow which could suggest a mature market with saturated consumer demand.\nBased on the above thought, the answer is:\nThe United States displays high consumption but slow growth, suggesting the presence of a stable, but saturated market." } ], "id_raw": 294 }, { "id": "sSNkGYGRGJQYYyjr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_9.png", "conversations": [ { "from": "human", "value": "\nWhich country has the highest consumption?" }, { "from": "gpt", "value": "To determine the country with the highest consumption, I must identify the blue bar reaching the highest point on the y-axis. By examining the bar height, I can observe that the longest blue bar corresponds to {USA}[0.326, 0.118, 0.364, 0.917], marking a height of about 4500 on the y-axis {yticks}{\"4500\": [0.029, 0.199, 0.058, 0.219]}\nBased on the above thought, the answer is:\nUSA" } ], "id_raw": 294 }, { "id": "gPHPOQVA2sboNet4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the progression of 'Stable Growth' and 'Rapid Decline' over time into a CSV format table for effectives comparison?" }, { "from": "gpt", "value": "From the x-axis ticks {['1', '2', '3', '4', '5', '6', '7', '8']}<{\"1\"}[0.165, 0.903, 0.177, 0.931]; {\"2\"}[0.271, 0.903, 0.282, 0.931]; {\"3\"}[0.377, 0.903, 0.387, 0.931]; {\"4\"}[0.482, 0.903, 0.493, 0.931]; {\"5\"}[0.588, 0.903, 0.598, 0.931]; {\"6\"}[0.693, 0.903, 0.704, 0.931]; {\"7\"}[0.799, 0.903, 0.809, 0.931]; {\"8\"}[0.904, 0.903, 0.915, 0.931]>, I can tell that the x-axis represents the timeframe. The y-axis represents frequencies, as can be determined from the y-axis label {ylabel}[0.019, 0.385, 0.036, 0.528] and it varies from {0}[0.076, 0.872, 0.087, 0.900] to {2000}[0.043, 0.060, 0.087, 0.088]. Observing the legend {legend}[0.786, 0.044, 0.973, 0.136] and the items within, the first group of bars represent 'Stable Growth' shown by {Stable Growth handle}[0.793, 0.058, 0.828, 0.077] and the second group of bars represent 'Rapid Decline' shown by {Rapid Decline handle}[0.793, 0.099, 0.828, 0.119]. I can also see the specific numbers on top of bars for frequencies in each timeframe. The bars representing each timeframe for 'Stable Growth' are {Bars of Stable Growth}[0.139, 0.680, 0.171, 0.883; 0.245, 0.721, 0.276, 0.883; 0.350, 0.680, 0.382, 0.883; 0.456, 0.701, 0.488, 0.883; 0.561, 0.680, 0.593, 0.883; 0.667, 0.741, 0.699, 0.883; 0.772, 0.721, 0.804, 0.883; 0.878, 0.680, 0.910, 0.883] and 'Rapid Decline' are {Bars of Rapid Decline}[0.171, 0.071, 0.203, 0.883; 0.276, 0.172, 0.308, 0.883; 0.382, 0.274, 0.414, 0.883; 0.488, 0.375, 0.519, 0.883; 0.593, 0.477, 0.625, 0.883; 0.699, 0.558, 0.730, 0.883; 0.804, 0.640, 0.836, 0.883; 0.910, 0.721, 0.941, 0.883]. Taking all those inputs into consideration, I can generate the CSV formatted data.\nBased on the above thought, the answer is:\nTimeframe, Stable Growth, Rapid Decline\n1, 500, 2000\n2, 400, 1750\n3, 500, 1500\n4, 450, 1250\n5, 500, 1000\n6, 350, 800\n7, 400, 600\n8, 500, 400" } ], "id_raw": 295 }, { "id": "Vl3KxvTuNTeb98sR", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you suggest for the future learning trend based on the current 'Stable Growth' and 'Rapid Decline' trend scenarios?" }, { "from": "gpt", "value": "'Rapid Decline' trend as represented by the {red bars}<{\"Rapid Decline\"}[0.792, 0.099, 0.828, 0.119]>shows a considerable decrease in frequency over time while 'Stable Growth' trend represented by the {green bars}<{\"Stable Growth\"}[0.793, 0.058, 0.828, 0.077]> appears to be more balanced with slight fluctuations. This suggests that if we continue with the current trends, we might witness a decline in the popularity of online education.\nBased on the above thought, the answer is:\nTherefore, based on the current trends, the education technology and online learning sectors might need to reevaluate their strategies and make necessary improvements to ensure a steady growth rate. They could possibly focus on factors that encourage 'Stable Growth' to counteract the 'Rapid Decline'. This could even include implementing innovative techniques, improving interface and user friendliness, offering diverse courses or even reducing fees." } ], "id_raw": 295 }, { "id": "0QxQayn3NgUr3bSR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum decrease in frequency from one timeframe to another for 'Rapid Decline'?" }, { "from": "gpt", "value": "To find this out, I need to look at the difference of y-values for each subsequent bar in the 'Rapid Decline' group. The corresponding bars are {['1', '2', '3', '4', '5', '6', '7', '8']}<0.171, 0.071, 0.203, 0.883; 0.276, 0.172, 0.308, 0.883; 0.382, 0.274, 0.414, 0.883; 0.488, 0.375, 0.519, 0.883; 0.593, 0.477, 0.625, 0.883; 0.699, 0.558, 0.730, 0.883; 0.804, 0.640, 0.836, 0.883; 0.910, 0.721, 0.941, 0.883>. The differences in frequency from one timeframe to another are: 2000-1750=250, 1750-1500=250, 1500-1250=250, 1250-1000=250, 1000-800=200, 800-600=200, and 600-400=200. So, the maximum decrease is 250 found in the first four observations.\nBased on the above thought, the answer is:\n250" } ], "id_raw": 295 }, { "id": "Wf2J976ZS3JD9UMH", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV formatted table from the expenses data in the bar chart?" }, { "from": "gpt", "value": "According to the bar chart, the x-axis represents different business processes, ranging from {Expense}[0.124, 0.758, 0.159, 0.830] to {Delivery}[0.895, 0.758, 0.929, 0.829]. The y-axis indicates the amount of expenses, from {0}[0.051, 0.739, 0.058, 0.756] to {4000}[0.029, 0.019, 0.058, 0.036]. The two colored bars under each process represent the expenses in 2010 and 2020, as specified by {the legend}[0.072, 0.034, 0.143, 0.092]. The light blue bars, labeled {2010}[0.109, 0.041, 0.138, 0.059], represent the expenses in the year 2010, and the orange bars, labeled {2020}[0.109, 0.067, 0.138, 0.085], represent the expenses in the year 2020. The height of each bar indicates the exact value of expenses. Hence, I can create a CSV table by notating the exact amount of expenses for each year and for each process.\nBased on the above thought, the answer is:\nProcesses,2010,2020\nExpense,1000,1200\nCustomer_Meeting,1200,1000\nMarket_Research,800,900\nProduct_Development,900,1200\nStaff_Training,700,800\nSupply_Management,1500,1600\nOrder_Processing,2000,2400\nWarehouse_Management,2200,2300\nDelivery,2100,2300" } ], "id_raw": 296 }, { "id": "zKpefTTKIgWQACFY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nIs there a consistent trend in the expense report from 2010 to 2020 for all processes?" }, { "from": "gpt", "value": "{The legend}[0.072, 0.034, 0.143, 0.092] denotes the two colored bars for each process, the light blue represented by {2010}[0.109, 0.041, 0.138, 0.059] and orange for {2020}[0.109, 0.067, 0.138, 0.085]. Across all processes; {Expense}[0.108, 0.566, 0.175, 0.746] and [0.108, 0.350, 0.175, 0.566]; {Customer_Meeting}[0.204, 0.530, 0.272, 0.746] and [0.204, 0.350, 0.272, 0.530]; {Market_Research}[0.301, 0.602, 0.368, 0.746] and [0.301, 0.440, 0.368, 0.602]; {Product_Development}[0.397, 0.584, 0.464, 0.746] and [0.397, 0.368, 0.464, 0.584]; {Staff_Training}[0.493, 0.620, 0.561, 0.746] and [0.493, 0.476, 0.561, 0.620]; {Supply_Management}[0.589, 0.476, 0.657, 0.746] and [0.589, 0.188, 0.657, 0.476]; {Order_Processing}[0.686, 0.386, 0.753, 0.746] and [0.686, -0.046, 0.753, 0.386]; {Warehouse_Management}[0.782, 0.350, 0.849, 0.746] and [0.782, -0.064, 0.849, 0.350]; {Delivery}[0.878, 0.368, 0.946, 0.746] and [0.878, -0.046, 0.946, 0.368], you can see that all processes except Customer Meeting and Staff Training experienced an increase in expenses from 2010 to 2020. Therefore, the consistent trend observed is an overall increase in expenses; however, the exception to this trend is that the expenses for Customer Meeting and Staff Training have decreased.\nBased on the above thought, the answer is:\nGenerally, there is an increasing trend in the expenses from 2010 to 2020 for most processes. However, the trend is inconsistent as there is a decrease in expenses for the 'Customer Meeting' and 'Staff Training' processes." } ], "id_raw": 296 }, { "id": "uWg4CFtu862TYz5c", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_11.png", "conversations": [ { "from": "human", "value": "\nIn what business process is the maximum difference in expenses observed over the 10 years?" }, { "from": "gpt", "value": "Compare the bars from every process in 2010 and 2020 for the difference in expenses. By calculating and comparing {Expense}[0.108, 0.566, 0.175, 0.746] and [0.108, 0.350, 0.175, 0.566]; {Customer_Meeting}[0.204, 0.530, 0.272, 0.746] and [0.204, 0.350, 0.272, 0.530]; {Market_Research}[0.301, 0.602, 0.368, 0.746] and [0.301, 0.440, 0.368, 0.602]; {Product_Development}[0.397, 0.584, 0.464, 0.746] and [0.397, 0.368, 0.464, 0.584]; {Staff_Training}[0.493, 0.620, 0.561, 0.746] and [0.493, 0.476, 0.561, 0.620]; {Supply_Management}[0.589, 0.476, 0.657, 0.746] and [0.589, 0.188, 0.657, 0.476]; {Order_Processing}[0.686, 0.386, 0.753, 0.746] and [0.686, -0.046, 0.753, 0.386]; {Warehouse_Management}[0.782, 0.350, 0.849, 0.746] and [0.782, -0.064, 0.849, 0.350]; {Delivery}[0.878, 0.368, 0.946, 0.746] and [0.878, -0.046, 0.946, 0.368]. It appears that the maximum difference in expenses over the 10 years is observed in Warehouse Management, with an increase of 1000 because the values go from 2200 to 3300 over two bars.\nBased on the above thought, the answer is:\nWarehouse Management." } ], "id_raw": 296 }, { "id": "M2gGxKYyn0Z7GMYv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nTransform the data in this bar graph into a CSV format." }, { "from": "gpt", "value": "By examining the bar chart, I can see that the x-axis {categories labels}[{\"Green Practises\"}[0.138, 0.919, 0.274, 0.942]; {\"Waste Levels\"}[0.316, 0.919, 0.430, 0.942]; {\"Energy Consumption\"}[0.450, 0.919, 0.631, 0.942]; {\"CO2 Emissions\"}[0.643, 0.919, 0.771, 0.942]; {\"Pollution Index\"}[0.811, 0.919, 0.938, 0.942]] represents different environmental categories. The y-axis signifies the respective measurements, ranging from {0}[0.076, 0.894, 0.087, 0.917] to {4000}[0.043, 0.025, 0.087, 0.048]. Each category has a pair of bars, with the first one {2010}[0.922, 0.055, 0.966, 0.078] in red and the second one {2020}[0.922, 0.090, 0.965, 0.113] in blue. The height of the bar determines the measurement for each category. For instance, in the \"Green Practises\" category, the 2010 bar {red bar}[0.139, 0.360, 0.206, 0.903] is about midway up between 2000 and 3000 units, which implies a measure of approximately 2500 units. Similarly, the 2020 bar {blue bar}[0.206, 0.251, 0.273, 0.903] in the same category is at about three-fourths, implying a measurement of around 3000 units. This process needs to be reiterated for each category to convert the data into a CSV format.\nBased on the above thought, the answer is:\nCategories,2010,2020\nGreen Practises,2500,3000\nWaste Levels,3000,2000\nEnergy Consumption,2500,1500\nCO2 Emissions,3500,2000\nPollution Index,1000,500" } ], "id_raw": 297 }, { "id": "c87kHN5fTB7VQRIH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the graph, can you tell if there was progress regarding environmental issues between 2010 and 2020?" }, { "from": "gpt", "value": "To analyze the graph for progress regarding environmental issues, I need to look for decreasing measurements in categories with negative impacts and increasing measurements for environmentally friendly practices. Examining the {Waste Levels}[{\"2010\"}[0.306, 0.251, 0.373, 0.903], {\"2020\"}[0.373, 0.469, 0.440, 0.903]], {Energy Consumption}[{\"2010\"}[0.473, 0.360, 0.540, 0.903], {\"2020\"}[0.540, 0.577, 0.607, 0.903]], and {CO2 Emissions}[{\"2010\"}[0.640, 0.143, 0.707, 0.903], {\"2020\"}[0.707, 0.469, 0.774, 0.903]] categories, the bars for 2020 are lower than 2010, indicating a decline in these environmental hazards. On the other hand, in the {Green Practises category}[{\"2010\"}[0.139, 0.360, 0.206, 0.903], {\"2020\"}[0.206, 0.251, 0.273, 0.903]], which is a positive environmental practice, the 2020 bar is higher than the 2010 bar, showing increased efforts in this area. This data suggests an overall improvement. However, it must be noted that even though there is a decrease in the {Pollution Index}[{\"2010\"}[0.808, 0.686, 0.874, 0.903], {\"2020\"}[0.874, 0.794, 0.941, 0.903]] category, there is a still significant room for improvement.\nBased on the above thought, the answer is:\nYes, there appears to be progression regarding environmental issues between 2010 and 2020. This is evident from the reductions in waste levels, energy consumption, and CO2 emissions, and the increase in green practices. However, the pollution index is still a concern." } ], "id_raw": 297 }, { "id": "lRruKvF5lV9JM0HD", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_12.png", "conversations": [ { "from": "human", "value": "\nIn the context of which category has the most significant decrease from 2010 to 2020?" }, { "from": "gpt", "value": "To find out which category has seen the most substantial reduction from 2010 to 2020, I must compare the differences between the 2010 bars and the 2020 bars for each category. Looking from left to right, the first difference is found at {Green Practises bars}[{\"2010\"}[0.139, 0.360, 0.206, 0.903], {\"2020\"}[0.206, 0.251, 0.273, 0.903]]. The difference here is nearly 500 units. This same approach can be used for all categories. After all these steps, I can see that the maximum decrease is in the {Energy Consumption category}[{\"2010\"}[0.473, 0.360, 0.540, 0.903], {\"2020\"}[0.540, 0.577, 0.607, 0.903]] with a decrease of around 1000 units.\nBased on the above thought, the answer is:\nEnergy Consumption" } ], "id_raw": 297 }, { "id": "34qV91AexFlAFsjk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nGiven the bar chart you observed, could you provide the data it represents? We would like it in CSV format." }, { "from": "gpt", "value": "The bar chart contains seven characteristics related to Entrepreneurial spirit and Entrepreneurship on the X-axis: {Self-confidence}[0.116, 0.919, 0.222, 0.942], {Optimism}[0.257, 0.919, 0.324, 0.942], {Persistence}[0.372, 0.919, 0.451, 0.942], {Passion}[0.506, 0.919, 0.558, 0.942], {Discipline}[0.620, 0.919, 0.687, 0.942], {Acceptance of failure}[0.701, 0.919, 0.848, 0.942], {Creativity}[0.862, 0.919, 0.929, 0.942]. Four trends categories represented by different colors and located at {the legend coordinate}[0.086, 0.046, 0.264, 0.192], correspond to four bars in each characteristic, namely \"Steady decline\", \"Minimal change\", \"Inexorable decline\" and \"Upward trend\". By examining the height of each bar and linking it to the numerical values labelled on the y-axis, I can deduce each numerical statistic for all categories. For example, for 'Self-confidence' in the 'Steady decline' category, the corresponding bar[0.121, 0.469, 0.217, 0.903] almost reaches up to the 4000 line, so the value is 4000. Similarly, doing the same for all bars, I am able to construct a CSV data format.\nBased on the above thought, the answer is:\nCharacteristics,Steady decline,Minimal change,Inexorable decline,Upward trend\nSelf-confidence,4000,1000,3000,500\nOptimism,3800,1000,2700,700\nPersistence,3600,1000,2400,900\nPassion,3400,1000,2100,1100\nDiscipline,3200,1000,1800,1300\nAcceptance of failure,3000,1000,1500,1500\nCreativity,2800,1000,1200,1700" } ], "id_raw": 298 }, { "id": "QCeKcVcYPjyKWVSO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which characteristic shows only minimal change over time? And explain your reasoning." }, { "from": "gpt", "value": "To answer the question, I need to look at the bars associated with the \"Minimal change\" trend, which is represented by the color green[0.092, 0.092, 0.120, 0.108] according to the legend. The green bars under each characteristic are of the same height; they correspond to the value of 1000 on the y-axis based on their starting and ending y coordinates. This indicates that the value of all characteristics remain the same in the \"Minimal change\" trend. Therefore, all characteristics show only minimal change over time.\nBased on the above thought, the answer is:\nAll characteristics (Self-confidence, Optimism, Persistence, Passion, Discipline, Acceptance of failure, Creativity). They all have consistent values in the \"Minimal change\" trend, indicating that there's minimal change over time." } ], "id_raw": 298 }, { "id": "2ytqRZ812Ou8CkwZ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value for the \"Inexorable decline\" trend, and what characteristic is it associated with?" }, { "from": "gpt", "value": "The \"Inexorable decline\" trend is in grey[0.092, 0.127, 0.120, 0.143] according to the legend. I must compare the heights of the grey bars in each characteristic to find the highest one. From left to right, the gray bar ranges for each characteristic are around 3000, 2700, 2400, 2100, 1800, 1500, and 1200. The highest value is {the 3000}[0.363, 0.143, 0.460, 0.403] in the \"Self-confidence\" characteristic[0.116, 0.919, 0.222, 0.942].\nBased on the above thought, the answer is:\nSelf-confidence." } ], "id_raw": 298 }, { "id": "vLOIVNsBlB4ulu34", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nCould you help turn the data represented on the bar chart into a CSV table please?" }, { "from": "gpt", "value": "From the labels I observe, {\"The Product Labels\"}[{\"Clothing\"}[0.147, 0.919, 0.212, 0.942]; {\"Electronics\"}[0.241, 0.919, 0.325, 0.942]; {\"Jewelry\"}[0.358, 0.919, 0.414, 0.942]; {\"Footwear\"}[0.453, 0.919, 0.525, 0.942]; {\"HomeGoods\"}[0.545, 0.919, 0.639, 0.942]; {\"Cosmetics\"}[0.656, 0.919, 0.735, 0.942]; {\"Books\"}[0.775, 0.919, 0.822, 0.942]; {\"Furniture\"}[0.867, 0.919, 0.936, 0.942]], these would be considered as the products sold. The y-axis represents the units sold/returned and the units go up to {\"12000 units\"}[0.038, -0.064, 0.087, -0.041]. Then I notice that our data has 4 different components that represent different things in sales: {\"Offline\"}[0.155, 0.046, 0.206, 0.069], {\"Online\"}[0.155, 0.081, 0.205, 0.104], {\"Sales\"}[0.155, 0.116, 0.196, 0.139], and {\"Returns\"}[0.155, 0.151, 0.214, 0.174]. Hence, to generate the csv data table, I will extract the four components which are easily distinguished by their colours, and hence y heights represented for each product category based on these components - for example, the \"Clothing\" bar starts at the base with the red \"Offline\" sales, which goes up to 1000 units. After that, the green segment represents \"Online\" sales and goes up to 3000 units. Then, the grey segment which represents \"Sales\" goes up to 6000 units, and finally, the orange segment representing \"Returns\" goes up to 6200 units.\nBased on the above thought, the answer is:\nProduct,Offline,Online,Sales,Returns \nClothing,1000,2000,3000,200 \nElectronics,3000,5000,8000,1000 \nJewelry,500,1500,2000,50 \nFootwear,700,1300,2000,70 \nHomeGoods,800,3000,3800,200 \nCosmetics,300,1500,1800,50 \nBooks,2000,3000,5000,3000 \nFurniture,3000,7000,10000,500" } ], "id_raw": 299 }, { "id": "YVk3ZVD2pG9GrGHe", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nGiven that smaller return rates are better for business efficacy, which product appears to be the most efficient for the business based on return rates and demand?" }, { "from": "gpt", "value": "For business efficiency, it's important to find a product with high demand (Sales) and with a small return rate (Returns). We can guess that a high Sales grey bar and a low Returns orange bar would reflect this efficiently. With this in mind, looking at the bars they show the two components stacked one on top of the other. Among all products, the bar for \"Jewelry\" with Sales and Returns data marked by {\"Jewelry Sales\"}[0.345, 0.584, 0.427, 0.743], and {\"Jewelry Returns\"}[0.345, 0.580, 0.427, 0.584] seems to have one of the highest Sales and one of the lowest Returns which suggests it might be the most efficient product for the business.\nBased on the above thought, the answer is:\nJewelry." } ], "id_raw": 299 }, { "id": "xrRUl2XOPTIpudOG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_20_14.png", "conversations": [ { "from": "human", "value": "\nWhich product category has the most total units sold and returned?" }, { "from": "gpt", "value": "We are looking for the highest total units sold and returned, suggested by {\"the y-axis\"}[0.017, 0.349, 0.032, 0.579] going up to 12000. Looking at the heights of the vertical bars, the biggest bar represents the \"Furniture\" category where the bar goes up to {\"12000 units\"}[0.038, -0.064, 0.087, -0.041], which is the highest point. The total amount of units sold and returned for the \"Furniture\" category is indicated by the height of the bar {\"Furniture\"}[0.861, -0.733, 0.943, -0.693].\nBased on the above thought, the answer is:\nFurniture" } ], "id_raw": 299 }, { "id": "dfAycs5l2n8q72Ax", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the grouped bar chart you observed, can you generate a structured table that presents the percentage of digital content creation on different platforms at different time intervals?" }, { "from": "gpt", "value": "To generate the CSV table, I need to start from the {title}[0.146, 0.025, 0.645, 0.055]. When checking the {x-axis labels}[0.105, 0.919, 0.114, 0.942]; [0.196, 0.919, 0.214, 0.942]; [0.292, 0.919, 0.309, 0.942]; [0.387, 0.919, 0.404, 0.942]; [0.482, 0.919, 0.500, 0.942]; [0.578, 0.919, 0.595, 0.942]; [0.673, 0.919, 0.690, 0.942], I know the values stand for different time intervals. I then check the {y-axis labels}[0.052, 0.894, 0.061, 0.917]; [0.043, 0.747, 0.061, 0.770]; [0.043, 0.600, 0.061, 0.623]; [0.043, 0.453, 0.061, 0.476]; [0.043, 0.306, 0.061, 0.329]; [0.035, 0.159, 0.061, 0.183]; [0.035, 0.012, 0.061, 0.036] to identify the percentage of content creation. The stacked bars in the plot stand for each platform\u2019s contribution at different time intervals. The height of each component can be determined by using the y-axis labels. The color of components is indicated in the {legend}[0.821, 0.074, 0.941, 0.255]. By cross-referencing between bars and the legend, we can assign the correct percentage of content creation on different platforms at different time intervals.\nBased on the above thought, the answer is:\nTime,YouTube,Instagram,TikTok,Blogs,Podcasts\n0,20,10,25,30,15\n10,30,15,10,22,23\n20,32,20,12,20,16\n30,38,19,13,21,18\n40,25,22,12,25,16\n50,10,20,18,28,24\n60,5,25,13,20,27" } ], "id_raw": 305 }, { "id": "vj4H13qzPT6R3urL", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which platform shows a continuous decline in digital content creation?" }, { "from": "gpt", "value": "To answer this question, I need to observe the change of bar heights of each platform over different time intervals. By looking at the {legend}[0.821, 0.074, 0.941, 0.255], I can identify the color of each platform's bars. From the bars whose color matches with YouTube, Instagram, TikTok, Blogs, and Podcasts in the legend, I can see that only the bars represent YouTube shows a continuous decline. Starting from the {first bar}[0.100, 0.756, 0.119, 0.903] in the 0-hour interval that corresponds to 20% of content creation. Then go to the next {bar}[0.196, 0.683, 0.215, 0.903] at the 10-hour interval, it increases to 30% of content creation. However, from the {20-hour interval bar}[0.291, 0.668, 0.310, 0.903] onwards, the trend continuously decreases until the end.\nBased on the above thought, the answer is:\nYouTube." } ], "id_raw": 305 }, { "id": "QKXwmTLvbJLuw2ST", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_21_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, at which time interval does TikTok have the lowest digital content creation?" }, { "from": "gpt", "value": "To find the time when TikTok has the lowest digital content creation, I'll check the length of the bars that represent TikTok in each group. Based on the colors given in the {legend}[0.821, 0.074, 0.941, 0.255], I identified the bars representing TikTok. By checking the height of the bars for TikTok at {0 hour}[0.100, 0.499, 0.119, 0.683], {10 hours}[0.196, 0.499, 0.215, 0.572], {20 hours}[0.291, 0.433, 0.310, 0.521], {30 hours}[0.386, 0.389, 0.405, 0.484], {40 hours}[0.481, 0.470, 0.501, 0.558], {50 hours}[0.577, 0.550, 0.596, 0.683], and {60 hours}[0.672, 0.587, 0.691, 0.683], I can see that the shortest one is at 30 hours.\nBased on the above thought, the answer is:\n30 hours." } ], "id_raw": 305 }, { "id": "sTFyH0HfD4cpCTBr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nConvert the stacked bar chart data into a structured CSV table format, showing the resource usage as a percentage over time." }, { "from": "gpt", "value": "By analyzing the stacked bar chart, I see that the x-axis labeled with {Time (s)}[0.347, 0.903, 0.440, 0.950] represents different time intervals and the y-axis labeled with {% Distribution}[0.025, 0.301, 0.048, 0.629] denotes percentages. Each stacked bar combines multiple resources, indicated by the color-coded legend: {EBooks}[0.639, 0.170, 0.685, 0.203], {Workbooks}[0.639, 0.240, 0.685, 0.273], {Video Materials}[0.639, 0.310, 0.685, 0.342], {Webinars}[0.639, 0.380, 0.685, 0.412], {Live Seminars}[0.639, 0.450, 0.685, 0.482]. To construct the CSV table, each bar's segment within the stacked bars represents the distribution percentage which is extracted by looking at the height and position of each segment relative to the y-axis ticks.\nBased on the above thought, the answer is:\nTime,EBooks,Workbooks,Video Materials,Webinars,Live Seminars\n0,0.3,0.3,0.1,0.2,0.1\n10,0.25,0.28,0.15,0.2,0.12\n20,0.20,0.25,0.18,0.20,0.17\n30,0.18,0.24,0.2,0.21,0.17\n40,0.16,0.2,0.22,0.22,0.20\n50,0.15,0.18,0.25,0.24,0.18\n60,0.15,0.17,0.27,0.23,0.18\n70,0.12,0.16,0.3,0.23,0.19\n80,0.1,0.15,0.33,0.22,0.20" } ], "id_raw": 306 }, { "id": "zO7AxXeGICEYReZh", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nDiscuss the change in resource distribution for EBooks and Workbooks over time based on the stacked bar chart." }, { "from": "gpt", "value": "To understand the change in resource distribution for {EBooks}[0.639, 0.170, 0.685, 0.203] and {Workbooks}[0.639, 0.240, 0.685, 0.273], I focus on the segments corresponding to these resources in each timed {stacked bar}[0.136, 0.157, 0.651, 0.806]. From time {0}[0.135, 0.838, 0.150, 0.885] to {80s}[0.630, 0.838, 0.659, 0.885], the segments representing EBooks and Workbooks steadily decrease in height, indicating a reduction in their usage over the observed time intervals. This trend suggests a shift from traditional reading materials to a preference for more interactive or different educational resources over time.\nBased on the above thought, the answer is:\nThe resource distribution for both EBooks and Workbooks has consistently decreased over time. The graph shows that EBooks and Workbooks started with higher usage at the initial point, but as time progressed, their distribution dropped, reflecting a transition to other types of educational resources." } ], "id_raw": 306 }, { "id": "ZxaSxvqFR6XwD6C8", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_21_6.png", "conversations": [ { "from": "human", "value": "\nAt what time interval did Video Materials have their highest percentage usage?" }, { "from": "gpt", "value": "To determine when Video Materials had the highest usage percentage, I assess the size of the {Video Materials}[0.639, 0.310, 0.685, 0.342] segments within each of the stacked bars across the x-axis {time intervals}[0.135, 0.838, 0.659, 0.885]. I notice a spike in the height of the bar for Video Materials between the x-axis labels {60}[0.504, 0.838, 0.534, 0.885] and {70}[0.567, 0.838, 0.596, 0.885]. Comparing this to the rest of the segments, this bar is the tallest, indicating the highest usage percentage.\nBased on the above thought, the answer is:\n70s" } ], "id_raw": 306 }, { "id": "9Zvm18JGCbym5125", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nGiven the line chart you've just looked at, could you turn the displayed information into a table with CSV formatting?" }, { "from": "gpt", "value": "Looking at the chart title {\"Global Trends and Changes Observation\"}[0.105, 0.050, 0.660, 0.110], I can understand the overall subject. The x-axis represents the years with its tick labels shown at coordinates from {2005}[0.099, 0.838, 0.157, 0.885] to {2045}[0.608, 0.838, 0.666, 0.885]. The y-axis tick labels displayed from {0}[0.058, 0.787, 0.072, 0.834] to {5}[0.058, 0.097, 0.072, 0.144] represent an index of Global Changes. Per the values listed in {the legend}[0.669, 0.148, 0.900, 0.511], it appears to represent different factors of Global Changes: Population, GDP, Technology, Environment, and Politics. To convert this into a table, I can note the height of the bars for each factor in each year and correlate them with the y-axis tick labels that represent the corresponding index values. For instance, in year 2005, the bar corresponding to \"Population\" {first bar in group 0}[0.115, 0.778, 0.141, 0.806] reaches about the position for index value 0.2 on the y-axis.\nBased on the above thought, the answer is:\nYear,Population,GDP,Technology,Environment,Politics\n2005,0.2,0.1,0.3,0.2,0.3\n2015,0.4,0.2,0.6,0.3,0.5\n2025,0.6,0.3,0.9,0.4,0.7\n2035,0.8,0.4,1.2,0.5,0.9\n2045,1.0,0.5,1.5,0.6,1.1" } ], "id_raw": 310 }, { "id": "mTUQZuSifTX07ypl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nWhat general trend do you see in the global changes index across all the fields from 2005 to 2045?" }, { "from": "gpt", "value": "Reviewing each of the data series from the chart, including {Population}[0.115, 0.778, 0.141, 0.806] to [0.624, 0.668, 0.650, 0.806], {GDP}[0.115, 0.764, 0.141, 0.778] to [0.624, 0.599, 0.650, 0.668], {Technology}[0.115, 0.723, 0.141, 0.764] to [0.624, 0.392, 0.650, 0.599], {Environment}[0.115, 0.695, 0.141, 0.723] to [0.624, 0.309, 0.650, 0.392], and {Politics}[0.115, 0.654, 0.141, 0.695] to [0.624, 0.157, 0.650, 0.309], they all show an upward trend. All these fields are seeing an increase in their global changes index values over the years from 2005 to 2045. In particular, the Technology field is showing the most noticeable growth, reaching from an index value of 0.3 in 2005 to 1.5 in 2045, showing a fivefold increase, as seen from {the Technology bars}.\nBased on the above thought, the answer is:\nThe general trend observed across all the fields is a strong upward trend from 2005 to 2045, with Technology experiencing the most dramatic increase." } ], "id_raw": 310 }, { "id": "DGaDV699G6gfNJtz", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_21_10.png", "conversations": [ { "from": "human", "value": "\nBy how much does the Global Changes Index of GDP increase from 2005 to 2045?" }, { "from": "gpt", "value": "To find the increase in the value of GDP from 2005 to 2045, I need to compare the bar heights for GDP in these two years. The bar for GDP in 2005 has the coordinates {GDP bar 2005}[0.115, 0.764, 0.141, 0.778]. Referring to the y-axis tick labels, its top reaches to the position of index value 0.1. The bar for GDP in 2045 is from {GDP bar 2045}[0.624, 0.599, 0.650, 0.668]. Its top reaches to the y-axis value of 0.5. The difference between the two is 0.5 (value in 2045) minus 0.1 (value in 2005), resulting in a total increase of 0.4.\nBased on the above thought, the answer is:\n0.4." } ], "id_raw": 310 }, { "id": "6xSlim5cIp0Bdl9s", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into CSV format showing the social demographics over time?" }, { "from": "gpt", "value": "Observing the chart titled {\"Social Demographics Over Time\"}[0.172, 0.050, 0.615, 0.110], the x-axis is labeled as 'Time' with {the time intervals}[{\"0\"}[0.135, 0.838, 0.150, 0.885]; {\"80\"}[0.630, 0.838, 0.659, 0.885]] ranging from 0 to 80. The y-axis {\"% of Total Population\"}[0.025, 0.223, 0.048, 0.708] labels the population percentage from 0.0 to 1.2. Each category such as 'Employed', 'Unemployed', etc. is represented by unique colors identified in {the legend}<[0.662, 0.148, 0.891, 0.511]>. The values for each category at every noted time can be read from the height of the bars, for example, 'Employed' at time 0 is between {0.00 and 0.25}[0.136, 0.644, 0.149, 0.806].\nBased on the above thought, the answer is:\nTime,Employed,Unemployed,Married,Single,Divorced\n0,0.25,0.15,0.35,0.20,0.05\n10,0.30,0.15,0.32,0.20,0.03\n20,0.35,0.13,0.30,0.19,0.03\n30,0.37,0.12,0.28,0.18,0.05\n40,0.39,0.11,0.27,0.17,0.06\n50,0.40,0.11,0.26,0.18,0.05\n60,0.41,0.10,0.25,0.19,0.05\n70,0.42,0.09,0.24,0.20,0.05\n80,0.43,0.09,0.23,0.21,0.04" } ], "id_raw": 311 }, { "id": "s1xReYYa9raAm95r", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nWhich category demonstrates the most stability over time based on the bar chart?" }, { "from": "gpt", "value": "To determine the stability, we analyze the fluctuation of bar heights in each category across all time points. For instance, the {Unemployed category}<[0.736, 0.236, 0.882, 0.283]> shows bars with heights representing values that start at 0.15 and decrease progressively but slightly to 0.09. Reviewing all categories, 'Unemployed' shows the least variation in its change, indicating greater stability compared to others where the changes are more significant.\nBased on the above thought, the answer is:\nUnemployed. It maintains a smaller range of fluctuation in percentage from 0.15 to 0.09 over the 80 units of time, showing the most consistency and stability in its values." } ], "id_raw": 311 }, { "id": "IRuJOBsZ5DyJGLyF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_21_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease of the Married category from time 0 to time 80?" }, { "from": "gpt", "value": "From the chart, the 'Married' category is plotted along the time axis with initial and final values highlighted on the bars at {time 0}[0.136, 0.319, 0.149, 0.546] and {time 80}[0.638, 0.319, 0.651, 0.468]. Initially, the value for 'Married' is 0.35 and at time 80, it is 0.23. The decrease can be calculated as initial value minus final value.\nBased on the above thought, the answer is:\n0.12" } ], "id_raw": 311 }, { "id": "7peUtbPWBFN7oeKu", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nBased on the provided bar chart display, can you present the contained data as a CSV format table?" }, { "from": "gpt", "value": "The title of the chart is {\"Trends in Education and Training Technology Innovation\"}[0.333, 0.019, 0.717, 0.041] indicating that we are visualizing trends of various education technologies. The x-axis represents the seven different technologies listed: {\"Collaboration Tools\"}[0.145, 0.939, 0.152, 0.957], {\"Multimedia Learning\"}[0.270, 0.939, 0.278, 0.957], {\"VR Learning\"}[0.396, 0.939, 0.403, 0.957], {\"Mobile Learning\"}[0.521, 0.939, 0.529, 0.957], {\"Simulation\"}[0.647, 0.939, 0.654, 0.957], {\"Data Mining\"}[0.772, 0.939, 0.780, 0.957], and {\"Game-based Learning\"}[0.898, 0.939, 0.905, 0.957]. The y-axis shows the trend percentages ranging from 0.00 to 0.30 based on {the y-axis tick labels}[{\"0.00\"}[0.029, 0.920, 0.054, 0.938], {\"0.05\"}[0.029, 0.771, 0.054, 0.788], {\"0.10\"}[0.029, 0.621, 0.054, 0.638], {\"0.15\"}[0.029, 0.471, 0.054, 0.489], {\"0.20\"}[0.029, 0.321, 0.054, 0.339], {\"0.25\"}[0.029, 0.172, 0.054, 0.189], {\"0.30\"}[0.029, 0.022, 0.054, 0.039]]. Looking at the colors of the bars, there are two trends labeled as \"Time 1\" and \"Time 2\" in {the legend}[0.901, 0.055, 0.982, 0.113]. Using these mappings, we can represent the data as a CSV format table.\nBased on the above thought, the answer is:\nTech,Time 1,Time 2\nCollaboration Tools,0.12,0.09\nMultimedia Learning,0.25,0.15\nVR Learning,0.28,0.15\nMobile Learning,0.09,0.12\nSimulation,0.05,0.13\nData Mining,0.12,0.18\nGame-based Learning,0.09,0.18" } ], "id_raw": 313 }, { "id": "0FEtUhx8T1NAkyYj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nJudging from the charts, which technology seems to be gaining more popularity?" }, { "from": "gpt", "value": "The {bar chart}[0.063, 0.047, 0.988, 0.927] depicts the trend percentages of varying education technologies over two different intervals of time. Among all technologies, {Game-based Learning}[0.898, 0.939, 0.905, 0.957] has shown the most considerable rise from Time 1 to Time 2. The comparison of the {two bars} for Game-based Learning {[0.858, 0.658, 0.902, 0.927] and [0.902, 0.388, 0.945, 0.927]} highlights a rise from 0.09 to 0.18, indicating an increase in its popularity.\nBased on the above thought, the answer is:\nGame-based Learning. This is based on the fact that Game-based Learning shows the most significant increase in trend percentage from Time 1 to Time 2 compared to the other technology types." } ], "id_raw": 313 }, { "id": "Xo8S06psp7N1rqdB", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_21_13.png", "conversations": [ { "from": "human", "value": "\nWhich technology showed the biggest increase from Time 1 to Time 2?" }, { "from": "gpt", "value": "By comparing the height of the bars representing Time 1 and Time 2 for each technology, I can identify the technology that showed the biggest increase in trend percentage. For instance, the bars for Collaboration Tools {[0.105, 0.568, 0.148, 0.927] and [0.148, 0.658, 0.192, 0.927]} represent a decrease from 0.12 to 0.09. This trend is followed for all technologies until I reach Game-based Learning represented by the bars at {[0.858, 0.658, 0.902, 0.927] and [0.902, 0.388, 0.945, 0.927]} which show an increase from 0.09 to 0.18. This is the largest increase amongst all the technologies.\nBased on the above thought, the answer is:\nGame-based Learning." } ], "id_raw": 313 }, { "id": "CEUZ2mCxGfcuYEGi", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nCould you help me convert the data displayed in the bar chart into a CSV format table?" }, { "from": "gpt", "value": "I can see that the bar chart is about \"Greenhouse Gas Emissions Over Time\" from {the title}[0.148, 0.050, 0.676, 0.110] and the x-axis shows the emission year, from the year 2000 to 2020, as I can see from {the x-axis labels}[\"2000\"[0.164, 0.838, 0.222, 0.885]; \"2005\"[0.273, 0.838, 0.331, 0.885]; \"2010\"[0.382, 0.838, 0.441, 0.885]; \"2015\"[0.492, 0.838, 0.550, 0.885]; \"2020\"[0.601, 0.838, 0.660, 0.885]. The y-axis represents the emissions in millions of metric tons with the labels ranges from 0 to 25000, as indicated by {the y-axis labels}[\"0\"[0.116, 0.787, 0.131, 0.834]; \"5000\"[0.072, 0.649, 0.131, 0.695]; \"10000\"[0.058, 0.510, 0.131, 0.556]; \"15000\"[0.058, 0.371, 0.131, 0.418]; \"20000\"[0.058, 0.232, 0.131, 0.279]; \"25000\"[0.058, 0.093, 0.131, 0.140].\nThe chart is divided into multiple groups of bars, each group representing a year, and each bar within a group representing a particular kind of gas. The types of gases are identified in {the legend}[0.735, 0.148, 0.877, 0.511] as CO2, CH4, N2O, CF4, and C2F6.\nThe emissions for each gas in each year can be inferred from the height of the corresponding bars. For instance, the emission of CO2 in the year 2000 can be deduced by looking at the coordination of the bar<{\"CO2 bar in 2000\"}[0.171, 0.350, 0.215, 0.806] on this chart. Taking this reasoning into account, I will deduce the emissions for each gas in each year to create the CSV table.\nBased on the above thought, the answer is:\nYear,CO2,CH4,N2O,CF4,C2F6\n2000,16433.416,454.036,156.307,23.0,4.920\n2005,17610.762,477.512,172.597,24.6,5.330\n2010,18630.101,501.988,188.887,26.2,5.740\n2015,20497.379,526.464,205.177,27.8,6.150\n2020,22564.662,550.940,221.467,29.4,6.560" } ], "id_raw": 314 }, { "id": "DBAKlx1XjnK4wDgO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat is your interpretation of the trend for each greenhouse gas over the period from 2000 to 2020?" }, { "from": "gpt", "value": "The bar chart gives a clear visual representation of the changes for each type of greenhouse gas over time. Let's take each gas type and consider its bars over the years 2000-2020. Starting with {CO2}[0.744, 0.170, 0.790, 0.203] from legend, the height on the bars shows a steady uptick {CO2 bar heights}[0.171, 0.350, 0.215, 0.806]; [0.280, 0.317, 0.324, 0.806]; [0.390, 0.289, 0.434, 0.806]; [0.499, 0.237, 0.543, 0.806]; [0.609, 0.179, 0.652, 0.806] suggesting increasing emissions of CO2 over the time period. \n\nMoving next to {CH4}[0.744, 0.240, 0.790, 0.273] from legend, you can observe that the increases are much smaller than those of CO2, highlighting a moderate increase in CH4 emissions over the years {CH4 bar heights}[0.171, 0.337, 0.215, 0.350]; [0.280, 0.304, 0.324, 0.317]; [0.390, 0.275, 0.434, 0.289]; [0.499, 0.222, 0.543, 0.237]; [0.609, 0.164, 0.652, 0.179]. \n\nFor {N2O}[0.744, 0.310, 0.790, 0.342] from legend, the emissions show a slightly noticeable increase {N2O bar heights}[0.171, 0.333, 0.215, 0.337]; [0.280, 0.299, 0.324, 0.304]; [0.390, 0.269, 0.434, 0.275]; [0.499, 0.216, 0.543, 0.222]; [0.609, 0.158, 0.652, 0.164]. \n\nIn the case of {CF4}[0.744, 0.380, 0.790, 0.412] from legend emissions, you can see a downward trajectory in the bars {CF4 bar heights}[0.171, 0.332, 0.215, 0.333]; [0.280, 0.298, 0.324, 0.299]; [0.390, 0.269, 0.434, 0.269]; [0.499, 0.216, 0.543, 0.216]; [0.609, 0.157, 0.652, 0.158], indicating a decreasing trend in CF4 emissions, which is a positive sign. \n\nLastly, {C2F6}[0.744, 0.450, 0.790, 0.482] from legend emissions also show a slight rise {C2F6 bar heights}[0.171, 0.332, 0.215, 0.332]; [0.280, 0.298, 0.324, 0.298]; [0.390, 0.268, 0.434, 0.269]; [0.499, 0.215, 0.543, 0.216]; [0.609, 0.157, 0.652, 0.157] during 2000-2020.\nBased on the above thought, the answer is:\nFrom the year 2000 to 2020, CO2 and N2O emissions have shown a significant increase, while CH4 and C2F6 have shown a moderate increase. In contrast, CF4 has shown a declining trend in emissions. This dispersion of emissions indicates the diverse impacts of these gases on environmental health." } ], "id_raw": 314 }, { "id": "2l6YLGmkMb87uuL5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the most emitted gas type in 2020 according to the chart?" }, { "from": "gpt", "value": "To find the most emitted gas type in 2020, I must examine the heights of all the bars associated with the year. Looking at 2020, which is represented by the last group of bars with {coordinates of bars in 2020}[\"CO2\"[0.609, 0.179, 0.652, 0.806]; \"CH4\"[0.609, 0.164, 0.652, 0.179]; \"N2O\"[0.609, 0.158, 0.652, 0.164]; \"CF4\"[0.609, 0.157, 0.652, 0.158]; \"C2F6\"[0.609, 0.157, 0.652, 0.157], it becomes clear that {the bar}[0.609, 0.179, 0.652, 0.806] which symbolizes CO2 on {the legend}[0.744, 0.170, 0.790, 0.203] has the highest level in 2020.\nBased on the above thought, the answer is:\nCO2" } ], "id_raw": 314 }, { "id": "6gUNoyvpox8WvNhJ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nCan you generate a CSV table from the observed horizontal bar chart showing the economic indicators over a 10-year span?" }, { "from": "gpt", "value": "Analyzing the chart, I noticed there are four distinct color bars under each year, represented in the {legend}[0.857, 0.055, 0.985, 0.165], corresponding to different economic indicators: {Accelerating Decline}[0.860, 0.064, 0.879, 0.076], {Slow Decline}[0.860, 0.090, 0.879, 0.102], {Inconsistent Growth}[0.860, 0.116, 0.879, 0.128], and {Fluctuating Decline}[0.860, 0.142, 0.879, 0.155]. Observing the horizontal bars, I see they are aligned with the y-axis, which shows years from {2011}[0.053, 0.854] through {2020}[0.053, 0.119], and the length of the bars on the x-axis indicates the metric values from {0}[0.050, 0.939, 0.056, 0.957] to {120}[1.115, 0.939, 1.133, 0.957]. For instance, the bar for {Accelerating Decline in 2011}[0.053, 0.871, 0.945, 0.887] stretches to a value close to 100, indicating that for 2011, Accelerating Decline is at 100. I will generate the table based on the metric values at the end of each bar for each indicator and year.\nBased on the above thought, the answer is:\nYear,Accelerating Decline,Slow Decline,Inconsistent Growth,Fluctuating Decline\n2011,100,90,80,85\n2012,85,89,83,82\n2013,70,88,86,79\n2014,52,87,89,76\n2015,40,86,92,73\n2016,25,85,98,70\n2017,12,84,96,67\n2018,5,83,94,64\n2019,2,82,92,61\n2020,1,81,90,58" } ], "id_raw": 327 }, { "id": "ijKqHNSnIrQMrGzp", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nWhich economic indicator has shown the most stability over the 10 years?" }, { "from": "gpt", "value": "To determine stability, I need to look for minimal variance in metric values across the years. Observing the bars, I see that {Slow Decline}[0.860, 0.090, 0.879, 0.102] has a small deviation in the length of its bars from {90}[0.856, 0.854, 0.877, 0.871] in 2011 to {81}[0.776, 0.119, 0.796, 0.136] in 2020, indicating a slow and consistent decline. Comparing this with the other indicators, such as {Accelerating Decline}[0.860, 0.064, 0.879, 0.076] which shows a more prominent decrease from {100}[0.945, 0.870, 0.972, 0.888] to {1}[0.062, 0.135, 0.077, 0.153], or {Inconsistent Growth}[0.860, 0.116, 0.879, 0.128] and {Fluctuating Decline}[0.860, 0.142, 0.879, 0.155], which both show fluctuations, Slow Decline remains the most stable throughout the given period.\nBased on the above thought, the answer is:\nSlow Decline. It has exhibited gradual and consistent values throughout the 10 years, in contrast to the other economic indicators which display more significant fluctuations or a rapid rate of decline." } ], "id_raw": 327 }, { "id": "3HASm5vSMrF6LGkP", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the average value of the 'Inconsistent Growth' metric over the 10-year period?" }, { "from": "gpt", "value": "To calculate the average value, I will sum up the metric values across all years for {Inconsistent Growth}[0.860, 0.116, 0.879, 0.128] and then divide by the number of years. Adding the values {80}[0.767, 0.838, 0.787, 0.855] for 2011, {83}[0.794, 0.756, 0.814, 0.773] for 2012, and so on until {90}[0.856, 0.102, 0.877, 0.120] for 2020, which gives a total sum. Dividing this sum by 10 will yield the average value for 'Inconsistent Growth'.\nBased on the above thought, the answer is:\n88.6" } ], "id_raw": 327 }, { "id": "pXQBxLusZXiuBTUn", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart image you are examining, could you retrieve the data and convert it into a CSV table?" }, { "from": "gpt", "value": "The bar chart contains information regarding the 'Income Growth' and 'Risk Control' in different regions as represented by the labels on x-axis {EN, DE, FR, IT, ES}. The y-axis mentions the values in percentages which range from around -4.5% to 1%, all represented by a combination of bars and numerical values annotated on them. By examining the height of each bar and the corresponding numerical value, we can retrieve the raw data from each category. For instance, {the EN bar for 'Income Growth'}[0.21, 0.49, 0.26, 0.75] is annotated with {-1.2%}[0.24, 0.46, 0.27, 0.49]. Similarly by checking all the bars of all the regions, the information can be compiled in a CSV format table.\nBased on the above thought, the answer is:\nModel,EN,DE,FR,IT,ES\nIncome Growth,%-1.2,%-0.8,%-3.5,%-0.3,%0.7\nRisk Control,%-3.1,%-1.8,%-0.5,%-4.3,%0.2" } ], "id_raw": 330 }, { "id": "xD3nh0y0uwQtDiRz", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nCan you analyze overall regional performance in financial risk control and income growth based on the charts observed?" }, { "from": "gpt", "value": "By examining {the bars of 'Income Growth'}[0.21, 0.26, 0.31, 0.75] and {the bars of 'Risk Control'}[0.51, 0.56, 0.61, 0.75], we can see that all the regions, except {ES bar for 'Income Growth'}[0.36, 0.52, 0.41, 0.75] and {ES bar for 'Risk Control'}[0.66, 0.73, 0.71, 0.75], show negative growth. The ES region appears to be the only one that shows slight improvement in both income growth and risk control, whereas all the other regions are in decline for both metrics. The Italian IT region shows the greatest decline in 'Risk Control', with the lowest bar for this metric. Hence, we can state that the overall regional performance in financial risk control and income growth is generally declining, with ES being the exception.\nBased on the above thought, the answer is:\nIn the bar chart, most regions are showing a decline in both financial risk control and income growth. However, the ES region shows slight improvements on both fronts. Particularly, the IT region shows the steepest decline in risk control." } ], "id_raw": 330 }, { "id": "vPSyqdbkvfBqgYwu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_23_0.png", "conversations": [ { "from": "human", "value": "\nWhich metric shows the biggest overall percentage decline across all regions according to the bar chart?" }, { "from": "gpt", "value": "The chart displays two metrics, 'Income Growth' and 'Risk Control' across 5 regions. To determine the metric with the biggest overall percentage decline, I need to sum up the declines across all regions for each metric. Observing the bars and corresponding values annotated on them for both metrics, for example, {the EN bar for 'Income Growth'}[0.21, 0.49, 0.26, 0.75] is annotated with {-1.2%}[0.24, 0.46, 0.27, 0.49] and {the EN bar for 'Risk Control'}[0.51, 0.43, 0.56, 0.75] is annotated with {-3.1%}[0.54, 0.40, 0.57, 0.43]. By checking all the bars for each region, I see that the overall percentage decline in 'Risk Control' across all regions is larger than the decline in 'Income Growth'.\nBased on the above thought, the answer is:\n'Risk Control'" } ], "id_raw": 330 }, { "id": "pHvT5EKDDIiKDiPE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nCould you transform the contents of the bar chart into a CSV format table?" }, { "from": "gpt", "value": "By looking at the {subplot coordinates}[0.076, 0.062, 0.932, 0.356] and [0.076, 0.429, 0.932, 0.723], I can see that there are two situations that the electric vehicles are used in, namely Sustainable City Living and Long Interstate Trip. This information is displayed through tick labels. For instance, the label {\"Sustainable City Living\"}[0.015, 0.739, 0.137, 0.942] and {\"Long Interstate Trip\"}[0.880, 0.739, 0.985, 0.915].\nFrom the {legend}[0.818, 0.074, 0.925, 0.185] and the {legend items}[0.862, 0.118, 0.911, 0.141] [0.862, 0.153, 0.920, 0.176], we see two types of batteries or EVs, 48KWh and 100KWh. Each bar in the subplots corresponds to an EV type, and the height of the bar corresponds to the range in miles, annotated by the {text coordinates}[0.269, 0.210, 0.295, 0.233] [0.269, 0.318, 0.295, 0.341] [0.713, 0.044, 0.740, 0.067] [0.713, 0.210, 0.740, 0.233]. Combining all this information, I can generate a CSV table.\nBased on the above thought, the answer is:\nConditions,48KWh EV Range,100KWh EV Range\nSustainable City Living,320,490\nLong Interstate Trip,210,320" } ], "id_raw": 335 }, { "id": "9iPho8QxxqPqNQPy", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nJudging from the bar chart, what are your thoughts on the range performance of the two different EVs under the two usage scenarios?" }, { "from": "gpt", "value": "From the bar chart, we can see two scenarios for using the EVs - Sustainable City Living and Long Interstate Trip. Within each scenario, there is a comparison of two EVs with different battery capacities \u2013 48KWh and 100KWh. The 48KWh EV's ranges are located at [0.269, 0.210, 0.295, 0.233] and [0.269, 0.318, 0.295, 0.341] on the respective scenario axes, while the 100KWh EV's ranges are located at [0.713, 0.044, 0.740, 0.067] and [0.713, 0.210, 0.740, 0.233] on the respective scenario axes. From these ranges specified, we can observe that the 100KWh EV consistently outperforms the 48KWh in both scenarios, suggesting a direct relation between battery size and range. Also, both the EVs have a reduced range in the Long Interstate Trip scenario when compared to the Sustainable City Living scenario. This implies that driving conditions and patterns significantly affect EV range.\nBased on the above thought, the answer is:\nIt is clear that the Electric Vehicle (EV) with the 100KWh battery has a superior range compared to its 48KWh counterpart. This performance difference is seen in both scenarios - Sustainable City Living and Long Interstate Trip. Furthermore, the range of both vehicles decreases for the Long Interstate Trip when compared to the Sustainable City Living condition, highlighting how different usage scenarios can affect EV range. Therefore, whilst choosing an EV, both battery size and intended usage scenario should be considered." } ], "id_raw": 335 }, { "id": "V8QtFJP861AgCtVP", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_23_5.png", "conversations": [ { "from": "human", "value": "\nBy how much does the EV range increase when we switch from the 48KWh battery to the 100KWh battery on a long interstate trip?" }, { "from": "gpt", "value": "To answer this question, I need to compare the values for Long Interstate Trip for both the 48KWh and the 100KWh battery. The y-coordinate of the 48KWh bar in the Long Interstate Trip plot is [0.115, 0.346, 0.449, 0.551] with its value at [0.269, 0.318, 0.295, 0.341] showing a range of 210 miles, while the y-coordinate of the 100KWh bar is [0.560, 0.238, 0.893, 0.551] with its corresponding value showing at [0.713, 0.210, 0.740, 0.233] showing a range of 320 miles. The difference between them will give us the required increase in EV range when we switch the battery.\nBased on the above thought, the answer is:\n110 miles." } ], "id_raw": 335 }, { "id": "ixemZ3RbflmPKgtA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nCan you please organize all the sales data for Toaster A in tabular form?" }, { "from": "gpt", "value": "To provide the sales data for Toaster A in a structured format, I need to consider the height and the value annotations at the top of each bar. Each month starting from {Jan}[0.156, 0.919, 0.177, 0.942] to {Jun}[0.878, 0.919, 0.900, 0.942] has a corresponding bar for Toaster_A in the upper subchart, by calculating each {bars'[height]} from [{Jan's bar}[0.112, 0.839, 0.221, 1.031]] to [{Jun's bar}[0.835, 0.139, 0.943, 0.599]], and I can extract the corresponding sales values of each month.\nBased on the above thought, the answer is:\nMonth, Sales\nJan,200\nFeb,220\nMar,240\nApr,260\nMay,280\nJun,300" } ], "id_raw": 337 }, { "id": "CYBPzz0RGR0VGXrI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which product had a more consistent sales performance in the first half of the year?" }, { "from": "gpt", "value": "To evaluate the consistency of sales performance, we should observe the variability of the bar heights. A product with less fluctuation of sales is more consistent. The upper subchart that shows the sales performance of {Toaster_A}[0.906, 0.118, 0.972, 0.142] with bars from {Jan}[0.112, 0.292, 0.221, 0.599] to {Jun}[0.835, 0.139, 0.943, 0.599] exhibits a steady and gradual increase in the heights depicting consistent growth. In comparison, the sales performance of Toaster_B presented in the lower subchart with bars from {Jan's bar}[0.112, 0.839, 0.221, 1.031] to {Jun's bar}[0.835, 0.139, 0.943, 0.599] exhibit drastic fluctuations especially the {June's bar}[0.835, 0.139, 0.943, 0.599]. Therefore, Toaster A has a more stable sales performance.\nBased on the above thought, the answer is:\nToaster A. Its sales grew steadily from January to June with smaller fluctuations compared to Toaster B." } ], "id_raw": 337 }, { "id": "S86XH3gCulbxWjjf", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_23_7.png", "conversations": [ { "from": "human", "value": "\nFor Toaster_A, in which month is the sales growth the highest relative to the previous month?" }, { "from": "gpt", "value": "To identify the month with the highest sales growth for Toaster_A compared to the previous month, I am comparing the heights of the bars in the upper subchart for each month starting from {Feb}[0.299, 0.919, 0.323, 0.942] to {Jun}[0.878, 0.919, 0.900, 0.942]. I will find the bar with the biggest upward shift from the previous bar. From the analysis, {Apr's bar}[0.546, 0.200, 0.654, 0.599] has the most noticeable upward shift from the previous month of {Mar's bar}[0.401, 0.231, 0.510, 0.599].\nBased on the above thought, the answer is:\nApril" } ], "id_raw": 337 }, { "id": "oPKvhceO1tSKrVyE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV table showing the number of participants in each sport at different times of the day for Week 1?" }, { "from": "gpt", "value": "To construct a CSV table for Week 1, I first locate the area corresponding to Week 1 in the chart, which can be seen in the {first subplot}[0.062, 0.062, 0.950, 0.381]. Within this subplot, various bars represent the number of participants in different sports at various times of the day. The chart displays bars grouped together for each sport, each group representing a different time of day, as indicated by the legends: {Morning}[0.843, 0.120, 0.871, 0.136], {Noon}[0.843, 0.155, 0.871, 0.171], {Evening}[0.843, 0.190, 0.871, 0.206], and {Night}[0.843, 0.225, 0.871, 0.241]. The vertical axis is labelled with numbers indicating the count of participants, with y-tick coordinates such as for {0}[0.062, 0.381] and {20}[0.062, 0.229]. Each bar's height corresponds to the count of participants, and I determine each value by its ending coordinate on the y-axis. For example, the {bar for Basketball in the Morning}[0.102, 0.214, 0.264, 0.381] reaches a value slightly above {20}[0.062, 0.229], indicating approximately 22 participants. Following this process for all bars, I compile the following table.\nBased on the above thought, the answer is:\nTime, Basketball, Football, Swimming, Running, Gym\nMorning,22,12,10,28,40\nNoon,15,8,8,30,30\nEvening,10,9,5,22,20\nNight,5,15,3,15,10" } ], "id_raw": 338 }, { "id": "hnJdNltX2pqYH6Io", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nWhich time of day showed the greatest increase in running participants from Week 1 to Week 2?" }, { "from": "gpt", "value": "To identify the time of day with the greatest increase in running participants, I compare the bars representing Running in each time slot from {Week 1}[0.062, 0.062, 0.950, 0.381] to {Week 2}[0.062, 0.445, 0.950, 0.764]. For {Morning}[0.843, 0.120, 0.871, 0.136], the {Running bar in Week 1}[0.079, 0.528, 0.149, 0.764] indicates about 28 participants, and in {Week 2}[0.172, 0.548, 0.242, 0.764] about 35 participants, an increase of 7. This process is repeated for {Noon}[0.843, 0.155, 0.871, 0.171], {Evening}[0.843, 0.190, 0.871, 0.206], and {Night}[0.843, 0.225, 0.871, 0.241]. Upon comparing these increases, I determine that the period with the greatest increase is from {the Running bars}[0.079, 0.528, 0.149, 0.764] to [0.172, 0.548, 0.242, 0.764] corresponding to Morning.\nBased on the above thought, the answer is:\nThe Morning showed the greatest increase in running participants, which was 7, from Week 1 to Week 2." } ], "id_raw": 338 }, { "id": "lFbAku7vZlYbqpXL", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_23_8.png", "conversations": [ { "from": "human", "value": "\nHow many more participants were there in Gym workouts during the Morning in Week 2 compared to Week 1?" }, { "from": "gpt", "value": "To calculate the increase in participants for Gym workouts during the Morning from Week 1 to Week 2, I analyze the height of the corresponding bars. Locating the {bar for Gym during Morning of Week 1}[0.079, 0.460, 0.149, 0.764], which corresponds to a value of 40 participants when compared to the y-axis labels. Then I locate {the bar for Gym during Morning of Week 2}[0.172, 0.528, 0.242, 0.764], which corresponds to a value of 45 participants. The difference is calculated as 45 minus 40.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 338 }, { "id": "TvHDK5eLwIsGnvK1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the dataset present in the bar chart into a table in CSV format?" }, { "from": "gpt", "value": "The x-axis label {Fiscal Quarter}[0.480, 0.942, 0.576, 0.970] suggests that the columns of the table will be the fiscal quarters, labeled as {\"1\"}[0.177, 0.903, 0.186, 0.931], {\"2\"}[0.350, 0.903, 0.359, 0.931], {\"3\"}[0.523, 0.903, 0.532, 0.931], {\"4\"}[0.697, 0.903, 0.705, 0.931], {\"5\"}[0.870, 0.903, 0.879, 0.931]. The two types of bars and corresponding text labels within the sublegend: {Facebook Interaction}[0.366, 0.039, 0.511, 0.067] and {Instagram Interaction}[0.578, 0.039, 0.728, 0.067] represent the two types of interactions we will be listing in our table. The number above each bar, such as {\"45\"}[0.138, 0.559, 0.156, 0.587], {\"65\"}[0.311, 0.427, 0.329, 0.455] for Facebook and {\"40\"}[0.207, 0.592, 0.225, 0.620], {\"50\"}[0.381, 0.526, 0.398, 0.554] for Instagram, indicates the interaction count for each social network in each quarter. From this, I can construct a CSV formatted table that includes all these details.\nBased on the above thought, the answer is:\n,Quarter 1,Quarter 2,Quarter 3,Quarter 4,Quarter 5\nFacebook Interaction,45,65,90,83,99\nInstagram Interaction,40,50,75,85,98" } ], "id_raw": 345 }, { "id": "7N0mbh0szYZZJafe", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nGiven the data available, which social network platform, Facebook or Instagram, would you recommend for maximizing user interactions, and why?" }, { "from": "gpt", "value": "To answer this question, it's important to consider the interaction rates across all quarters for both platforms. By examining the height of bars representing {Facebook Interaction}[0.366, 0.039, 0.511, 0.067] and {Instagram Interaction}[0.578, 0.039, 0.728, 0.067], it can be seen that in Quarter 1 and Quarter 2, Facebook has higher interaction rates as suggested by {Facebook bars}[{\"Q1\"}[0.112, 0.587, 0.181, 0.883]; {\"Q2\"}[0.285, 0.455, 0.355, 0.883]]are higher than {Instagram bars}[{\"Q1\"}[0.181, 0.620, 0.251, 0.883]; {\"Q2\"}[0.355, 0.554, 0.424, 0.883]]. However, in Quarter 3, Instagram starts to close the gap as seen from the higher bar at {Instagram bar for Q3}[0.528, 0.389, 0.597, 0.883], and in Quarter 4 and Quarter 5, Instagram interactions are fairly close to, but still lower than, Facebook's. Therefore, while Instagram has shown improvement in the engagement rate, Facebook consistently maintains a higher engagement rate throughout all the fiscal quarters.\nBased on the above thought, the answer is:\nFacebook, due to its consistently higher user interaction rate over all the fiscal quarters compared to Instagram." } ], "id_raw": 345 }, { "id": "A4EmyM9tSrmyB3Yj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_0.png", "conversations": [ { "from": "human", "value": "\nWhich fiscal quarter had the highest increase in Instagram interactions?" }, { "from": "gpt", "value": "To answer this query, I need to calculate the increase in interactions for Instagram in each quarter. For example, the difference in interactions between Quarter 1 and Quarter 2 for Instagram can be calculated by subtracting the height of the {Instagram bar in Quarter 1}[0.181, 0.620, 0.251, 0.883], labeled {\"40\"}[0.207, 0.592, 0.225, 0.620], from the height of the {Instagram bar in Quarter 2}[0.355, 0.554, 0.424, 0.883], labeled {\"50\"}[0.381, 0.526, 0.398, 0.554]. Similarly, this can be done for the rest of the quarters - 3, 4, and 5. I can see that the highest increase occurs between Quarter 2 labeled {\"50\"}[0.381, 0.526, 0.398, 0.554], and Quarter 3, labeled {\"75\"}[0.554, 0.361, 0.571, 0.389], with a difference of 25 interactions.\nBased on the above thought, the answer is:\nQuarter 3." } ], "id_raw": 345 }, { "id": "kvduxddLYRdczAT7", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, can you extract the data for Social Networks into CSV format?" }, { "from": "gpt", "value": "I can see the bars for {Social Networks}[0.106, 0.330, 0.176, 0.488];[0.281, 0.172, 0.351, 0.488];[0.455, 0.212, 0.525, 0.488];[0.629, 0.275, 0.699, 0.488];[0.804, 0.125, 0.873, 0.488], each matched with a label below showing the fiscal quarter from 1 to 5. These bar labels are given by {the x-axis tick labels}[0.172, 0.903, 0.181, 0.931];[0.346, 0.903, 0.355, 0.931];[0.520, 0.903, 0.529, 0.931];[0.695, 0.903, 0.704, 0.931];[0.869, 0.903, 0.878, 0.931]. The value labels are written at the top of each bar, for example, the value label for the first bar for Social Networks is {the value annotation \"2.0\"}[0.130, 0.302, 0.152, 0.330]. Putting these together, I can compile the data of Social Networks into a CSV format.\nBased on the above thought, the answer is:\nFiscal Quarter,Social Networks\n1,2.0\n2,4.0\n3,3.5\n4,2.7\n5,4.6" } ], "id_raw": 346 }, { "id": "jXyjIwp5uwo9ds2z", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you summarize the trend of Interpersonal Relationships throughout the fiscal quarters?" }, { "from": "gpt", "value": "The Interpersonal Relationships is represented by the second bar of each pair, with its corresponding legend in {the legend}[0.538, 0.039, 0.728, 0.067]. The {bars}[0.176, 0.172, 0.246, 0.488]; [0.351, 0.330, 0.420, 0.488]; [0.525, 0.291, 0.595, 0.488]; [0.699, 0.156, 0.769, 0.488]; [0.873, 0.251, 0.943, 0.488] represent the change of Interpersonal Relationships through fiscal quarters. By observing the height of each bar, it seems to start high in the first quarter, then it has a downward turn in the second quarter. Going upward but it a slower pace in the third quarter, a steep climb in the fourth quarter, later to fall back to a lower level again in the fifth quarter.\nBased on the above thought, the answer is:\nThe level of Interpersonal Relationships started high in the first quarter, went through a downward turn in the second quarter, showed small growth in the third quarter, and surged in the fourth quarter, but went back down again in the fifth quarter." } ], "id_raw": 346 }, { "id": "Z938Zvnu3FqVszE2", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_1.png", "conversations": [ { "from": "human", "value": "\nHow many quarters that the level of Interpersonal Relationships is lower than the level of Social Networks?" }, { "from": "gpt", "value": "In order to determine the number of quarters that the level of Interpersonal Relationships was lower than Social Networks, I have to compare the heights of each of the bars side by side. For the first quarter, {Interpersonal Relationships bar}[0.176, 0.172, 0.246, 0.488] is higher than {Social Networks bar}[0.106, 0.330, 0.176, 0.488]. Similar comparisons are made for all quarters. Only when {Interpersonal Relationships bar in 4th}[0.699, 0.156, 0.769, 0.488] is lower than {Social Networks bar in 4th}[0.629, 0.275, 0.699, 0.488]. So, there is only one quarter where Interpersonal Relationships are lower than Social Networks.\nBased on the above thought, the answer is:\n1" } ], "id_raw": 346 }, { "id": "RDeSRsBGI0JcSdlM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart you see, can the data information be converted into a CSV format table?" }, { "from": "gpt", "value": "From the subtitle {\"Figure: Fiscal Quarter\"}[0.483, 0.942, 0.580, 0.970] and the {x-axis tick labels}[{\"1\"}[0.183, 0.903, 0.192, 0.931]; {\"2\"}[0.355, 0.903, 0.364, 0.931]; {\"3\"}[0.527, 0.903, 0.536, 0.931]; {\"4\"}[0.699, 0.903, 0.708, 0.931]; {\"5\"}[0.871, 0.903, 0.879, 0.931]], I can see that the x-axis represents different quarters. The y-axis tick labels represent the corresponding values, ranging from {-2.5}[0.035, 0.872, 0.068, 0.900] to {3.5}[0.046, 0.108, 0.068, 0.136]. According to the {legend}[0.353, 0.028, 0.710, 0.078], split color bars in each quarter represent {Risk Assessment}[0.397, 0.039, 0.513, 0.067] and {Risk Management}[0.580, 0.039, 0.705, 0.067]. To generate the CSV table, I need to extract the values for Risk Assessment and Risk Management in each quarter. I can use the heights of the bars and the value annotations at the top for calculation. For example, the {Risk Assessment bar for Quarter 1}[0.119, 0.463, 0.188, 0.565] is at 0.8 as shown by {a value annotation \"0.8\"}[0.142, 0.435, 0.164, 0.463], thus the value of Risk Assessment for the \"Quarter 1\" is 0.8.\nBased on the above thought, the answer is:\nQuarter,Risk Assessment,Risk Management\n1,0.8,2.5\n2,-1.2,1.3\n3,2.4,-0.9\n4,-1.0,3.7\n5,1.6,-2.0" } ], "id_raw": 347 }, { "id": "NTz7UuItMCYMWfcj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which of the two fields, Risk Assessment or Risk Management, experienced more fluctuations? Please explain your reasons." }, { "from": "gpt", "value": "To answer this question, I need to observe the fluctuations of {Risk Assessment}[0.397, 0.039, 0.513, 0.067] and {Risk Management}[0.580, 0.039, 0.705, 0.067] across all quarters. Beginning with the initial quarter, I need to check the bar height of each field in every subsequent quarter and observe any reductions or increases. For Risk Management, we first see a significant increase in {Quarter 1}[0.188, 0.246, 0.257, 0.565], which is followed by a decrease in measures in the {Quarters 2}[0.360, 0.399, 0.428, 0.565] and {3}[0.531, 0.565, 0.600, 0.679]. {Fourth Quarter}[0.703, 0.093, 0.772, 0.565] exhibits a significant surge, then a large decrease in measures in {Quarter 5}[0.875, 0.565, 0.944, 0.820]. Conversely, Risk Assessment initially increased slightly in {Quarter 1}[0.119, 0.463, 0.188, 0.565] then underwent considerable reduction in {Quarter 2}[0.291, 0.565, 0.360, 0.718]. It grew again in {Quarter 3}[0.463, 0.259, 0.531, 0.565], decreased in {Quarter 4}[0.635, 0.565, 0.703, 0.692] and finally rose slightly in {Quarter 5}[0.806, 0.361, 0.875, 0.565]. By reviewing these trends, we can infer that Risk Management experienced more drastic jumps and dips, indicating a higher volatility compared to Risk Assessment.\nBased on the above thought, the answer is:\nRisk Management. This field experienced more drastic jumps and dips across the 5 Quarters, showing a higher level of volatility as compared to Risk Assessment." } ], "id_raw": 347 }, { "id": "A8csG4NdsOJjFVXA", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_2.png", "conversations": [ { "from": "human", "value": "\nIn which Quarter did the Risk Management measure reveal the maximum increase?" }, { "from": "gpt", "value": "To find when the Risk Management measure revealed the maximum increase, I must compare the heights of the Risk Management bars for each Quarter. Starting with the first Risk Management value in {Quarter 1}[0.188, 0.246, 0.257, 0.565], then to {Quarter 2}[0.360, 0.399, 0.428, 0.565], {Quarter 3}[0.531, 0.565, 0.600, 0.679], {Quarter 4}[0.703, 0.093, 0.772, 0.565], and finally, {Quarter 5}[0.875, 0.565, 0.944, 0.820]. By comparing these ranges, we can see that {Risk Management in Quarter 4}[0.703, 0.093, 0.772, 0.565] showed remarkable growth, maxing at 3.7 above the zero mark. Hence, Quarter 4 had the highest upswing in the Risk Management measure.\nBased on the above thought, the answer is:\nQuarter 4" } ], "id_raw": 347 }, { "id": "uYWJQ4i4hBtXrtiX", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, please transform the data into a comma-separated values (CSV) format." }, { "from": "gpt", "value": "From {the y-label}[0.015, 0.323, 0.029, 0.653], it's indicated that the y-axis represents the \"Increase in revenue (%)\". The x-axis represents different fiscal quarters. Value annotations at the top of each bar denote the exact value of increase in revenue percentage for each sector in every quarter. For instance, on Fiscal Quarter 1, {the Entertainment bar}[0.106, 0.521, 0.176, 0.686] has a value annotation of 2.5 and {the Gaming bar}[0.176, 0.686, 0.246, 0.785] has a value annotation of -1.5. This pattern continues for all other fiscal quarters. According to {the legend}[0.389, 0.028, 0.660, 0.078], a greenish bar represents the Entertainment industry while an orangish bar represents the Gaming industry. Following the same procedure for each quarter we can convert the raw data into CSV format.\nBased on the above thought, the answer is:\nFiscal Quarter, Entertainment, Gaming\n1, 2.5, -1.5\n2, 3.8, -0.3\n3, 5.6, -2.0\n4, 6.9, -1.9\n5, 8.0, -2.4" } ], "id_raw": 349 }, { "id": "W94QshlDlvFM7psG", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nFrom the given chart, what could be inferred about the performance of the Entertainment and Gaming sectors over these 5 quarters, particularly the trend and its implications?" }, { "from": "gpt", "value": "It is clear from the bar chart that the Entertainment and Gaming industries are moving in opposite directions. The Entertainment industry, represented by {the greenish bars}[0.106, 0.521, 0.176, 0.686; 0.281, 0.436, 0.351, 0.686; 0.455, 0.317, 0.525, 0.686; 0.629, 0.231, 0.699, 0.686; 0.804, 0.159, 0.873, 0.686], shows a steady rise in revenue quarter-by-quarter, which implies the continued growth and stability of this industry. Meanwhile, the Gaming industry, represented by {the orangish bars}[0.176, 0.686, 0.246, 0.785; 0.351, 0.686, 0.420, 0.706; 0.525, 0.686, 0.595, 0.818; 0.699, 0.686, 0.769, 0.811; 0.873, 0.686, 0.943, 0.844], the sector experiences a decrease in revenue in every quarter, suggesting this industry is facing tough challenges, and unfavorable market conditions. In particular, the revenue decreases appear to be accelerating, signaling a worrying trend.\nBased on the above thought, the answer is:\nThe Entertainment and Gaming industries are trending in completely opposite directions over these five quarters. The Entertainment industry shows a consistent upward trend, meaning it's doing well, indicating continual growth and stability. On the contrary, the Gaming industry trends downward, indicating it faces significant challenges and possibly declining in popularity or profitability. The acceleration of its decline may suggest critical, potentially structural issues within the industry." } ], "id_raw": 349 }, { "id": "o3ENIlI1zmJP8GwC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_4.png", "conversations": [ { "from": "human", "value": "\nIn what fiscal quarter did the Entertainment industry see the greatest increase in revenue?" }, { "from": "gpt", "value": "To answer this question, I have to examine all five quarters, keeping my attention on {the greenish Entertainment bars}[0.106, 0.521, 0.176, 0.686; 0.281, 0.436, 0.351, 0.686; 0.455, 0.317, 0.525, 0.686; 0.629, 0.231, 0.699, 0.686; 0.804, 0.159, 0.873, 0.686]. By checking the value annotations at the top of each bar, it's clear that {the highest increase}[0.828, 0.131, 0.850, 0.159] in revenue for Entertainment industry happened in Fiscal Quarter 5 with the value of 8.0.\nBased on the above thought, the answer is:\nFiscal Quarter 5." } ], "id_raw": 349 }, { "id": "GzZh2FUN4U0zlXpp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nCan you transform the information demonstrated in the bar chart into a CSV table?" }, { "from": "gpt", "value": "The chart is composed of two stages of the value chain ['Manufacturing', 'Marketing'], each represented in two different business scenarios ['Stable Growth', 'Volatile Movement'] spotted in the x-axis ticks with coordinates [{'label': 'Stable Growth', 'label_coordinates': '[0.229, 0.942, 0.328, 0.970]'}, {'label': 'Volatile Movement', 'label_coordinates': '[0.706, 0.942, 0.835, 0.970]'}]. The y-axis shows the value change of these stages under each scenario from -3 to +1 according to the yticks coordinates {'yticks': [{'label': '\u22123', 'label_coordinates': '[0.035, 0.912, 0.055, 0.940]'}, {'label': '\u22122', 'label_coordinates': '[0.035, 0.733, 0.055, 0.761]'},... {'label': '0', 'label_coordinates': '[0.046, 0.376, 0.055, 0.404]'}, {'label': '1', 'label_coordinates': '[0.046, 0.198, 0.055, 0.226]'}]}. \nThe height of each bar and the respective displayed values, for instance, the first manufacturing bar's{'bar_coordinates': '[0.107, 0.244, 0.279, 0.387]'}, will assist in matching accurate values with the different stages of the value chain under the respective scenarios.\nBased on the above thought, the answer is:\nScenario, Manufacturing, Marketing\nStable_Growth, 0.8, 0.6\nVolatile_Movement, 1.5, -2.0" } ], "id_raw": 351 }, { "id": "rCm288J5aVdTsKlA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you observed, what impacts could occur when experiencing Volatile Movement scenario?" }, { "from": "gpt", "value": "The \"Volatile Movement\" scenario, as its name suggests, introduces instability in business trends. Looking at the bars representing this scenario in the chart {'label': 'Volatile Movement', 'label_coordinates': '[0.706, 0.942, 0.835, 0.970]'}, it appears to demonstrate significant divergence in the two sectors. Manufacturing with bar at coordinates {'bar_coordinates': '[0.599, 0.119, 0.771, 0.387]'} reveals an increase by 1.5%, hinting at a successful adaption or even capitalization in such a volatile market. In contrast, the Marketing sector, represented by the bar with coordinates {'bar_coordinates': '[0.771, 0.387, 0.943, 0.744]'}, shows a decline of 2%. This may indicate inadequate preparedness or inability to adjust rapidly, resulting in a loss during volatile periods. Therefore, the Volatile Movement scenario could lead to diverse impacts across different sectors, making it essential for companies to strategize accordingly for different sectors in such unstable periods.\nBased on the above thought, the answer is:\nThe potential impacts could be diverse for different sectors. For instance, sectors like Manufacturing may experience growth due to successful adaptation or capitalization during volatility. In contrast, Marketing might see a decline, which could be because of lack of readiness to adjust to sudden market changes, leading to losses." } ], "id_raw": 351 }, { "id": "PzElBCaocpm7PwCe", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_6.png", "conversations": [ { "from": "human", "value": "\nWhich stage of the value chain demonstrated the highest percentage change, considering Volatile Movement scenario?" }, { "from": "gpt", "value": "To determine this, I will compare the height of the second pair of bars which stands for the Volatile Movement scenario as denoted by {'label': 'Volatile Movement', 'label_coordinates': '[0.706, 0.942, 0.835, 0.970]'}. The corresponding bars are as follow: manufacturing bar with coordinates of {'bar_coordinates': '[0.599, 0.119, 0.771, 0.387]'} and marketing bar with coordinates of {'bar_coordinates': '[0.771, 0.387, 0.943, 0.744]'}. It's clear that the manufacturing bar height is up to around y = 1.5, and the marketing bar height drops to around y= -2. So the Marketing stage, even though experienced a drop, has the most significant absolute percentage change.\nBased on the above thought, the answer is:\nMarketing." } ], "id_raw": 351 }, { "id": "hNl16NK6rEBBOJnB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nLet's convert the barchart data showing investment strategy changes over five quarters into a CSV format. How would you proceed?" }, { "from": "gpt", "value": "The chart title {\"Investment Change (%) vs. Fiscal Quarter\"}[0.481, 0.942, 0.577, 0.970] implies that the data relates to changes in investment strategies over five fiscal quarters. The x-axis represents the five quarters {xticks}[{\"1\"}[0.180, 0.903, 0.188, 0.931]; {\"2\"}[0.352, 0.903, 0.361, 0.931]; {\"3\"}[0.525, 0.903, 0.534, 0.931]; {\"4\"}[0.698, 0.903, 0.706, 0.931]; {\"5\"}[0.870, 0.903, 0.879, 0.931]]. The y-axis denotes the percentage change {ylabel}[0.015, 0.324, 0.029, 0.653] and can be referenced from the given ticks. We have two groups of bars: blue ones denoting {\"High-Risk High-Return\"}[0.393, 0.039, 0.546, 0.067] strategy and orange ones denoting {\"Conservative\"}[0.613, 0.039, 0.704, 0.067] strategy. I can calculate percentage changes using the bars' heights denoted by the y-axis ticks. Thus, CSV data can be generated.\nBased on the above thought, the answer is:\nQuarter,High-Risk High-Return,Conservative\n1,-1,2\n2,5,3\n3,3,3\n4,-3,2\n5,7,2" } ], "id_raw": 352 }, { "id": "sKNwvBQtm7ts9Ype", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nLooking at the trends, what can you tell about the two investment strategies?" }, { "from": "gpt", "value": "The 'High-Risk High-Return' strategy, represented by the {blue bars}[0.393, 0.039, 0.546, 0.067], indicates higher volatility, as the investment results bounce between positive and negative returns, reflecting higher risk. However, it shows a significant return in the fifth quarter, indicating the potential for high returns despite the risk. The 'Conservative' strategy, represented by the {orange bars}[0.613, 0.039, 0.704, 0.067], meanwhile, remains consistently positive across all five quarters but does not reach the high returns of the high risk strategy in quarter 5.\nBased on the above thought, the answer is:\nThe 'High-Risk High-Return' strategy showed higher volatility, with investment outcomes fluctuating between negative and positive. Despite this risk, it demonstrated potential for high returns. Conversely, the 'Conservative' strategy appeared stable and consistently positive across all five quarters, but it didn't reach as high a return as the high risk strategy in the last quarter. Hence, we observe the usual risk-return trade-off. An investor should decide between the two based on their risk tolerance." } ], "id_raw": 352 }, { "id": "PVkNw5SrTJW7LuJt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_7.png", "conversations": [ { "from": "human", "value": "\nWhich quarter saw the highest percentage change in the high-risk high-return strategy?" }, { "from": "gpt", "value": "For finding the quarter with maximum percentage change, I need to look at the blue bars representing the \"High-Risk High-Return\" strategy and find the one with highest magnitude. The highest bar appears to be in the 5th quarter [0.805, 0.212, 0.875, 0.488], corresponding to a change of 7% [0.829, 0.184, 0.851, 0.212], as indicated by the value annotation at the top of the bar.\nBased on the above thought, the answer is:\nQuarter 5" } ], "id_raw": 352 }, { "id": "W1R95MLccTIBHQlo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nCan you help me transform the data from the bar chart into a CSV format table?" }, { "from": "gpt", "value": "From the chart's {xlabel}[0.477, 0.942, 0.573, 0.970], we can see that the x-axis represents five fiscal quarters. Also, the y-axis is the respective scores of two measures, {Employee Productivity}[0.343, 0.043, 0.499, 0.071] and {Employee Job Satisfaction}[0.565, 0.043, 0.746, 0.071], as indicated in the legend. To convert this into a CSV table, I'll need to gather the scores of both parameters for each quarter. I can evaluate these scores based on the bar height relative to the y-axis scale: quarter 1 productivity is {5.1}[0.107, 0.089, 0.176, 0.490] as per the green bar and satisfaction is {4.6}[0.176, 0.128, 0.246, 0.490] from the orange bar, and so on.\nBased on the above thought, the answer is:\nFiscal Quarter,Employee Productivity,Employee Job Satisfaction\nQ1,5.1,4.6\nQ2,5.3,4.3\nQ3,4.8,3.9\nQ4,5.5,4.1\nQ5,5.2,3.8" } ], "id_raw": 353 }, { "id": "EqjuhsV1kuyuBhUm", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nBased on the data in the chart, can you figure out where the business is lacking and explain your reasoning?" }, { "from": "gpt", "value": "This question requires an examination of both Employee Productivity and Employee Job Satisfaction metrics across all fiscal quarters. If we look closely at Employee Job Satisfaction {orange bars}, we\u2019ll see a continuous decrease over five quarters. Considering the Q5 bar of Employee Job Satisfaction [0.873, 0.191, 0.943, 0.490] in relation to its corresponding Q1 bar [0.176, 0.128, 0.246, 0.490], it's evident that employee morale has been steadily declining in the company. While Employee Productivity metrics {green bars} exhibit a fluctuating tendency, they outperform Employee Job Satisfaction across quarters. This suggests the team might be overworked but feel underappreciated or lacks drive, reflecting negatively in the Job Satisfaction metric.\nBased on the above thought, the answer is:\nThe business is lacking in employee satisfaction. This is based on the continued decrease in the Employee Job Satisfaction across five quarters, despite fluctuating but relatively higher Employee Productivity scores, indicating potential issues with employee morale and motivation." } ], "id_raw": 353 }, { "id": "owBXJeTYnCkxKEPs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_8.png", "conversations": [ { "from": "human", "value": "\nIn which fiscal quarter is the gap between Employee Productivity and Employee Job Satisfaction the largest?" }, { "from": "gpt", "value": "To find the quarter with the largest gap between Employee Productivity and Employee Job Satisfaction, I must compare the differences between the bar heights representing Employee Productivity and Employee Job Satisfaction under each quarter. By visually scanning the chart from left to right, I can observe the following: For Q1, {the difference between the bars}[0.107, 0.089, 0.176, 0.490],[0.176, 0.128, 0.246, 0.490] is 0.5; for Q2, {the difference between the bars}[0.281, 0.073, 0.351, 0.490],[0.351, 0.152, 0.420, 0.490] is 1.0; and so on. This way, by measuring the differences for other quarters, I found the largest difference in Q3, where {the two bars}[{Productivity}[0.455, 0.112, 0.525, 0.490]; {Satisfaction}[0.525, 0.183, 0.595, 0.490]] have a difference of 0.9, which is the largest among all quarters.\nBased on the above thought, the answer is:\nQ3" } ], "id_raw": 353 }, { "id": "5qTm2sAnqGwgXafg", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nBased on my observation of the bar chart, could you please help me to convert the data into a CSV format table?" }, { "from": "gpt", "value": "Sure, from the chart, I can see that the x-axis represents different epochs in human history, which from left to right are {Stone Age}[0.126, 0.903, 0.197, 0.931], {Bronze Age}[0.267, 0.903, 0.346, 0.931], {Iron Age}[0.422, 0.903, 0.480, 0.931], {Middle Ages}[0.554, 0.903, 0.638, 0.931], {Industrial Age}[0.692, 0.903, 0.789, 0.931], and {Digital Age}[0.847, 0.903, 0.923, 0.931]. For each epoch, there are two types of events represented by two bars of different colors: {Innovations}[0.439, 0.039, 0.520, 0.067], represented by the green bars, and {Conflicts}[0.587, 0.039, 0.647, 0.067], represented by the orange bars. The y-axis represents the numbers of events, ranging from 0 to 20. For example, during the Stone Age, the number of innovations, which is the height of the {green bar}[0.104, 0.725, 0.162, 0.883], is 5, and the number of conflicts, which is the height of the {orange bar}[0.162, 0.852, 0.220, 0.883], is 1. So, to generate the CSV table, I repeat this process for all epochs.\nBased on the above thought, the answer is:\nEpoch,Innovations,Conflicts\nStone Age,5,1\nBronze Age,7,2\nIron Age,9,3\nMiddle Ages,10,5\nIndustrial Age,15,7\nDigital Age,20,10" } ], "id_raw": 354 }, { "id": "zAr7tmHT9LGa4uno", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you describe the trend of innovations and conflicts across different epochs?" }, { "from": "gpt", "value": "Yes, according to the {green bars for innovations}[[0.104, 0.725, 0.162, 0.883];[0.249, 0.662, 0.306, 0.883];[0.393, 0.599, 0.451, 0.883];[0.538, 0.567, 0.596, 0.883];[0.683, 0.409, 0.740, 0.883];[0.827, 0.251, 0.885, 0.883]], there are clear upward trends with each passing epoch from Stone Age to Digital Age, indicating an increase in the number of innovations over time. In parallel, the {orange bars for conflicts}[[0.162, 0.852, 0.220, 0.883];[0.306, 0.820, 0.364, 0.883];[0.451, 0.789, 0.509, 0.883];[0.596, 0.725, 0.654, 0.883];[0.740, 0.662, 0.798, 0.883];[0.885, 0.567, 0.943, 0.883]] also show a similar increasing trend with each epoch, this indicates that the number of conflicts also increased over time. Therefore, the number of both innovations and conflicts has increased over different epochs, indicating that while human civilization was advancing, the number of conflicts was also on the rise.\nBased on the above thought, the answer is:\nThere is an increasing trend for both innovations and conflicts with each passing epoch. The chart indicates that as human society progressed and the number of innovations increased, the number of conflicts also went up. Essentially, while there was an advancement in innovations, conflicts also rose over the epochs." } ], "id_raw": 354 }, { "id": "ZlBGq69nuNSeHuHB", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_9.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, in which epoch did the number of conflicts exceed the number of innovations?" }, { "from": "gpt", "value": "To find the epoch where the number of conflicts is greater than the number of innovations, I need to compare the height of the green bar and the orange bar within each epoch. By comparing the bars of the two events from left to right, I can conclude that none of the {orange bars for conflicts}[[0.162, 0.852, 0.220, 0.883];[0.306, 0.820, 0.364, 0.883];[0.451, 0.789, 0.509, 0.883];[0.596, 0.725, 0.654, 0.883];[0.740, 0.662, 0.798, 0.883];[0.885, 0.567, 0.943, 0.883]] is taller than the corresponding {green bar for innovations}[[0.104, 0.725, 0.162, 0.883];[0.249, 0.662, 0.306, 0.883];[0.393, 0.599, 0.451, 0.883];[0.538, 0.567, 0.596, 0.883];[0.683, 0.409, 0.740, 0.883];[0.827, 0.251, 0.885, 0.883]]. Therefore, there is no epoch in which the number of conflicts exceeded the number of innovations.\nBased on the above thought, the answer is:\nThere isn't any." } ], "id_raw": 354 }, { "id": "cb96I8NCb6ZkZh0W", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the data given in the bar chart into a csv file?" }, { "from": "gpt", "value": "To convert the bar chart data into a CSV format, we first need to extract the X-axis data (Years) and the Y-axis data (GDP growth rate) for the two countries. {The x-axis}[{\"1985\"}[0.105, 0.903, 0.140, 0.931]; {\"1990\"}[0.306, 0.903, 0.341, 0.931]; {\"1995\"}[0.507, 0.903, 0.543, 0.931]; {\"2000\"}[0.708, 0.903, 0.743, 0.931]; {\"2005\"}[0.910, 0.903, 0.945, 0.931]] contains labels for the years from 1985 to 2005. Then, we have two groups of bars, one group for each country. Looking at {the legend}[0.430, 0.028, 0.620, 0.078], {the green bars} represent GDP growth rate for USA and {the orange bars} represent Russia. For example, for the year 1985, {the green bar for USA}[0.107, 0.540, 0.123, 0.676] corresponds to a GDP growth rate of 0.8, and {the orange bar for Russia}[0.123, 0.676, 0.139, 0.848] corresponds to a GDP growth rate of -1.0. Repeating this process for each year will allow us to generate the final CSV table.\nBased on the above thought, the answer is:\nYear,USA,Russia\n1985,0.8,-1.0\n1990,1.5,-0.8\n1995,2.2,-0.6\n2000,2.5,-0.4\n2005,3.2,-0.2" } ], "id_raw": 355 }, { "id": "kj6WiBuqhjz4QnkV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nWhich country showed consistent economic growth from 1985 to 2005 based on the chart?" }, { "from": "gpt", "value": "To answer this question, I will compare the height of the bars for each country from 1985 to 2005. Starting from 1985, the {green bar corresponding to the USA's GDP growth rate}[0.107, 0.540, 0.123, 0.676] is positive and seems to increases consistently over the years, showing an upward trend. In contrast, the {orange bar corresponding to Russia's GDP growth rate}[0.123, 0.676, 0.139, 0.848] starts from a negative growth rate but improves over time, showing a recovering trend rather than a consistent growth. Therefore, based on the chart, the USA showed consistent economic growth from 1985 to 2005.\nBased on the above thought, the answer is:\nUSA" } ], "id_raw": 355 }, { "id": "gtU2Qo4CmUSWWUoE", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat was the GDP growth rate of USA in 2005 according to the chart?" }, { "from": "gpt", "value": "Looking at the year 2005 on the x-axis with coordinates [0.910, 0.903, 0.945, 0.931], there is an associated {green bar}[0.911, 0.129, 0.927, 0.676] that corresponds to USA's GDP growth rate, which has a text annotation indicating a value of 3.2. This suggests that in 2005, the GDP growth rate of the USA was 3.2%.\nBased on the above thought, the answer is:\n3.2%" } ], "id_raw": 355 }, { "id": "DI6j0kFvz2Y0U4yc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from each bar and present it in a CSV format?" }, { "from": "gpt", "value": "From the bar chart, there are two groups of bars presented: {Greenhouse Gas Emissions}[0.285, 0.545, 0.355, 0.883] and {Ozone Depletion Potential}[0.355, 0.861, 0.424, 0.883], as noted by {the legend}[0.285, 0.028, 0.770, 0.078].\nEach fiscal quarter noted on the {x-axis}[0.480, 0.942, 0.576, 0.970] has a pair of bars, one for each group. For example, in quarter 1, the {Greenhouse Gas Emissions bar}[0.112, 0.658, 0.181, 0.883] marks approximately 100 units and the {Ozone Depletion Potential bar}[0.181, 0.838, 0.251, 0.883] marks approximately 20 units. Following this procedure, I can extract the value for each bar according to their heights, and place them in a structured table.\nBased on the above thought, the answer is:\nQuarter,Greenhouse Gas Emissions,Ozone Depletion Potential\n1,100,20\n2,150,10\n3,200,30\n4,180,15\n5,300,25" } ], "id_raw": 356 }, { "id": "tMJDNg7BCb7vmMxL", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nCan you analyze and interpret the trends of Greenhouse Gas Emissions and Ozone Depletion Potential based on the chart?" }, { "from": "gpt", "value": "The chart represents trends in two environmental indicators over five quarters. The green bars for the {Greenhouse Gas Emissions}[0.112, 0.658, 0.181, 0.883]; [0.285, 0.545, 0.355, 0.883]; [0.459, 0.432, 0.528, 0.883]; [0.632, 0.477, 0.701, 0.883]; [0.805, 0.206, 0.874, 0.883] demonstrate an overall increasing trend, starting from 100 units in the first quarter and escalating to 300 units in the fifth quarter, despite a slight dip in the fourth quarter. This could indicate an increasing contribution to greenhouse gases over these periods.\nThe blue bars for the {Ozone Depletion Potential}[0.181, 0.838, 0.251, 0.883]; [0.355, 0.861, 0.424, 0.883]; [0.528, 0.816, 0.597, 0.883]; [0.701, 0.850, 0.770, 0.883]; [0.874, 0.827, 0.943, 0.883] show more fluctuation across the quarters, peaking at 30 units in the third quarter. This might suggest that ozone depletion varied more erratically in response to certain factors, but the cause behind these changes would require further investigation.\nBased on the above thought, the answer is:\nThe Greenhouse Gas Emissions saw a generally increasing trend over the five quarters, suggesting a growing contribution to greenhouse gases. In contrast, the Ozone Depletion Potential showed more significant fluctuations, peaking in the third quarter, which indicates that factors affecting ozone depletion might have varied more drastically during these periods." } ], "id_raw": 356 }, { "id": "VmRSFKL2E6fbq9sz", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_11.png", "conversations": [ { "from": "human", "value": "\nIn which quarter did the Greenhouse Gas Emissions reach the maximum?" }, { "from": "gpt", "value": "To find the quarter with the maximum Greenhouse Gas Emissions, I need to identify the tallest bar among the group of {Greenhouse Gas Emissions bars}[0.112, 0.658, 0.181, 0.883]; [0.285, 0.545, 0.355, 0.883]; [0.459, 0.432, 0.528, 0.883]; [0.632, 0.477, 0.701, 0.883]; [0.805, 0.206, 0.874, 0.883]. The bar with the greatest height is in the fifth quarter[0.805, 0.206, 0.874, 0.883], which indicates that the Greenhouse Gas Emissions reached their peak during this period.\nBased on the above thought, the answer is:\nQuarter 5." } ], "id_raw": 356 }, { "id": "cL6pPigjOI6q4VYr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you're seeing, can you derive a table representation of the chart data in a CSV format?" }, { "from": "gpt", "value": "I can see two different types of bars in the chart, indicating the data series for 'Number of People in the Household' represented by {the green bars}[0.104, 0.808, 0.290, 0.883];[0.570, 0.801, 0.757, 0.883] and 'Percentage of Single-parent Households' represented by {the orange bars}[0.290, 0.156, 0.477, 0.883];[0.757, 0.125, 0.943, 0.883]. The height of each bar corresponds to its value, and from {the annotations above the bars}[0.186, 0.780, 0.208, 0.808];[0.652, 0.773, 0.674, 0.801];[0.368, 0.128, 0.399, 0.156];[0.834, 0.097, 0.865, 0.125], I get the exact value for each bar. The years are labeled along the x-axis, so I can derive the CSV format data from these details.\nBased on the above thought, the answer is:\nYear, Number of People in Household, Percentage of Single-parent Households\n1, 2.4, 23%\n2, 2.6, 24%" } ], "id_raw": 357 }, { "id": "Pu8G5XDnRBWK53Rt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nFrom the chart can you derive any trends or notable changes in the family structure over the years?" }, { "from": "gpt", "value": "From the chart visuals we can observe two aspects. Firstly, the average 'Number of People in Household' stands at [0.186, 0.780, 0.208, 0.808] as 2.4 in Year 1 and increases to 2.6 at [0.652, 0.773, 0.674, 0.801] in Year 2 indicating a slight increase in the number of people per household. Secondly, the 'Percentage of Single-parent Households' starting at a value of 23% in Year 1 [0.368, 0.128, 0.399, 0.156] increases to 24% in Year 2 [0.834, 0.097, 0.865, 0.125]. The chart shows an overall increase in the family size as well as an increase in the single-parent households over the years. This suggests a shift in family dynamics with a potential increase in extended families and single parent families.\nBased on the above thought, the answer is:\nYes, there is a slight growth in the number of people per households over the years. There is also an increase in the percentage of single-parent households over the same period, suggesting changing family dynamics with potential growth in extended families and single-parent families." } ], "id_raw": 357 }, { "id": "qkkbhn2JwMl6bMdC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_12.png", "conversations": [ { "from": "human", "value": "\nIn the given chart, which variable increased more from Year 1 to Year 2?" }, { "from": "gpt", "value": "The green bar in Year 1 indicates the 'Number of People in Household' and its height at [0.104, 0.808, 0.290, 0.883] shows the value of 2.4. In Year 2, the value of 'Number of People in Household' increases to 2.6 at [0.570, 0.801, 0.757, 0.883]. The increment is 0.2. The orange bar representing 'Percentage of Single-parent Households' is at [0.290, 0.156, 0.477, 0.883] in Year 1, indicating a value of 23%. In Year 2, the value increases to 24% at [0.757, 0.125, 0.943, 0.883]. The increment is 1%. Comparing these results, 'Percentage of Single-parent Households' increased more.\nBased on the above thought, the answer is:\nPercentage of Single-parent Households" } ], "id_raw": 357 }, { "id": "1OPN3CKjRpqrlibT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart before you, could you compile the data into a CSV format table?" }, { "from": "gpt", "value": "The bar chart appears to have been titled {\"\"}[0.523, 0.077, 0.523, 0.077], but there is no visible title. However, I can identify the X-axis through the {axis label \"Year\"}[0.509, 0.952, 0.538, 0.975] and its {tick labels}[{\"2018\"}[0.259, 0.919, 0.294, 0.942]; {\"2019\"}[0.753, 0.919, 0.788, 0.942]]. Similarly, the Y-axis displays numerical values pertaining to market share, as indicated by the {label \"Market Share (%)\"}[0.015, 0.396, 0.029, 0.597]. The bar chart contains two bars for each year, which represents the market share of two categories: Music and Books. This is confirmed by a {legend}[0.424, 0.021, 0.622, 0.063] at the bottom of the chart, with two entries: {Music}[0.469, 0.030, 0.508, 0.053] and {Books}[0.575, 0.030, 0.617, 0.053]. By inspecting the height of the bars and their vertical position with respect to the Y-axis, I can gather that 'Music' had a market share of 32.11% in 2018 and 34.85% in 2019 while 'Books' had a market share of 44.23% in 2018 and 46.56% in 2019.\nBased on the above thought, the answer is:\nYear,Music,Books\n2018,32.11,44.23\n2019,34.85,46.56" } ], "id_raw": 358 }, { "id": "HXqvCTvVBLVOFYFN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can we say that the Music industry is doing better than the Books industry? Why or why not?" }, { "from": "gpt", "value": "As the chart indicates, both the 'Music' industry {represented by green bars}[0.104, 0.381, 0.277, 0.903; 0.597, 0.337, 0.770, 0.903] and the 'Books' industry {represented by orange bars}[0.277, 0.184, 0.449, 0.903; 0.770, 0.146, 0.943, 0.903] have experienced growth from 2018 to 2019. Since the question doesn't specify what metric defines an industry as \"doing better\", we can make a comparison based on two aspects: the absolute market share and the growth rate. When we look at the absolute market share, the 'Books' industry consistently had a higher market share for both years. But if we look at the growth rate, the 'Music' industry had seen a slightly higher increase in its market share. In conclusion, if we are focusing on the market share alone, 'Books' industry is doing better than the 'Music' industry. However, if we are talking about the growth rate, the 'Music' industry did better with slightly a higher increase.\nBased on the above thought, the answer is:\nIt depends on the metric. If we're comparing the absolute market share, then the Books industry is doing better than the Music industry. If we're looking at the growth rate, then the Music industry is performing slightly better." } ], "id_raw": 358 }, { "id": "a7VnciKRtDZPVlml", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_13.png", "conversations": [ { "from": "human", "value": "\nAcross the two years, in which industry did the market share increase the most?" }, { "from": "gpt", "value": "I will compute the difference in the market share for each industry by comparing the heights of the bars corresponding to 2018 and 2019. For the 'Music' industry, the {bar in 2018}[0.104, 0.381, 0.277, 0.903] is lower than the {corresponding bar in 2019}[0.597, 0.337, 0.770, 0.903], indicating an increase in market share. The height difference corresponds to an increase of approximately 2.74%. Similarly, the 'Books' industry's {market share in 2018}[0.277, 0.184, 0.449, 0.903] is lower than its {market share in 2019}[0.770, 0.146, 0.943, 0.903]. The height difference indicates an increase of approximately 2.33%. From this, we can see that the market share increase for the 'Music' industry is slightly higher than that of the 'Books' industry.\nBased on the above thought, the answer is:\nMusic" } ], "id_raw": 358 }, { "id": "3USIXxXRCSF6ygJQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nCan you turn the data presented in the bar chart into a structured table?" }, { "from": "gpt", "value": "Based on {the x-axis}[0.522, 0.942, 0.551, 0.970] labelled 'Year' and {the y-axis}[0.015, 0.447, 0.029, 0.530] labelled 'Count', I can identify the data dimensions. The chart's legend at the bottom with {[0.324, 0.028, 0.749, 0.078]} indicates that this chart contains data of 'New Educational Institutes' and 'Students Enrolled'. To structure the data into a table, I need to identify the height of each bar and match it to the corresponding tick label on the y-axis. By combining this information with the year located at the base of each bar, the table can be constructed.\nBased on the above thought, the answer is:\nYear,New_Educational_Institutes,Students_Enrolled\n2022,10,20000\n2023,15,30000\n2024,11,27000\n2025,10,25000\n2026,30,60000" } ], "id_raw": 359 }, { "id": "NYUIRhhodfRX8ECa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the correlation between the number of new educational institutes and students enrolled?" }, { "from": "gpt", "value": "The given bar chart provides insights into the correlation between the number of new educational institutes and students enrolled. The bars representing the 'New Educational Institutes', found at coordinates {[0.129, 0.883, 0.197, 0.883]; [0.299, 0.883, 0.367, 0.883]; [0.469, 0.883, 0.537, 0.883]; [0.638, 0.883, 0.706, 0.883]; [0.808, 0.883, 0.876, 0.883]}, and the 'Students Enrolled', which are at {[0.197, 0.658, 0.265, 0.883]; [0.367, 0.545, 0.435, 0.883]; [0.537, 0.579, 0.605, 0.883]; [0.706, 0.601, 0.774, 0.883]; [0.876, 0.206, 0.944, 0.883]} respectively, express this correlation. As the height of the bars for 'New Educational Institutes' rises, so does the height of the bars for 'Students Enrolled'. That is, the two seem to share a positive correlation- as the number of new institutes increases, so does the number of students enrolling in them, especially seen in the year 2026 marked with coordinates {[0.808, 0.883, 0.876, 0.883]; [0.876, 0.206, 0.944, 0.883]}.\nBased on the above thought, the answer is:\nThere appears to be a positive correlation between the establishment of new educational institutes and the number of students enrolled. As the number of new institutes increases, there also seems to be an increase in the number of students attracted to these institutions." } ], "id_raw": 359 }, { "id": "43bynCT4rDKLovwd", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_24_14.png", "conversations": [ { "from": "human", "value": "\nIn which year did we see the most significant increase in newly established educational institutes?" }, { "from": "gpt", "value": "To identify the year with the most significant increase in new educational establishments, I need to compare the heights of the green bars representing this data {green bars at}[0.129, 0.883, 0.197, 0.883]; [0.299, 0.883, 0.367, 0.883]; [0.469, 0.883, 0.537, 0.883]; [0.638, 0.883, 0.706, 0.883] and {at}[0.808, 0.883, 0.876, 0.883] in consecutive years. The bar for the year 2026 [0.808, 0.883, 0.876, 0.883] shows the most significant increase compared to the bars for the other years.\nBased on the above thought, the answer is:\n2026." } ], "id_raw": 359 }, { "id": "KggA3tI7YjPqWumQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table capturing the project names and their corresponding scores from the bar chart?" }, { "from": "gpt", "value": "From {the chart title}[0.462, 0.019, 0.736, 0.041], I can tell that my task is to extract the innovation projects and their corresponding evaluation scores from this bar chart. The innovation projects are annotated on the y-axis, and their respective evaluation scores can be found by inspecting the lengths of the bars and text annotations at the end of the bars. For instance, {the bar for 'Website Redesign'} [0.211, 0.806, 0.859, 0.870] has a corresponding text annotation {\"85\"}[0.867, 0.829, 0.881, 0.847], indicating that this project has an evaluation score of 85. Hence, by extracting the information in this same manner for all the bars, I can generate the table.\nBased on the above thought, the answer is:\nInnovation Project,Evaluation Score\nWebsite Redesign,85\nCloud Migration,80\nCustomer Relationship Management System,92\nOnline Marketing Campaign,75\nA.I. Customer Service Chatbots,97\nCompany Mobile App,88\nE-commerce Platform,83\nData Analytics System,90\nCybersecurity Improvement,86\nVirtual Reality Product Showroom,94" } ], "id_raw": 361 }, { "id": "Ik4dZVGgHPEOufk8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nCan you suggest a recommendation on which innovation project should be given priority based on their evaluation scores?" }, { "from": "gpt", "value": "This recommendation can be made by comparing the evaluation scores of the innovation projects, represented as the lengths of the bars. The innovation project with the highest evaluation score should be prioritized. In this case, {the bar for \"A.I. Customer Service Chatbots\"}[0.211, 0.486, 0.951, 0.550] is longest, indicating the highest evaluation score. This is confirmed by the score {\"97\"}[0.958, 0.509, 0.973, 0.527] annotated at the end of the bar. Therefore, I would recommend prioritizing the \"A.I. Customer Service Chatbots\" project.\nBased on the above thought, the answer is:\n\"A.I. Customer Service Chatbots\" should be given priority because it has the highest evaluation score." } ], "id_raw": 361 }, { "id": "l3myYNJbo05fptXl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_1.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, what is the lowest project evaluation score?" }, { "from": "gpt", "value": "To find the lowest project evaluation score, I can compare the lengths of the purple bars on the x-axis. It appears that {the bar for \"Online Marketing Campaign\"}[0.211, 0.566, 0.783, 0.630] is shorter than all others, indicating it has the lowest score. The {text annotation at the end of this bar}[0.790, 0.589, 0.805, 0.607] confirms this, as it displays the score: \"75\".\nBased on the above thought, the answer is:\n75." } ], "id_raw": 361 }, { "id": "9LrSPKuifTLF5tSK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into a CSV structured table?" }, { "from": "gpt", "value": "To transform the information on the chart to CSV format, the key elements we need are the names of the business practices from the y-axis and their respective quantities from the x-axis. I can see from the values located at {yticks['yticks'] label_coordinates} that we have 10 different business practices ranging from CSR Activities and down to Animal Welfare. Similarly, each business practice or activity has a specific value associated with it, illustrated by the widths of the bars and accompanying labels at {bars['bars_of_group_0']['rectangles'] bar and text coordinates}. Therefore, by mapping these categories to their quantities, we can tabulate the data as a CSV.\nBased on the above thought, the answer is:\nBusiness Pratice, Instances\\n\nCSR Activities,8000\\n\nSustainable Packaging,7400\\n\nCommunity Development,6900\\n\nEmission Reduction,6500\\n\nRenewable Energy,6100\\n\nWaste Reduction,5800\\n\nWater Conservation,5300\\n\nLabor Practices,5000\\n\nEthical Sourcing,4600\\n\nAnimal Welfare,4200\\n" } ], "id_raw": 362 }, { "id": "NxRt09GHExfKNn6q", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat can we infer about the priority of the business practices based on their respective instances in this report of 2021?" }, { "from": "gpt", "value": "Evaluating the chart, it is noticeable that the number of instances of a practice corresponds with its priority in businesses. CSR Activities represented by {CSR Activities}[0.061, 0.848, 0.126, 0.945] had the most instances, which suggests it was given the highest priority. Conversely, Animal Welfare represented by {Animal Welfare}[0.055, 0.112, 0.126, 0.218] had the least instances, showing it might have been the least prioritized. In the middle-ground, practices such as Sustainable Packaging, Community Development, and beyond showed a staggered decrease in instances, showcasing a sliding scale of priorities.\nBased on the above thought, the answer is:\nThe practices with the highest instances, such as CSR Activities, are likely given the most priority. In contrast, those with the least instances, such as Animal Welfare, are possibly given less priority. Practices with instances in between display a spectrum of prioritization." } ], "id_raw": 362 }, { "id": "HIZywefNVjJ5Wmub", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_2.png", "conversations": [ { "from": "human", "value": "\nWhich business practice had the least instances performed in 2021, and how many instances were there?" }, { "from": "gpt", "value": "To find the business practice with the least instances in 2021, I can start reading the bar chart from {CSR Activities}[0.061, 0.848, 0.126, 0.945], which has the most instances, and move downwards. Looking at the lengths of the bars and the numeric labels next to them, as specified by the {bars_coordinates_and_text}, I can see that the bar with the smallest length represents the business practice with the least performed instances. This bar is found at {Animal Welfare}[0.055, 0.112, 0.126, 0.218] which also has its number of instances listed as 4200.\nBased on the above thought, the answer is:\nAnimal Welfare, 4200." } ], "id_raw": 362 }, { "id": "SXesgGN5Hgzkl6vf", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nCould you translate the visual data represented in the bar chart into a CSV formatted table?" }, { "from": "gpt", "value": "By closely reading it, I can see that this bar chart presents the number of research papers across various areas of Artificial Intelligence. The areas are shown along the y-axis, and they include: {Deep Learning}[0.062, 0.862, 0.120, 0.943], {Reinforcement Learning}[0.029, 0.778, 0.120, 0.905], {Automated Reasoning}[0.036, 0.694, 0.120, 0.812], {Computer Vision}[0.055, 0.610, 0.120, 0.701], {Natural Language Processing}[0.011, 0.526, 0.120, 0.679], {Knowledge Representation}[0.019, 0.442, 0.120, 0.584], {Planning and Navigation}[0.027, 0.358, 0.120, 0.488], {Multi-agent Systems}[0.041, 0.274, 0.120, 0.385], {Swarm Intelligence}[0.046, 0.190, 0.120, 0.294], {Evolutionary Computation}[0.021, 0.106, 0.120, 0.245]. The x-axis indicates the number of research papers, setting the range from {0}[0.124, 0.951, 0.130, 0.965] to {4500}[1.050, 0.951, 1.075, 0.965]. The bar associated with each area represents the number of research papers for that area. Using the positions of these bars and the values annotated on them, I can effectively extract and compile the data into a CSV format.\nBased on the above thought, the answer is:\nAI Area, Number of Research Papers\nDeep Learning,3950\nReinforcement Learning,2790\nAutomated Reasoning,765\nComputer Vision,3100\nNatural Language Processing,3700\nKnowledge Representation,675\nPlanning and Navigation,1815\nMulti-agent Systems,1325\nSwarm Intelligence,825\nEvolutionary Computation,930" } ], "id_raw": 363 }, { "id": "REOeDWyymSkRyxTd", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, where should we focus more on to achieve significant progress in Artificial Intelligence in terms of research work?" }, { "from": "gpt", "value": "Reflecting on the bar chart, it's clear that there has been significant research done in the areas of {Deep Learning}[0.127, 0.833, 0.948, 0.901] and {Natural Language Processing}[0.127, 0.498, 0.896, 0.565]. However, there are some areas such as {Automated Reasoning}[0.127, 0.666, 0.286, 0.733], {Knowledge Representation}[0.127, 0.414, 0.268, 0.481], and {Swarm Intelligence}[0.127, 0.162, 0.299, 0.229], where comparatively less research has been conducted. In the interest of pushing our boundaries of understanding and to maintain balanced progress across all avenues of artificial intelligence, increased focus should be put on these under-explored areas. A more evenly distributed volume of research across all fields can lead to more comprehensive and stable development of artificial intelligence as a whole.\nBased on the above thought, the answer is:\nAreas such as 'Automated Reasoning', 'Knowledge Representation', and 'Swarm Intelligence' should get more focus to achieve balanced and significant progress in Artificial Intelligence." } ], "id_raw": 363 }, { "id": "jSdzSeKWeQ85ymY3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_3.png", "conversations": [ { "from": "human", "value": "\nIn which area of Artificial Intelligence is the number of research papers second highest?" }, { "from": "gpt", "value": "In order to determine this, I have to examine the lengths of all the bars in the figure. The x-axis of the bar chart indicates the number of research papers, with each bar representing a different AI area. From looking at the bars, it's clear that {Deep Learning}[0.127, 0.833, 0.948, 0.901] area has the highest number of research papers. If I want to identify the second highest, I have to compare the other areas. For instance, {Computer Vision}[0.127, 0.582, 0.772, 0.649] area has a high number of research papers, but when compared to the {Natural Language Processing}[0.127, 0.498, 0.896, 0.565] area, it's clear that Natural Language Processing has the second highest number of research papers.\nBased on the above thought, the answer is:\nNatural Language Processing" } ], "id_raw": 363 }, { "id": "2Qk07FfJyOFsLybs", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the data observed from the bar chart into a table using CSV format?" }, { "from": "gpt", "value": "From {the title}[0.410, 0.019, 0.703, 0.041] of the chart, \"Entrepreneurial Initiatives by Activity Type\", it's clear that this chart is showing the number of initiatives in different types of entrepreneurial activity. The y-axis labels represent the types of entrepreneurial activities, ranging from {Franchise Ownership}[0.025, 0.834, 0.118, 0.973] to {Angel Investing}[0.047, 0.111, 0.118, 0.219]. The x-axis labels represent the corresponding values, ranging from {0}[0.123, 0.924, 0.130, 0.941] to {4500}[0.996, 0.924, 1.025, 0.941]. To generate the CSV table, I need to extract the values from each type of activity. I can do this by examining the length of each bar and correlating it with the x-axis scale. For instance, the length of the bar for {Franchise Ownership}[0.127, 0.808, 0.415, 0.872] is a little bit less than {1500}[0.407, 0.924, 0.436, 0.941] according to x-axis. Thus, the number of initiatives for Franchise Ownership is 1470.\nBased on the above thought, the answer is:\nType of Activity, Number of Initiatives\nFranchise Ownership, 1470\nStartup Founding, 3192\nProduct Innovation, 2422\nFreelance Contracting, 1848\nBusiness Consulting, 2162\nMarket Expansion, 1905\nBootstrapping, 1390\nCrowdfunding, 3000\nSocial Entrepreneurship, 4166\nAngel Investing, 1000" } ], "id_raw": 364 }, { "id": "Wu8MWrwhDyfjSFhV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which field of entrepreneurial activity seems to be the most popular among entrepreneurs, and why would you say so?" }, { "from": "gpt", "value": "The bar chart shows the number of initiatives in different entrepreneurial activities, which could reflect the popularity of these activities among entrepreneurs. As the number of initiatives represents the frequency or enthusiasm of entrepreneurs in an activity, the larger the number, the more popular the activity is. So, I can say {Social Entrepreneurship}[0.127, 0.166, 0.945, 0.230], which has the longest bar among all the activities, appears to be the most popular entrepreneurial activity, followed by {Startup Founding}[0.127, 0.728, 0.754, 0.792].\nBased on the above thought, the answer is:\nThe field of Social Entrepreneurship seems to be the most popular among entrepreneurs because it has the most number of initiatives compared to the other activities, indicating that a lot of entrepreneurs are becoming increasingly interested in contributing to society and addressing social issues through their entrepreneurial ventures. On the other hand, Startup Founding also has a high number of initiatives, showing that creating new businesses is also a popular activity among entrepreneurs." } ], "id_raw": 364 }, { "id": "UrOI2X5Ec3J5dZ5j", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_4.png", "conversations": [ { "from": "human", "value": "\nWhich type of entrepreneurial activity has the most initiatives?" }, { "from": "gpt", "value": "To find the activity type with the maximum number of initiatives, I need to find the longest bar. From left to right, the bars at {Franchise Ownership}[0.127, 0.808, 0.415, 0.872], {Startup Founding}[0.127, 0.728, 0.754, 0.792], {Product Innovation}[0.127, 0.648, 0.602, 0.712], and {Social Entrepreneurship}[0.127, 0.166, 0.945, 0.230] seem to be sizable. However, the one at {Social Entrepreneurship}[0.127, 0.166, 0.945, 0.230] extends nearly to the end of the scale at {4500}[0.996, 0.924, 1.025, 0.941], making it the lengthiest. Consequently, the entrepreneurial activity with the most initiatives is Social Entrepreneurship.\nBased on the above thought, the answer is:\nSocial Entrepreneurship." } ], "id_raw": 364 }, { "id": "4iiyHNSxBWLyjbGF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart's data into a structured CSV table according to your visual analysis?" }, { "from": "gpt", "value": "In the chart titled {Estimated Financial Losses by Risk Category}[0.414, 0.019, 0.722, 0.041], different categories are displayed on the vertical axis, shown in {yticks' label}[0.049, 0.848, 0.141, 0.865] to [0.028, 0.112, 0.141, 0.130], and the estimated losses are marked on the horizontal axis, where {0}[0.145, 0.939, 0.153, 0.957] denotes the smallest loss and {5,000}[1.028, 0.939, 1.057, 0.957] denotes the maximum loss. Each bar represents a different category from the risk management domain. The length of each bar, such as {Security Breach}[0.149, 0.822, 0.721, 0.887], corresponds to the estimated financial loss for the corresponding category. The length is labeled with the exact value at the end of the bar like {3,200}[0.730, 0.846, 0.759, 0.863]. Hence, the CSV table can be generated from the chart by pairing each category with its respective estimated financial loss.\nBased on the above thought, the answer is:\nRisk Factor,Estimated Losses\nSecurity Breach,3200\nNatural Disasters,1950\nOperational Risks,2750\nMarket Shifts,2335\nReputation Damage,4467\nStrategic Risks,1980\nCompliance Violations,2876\nFinancial Risks,3765\nHuman Errors,3900\nTechnology Failures,3030" } ], "id_raw": 365 }, { "id": "HB1BGJkKvfZa1BOO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what could be a potential strategy for the company to minimize the overall risk of financial loss?" }, { "from": "gpt", "value": "From the chart, the largest financial loss seems to be due to {'Reputation Damage'}[0.149, 0.495, 0.947, 0.560], labeled with {'4467'}[0.956, 0.519, 0.985, 0.536], followed by {'Human Errors'}[0.149, 0.168, 0.846, 0.234] at {'3900'}[0.855, 0.192, 0.884, 0.210]. Therefore, the company could prioritize creating an in-depth strategy to prevent reputation damage by improving customer relations, transparent communication, and promptly addressing any issues that could tarnish the company's reputation. Simultaneously, it should focus on reducing human errors by providing comprehensive training programs, implementing strict compliance and safety protocols, and promoting a well-structured work environment. Moreover, looking down the chart to lesser but significant losses, the company should not ignore other risk factors. Regular risk assessments and updates on risk management strategies could help control these risks effectively.\nBased on the above thought, the answer is:\nThe company could focus on preventing reputation damage, reducing human errors, and regularly updating risk management strategies for other significant risk factors." } ], "id_raw": 365 }, { "id": "3auHhE1qCduoOCBz", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_5.png", "conversations": [ { "from": "human", "value": "\nConsidering your visual analysis, which risk category contributes to the highest expected financial loss?" }, { "from": "gpt", "value": "In order to find out the risk category contributing to the highest expected financial loss, one must observe the length of all the bars displayed. As per my observation, the bar representing {'Reputation Damage'}[0.149, 0.495, 0.947, 0.560] is the longest reaching almost towards the end of the x-axis. At the end of this bar, there's a text {'4467'}[0.956, 0.519, 0.985, 0.536] marking the estimated financial loss which is the highest number among all the bars' endpoints. That indicates that 'Reputation Damage' is the risk category with the highest expected financial loss.\nBased on the above thought, the answer is:\nReputation Damage." } ], "id_raw": 365 }, { "id": "QtEHrAKJsefnK9ch", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured table representing the stock values for each company as shown in the bar chart?" }, { "from": "gpt", "value": "Starting by reading the title {Stock Value Trend for Tech Companies}[0.396, 0.019, 0.659, 0.041], we can tell structure our data into companies and their corresponding stock values. The y-axis ticks provide the company names from {Amazon}[0.018, 0.848, 0.059, 0.910] to {Oracle}[0.024, 0.112, 0.059, 0.165]. The length of each bar represent stock values, for example in the case of {Amazon}[0.067, 0.822, 0.637, 0.887] the value represented is {5450}[0.642, 0.846, 0.671, 0.863] on the far right side of the bar. Following the same process for each company, we can generate the table.\nBased on the above thought, the answer is:\nCompany, Stock Value\nAmazon, 5450\nApple, 5350\nGoogle, 2900\nNetflix, 4500\nFacebook, 4200\nTesla, 8390\nMicrosoft, 6100\nIBM, 3580\nCisco, 2400\nOracle, 2800" } ], "id_raw": 366 }, { "id": "q9VxsS0PHz9tVyKH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which tech company should investors prioritize for investment? Please provide reasoning." }, { "from": "gpt", "value": "From observing the chart, {Tesla}[0.031, 0.439, 0.059, 0.482] with the longest green bar {[0.067, 0.413, 0.944, 0.479]} has the highest stock value of {8390}[0.949, 0.437, 0.978, 0.455], which indicates the highest price of its stocks and as such the company's strong financial performance and high market confidence. Therefore, given these financial factors, investors might prioritize Tesla for investment.\nBased on the above thought, the answer is:\nTesla. Because it has the highest stock value, indicating a strong financial performance and high market confidence, which are important considerations for investors." } ], "id_raw": 366 }, { "id": "mcvspqyuTZ7wM5Qw", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_6.png", "conversations": [ { "from": "human", "value": "\nWhich company has the highest stock value?" }, { "from": "gpt", "value": "To find the company with the highest stock value, we look for the longest green bar on the chart. This corresponds to the {Tesla bar}[0.067, 0.413, 0.944, 0.479] and its value annotation {8390}[0.949, 0.437, 0.978, 0.455] is higher than that of any other company.\nBased on the above thought, the answer is:\nTesla." } ], "id_raw": 366 }, { "id": "wMotPajHsHz6REDG", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nConvert the data displayed on the bar chart into a tabulated format." }, { "from": "gpt", "value": "The investment types are indicated along the y-axis with {labels}[{'Mutual Fund'}[0.046, 0.848, 0.104, 0.936]; {'Blue-Chip Stocks'}[0.027, 0.766, 0.104, 0.882]; {'Real Estate'}[0.050, 0.684, 0.104, 0.766]; {'Bonds'}[0.071, 0.603, 0.104, 0.653]; {'Startup Investments'}[0.012, 0.521, 0.104, 0.658]; {'Cryptocurrency'}[0.033, 0.439, 0.104, 0.546]; {'Penny Stocks'}[0.042, 0.358, 0.104, 0.451]; {'ETFs'}[0.077, 0.276, 0.104, 0.317]; {'Gold'}[0.077, 0.194, 0.104, 0.235]; {'Savings Account'}[0.028, 0.112, 0.104, 0.226]]. The return rates are indicated by the {length of the bars}[{Mutual Fund}[0.112, 0.822, 0.246, 0.887]; {Blue-Chip Stocks}[0.112, 0.740, 0.521, 0.805]; {Real Estate}[0.112, 0.658, 0.367, 0.724]; {Bonds}[0.112, 0.577, 0.292, 0.642]; {Startup Investments}[0.112, 0.495, 0.946, 0.560]; {Cryptocurrency}[0.112, 0.413, 0.550, 0.479]; {Penny Stocks}[0.112, 0.332, 0.200, 0.397]; {ETFs}[0.112, 0.250, 0.433, 0.315]; {Gold}[0.112, 0.168, 0.179, 0.234]; {Savings Account}[0.112, 0.087, 0.133, 0.152]]. The {labels on the ends of the bars} displays the exact return rates. Hence, by aligning the corresponding investment types with their return rates, we convert the graph into tabulated data.\nBased on the above thought, the answer is:\nInvestment Type,Return Rate (%)\nMutual Fund,3.2\nBlue-Chip Stocks,9.8\nReal Estate,6.1\nBonds,4.3\nStartup Investments,20.0\nCryptocurrency,10.5\nPenny Stocks,2.1\nETFs,7.7\nGold,1.6\nSavings Account,0.5" } ], "id_raw": 367 }, { "id": "qF2LpmgqfD58WlN3", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nCan you provide an overview of the investment types and their associated risks based on the provided data?" }, { "from": "gpt", "value": "Based on the chart, the investment types are ranked in descending order of return rates. Starting with {Startup Investments}[0.112, 0.495, 0.946, 0.560] at the top with the highest return rate, and ending with {Savings Account}[0.112, 0.087, 0.133, 0.152] at the bottom. Usually, a higher return rate suggests a higher risk and a lower return rate suggests a lower risk. Therefore, the risk for each type of investment may also be inferred. For example, the highest return rate is associated with Startup Investments, which are often considered to be high risk. On the other hand, Savings Accounts, with the lowest return, are considered to be the safest form of investment.\nBased on the above thought, the answer is:\nThe investment types with the highest return rates, such as Startup Investments and Cryptocurrency, are typically considered to be high-risk, high-return investments. Then there are moderate-risk, moderate-return types such as Real Estate and ETFs. Finally, investment types with lower return rates, such as Savings Accounts and Gold, are usually considered to be low-risk investments." } ], "id_raw": 367 }, { "id": "CD2448obwV3s2d75", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_7.png", "conversations": [ { "from": "human", "value": "\nWhich type of investment has the highest return rate according to the chart?" }, { "from": "gpt", "value": "The return rate is indicated by the {horizontal length of bars}. By comparing each bar, it appears that the bar for {Startup Investments}[0.112, 0.495, 0.946, 0.560] is far ahead compared to others. Therefore, it has the highest return rate.\nBased on the above thought, the answer is:\nStartup Investments." } ], "id_raw": 367 }, { "id": "ipoP75a3zbpvZYpI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you pull from the raw data to create a table including industry and the number of internet users?" }, { "from": "gpt", "value": "From the labels at the y-axis {\"yticks\"}[{\"Cloud Computing\"}[0.042, 0.848, 0.122, 0.966], {\"Cybersecurity\"}[0.057, 0.766, 0.122, 0.864], {\"Online Shopping\"}[0.046, 0.684, 0.122, 0.798], {\"Data Analysis\"}[0.058, 0.603, 0.122, 0.699], {\"AI and Machine Learning\"}[0.013, 0.521, 0.122, 0.685], {\"Telecommunications\"}[0.030, 0.439, 0.122, 0.577], {\"Social Media Marketing\"}[0.018, 0.358, 0.122, 0.513], {\"Web Development\"}[0.038, 0.276, 0.122, 0.402], {\"Software Development\"}[0.020, 0.194, 0.122, 0.347], {\"E-commerce\"}[0.062, 0.112, 0.122, 0.202]], I can see that it represents different industries. The values accompany each bar, e.g., continuing from top to bottom, the first bar {\"Cloud Computing\"}<{\"bar\"}[0.130, 0.822, 0.308, 0.887]> with \"{the number of internet users}\"<{\"label\"}[0.323, 0.846, 0.360, 0.863]> as 24000, the second bar {\"Cybersecurity\"}<{\"bar\"}[0.130, 0.740, 0.343, 0.805]> with \"{the number of internet users}\"<{\"label\"}[0.358, 0.764, 0.394, 0.782]> as 28650, and so on. This will be the process to generate the CSV table, with one column for the specific industry, and another column for the corresponding number of internet users.\nBased on the above thought, the answer is:\nIndustry,Number of Internet Users\nCloud Computing,24000\nCybersecurity,28650\nOnline Shopping,35200\nData Analysis,40500\nAI and Machine Learning,52750\nTelecommunications,61000\nSocial Media Marketing,74200\nWeb Development,83200\nSoftware Development,98650\nE-commerce,107200" } ], "id_raw": 368 }, { "id": "38xgCr8QhT5NI9TN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nIf a company only has the resources to enter one industry, which industry should they potentially consider for the greatest exposure to internet users?" }, { "from": "gpt", "value": "The chart shows the number of users interested in different sectors of the Internet and Communication Industry and \"{E-commerce}\"<{\"bar\"}[0.130, 0.087, 0.927, 0.152]> has the highest number of users. So if a company wants to get exposed to the most significant number of internet users, they should probably consider the 'E-commerce' industry.\nBased on the above thought, the answer is:\nE-commerce" } ], "id_raw": 368 }, { "id": "DTWlxrIJXl1QT8It", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_8.png", "conversations": [ { "from": "human", "value": "\nWhat was the approximate increase in internet users when considering engagement from 'Cloud Computing' to 'E-commerce'?" }, { "from": "gpt", "value": "By looking at the bars, we can see that the first bar \"{Cloud Computing}\"<{\"bar\"}[0.130, 0.822, 0.308, 0.887]> represents 24000 users, and the lowest bar \"{E-commerce}\"<{\"bar\"}[0.130, 0.087, 0.927, 0.152]> represents 107200 users. To find the increase in users, I need to subtract the number of users in 'Cloud Computing' from the number of users in 'E-commerce'.\nBased on the above thought, the answer is:\n83200" } ], "id_raw": 368 }, { "id": "799DClvgI5bxlymS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nFrom your analysis, please provide the data from the chart in a structured CSV format." }, { "from": "gpt", "value": "To convert the chart data into a CSV format, I am observing that the y-axis labels consist of different {Big Data Tools}[0.041, 0.848, 0.078, 0.902]; [0.052, 0.766, 0.078, 0.804]; [0.064, 0.684, 0.078, 0.705]; [0.053, 0.603, 0.078, 0.639]; [0.038, 0.521, 0.078, 0.581]; [0.038, 0.439, 0.078, 0.499]; [0.013, 0.358, 0.078, 0.455]; [0.053, 0.276, 0.078, 0.312]; [0.048, 0.194, 0.078, 0.239]; [0.036, 0.112, 0.078, 0.174]. By observing the lengths and given values on each bar, it is clear that these represent the {Number of Employees}[0.881, 0.846, 0.910, 0.863]; [0.750, 0.764, 0.780, 0.782]; [0.716, 0.682, 0.745, 0.700]; [0.855, 0.601, 0.884, 0.618]; [0.768, 0.519, 0.797, 0.536]; [0.820, 0.437, 0.849, 0.455]; [0.855, 0.356, 0.884, 0.373]; [0.672, 0.274, 0.702, 0.291]; [0.915, 0.192, 0.945, 0.210]; [0.455, 0.111, 0.485, 0.128] proficient in each big data tool.\nBased on the above thought, the answer is:\nBig Data Tool,No. of Employees\nPython,9500\nJava,8000\nR,7600\nSQL,9200\nHadoop,8200\nTableau,8800\nApache Spark,9200\nSAS,7100\nExcel,9900\nMATLAB,4600" } ], "id_raw": 369 }, { "id": "euqZmkZptY1HVBqm", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nFrom the graph data, which big data tool do you observe to be trending among most employees? And why do you think so?" }, { "from": "gpt", "value": "In the graph, we can clearly see the tools on the y-axis with each corresponding bar length on the x-axis representing the number of employees proficient in that specific tool. The longest bar that is easy to spot belongs to the tool {Excel}[0.086, 0.168, 0.945, 0.234]. This bar represents the highest number of employees proficient, with the number being {9900}[0.915, 0.192, 0.945, 0.210]. Therefore, it can be stated that among all the Big Data Tools displayed on the graph, Excel is currently trending with the most number of employees proficient in it.\nBased on the above thought, the answer is:\nExcel. Because it has the highest number of proficient employees, which implies that it is most commonly being used amongst employees." } ], "id_raw": 369 }, { "id": "lx7zbCALCGdLJBUL", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_9.png", "conversations": [ { "from": "human", "value": "\nWhich tool is used by the least number of employees?" }, { "from": "gpt", "value": "To find the tool with the least number of employees, I need to locate the shortest bar on the chart. The shortest bar that I can observe is {MATLAB}[0.086, 0.087, 0.485, 0.152] with the number of employees given at the right end of the bar as {4600}[0.455, 0.111, 0.485, 0.128]. This bar is clearly the shortest among all other bars representing various tools.\nBased on the above thought, the answer is:\nMATLAB" } ], "id_raw": 369 }, { "id": "JxIO3ZvGFRX6Cd74", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you've analyzed, please arrange the E-commerce categories in descending order of annual revenue growth and present the data in a CSV format." }, { "from": "gpt", "value": "By examining the bar chart, I note that different E-commerce categories are represented on the y-axis with labels such as {Online Retail}[0.056, 0.848, 0.116, 0.938], {Digital Media}[0.054, 0.766, 0.116, 0.859] among others. The annual growth percentage for each category can be determined by examining the corresponding bar length and the value it reaches on the x-axis, such as the 18% value for {Online Retail bar}[0.124, 0.822, 0.617, 0.887] and 25% value for {Digital Media bar}[0.124, 0.740, 0.809, 0.805]. By looking at all the bars and comparing their lengths, I can extract the entire E-commerce types and their corresponding annual growth percentages.\nBased on the above thought, the answer is:\nE-commerce Type,Annual Revenue Growth (%)\nDigital Banking,30\nOnline Fashion,27\nOnline Education,26\nDigital Media,25\nE-healthcare,23\nFood Delivery,22\nOnline Market Place,28\nOnline Retail,18\nTravel & Transportation,15\nE-utilities,12" } ], "id_raw": 370 }, { "id": "TRHZeIihaJ1Q05j8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis of the chart, which sectors of E-commerce should one consider investing in, and why?" }, { "from": "gpt", "value": "From the chart, we can see that areas like {Digital Banking}[0.045, 0.358, 0.116, 0.464], {Online Fashion}[0.048, 0.112, 0.116, 0.214], and {Online Education}[0.038, 0.276, 0.116, 0.393] have longer bar lengths, exposing that these sectors indicate higher growth percentages. Indicatively, {Digital Banking bar}[0.124, 0.332, 0.946, 0.397] has the longest bar in the chart, implying the maximum revenue growth of 30%. It is important to note that these high growth sectors suggest a rapid expansion which could open up significant investment opportunities.\nBased on the above thought, the answer is:\nOne should consider investing in Digital Banking, Online Fashion, and Online Education sectors as they have shown the highest annual revenue growth, indicating these sectors are rapidly expanding and could potentially bring high returns." } ], "id_raw": 370 }, { "id": "eYDiczoNwRAyPIFU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_10.png", "conversations": [ { "from": "human", "value": "\nWhich E-commerce type demonstrates the lowest annual revenue growth percentage according to the bar chart?" }, { "from": "gpt", "value": "In order to determine the E-commerce type with the lowest annual revenue growth percentage, I need to analyze the lengths of the bars from each category relative to their percentages on the x-axis. The shortest bar length corresponds to the lowest value. I can see that {E-utilities}[0.070, 0.439, 0.116, 0.509] has {the shortest bar}[0.124, 0.413, 0.453, 0.479] when compared with others on the chart indicating the lowest growth percentage of 12%.\nBased on the above thought, the answer is:\nE-utilities" } ], "id_raw": 370 }, { "id": "Q9R5BdgshYQXyDwQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nCould you present the data from the bar chart in CSV format?" }, { "from": "gpt", "value": "The vertical labels on the chart represent different sectors. I note labels named {High Tech}[0.089, 0.838, 0.147, 0.920], {Utility}[0.108, 0.758, 0.147, 0.813], {Healthcare}[0.083, 0.677, 0.147, 0.768], {Consumer Discretionary}[0.018, 0.597, 0.147, 0.781], {Real Estate}[0.081, 0.517, 0.147, 0.611], {Energy}[0.102, 0.437, 0.147, 0.500], {Finance}[0.099, 0.357, 0.147, 0.425], {Communication Services}[0.015, 0.277, 0.147, 0.465], {Materials}[0.092, 0.197, 0.147, 0.276], and {Industrials}[0.086, 0.117, 0.147, 0.204]. The length of each bar corresponds to the number of shares traded in that sector. For example, for High Tech, the corresponding bar with the coordinates [0.156, 0.813, 0.592, 0.877] shows a number of shares around 2308. Subsequently, I will extract the data for each sector in the same way.\nBased on the above thought, the answer is:\nProfession,Number of Shares\nHigh Tech,2308\nUtility,1520\nHealthcare,1258\nConsumer Discretionary,934\nReal Estate,1048\nEnergy,1915\nFinance,2160\nCommunication Services,1643\nMaterials,4150\nIndustrials,3275" } ], "id_raw": 371 }, { "id": "6mBqaWS85vA3UoB2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat information does the chart give us about the health of each sector of the financial market?" }, { "from": "gpt", "value": "To analyze the health of each sector, we need to consider the number of shares traded which may correlate with the sector's activity and interest from investors. For instance, the {Materials sector}[0.092, 0.197, 0.147, 0.276]shows the longest bar[0.156, 0.173, 0.939, 0.237], suggesting a high volume of shares traded, possibly indicating robust health and high investor interest. In contrast, sectors with shorter bars, such as {Consumer Discretionary}[0.018, 0.597, 0.147, 0.781] with bar[0.156, 0.573, 0.332, 0.637], signify fewer shares traded, which could be a sign of lesser activity or potentially less investor interest. It is crucial to note that numerous other factors influence a sector's health, and the number of shares traded is just one indicator that can influence the general sentiment towards a sector.\nBased on the above thought, the answer is:\nThis chart indicates the relative activity levels and potentially the investor interest in each sector of the financial market. Materials sector appears to be the most active or has the highest investor interest, whereas the Consumer Discretionary sector seems to show the lowest activity levels or investor interest. These interpretations are based on the number of shares traded, which is the visible representation in the chart." } ], "id_raw": 371 }, { "id": "AvioTifAS7hUkyjt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_11.png", "conversations": [ { "from": "human", "value": "\nWhich sector has the least number of shares traded?" }, { "from": "gpt", "value": "To determine which sector trades the least number of shares, I need to analyze the length of the bars corresponding to each sector. After scanning from left to right, I find that the {Consumer Discretionary bar}[0.156, 0.573, 0.332, 0.637] is shorter, indicating it has fewer shares. To confirm, I check the bar labels and find the value of shares traded for this sector is 934.\nBased on the above thought, the answer is:\nConsumer Discretionary" } ], "id_raw": 371 }, { "id": "aOTfre33JDn6l1o8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a CSV table?" }, { "from": "gpt", "value": "Looking at the various activities in the value chain from {the y-axis labels}[{\"Sourcing\"}[0.053, 0.833, 0.097, 0.899]; {\"Production\"}[0.045, 0.715, 0.097, 0.793]; {\"Packaging\"}[0.047, 0.598, 0.097, 0.673]; {\"Transportation\"}[0.030, 0.480, 0.097, 0.581]; {\"Distribution\"}[0.041, 0.362, 0.097, 0.446]; {\"Retail\"}[0.066, 0.245, 0.097, 0.292]; {\"After-Sales Service\"}[0.012, 0.127, 0.097, 0.254]], I can see that these are the rows of the requested table. The {transaction volumes}[{\"1250\"}[0.365, 0.831, 0.394, 0.849]; {\"1700\"}[0.455, 0.714, 0.484, 0.731]; {\"2350\"}[0.585, 0.596, 0.614, 0.613]; {\"2800\"}[0.675, 0.478, 0.704, 0.496]; {\"3250\"}[0.765, 0.360, 0.794, 0.378]; {\"4200\"}[0.954, 0.243, 0.984, 0.260]; {\"3850\"}[0.885, 0.125, 0.914, 0.143]] that sit on top of the bars for each activity will fill the second column of the table. Therefore, I can construct a table using these values where the first column represents the various activities of the value chain and the second column contains the corresponding transaction volume.\nBased on the above thought, the answer is:\nValue Chain Activity,Transaction Volume\nSourcing,1250\nProduction,1700\nPackaging,2350\nTransportation,2800\nDistribution,3250\nRetail,4200\nAfter-Sales Service,3850" } ], "id_raw": 372 }, { "id": "rrtjEKRjV9bG2E73", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the slight decline in transaction volume in the After-Sales Service stage suggest about the overall function of the value chain?" }, { "from": "gpt", "value": "Based on the bar graph, we can see that most of the categories have an increasing transaction volume, with the exception of {After-Sales Service}[0.012, 0.127, 0.097, 0.254], which has {\"a slightly lesser value of 3850\"}[0.885, 0.125, 0.914, 0.143] than Retail {\"of 4200\"}[0.954, 0.243, 0.984, 0.260]. This indicates that while the bulk of transactions take place up until the point of sale, there might be a reduction in activities after the product has been sold, such as returns, exchanges, or customer inquiries, leading to a slightly lower volume of transactions in the After-Sales Service stage.\nBased on the above thought, the answer is:\nThe slight decline in transaction volume in the After-Sales Service stage suggests that after a product is sold, there might be fewer transactions related to return, exchange, or customer queries. This might indicate a strong product quality, effective upfront services, or a lack of strong after-sales policies and services." } ], "id_raw": 372 }, { "id": "mkgef0KHcI0gv5yW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the largest transaction volume represented in the chart?" }, { "from": "gpt", "value": "To find the largest transaction volume, I need to analyze each of the numbers provided at the end of the bar chart. Starting with {\"1250\"}[0.365, 0.831, 0.394, 0.849] for Sourcing, {\"1700\"}[0.455, 0.714, 0.484, 0.731] for Production, {\"2350\"}[0.585, 0.596, 0.614, 0.613] for Packaging, {\"2800\"}[0.675, 0.478, 0.704, 0.496] for Transportation, {\"3250\"}[0.765, 0.360, 0.794, 0.378] for Distribution, {\"4200\"}[0.954, 0.243, 0.984, 0.260] for Retail, and finally {\"3850\"}[0.885, 0.125, 0.914, 0.143] for After-Sales Service, it seems that Retail at 4200 has the largest transaction volume.\nBased on the above thought, the answer is:\n4200" } ], "id_raw": 372 }, { "id": "nGkrspQhfx0i1U4X", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart presented, can you help me to structure the data into CSV format data?" }, { "from": "gpt", "value": "From the subtitle {\"Number of Employees under each managerial role\"}[0.368, 0.019, 0.718, 0.041] and the x-axis label {\"Number of Employees\"}[0.479, 0.964, 0.607, 0.981], I can see that the x-axis represents the number of employees managed by each managerial role. The y-axis labels represent different management roles, including {CEO}[0.065, 0.844, 0.091, 0.882], {President}[0.044, 0.753, 0.091, 0.823], {Director}[0.049, 0.662, 0.091, 0.724], and so on. For each role, there is a single coloured bar to represent the number of employees. For example, the bar for {CEO}[0.099, 0.814, 0.945, 0.887] extends to approximately the 7000 marker, and there is {a value annotation \"7000\"}[0.951, 0.842, 0.980, 0.859] associated with it, so the number of employees managed by the CEO is 7000.\nBased on the above thought, the answer is:\nmanager_type,number_of_employees\nCEO,7000\nPresident,5000\nDirector,4000\nSenior_Manager,3000\nManager,2000\nJunior_Manager,1500\nTeam_Lead,1000\nTrainee_Manager,800\nIntern,100" } ], "id_raw": 373 }, { "id": "F0tN3dpFAFk5cNrk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nHow can the bar chart depict the hierarchy in the company?" }, { "from": "gpt", "value": "This bar chart intuitively reflects a hierarchical structure of a company based on the number of employees managed by each role. The y-axis categorizes the roles from {CEO}[0.065, 0.844, 0.091, 0.882] to {Intern}[0.058, 0.116, 0.091, 0.165], and the length of the corresponding bar on the x-axis represents the number of employees managed by the role. Generally, the higher the management level is, the more employees they manage, which stands out in high to low order on the chart, although exceptions exist such as between the {Junior Manager}[0.019, 0.389, 0.091, 0.497] and {Team Lead}[0.037, 0.298, 0.091, 0.379]. This visually presents a hierarchy from the CEO at the top down to the intern at the bottom in the managerial order.\nBased on the above thought, the answer is:\nThe bar chart depicts the hierarchy in the company by differentiating the managerial roles with the quantity of employees each role manages. The higher up in the hierarchy, the longer the bar, indicating more employees under its jurisdiction. This visually represents a management hierarchy from the CEO at the top to the intern at the bottom." } ], "id_raw": 373 }, { "id": "MfgN5SzeW6z4NRpb", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_13.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which managerial role manages the least number of employees?" }, { "from": "gpt", "value": "By observing the length of the bars from left to right, it can be seen that the shortest bar is {Intern}[0.099, 0.087, 0.111, 0.159]. This is supported by the value annotation {\"100\"}[0.117, 0.114, 0.139, 0.132] at the end of this bar. It indicates that the Intern role manages the least number of employees.\nBased on the above thought, the answer is:\nIntern." } ], "id_raw": 373 }, { "id": "80fDvXegAtXt1o8P", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert its data into a CSV format table." }, { "from": "gpt", "value": "Judging from the information given by {the subtitle}<[0.389, 0.019, 0.755, 0.041]>, the chart is talking about the number of entrepreneurs by different entrepreneurial activity. The bars span along the x-axis, with their lengths representing the number of entrepreneurs from {0}<[0.153, 0.939, 0.160, 0.957]> to {6000}<[1.021, 0.939, 1.050, 0.957]>. The y-axis present 10 types of entrepreneurial activities. Also, for each bar, there is {a value annotation}[like {this one}<[0.237, 0.846, 0.259, 0.863]> for \"Bootstrapping\"] on the right end of the bar, indicating the exact number of entrepreneurs for a specific activity. The y-label of each bar, like {this one}<[0.082, 0.848, 0.149, 0.947]> for \"Bootstrapping\", tells us the type of the entrepreneurial activity.\nBased on the above thought, the answer is:\nEntrepreneurial Activity, Number of Entrepreneurs\nBootstrapping, 500\nVenture Capital, 1500\nAngel Investment, 2000\nCrowdfunding, 2100\nFranchising, 2200\nPartnership, 3200\nSelf-funding, 3800\nSmall Business Loans, 4200\nEntrepreneurship Competitions, 4500\nGovernment Programs, 5400" } ], "id_raw": 374 }, { "id": "aoeJAuTs0HSOESGj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nGiven the chart data, which type of entrepreneurial activity would you recommend a newcomer in entrepreneurship to engage in and why?" }, { "from": "gpt", "value": "To recommend a suitable entrepreneurial activity for entertainers, one should interpret the bar chart in a comprehensive way. The x-axis represents the 'Number of Entrepreneurs' while different bars indicate different types of entrepreneurial activities. Higher bars, like {Government Programs}<[0.157, 0.087, 0.948, 0.152]> and {Entrepreneurship Competitions}<[0.157, 0.168, 0.816, 0.234]>, suggest that more entrepreneurs engage in these activities. While this could mean these activities are popular and potential-filled, it also implies high competition. On the contrary, bars with reasonable length, such as {Partnership}<[0.157, 0.413, 0.626, 0.479]>, suggest that these activities are quite participatory but have less fierce competition, which may be more suitable for newcomers.\nBased on the above thought, the answer is:\nPartnership. Because it sees a reasonable amount of entrepreneurs involved which suggest a certain degree of practical potential, meanwhile, compared to the most popular activities, the competition it faces is less fierce. This load of competition may be more suitable for newcomers." } ], "id_raw": 374 }, { "id": "lOuX5Wz2hhIuteus", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_25_14.png", "conversations": [ { "from": "human", "value": "\nWhich type of entrepreneurial activity faces the most number of entrepreneurs?" }, { "from": "gpt", "value": "The 'Number of Entrepreneurs' is represented by the length of bars along the x-axis. Thus, the one with the longest bar would be the activity facing the highest number of entrepreneurs. Upon observing the bar chart, it's evident that the bar corresponding to {Government Programs}<[0.157, 0.087, 0.948, 0.152]> has the longest length, and goes all the way to {5400}<[0.955, 0.111, 0.984, 0.128]>, which is also confirmed by the value annotation.\nBased on the above thought, the answer is:\nGovernment Programs." } ], "id_raw": 374 }, { "id": "Oxx7V5uP3hd5YzX8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nCan you transform the friendship data seen in the bar chart into a structured table format detailing the number of friends made and lost for both 'Ours' and 'Saliency' categories across the different time periods?" }, { "from": "gpt", "value": "Observing the bar charts through their given coordinates, the {x-axis labels}[0.043, 0.432, 0.099, 0.455]; [0.235, 0.432, 0.272, 0.455]; [0.403, 0.432, 0.470, 0.455]; [0.591, 0.432, 0.647, 0.455]; [0.783, 0.432, 0.821, 0.455] describe the time periods: Morning, Noon, Afternoon, Evening, and Night. The data contains two types of information, 'friends made' and 'friend loss', represented by bars in two separate {subplots}[0.071, 0.034, 0.985, 0.415]; [0.071, 0.522, 0.985, 0.903]. Extraction of values can be performed based on the heights of the bars for 'Ours' and 'Saliency' with colors identified in the {legend}[0.396, -0.018, 0.604, 0.023], where {Ours' color}[0.402, -0.007, 0.430, 0.009] is identified by a specific shade and {Saliency's color}[0.501, -0.007, 0.529, 0.009] by another. For example, in the Morning, {Ours' bar for friends made}[-0.002, 0.288, 0.144, 0.415] corresponds to a value range of {0 to 40}[0.071, 0.288], while {Saliency\u2019s bar}[-0.002, 0.304, 0.144, 0.415] matches a range of {0 to 35}[0.071, 0.304]. By progressing through each time period and category, one can match these height-to-value correspondences and create the output table.\nBased on the above thought, the answer is:\nTime Period,Friends Made Ours,Friends Made Saliency,Friend Loss Ours,Friend Loss Saliency\nMorning,40,35,5,10\nNoon,60,55,10,12\nAfternoon,80,70,15,17\nEvening,100,90,25,20\nNight,25,20,8,10" } ], "id_raw": 375 }, { "id": "Vnt58LBGnQ6ZCgmV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nWhich category shows a trend of growing friend loss as the day progresses, and how can this be determined from the chart?" }, { "from": "gpt", "value": "To ascertain a trend, we need to examine the bars representing friend loss for both categories across different time periods. This requires looking at the second set of bar charts within the {subplot}[0.071, 0.522, 0.497, 0.903]. {Ours bars for friend loss}[\n[0.037, 0.848, 0.105, 0.903],\n[0.122, 0.794, 0.190, 0.903],\n[0.207, 0.739, 0.275, 0.903],\n[0.292, 0.631, 0.360, 0.903],\n[0.378, 0.816, 0.446, 0.903]\n] show friend loss values of 5, 10, 15, 25, and 8 for Morning, Noon, Afternoon, Evening, and Night, respectively. Similarly, {Saliency\u2019s bars for friend loss}[\n[0.525, 0.794, 0.593, 0.903],\n[0.610, 0.772, 0.678, 0.903],\n[0.695, 0.718, 0.763, 0.903],\n[0.780, 0.685, 0.849, 0.903],\n[0.866, 0.794, 0.934, 0.903]\n] indicate values of 10, 12, 17, 20, and 10, respectively. While both demonstrate an increasing trend, it\u2019s evident that the 'Ours' category not only starts higher but also increases more substantially before the drop at Night. Thus, 'Ours' clearly shows a stronger growing trend till the Evening.\nBased on the above thought, the answer is:\nThe 'Ours' category demonstrates a notable increasing trend in friend loss as the day progresses. This can be established by analyzing the heights of the bars in the second set of bar charts, which increase more drastically for 'Ours' than for 'Saliency', peaking at Evening before the Night reveals a decrease." } ], "id_raw": 375 }, { "id": "QSKsAk3gzuNQSxQ9", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_26_0.png", "conversations": [ { "from": "human", "value": "\nDuring which time period did the 'Saliency' category have the most significant increase in friends made compared to the 'Ours' category?" }, { "from": "gpt", "value": "To determine the time period where 'Saliency' had the most significant relative increase in friends made compared to the 'Ours' category, I need to analyze the height differences between corresponding pairs of bars. {Ours bars}[\n[-0.002, 0.288, 0.144, 0.415],\n[0.180, 0.225, 0.327, 0.415],\n[0.363, 0.161, 0.510, 0.415],\n[0.546, 0.098, 0.692, 0.415],\n[0.729, 0.336, 0.875, 0.415]\n] and {Saliency bars}[\n[-0.002, 0.304, 0.144, 0.415],\n[0.180, 0.241, 0.327, 0.415],\n[0.363, 0.193, 0.510, 0.415],\n[0.546, 0.129, 0.692, 0.415],\n[0.729, 0.352, 0.875, 0.415]\n] must be individually considered. Starting from the Morning, I can see the difference is 5 for both categories. Moving through the time periods, the differences in friends made are 5, 5, 10, 10, and 5 respectively. The Afternoon period presents the most significant increase with Saliency having 70 friends made against Ours with 80 friends made, showing a smaller gap between the two compared to other periods.\nBased on the above thought, the answer is:\nAfternoon." } ], "id_raw": 375 }, { "id": "JgIi2sHNPrPixr55", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the bar subplots and compile it into a CSV format table?" }, { "from": "gpt", "value": "In order to generate a CSV table from the bar charts, we must first identify the x and y axes of each subplot. The x-axes for the two subplots represent the 10 regions across which the sales data is divided. The y-axes represent the sales figures. For the {first subplot}[0.071, 0.021, 0.985, 0.424], we see that the plot represents data from the 'Consumer Electronics' market, colored in {#ee8882}[0.112, 0.397, 0.139, 0.638]. Similarly, in the {second subplot}[0.071, 0.515, 0.985, 0.917], we see data from the 'Apparel' market, represented in {#8db7db}[0.112, 0.901, 0.139, 1.045]. We now deduce the individual sales figures for each market in the regions. For instance, in the first region, the sales figures for 'Consumer Electronics' and 'Apparel' are ~90 and ~90, respectively. The same pattern can be followed to deduce the data for all 10 regions. Using this information, the CSV table can be generated as follows:\nBased on the above thought, the answer is:\nRegion,Consumer Electronics, Apparel\n1,90,90\n2,85,85\n3,120,80\n4,150,100\n5,200,200\n6,220,220\n7,190,240\n8,160,250\n9,180,300\n10,170,300" } ], "id_raw": 376 }, { "id": "Gy2Jqd0LwifOWR4h", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nWhich market seems to have a more steady sales trend over the regions based on the chart?" }, { "from": "gpt", "value": "Analyzing the relative steadiness of the sales trend across regions involves comparing the fluctuations in bar heights for both markets across the regions. For the 'Consumer Electronics' market, represented in the {first subplot}[0.071, 0.021, 0.985, 0.424], the sales data fluctuates significantly, with certain regions showing high sales figures and others being significantly lower at the beginning, the trend increases and then drops. On the contrary, for the 'Apparel' market, represented in the {second subplot}[0.071, 0.515, 0.985, 0.917], the sales trends appear to be more unpredictable initially, but generally increases towards the tenth region. Given these observations, it appears that the 'Apparel' market has a less variable and more steady sales trend over regions.\nBased on the above thought, the answer is:\nApparel Market" } ], "id_raw": 376 }, { "id": "GPZLYw9ciix1dTBW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest sales figure observed in the 'Apparel' market?" }, { "from": "gpt", "value": "To determine the highest sales figure for the 'Apparel' market, we need to analyze the {second subplot}[0.071, 0.515, 0.985, 0.917]. We can see the bars representing sales data in {#8db7db}[0.112, 0.901, 0.139, 1.045]. By looking at the heights of each of the 10 bars, we can find that the highest bar corresponds to the 9th and 10th regions which reaches near the top y-axis tick with the label '330'. Therefore, based on the bar height, I would say the highest sales figure for 'Apparel' is near 300.\nBased on the above thought, the answer is:\nAround 300" } ], "id_raw": 376 }, { "id": "ozjHGiKrcmC0j2Hc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table listing the Cultural Heritage and Cultural Integration trends for the represented countries based on the displayed bar chart?" }, { "from": "gpt", "value": "To construct the table, I will analyze both the Cultural Heritage and the Cultural Integration trends for each country as represented on the bar chart. Starting with the Cultural Heritage trend, the {x-axis labels of subplot_0}[0.162, 0.919, 0.531, 0.942] go from -20 to 120, and the {y-axis labels}[0.025, 0.085, 0.099, 0.848] list various countries such as Rome, Greece, Egypt, and China. Each bar's length corresponds to a value for that country's Cultural Heritage trend. For instance, Rome's Cultural Heritage trend bar ranges from {0.00 to 34.10}[0.165, 0.804, 0.267, 0.863]. Similarly, for the Cultural Integration trend shown in subplot_1, the {x-axis labels}[0.560, 0.919, 1.032, 0.942] also go from -60 to 120, and each country's bar represents the value trend, like the Rome bar showing values from {0.00 to 86.17}[0.721, 0.804, 0.938, 0.863]. By analyzing each bar's coordinates, the table with Cultural Heritage and Cultural Integration trends can be constructed.\nBased on the above thought, the answer is:\nCountry,Cultural Heritage,Cultural Integration\nRome,34.10,86.17\nGreece,58.36,-25.31\nEgypt,105.06,-18.18\nChina,102.20,21.95\nIndia,41.22,13.64\nFrance,70.34,-38.90\nUnited Kingdom,23.66,99.70\nSpain,88.81,75.97\nTurkey,85.03,37.53\nRussia,-6.57,53.44\nUSA,-12.37,4.24" } ], "id_raw": 377 }, { "id": "ecePtWb8Qru2Nd7I", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nObserving the trends in Cultural Heritage and Cultural Integration, which country seems to have a more consistent performance across both categories?" }, { "from": "gpt", "value": "To evaluate consistency in performance across the Cultural Heritage and Cultural Integration categories, I will compare the proximity of each country's values in both categories to determine the smallest disparity. For instance, {China's bar for Cultural Heritage}[0.165, 0.582, 0.469, 0.641] shows a value of 102.20, and {China's bar for Cultural Integration}[0.721, 0.582, 0.776, 0.641] indicates 21.95. This demonstrates a consistent performance as both values are positive with a reasonable difference. Performing the same method across other countries, it becomes evident that while some countries have high disparities, China maintains a more consistent performance, having both values in a positive range and a modest difference.\nBased on the above thought, the answer is:\nChina. When comparing the difference between Cultural Heritage and Cultural Integration trends, China shows moderate and consistent positive values in both categories, indicating a balanced performance in promoting cultural dynamics." } ], "id_raw": 377 }, { "id": "5K4FLT3pV1vcCZ6i", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_26_2.png", "conversations": [ { "from": "human", "value": "\nWhich country shows the greatest positive difference between Cultural Heritage and Cultural Integration?" }, { "from": "gpt", "value": "To identify the country with the greatest positive difference between the Cultural Heritage and Cultural Integration trends, I must first determine the difference for each country and then compare these values. Focusing on the Cultural Heritage subplot, I see {Rome's bar}[0.165, 0.804, 0.267, 0.863] which indicates a value of 34.10. In the Cultural Integration subplot, I observe {Rome's bar}[0.721, 0.804, 0.938, 0.863] indicating a value of 86.17. The difference is therefore 52.07 for Rome. I will repeat this calculation for all countries and conclude that the country with {the greatest positive difference}[content relevant coordinates for that country] is the United Kingdom, with a Cultural Heritage value of 23.66 and a Cultural Integration value of 99.70, which gives a difference of 76.04.\nBased on the above thought, the answer is:\nUnited Kingdom" } ], "id_raw": 377 }, { "id": "eBfAurBaKIQgNl2o", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nCould you provide the participation rates for University Programs in CSV format from the provided bar chart?" }, { "from": "gpt", "value": "Observing the bar chart subset dedicated to {University Programs}[0.087, 0.062, 0.492, 0.448], I notice two bars corresponding to the methods 'Ours' and 'Trend-based Sampling'. The {y-axis labels}[0.043, 0.053, 0.077, 0.488] range from '\u22120.6' to '1.0', and the {x-axis labels}[0.171, 0.464, 0.469, 0.487] detail the method names. The first bar labeled {Ours}[0.105, 0.088, 0.269, 0.319] indicates a rate of '0.9' and the second bar labeled {Trend-based Sampling}[0.310, 0.101, 0.474, 0.319] signifies a rate of '0.85'.\nBased on the above thought, the answer is:\nMethod,Participation Rate\nOurs,0.9\nTrend-based Sampling,0.85" } ], "id_raw": 379 }, { "id": "LWFa0D2JIpB5luAj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nBased on the data, which method appears to be more successful in engaging participants across the educational opportunities?" }, { "from": "gpt", "value": "To evaluate the overall success of the engagement in educational opportunities, a comparative analysis of the bars representing {Ours}[0.105, 0.088, 0.269, 0.319] and {Trend-based Sampling}[0.310, 0.101, 0.474, 0.319] for each category like {University Programs}[0.087, 0.062, 0.492, 0.448], {Adult Education}[0.579, 0.062, 0.985, 0.448], {Online Certificate Programs}[0.087, 0.550, 0.492, 0.935], and {Workplace Trainings}[0.579, 0.550, 0.985, 0.935] must be performed. In comparison for each category, the bar for 'Ours' is consistently at the same level or taller than the bar for 'Trend-based Sampling', denoting a higher or equal participation rate.\nBased on the above thought, the answer is:\nThe \"Ours\" method. The visual data suggests that 'Ours' is either outperforming or matching the 'Trend-based Sampling' method in participant engagement, as indicated by the consistent bar height equalities or advantages in all categories." } ], "id_raw": 379 }, { "id": "F905QQsrECQP7DO3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the exact participation rate change difference between the methods for Workplace Trainings?" }, { "from": "gpt", "value": "To determine the participation rate change difference, I compare the lengths of bars in the {Workplace Trainings}[0.579, 0.550, 0.985, 0.935] section of the chart. The {bar for \"Ours\"}[0.597, 0.704, 0.762, 0.807] reaches up to '0.4' and the {bar for \"Trend-based Sampling\"}[0.803, 0.717, 0.967, 0.807] extends to '0.35'. Calculating the exact difference, we get 0.4 - 0.35 which equals 0.05, or 5 percentage points.\nBased on the above thought, the answer is:\n0.05" } ], "id_raw": 379 }, { "id": "iJ9RAP8EwId2LcwB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_26_6.png", "conversations": [ { "from": "human", "value": "\nPlease convert the growth data depicted in the chart into a table that can be understood in CSV format." }, { "from": "gpt", "value": "According to my visual analysis of the figure, I infer that the x-axis represents different categories of digital content creation and the y-axis represents their respective growth trends. Each category contains two bars: one for Startups and the other for Funded Startups. The categories (x-axis) are {Technology}[0.145, 0.919, 0.223, 0.942], {Photography}[0.312, 0.919, 0.401, 0.942], {3D Modelling}[0.484, 0.919, 0.575, 0.942], {Social Media}[0.658, 0.919, 0.746, 0.942] and {VFX}[0.861, 0.919, 0.888, 0.942]. I can derive the exact data details of the growth trends by observing the corresponding height of each bar per category. To come up with a CSV-formatted table, it is evident that each company's growth trending is either above - if positive - or below - if negative - the 0 value indicated on the y-axis. For instance, the {Technology startup bar}[0.115, 0.304, 0.253, 0.544] has its top at approximately 30 on the y-axis, so the growth trend is 'Moderate'; whereas the {Technology Funded Startup bar}[0.115, 0.544, 0.253, 0.544] remains at 0, which could suggest 'No Growth'. This will be repeated for all the categories to decode their growth levels, thus enabling the construction of a comprehensive CSV table.\nBased on the above thought, the answer is:\nDigital Content Category, Startup, Funded Startup\nTechnology, Moderate, No Growth\nPhotography, Sharp rise, Steady decline\n3D Modelling, Moderate, Substantial Decrease\nSocial Media, Volatile, Sustained\nVFX, Moderate, Substantial decrease" } ], "id_raw": 381 }, { "id": "SZGYSV0SpTFmxr9L", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_26_6.png", "conversations": [ { "from": "human", "value": "\nCan you give an evaluation of the market trend for all startups in the digital content industry based on the chart?" }, { "from": "gpt", "value": "By analyzing the bars representing Startups on the graph, an evaluation of the market trend can be given. {The bars for Startups}[0.115, 0.304, 0.253, 0.544]; [0.287, 0.065, 0.426, 0.544]; [0.460, 0.384, 0.598, 0.544]; [0.633, 0.225, 0.771, 0.544]; [0.805, 0.544, 0.944, 0.624] range in color from light to deep pink, indicating startups in different digital content creation categories. The categories themselves are Technology, Photography, 3D Modelling, Social Media, and VFX. The length of these bars reflects the growth trends of each Startup. Looking at the lengths of these bars, it can be observed that the length varies from category to category, which suggests a varying growth trend across different sectors of the digital content industry. For instance, the Photography startup has the longest bar, which could suggest a sharp rise, while the VFX startup has the shortest bar, suggesting a moderate rise. Therefore, it can be concluded that in terms of startup companies, the market trend is generally upward with variations across different sectors.\nBased on the above thought, the answer is:\nThe market trend for startups in the digital content creation industry is overall positive, though the level of growth varies greatly across different categories. Notably, startups in the Photography area show a sharp rise, while those in the VFX show a moderate rise." } ], "id_raw": 381 }, { "id": "YPv7cDgcLvixBXVI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_26_6.png", "conversations": [ { "from": "human", "value": "\nWhich category shows the greatest disparity between the growth trend of startup and funded startup companies?" }, { "from": "gpt", "value": "To answer this, it is necessary to compare the differences in growth trends between the startup and funded startup companies within each category. For each category, the difference can be measured in terms of the divergence in y-axis values.\nFor instance, in the category of {Photography}[0.312, 0.919, 0.401, 0.942], the difference is generated by subtracting the y-coordinate of the {Photography Funded Startup bar}[0.287, 0.544, 0.426, 0.783] from that of the {Photography startup bar}[0.287, 0.065, 0.426, 0.544], which equals approximately 60. We apply a similar approach across all categories, finding out that the greatest disparity is in the category of 3D Modelling.\nBased on the above thought, the answer is:\n3D Modelling." } ], "id_raw": 381 }, { "id": "9v6TBBizAWS8lBsa", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the two bar charts, can you generate a CSV table that contains the data of Trade Volume and Globalization Index for each year?" }, { "from": "gpt", "value": "Looking at the chart, I can see that the x-axis represents the year and the y-axis represents the values corresponding to Trade Volume and Globalization Index. To get the data, I need to analyze the height of each bar. For instance, {the bar for the year 1990}[0.129, 0.209, 0.180, 0.415] in the top chart corresponds to the Trade Volume in 1990. Although there isn't a direct value annotation, by referencing the y-axis labels and the height of the bar, I can deduce that the Trade Volume in 1990 is around 5554 billion USD. Similarly, I can get the data for Globalization Index by analyzing {the bar for the year 1990}[0.129, 0.649, 0.180, 0.903] in the bottom chart, which corresponds to 60. By repeating this process for all years from 1990 to 2020, I can create a CSV table.\nBased on the above thought, the answer is:\nYear,Trade Volume (Billions USD),Globalization Index\n1990,5554,60\n1995,6778,65\n2000,7544,70\n2005,8272,75\n2010,9157,80\n2015,10025,85\n2020,9000,88" } ], "id_raw": 384 }, { "id": "b2O5rER3FqJQrB2b", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis, how can you compare the trend of Globalization index to the trend of Trade Volume?" }, { "from": "gpt", "value": "This question involves analyzing the overall trends of the two bar charts. From bar charts, the bars for both Globalization Index and Trade Volume generally increase over time as the bars get progressively taller from {\"1990\"}[0.129, 0.209, 0.180, 0.415] to {\"2015\"}[0.766, 0.044, 0.817, 0.415]. The height of the bars indicates that both the Trade Volume and Globalization Index increased with time. However, the upward trend in Trade Volume is interrupted in {\"2020\"}[0.893, 0.082, 0.944, 0.415], where the drop of the bar indicates a decrease in Trade Volume from its peak in 2015. In contrast, the bar for the Globalization Index in {\"2020\"}[0.893, 0.531, 0.944, 0.903] continues to grow, albeit at a slower rate which indicates that the Globalization trend has been more stable over the years.\nBased on the above thought, the answer is:\nBoth Globalization Index and Trade Volume have increased over time, indicating a positive trend. However, the trend in the Trade Volume is affected by a decline in 2020, whereas the Globalization Index has been more stable, steadily increasing even in 2020." } ], "id_raw": 384 }, { "id": "NQeEyiegaCs3Cwv2", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_26_9.png", "conversations": [ { "from": "human", "value": "\nIn which year did we see the highest amount of International Trade according to the top bar-chart?" }, { "from": "gpt", "value": "The bar height in the top chart indicates the value of the Trade Volume in each year. Therefore, to find out the year with the highest trade volume, I should look for the tallest bar of the chart. After examining each bar from {\"1990\"}[0.129, 0.209, 0.180, 0.415] to {\"2020\"}[0.893, 0.082, 0.944, 0.415], it is clear that the bar corresponding to {\"2015\"}[0.766, 0.044, 0.817, 0.415] is the highest, therefore, the trade volume was the highest in 2015.\nBased on the above thought, the answer is:\n2015." } ], "id_raw": 384 }, { "id": "8psGbKGzKfdLuS6u", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data into a structured table with health parameters and the corresponding values for Random Systematic and Accelerated Gradient trends?" }, { "from": "gpt", "value": "By observing the provided chart, I note the {x-axis labels}[0.163, 0.243, 0.906, 0.430], which include health parameters such as health_factors, medical_bill, appointment_frequency, lab_test_intervals, medication_quantity, and patient_noshow_rate. I then focus on the {bars}[0.022, 0.117, 0.074, 0.226] and {bar_labels}[0.035, 0.217, 0.064, 0.241] to read off the corresponding y-values for the Random Systematic trend and the {bars}[0.074, 0.050, 0.125, 0.226] and {bar_labels}[0.046, 0.034, 0.064, 0.057] for the Accelerated Gradient trend for each health parameter. With this analysis, I am able to construct the following tabular data.\nBased on the above thought, the answer is:\nHealth Parameters,Random Systematic,Accelerated Gradient\nhealth_factors,-2.4,18.1\nmedical_bill,-25.4,9.5\nappointment_frequency,8.1,-10.2\nlab_test_intervals,-15.5,5.4\nmedication_quantity,-5.1,10.9\npatient_noshow_rate,-3.6,20.2" } ], "id_raw": 385 }, { "id": "14eEgvusEmcbMLct", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nWhich of the two trends tends to show an overall positive effect on the health parameters?" }, { "from": "gpt", "value": "To determine the trend with an overall positive effect, I observe the {bars for each trend}[0.022, 0.117, 0.074, 0.226];[0.074, 0.050, 0.125, 0.226]. The Random Systematic trend mostly shows negative figures with {negative bars}[0.022, 0.117, 0.074, 0.226];[0.151, 0.117, 0.203, 0.226];[0.410, 0.117, 0.462, 0.226];[0.539, 0.117, 0.591, 0.226];[0.669, 0.117, 0.720, 0.226], apart from a single positive entry for appointment_frequency. In contrast, the Accelerated Gradient trend shows predominantly positive values with {positive bars}[0.074, 0.050, 0.125, 0.226];[0.203, 0.082, 0.255, 0.226];[0.591, 0.077, 0.643, 0.226];[0.720, 0.043, 0.772, 0.226] for health_factors, medical_bill, medication_quantity, and patient_noshow_rate respectively, and only a single negative value for appointment_frequency.\nBased on the above thought, the answer is:\nThe Accelerated Gradient trend tends to show an overall positive effect on the health parameters, as it has more parameters where the values are rising as opposed to declining." } ], "id_raw": 385 }, { "id": "qhe4i8ylqM0OjLzJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the combined negative value for the Random Systematic trend across all the health parameters?" }, { "from": "gpt", "value": "To compute the combined negative value for the Random Systematic trend, I locate the {negative bars for Random Systematic}[0.022, 0.117, 0.074, 0.226];[0.151, 0.117, 0.203, 0.226];[0.410, 0.117, 0.462, 0.226];[0.539, 0.117, 0.591, 0.226];[0.669, 0.117, 0.720, 0.226]. I sum up the negative values provided for the health parameters, excluding appointment_frequency as it is positive for this trend. The values are -2.4 for health_factors, -25.4 for medical_bill, -15.5 for lab_test_intervals, -5.1 for medication_quantity, and -3.6 for patient_noshow_rate.\nBased on the above thought, the answer is:\n-51.6" } ], "id_raw": 385 }, { "id": "YF2E6Lyc5KmpAG7l", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nCould you transform the interaction rates from the 'Ours' method chart into a structured table based on your analysis?" }, { "from": "gpt", "value": "I am focusing on the central subplot, specifically {the y-axis}[0.015, 0.280, 0.029, 0.657] which shows the interaction rate in millions per month. The x-axis, identified by {the x-label}[0.517, 0.627, 0.550, 0.650], lists the social media platforms: Facebook, Instagram, Twitter, YouTube, LinkedIn, Snapchat, and Pinterest. Each platform has a corresponding rectangle representing its specific interaction rate, like {the bar for \"Facebook\"}[0.123, 0.505, 0.220, 0.554] showing a rate from 0 to 200 million interactions. Repeating this examination for each bar gives us the full data set from the 'Ours' method.\nBased on the above thought, the answer is:\nPlatform,Interaction Rate (millions/month)\nFacebook,200\nInstagram,450\nTwitter,550\nYouTube,650\nLinkedIn,350\nSnapchat,500\nPinterest,550" } ], "id_raw": 386 }, { "id": "a4PPvYZwZkBIvrJ2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform has demonstrated the most consistent performance between both monitoring methods?" }, { "from": "gpt", "value": "Consistency between 'Ours' and 'Saliency-based Sampling' can be identified by analyzing {the bars}[0.082, 0.034, 0.985, 0.253] displaying engagement, and comparing the height variance in both methods per platform. Minimal variance indicates consistency. {The bars for \"Twitter\"}[0.365, 0.204, 0.461, 0.229] under 'Ours' and [0.365, 0.769, 0.461, 0.879] under 'Saliency-based Sampling' have close starting and ending points, showing that Twitter's interaction rates don't fluctuate significantly between the two methods.\nBased on the above thought, the answer is:\nTwitter. Because comparing the vertical positioning of the bars associated with Twitter across both 'Ours' and 'Saliency-based Sampling' reveals minimal variance, suggesting consistent performance." } ], "id_raw": 386 }, { "id": "KNfmTMb1KFsJBWMt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_26_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the ratio of engagement between 'Saliency-based Sampling' on LinkedIn and 'Ours' on Snapchat?" }, { "from": "gpt", "value": "To find the ratio, I need both engagement numbers. For LinkedIn, under 'Saliency-based Sampling', we have {the bar for \"LinkedIn\"}[0.606, 0.842, 0.703, 0.879], indicating 150 million interactions. For Snapchat, with 'Ours', the engagement is represented by {the bar for \"Snapchat\"}[0.727, 0.432, 0.823, 0.554] with 500 million interactions. Computing the ratio involves dividing LinkedIn's 'Saliency-based Sampling' value by Snapchat's 'Ours' value.\nBased on the above thought, the answer is:\n0.3" } ], "id_raw": 386 }, { "id": "ieROlzQek0bmhYwa", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nCan you turn the data represented in this bar chart into a CSV format?" }, { "from": "gpt", "value": "The chart uses horizontal bars to represent different trends in Chinese and French gastronomy as marked by the y-axis labels. Each type of gastronomy has bars corresponding to the categories defined in the y-axis. For Chinese cuisine, the bars are blue, and for French cuisine, the bars are green. For instance, the bar for 'Accelerated growth' in Chinese cuisine{Chinese bar for 'Accelerated growth'}[0.160, 0.092, 0.415, 0.178] has a length that suggests its value is about 60, according to the x-axis. Also, the bar for 'Accelerated growth' for French cuisine{French bar for 'Accelerated growth'}[0.415, 0.092, 0.568, 0.178] suggests it is about 36. In this manner, I can extract the trends values for both Chinese and French cuisine for each category in the y-axis.\nBased on the above thought, the answer is:\nTrends,Chinese Cuisine,French Cuisine\nAccelerated growth,60,36\nSporadic growth,70,45\nGradual downturn,90,70\nSustained decline,85,80\nInexorable decline,95,90\nSlow recovery,70,60" } ], "id_raw": 390 }, { "id": "mhKcyzSH8bVzVTRi", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, please explain the changes in gastronomy trends for the Chinese Cuisine over time." }, { "from": "gpt", "value": "The graph represents trends in Chinese cuisine by the length of blue bars for each category. From the bar for 'Accelerated growth'{Chinese bar for 'Accelerated growth'}[0.160, 0.092, 0.415, 0.178], which is about 60, there is a trend of increasing growth, reaching its peak at 'Inexorable decline'{Chinese bar for 'Inexorable decline'}[0.160, 0.666, 0.564, 0.752] with a value of about 95. Then, there is a noticeable decrease in the value for 'Slow recovery'{Chinese bar for 'Slow recovery'}[0.160, 0.809, 0.457, 0.895], denoting a value of about 70. It's important to note that the change in trends for Chinese cuisine does not strictly follow the order presented on the y-axis.\nBased on the above thought, the answer is:\nThe Chinese gastronomy was characterized by a trend of increasing growth reaching its peak at the period of 'Inexorable decline'. However, this growth did not last as there was a noticeable decrease during the 'Slow recovery' period." } ], "id_raw": 390 }, { "id": "cNRgBolSEYWlkJz8", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_0.png", "conversations": [ { "from": "human", "value": "\nIn which category did the French Cuisine have the least trend value?" }, { "from": "gpt", "value": "To answer this question, I need to locate the shortest green bar that represents French Cuisine on the chart. If I look at the French bars, it\u2019s clear that the shortest one is for 'Accelerated growth'{French bar for 'Accelerated growth'}[0.415, 0.092, 0.568, 0.178]. This shortest bar for 'Accelerated growth' equates to a value of about 36 according to the x-axis.\nBased on the above thought, the answer is:\nAccelerated growth." } ], "id_raw": 390 }, { "id": "nyOBmh6MuglXF4iB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you generate a CSV table with each row representing a different project comparison and columns representing the percentage of \"win\", \"tie\", and \"loss\" respectively?" }, { "from": "gpt", "value": "The y-axis of the chart represents different project comparisons, such as {Education Infrastructure vs. Healthcare Infrastructure}[0.015, 0.126, 0.388, 0.149], which can be used as the index of the table. The x-axis represents the respective percentages (ranging from {0 %}[0.393, 0.952, 0.402, 0.975] to {100 %}[0.944, 0.952, 0.970, 0.975]), of each outcome recorded under these categories. The bars are divided into three categories based on color and content according to {the legend}[0.502, 0.019, 0.763, 0.061]: {Win}[0.547, 0.029, 0.573, 0.052] (blue bars), {Tie}[0.640, 0.029, 0.660, 0.052] (green bars), and {Loss}[0.727, 0.029, 0.757, 0.052] (red bars), corresponding to the effectiveness of different projects. The length of the bars in each category combined with their labels determines the value for each category, for example, for {Win in Education Infrastructure vs. Healthcare Infrastructure}[0.397, 0.092, 0.509, 0.178], the value label is {20.0 %}[0.431, 0.123, 0.475, 0.147].\nBased on the above thought, the answer is:\nProject Comparisons,Win,Tie,Loss\nEducation Infrastructure vs. Healthcare Infrastructure,20.0 %,60.0 %,20.0 %\nEducation Infrastructure vs. Road Construction,55.0 %,40.0 %,5.0 %\nEducation Infrastructure vs. Waste Management,35.0 %,55.0 %,10.0 %\nHealthcare Infrastructure vs. Road Construction,45.0 %,50.0 %,5.0 %\nHealthcare Infrastructure vs. Waste Management,30.0 %,65.0 %,5.0 %\nRoad Construction vs. Waste Management,55.0 %,40.0 %,5.0 %" } ], "id_raw": 391 }, { "id": "mOqBvu3K50ijn9oW", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat can we learn about the effectiveness of different projects from this chart?" }, { "from": "gpt", "value": "The bar chart provides a comparative analysis between the effectiveness of various local government project pairs. According to {the legend}[0.502, 0.019, 0.763, 0.061], it divides the bars into three categories of outcomes: wins, ties and losses. High percentages of wins indicate that in most comparisons, one project (listed first) is more effective than the other. High percentages of losses suggest the reverse whilst high percentages of ties indicate that there is little to choose between the two projects in terms of effectiveness. Based on the heights of the bars, we can discern that none of the project pairs have a high percentage of losses showing that there is clear preference for one project over another in most cases. Particularly, {Education Infrastructure vs. Road Construction}[0.397, 0.236, 0.705, 0.322] and {Road Construction vs. Waste Management}[0.397, 0.809, 0.705, 0.895] have a high percentage of wins indicating those two projects have a clear advantage over their pairs. However, in most project comparisons the tie percentages are significantly high, indicating that in many cases the projects are equally effective. A particularly high tie percentage is seen in {Healthcare Infrastructure vs. Waste Management}[0.565, 0.666, 0.929, 0.752] where 65% of the time neither project is superior. This could mean that both projects are equally important and effective, hence local government and community should equally focus on both projects.\nBased on the above thought, the answer is:\nThe chart indicates that in many project pair comparisons, there is a clear preference for one project over another as is evidenced by the high win percentages for pairs Education Infrastructure vs. Road Construction and Road Construction vs. Waste Management. However, it also shows that in many cases, both projects are equally effective as demonstrated by the significantly high tie percentages. A particularly high tie percentage in Healthcare Infrastructure vs. Waste Management suggests that both projects are equally important and effective. Hence, local government and communities should equally focus on both projects." } ], "id_raw": 391 }, { "id": "sROhxpUpqB5VRLwQ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest percentage of a tie in project comparisons?" }, { "from": "gpt", "value": "To find out the project comparison with the highest percentage of a \"tie\", I need to inspect the green bars under each project comparison. The green bars represent the ties according to the {legend}[0.502, 0.019, 0.763, 0.061]. Starting from the top, {Road Construction vs. Waste Management}[0.705, 0.809, 0.929, 0.895] has a tie of {40 %}[0.795, 0.841, 0.839, 0.864], {Healthcare Infrastructure vs. Waste Management}[0.565, 0.666, 0.929, 0.752] has a tie of {65 %}[0.725, 0.697, 0.769, 0.721], and so on. After comparing all the green bars, it can be seen that {Healthcare Infrastructure vs. Waste Management} has the highest tie of 65 %.\nBased on the above thought, the answer is:\n65 %" } ], "id_raw": 391 }, { "id": "yohbeTi5ukdMgDpq", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nCould you put the win, tie, and loss percentages data for each era into a structured table?" }, { "from": "gpt", "value": "Sure. The y-axis of the chart are the eras, and the x-axis are the percentages. According to {the yticks}[{0.168, 0.809, 0.935, 0.099}], there are 7 eras: {Industrial Revolution}[0.015, 0.799, 0.158, 0.823], {Renaissance}[0.072, 0.681, 0.158, 0.705], {Middle Ages}[0.074, 0.563, 0.158, 0.586], {Dark Ages}[0.086, 0.445, 0.158, 0.468], {Iron Age}[0.100, 0.327, 0.158, 0.350], {Bronze Age}[0.079, 0.208, 0.158, 0.232], and {Stone Age}[0.087, 0.090, 0.158, 0.114]. The lengths of the bars in each group represent the percentages of win, tie, and loss for each era respectively. I can extract these percentages from {the text inside each bar}[0.302, 0.797, 0.345, 0.820; 0.263, 0.679, 0.306, 0.702; 0.231, 0.561, 0.275, 0.584; 0.204, 0.442, 0.248, 0.466; 0.243, 0.324, 0.287, 0.348; 0.302, 0.206, 0.345, 0.229; 0.224, 0.088, 0.267, 0.111].\nBased on the above thought, the answer is:\nEra,Win,Tie,Loss\nIndustrial Revolution,40%,40%,20%\nRenaissance,30%,40%,30%\nMiddle Ages,22%,38%,40%\nDark Ages,15%,40%,45%\nIron Age,25%,45%,30%\nBronze Age,40%,35%,25%\nStone Age,20%,50%,30%" } ], "id_raw": 393 }, { "id": "DqHZUQRTMEN2D7TK", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nWhich era could be considered the peak of civilization development, according to the chart?" }, { "from": "gpt", "value": "The peak of civilization development could be inferred where the 'Win' attribute was maximized and the 'Loss' attribute was minimized. The 'Win' attribute bars are the blue bars {[0.168, 0.773, 0.479, 0.844; 0.168, 0.655, 0.401, 0.726; 0.168, 0.537, 0.339, 0.608; 0.168, 0.419, 0.284, 0.489; 0.168, 0.300, 0.362, 0.371; 0.168, 0.182, 0.479, 0.253; 0.168, 0.064, 0.323, 0.135}. Industrial Revolution and Bronze Age both have the longest 'Win' bars, but the 'Loss' bar of the Industrial Revolution [0.790, 0.773, 0.946, 0.844] is smaller than the 'Loss' bar of the Bronze Age [0.751, 0.182, 0.946, 0.253].\nBased on the above thought, the answer is:\nIndustrial Revolution." } ], "id_raw": 393 }, { "id": "ZTE2Rir975ZNVpJJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_3.png", "conversations": [ { "from": "human", "value": "\nIn what era was the win percentage the lowest?" }, { "from": "gpt", "value": "To find the era with the lowest win percentage, I need to compare the lengths of the win bars in each era, which are the blue bars nested in {the bars for each group}[{0.168, 0.773, 0.479, 0.844; 0.168, 0.655, 0.401, 0.726; 0.168, 0.537, 0.339, 0.608; 0.168, 0.419, 0.284, 0.489; 0.168, 0.300, 0.362, 0.371; 0.168, 0.182, 0.479, 0.253; 0.168, 0.064, 0.323, 0.135}]. The win bar for Dark Ages at [0.168, 0.419, 0.284, 0.489] is the shortest among all, and from the value annotation, it is 15%.\nBased on the above thought, the answer is:\nDark Ages." } ], "id_raw": 393 }, { "id": "B0ScgSEkDnA1iwEm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nCould you structure data observed in the bar chart as a CSV format table?" }, { "from": "gpt", "value": "Yes, according to the title {\"Efficiency Improvement Potential Assessment\"}[0.285, 0.025, 0.863, 0.058] the chart is about efficiency improvement in different areas. As observed from {yticks label}[{\"Resource Utilisation\"}[0.016, 0.319, 0.154, 0.343]; {\"Energy Efficiency\"}[0.033, 0.148, 0.154, 0.171]] and other areas, along the y-axis which are 'Energy Efficiency', 'Resource Utilisation', 'Waste Management', 'Process Automation', and 'Overall Efficiency'. Each y-axis label represents an evaluation area. For each area, two parallel bars that differ in color represent 'Potential Improvement' and 'Actual Improvement'. According to {the legend}[0.366, 0.034, 0.783, 0.075], the light blue bars({Potential Improvement})[0.410, 0.043, 0.569, 0.066] represent 'Potential Improvement' while the light green bars ({Actual Improvement})[0.636, 0.043, 0.777, 0.066] represent 'Actual Improvement'. The actual values of each area can be obtained by observing the lengths of the bars and the annotations above them. For example, the 'Potential Improvement' for 'Resource Utilisation' is about 62.5 and the 'Actual Improvement' is about 45.0, which can be determined by {the blue bar}[0.163, 0.277, 0.511, 0.380] and {the green bar}[0.511, 0.277, 0.762, 0.380]. I can do this for every area to extract all data.\nBased on the above thought, the answer is:\nEvaluation Item, Potential Improvement, Actual Improvement\nEnergy Efficiency, 70.5, 50.0\nResource Utilisation, 62.5, 45.0\nWaste Management, 85.5, 55.0\nProcess Automation, 80.0, 60.0\nOverall Efficiency, 78.0, 58.0" } ], "id_raw": 394 }, { "id": "4ZJkVp5zUe8k9GJG", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you tell which area has the largest gap between potential and actual improvement?" }, { "from": "gpt", "value": "This question requires me to compare the length of blue and green bars of each area. For example, looking at {Energy Efficiency} [0.033, 0.148, 0.154, 0.171], the {Potential Improvement bar} [0.163, 0.105, 0.556, 0.208] is longer than the {Actual Improvement bar} [0.556, 0.105, 0.834, 0.208]. This means the potential improvement is higher than the actual improvement for energy efficiency. Similar comparisons can be made for other areas such as Resource Utilisation, Waste Management, Process Automation, and Overall Efficiency. The biggest gap is observed in Waste Management represented by the coordinates [0.015, 0.491, 0.154, 0.515] where the {Potential Improvement bar}[0.163, 0.449, 0.640, 0.552] and {Actual Improvement bar}[0.640, 0.449, 0.946, 0.552] show a difference of approximately 30 percentage points, which is the largest among all areas.\nBased on the above thought, the answer is:\nWaste Management." } ], "id_raw": 394 }, { "id": "psuJyGcFr7sJWRwl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of potential improvement shown on the x-axis?" }, { "from": "gpt", "value": "The x-axis of this chart represents the percentage of efficiency improvement. By examining the {x-ticks}[{\"label\": '0', 'coordinates': '[0.159, 0.952, 0.168, 0.975]'}, {\"label\": '100', 'coordinates': '[0.707, 0.952, 0.733, 0.975]'}], it can be seen that the range of potential improvement is from 0 to 100.\nBased on the above thought, the answer is:\n0 to 100" } ], "id_raw": 394 }, { "id": "IgJ8bHLwiWTYZepy", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nFrom your view of the bar chart, can you convert the presented data into a CSV format table?" }, { "from": "gpt", "value": "The chart illustrates 'Entrepreneurial Spirit' and 'Entrepreneurship' percentages in various industries, namely 'Healthcare', 'Technology', 'Finance', 'Retail', 'Education', and 'Non-Profit'. Each industry has two corresponding bars, as indicated in the legend[0.263, 0.019, 0.648, 0.061]. The blue bar represents 'Entrepreneurial Spirit' and the green bar denotes 'Entrepreneurship'. The x-axis includes 6 labels that represent these industries and can be found at [{\"Healthcare\"}[0.017, 0.126, 0.093, 0.149], {\"Technology\"}[0.015, 0.269, 0.093, 0.293], {\"Finance\"}[0.040, 0.413, 0.093, 0.436], {\"Retail\"}[0.053, 0.556, 0.093, 0.580], {\"Education\"}[0.024, 0.700, 0.093, 0.723], {\"Non-Profit\"}[0.024, 0.843, 0.093, 0.866]]. The y-axis represents the percentage of 'Entrepreneurial Spirit' or 'Entrepreneurship' that ranges from 0% to 100%. Each bar corresponds to a certain percentage that represents the attribute it is linked with for a particular industry. For instance, the 'Entrepreneurial Spirit' bar for 'Healthcare' can be found at [0.102, 0.092, 0.604, 0.178] with a text indicating '80.0%' at the top [0.331, 0.123, 0.375, 0.147]. This process is repeated for each industry to fetch both 'Entrepreneurial Spirit' and 'Entrepreneurship' values.\nBased on the above thought, the answer is:\nIndustry,Entrepreneurial Spirit,Entrepreneurship\nHealthcare,80.0,52.0\nTechnology,86.0,48.0\nFinance,45.0,18.5\nRetail,60.0,16.5\nEducation,70.0,25.5\nNon-Profit,55.0,35.5" } ], "id_raw": 395 }, { "id": "aJbKPrCYr5uXhFEb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nFrom what you can see in the chart, which industry shows the highest Entrepreneurship percentage and how might this indicate that industry's future growth?" }, { "from": "gpt", "value": "According to the chart, each industry has a green bar which represents Entrepreneurship. By comparing the lengths of these bars, we can determine which industry has the highest Entrepreneurship percentage. The highest bar corresponds to the Healthcare industry where its rectangle's coordinates can be seen at [0.604, 0.092, 0.930, 0.178] with an accompanying text '52.0%' at [0.745, 0.123, 0.789, 0.147]. High levels of Entrepreneurship could indicate that the industry is ripe for innovation, as fresh ideas and new businesses are key factors that drive growth. Thus, I would anticipate that the healthcare industry may experience considerable growth in the near future based on the insights from this chart.\nBased on the above thought, the answer is:\nThe 'Healthcare' industry shows the highest Entrepreneurship percentage. This could bode well for its future as high Entrepreneurship percentages typically indicate a strong presence of innovation and progressive business models, which are key drivers for an industry's growth." } ], "id_raw": 395 }, { "id": "1tSaVAqeKSCq7Qb4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in 'Entrepreneurial Spirit' between the Healthcare and Technology sectors?" }, { "from": "gpt", "value": "The 'Entrepreneurial Spirit' value for 'Healthcare' can be identified by the height of the blue bar labeled \"Entrepreneurial Spirit\" and its associated text, which is found at [0.331, 0.123, 0.375, 0.147]. The text clearly denotes a percentage of 80.0. Similarly, 'Technology's' 'Entrepreneurial Spirit' value can be identified at [0.350, 0.267, 0.394, 0.290] with a value of 86.0 indicated. To find the difference, we subtract the two values.\nBased on the above thought, the answer is:\n6.0" } ], "id_raw": 395 }, { "id": "B47rbOnpuJS6kSaU", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the bar chart representation into a structured CSV format table?" }, { "from": "gpt", "value": "On examining the bar chart, I see three distinct categories in the chart - represented through colors for which {the legend}[0.235, 0.019, 0.786, 0.061] is provided at the bottom. The labels denote {Grants Provided}[0.280, 0.029, 0.391, 0.052] (blue), {Volunteer Hours}[0.458, 0.029, 0.569, 0.052] (green), and {Communities Served}[0.636, 0.029, 0.780, 0.052] (red). Each colored bar corresponds to the respective category under different fields. The y-axis represents different fields: {Religion, Arts, Community Development, Environment, Health, Education}[0.138, 0.843, 0.193, 0.866];[0.165, 0.700, 0.193, 0.723];[0.015, 0.556, 0.193, 0.580];[0.105, 0.413, 0.193, 0.436];[0.148, 0.269, 0.193, 0.293];[0.124, 0.126, 0.193, 0.149]. Each field has each of the three colored bars. The length of each colored bar box represents a numeric value associated with that category for a particular field. For instance, in {Religion}[0.138, 0.843, 0.193, 0.866], the values for {Grants Provided}[0.203, 0.809, 0.299, 0.895], {Volunteer Hours}[0.299, 0.809, 0.651, 0.895], and {Communities Served}[0.651, 0.809, 0.844, 0.895] are 15.0, 55.0, and 30.0 respectively. From these, I can create the CSV table.\nBased on the above thought, the answer is:\nField,Grants Provided,Volunteer Hours,Communities Served\nEducation,25.0,55.0,20.0\nHealth,20.0,35.0,45.0\nEnvironment,30.0,45.0,25.0\nCommunity Development,42.0,48.0,10.0\nArts,28.0,51.0,21.0\nReligion,15.0,55.0,30.0" } ], "id_raw": 396 }, { "id": "I6nFXpam2DhHw81p", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell from the chart which type of charity contribution is prioritized most across all the fields?" }, { "from": "gpt", "value": "From the chart, it seems that each of the three types of contributions, named {Grants Provided, Volunteer Hours, Communities Served}[0.280, 0.029, 0.391, 0.052];[0.458, 0.029, 0.569, 0.052];[0.636, 0.029, 0.780, 0.052] in the legend is depicted as its own separate color. By examining each set of bars, I can gauge the extent of each type of contribution. From what I observe, {Volunteer Hours}[0.458, 0.029, 0.569, 0.052]that is {the green bar}[0.299, 0.809, 0.651, 0.895];[0.382, 0.666, 0.709, 0.752];[0.472, 0.522, 0.780, 0.608];[0.395, 0.379, 0.683, 0.465];[0.331, 0.236, 0.555, 0.322];[0.363, 0.092, 0.716, 0.178] tends to be the most extensive category in the majority of the fields. Only in {Community Development}[0.015, 0.556, 0.193, 0.580] does the Grants Provided bar outstretch the others, but in the remaining five fields, the length of the Volunteer Hours bar tends to be the greatest. This suggests that, overall, Volunteer Hours may be the type of contribution prioritized most across all the fields.\nBased on the above thought, the answer is:\nVolunteer Hours. The green-colored bars representing Volunteer Hours are the longest in most fields, indicating that it is the type of contribution that is prioritized across all the fields." } ], "id_raw": 396 }, { "id": "pKYeWtKen1c3IwQX", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_6.png", "conversations": [ { "from": "human", "value": "\nIn which field is the sum of the values of Grants Provided, Volunteer Hours, and Communities Served the highest?" }, { "from": "gpt", "value": "To find the field where the sum of the values - Grants Provided, Volunteer Hours, and Communities Served - is the highest, I need to sum up the values of the three categories for every field. Let's start with {Religion}[0.138, 0.843, 0.193, 0.866], with the values being {15.0}[0.236, 0.841, 0.266, 0.864], {55.0}[0.460, 0.841, 0.491, 0.864], and {30.0}[0.732, 0.841, 0.763, 0.864] for Grants Provided, Volunteer Hours, and Communities Served respectively. I repeat this for the remainder of the fields: {Arts}[0.165, 0.700, 0.193, 0.723], {Community Development}[0.015, 0.556, 0.193, 0.580], {Environment}[0.105, 0.413, 0.193, 0.436], {Health}[0.148, 0.269, 0.193, 0.293], {Education}[0.124, 0.126, 0.193, 0.149]. I need to compare the sums of each field. Eventually, it seems that {Community Development}[0.015, 0.556, 0.193, 0.580] has the highest sum: {42.0}[0.322, 0.554, 0.353, 0.577], {48.0}[0.610, 0.554, 0.641, 0.577], and {10.0}[0.796, 0.554, 0.827, 0.577] respectively, totaling 100.0.\nBased on the above thought, the answer is:\nCommunity Development." } ], "id_raw": 396 }, { "id": "kg4m3KufkIDmB4Ab", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "This chart is titled as an empty string {}, but from the context, it is understood that it compares win, tie, and loss percentages between different tech sectors. The y-axis{\"PropTech vs. AdTech\"}[0.043, 0.843, 0.182, 0.866] states the tech sector listings and the x-axis values {0}[0.187, 0.952, 0.196, 0.975] to {100}[0.934, 0.952, 0.960, 0.975] represent the percentages of win, tie, and loss. The bars are color-coded with {blue bars}[0.423, 0.029, 0.450, 0.052] representing 'win', {green bars}[0.516, 0.029, 0.537, 0.052] representing 'tie', and {red bars}[0.603, 0.029, 0.634, 0.052] representing 'loss'. Depending on the color of the bars, each one provides a percentage value for 'win', 'tie', or 'loss' for each tech sector. For example, for {'Blockchain vs. AI'}[0.065, 0.126, 0.182, 0.149], the blue bar has a value of {30.3%}[0.284, 0.123, 0.328, 0.147], the green bar has a value of {50.2%}[0.588, 0.123, 0.632, 0.147], and the red bar has a value of {19.5%}[0.852, 0.123, 0.896, 0.147].\nBased on the above thought, the answer is:\nTech sectors,Win (%),Tie (%),Loss (%)\nBlockchain vs. AI,30.3,50.2,19.5\nAI vs. Fintech,33.1,41.6,25.3\nFintech vs. Edtech,25.0,50.0,25.0\nEdtech vs. HealthTech,40.5,40.0,19.5\nHealthTech vs. PropTech,45.5,35.2,19.3\nPropTech vs. AdTech,38.6,35.6,25.8" } ], "id_raw": 397 }, { "id": "4JFEztWV70oFbBcz", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which tech sector comparison seems to interest investors equally?" }, { "from": "gpt", "value": "This kind of question requires considering the green bars that represent cases of a tie in the sectors investments. The height of these bars will tell us where the investments between the two tech sectors have been equal. In the \"Fintech vs. Edtech\" comparison, represented by the {green bar}[0.381, 0.379, 0.758, 0.465], the investments between these sectors have been equally attracting 50% of the investments. This is apparent from the {50.0%}[0.548, 0.410, 0.591, 0.434] text annotation accompanying the green bar. Hence, we can infer that the \"Fintech\" and \"Edtech\" sectors have equally piqued the interest of investors.\nBased on the above thought, the answer is:\nFintech vs. Edtech. Because in this comparison, the investors are equally interested in both sectors, which is represented by a tie percentage of 50.0%." } ], "id_raw": 397 }, { "id": "InN6211uki8IMsPu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_7.png", "conversations": [ { "from": "human", "value": "\nWhich tech sector comparison had the highest win percentage?" }, { "from": "gpt", "value": "To find out which tech sector comparison had the highest win percentage, I will compare the lengths of the {blue bars}[0.423, 0.029, 0.450, 0.052]. They represent the win percentage in each tech sector comparison. For example, in the {\"Blockchain vs. AI\"}[0.065, 0.126, 0.182, 0.149] category, the {blue bar}[0.192, 0.092, 0.421, 0.178] approximately reaches 30.3% on the x-axis. Looking at all the sectors, the longest blue bar corresponds to the sector {\"HealthTech vs. PropTech\"}[0.015, 0.700, 0.182, 0.723], with a height representative of {45.5%}[0.342, 0.697, 0.386, 0.721].\nBased on the above thought, the answer is:\nHealthTech vs. PropTech." } ], "id_raw": 397 }, { "id": "zONcac41dGRe6y3T", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart into CSV format based on the Stacked bar chart you observed?" }, { "from": "gpt", "value": "From the chart title at the top {\"Efficiency Evaluation in Different Regions\"}[0.5, 0.96, 0.69, 0.99] and the {Y-axis tick labels}[{\"Asia Pacific\"}[0.05, 0.24, 0.1, 0.26]; {\"Americas\"}[0.05, 0.5, 0.1, 0.52]; {\"EMEA\"}[0.05, 0.79, 0.1, 0.81]], it's clear that the Y-axis represents different regions. The X-axis tick labels represent percentages from 0 to 100. For each region, there are three horizontal stacked bars representing 'Win', 'Tie', and 'Loss' in that order. According to {the legend}[0.325, 0.019, 0.586, 0.061], the blue, green, and red colors represent 'Win', 'Tie', and 'Loss' respectively. From the length of the color sections and the performance percentage at the end of each section, the values can be extracted. For instance, in the \"Asia Pacific\" category, the length of {the blue section}[0.1, 0.1, 0.4, 0.27] ends approximately at the 35 position on the X-axis, confirmed by the {annotated value \"35.0%\"}[0.23, 0.18, 0.27, 0.2] at the end of the section.\nBased on the above thought, the answer is:\nRegions,Win,Tie,Loss\nAsia Pacific,35.0,40.0,25.0\nAmericas,45.0,30.0,25.0\nEMEA,20.0,50.0,30.0" } ], "id_raw": 398 }, { "id": "1gCh0FIRGm9eDYDX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which region do you think has the best manufacturing efficiency and why?" }, { "from": "gpt", "value": "To answer this question, we should focus on the 'Win' sections of the bars, which represent consistent expansion in manufacturing efficiency. The 'Win' sections are the {blue sections}[{\"EMEA\"}[0.1, 0.7, 0.27, 0.89]; {\"Americas\"}[0.1, 0.4, 0.48, 0.58]; {\"Asia Pacific\"}[0.1, 0.09, 0.4, 0.27]]. Evidently, the length of the 'Win' section for the 'Americas' region extends furthest to approximately 45% on the X-axis, compared to about 35% for 'Asia Pacific' and 20% for 'EMEA'. This indicates that the 'Americas' region has a higher rate of consistent expansion in manufacturing efficiency. Therefore, we can conclude that the 'Americas' region has the best manufacturing efficiency.\nBased on the above thought, the answer is:\nThe Americas region. This is because it has the highest percentage of 'Win', which indicates a consistent expansion in terms of efficiency, compared to the other regions." } ], "id_raw": 398 }, { "id": "7X8cn5xqZppI4tyN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_8.png", "conversations": [ { "from": "human", "value": "\nWhich region shows the highest percentage of 'Loss'?" }, { "from": "gpt", "value": "To find the region with the highest 'Loss' percentage, I need to compare the length of the 'Loss' sections of each region, which are the {red sections}[{\"EMEA\"}[0.68, 0.72, 0.94, 0.89]; {\"Americas\"}[0.73, 0.42, 0.94, 0.58]; {\"Asia Pacific\"}[0.73, 0.09, 0.94, 0.28]]. Looking from top to bottom, the 'Loss' sections for the 'EMEA', 'Americas', and 'Asia Pacific' regions extend to approximately 100%, 100%, and 100% on the X-axis, respectively. However, by checking the annotated values at the end of each red section, the 'Loss' percentage for EMEA is 30%, for Americas it is 25%, and for Asia Pacific, it is also 25%. Therefore, the 'EMEA' region has the highest 'Loss' percentage.\nBased on the above thought, the answer is:\nEMEA" } ], "id_raw": 398 }, { "id": "CllCyQQY2eTdCxfT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table using the bar chart data that compiles the win, tie, and loss percentages for each investment category?" }, { "from": "gpt", "value": "Yes, I can generate that information using the bar chart details. Firstly, we consider the y-axis labels, which represent different investment categories. These categories, denoted by {'Stable growth'}[0.049, 0.135, 0.146, 0.159], {'Gradual recovery'}[0.025, 0.310, 0.146, 0.333], {'Steady recovery'}[0.032, 0.485, 0.146, 0.508], {'Fluctuating growth'}[0.015, 0.659, 0.146, 0.682] and {'Slow increase'}[0.051, 0.834, 0.146, 0.857], form our rows. The x-axis, labeled with percentages from {0}[0.151, 0.952, 0.160, 0.975] to {100}[0.932, 0.952, 0.959, 0.975], presents the potential outcomes: Win, Tie, and Loss. These outcomes, as per the color-coded legend {'Win'}[0.402, 0.029, 0.428, 0.052], {'Tie'}[0.495, 0.029, 0.515, 0.052] and {'Loss'}[0.582, 0.029, 0.612, 0.052], respectively, will form our columns. Each category has three bars that correlate to these outcomes. The length of each bar segment, when measured against the from 0 to 100, provide the values to fill in our table. For example, for 'Stable growth', the three segments of the bar at coordinates {Win}[0.156, 0.092, 0.393, 0.197], {Tie}[0.393, 0.092, 0.788, 0.197], and {Loss}[0.788, 0.092, 0.946, 0.197] represent 30%, 50%, and 20% respectively. In the same way the values for the other categories can be obtained.\nBased on the above thought, the answer is:\nInvestment Category, Win, Tie, Loss\nStable growth, 30%, 50%, 20%\nGradual recovery, 20%, 45%, 35%\nSteady recovery, 40%, 45%, 15%\nFluctuating growth, 25%, 50%, 25%\nSlow increase, 35%, 45%, 20%" } ], "id_raw": 399 }, { "id": "hxV5jJVO1ZHOWmMz", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nWhich strategy would you recommend for someone who wants to minimize losses?" }, { "from": "gpt", "value": "If the aim is to minimize losses, the strategy with the least losses should be considered. This can be determined by analyzing the 'Loss' segment, indicated in the legend as 'Loss'[0.582, 0.029, 0.612, 0.052]. This segment is represented by the rightmost part of each horizontal bar. We compare each 'Loss' segment beginning from 'Stable growth' - {Loss}[0.788, 0.092, 0.946, 0.197] (20%), 'Gradual recovery' - {Loss}[0.669, 0.267, 0.946, 0.371] (35%), 'Steady recovery' - {Loss}[0.827, 0.441, 0.946, 0.546] (15%), 'Fluctuating growth' - {Loss}[0.748, 0.616, 0.946, 0.721] (25%), 'Slow increase' - {Loss}[0.788, 0.791, 0.946, 0.895] (20%). From the comparison, it's clear that 'Steady recovery' has the least 'Loss' segment, indicating that it has the least amount of losses.\nBased on the above thought, the answer is:\nThe 'Steady recovery' strategy is recommended for someone who wants to minimize losses, as it has the smallest loss percentage." } ], "id_raw": 399 }, { "id": "ZKPxM7MBERdP1mtE", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_9.png", "conversations": [ { "from": "human", "value": "\nWithin these strategies, which has the highest win rate?" }, { "from": "gpt", "value": "To find the strategy with the highest win percentage, I need to compare the length of the 'Win' segment of the horizontal bars for each strategy. The 'Win' segment is represented by the first section of each bar, in the color labeled as 'Win' in the legend {Win}[0.402, 0.029, 0.428, 0.052]. Looking at the bar segments from bottom to top, the lengths of the 'Win' segment for each strategy are as follows: 'Stable growth' at {Win coordinate}[0.156, 0.092, 0.393, 0.197] is 30%, 'Gradual recovery' at {Win coordinate}[0.156, 0.267, 0.314, 0.371] is 20%, 'Steady recovery' at {Win coordinate}[0.156, 0.441, 0.472, 0.546] is 40%, 'Fluctuating growth' at {Win coordinate}[0.156, 0.616, 0.353, 0.721] is 25%, and 'Slow increase' at {Win coordinate}[0.156, 0.791, 0.432, 0.895] is 35%. Out of these, 'Steady recovery' has the longest 'Win' segment, meaning it had the highest winning percentage.\nBased on the above thought, the answer is:\nSteady recovery." } ], "id_raw": 399 }, { "id": "91x4oGmEAY8ckeT3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you observed, can you convert the data on the bar chart into a CSV format table?" }, { "from": "gpt", "value": "Yes. First, from the ylabel of the chart, we can observe that the labels are of the format {A vs B}[0.015, 0.843, 0.139, 0.866], such kind of patterns are present throughout the chart. The xticks {\"0\"}[0.144, 0.952, 0.153, 0.975] to {\"100\"}[0.932, 0.952, 0.958, 0.975] indicates that the values are in the percentage format. Looking at the legend box {sublegend}[0.353, 0.019, 0.613, 0.061] where we have labels for {\"Win\"}[0.397, 0.029, 0.423, 0.052], {\"Tie\"}[0.490, 0.029, 0.511, 0.052], and {\"Loss\"}[0.577, 0.029, 0.607, 0.052], we infer that the parameters that are being compared are the win percentages, tie percentages, and loss percentages. For each comparison, there are three bars, and the length of each bar correlates with the respective percentage of wins, ties and losses. The values can be inferred from the position of each bar on the x-axis scale, as each bar has a text tag with the corresponding value, like {45.0%}[0.306, 0.841, 0.349, 0.864].\nBased on the above thought, the answer is:\nComparison,Win%,Tie%,Loss%\nSolar vs. Wind,25.0,50.0,25.0\nSolar vs. Nuclear,20.0,60.0,20.0\nSolar vs. Hydro,30.0,40.0,30.0\nWind vs. Nuclear,35.0,55.0,10.0\nWind vs. Hydro,40.0,50.0,10.0\nNuclear vs. Hydro,45.0,45.0,10.0" } ], "id_raw": 400 }, { "id": "gloo20eaU0pBEdvQ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nConsidering the chart, which energy source seems to have the most promising future? And explain why." }, { "from": "gpt", "value": "From this chart, the promising future would primarily depend on the number of wins a particular energy source has. Let's evaluate each energy source. {Solar vs. Wind}[0.148, 0.092, 0.347, 0.178], {Solar vs. Nuclear}[0.148, 0.236, 0.308, 0.322], and {Solar vs. Hydro}[0.148, 0.379, 0.387, 0.465] we see that Solar has wins percentage as 25%, 20% and 30% respectively. For {Wind vs. Nuclear}[0.148, 0.522, 0.427, 0.608] and {Wind vs. Hydro}[0.148, 0.666, 0.467, 0.752] wins values are 35% and 40% respectively. For {Nuclear vs. Hydro}[0.148, 0.809, 0.507, 0.895], Nuclear has win percentage of 45%. Now coming to Hydro it does not win in any comparisons. So, considering everything Wind and Nuclear have the highest win percentages in their respective comparisons and hence, can be considered to have the most promising future.\nBased on the above thought, the answer is:\nIt's a tie between Wind and Nuclear. As they both have higher win percentages in their respective comparisons, these energy sources seem to have the most promising future." } ], "id_raw": 400 }, { "id": "4anw44qbFoYFaSJH", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_10.png", "conversations": [ { "from": "human", "value": "\nIn which comparison is the percentage of 'Win' highest?" }, { "from": "gpt", "value": "To find the comparison with the highest percentage of 'Win', I need to compare the length of the 'Win' segment (colored blue as per the {legend}[0.353, 0.019, 0.613, 0.061]) for each horizontal bar. Starting with the 'Solar vs. Wind' comparison at the bottom of the chart and working up, I see the 'Win' segment for 'Solar vs. Wind' starts at {0.148}[0.148, 0.092, 0.347, 0.178] and goes up to {25.0%}[0.226, 0.123, 0.270, 0.147] on the x-axis. Similar inspection can be done for rest of the comparisons like 'Solar vs. Nuclear', 'Solar vs. Hydro', 'Wind vs. Nuclear', 'Wind vs. Hydro' and 'Nuclear vs. Hydro'. Among them, 'Nuclear vs. Hydro' has the maximum length for the 'Win' segment from {0.148}[0.148, 0.809, 0.507, 0.895] to {45.0%}[0.306, 0.841, 0.349, 0.864] on the x-axis, which is approximately 45%.\nBased on the above thought, the answer is:\nNuclear vs. Hydro." } ], "id_raw": 400 }, { "id": "rgIJoqkzNgIuJlNR", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nCould you help me convert the given chart data into a structured CSV format?" }, { "from": "gpt", "value": "Sure, to do that, first I have to identify the distribution of data in the chart. The x-axis in this stacked bar chart contains percentages ranging from {0.00}[0.199, 0.984, 0.203, 0.975] to {100.00}[0.948, 0.984, 0.961, 0.975]. The y-axis contains comparison categories, which are {'Combines vs. Harvesters'}[0.199, 0.092, 0.360, 0.178], {'Tractors vs. Ploughs'}[0.199, 0.236, 0.371, 0.322], {'Irrigation vs. Fertilizer'}[0.199, 0.379, 0.412, 0.465], {'Organic vs. Conventional'}[0.199, 0.522, 0.453, 0.608], {'Dairy vs. Poultry'}[0.199, 0.666, 0.427, 0.752], {'Fruits vs. Grains'}[0.199, 0.809, 0.334, 0.895]. For each category, there are three sections symbolizing 'Win', 'Tie', 'Loss', according to the color legend {'Win'}[0.389, 0.031, 0.417, 0.047], {'Tie'}[0.482, 0.031, 0.509, 0.047] and {'Loss'}[0.569, 0.031, 0.597, 0.047]. The length of each section in a bar represents the percentage of that value which can be read from the x-axis labels. Now, let me convert each category into one line in CSV format.\nBased on the above thought, the answer is:\nCategory,Win,Tie,Loss\nCombines vs. Harvesters,21.5,60.5,18.0\nTractors vs. Ploughs,23.0,64.0,13.0\nIrrigation vs. Fertilizer,28.5,66.5,5.0\nOrganic vs. Conventional,34.0,52.0,14.0\nDairy vs. Poultry,30.5,62.0,7.5\nFruits vs. Grains,18.0,72.0,10.0" } ], "id_raw": 401 }, { "id": "ibSm1IvdNdVFX8ON", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nIf a person wants to focus on winning strategies, which two categories should he focus on according to the chart?" }, { "from": "gpt", "value": "To answer this, I should analyze the 'Win' sections in all categories as 'Win' results represent positive aspects. These sections are marked in color marked as {'Win'}[0.389, 0.031, 0.417, 0.047] in the color legend. The lengths of 'Win' sections represent the percentage distribution and are the first sections from the left in each category. These sections include {'Combines vs. Harvesters'}[0.199, 0.092, 0.360, 0.178], {'Tractors vs. Ploughs'}[0.199, 0.236, 0.371, 0.322], {'Irrigation vs. Fertilizer'}[0.199, 0.379, 0.412, 0.465], {'Organic vs. Conventional'}[0.199, 0.522, 0.453, 0.608], {'Dairy vs. Poultry'}[0.199, 0.666, 0.427, 0.752], {'Fruits vs. Grains'}[0.199, 0.809, 0.334, 0.895]. The 'Organic vs. Conventional' and 'Dairy vs. Poultry' categories have the longer 'Win' sections, meaning they have higher win percentages. Thus, if a person wants to concentrate on winning strategies, they should focus on these two categories.\nBased on the above thought, the answer is:\nThe person should focus on 'Organic vs. Conventional' and 'Dairy vs. Poultry'" } ], "id_raw": 401 }, { "id": "qhmVfgGIAEaJMKcv", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_11.png", "conversations": [ { "from": "human", "value": "\nWhich comparison category has the fewest losses according to the chart?" }, { "from": "gpt", "value": "To answer this question, I'll focus on identifying the lengths of the color sections representing 'Loss' in each category bar. Looking at the provided data, for each category bar, 'Loss' is represented by the last section of the bar, and the corresponding color is indicated by the {'Loss'}[0.569, 0.031, 0.597, 0.047] in the color legend. Now, I need to observe the lengths of those 'Loss' sections one by one: {'Combines vs. Harvesters'}[0.813, 0.092, 0.948, 0.178], {'Tractors vs. Ploughs'}[0.850, 0.236, 0.948, 0.322], {'Irrigation vs. Fertilizer'}[0.910, 0.379, 0.948, 0.465], {'Organic vs. Conventional'}[0.843, 0.522, 0.948, 0.608], {'Dairy vs. Poultry'}[0.891, 0.666, 0.948, 0.752], {'Fruits vs. Grains'}[0.873, 0.809, 0.948, 0.895]. Among all the categories, the 'Loss' section for the 'Irrigation vs. Fertilizer' category has the shortest length, representing the fewest losses.\nBased on the above thought, the answer is:\nIrrigation vs. Fertilizer" } ], "id_raw": 401 }, { "id": "apuHUfsW8ypSeEXd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nConvert the entire data as you see in the bar chart into a structured CSV table format." }, { "from": "gpt", "value": "The x-axis has numeric labels representing the percentage of purchases from 0 to 100 incrementing by 20s at positions {0}[0.180, 0.952, 0.189, 0.975], {20}[0.328, 0.952, 0.346, 0.975], {40}[0.481, 0.952, 0.498, 0.975], {60}[0.633, 0.952, 0.651, 0.975], {80}[0.786, 0.952, 0.803, 0.975], {100}[0.934, 0.952, 0.960, 0.975]. The y-axis has labels indicating different consumer behavior phases including: {\"Customer Preferences\"}[0.021, 0.847, 0.175, 0.870], {\"Return to Normalcy\"}[0.040, 0.710, 0.175, 0.733], {\"Lockdown Impact\"}[0.053, 0.572, 0.175, 0.596], {\"Innovations in Delivery\"}[0.015, 0.435, 0.175, 0.459], {\"Advent of Markets\"}[0.048, 0.298, 0.175, 0.321], and {\"Rise of E-commerce\"}[0.037, 0.161, 0.175, 0.184]. Each phase has 3 bars encapsulating in-store purchases, online purchases, and e-commerce app purchases, with their respective percentages included which I interpret from the length of the bars. For instance, in \"Customer Preferences\" phase, purchasing methods presented are {In-store purchase}[0.184, 0.815, 0.380, 0.897] labelled as \"25.7%\", {Online purchase}[0.380, 0.815, 0.703, 0.897] labelled as \"42.3%\", and {E-commerce Apps}[0.703, 0.815, 0.947, 0.897] labelled as \"32.0%\". The same method applies for every phase and for every type of purchase.\nBased on the above thought, the answer is:\nPhase,In-store purchase,Online purchase,E-commerce Apps\nRise of E-commerce,20.0,50.0,30.0\nAdvent of Markets,30.0,25.5,44.5\nInnovations in Delivery,25.5,35.0,39.5\nLockdown Impact,15.7,59.1,25.2\nReturn to Normalcy,30.5, 45.5,24.0\nCustomer Preferences,25.7,42.3,32.0" } ], "id_raw": 402 }, { "id": "A3AfCEgtjRBklDBX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which purchasing method was most preferred by consumers during the lockdown?" }, { "from": "gpt", "value": "To determine the most preferred purchasing method during the lockdown, I need to examine the bars corresponding to the \"Lockdown Impact\" phase. In this phase, there are three bars each representing a method of purchasing: {In-store purchase}[0.184, 0.540, 0.304, 0.623] with a value label of \"15.7%\", {Online purchase}[0.304, 0.540, 0.755, 0.623] with a value label of \"59.1%\", and {E-commerce Apps}[0.755, 0.540, 0.947, 0.623] with a value label of \"25.2%\". Comparing the lengths, I can see that the longest one is the green bar representing online purchases, thus indicates it was the most popular purchasing method during lockdown.\nBased on the above thought, the answer is:\nOnline purchase. This was the most preferred method since the highest percentage of purchases during the lockdown was made online." } ], "id_raw": 402 }, { "id": "ilAxJNsrl1JeCaXC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_12.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, during which phase was the percentage of in-store purchases the highest?" }, { "from": "gpt", "value": "To find the phase where the percentage of in-store purchases is the highest, I need to look for the longest blue bar. The length of the blue bar indicates the percentage of in-store purchases. For each category in the y-axis, I examined the in-store purchase bar. By comparing these bars, I can see that the longest blue bar is under the \"Return to Normalcy\" phase, and its coordinate is [0.184, 0.678, 0.417, 0.760]. This bar represents the highest percentage of in-stores purchases at 30.5% according to the text atop the bar [0.279, 0.707, 0.323, 0.730].\nBased on the above thought, the answer is:\nReturn to Normalcy" } ], "id_raw": 402 }, { "id": "lDwW2p2qwWujoiSe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, construct a CSV formatted table capturing each Media Outlet's percentage of Influence and Public Opinion Guidance." }, { "from": "gpt", "value": "Observing the chart, the {y-ticks}[0.129, 0.052, 0.985, 0.935] on the left represent different Media Outlets, including Twitter, Facebook, News Websites, Television, Radio, and Print Media. There are two bars for each outlet, according to the {legend}[0.297, 0.019, 0.646, 0.061], the blue bar represents the 'Influence' and the green bar represents 'Public Opinion Guidance'. The x-ticks represent percentages ranging from 0 to 100. I will then analyze the length of each bar to get the exact percentage that each bar represents. For example, the first pair of bars represents Twitter's 'Influence' and 'Public Opinion Guidance'. The length of the blue bar is from [0.129, 0.809, 0.296, 0.895] with a value of 27.3% written on it, and the green bar ranges from [0.350, 0.092, 0.944, 0.178] with a value of 73.5% written on it. By following this pattern, we can get the percentage for other Media Outlets as well.\nBased on the above thought, the answer is:\nMedia Outlet,Influence,Public Opinion Guidance\nTwitter,27.3,73.5\nFacebook,49.9,50.1\nNews Websites,41.3,58.7\nTelevision,39.2,60.8\nRadio,28.4,71.6\nPrint Media,20.7,79.3" } ], "id_raw": 403 }, { "id": "8BalaeWBfTPOP22p", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nBased on this dataset, which medium do you think would be best for persuasion purposes, and why?" }, { "from": "gpt", "value": "Considering persuasion targets to influence public opinion, we need to select one which has a good balance between Influence and Public Opinion Guidance. Looking at the bars for Facebook, it represents {Influence}[0.129, 0.236, 0.533, 0.322] with 49.9% and {Public Opinion Guidance}[0.533, 0.236, 0.938, 0.322] with 50.1%. These two values are almost equal, indicating that Facebook has been quite effective at both influencing people's thoughts and guiding public opinion. Therefore, this Media Outlet is likely to be a powerful tool for persuasion.\nBased on the above thought, the answer is:\nFacebook. Because Facebook carries nearly equal weight in both influencing people's thoughts and guiding public opinion, hence, it can be quite effective for persuasion purposes." } ], "id_raw": 403 }, { "id": "AJxC3ETK9TSzJVpI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_13.png", "conversations": [ { "from": "human", "value": "\nWhich media outlet has the smallest difference between its Influence and Public Opinion Guidance percentages?" }, { "from": "gpt", "value": "To find the media outlet with the smallest difference between its Influence and Public Opinion Guidance percentages, I need to subtract the Influence value from the Public Opinion Guidance value for each Media Outlet. Starting with Twitter, whose {Influence value}[0.129, 0.809, 0.296, 0.895] is 27.3% and {Public Opinion Guidance value} [0.350, 0.092, 0.944, 0.178] is 73.5%, the difference is 46.2%. Continuing this analysis through Facebook, News Websites, Television, Radio, and finally Print Media, it can be deduced that Facebook with {Influence value}[0.129, 0.236, 0.533, 0.322] of 49.9% and {Public Opinion Guidance value}[0.533, 0.236, 0.938, 0.322] of 50.1% has the smallest difference of 0.2%.\nBased on the above thought, the answer is:\nFacebook" } ], "id_raw": 403 }, { "id": "f880AS6nsXv9yGuD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nCould you present the data on the chart in a tabular form?" }, { "from": "gpt", "value": "Looking at the chart title {education and training industry trends}[0.571, 0.038, 0.571, 0.038] and the {legend}[0.346, 0.019, 0.635, 0.096], I see that the chart shows five different trends over a period of 5 years from 2017 to 2021. The x-axis represents the percentage values, varying from {0}[0.165, 0.952, 0.174, 0.975] to {100}[0.959, 0.952, 0.985, 0.975], and the y-axis stands for different types of trends, including {Increase}[0.101, 0.135, 0.160, 0.159], {Stable}[0.116, 0.310, 0.160, 0.333], {Abrupt drop}[0.076, 0.485, 0.160, 0.508], {Pronounced Increase}[0.015, 0.659, 0.160, 0.682], and {Upward Trend}[0.064, 0.834, 0.160, 0.857]. To construct the table, I can represent each bar as a cell in the table, using the x-coordinate of the end side of each bar and the corresponding year from the legend for each value. For example, the bar for {Increase for 2017}[0.170, 0.092, 0.250, 0.197] ends approximately at the 10 position on the x-axis, indicating a value of 10%.\nBased on the above thought, the answer is:\nTrends,2017,2018,2019,2020,2021\nIncrease,10,12,15,18,21\nStable,50,51,49,50,51\nAbrupt drop,80,60,40,20,10\nPronounced Increase,15,25,35,45,55\nUpward Trend,5,10,15,20,25" } ], "id_raw": 404 }, { "id": "qYOV7s2mnomoiUYY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about the overall trend in the Education and Training Industry over the years?" }, { "from": "gpt", "value": "By looking at the bars of each trend, there appears to be a consistent pattern. From 2017 to 2021, the trend categories that have shown growth are {Upward Trend}, {Pronounced Increase}, and {Increase}. From this, I can infer that there is an upward trajectory overall in this industry. However, the {Abrupt drop} category appears to have reduced significantly over the years, suggesting that there are some areas in this industry that are losing traction or are less in demand. The {Stable} category has remained more or less consistent throughout these years, indicating certain aspects of the industry have not changed substantially.\nBased on the above thought, the answer is:\nThe overall trend in the Education and Training Industry appears to be positive, although there are certain aspects that have experienced a significant decline, and others that have remained constant over the years." } ], "id_raw": 404 }, { "id": "5hp0PHIU2gT1GeJN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_27_14.png", "conversations": [ { "from": "human", "value": "\nWhich category experienced the biggest drop from 2017 to 2021?" }, { "from": "gpt", "value": "To answer this, I must examine the beginning and end values of each trend. For {Abrupt drop}, the 2017 bar<{\"2017\"}[0.170, 0.441, 0.811, 0.546]> ends near the 80 on x-axis and 2021 bar<{\"2021\"}[0.170, 0.441, 0.250, 0.546]> ends near 10, indicating a drop of 70%. Similarly, for each category, I need to compare the differences between 2017 and 2021. Therefore, by examining all categories, I can detemine which one has the biggest drop.\nBased on the above thought, the answer is:\nAbrupt drop" } ], "id_raw": 404 }, { "id": "rF13GB42BB1DReuE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you see, can you convert the data for both products into a CSV format table?" }, { "from": "gpt", "value": "From the data I see in the bar chart, both products: {New Improved Product}[0.203, 0.060, 0.535, 0.095] and {Declining Product Sales}[0.570, 0.060, 0.842, 0.095] have sales data for 4 quarters (Q1,Q2,Q3,Q4). The x-axis tick labels represent these quarters. The y-axis tick labels represent the corresponding sales values. The y-axis for the New Improved Product ranges from {0}[0.087, 0.889, 0.102, 0.924] to {450}[0.058, 0.127, 0.102, 0.162]. The y axis for the Declining Product Sales has a negative range from {-1000}[0.879, 0.968, 0.957, 1.003] to {0}[0.879, 0.160, 0.894, 0.195]. The interpretation of values for the Declining Product Sales is reversed because it is a declining trend. The sales values for each product can be ascertained from the height of the bars and the value annotations at the top of each bar. For instance, the height of {the bar for Q1 for the New Improved Product Sales}[0.152, 0.564, 0.231, 0.903] shows approximately the 200 sales figure. The value annotation at its top also shows a {200K}[0.162, 0.513, 0.221, 0.548] sales value, thus the sale of 'New Improved Product' for Q1 is 200.\nBased on the above thought, the answer is:\nQuarter,New Improved Product Sales,Declining Product Sales\nQ1,200,860\nQ2,275,820\nQ3,335,735\nQ4,410,670" } ], "id_raw": 406 }, { "id": "W75OmjUliJ57kmJR", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which product seems more favorable in Q4 and why?" }, { "from": "gpt", "value": "To determine which product is more favorable in Q4, I need to compare the bar heights of the two products in Q4. For the {New Improved Product Sales} [0.203, 0.060, 0.535, 0.095], the Q4 sales bar is located at [0.749, 0.209, 0.829, 0.903], and its height approximately corresponds to a 410K value on the y-axis. For the {Declining Product Sales}[0.570, 0.060, 0.842, 0.095], the bar for Q4 is located at [0.749, 0.174, 0.829, 0.715], and its height corresponds to a 670K value on the y-axis (Note: As the y-axis for the Declining Product Sales is inverted, the higher the bar, the lower the value). Therefore, in Q4, the New Improved Product Sales has lower sales than the Declining Product Sales.\nBased on the above thought, the answer is:\nThe Declining Product Sales. Even though it demonstrates a declining trend, the sales for this product are still significantly higher than the New Improved Product Sales in Q4." } ], "id_raw": 406 }, { "id": "4VfYXZxHrWJ9YunQ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_1.png", "conversations": [ { "from": "human", "value": "\nHow much has the Declining Product Sales fallen from Q1 to Q4?" }, { "from": "gpt", "value": "In order to find the decline in sales for the Declining Product Sales from Q1 to Q4, I need to look at the difference in bar heights at the start of Q1 and the end of Q4. At Q1, the Declining Product Sales's bar is located at [0.152, 0.174, 0.231, 0.868], and the height from the y-axis to the bar top is about 860K. At Q4, the bar is located at [0.749, 0.174, 0.829, 0.715], and the height from the y-axis to the bar top is about 670K. Therefore, the decrease in sales from Q1 to Q4 for the Declining Product Sales can be calculated as the difference between 860K and 670K.\nBased on the above thought, the answer is:\n190K" } ], "id_raw": 406 }, { "id": "iN22py2WHAOnfjTK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, could you create a CSV table for the AI Adoption and Automation Trend over five periods?" }, { "from": "gpt", "value": "From the chart title {\"AI Adoption Vs Automation Trend\"}[0.268, 0.118, 0.724, 0.163], the x-axis represents the timeline. For y-axis, {AI Adoption}[0.025, 0.458, 0.048, 0.658] and {Automation Trend}[0.952, 0.403, 0.975, 0.712] labels reflect the two different trends. The AI Adoption data is displayed by {the grey bars}[0.139, 0.462, 0.204, 0.941] and the Automation Trend data is presented by {the pink bars}[0.139, 0.558, 0.204, 0.941]. To populate the CSV table, the read-out values annotated in white on top of each bar correspond to the respective trend value for each period.\nBased on the above thought, the answer is:\nPeriod,AI Adoption,Automation Trend\n1,50,-40\n2,55,-45\n3,40,-50\n4,65,-60\n5,70,-40" } ], "id_raw": 407 }, { "id": "SQJCVJju3awcT9FF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you analyze and tell which trend (AI adoption or Automation Trend) has been more stable over the years?" }, { "from": "gpt", "value": "This requires a comparison between the degree of fluctuation in {AI adoption}[0.025, 0.458, 0.048, 0.658] and {Automation Trend}[0.952, 0.403, 0.975, 0.712]. If the changes are small, it means that the trend is relatively stable. So, we can conclude its stability by measuring the changes in the heights of the respective bars over different periods. From the chart, it is noticeable that the AI adoption bars are making significant jumps between periods. In contrast, the change in height for the Automation Trend bars is less drastic, indicating a smoother trend over time and thus, a higher degree of stability.\nBased on the above thought, the answer is:\nThe Automation Trend has been more stable over the years." } ], "id_raw": 407 }, { "id": "iYjpmFpBGGs6Kkkm", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_2.png", "conversations": [ { "from": "human", "value": "\nWhich period had the highest AI Adoption value?" }, { "from": "gpt", "value": "To find the period with the highest AI Adoption value, I have to check the value at the top of each {grey bar}[0.139, 0.462, 0.204, 0.941]. By observation, {the grey bar in the fifth period}[0.788, 0.270, 0.853, 0.941] is the highest with the value of 70.\nBased on the above thought, the answer is:\nPeriod 5." } ], "id_raw": 407 }, { "id": "JwTcufauRsFIsFgj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nCan you transform the bar chart data into a two-column CSV format reflecting the effects of Monetary and Fiscal Policies over the years?" }, { "from": "gpt", "value": "Indeed, examining {the bars corresponding to Monetary Policy}[0.126, 0.273, 0.158, 0.564] and their respective {percentage annotations}, like [0.114, 0.274, 0.169, 0.298] denoting 73.4%, I observe a consistent sequence for monetary policy data points, with the first value starting at 73.4%. Similarly, for {Fiscal Policy}, {the first bar}[0.126, 0.564, 0.158, 0.882] has an {annotated value of 80.2%}[0.114, 0.884, 0.169, 0.907]. I would proceed year by year for both policies, finding corresponding bars and their annotations to compile the CSV format data accurately.\nBased on the above thought, the answer is:\nYear,Monetary Policy,Fiscal Policy\n1,73.4,80.2\n2,82.6,79.3\n3,79.5,84.1\n4,67.4,76.0\n5,62.2,72.1\n6,58.3,71.4\n7,55.6,70.5\n8,54.3,67.3\n9,53.1,66.4\n10,51.5,60.7" } ], "id_raw": 408 }, { "id": "eRtBP7ut32U0ZgfT", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the average effect of Fiscal Policy across the decade?" }, { "from": "gpt", "value": "To calculate the average effect of Fiscal Policy, I must review and sum up the values present in each of the {fiscal policy bar annotations}, starting from [0.114, 0.884, 0.169, 0.907] at 80.2%, through subsequent years, and ending at [0.831, 0.806, 0.886, 0.830] at 60.7%. Having ten data points in total, I will sum these percentages and then divide the sum by 10 to find the average.\nBased on the above thought, the answer is:\n71.72" } ], "id_raw": 408 }, { "id": "CHKqlytGmvmmhGfD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nBased on the dual bar chart you observe, can you help me structure this data in a CSV format table?" }, { "from": "gpt", "value": "{The chart's title}[0.239, 0.118, 0.761, 0.163] states the data represents scores difference in different education fields between Traditional and Modern teaching methods. The x-axis tick labels which are {\"Science\"}[0.181, 0.927, 0.270, 0.962], {\"Literature\"}[0.352, 0.927, 0.465, 0.962], {\"Math\"}[0.562, 0.927, 0.620, 0.962], and {\"History\"}[0.733, 0.927, 0.815, 0.962] represent the various academic fields. The height of bars represents scores of each teaching method provided as texts are on top of each bar. For instance, the bar for Traditional method in Science at [0.153, 0.721, 0.226, 1.632] has a score of 75. Hence, by observing each bar, extracting the value and associating it with the respective field and method, we can structure the information in CSV format.\nBased on the above thought, the answer is:\nEducation Field,Traditional Method,Modern Method\nScience,75,90\nLiterature,68,80\nMath,82,98\nHistory,95,110" } ], "id_raw": 409 }, { "id": "QyoMI3wcztYPNA41", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the Data Visualized on the chart, what would you suggest about the teaching methods?" }, { "from": "gpt", "value": "Inspecting the bar heights representing Traditional method and Modern Method scores, for all fields like {\"Science\"}[0.153, 0.721, 0.226, 1.632], {\"Literature\"}[0.335, 0.806, 0.409, 1.632], {\"Math\"}[0.518, 0.636, 0.591, 1.632], {\"History\"}[0.701, 0.478, 0.774, 1.632] it is clear that Modern Method has outperformed. It has resulted in higher scores consistently in all fields. This comparison shows that the Modern Method of teaching seems to be more effective.\nBased on the above thought, the answer is:\nBased on the data, It would be recommended to use the Modern Method of teaching because the scores observed under this method were consistently higher in all fields, indicating a significant positive impact on the academic performance of students." } ], "id_raw": 409 }, { "id": "FWddr5mHG6rf2r7m", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_4.png", "conversations": [ { "from": "human", "value": "\nIn which field, the score improvement from Traditional to Modern method was maximum?" }, { "from": "gpt", "value": "To establish the field with the largest improvement from Traditional to Modern methods, I need to examine the difference in each field's scores. Following left to right from the first pair of bars which are located at [0.153, 0.721, 0.226, 1.632] for Traditional Method and [0.226, 0.539, 0.299, 1.632] for Modern Method, representing the Science field, and find that the difference between the two is 15; going through each field, one can find that the maximum difference is in {\"History\"}[0.733, 0.927, 0.815, 0.962] field where the two method bars at [0.701, 0.478, 0.774, 1.632] for Traditional Method and [0.774, 0.296, 0.847, 1.632] for Modern Method show a score difference of 15, which is the maximum difference among all fields.\nBased on the above thought, the answer is:\nHistory" } ], "id_raw": 409 }, { "id": "lLRLV8JHhOmueARA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nCan you translate the data from the bar chart into a structured CSV format?" }, { "from": "gpt", "value": "The chart represents the data of two geographical areas marked {Rapid recovery}[0.155, 0.348, 0.330, 0.383] and {Abrupt Drop}[0.687, 0.348, 0.829, 0.383] with two different types of ecological data: {Mountains}[0.189, 0.175, 0.309, 0.210] and {Ocean}[0.802, 0.175, 0.876, 0.210]. To convert these data points into a structured table, we can take each geographic area as a row and each type of data as a column. The values can be inferred from the height of the bars, additionally with the help of the percentage values mentioned above each bar, for instance, {55.3%}[0.206, 0.153, 0.279, 0.188] for the Mountains under Rapid recovery and {32.1%}[0.721, 0.211, 0.794, 0.246] for the Mountains under Abrupt Drop. Similarly, we can find out the values for the Ocean in the same way.\nBased on the above thought, the answer is:\nArea,Mountains,Ocean\nRapid recovery,55.3,44.7\nAbrupt drop,32.1,47.2" } ], "id_raw": 410 }, { "id": "PJvjgVQvi0JU9lEs", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nWhich geographical area represented in the chart faced a severe ecological crisis considering the compared data from Mountains and Ocean regions?" }, { "from": "gpt", "value": "To answer this, we have to look at the overall decline in both the mountain and ocean regions of both geographic areas. In the geographical area labeled {Abrupt Drop}[0.687, 0.348, 0.829, 0.383], the mountain region shows {a decrease of 32.1%}[0.721, 0.211, 0.794, 0.246] and the {ocean region a decrease of 47.2%}[0.721, 0.419, 0.794, 0.454]. However, in the {Rapid Recovery}[0.155, 0.348, 0.330, 0.383] area, the mountainous and ocean regions have increased by {55.3%}[0.206, 0.153, 0.279, 0.188] and {44.7%}[0.206, 0.412, 0.279, 0.447] respectively. Therefore, the Abrupt Drop area faced the severe ecological crisis.\nBased on the above thought, the answer is:\nAbrupt Drop" } ], "id_raw": 410 }, { "id": "WrVOlz9ged2ZqAoH", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_5.png", "conversations": [ { "from": "human", "value": "\nIn which area is the percentage of ecological change in the mountainous regions greater?" }, { "from": "gpt", "value": "Comparing the percentage of ecological change in the mountainous regions in both the areas, we can gauge that the bar representing the mountains in {Rapid recovery}[0.139, 0.186, 0.345, 0.324] is taller than the bar representing mountains in {Abrupt Drop}[0.655, 0.244, 0.861, 0.324]. The respective percentages stated above each bar, {55.3%}[0.206, 0.153, 0.279, 0.188] for Rapid recovery and {32.1%}[0.721, 0.211, 0.794, 0.246] for Abrupt Drop, also affirm our observation.\nBased on the above thought, the answer is:\nRapid recovery" } ], "id_raw": 410 }, { "id": "zSh6UJpR4UsL3TVl", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nCould you please form a CSV table representing the data displayed in the bar chart?" }, { "from": "gpt", "value": "After analyzing the bar chart and its provided components, I can coordinate the scores between the two key areas: Project Management and Project Innovation. The Y-axis represents the percentage scores ranging from {0%}[0.072, 0.478, 0.087, 0.513] to {60%}[0.058, 0.185, 0.087, 0.220], as listed on the left. Now looking at the bars, the bars colored in {#404346}[0.189, 0.093, 0.428, 0.128] correspond to the Project Management scores, and the bars labeled with {#dc9dae}[0.658, 0.093, 0.868, 0.128] represent the Project Innovation scores. These values can be obtained by analyzing the heights of the bars aligned within both sectors.\nBased on the above thought, the answer is:\n,Project Management,Project Innovation\n1,45.1,35.2\n2,57.2,27.3\n3,32.8,50.5\n4,53.2,45.6\n5,12.3,20.7" } ], "id_raw": 411 }, { "id": "aaS4czR5svkl969o", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nConsidering the data visualized in the bar chart, which aspect appears to have more consistent performances across the various projects, Project Management or Project Innovation? Explain your reasoning." }, { "from": "gpt", "value": "To answer this question, I need to evaluate the variability of scores among the bars. For the Project Management section, the bars colored in {#404346}[0.189, 0.093, 0.428, 0.128], there is a particularly low outlier at {12.3%}[0.784, 0.429, 0.857, 0.464] which greatly deviates from the other values. This suggests that the Project Management performance varies considerably. On the other hand, in the Project Innovation section, represented by bars colored in {#dc9dae}[0.658, 0.093, 0.868, 0.128], there seems to be less variation in the heights of the bars, indicating that the performance was more consistent across the projects. Hence, the aspect with more consistent performances appears to be Project Innovation.\nBased on the above thought, the answer is:\nProject Innovation. This is because the performance scores in this aspect have less variation compared to Project Management, revealing a more consistent performance across the projects." } ], "id_raw": 411 }, { "id": "CbgpfC04xXBjxwIt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_6.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which project number has the least percentage in Project Management?" }, { "from": "gpt", "value": "In order to find the project with the lowest percentage in Project Management, I need to identify the shortest bar in {#404346}[0.189, 0.093, 0.428, 0.128]. After comparing all five bars within this group, I can see that {the fifth bar}[0.788, 0.432, 0.853, 0.492] is the shortest, corresponding to a score of {12.3%}[0.784, 0.429, 0.857, 0.464]. Therefore, the lowest percentage in Project Management is associated with the fifth project.\nBased on the above thought, the answer is:\nProject 5" } ], "id_raw": 411 }, { "id": "DeqY4MyEk5pzD4SW", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nJudging by the bar chart, could you extract and present the performance data in a table format for 2016 to 2020?" }, { "from": "gpt", "value": "Yes, the chart offers a comparison of manufacturing production efficiency evaluation for Product A and Product B between 2016 and 2020. The blue bars represent {\"Product A Efficiency\"}[0.113, 0.035, 0.252, 0.058], with the respective percentages annotated at the top of each bar. Meanwhile, the pink bars signify the {\"Product B Efficiency\"}[0.782, 0.035, 0.921, 0.058], again with percentages marked. By reading these corresponding data, we can construct a table.\nBased on the above thought, the answer is:\nYear,Product A Efficiency,Product B Efficiency\n2016,59.2,40.8\n2017,58.1,41.9\n2018,54.6,45.4\n2019,65.4,34.6\n2020,70.2,29.8" } ], "id_raw": 412 }, { "id": "qsrvRowsYCbVaS2S", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, did the introduction of a new manufacturing process for Product A in 2019 have a significant impact?" }, { "from": "gpt", "value": "The impact of the new manufacturing process for Product A can be assessed by examining the changes in its efficiency bar widths. Looking at the bar for {\"Product A\"}[0.642, 0.238, 0.714, 0.551] in 2019, it is significantly wider compared to the bar [0.462, 0.289, 0.534, 0.551] in 2018 which implies a higher percentage of efficiency. This increase can thus be interpreted as a significant positive impact of the new manufacturing process.\nBased on the above thought, the answer is:\nYes, the new manufacturing process introduced in 2019 significantly improved the manufacturing production efficiency for Product A." } ], "id_raw": 412 }, { "id": "585mFmzcwFaXvXWD", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_7.png", "conversations": [ { "from": "human", "value": "\nIn which year was the percentage difference between Product A and Product B efficiencies the widest?" }, { "from": "gpt", "value": "By visually examining the chart, I can see that both set of data have different widths. So to determine the greatest percentage difference, I would need to compare the differences between the percentages given for Product A and Product B for each year. Taking this into account, I can visually identify that the year where the percentage difference was the widest was during {\"2020\"}[0.840, 0.952, 0.875, 0.975] as the bar width for Product A [0.822, 0.214, 0.894, 0.551] was visibly greater than the bar width for Product B [0.822, 0.551, 0.894, 0.742].\nBased on the above thought, the answer is:\n2020." } ], "id_raw": 412 }, { "id": "oqAkhtjA6h32dGBT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you convert the data into a structured table with CSV format?" }, { "from": "gpt", "value": "As seen from the title {\"Comparison of Growth Rates in Digital Content and SEO\"}[0.109, 0.118, 0.883, 0.163], this chart compares the growth rates of different digital content and SEO strategies. According to the x-axis, we have types of digital content and SEO strategies including {\"Video Production\"}[0.115, 0.927, 0.247, 0.962], {\"Podcast\"}[0.346, 0.927, 0.436, 0.962], {\"Content Writing\"}[0.524, 0.927, 0.678, 0.962], {\"Graphic Design\"}[0.750, 0.927, 0.873, 0.962]. The y-axis shows the rate of growth from {\"0\"}[0.072, 0.525, 0.087, 0.560] to {\"60\"}[0.058, 0.160, 0.087, 0.195] percent, which can be inferred by the height of the bars seen above each strategy category. For instance, the bar height in the {\"Video Production\"}[0.139, 0.295, 0.223, 0.539] category suggests a growth of 40.1% which corresponds with the text {\"40.1%\"}[0.144, 0.298, 0.217, 0.333] at the top of the bar. Interpreting similarly for the other categories, we can convert these into a table format.\nBased on the above thought, the answer is:\nDigital Content and SEO Strategy,Growth Rate\nVideo Production,40.1\nPodcast,51.8\nContent Writing,47.3\nGraphic Design,22.5" } ], "id_raw": 413 }, { "id": "oJiz3GLdXBRQvW4B", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart suggest about the current trends in New Media and Digital Content Creation?" }, { "from": "gpt", "value": "The chart illustrates a comparison of growth rates in the New Media and Digital Content Creation sector as depicted by the chart title {\"Comparison of Growth Rates in Digital Content and SEO\"}[0.109, 0.118, 0.883, 0.163]. We can glean insights by looking at the growth rates of the different strategies. High growth is observed in {\"Podcast\"}[0.346, 0.927, 0.436, 0.962] creation at {\"51.8%\"}[0.354, 0.227, 0.428, 0.262] and {\"Content Writing\"} [0.524, 0.927, 0.678, 0.962] at {\"47.3%\"}[0.565, 0.254, 0.638, 0.289], indicating these strategies have been rapidly successful. In comparison, the {\"Graphic Design\"}[0.750, 0.927, 0.873, 0.962] strategy seems to be growing at a slower pace of {\"22.5%\"}[0.775, 0.405, 0.848, 0.440].\nBased on the above thought, the answer is:\nThe chart suggests that Podcast Creation and Content Writing are rapidly increasing strategies in the realm of New Media and Digital Content Creation. On the other hand, Graphic Design is gaining traction at a comparatively slower pace. These trends suggest a greater focus on content-based strategies in the current market." } ], "id_raw": 413 }, { "id": "EJ8lNx4gAtT57c92", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_8.png", "conversations": [ { "from": "human", "value": "\nWhich category shows the highest growth rate in terms of digital content creation?" }, { "from": "gpt", "value": "To figure this out, I'll need to examine the bars corresponding to each category on the chart and determine which one reaches the highest point on the y-axis. Observing, I can see that the growth rate for {\"Podcast\"}[0.346, 0.927, 0.436, 0.962] {\"51.8%\"}[0.354, 0.227, 0.428, 0.262] is the highest among all categories.\nBased on the above thought, the answer is:\nPodcast." } ], "id_raw": 413 }, { "id": "SICcWZXv4LY9sQle", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data represented in the chart figure to a CSV structured table?" }, { "from": "gpt", "value": "Observing {the graph title}[0.366, 0.118, 0.634, 0.163] and the specific bars for the categories 'Urban Development Index' and 'Rural Development Index', found in[{'Urban Development Index'}[0.139, 0.338, 0.345, 0.539]; {Rural Development Index}[0.139, 0.539, 0.345, 0.703]], I can infer that these bars represent the development indices for the years 2018 and 2020. By comparing these bar heights to the values on the Y-axes[{Urban Development Index Y-axis}[0.025, 0.311, 0.048, 0.767]; {Rural Development Index Y-axis}[0.952, 0.318, 0.975, 0.759]], I can convert the indices into meaningful numbers. The bar height of the Urban Development Index for 2018 is at 55, and for 2020 it is at 75. The height of the Rural Development Index bar for 2018 is at 45, and for 2020 it is at 60.\nBased on the above thought, the answer is:\nYear,Urban Development Index,Rural Development Index\n2018,55,45\n2020,75,60" } ], "id_raw": 414 }, { "id": "zlcXvbEW8dnE1Soi", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nCan you suggest what the changing trend of these indices reflect in terms of development?" }, { "from": "gpt", "value": "Observing the bars and their locations, it is evident that both the Urban Development Index [{Urban Development Index24}[0.217, 0.329, 0.268, 0.364]; {Urban Development Index20}[0.732, 0.256, 0.783, 0.291]] and the Rural Development Index [{Rural Development Index18}[0.217, 0.693, 0.268, 0.728]; {Rural Development Index20}[0.732, 0.748, 0.783, 0.783]] have increased from 2018 to 2020. This clearly indicates development in both the urban and rural areas during these years. The larger increase in the Urban Development Index suggests that urban areas have seen more development than rural areas in this period.\nBased on the above thought, the answer is:\nThe increasing trends in both development indices indicate that there has been growth and development in both urban and rural areas from 2018 to 2020. The greater increase in the Urban Development Index may suggest that urban areas saw more development relative to rural areas during this period." } ], "id_raw": 414 }, { "id": "DGjZ2D8C3BNepZCm", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me by how much the Rural Development Index increased from 2018 to 2020?" }, { "from": "gpt", "value": "Looking at the bar for the Rural Development Index in 2018 located at [0.139, 0.539, 0.345, 0.703] and comparing its height to the values on the {Rural Development Index Y-axis}[0.952, 0.318, 0.975, 0.759], I determine that the value was 45. Moving on to the bar for the Rural Development Index in 2020[{bar}[0.655, 0.539, 0.861, 0.757]], and taking a comparison to the same Y-axis, tells me that the value for 2020 was 60. The difference is therefore 60 - 45.\nBased on the above thought, the answer is:\n15" } ], "id_raw": 414 }, { "id": "X0xnfM9BhiUkSItp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "The chart's subtitle {Health Membership & Outdoor Sports}[0.229, 0.118, 0.749, 0.163] tells me that the data I need to convert into a CSV format involves two items, namely: Health Membership and Outdoor Sports. The x-axis contains years {2017}[0.150, 0.927, 0.209, 0.962, 0.179, 0.903], {2018}[0.356, 0.927, 0.415, 0.962, 0.386, 0.903], {2019}[0.562, 0.927, 0.621, 0.962, 0.592, 0.903] and {2020}[0.769, 0.927, 0.827, 0.962, 0.798, 0.903]. Meanwhile, the y-axis, as indicated by {Health Membership label}[0.025, 0.369, 0.048, 0.708] and {Outdoor Sports label}[0.952, 0.407, 0.975, 0.671] corresponds to the percentage values of the two categories. Let's look at the bars now, Health Membership {2017 bar}[0.138, 0.591, 0.221, 2.153], {2018 bar}[0.344, 0.549, 0.427, 2.153], {2019 bar}[0.550, 0.466, 0.633, 2.153] and {2020 bar}[0.757, 0.570, 0.839, 2.153] provide the corresponding values for each year in the dataset. The same goes for the Outdoor Sports bars {2017 bar}[0.138, 0.558, 0.221, 0.884], {2018 bar}[0.344, 0.558, 0.427, 0.857], {2019 bar}[0.550, 0.558, 0.633, 0.899] and {2020 bar}[0.757, 0.558, 0.839, 0.872]. By comparing these bars with the y-axis, I can derive the percentage values for both categories over the noted years.\nBased on the above thought, the answer is:\nType,2017,2018,2019,2020\nHealth Membership,75,77,81,76\nOutdoor Sports,85,78,89,82" } ], "id_raw": 415 }, { "id": "YX9Nx2p5Erp5vfAE", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart analysis, which category seems to be more consistent?" }, { "from": "gpt", "value": "By looking at the length of the bars for both categories over the years, I can assess the consistency of Health Membership and Outdoor Sports. Considering Health Membership {2017 bar}[0.138, 0.591, 0.221, 2.153], {2018 bar}[0.344, 0.549, 0.427, 2.153], {2019 bar}[0.550, 0.466, 0.633, 2.153] and {2020 bar}[0.757, 0.570, 0.839, 2.153], it can be seen that there is a small fluctuation in the values. It consistently remains in the range of 75 to 81 over the observed period. However, in the case of Outdoor Sports {2017 bar}[0.138, 0.558, 0.221, 0.884], {2018 bar}[0.344, 0.558, 0.427, 0.857], {2019 bar}[0.550, 0.558, 0.633, 0.899] and {2020 bar}[0.757, 0.558, 0.839, 0.872], the fluctuation is quite significant, moving from a percentage of 85 in 2017 to 78 in 2018 and climbing up to 89 in 2019 then dropping again to 82 in 2020. Thus, with the more stable values over four years, Health Membership is the more consistent category.\nBased on the above thought, the answer is:\nHealth Membership appears to be most consistent, given the relative stability of its values over the four-year period." } ], "id_raw": 415 }, { "id": "c0dmV1RmUL4Z1rmw", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_10.png", "conversations": [ { "from": "human", "value": "\nConsidering the values, in which year is the difference between Health Membership and Outdoor Sports the smallest?" }, { "from": "gpt", "value": "To answer this question, I need to calculate the absolute difference between the percentage values for Health Membership and Outdoor Sports in each year. Let's start with {2017}[0.150, 0.927, 0.209, 0.962, 0.179, 0.903], the Health Membership {2017 bar}[0.138, 0.591, 0.221, 2.153] has a value of 75 and the Outdoor Sports {2017 bar}[0.138, 0.558, 0.221, 0.884] has a value of 85, resulting in a difference of 10. For {2018}[0.356, 0.927, 0.415, 0.962, 0.386, 0.903], the Health Membership {2018 bar}[0.344, 0.549, 0.427, 2.153] shows a value of 77 and the Outdoor Sports {2018 bar}[0.344, 0.558, 0.427, 0.857] is at 78, the difference being 1. Applying the same process to {2019}[0.562, 0.927, 0.621, 0.962, 0.592, 0.903] and {2020}[0.769, 0.927, 0.827, 0.962, 0.798, 0.903], I notice the smallest difference occurs in 2018.\nBased on the above thought, the answer is:\n2018." } ], "id_raw": 415 }, { "id": "pIPYxGy9RtrmbabP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart into a CSV format table that captures the details of the trends in the Transportation and Logistics industry across different vehicle types?" }, { "from": "gpt", "value": "Sure. The title of this chart {'Transportation and Logistics Industry Trends'}[0.198, 0.118, 0.809, 0.163] indicates that the data is about the industry trends categorized by different vehicle types in Transportation and Logistics. There are 4 categories on the x-axis represented by the labels {'Vehicles'}[0.147, 0.927, 0.241, 0.962], {'Ships'}[0.369, 0.927, 0.431, 0.962], {'Trains'}[0.572, 0.927, 0.640, 0.962], and {'Aircraft'}[0.770, 0.927, 0.855, 0.962]. With that understood, I can extract each bar's numerical information from the annotation above it, to determine the corresponding percentage in each category. For example, in the category of Vehicles, the {'Transportation'}[0.157, 0.371, 0.231, 0.406] and {'Logistics'}[0.157, 0.741, 0.231, 0.776] values are 68.2% and 55.1% respectively. I would complete this process for each category to generate the CSV table.\nBased on the above thought, the answer is:\nVehicle_Type,Transportation,Logistics\nVehicles,68.2,55.1\nShips,78.9,62.5\nTrains,63.3,30.1\nAircraft,25.6,40.0" } ], "id_raw": 416 }, { "id": "FfJPOHp2ZZZEoaZB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart data, what can you infer about the performance of the transportation and logistics sectors across the various types of vehicles?" }, { "from": "gpt", "value": "This question requires a comprehensive comparison of the data from the chart. Looking at the Transportation trend, for {'Vehicles'}[0.153, 0.174, 0.235, 0.748] and {'Trains'}[0.565, 0.174, 0.648, 0.488], the performance is moderate, while for {'Ships'}[0.359, 0.174, 0.441, 0.825], it is the highest, indicating that most transportation likely occurs via Ships. {'Aircraft'}[0.771, 0.174, 0.854, 0.591], however, shows the lowest performance in comparison to all the other vehicle types. As for Logistics, Ships show the highest performance again, while Trains demonstrate the lowest performance. It is likely that Aircraft logistics services are underutilized given their relatively low score. The comparison indicates that Ships are most frequently used for both Transportation and Logistics purposes, suggesting they might be the most effective type of vehicle for these industries.\nBased on the above thought, the answer is:\nShips are the most effective type of vehicle in both the Transportation and Logistics sectors, while Aircraft are relatively underutilized in these sectors, and Trains perform poorly in Logistics." } ], "id_raw": 416 }, { "id": "DowpYlijPNdppdCj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest percentage value for the Logistics across all vehicle types and to which vehicle does it relate?" }, { "from": "gpt", "value": "To answer this question, I need to compare the Logistics values of all vehicle types. The Logistics values are displayed as negative on the graph, annotated above the corresponding bars. Starting with Vehicles, the {'Logistics bar for Vehicles'}[0.153, 0.174, 0.235, 0.748] is annotated as {'55.1%'}[0.157, 0.741, 0.231, 0.776]. Then, for Ships, the {'Logistics bar for Ships'}[0.359, 0.174, 0.441, 0.825] is annotated with {'62.5%'}[0.364, 0.818, 0.437, 0.853]. The Logistics bars for Trains and Aircraft are annotated as 30.1% and 40.0%, respectively. Therefore, based on my analysis, the highest percentage value in Logistics is related to Ships.\nBased on the above thought, the answer is:\nShips." } ], "id_raw": 416 }, { "id": "I2iyDd9bhQOWEQFm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nCould you interpret the bar chart data and create a structured table in CSV format?" }, { "from": "gpt", "value": "Observing the {title}[0.366, 0.130, 0.645, 0.160] 'Social Media Data Analysis', it's noticeable that the x-axis exhibits different social media platforms from {Twitter}[0.142, 0.952, 0.200, 0.975] to {Snapchat}[0.799, 0.952, 0.881, 0.975]. The y-axis is divided into two parts: 'Average Usage Time' with range from {0}[0.065, 0.926, 0.076, 0.950] to {250}[0.043, 0.158, 0.076, 0.182] and 'Average Post Engagement' from {0}[0.935, 0.158, 0.946, 0.182] to {50}[0.935, 0.926, 0.957, 0.950]. This information is represented by bars. For example, the bar corresponding to Twitter's average usage time '[0.126, 0.467, 0.215, 0.935]' reads '152.6 mins', the value can be confirmed by the corresponding {label}[0.123, 0.494, 0.219, 0.518]. Similarly, the bar corresponding to Twitter's average post engagement '[0.126, 0.167, 0.215, 0.522]' reads '23.1%', the value can be affirmed by the descriptive {text}[0.143, 0.550, 0.198, 0.573]. Following this method, we can extract the usage time and post engagement for all platforms.\nBased on the above thought, the answer is:\nPlatform, Average Usage Time (Minutes), Average Post Engagement (%)\nTwitter, 152.6, 23.1\nInstagram, 192.3, 35.7\nFacebook, 226.5, 29.9\nSnapchat, 80.2, 11.1" } ], "id_raw": 417 }, { "id": "VFO0y0Qy17l5m64t", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nConsidering the usage times and post engagements, which social media platform seems to be the most effective and why?" }, { "from": "gpt", "value": "To answer this, I need to consider both the average usage time and post engagement metrics for each platform. Let's start with {'Twitter'}[0.142, 0.952, 0.200, 0.975] which has {usage time}[0.123, 0.494, 0.219, 0.518] of 152.6 mins and {engagement}[0.143, 0.550, 0.198, 0.573] of 23.1%. Next, let's evaluate {'Instagram'}[0.350, 0.952, 0.438, 0.975] with {usage time}[0.346, 0.372, 0.442, 0.396] of 192.3 mins and {engagement}[0.367, 0.744, 0.421, 0.767] of 35.7%. Thirdly, {'Facebook'}[0.576, 0.952, 0.658, 0.975] offers {usage time}[0.569, 0.267, 0.665, 0.291] of 226.5 mins and {engagement}[0.590, 0.654, 0.645, 0.678] of 29.9%. Lastly, {'Snapchat'}[0.799, 0.952, 0.881, 0.975] carries {usage time}[0.797, 0.717, 0.883, 0.740] of 80.2 mins and {engagement}[0.813, 0.366, 0.868, 0.389] of 11.1%. Comparing these statistics, Instagram has a high usage time and the highest post-engagement, hence, it is the most effective.\nBased on the above thought, the answer is:\nInstagram. Because it's approximately the second-highest in terms of average usage time and the top performing in the average post engagement statistics, suggesting users spend a significant amount of time on it and are highly engaged with the content, making it the most effective platform according to the data presented." } ], "id_raw": 417 }, { "id": "DHqOlq7GK82l1dMM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_12.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform has the lowest average usage time?" }, { "from": "gpt", "value": "To answer this question, I need to compare the heights of the bars representing the average usage time for each social media platform. The bars are located at {'Twitter'}[0.126, 0.467, 0.215, 0.935], {'Instagram'}[0.349, 0.345, 0.439, 0.935], {'Facebook'}[0.572, 0.240, 0.662, 0.935], and {'Snapchat'}[0.796, 0.689, 0.885, 0.935]. Here, the lowest bar stands with Snapchat.\nBased on the above thought, the answer is:\nSnapchat" } ], "id_raw": 417 }, { "id": "3j5q860nmrkaca6L", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nCan you create a table representing the trend of 'Slow Growth' from the chart?" }, { "from": "gpt", "value": "Analyzing the bar chart, the 'Slow Growth' trend can be identified by {cyan bars}[0.116, 0.623, 0.152, 0.935] and corresponding {label coordinates}[0.114, 0.584, 0.153, 0.608] which indicate the value above each bar. Each bar's height on the y-axis indicates the trend value for a year, labeled on the x-axis. To construct the table, the coordinate data of each cyan bar and their top-bound text will be cross-referenced with the x-axis {years}[0.112, 0.952, 0.156, 0.975] and y-axis values.\nBased on the above thought, the answer is:\nYear,Slow Growth Value\n2022,15.5\n2023,18.7\n2024,21.1\n2025,23.2\n2026,25.4\n2027,27.3\n2028,29.1\n2029,30.863\n2030,32.998" } ], "id_raw": 418 }, { "id": "OHhi4N8XA4fpZ82Z", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Slow Growth' or 'Unstable Behavior' show a more consistent trend between the years 2022 to 2030?" }, { "from": "gpt", "value": "By examining the bar charts, the consistency of a trend can be evaluated by looking at the direction and size of the bars for each consecutive year. For 'Slow Growth', the bars show a consistent upwards trend, as seen by the increasing height of the {cyan bars}[0.116, 0.623, 0.152, 0.935]; while 'Unstable Behavior' has bars that first decrease then increase, as evidenced by the changing direction of the {hot pink bars}[0.116, 0.129, 0.152, 0.721]. The coordinates of each bar and their progression over the years indicate the consistency of the trend.\nBased on the above thought, the answer is:\n'Slow Growth' shows a more consistent trend as its values gradually increase year over year, whereas 'Unstable Behavior' exhibits fluctuations with notable rebounds, indicating less consistency." } ], "id_raw": 418 }, { "id": "ZCAsPXCj53PqJrmf", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease in 'Unstable Behavior' from 2022 to 2023?" }, { "from": "gpt", "value": "First, I locate the values for 'Unstable Behavior' in 2022 and 2023 using their bar coordinates and the text above them: for 2022, {bar}[0.116, 0.129, 0.152, 0.721] with {value text -25.7}[0.115, 0.749, 0.153, 0.772] and for 2023, {bar}[0.206, 0.129, 0.243, 0.806] with {value text -29.4}[0.205, 0.834, 0.244, 0.858]. Then, I calculate the percentage decrease using the formula ((Old Value - New Value) / Old Value) * 100, which equates to ((-25.7 - (-29.4)) / -25.7) * 100.\nBased on the above thought, the answer is:\n14.4%" } ], "id_raw": 418 }, { "id": "8c8bMF2NuZRcckph", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nCan you construct a CSV format table based on the dual colored bar chart you observed?" }, { "from": "gpt", "value": "The chart title {\"Global Trade & Supply Chain Management Growth 2020 Vs 2021\"}[0.059, 0.118, 0.953, 0.163] labels the two categories compared. On the main chart, the bars represent the data for each year, specifically {\"2020\"}[0.218, 0.927, 0.276, 0.962] and {\"2021\"}[0.735, 0.927, 0.793, 0.962] on the x-axis. Looking at the first bar chart, the {global trade bar for 2020}[0.144, 0.539, 0.351, 0.728], the value for 2020 is {\"-2.6%\"}[0.218, 0.737, 0.281, 0.772]; Similarly for {2021}[0.661, 0.269, 0.867, 0.539], the value is {\"3.7%\"}[0.735, 0.278, 0.793, 0.313]. For the supply chain management growth, the {bar for 2020}[0.144, 0.539, 0.351, 0.641] shows a value of {\"1.4%\"}[0.218, 0.650, 0.277, 0.685] and similarly the {bar for 2021}[0.661, 0.480, 0.867, 0.539] has the value {\"-0.8%\"}[0.731, 0.489, 0.797, 0.524]. Thus, we can generate a csv table from the bars and their corresponding labels.\nBased on the above thought, the answer is:\nYear,Global Trade,Supply Chain Management\n2020,-2.6%,1.4%\n2021,3.7%,-0.8%" } ], "id_raw": 419 }, { "id": "ICBNaZnu29arkcQl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, did Supply Chain Management perform well in 2021 compared to 2020? Discuss your observation." }, { "from": "gpt", "value": "To answer this question, I need to compare the performance of Supply Chain Management in 2020 and 2021. The value of the {Supply Chain Management bar for 2020}[0.144, 0.539, 0.351, 0.641] is {\"1.4%\"}[0.218, 0.650, 0.277, 0.685]. It showed a growth of 1.4% in 2020. However, when I look at the {Supply Chain Management bar for 2021}[0.661, 0.480, 0.867, 0.539], the value {\"-0.8%\"}[0.731, 0.489, 0.797, 0.524] indicates that it declined by 0.8% in 2021. Therefore, compared to 2020, the performance of Supply Chain Management in 2021 dropped.\nBased on the above thought, the answer is:\nNo, according to the chart, the Supply Chain Management sector did not perform well in 2021 compared to 2020. It had shown growth in 2020 but then experienced a decline in 2021." } ], "id_raw": 419 }, { "id": "j84RdlakeU2y64d5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_28_14.png", "conversations": [ { "from": "human", "value": "\nHow much improvement percentage did the Global Trade show from 2020 to 2021?" }, { "from": "gpt", "value": "To find the improvement percentage in Global Trade, I need to subtract the 2020 data from the 2021 data. The {Global Trade bar for 2020}[0.144, 0.539, 0.351, 0.728] has a value of {\"-2.6%\"}[0.218, 0.737, 0.281, 0.772] and the {Global Trade bar for 2021}[0.661, 0.269, 0.867, 0.539] has a value of {\"3.7%\"}[0.735, 0.278, 0.793, 0.313]. So the improvement from 2020 to 2021 would be the value of 2021 (3.7%) minus the value of 2020 (-2.6%).\nBased on the above thought, the answer is:\n6.3%" } ], "id_raw": 419 }, { "id": "wzmObDt1e7hFko1m", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a structured CSV table showing the performance scores for each AI model across all innovation categories?" }, { "from": "gpt", "value": "Observing the complex structure of the bar chart across multiple subplots, each subplot represents a specific category of innovation, such as {Independent Innovation}[0.037, 0.031, 0.331, 0.270], {Merged Innovation}[0.367, 0.031, 0.661, 0.270], and others. Within these subplots, the bars and their corresponding labels such as {AIBot-983X}[0.032, 0.274, 0.061, 0.317] under the first subplot show their performance scores. I will compile these performance values from each bar's text label across all categories for each model.\nBased on the above thought, the answer is:\nModel,Independent Innovation,Merged Innovation,Creative Destruction,Incremental Innovation,Technological Discontinuity,Technological Leap,Fraudulent Patents\nAIBot-983X,87.5,91.2,68.9,84.5,69.5,90.0,0.4\nTuring-1099S,89.3,90.5,71.2,85.1,66.4,93.6,0.1\nDataEasel-8V,90.4,88.9,69.2,83.4,71.7,89.6,0.6\nMindfulB-66X,85.6,92.3,67.3,82.9,70.2,88.1,0.5\nWarpSpeed-5G,84.3,89.6,70.3,81.3,72.3,91.4,0.3\nGenie-101T,88.1,90.7,69.5,84.7,73.4,92.8,0.2\nSiliconM-112L,86.2,91.8,68.2,82.1,74.9,90.9,0.4\nNeuraVista-6Z,85.4,92.1,66.8,84.9,75.5,89.6,0.1\nAstra-778E,87.7,90.1,71.6,85.3,76.8,93.8,0.3\nMatrixQ-999R,88.4,91.7,70.7,83.8,77.3,90.3,0.4" } ], "id_raw": 420 }, { "id": "daB0F1z0YOWWn0N6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the consistent performance in the technological leap category suggest about these AI models?" }, { "from": "gpt", "value": "Reviewing the technology leap category, noticeable by its subtitle {Technological Leap}<[0.801, 0.342, 0.888, 0.357]> and analyzing the bar heights such as {AIBot-983X}<[0.711, 0.384, 0.733, 0.599]>, it can be seen that scores are high compared to other categories. The consistent high performance in this category suggests that despite the challenges of drastic technological shifts, these AI models are well-equipped to adapt and thrive in rapidly changing technological environments, as indicated by their capability to achieve high scores across various models.\nBased on the above thought, the answer is:\nThe consistent high performance in the Technological Leap category for these AI models suggests they are highly capable of adapting to and thriving in rapidly changing technological environments." } ], "id_raw": 420 }, { "id": "Uk30BmUQCcIYOhY4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_0.png", "conversations": [ { "from": "human", "value": "\nWhich model has the highest average performance across all categories?" }, { "from": "gpt", "value": "To find the model with the highest average performance, I need to compute the average of the performance scores listed for each model across all innovation categories. By examining each model's performance through their respective bar charts such as {AIBot-983X}<[0.032, 0.274, 0.061, 0.317]> and others, a numerical average can be calculated. After computing these averages, I can determine which model has the highest value.\nBased on the above thought, the answer is:\nMatrixQ-999R" } ], "id_raw": 420 }, { "id": "rJGhEDnsm6M2P4HQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nHow should the energy production capacities from the year 2015 to 2020 be summarized in a table according to the dataset?" }, { "from": "gpt", "value": "Upon observing the 9 sub-charts, each representing different energy sources like {Solar}[0.075, 0.047, 0.328, 0.294], {Wind}[0.403, 0.047, 0.657, 0.294], and so on, I can see bars corresponding to respective years marked by {the x-axis tick labels}[{2015}[0.081, 0.300, 0.102, 0.327], {2016}[0.121, 0.300, 0.142, 0.327], etc.]. Each bar's height represents the energy capacity, and the value can be found in {the text coordinates}[{solar 2015}[0.094, 0.280, 0.110, 0.292], {wind 2015}[0.422, 0.275, 0.438, 0.287], etc.]. From this inspection of the bar charts, I can present the data as follows in a structured CSV format table.\nBased on the above thought, the answer is:\nYear,Solar (TWh),Wind (TWh),Nuclear (TWh),Oil (TWh),Gas (TWh),Hydro (TWh),Biomass (TWh),Coal (TWh),Geothermal (TWh)\n2015,118,341,2152,7434,9820,2750,268,530,48\n2016,128,370,2205,7412,9954,2780,280,520,50\n2017,147,411,2275,7325,10142,2818,290,510,51\n2018,176,465,2352,7238,10315,2854,302,499,53\n2019,210,530,2402,7100,10520,2886,315,485,55\n2020,240,594,2460,6985,10700,2920,330,470,56" } ], "id_raw": 421 }, { "id": "0Sjui7CpqPPTKtSu", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nWhich energy source shows the most significant growth in production capacity from 2015 to 2020 as observed in the bar sub-charts?" }, { "from": "gpt", "value": "Ascertaining the most significant growth in production capacity involves assessing the difference in values from 2015 to 2020 within each energy source sub-chart. By comparing the height of the bars in the first and last position, such as {solar}[0.086, 0.292, 0.118, 0.294] for 2015 and {solar}[0.285, 0.289, 0.317, 0.294] for 2020, {wind}[0.415, 0.287, 0.446, 0.294] for 2015 and {wind}[0.613, 0.281, 0.645, 0.294] for 2020, it becomes evident through this cumulative assessment that solar energy, showing a growth from 118 TWh to 240 TWh, exhibits the most growth in capacity terms.\nBased on the above thought, the answer is:\nSolar energy shows the most significant growth." } ], "id_raw": 421 }, { "id": "dciZj0nW17c3NfvA", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total energy production capacity for all sources in 2020 according to the sub-charts?" }, { "from": "gpt", "value": "To calculate the total energy production capacity for all sources in 2020, I need to sum up the values highlighted in the text coordinates {solar}[0.293, 0.278, 0.309, 0.289], {wind}[0.621, 0.269, 0.637, 0.281], {nuclear}[0.947, 0.227, 0.968, 0.238], and so on for oil, gas, hydro, biomass, coal, and geothermal sources for the year 2020. Summing these figures will give me the total capacity.\nBased on the above thought, the answer is:\n31456 TWh." } ], "id_raw": 421 }, { "id": "M2TRsYvlUwhCYJWY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV table from the bar charts showing the active users on each social media platform for each quarter?" }, { "from": "gpt", "value": "Firstly, I identify the title and labels on the x-axis to determine the platforms, which are {TikTok}[0.080, 0.299, 0.104, 0.329], {Facebook}[0.138, 0.299, 0.172, 0.342], {Instagram}[0.204, 0.299, 0.241, 0.344], {Twitter}[0.283, 0.299, 0.309, 0.331], {Reddit}[0.352, 0.299, 0.377, 0.331], and {LinkedIn}[0.414, 0.299, 0.445, 0.338]. The y-axis represents active users in millions with labels ranging from {0}[0.043, 0.286, 0.052, 0.303] to {70}[0.035, 0.040, 0.052, 0.057]. Each bar represents a quarter, starting with {Q1}[0.263, 0.019, 0.287, 0.041] continuing with {Q2}[0.755, 0.019, 0.779, 0.041], {Q3}[0.263, 0.363, 0.287, 0.386], and {Q4}[0.756, 0.363, 0.779, 0.386]. Using the heights of the bars, for instance, {TikTok\u2019s Q1 bar}[0.077, 0.222, 0.131, 0.293] at about 20 million active users, I can construct the table.\nBased on the above thought, the answer is:\nQuarter,TikTok,Facebook,Instagram,Twitter,Reddit,LinkedIn\nQ1,20,35,30,45,10,15\nQ2,30,40,25,40,20,18\nQ3,45,33,35,37,15,20\nQ4,60,31,33,39,14,22" } ], "id_raw": 422 }, { "id": "SBbWIzVAWHESEBIX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nLooking at the data, which social media platform showed the most consistent user base throughout the year?" }, { "from": "gpt", "value": "Consistency in user numbers can be inferred by looking at the height of the bars across all four quarters. Taking each platform one by one, we look at the bars for {Twitter}[0.282, 0.134, 0.336, 0.293], {Q2 Twitter}[0.774, 0.152, 0.829, 0.293], {Q3 Twitter}[0.282, 0.507, 0.336, 0.637], and {Q4 Twitter}[0.774, 0.500, 0.829, 0.637]. The height of Twitter\u2019s bars shows the least variation from Q1 through Q4, indicating a consistent user base compared to the more fluctuating bars of other platforms.\nBased on the above thought, the answer is:\nTwitter. The height of Twitter's bars across all four quarters shows minimal variation, illustrating the most consistent user base among the platforms represented in the chart." } ], "id_raw": 422 }, { "id": "ZGzReinDYsK9lFXc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in active users for TikTok from Q1 to Q4?" }, { "from": "gpt", "value": "To calculate the percentage increase for TikTok, I examine the bars for TikTok in {Q1}[0.077, 0.222, 0.131, 0.293] and {Q4}[0.569, 0.426, 0.624, 0.637]. The value for Q1 is {20 million users}[0.099, 0.208, 0.109, 0.219], and for Q4 is {60 million users}[0.591, 0.411, 0.602, 0.423]. To find the percentage increase: [(60 - 20) / 20] * 100.\nBased on the above thought, the answer is:\n200%" } ], "id_raw": 422 }, { "id": "wuO7OkdaoR46RVI3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nCan you create a table displaying the metric values across all subplots for the year 2017?" }, { "from": "gpt", "value": "In order to create this table, I am observing the bar heights and the text labels present in each subplot for the year {2017}[0.126, 0.628, 0.147, 0.654]. Starting with 'Career Development', the text label {\u201c85\u201d}[0.142, 0.093, 0.153, 0.104] suggests the score for this metric. Proceeding with 'Workplace Environment', the corresponding text label {\u201c75\u201d}[0.470, 0.135, 0.481, 0.146] indicates the score. I repeat this process for each subplot, including 'Upskilling', 'Leadership Development', 'Work-Life Balance', 'Employee Engagement', 'Diversity & Inclusion', 'Digital Transformation', and 'Company Culture' to compile the required values for the year 2017.\nBased on the above thought, the answer is:\nMetric,2017\nCareer Development,85\nWorkplace Environment,75\nUpskilling,52\nLeadership Development,88\nWork-Life Balance,67\nEmployee Engagement,77\nDiversity & Inclusion,74\nDigital Transformation,65\nCompany Culture,82" } ], "id_raw": 423 }, { "id": "gLQ2bqKpzF6Y3M30", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nWhich metric shows the most significant improvement from the year 2016 to 2020?" }, { "from": "gpt", "value": "To determine the metric with the most significant improvement, I will compare the increase in values from 2016 to 2020 for each metric. I begin with 'Career Development' which increased from {\u201c80\u201d}[0.092, 0.114, 0.103, 0.125] to {\u201c88\u201d}[0.291, 0.081, 0.302, 0.092]. I perform similar comparisons for each subsequent subplot by examining the text labels for 'Workplace Environment', 'Upskilling', 'Leadership Development', 'Work-Life Balance', 'Employee Engagement', 'Diversity & Inclusion', 'Digital Transformation', and 'Company Culture'. The metric with the largest difference in values between {2016}[0.076, 0.628, 0.098, 0.654] and {2020}[0.275, 0.628, 0.297, 0.654] will be considered to have the most significant improvement.\nBased on the above thought, the answer is:\nWork-Life Balance. The rise from \u201c65\u201d in {2016}[0.421, 0.503, 0.431, 0.514] to \u201c78\u201d in {2020}[0.619, 0.449, 0.630, 0.460] represents the largest increase among all the metrics from the provided data." } ], "id_raw": 423 }, { "id": "MFcx7pex9rUh4Ru9", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the total sum of metric values across all categories for the year 2018?" }, { "from": "gpt", "value": "To calculate the total sum for the year 2018, I will add up the values indicated by the text labels on top of each bar corresponding to {2018}[0.176, 0.628, 0.197, 0.654]. I locate each label, starting with 'Career Development' {\u201c87\u201d}[0.192, 0.085, 0.202, 0.096], 'Workplace Environment' {\u201c68\u201d}[0.520, 0.163, 0.531, 0.175], 'Upskilling' {\u201c57\u201d}[0.848, 0.209, 0.859, 0.220], and so on for other categories. Each value is added to form the cumulative sum.\nBased on the above thought, the answer is:\n626" } ], "id_raw": 423 }, { "id": "MGpMxYL0Kfx36lCU", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into a CSV table format that includes technology types and their patent applications and grants?" }, { "from": "gpt", "value": "Analyzing the bar charts, I can identify six different technology types as subtitled in each subplot, with respective bar details capturing data on patent applications and patents granted. Observing closely, each bar has a label just beneath it naming the category and text above each bar indicating the numerical value. For instance, for Artificial Intelligence {the bars}[{\"Patent Applications\"}[0.094, 0.064, 0.193, 0.405]], which represents patent applications, has a number {28325}[0.131, 0.052, 0.157, 0.064] at the top, while {the bars}[{\"Patents Granted\"}[0.218, 0.223, 0.317, 0.405]], representing patents granted, has a corresponding number {15090}[0.255, 0.212, 0.281, 0.223]. I will systematically extract similar data for each technology from their respective subplots, and convert this information into a structured CSV format.\nBased on the above thought, the answer is:\nTechnology,Patent Applications,Patents Granted\nArtificial Intelligence,28325,15090\nBlockchain,13323,5602\nQuantum Computing,8743,4127\n5G Technology,55220,29127\nIoT,41205,22030\nBig Data,33184,17847" } ], "id_raw": 424 }, { "id": "cXibKC8uCSocv35s", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nCan you explain why artificial intelligence and 5G technology have higher numbers of patent applications and grants compared to other technologies?" }, { "from": "gpt", "value": "Observing the charts, it\u2019s evident that both {Artificial Intelligence}{r>[0.083, 0.047, 0.328, 0.405]} and {5G Technology}{r>[0.083, 0.537, 0.328, 0.896]} have significantly higher bars corresponding to both patent applications and patents granted. The height of these bars, compared to those of other technologies like {Blockchain}[0.412, 0.047, 0.657, 0.405] or {Quantum Computing}[0.740, 0.047, 0.985, 0.405], is quite substantial. The greater height represents a larger volume of patents, indicating a higher rate of innovation and industry investment in these fields. Artificial Intelligence and 5G are leading-edge technologies with broad applications in various sectors, driving the need for extensive patenting to protect novel inventions and competitive advantages.\nBased on the above thought, the answer is:\nArtificial intelligence and 5G technology show higher patent numbers due to their groundbreaking potential and wide applications across different industries. Increased investments and rapid innovations in these areas necessitate robust patenting to safeguard intellectual property, setting them apart from more nascent fields like Blockchain and Quantum Computing." } ], "id_raw": 424 }, { "id": "AKfkAAqkUpuRoMBk", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of patent applications for all technologies combined?" }, { "from": "gpt", "value": "For calculating the total number of patent applications, I will sum up the numbers from each technology's patent application bar. Observing {the number above patent applications}[{\"Artificial Intelligence\"}[0.131, 0.052, 0.157, 0.064]; {\"Blockchain\"}[0.459, 0.052, 0.486, 0.064]; {\"Quantum Computing\"}[0.790, 0.052, 0.811, 0.064]; {\"5G Technology\"}[0.131, 0.543, 0.157, 0.554]; {\"IoT\"}[0.459, 0.543, 0.485, 0.554]; {\"Big Data\"}[0.787, 0.543, 0.814, 0.554]], where each has the value for patent applications listed. Adding these values gives a combined total for the observed technologies.\nBased on the above thought, the answer is:\n168800" } ], "id_raw": 424 }, { "id": "xm0L6lzsM90EuOET", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nCould you convert the return rate data for the investment company \"Clearbell\" from the bar chart into a CSV table?" }, { "from": "gpt", "value": "Observing the area designated for \"Clearbell\", {the subplot}[0.397, 0.701, 0.657, 0.949], which includes {the bars}[0.409, 0.910, 0.428, 0.910] representing the year 2010 through {the bars}[0.954, 0.701, 0.973, 0.910] for the year 2019. Using the y-axis {tick labels}[0.363, 0.942, 0.392, 0.959] to [0.375, 0.694, 0.392, 0.711], the data points can be extracted. I will list out the year along with the corresponding return rate, as visible from the chart, for each bar.\nBased on the above thought, the answer is:\nYear,Return Rate (%)\n2010,0\n2011,-5\n2012,-10\n2013,-10\n2014,-5\n2015,0\n2016,5\n2017,10\n2018,15\n2019,20" } ], "id_raw": 425 }, { "id": "JjgmG9C7irIlFONl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nWhich company experienced the most dramatic change in their return rate between 2010 and 2019?" }, { "from": "gpt", "value": "Identifying the most dramatic change requires comparing the initial and final return rates for each company. Scanning through the subplots, starting with {GreenOak}[0.069, 0.047, 0.328, 0.294], {Fortress}[0.397, 0.047, 0.657, 0.294], {GGP}[0.725, 0.047, 0.985, 0.294], and continuing with the others, I extract the rates for the first and last bars of each, such as {for GreenOak}[0.080, 0.218, 0.100, 0.256] and [0.297, 0.161, 0.317, 0.256]. The most significant change is noted by comparing these values for all companies and recognizing the largest difference.\nBased on the above thought, the answer is:\nNREP. Starting with {1% in 2010}[0.737, 0.580, 0.756, 0.583] and ending with {0% in 2019}[0.954, 0.583, 0.973, 0.583], the difference is a complete decline of 1% over the decade, which is a considerable change rate-wise, given the initial low base rate." } ], "id_raw": 425 }, { "id": "tMTjcS9pZc2M211T", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the average return rate for Blackstone over the entire period?" }, { "from": "gpt", "value": "To calculate the average return rate for \"Blackstone\", I need to sum up the individual yearly return rates and divide by the number of years. Examining {the subplot for Blackstone}[0.069, 0.374, 0.328, 0.621] including {bars}[0.080, 0.583, 0.100, 0.621] through [0.297, 0.469, 0.317, 0.583], I obtain the following return rates: -10, -8, -5, 0, 5, 10, 15, 20, 27, and 30. Summing these values and dividing by 10 provides the average.\nBased on the above thought, the answer is:\n8.4" } ], "id_raw": 425 }, { "id": "2FN4ACuyI2fRbaU0", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nCan you represent the percentage performance score for each energy technology in 'Slow recovery' trend as a structured data table?" }, { "from": "gpt", "value": "Observing the lower half of the bar chart where 'Slow recovery' is indicated as the subtitle at {the bottom chart subtitle}[0.467, 0.019, 0.584, 0.041], I see various bars corresponding to different energy technologies as labeled on the x-axis at {tick labels}[0.100, 0.422, 0.909, 0.470]. The values at the top of each bar represent the performance score percentages for each technology. For instance, the bar for 'Solar Power' has coordinates at {bar for Solar Power}[0.108, 0.277, 0.176, 0.508] with a height indicating a performance score that can be deciphered from the value labelled '50.2%' at {text above Solar Power bar}[0.128, 0.261, 0.155, 0.272]. By analyzing the bars' heights and their top values for each technology, I can construct a CSV structured table.\nBased on the above thought, the answer is:\nTechnology, Slow Recovery (%)\nSolar Power, 50.2\nWind Power, 35.2\nHydro Power, 60.5\nGeothermal, 27.0\nBioenergy, 50.9\nHydrogen, 45.0\nElectric Vehicles, 30.7\nEnergy Storage, 56.0\nCarbon Capture, 46.8\nSmart Grid, 41.9" } ], "id_raw": 427 }, { "id": "U5kqwX6BJjQD2ohQ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nWhich energy technologies have a lower performance score in 'Consistent expansion' compared to 'Slow recovery'?" }, { "from": "gpt", "value": "To compare the performance scores between the trends, I must look at the bars corresponding to each energy technology under both trends and note which ones have a lower height in 'Consistent expansion' compared to 'Slow recovery'. Starting with 'Solar Power', I can see that its bar in 'Slow recovery' at {bar for Solar Power in Slow recovery}[0.108, 0.277, 0.176, 0.508] is shorter than its counterpart in 'Consistent expansion' at {bar for Solar Power in Consistent expansion}[0.108, 0.673, 0.176, 0.999]. Investigating the bars of all other technologies using their coordinates, I will determine if any have lower values in the 'Consistent expansion' trend compared to 'Slow recovery'.\nBased on the above thought, the answer is:\nNone of the technologies have a lower performance score in 'Consistent expansion' compared to 'Slow recovery'. Each technology's performance score is higher or equal in the 'Consistent expansion' trend, which is apparent from the height of the bars being taller or at least equal in the upper chart compared to the lower chart for each respective technology." } ], "id_raw": 427 }, { "id": "j8Iw8pMSCZihrbgs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage for 'Hydro Power' combining both 'Slow recovery' and 'Consistent expansion'?" }, { "from": "gpt", "value": "I will look at the bars corresponding to 'Hydro Power' for both trends to sum their performance scores. The bar for the 'Slow recovery' trend is found at {bar for Hydro Power in Slow recovery}[0.278, 0.229, 0.346, 0.508], with its value label '60.5%' located at {text above Hydro Power bar in Slow recovery}[0.299, 0.213, 0.325, 0.224]. Similarly, the bar for the 'Consistent expansion' trend is found at {bar for Hydro Power in Consistent expansion}[0.278, 0.606, 0.346, 0.999], with its corresponding value label '85.1%' at {text above Hydro Power bar in Consistent expansion}[0.299, 0.590, 0.325, 0.601]. Adding both values: 60.5 + 85.1 gives the total percentage.\nBased on the above thought, the answer is:\n145.6" } ], "id_raw": 427 }, { "id": "Ext9uzrwNwTKO9DU", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nCan you create a table that shows the progression of 'Customer feedbacks' over the 12 months as displayed in the bar chart?" }, { "from": "gpt", "value": "By analyzing the specific subplot dedicated to 'Customer feedbacks' with its axes at {the axes}[0.075, 0.537, 0.493, 0.932], I observe each of the 12 bars that correspond to a month of the year, starting from {Month 1 bar}[0.094, 0.927, 0.119, 0.936] to {Month 12 bar}[0.448, 0.910, 0.474, 0.936]. The height of each bar indicates the quantity of feedbacks received with the y-axis tick labels providing a scale from {0}[0.075, 0.928, 0.097, 0.936] to values over {50}[0.427, 0.898, 0.449, 0.909]. I can develop a CSV table structure reflecting each bar's label and height, which corresponds to the number of customer feedbacks in each month.\nBased on the above thought, the answer is:\nMonth,Customer Feedbacks\nMonth 1,50\nMonth 2,60\nMonth 3,75\nMonth 4,80\nMonth 5,90\nMonth 6,95\nMonth 7,100\nMonth 8,105\nMonth 9,110\nMonth 10,120\nMonth 11,130\nMonth 12,140" } ], "id_raw": 428 }, { "id": "gNlsemi7RSWKG3UU", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nWhich quarter shows the greatest increase in 'Emails processed' and by how much did it increase over the previous quarter?" }, { "from": "gpt", "value": "To assess quarterly increases, I will examine the 'Emails processed' subplot, specifically looking at month-wise bars and compare the last month of each quarter to the last month of the previous quarter. For example, comparing {Month 3}[0.158, 0.208, 0.184, 0.445] with {Month 6}[0.255, 0.132, 0.280, 0.445], {Month 9}[0.351, 0.104, 0.377, 0.445], and {Month 12}[0.448, 0.066, 0.474, 0.445], I'll use the values annotated above the bars like for {Month 3 '1250'}[0.160, 0.197, 0.181, 0.208] and {Month 6 '1650'}[0.257, 0.121, 0.278, 0.132]. The greatest increase will be the quarter where the difference between these end-month values is the highest.\nBased on the above thought, the answer is:\nQ2 to Q1. The increase was by 400, from 1250 in {Month 3}[0.158, 0.208, 0.184, 0.445] to 1650 in {Month 6}[0.255, 0.132, 0.280, 0.445]." } ], "id_raw": 428 }, { "id": "kVMrlP5lwl9MpXGM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_8.png", "conversations": [ { "from": "human", "value": "\nWhat was the total number of 'New contracts' signed across all 12 months?" }, { "from": "gpt", "value": "To calculate the total number of 'New contracts' over the year, I shall sum up the values presented above each bar in the respective subplot, starting from {Month 1}[0.586, 0.931, 0.612, 0.936] through {Month 12}[0.940, 0.926, 0.966, 0.936]. The annotations, such as {Month 1 '25'}[0.594, 0.920, 0.604, 0.931] and {Month 12 '55'}[0.948, 0.914, 0.958, 0.925], indicate the respective number of contracts. By summing all the annotated values, I'd get the total number of new contracts for the year.\nBased on the above thought, the answer is:\n444" } ], "id_raw": 428 }, { "id": "hyVOEbWoAeWdrKLm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide a CSV-formatted table representing the performance of various softwares in the Encrypted Data Leak category?" }, { "from": "gpt", "value": "The subplot for Encrypted Data Leak {\"Encrypted Data Leak\"}[0.192, 0.509, 0.366, 0.532] shows eleven bars representing different software performances. Each bar has a text label above it indicating the performance score. Based on the position of the bars on the y-axis, which has tick labels from {30}[0.043, 0.899, 0.061, 0.917] to {100}[0.035, 0.530, 0.061, 0.548], I will determine the scores for each software. For example, {the bar for \"Cyberlock-5d\"}[0.085, 0.590, 0.114, 1.065] has a text label above {\"90.0\"}[0.090, 0.573, 0.109, 0.585], indicating a performance score of 90.0. I'll repeat this process for all softwares listed along the x-axis, including their {labels}[0.054, 0.912, 0.459, 0.972].\nBased on the above thought, the answer is:\nSoftware,Encrypted Data Leak Performance Score (%)\nCyberlock-5d,90.0\nFort-7m,85.8\nAven-110z-1.3,82.8\nYz-54z,77.4\nMirage-1b-alpha,74.5\nWi-300b,70.1\nElectra-29b,68.2\nYan-PaLA,63.4\nYan-PaLA 2,63.0\nIPT-3,58.0\nGemini Shield,55.5" } ], "id_raw": 429 }, { "id": "agZCfjZToJfMGjQr", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nWhich software shows a decline in performance for both Encrypted Data Leak and Darkweb Activity categories?" }, { "from": "gpt", "value": "To identify the software that shows decline in both Encrypted Data Leak and Darkweb Activity, I need to analyze the bar charts for each category. In the {Encrypted Data Leak}[0.066, 0.537, 0.493, 0.906] category, I observe the height of the bars and the associated text labels placed above the bars to gauge the performance scores, such as the 74.5 score for {Mirage-1b-alpha}[0.229, 0.672, 0.258, 1.065] with its label {\"74.5\"}[0.234, 0.655, 0.252, 0.666]. Next, I move to the {Darkweb Activity}[0.558, 0.537, 0.985, 0.906] chart and do the same, where the performance score for {Mirage-1b-alpha} is {\"76.7\"}[0.726, 0.644, 0.745, 0.655]. By analyzing the scores for the same software across both categories, I can spot which ones show a decrease in scores. This process is repeated for all software listed on the x-axis.\nBased on the above thought, the answer is:\nMirage-1b-alpha. Its performance score decreased from 77.0 in Incognito Surveilance to 74.5 in Encrypted Data Leak and further to 76.7 in Darkweb Activity." } ], "id_raw": 429 }, { "id": "pSPx9aAYZbdaTH7m", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the average performance score for Darkweb Activity across all softwares?" }, { "from": "gpt", "value": "To calculate the average performance score for Darkweb Activity, I need to sum all individual software scores and divide by the total number of software. The scores are displayed as text labels above each bar in the Darkweb Activity subplot {\"Darkweb Activity\"}[0.701, 0.509, 0.842, 0.532]. The text labels range from {\"60.6\"}[0.583, 0.728, 0.601, 0.740] for {Cyberlock-5d} to {\"93.5\"}[0.942, 0.555, 0.960, 0.566] for {Gemini Shield}. I will add each of these values obtained from the text labels above the bars {[0.578, 0.745, 0.966, 1.065]}, and then calculate the average.\nBased on the above thought, the answer is:\n77.1" } ], "id_raw": 429 }, { "id": "4KcYsV9nWPuvvRIn", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data into a table depicting the performance scores of each biotechnology model for Q1?" }, { "from": "gpt", "value": "Observing the chart, focusing on the first subplot titled {Incognito Surveilance}[0.189, 0.019, 0.369, 0.041] which signifies Q1 data, I scan the x-axis and locate the biotech models at the following coordinates: BioMed-X[0.054, 0.422, 0.100, 0.479], GenoTech[0.106, 0.422, 0.135, 0.459], MicroCure[0.122, 0.422, 0.171, 0.483], PatholPro[0.182, 0.422, 0.207, 0.454], HealthShape[0.188, 0.422, 0.243, 0.491], VitaVascular[0.248, 0.422, 0.279, 0.460], CelluFit[0.275, 0.422, 0.315, 0.472], NeuroNet[0.318, 0.422, 0.351, 0.463], ImmunInsight[0.348, 0.422, 0.387, 0.470]. I then cross-reference these labels with corresponding bar coordinates to extract performance values: BioMed-X[0.085, 0.337, 0.114, 0.574], GenoTech[0.121, 0.258, 0.150, 0.574], MicroCure[0.157, 0.205, 0.186, 0.574], PatholPro[0.193, 0.152, 0.222, 0.574], HealthShape[0.229, 0.168, 0.258, 0.574], VitaVascular[0.265, 0.141, 0.294, 0.574], CelluFit[0.301, 0.124, 0.329, 0.574], NeuroNet[0.337, 0.099, 0.365, 0.574], ImmunInsight[0.373, 0.084, 0.401, 0.574].\nBased on the above thought, the answer is:\nModel,Q1 Score\nBioMed-X,87.6\nGenoTech,83.2\nMicroCure,67.9\nPatholPro,72.1\nHealthShape,76.5\nVitaVascular,91.5\nCelluFit,84.3\nNeuroNet,75.4\nImmunInsight,88.7" } ], "id_raw": 430 }, { "id": "ubOkRjpCKF9DdqSg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nWhich biotech model demonstrated a consistent improvement over the four quarters?" }, { "from": "gpt", "value": "To answer this question, I must analyze the trend of each biotech model's performance scores across the subplots representing each quarter. I'll observe the bar heights for each model in all four subplots \u2013 {Incognito Surveilance}[0.189, 0.019, 0.369, 0.041], {Firewall Breach}[0.709, 0.019, 0.835, 0.041], {Encrypted Data Leak}[0.192, 0.509, 0.366, 0.532], and {Darkweb Activity}[0.701, 0.509, 0.842, 0.532]. Observing the bar coordinates: BioMed-X[0.578, 0.745, 0.606, 1.065], GenoTech[0.614, 0.732, 0.642, 1.065], MicroCure[0.650, 0.704, 0.678, 1.065], PatholPro[0.685, 0.682, 0.714, 1.065], HealthShape[0.721, 0.660, 0.750, 1.065], VitaVascular[0.757, 0.640, 0.786, 1.065], CelluFit[0.793, 0.625, 0.822, 1.065], NeuroNet[0.829, 0.613, 0.858, 1.065], ImmunInsight[0.865, 0.601, 0.894, 1.065]. The consistent improvement can be identified by noting the sequential increase in bar height across all quarters. According to the bar coordinates, MicroCure reflects a progressive rise in the bar heights representing an increase in performance scores from Q1 to Q4.\nBased on the above thought, the answer is:\nMicroCure. This model shows a consistent rise in its performance scores as visualized across all subplots, indicating an improvement in each successive quarter." } ], "id_raw": 430 }, { "id": "gE4gOqRUyJGSC9Jp", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest performance score observed in Q2?" }, { "from": "gpt", "value": "I will review the second subplot representing Q2 data titled {Firewall Breach}[0.709, 0.019, 0.835, 0.041]. I will scan the y-axis for the highest numerical value by examining the bars. I locate the performance score of each biotech model by identifying the top edge of their respective bars: Cyberlock-5d[0.578, 0.385, 0.606, 0.574], Fort-7m[0.614, 0.280, 0.642, 0.574], Aven-110z-1.3[0.650, 0.227, 0.678, 0.574], Yz-54z[0.685, 0.192, 0.714, 0.574], Mirage-1b-alpha[0.721, 0.221, 0.750, 0.574], Wi-300b[0.757, 0.205, 0.786, 0.574], Electra-29b[0.793, 0.178, 0.822, 0.574], Yan-PaLA[0.829, 0.159, 0.858, 0.574], Yan-PaLA 2[0.865, 0.149, 0.894, 0.574], IPT-3[0.901, 0.122, 0.930, 0.574], Gemini Shield[0.937, 0.097, 0.966, 0.574]. According to text coordinates, the highest performance score is associated with IPT-3 with a value found at [0.906, 0.106, 0.925, 0.117].\nBased on the above thought, the answer is:\n85.7" } ], "id_raw": 430 }, { "id": "1hGa0ftRaar5UWQ3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nHow would you organize the project volume data from the bar charts into a structured CSV table?" }, { "from": "gpt", "value": "Upon examining the bar chart, I am analyzing each sub-chart for different community types, starting with {\"Uptown\"}[0.165, 0.019, 0.229, 0.041], and moving right along to {\"Business District\"}[0.784, 0.673, 0.923, 0.695]. I notice the x-axis lists the types of community projects: {Residential Buildings}[0.021, 0.231, 0.090, 0.317], {Recreational Facilities}[0.049, 0.231, 0.121, 0.320], and so forth. The y-axis, located at {ylabel coordinates}[0.015, 0.071, 0.029, 0.200], scales from 0 to 100, representing the project volumes. For each type of project within the \"Uptown\" community, for instance, {bar chart rectangles}[0.078, 0.148, 0.102, 0.301] and {value labels}[0.085, 0.135, 0.095, 0.146], I extract their respective heights and note down the numerical values to transform this visual information into a CSV table.\nBased on the above thought, the answer is:\nCommunity,Residential Buildings,Recreational Facilities,Transport Infrastructure,Educational Institutions,Commercial Buildings,Gardens & Parks,Community Facilities,Healthcare Institutions\nUptown,60,50,65,70,80,45,55,75\nDowntown,65,70,55,70,60,65,70,85\nSuburbs,70,75,55,65,70,70,80,65\nCountryside,45,50,60,60,65,75,80,70\nHistoric,50,55,55,70,85,70,65,75\nRevitalization Zone,45,50,70,75,80,50,60,70\nEconomic Development Zone,70,75,55,80,85,70,80,65\nScenic Area,40,80,60,60,70,80,85,60\nBusiness District,80,65,70,70,90,45,40,70" } ], "id_raw": 431 }, { "id": "Uq9pgEeGVU9A3Nfd", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nCan you identify which type of community has the most diversified investment in projects?" }, { "from": "gpt", "value": "To find out which type of community has the most diversified investment in projects, I analyze the spread of project volumes across all categories for each community. I observe {the bars}[0.078, 0.148, 0.102, 0.301] in each sub-chart, representing project volumes, and check for the community that has the least variation in bar heights, indicating that the investment is spread out more evenly across different types of projects. After comparing all the communities, I notice that the {Economic Development Zone}[0.075, 0.673, 0.320, 0.695] demonstrates a relatively even distribution of project investments across most categories as indicated by the consistent heights of its bars in comparison to other communities.\nBased on the above thought, the answer is:\nThe Economic Development Zone has the most diversified investment, as the project volumes across different categories are almost evenly distributed, which indicates a balanced spread of investment across various types of projects." } ], "id_raw": 431 }, { "id": "O6AfZCDlfJlvHPhG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_11.png", "conversations": [ { "from": "human", "value": "\nWhich community has the highest number of projects for Recreational Facilities?" }, { "from": "gpt", "value": "To determine the community with the highest number of projects for Recreational Facilities, I need to compare the height of the bars corresponding to Recreational Facilities across all the sub-charts. I start with the {\"Uptown\"}[0.165, 0.019, 0.229, 0.041] sub-chart and go through each, looking specifically at the height of the bars related to Recreational Facilities, such as {Uptown's Recreational Facilities bar}[0.108, 0.174, 0.133, 0.301] and so on, until I find the tallest bar which indicates the highest project volume. Comparing the values as listed in the chart, {Scenic Area's Recreational Facilities bar}[0.437, 0.752, 0.461, 0.955] stands out as the tallest.\nBased on the above thought, the answer is:\nScenic Area." } ], "id_raw": 431 }, { "id": "pVRJETx8fwuzljrc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nCan you create a table for the 'Subdued Expansion' trend with the engagement scores for each social media platform?" }, { "from": "gpt", "value": "Observing the first subplot corresponding to the 'Subdued Expansion' trend, I analyze the data for each platform. I find {the bars}[0.086, 0.148, 0.125, 0.487] each with a text label above that gives the engagement score for each platform\u2014{Facebook}[0.086, 0.148, 0.125, 0.487] has a score of 5000, {Twitter}[0.134, 0.196, 0.173, 0.487] has a score of 4300, {Instagram}[0.182, 0.236, 0.221, 0.487] at 3700, {LinkedIn}[0.230, 0.175, 0.269, 0.487] at 4600, and {YouTube}[0.278, 0.121, 0.317, 0.487] scoring 5400.\nBased on the above thought, the answer is:\nSocialMedia,SubduedExpansion\nFacebook,5000\nTwitter,4300\nInstagram,3700\nLinkedIn,4600\nYouTube,5400" } ], "id_raw": 433 }, { "id": "dIfDmC3XBsb7A5h7", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nDuring which trend did LinkedIn show the most significant increase in engagement compared to its previous period?" }, { "from": "gpt", "value": "To determine LinkedIn's most significant increase, I assess each period where LinkedIn's engagement score rises from the previous period's score. I begin with the 'Subdued Expansion' subplot and note {LinkedIn's score}[0.230, 0.175, 0.269, 0.487]. Moving to the next subplot labeled 'Stable Growth', LinkedIn's score does not show an increase. Progressing through the subsequent trends, I notice in the 'Rapid Recovery' subplot, LinkedIn shows a significant score increase from its 'Volatile Decline' score, changing from 4200 to 4800, shown by {LinkedIn's bars}[0.559, 0.551, 0.597, 0.836] and [0.887, 0.511, 0.925, 0.836] respectively. Comparing this with other increases, I conclude which trend exhibited the greatest increase for LinkedIn.\nBased on the above thought, the answer is:\nRapid Recovery. The transition from the 'Volatile Decline' trend to the 'Rapid Recovery' trend shows the most substantial increase in engagement for LinkedIn, shifting from an engagement score of 4200 to 4800." } ], "id_raw": 433 }, { "id": "LPUxEjuyStZfnrP5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_13.png", "conversations": [ { "from": "human", "value": "\nHow much has the engagement on Twitter reduced from the 'Stable Growth' trend to the 'Rapid Decline' trend?" }, { "from": "gpt", "value": "To compare Twitter's engagement between the two trends, I first locate Twitter's bars. In the 'Stable Growth' subplot, {Twitter's bar}[0.462, 0.202, 0.501, 0.487] indicates an engagement score of 4200. Next, in the 'Rapid Decline' subplot, I analyze {Twitter's bar}[0.791, 0.223, 0.829, 0.487] to find an engagement score of 3900. Now, by calculating the difference between 4200 and 3900, I can find the reduction in engagement.\nBased on the above thought, the answer is:\n300" } ], "id_raw": 433 }, { "id": "Xdk0mblbl6cs826w", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV table showing the genres and their corresponding theater performance and art exhibition ratings?" }, { "from": "gpt", "value": "By analyzing the given data for different genres within the chart {subplot_0}[0.066, 0.047, 0.197, 0.458] and {subplot_1}[0.263, 0.047, 0.394, 0.458], I observe that the genres are listed along the x-axis, with their corresponding ratings depicted as bars. The bars have text annotations indicating the exact scores. By extracting each genre's theater and art exhibition ratings from the top of the respective bars such as {Regional Drama}[0.072, 0.252, 0.081, 0.869], I can compile the CSV table.\nBased on the above thought, the answer is:\nGenre,Theater Performance Rating,Art Exhibition Rating\nRegional Drama,75.0,69.0\nMusical,80.4,82.3\nContemporary Dance,81.8,82.0\nBallet,71.1,70.4\nStand-up Comedy,88.7,87.5\nOpera,94.8,88.9\nPantomime,78.5,68.6\nMimes,77.1,79.8\nImprovisational,92.0,76.9\nStorytelling,83.6,78.1" } ], "id_raw": 434 }, { "id": "KCowvA9JnlBSHNBl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nWhich genre shows a noteworthy disparity between Theater Performance and Art Exhibition Ratings, and can you explain why it may be significant?" }, { "from": "gpt", "value": "I will locate the bars within {subplot_0}[0.066, 0.047, 0.197, 0.458] for Theater Performance Rating and within {subplot_1}[0.263, 0.047, 0.394, 0.458] for Art Exhibition Rating to observe marked differences. For instance, I identify that the {Opera genre}[0.133, 0.089, 0.142, 0.869]; [0.330, 0.138, 0.339, 0.869] has a significant lead in both categories with an especially high Theater Performance Rating. This exceptional performance in both categories could reflect a higher audience appreciation for the Opera genre, possibly due to its unique offering of both visual arts and live performance, thus resonating more strongly with the audience compared to other genres.\nBased on the above thought, the answer is:\nThe Opera genre. It indicates a strong audience preference for Opera that is consistent across both performance venues, setting it apart as a particularly popular art form potentially due to its grandeur and rich artistic experience." } ], "id_raw": 434 }, { "id": "YZ4IUTcP5UJ1bfOQ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the average Theater Performance Rating for all the genres represented in the chart?" }, { "from": "gpt", "value": "In order to calculate the average Theater Performance Rating, I need to sum the ratings from each genre noted in the bars within {subplot_0}[0.066, 0.047, 0.197, 0.458] and divide by the number of genres present. The performance ratings for genres {Regional Drama}[0.072, 0.252, 0.081, 0.869], {Musical}[0.084, 0.208, 0.094, 0.869], and others bring us to a total sum. There are 10 genres in total. After doing the math, I arrive at the average rating.\nBased on the above thought, the answer is:\n81.99" } ], "id_raw": 434 }, { "id": "odPBaAJ11C1MPqwg", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nCan you organize the provided chart data into structured tabular form?" }, { "from": "gpt", "value": "The dual bar chart presents two different datasets over a 10-year period. {The left y-axis}[0.015, 0.367, 0.029, 0.630] of the chart shows the 'Career Progression' with increasing orange bars and the {right y-axis}[0.971, 0.373, 0.985, 0.624] shows the 'Obstacle Removal' with decreasing blue bars. There are ten sets of bars corresponding to each year labeled on the {x-axis}[0.111, 0.942, 0.869, 0.970]. For career progression, the bars start at the baseline [0.062, 0.923] and extend upwards, whereas for obstacle removal, the bars also start at the same baseline but extend downwards. By examining the height of each bar from the baseline to its top, I would tabulate the given values for all ten years.\nBased on the above thought, the answer is:\nYear,Career Progression,Obstacle Removal\nYear 1,10,-5\nYear 2,20,-7\nYear 3,23,-8\nYear 4,15,-6\nYear 5,28,-7\nYear 6,24,-9\nYear 7,29,-10\nYear 8,28,-12\nYear 9,30,-9\nYear 10,35,-10" } ], "id_raw": 435 }, { "id": "g3WlLaYTo9wXVAug", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nIs there a visible trend in the obstacle removal over the years, and if so, what does it indicate?" }, { "from": "gpt", "value": "To identify the trend in obstacle removal, I will analyze the height and direction of the {blue bars}[0.132, 0.075, 0.875, 0.499] across all x-axis labels from {Year 1} to {Year 10}[0.111, 0.942, 0.869, 0.970], in relation to the negative scale on the {right y-axis}[0.971, 0.373, 0.985, 0.624]. A downward trend in the height of the blue bars would signify an increase in the number of obstacles being removed over time.\nBased on the above thought, the answer is:\nYes, there is a visible trend of increasing obstacle removal over the years. The ascending height of the blue bars, especially noticeable in the 8th and 9th years, suggests that the individual is becoming progressively more effective at overcoming career obstacles, indicating a positive development in problem-solving and possibly a phase of accelerated career growth." } ], "id_raw": 435 }, { "id": "MzxGSgpbm5XuvTXw", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the total sum of obstacle removal over the 10-year period?" }, { "from": "gpt", "value": "To determine the total sum of the obstacle removal values, I will add up the negative values represented by each of the {blue bars}[0.132, 0.075, 0.875, 0.499] along the x-axis, which corresponds to the years from 'Year 1' to 'Year 10'. Each bar's value corresponds to the label on the {right y-axis}[0.971, 0.373, 0.985, 0.624], allowing me to accurately sum the obstacle removal values for each year.\nBased on the above thought, the answer is:\n-84" } ], "id_raw": 435 }, { "id": "Taijq1Sy3A3J20Sa", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nCan you put the data from the bar chart representing the popularity of various fashion subcultures in 2018 and 2019 into a table?" }, { "from": "gpt", "value": "Sure, we can start by dividing the chart by fashion subculture. Each subculture {Gothic}[0.152, 0.942, 0.197, 0.970], {Streetwear}[0.299, 0.942, 0.376, 0.970], {Vintage}[0.473, 0.942, 0.527, 0.970], {Grudge}[0.637, 0.942, 0.688, 0.970], and {Skater}[0.803, 0.942, 0.848, 0.970], has two bars that reflect popularity for the years 2018 and 2019. These are distinguished by different color bars according to {the legend}[0.763, 0.089, 0.922, 0.139]. The values can be estimated from the y-axis. For instance, for Gothic, {[2018 Popularity Bar][0.110, 0.287, 0.175, 1.347]} corresponds to approximately 500 and {[2019 Popularity Bar][0.175, 0.297, 0.240, 1.347]} corresponds to around 495. We can repeat this step for each subculture to determine the associated values.\nBased on the above thought, the answer is:\nSubculture,2018 Popularity,2019 Popularity\nGothic,500,495\nStreetwear,340,450\nVintage,400,380\nGrudge,360,385\nSkater,320,335" } ], "id_raw": 436 }, { "id": "aeK8R4lE3trUM4Qv", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nHow can you describe the popularity trend for the Streetwear category based on the chart?" }, { "from": "gpt", "value": "The Streetwear category has two bars corresponding to 2018 and 2019. The {Streetwear bar for 2018}[0.272, 0.626, 0.337, 1.347] has a value around 340 and the {Streetwear bar for 2019}[0.337, 0.393, 0.402, 1.347] is around 450. This indicates an increase in popularity for this subculture from 2018 to 2019. Thus, the trend for the Streetwear subculture seems to be on the rise.\nBased on the above thought, the answer is:\nThe Streetwear subculture showed a rising trend in popularity from the year 2018 to 2019." } ], "id_raw": 436 }, { "id": "LRYBa61JCuS5IjVw", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_1.png", "conversations": [ { "from": "human", "value": "\nWhich fashion subculture has seen the most significant increase in popularity from 2018 to 2019?" }, { "from": "gpt", "value": "In order to find out the subculture with the most significant increase, I need to look at the difference in height between the 2018 and 2019 bars for each subculture. Starting with {Gothic}'s bars<[2018 Popularity Bar][0.110, 0.287, 0.175, 1.347]; [2019 Popularity Bar][0.175, 0.297, 0.240, 1.347]>, it's a decreasing trend; Moving on to {Streetwear}'s bars<[2018 Popularity Bar][0.272, 0.626, 0.337, 1.347]; [2019 Popularity Bar][0.337, 0.393, 0.402, 1.347]>, it saw an increase of about 110 in popularity; and so on for each subculture. Comparing the differences of each subculture, I can see that {Streetwear} saw the most significant increase.\nBased on the above thought, the answer is:\nStreetwear" } ], "id_raw": 436 }, { "id": "07RoACsRV5KBU7pU", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into CSV formatted rows and columns?" }, { "from": "gpt", "value": "First, I observe that there are two sets of bars {2018 Sales}[0.120, 0.075, 0.200, 0.680], {2019 Sales}[0.200, 0.075, 0.280, 0.438]. These bars represent the Sales in 2018 and 2019, respectively. {The x-axis tick labels}{\"Food\"}[0.184, 0.942, 0.217, 0.970]; {\"Electronics\"}[0.362, 0.942, 0.438, 0.970]; {\"Books\"}[0.579, 0.942, 0.621, 0.970]; {\"Clothing\"}[0.771, 0.942, 0.829, 0.970] suggest these are four different categories. The bars' heights assist in determining the corresponding values and extracting this data into a CSV format, where the first column is the category, the second column represents the 2018 sales, and the third column represents the 2019 sales.\nBased on the above thought, the answer is:\nCategory,2018 Sales,2019 Sales\nFood,-100,-60\nElectronics,-120,-90\nBooks,-50,-60\nClothing,-100,-130" } ], "id_raw": 437 }, { "id": "9czRpTsquF94MqvJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which product category appeared to struggle the most over the given years and why?" }, { "from": "gpt", "value": "By observing the chart, it's clear that all product categories recorded a decrease in sales. However, {Clothing}[0.720, 0.075, 0.800, 0.680; 0.800, 0.075, 0.880, 0.862] showed a significant decline as the bars carved a steeper graph compared to other categories. The sales went from -100 in 2018 to -130 in 2019, which indicates a larger decrease than the other categories. Furthermore, the 2019 sales bar for clothing is also the longest among all the categories suggesting that not only did it have the highest drop from the previous year but it was also the lowest performing category in 2019.\nBased on the above thought, the answer is:\nThe Clothing category struggled the most based on the data from the chart. The significant decline in sales along with the lowest performance in sales in 2019 highlights this struggle." } ], "id_raw": 437 }, { "id": "KAtK3wtXgcDxkGBk", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_2.png", "conversations": [ { "from": "human", "value": "\nWhich product category shows the largest difference in sales from 2018 to 2019?" }, { "from": "gpt", "value": "To determine this, I need to compare the height difference between the two bars in each sales category. Starting with {Food category}[0.120, 0.075, 0.200, 0.680] and [0.200, 0.075, 0.280, 0.438], the difference is approximately 40; repeating this process for each of the remaining product categories. From these observations, it's visible that {Clothing}[0.720, 0.075, 0.800, 0.680; 0.800, 0.075, 0.880, 0.862] category shows the maximum change in sales from 2018 to 2019.\nBased on the above thought, the answer is:\nClothing" } ], "id_raw": 437 }, { "id": "3JCgnPwJouT64MB8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nLet's derive a table with a comparison of the number of papers in each academic field for the years 2019 and 2020 as portrayed in the bar chart." }, { "from": "gpt", "value": "From the title, {Annual Academic Research Papers in Different Fields}[0.354, 0.015, 0.646, 0.033], I can see that the data pertains to annual academic research papers in different fields for the years 2019 and 2020. The x-axis labels represent the fields {Computer Science}[0.098, 0.885, 0.165, 0.985], {Mathematics}[0.230, 0.885, 0.279, 0.958], {Physics}[0.362, 0.885, 0.392, 0.931], {Chemistry}[0.480, 0.885, 0.520, 0.945], {Biology}[0.607, 0.885, 0.638, 0.932], {Economics}[0.725, 0.885, 0.767, 0.948], and {Psychology}[0.847, 0.885, 0.890, 0.950]. The y-axis labels display the number of papers, ranging from {0}[0.035, 0.870, 0.041, 0.884] to {600}[0.023, 0.032, 0.041, 0.046]. The key is depicted by the sublegend, with the {2019 Papers} bar colour being [0.10a674] and the {2020 Papers} bar color being [0.ac3838]. I'll generate the table by reading the height of bars against the y-axis value for each field for both 2019 and 2020.\nBased on the above thought, the answer is:\nFields,2019,2020\nComputer Science,450,550\nMathematics,320,340\nPhysics,180,230\nChemistry,270,280\nBiology,360,390\nEconomics,530,570\nPsychology,250,220" } ], "id_raw": 438 }, { "id": "u3ARSQD0LGAGSrIh", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nWhich academic domain shows a decline in the number of research papers produced and why might this be according to the bar chart?" }, { "from": "gpt", "value": "From the bar chart, the {Psychology bars}[0.826, 0.526, 0.869, 0.875][0.869, 0.568, 0.912, 0.875] show a decline in the number of papers produced. If you look at the y-values of these two bars, the 2019 bar shows a y-value of 250 while the 2020 bar shows a y-value of 220. This means a decrease in the number of papers published in psychology from 2019 to 2020. The exact reason cannot be definitively concluded from the bar chart as it only provides quantitative data, not qualitative. However, one could hypothesize a potential shortfall in funding, author interest, or research opportunities within this field during 2020.\nBased on the above thought, the answer is:\nThe Psychology field exhibits a decline in academic papers from 2019 to 2020. While the bar chart's data does not clarify the reasons, possible explanations might include reduced funding, diminished author interest, or fewer research opportunities in this field in 2020." } ], "id_raw": 438 }, { "id": "M2akY3szo0wUUjqu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_3.png", "conversations": [ { "from": "human", "value": "\nWhich academic domain has seen the most significant number increase in papers published from 2019 to 2020?" }, { "from": "gpt", "value": "To find out which academic sector has seen the most significant rise in published papers from 2019 to 2020, I have to compare the difference in the heights of the {2019 bars} and the {2020 bars} in each field. For example, the {bar for Computer Science papers in 2019}[0.088, 0.247, 0.131, 0.875] reaches a y-value of 450 while the {bar for Computer Science in 2020}[0.131, 0.107, 0.174, 0.875] reaches a y-value of 550, indicating an increase of 100 papers. I'll need to repeat this process for each field and find the field with the largest difference.\nBased on the above thought, the answer is:\nComputer Science" } ], "id_raw": 438 }, { "id": "PUVjn15o90lQFJFn", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, could you please construct a CSV format table to present the reduced forest area and carbon emissions for 2020 and 2021?" }, { "from": "gpt", "value": "Sure, based on the bar chart and according to the y-axis labels of {reduced forest area}[0.015, 0.263, 0.029, 0.735] and {reduced carbon emissions}[0.971, 0.181, 0.985, 0.816], I can see that the reduced forest area data is represented by the coordinates [0.126, 0.357, 0.296, 0.923] and [0.551, 0.470, 0.721, 0.923] and the reduced carbon emissions data is represented by the coordinates [0.296, 0.357, 0.466, 0.923] and [0.721, 0.216, 0.891, 0.923]. The bars' top endpoint represents the value so by aligning them with the axis tick labels, I can see that in 2020, the reduced forest area was 10000 hectares and the reduced carbon emissions was 200 million tonnes; in 2021, the reduced forest area was 8000 hectares and the reduced carbon emissions had increased to 250 million tonnes. So, by also taking into account the x-axis labels [0.279, 0.942, 0.314, 0.970]; [0.704, 0.942, 0.739, 0.970], I can make the CSV structured data with these values.\nBased on the above thought, the answer is:\nYear,Reduced Forest Area (in hectares),Reduced Carbon Emissions (in million tonnes)\n2020,10000,200\n2021,8000,250" } ], "id_raw": 439 }, { "id": "jAX9i7lbav5oC6CO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nHow would you interpret the correlation between reduced forest area and carbon emissions based on the chart?" }, { "from": "gpt", "value": "Observing the bar chart, I note that the {bars}[0.126, 0.357, 0.296, 0.923]; [0.551, 0.470, 0.721, 0.923]; [0.296, 0.357, 0.466, 0.923]; [0.721, 0.216, 0.891, 0.923] represent reduced forest area and reduced carbon emissions for 2020 and 2021. In both years, the reduced forest area and carbon emissions have moved in opposite directions. In 2020, 10,000 hectares of forest were reduced, accompanied by 200 million tonnes reduced carbon emissions. In 2021, though the reduced forest area decreased to 8,000 hectares, the reduced carbon emissions increased to 250 million tonnes. This suggests an inverse relationship exists between reduced forest area and carbon emissions.\nBased on the above thought, the answer is:\nThe chart suggests an inverse correlation between reduced forest area and carbon emissions. As the reduced forest area decreases, carbon emissions reductions increase, indicating that less deforestation may lead to higher reductions in carbon emissions." } ], "id_raw": 439 }, { "id": "SPNJmXL37oMewK69", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in reduced carbon emissions from 2020 to 2021?" }, { "from": "gpt", "value": "Looking at the bar chart representing {reduced carbon emissions}[0.971, 0.181, 0.985, 0.816], I can align the coordinates of the bars [0.296, 0.357, 0.466, 0.923]; [0.721, 0.216, 0.891, 0.923] and find the correspondent values from the y-axis for 2020 and 2021. In 2020, the carbon emissions was reduced by 200 million tonnes, and such reduction increased to 250 million tonnes in 2021. With these numbers, I can compute the percentage increase using the formula ((New Value - Original Value) / ABS(Original Value))*100%.\nBased on the above thought, the answer is:\n25%" } ], "id_raw": 439 }, { "id": "71VKVsyX58RmmsSk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nCan you translate the data on the chart into a CSV format?" }, { "from": "gpt", "value": "Based on the chart, there are two categories: {Urban Living}[0.235, 0.942, 0.323, 0.970] and {Rural Living}[0.621, 0.942, 0.703, 0.970]. Each category has three bars: a bar representing the {Reduced Carbon Emissions}[0.140, 0.100, 0.457, 0.128], a bar representing the population for {2025}[0.140, 0.142, 0.175, 0.170], and a bar representing the population for {2030}[0.140, 0.183, 0.175, 0.211]. The bold blue bar for Urban Living is located at[r][0.126, -0.349, 0.279, 0.923] and has a height suggesting a value of {450 million}[0.0, 0.571, 0.1, 0.668] for 2025 and {520 million}[0.0, 0.646, 0.1, 0.753] for 2030. Similar analysis for the Rural Living category suggests corresponding values of {400 million}[0.0, 0.515, 0.1, 0.602] and {430 million}[0.0, 0.570, 0.1, 0.657] respectively. Based on these observations, the CSV data table can be constructed.\nBased on the above thought, the answer is:\nScenario,2025,2030\nUrban Living,450,520\nRural Living,400,430" } ], "id_raw": 440 }, { "id": "jNtqVvAFHyFTEg2w", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about the future population distribution from the data in the chart?" }, { "from": "gpt", "value": "From the given bar chart, it is clear that both Urban Living and Rural Living populations are expected to increase. As seen by the bars, the {Urban Living 2025}[0.126, -0.349, 0.279, 0.923] bar height indicates a population of {450 million}[0.0, 0.571, 0.1, 0.668] which goes up to {520 million}[0.0, 0.646, 0.1, 0.753] for the {Urban Living 2030}[0.279, -0.547, 0.432, 0.923] bar. Similarly, the {Rural Living 2025}[0.509, -0.208, 0.662, 0.923] bar height indicates a population of {400 million}[0.0, 0.515, 0.1, 0.602] which is lower than Urban Living and rises to {430 million}[0.0, 0.570, 0.1, 0.657] in {Rural Living 2030}[0.662, -0.293, 0.815, 0.923]. Hence, it can be inferred that over time, a greater proportion of the population is likely to shift towards Urban Living scenarios as compared to Rural Living. This trend may reflect either increased urbanization or preference for urban lifestyle.\nBased on the above thought, the answer is:\nIt infers that by 2030, more people will prefer to live in urban areas than rural areas as the increase in the urban population is significantly higher than the increase in the rural population." } ], "id_raw": 440 }, { "id": "9OilksimQ93hpKiu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_5.png", "conversations": [ { "from": "human", "value": "\nWhich living scenario experienced a greater increase in population from 2025 to 2030?" }, { "from": "gpt", "value": "To answer this question, I will need to compare the population increase in both the {Urban Living}[0.235, 0.942, 0.323, 0.970] and {Rural Living}[0.621, 0.942, 0.703, 0.970] scenarios between 2025 and 2030. For Urban Living, the population for 2025 is represented by the bar at {coordinates}[0.126, -0.349, 0.279, 0.923], showing a population of displayed as {450 million}[0.0, 0.571, 0.1, 0.668], and the population for 2030 is represented by the bar at coordinates[0.279, -0.547, 0.432, 0.923], showing a population of displayed as {520 million}[0.0, 0.646, 0.1, 0.753]. This suggests an increase of 70 million. For Rural Living, the population for 2025 is represented by the bar at {coordinates}[0.509, -0.208, 0.662, 0.923], showing a population of displayed as {400 million}[0.0, 0.515, 0.1, 0.602], and the population for 2030 is represented by the bar at coordinates[0.662, -0.293, 0.815, 0.923], showing a population of displayed as {430 million}[0.0, 0.570, 0.1, 0.657]. This suggests an increase of 30 million. By comparing these two increase amounts, I can deduce that the Urban Living scenario experienced a greater increase in population.\nBased on the above thought, the answer is:\nUrban Living" } ], "id_raw": 440 }, { "id": "hvpxVcmFOFGOkKgk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nPlease extract the trade volume data for 2016 and 2021 and express it in the form of a table as per your analysis of the chart." }, { "from": "gpt", "value": "The chart shows the trade volume for countries over the years 2016 and 2021. There are four countries represented with bars for these two years. From the labels and the lengths of bars, I can construct the data table. The first country, {Country 1}[0.177, 0.942, 0.245, 0.970], for the year 2016 has a bar with coordinates [0.134, 0.764, 0.211, 1.770] which corresponds to a trade volume of 95000. Similarly, for the year 2021, the bar with coordinates [0.211, 0.393, 0.288, 1.770] corresponds to the trade volume of 130000. By following the same process for {Country 2}[0.369, 0.942, 0.438, 0.970], {Country 3}[0.562, 0.942, 0.631, 0.970] and {Country 4}[0.755, 0.942, 0.823, 0.970] I'm able to extract the trade volume for these countries over the years 2016 and 2021.\nBased on the above thought, the answer is:\nCountry,2016 Trade Volume, 2021 Trade Volume\nCountry 1,95000,130000\nCountry 2,87000,122000\nCountry 3,123000,115000\nCountry 4,150000,144000" } ], "id_raw": 441 }, { "id": "jsoIlQIc42ifLpx5", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nLooking at the graph, could you comment on the trade volume trend for Country 3 from 2016 to 2021?" }, { "from": "gpt", "value": "The chart visibly illustrates the trade volume for {Country 3}[0.562, 0.942, 0.631, 0.970] in the years 2016 and 2021. In 2016, {the bar for Country 3}[0.519, 0.467, 0.596, 1.770] is quite tall, indicating a high trade volume of 123000. Nevertheless, in 2021, we see a decrease in trade volume as {the bar for 2021}[0.596, 0.552, 0.674, 1.770] is shorter than the 2016 bar. The trade volume dipped to 115000 in 2021. This suggests that the trade volume for Country 3 has decreased over this period.\nBased on the above thought, the answer is:\nThe trade volume of Country 3 shows a decline from 2016 to 2021." } ], "id_raw": 441 }, { "id": "TuCEYdMVHsJvCtQs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_6.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, which country showed the greatest increase in trade volume from 2016 to 2021?" }, { "from": "gpt", "value": "To determine which country had the greatest increase in trade volume from 2016 to 2021, we need to compare the rise in the heights of bars for both years for each country. For {Country 1}[0.177, 0.942, 0.245, 0.970], the trade volumes represented by bars [{2016 bar} [0.134, 0.764, 0.211, 1.770]; {2021 bar} [0.211, 0.393, 0.288, 1.770]] show an increase from 95000 to 130000 which is 35000. Comparing this height difference to {Country 2}[0.369, 0.942, 0.438, 0.970], {Country 3}[0.562, 0.942, 0.631, 0.970], and {Country 4}[0.755, 0.942, 0.823, 0.970] would reveal that Country 1 has shown the greatest increase in trade volume from 2016 to 2021.\nBased on the above thought, the answer is:\nCountry 1" } ], "id_raw": 441 }, { "id": "Iw8UA9ZnUU6lrDUj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nCan you help transform the chart data into a table with CSV format?" }, { "from": "gpt", "value": "The chart contains two sets of data, one showing the number of 2020 participants and the other showing the number of 2021 participants for different sports activities, namely {\"Jogging\"}[0.166, 0.942, 0.218, 0.970], {\"Gym\"}[0.381, 0.942, 0.414, 0.970], {\"Yoga\"}[0.586, 0.942, 0.619, 0.970] and {\"Swimming\"}[0.772, 0.942, 0.844, 0.970]. According to the chart, we can approximate the participant data for each year based on the length of bars. For example, {Jogging's bar for 2020}[0.110, 0.393, 0.192, 1.982] ends at 150 and {the 2021 bar} [0.192, 0.287, 0.274, 1.982] ends at 160.\nBased on the above thought, the answer is:\nYear, Jogging, Gym, Yoga, Swimming\n2020, 150, 120, 145, 160\n2021, 160, 110, 155, 170" } ], "id_raw": 442 }, { "id": "48V19rhywWDnubve", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nGiven the chart, which activity would you recommend for someone interested in joining a popular and growing trend in 2022 and why?" }, { "from": "gpt", "value": "To answer this question, I would choose an activity that shows consistent growth from 2020 to 2021, demonstrating its increasing popularity over time. Looking at {\"Jogging\"}[0.166, 0.942, 0.218, 0.970], the number of participants increased from 150 in 2020 to 160 in 2021 according to the heights of the bars located at [0.110, 0.393, 0.192, 1.982] for 2020 and [0.192, 0.287, 0.274, 1.982] for 2021. Other activities like {\"Yoga\"} [0.586, 0.942, 0.619, 0.970] and {\"Swimming\"} [0.772, 0.942, 0.844, 0.970] also show growth. Among these, Yoga has the highest growth rate, going from 145 participants in 2020 to 155 in 2021 according to the bars at [0.521, 0.446, 0.603, 1.982] for 2020 and [0.603, 0.340, 0.685, 1.982] for 2021.\nBased on the above thought, the answer is:\nYoga. Because it not only is a popular activity but also the growth rate in participation from 2020 to 2021 was the highest compared to other activities, indicating its increasing trend." } ], "id_raw": 442 }, { "id": "nt9IaP6BJAXCPnfa", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_7.png", "conversations": [ { "from": "human", "value": "\nWhich activity experienced growth in the number of participants from 2020 to 2021?" }, { "from": "gpt", "value": "To determine which activity experienced growth in number of participants, I need to compare the two bars under each activity. For instance, for Jogging, the 2020's bar and 2021's bar are located at [0.110, 0.393, 0.192, 1.982] and [0.192, 0.287, 0.274, 1.982] respectively, having heights of approximately 150 and 160. Repeating this process for Gym, Yoga, and Swimming, we can conclude that Jogging, Yoga, and Swimming all experienced growth.\nBased on the above thought, the answer is:\nJogging, Yoga, Swimming" } ], "id_raw": 442 }, { "id": "EdLqC68wC4Q4Xh1R", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "To organize the data of the chart into a CSV format, we'll start with the company names located in {the x-axis}[{\"Company A\"}[0.127, 0.952, 0.194, 0.975]; {\"Company B\"}[0.297, 0.952, 0.364, 0.975]; {\"Company C\"}[0.467, 0.952, 0.533, 0.975]; {\"Company D\"}[0.636, 0.952, 0.704, 0.975]; {\"Company E\"}[0.807, 0.952, 0.873, 0.975]]. We then refer to the bars which represent their market shares for the years 2020 and 2021. The {red bars}[{\"Company A 2020\"}[0.092, 0.436, 0.160, 0.935]; {\"Company B 2020\"}[0.262, 0.312, 0.330, 0.935]; {\"Company C 2020\"}[0.432, 0.187, 0.500, 0.935]; {\"Company D 2020\"}[0.602, 0.561, 0.670, 0.935]; {\"Company E 2020\"}[0.772, 0.686, 0.840, 0.935]] represent their market shares in 2020 while the {blue bars}[{\"Company A 2021\"}[0.160, 0.362, 0.228, 0.935]; {\"Company B 2021\"}[0.330, 0.287, 0.398, 0.935]; {\"Company C 2021\"}[0.500, 0.237, 0.568, 0.935]; {\"Company D 2021\"}[0.670, 0.486, 0.738, 0.935]; {\"Company E 2021\"}[0.840, 0.561, 0.908, 0.935]] signify their market shares in 2021. By estimating their height against the y-axis, which marks the percentages from {0%}[0.029, 0.926, 0.036, 0.950] to {35%}[0.029, 0.053, 0.043, 0.076], we can arrive at the values.\nBased on the above thought, the answer is:\nCompany,Market Share 2020,Market Share 2021\nCompany A,20,23\nCompany B,25,26\nCompany C,30,28\nCompany D,15,18\nCompany E,10,15" } ], "id_raw": 443 }, { "id": "Of440cS6xX5faWFC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which company do you think has made the most progress from 2020 to 2021? And explain the reasons." }, { "from": "gpt", "value": "To determine the company that has made the most progress from 2020 to 2021, I need to compare the increases in market share for each company from 2020 to 2021. These can be found by comparing the difference in height between the {red bars}[{\"Company A 2020\"}[0.092, 0.436, 0.160, 0.935]; {\"Company B 2020\"}[0.262, 0.312, 0.330, 0.935]; {\"Company C 2020\"}[0.432, 0.187, 0.500, 0.935]; {\"Company D 2020\"}[0.602, 0.561, 0.670, 0.935]; {\"Company E 2020\"}[0.772, 0.686, 0.840, 0.935]] for 2020 and the {blue bars}[{\"Company A 2021\"}[0.160, 0.362, 0.228, 0.935]; {\"Company B 2021\"}[0.330, 0.287, 0.398, 0.935]; {\"Company C 2021\"}[0.500, 0.237, 0.568, 0.935]; {\"Company D 2021\"}[0.670, 0.486, 0.738, 0.935]; {\"Company E 2021\"}[0.840, 0.561, 0.908, 0.935]] for 2021. Notably, {Company E}[0.807, 0.952, 0.873, 0.975] appears to have made the largest gain, moving from approximately {10%}[0.772, 0.686, 0.840, 0.935] in 2020 to around {15%}[0.840, 0.561, 0.908, 0.935] in 2021. This a percentage increase of around 5%, which is the highest among all companies.\nBased on the above thought, the answer is:\nCompany E. It experienced a 5% increase in the market share from 2020 to 2021, which is the largest gain among all other companies." } ], "id_raw": 443 }, { "id": "ARxk7qabLQUyW8oJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_8.png", "conversations": [ { "from": "human", "value": "\nIn 2021, which company has the highest market share?" }, { "from": "gpt", "value": "To find the company with the highest market share, I have to check the height of the {blue bars}[{\"Company A 2021\"}[0.160, 0.362, 0.228, 0.935]; {\"Company B 2021\"}[0.330, 0.287, 0.398, 0.935]; {\"Company C 2021\"}[0.500, 0.237, 0.568, 0.935]; {\"Company D 2021\"}[0.670, 0.486, 0.738, 0.935]; {\"Company E 2021\"}[0.840, 0.561, 0.908, 0.935]] representing the 2021 market share for each company. The bar that reaches the highest on the y-axis scale marks the company with the greatest market share. Among all the bars, it turns out {the blue bar for \"Company C\"}[0.500, 0.237, 0.568, 0.935] is the tallest, so Company C has the highest market share in 2021.\nBased on the above thought, the answer is:\nCompany C" } ], "id_raw": 443 }, { "id": "Uzt2OsFFnAibHbVL", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nLooking at the bar chart, can you generate a representation of the data in a CSV format table?" }, { "from": "gpt", "value": "The bar chart presents data on the popularity of two fashion trends: {Gothic Fashion}[0.226, 0.942, 0.371, 0.970] and {Streetwear Fashion}[0.606, 0.942, 0.797, 0.970]. Each trend is represented by two bars denoting the popularity in [2020][0.111, 0.858, 0.338, 0.909] shown by the {left bars} [{Gothic Fashion in 2020}[0.137, 0.655, 0.298, 1.347]; {Streetwear Fashion in 2020}[0.540, 0.456, 0.702, 1.347]], and [2021][0.661, 0.858, 0.889, 0.909] shown by the {right bars} [{Gothic Fashion in 2021}[0.298, 0.719, 0.460, 1.347]; {Streetwear Fashion in 2021}[0.702, 0.376, 0.863, 1.347]]. The height of the bars represents the popularity metric, according to the scale on the y-axis ranging from {100}[0.049, 0.912, 0.087, 0.940] to {300}[0.050, 0.064, 0.087, 0.092]. From the visual indication, Gothic Fashion had a popularity metric of approximately 163 in 2020, going down to approximately 148 in 2021. On the other hand, Streetwear Fashion's popularity increased from approximately 210 in 2020 to around 229 in 2021. Let's compile the data into a CSV table.\nBased on the above thought, the answer is:\nFashion Style,2020 Popularity,2021 Popularity\nGothic Fashion,163,148\nStreetwear Fashion,210,229" } ], "id_raw": 444 }, { "id": "jFhzVUC77vJeZJ32", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nConsidering the data provided in the chart, which fashion style was more prevalent in 2021?" }, { "from": "gpt", "value": "There are two bars on the chart, both representing the years of 2021. One bar {for Gothic Fashion}[0.298, 0.719, 0.460, 1.347] and a second one {for Streetwear Fashion}[0.702, 0.376, 0.863, 1.347]. Based on the y-axis metric and the bars' heights, it is evident that the Streetwear Fashion bar for 2021 is higher than the Gothic Fashion bar for 2021, meaning Streetwear Fashion was more popular in 2021.\nBased on the above thought, the answer is:\nStreetwear Fashion was more prevalent in 2021." } ], "id_raw": 444 }, { "id": "YXUo2N1aq2fNoV3H", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_9.png", "conversations": [ { "from": "human", "value": "\nBy what percent did the popularity of Streetwear Fashion increase from 2020 to 2021?" }, { "from": "gpt", "value": "The bar corresponding to Streetwear Fashion's popularity in {2020}[0.540, 0.456, 0.702, 1.347] visually indicates a metric of approximately 210. For {2021}[0.702, 0.376, 0.863, 1.347], the metric is around 229.This represents an increase. To calculate the percentage increase, I would subtract the 2020 value from the 2021 value, and then divide that difference by the 2020 value. After that, I multiply by 100 to get the percentage.\nBased on the above thought, the answer is:\nApproximately 9.05%" } ], "id_raw": 444 }, { "id": "xEIXNrzsCAJbjtb6", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data of Technological Innovations of 2020 and 2021 into a CSV format table?" }, { "from": "gpt", "value": "Examining the x-axis, I can see {four countries' labels}[\"Country 1\", \"Country 2\", \"Country 3\", \"Country 4\"] spaced evenly along the bottom of the chart. On the y-axis, I see that the measurements of innovations are in increments of 10 from 50 to 110. Inside the graph, there are two sets of bar plots: one set represents {2020 Innovations}[0.122, 0.100, 0.242, 0.128] with color #8b4513 and one set represents {2021 Innovations}[0.797, 0.100, 0.917, 0.128] with color #20b2aa. To convert the data into a CSV format, I need to extract the height of each bar for both 2020 and 2021 Innovations across all the countries. For example, in Country 1, the 2020 Innovation is measured by the height of the bar plotted at [0.110, 0.569, 0.175, 1.629], which corresponds to a value of 75, and the 2021 Innovation is measured by the bar plotted at [0.175, 0.456, 0.240, 1.629], which corresponds to a value of 83.\nBased on the above thought, the answer is:\nCountry,2020 Innovations,2021 Innovations\nCountry 1,75,83\nCountry 2,95,83\nCountry 3,92,105\nCountry 4,70,85" } ], "id_raw": 445 }, { "id": "hr881BlFRayIcCSF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which country seems to be leading in technological innovations in the year 2021 and why?" }, { "from": "gpt", "value": "From the bar chart of 2021 Innovations, it can be seen that the bar of {Country 3}[0.608, 0.145, 0.673, 1.629] is the highest among the four countries, indicating that Country 3 has the highest number of innovations in the year 2021. The height of this bar corresponds to about 105 Intellectual Property units (IPU), while the other countries' bars represent a lesser count of IPUs. Therefore, based on the 2021 Innovations chart, Country 3 appears to be leading in technological innovations.\nBased on the above thought, the answer is:\nCountry 3 leads in technological innovations in 2021 because it has the highest number of Intellectual Property Units (IPUs)." } ], "id_raw": 445 }, { "id": "7GxCVOtC39kUSIWi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar charts, in which country did the innovation increase the most from 2020 to 2021?" }, { "from": "gpt", "value": "To find the country with the greatest increase in innovation, I need to subtract the height of each bar representing 2020 Innovations from the height of each corresponding bar representing 2021 Innovations. For example, in Country 1, the height of the bar for 2020 Innovations is {approximately 75}[0.110, 0.569, 0.175, 1.629], and for 2021 Innovations is {approximately 83}[0.175, 0.456, 0.240, 1.629], so the increase is 8; similarly, for Country 2 the increase is -12; for Country 3, the increase is 13; and for Country 4, the increase is 15. Therefore, the country with the greatest increase in innovations from 2020 to 2021 is Country 4.\nBased on the above thought, the answer is:\nCountry 4." } ], "id_raw": 445 }, { "id": "tl9X17mAPFM0CRwY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nCould you generate the CSV table for the bar chart that depicts the innovation levels for Creativity and Design in 2020 and 2021?" }, { "from": "gpt", "value": "Sure. To transform the bar chart into a CSV table, I need to encode the bar heights that represent innovation levels of each product into numerical data. From {the legend}[0.881, 0.809, 0.985, 0.935], the red bars represent Creativity in 2020, the blue bars represent Design in 2020, the yellow bars denote Creativity in 2021, and the orange bars denote Design in 2021. For each product, there are four bars showing the levels of Creativity and Design across two years. The coordinates of the bars will aid in the extraction process, such as {bar examining Creativity 2020 for 'Product A'}[0.126, 0.588, 0.161, 1.836] is approximately at the level of 70.\nBased on the above thought, the answer is:\nProduct,Creativity 2020,Design 2020,Creativity 2021,Design 2021\nProduct A,70,65,90,88\nProduct B,85,78,90,80\nProduct C,76,80,95,93\nProduct D,91,69,98,97\nProduct E,92,95,100,96" } ], "id_raw": 446 }, { "id": "m1dx0c8QXuNICQb1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which product consistently had the highest innovation levels for both Creativity and Design in the span of two years?" }, { "from": "gpt", "value": "This question requires looking at the overall performance in both Creativity and Design of all products. Examining the bars of each product and comparing the heights, we can identify the product that has the highest scores in both categories for both years. For instance, {the bars for 'Product A'}[{red_Abar}[0.126, 0.588, 0.161, 1.836]; {blue_Abar}[0.090, 0.677, 0.126, 1.836]; {yellow_Abar}[0.161, 0.232, 0.197, 1.836]; {orange_Abar}[0.197, 0.267, 0.233, 1.836]] show moderate heights across categories. Following the same process for all products, it is found that {the bars for 'Product E'}[{red_Ebar}[0.840, 0.196, 0.876, 1.836]; {blue_Ebar}[0.804, 0.142, 0.840, 1.836]; {yellow_Ebar}[0.876, 0.053, 0.911, 1.836]; {orange_Ebar}[0.911, 0.125, 0.947, 1.836]] consistently have the tallest heights, indicating the highest innovation levels throughout the two years.\nBased on the above thought, the answer is:\nProduct E, as it consistently had the highest innovation level in both Creativity and Design over the two years." } ], "id_raw": 446 }, { "id": "XrA4AwukF3VGU5S6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_11.png", "conversations": [ { "from": "human", "value": "\nWhich product demonstrated the greatest improvement in creativity scores from 2020 to 2021?" }, { "from": "gpt", "value": "To find the product with the greatest improvement in creativity scores, we need to compare the height changes of the red and yellow bars under each product. Taking examples, {the bars for 'Product A'}<{red_Ebar}[0.126, 0.588, 0.161, 1.836]; {yellow_Ebar}[0.161, 0.232, 0.197, 1.836]> show a score difference of approximate 20. By doing the same for 'Product B', 'Product C', 'Product D' and 'Product E', I find that {the Creativity improvement for Product D}[0.661, 0.214, 0.697, 1.836; 0.697, 0.089, 0.733, 1.836] has the largest difference, from 91 to 98.\nBased on the above thought, the answer is:\nProduct D" } ], "id_raw": 446 }, { "id": "cs1HLy3sgMt6yaIP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nPlease extract the data from the observed chart and present it as a structured CSV table." }, { "from": "gpt", "value": "The chart shows two bar plots in each of three categories. For each category, we have two bars for 2019 and 2020 respectively. The categories are 'Deep Learning' and 'Machine Learning' as shown on the {x-axis labels}[0.200, 0.942, 0.301, 0.970] and [0.605, 0.942, 0.728, 0.970]. The number of research papers published and number of active developers in 2019 and 2020 are demonstrated by the bars of different colors and patterns. For 'Deep Learning' in 2019, the number of research papers published is around 50000 represented by {the bar}[0.126, 0.393, 0.209, 0.923] and the number of active developers is around 20000 represented by {the bar}[0.292, -0.531, 0.375, 0.923]. The other years and categories can be analyzed similarly. Thus, it provides us the below structured table.\nBased on the above thought, the answer is:\nCategory,Year,Number of Research Papers Published,Number of Active Developers\nDeep Learning,2019,50000,20000\nDeep Learning,2020,75000,30000\nMachine Learning,2019,60000,22000\nMachine Learning,2020,65000,24000" } ], "id_raw": 447 }, { "id": "4UiOL4IVzDUmBww7", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which field is attracting more active developers and why?" }, { "from": "gpt", "value": "The chart shows two categories - 'Deep Learning' and 'Machine Learning', with the number of active developers represented by {the bars}[0.292, -0.531, 0.375, 0.923],[0.375, -0.652, 0.458, 0.923],[0.708, 0.390, 0.791, 0.923] and [0.791, 0.341, 0.874, 0.923] respectively. Comparing the heights of the bars for both 2019 and 2020, we can see that 'Deep Learning' has attracted more developers. Although 'Machine Learning' also shows growth in the number of active developers, the growth in 'Deep Learning' is more substantial. This trend is likely due to the increasing popularity and applications of deep learning in recent years.\nBased on the above thought, the answer is:\nDeep Learning. The number of active developers in this area has seen a significant increase from 20000 to 30000 between 2019 and 2020." } ], "id_raw": 447 }, { "id": "50X6pwqudCACRWrr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_12.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which category saw an increase in the number of research papers published from 2019 to 2020?" }, { "from": "gpt", "value": "The chart presents the number of research papers published for 'Deep Learning' and 'Machine Learning' in 2019 and 2020, represented by {patterned bars}[0.126, 0.393, 0.209, 0.923],[0.542, 0.711, 0.625, 0.923],[0.209, -0.652, 0.292, 0.923] and [0.625, 0.605, 0.708, 0.923] respectively. The height of the bars of each category tells the data of respective years. Comparing the bar heights, we can see that both 'Deep Learning' and 'Machine Learning' witnessed an increase in the number of research papers published, with 'Deep Learning' having a substantial increase.\nBased on the above thought, the answer is:\nBoth 'Deep Learning' and 'Machine Learning'." } ], "id_raw": 447 }, { "id": "3U9eMyPbvisfwidL", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the chart you are observing into a structured CSV format?" }, { "from": "gpt", "value": "Based on the dual bar chart at hand, and the color guides as well as accompanying labels, I can see that the x-axis dedicates individual segments to each human ancestral era such as {Prehistoric}[0.170, 0.942, 0.243, 0.970], {Ancient}[0.376, 0.942, 0.428, 0.970], {Middle Ages}[0.556, 0.942, 0.640, 0.970], and {Modern}[0.767, 0.942, 0.820, 0.970] periods. It seems that the y-axis, on the other hand, showcases numerical data ranging from {-1200}[0.035, 0.989, 0.081, 1.017] to {0}[0.073, 0.064, 0.081, 0.092]. The plot features two types of bars in each era. As per the legends, one could infer the lighter red colored bars represent the 'Inventions' like in the {Prehistoric}[0.128, 0.075, 0.206, 0.113] and darker blue bars symbolize the level of 'Cultural Expansion' that took place during that era as can be seen for {Modern}[0.794, 0.075, 0.872, 0.807]. Given their colors and their relation to the y-axis, the number of major inventions and the cultural expansion during each era can be recognized. After analyzing the chart in detail, the data can be structured into a CSV format.\nBased on the above thought, the answer is:\nEra, Inventions, Cultural Expansion\nPrehistoric, -50, 0\nAncient, -100, -25\nMiddle Ages, -500, -400\nModern, -1000, -950" } ], "id_raw": 448 }, { "id": "elPCVblgssxv7pOO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which Era would you say witnessed the most rapidly accelerating decline in the Civilization Development?" }, { "from": "gpt", "value": "If we understand the Civilization Development as being represented collectively by the number of major Inventions and the rate of Cultural Expansion, then we would need to focus on both colored bars under each Era. Visually, the bars representing both cultural expansion and Inventions are longest for Modern era {Inventions}[0.715, 0.075, 0.794, 0.845], {Cultural Expansion}[0.794, 0.075, 0.872, 0.807] and shortest for the Prehistoric era {Inventions}[0.128, 0.075, 0.206, 0.113], {Cultural Expansion}[0.206, 0.075, 0.285, 0.075]. Looking at the bars under each category, it seems that the gap between Inventions and Cultural Expansion became increasingly wider the more recent the era, indicating a rapid decline in the development of civilization.\nBased on the above thought, the answer is:\nThe Modern era, because although the quantity of inventions was highest, it shows the most significant gap with Cultural Expansion, indicating an imbalance in development." } ], "id_raw": 448 }, { "id": "s8NA8NXpL8u0mchq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_13.png", "conversations": [ { "from": "human", "value": "\nIn which era was the societal Cultural Expansion the lowest?" }, { "from": "gpt", "value": "The lowest level of Cultural Expansion signifies the smallest bar in the 'Cultural Expansion' dataset. By comparing the different bars of the 'Cultural Expansion' dataset {Prehistoric}[0.206, 0.075, 0.285, 0.075], {Ancient}[0.402, 0.075, 0.480, 0.094], {Middle Ages}[0.598, 0.075, 0.676, 0.383], and {Modern}[0.794, 0.075, 0.872, 0.807], I see that the prehistoric era has no cultural expansion, indicating that the cultural expansion is lowest during that era.\nBased on the above thought, the answer is:\nPrehistoric" } ], "id_raw": 448 }, { "id": "XqBaPXJ8hrDLW4Fo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data presented in the double bar chart into a CSV file?" }, { "from": "gpt", "value": "The chart is divided into two main sections, with each section representing the sales performance of the five companies for a specific year. Each company is reported for {2020}[0.107, 0.858, 0.233, 0.909] and {2021}[0.767, 0.858, 0.893, 0.909]. The sales figures for each company can be deduced from its corresponding bar's height in each section. For instance, the bar for {Company A in 2020}[0.136, 0.075, 0.197, 0.680] extends approximately to the \"-25000\" mark on the left y-axis, suggesting it sold 25,000 units. Similarly, the bar for {Company A in 2021}[0.197, 0.075, 0.257, 0.753] extends approximately to the \"-28000\" placeholder, indicating that the company sold 28,000 units. This process is repeated for each company, allowing me to compile the data into a CSV file.\nBased on the above thought, the answer is:\nCompany,2020 Sales,2021 Sales\nCompany A,25000,28000\nCompany B,30000,26000\nCompany C,21000,29000\nCompany D,28000,30000\nCompany E,26000,22000" } ], "id_raw": 449 }, { "id": "ytGoHw8ZMfzANqkH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nFrom the chart's perspective, which company is more resilient in terms of sales against the market trend between the years 2020 and 2021?" }, { "from": "gpt", "value": "A company's resiliency can be gauged by examining its ability to increase sales or maintain stability amid changing market conditions. Observing the chart, I can see bars for each company showing sales for {2020}[0.107, 0.858, 0.233, 0.909] and {2021}[0.767, 0.858, 0.893, 0.909]. Most companies experienced either a decline or a slight increase in sales, except for {Company C}[0.439, 0.075, 0.500, 0.583][0.500, 0.075, 0.561, 0.777]. Company C showed a noticeable increase in sales from 21,000 units to approximately 29,000 units, resisting the negative trend and demonstrating a high level of resilience.\nBased on the above thought, the answer is:\nCompany C. Its sales trend shows resilience as it successfully increased its sales amidst changing market trends, showing an increase in sales from 21,000 units in 2020 to approximately 29,000 in 2021." } ], "id_raw": 449 }, { "id": "JgEgM3fFC7UZPCvo", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_30_14.png", "conversations": [ { "from": "human", "value": "\nWhich company showed the most significant increase in sales from 2020 to 2021?" }, { "from": "gpt", "value": "Comparing each company's bar heights for 2020 and 2021, I can calculate the change in sales. For {Company A}[0.136, 0.075, 0.197, 0.680][0.197, 0.075, 0.257, 0.753], the increase is approximately 3,000 units. Using the same process for {Company B}[0.288, 0.075, 0.348, 0.801][0.348, 0.075, 0.409, 0.705], {Company C}[0.439, 0.075, 0.500, 0.583][0.500, 0.075, 0.561, 0.777], {Company D}[0.591, 0.075, 0.652, 0.753][0.652, 0.075, 0.712, 0.801], and {Company E}[0.743, 0.075, 0.803, 0.705][0.803, 0.075, 0.864, 0.608], I conclude that Company C has the most significant growth in sales, approximately 8,000 units.\nBased on the above thought, the answer is:\nCompany C." } ], "id_raw": 449 }, { "id": "TemRVsVKWoWuX4iU", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nCan you help to transcribe this bar chart data into a CSV format table?" }, { "from": "gpt", "value": "To convert the chart data to a table, I can reference the 'Research Output in Academic Fields' title[0.395, 0.019, 0.644, 0.041], the x-axis labels[\"Computer Science\", \"Physics\", \"Chemistry\", \"Biology\", \"Mathematics\", \"Engineering\", \"Psychology\", \"Sociology\", \"Literature\", \"Musicology\"], and the y-axis labels[\"0\", \"10\", \"20\", \"30\", \"40\", \"50\", \"60\", \"70\"]. These labels correspond to academic fields and research output percentages respectively. Each bar in the chart represents an academic field, with the height representing the research output percentage. For example, the 'Computer Science' bar[0.094, 0.359, 0.164, 0.927] extends to the y coordinate of 0.359, corresponding to a research output of 45.2% as indicated by the text annotation above the bar[0.111, 0.329, 0.147, 0.346]. I will repeat this process across all the bars to transcribe the data into a CSV table.\nBased on the above thought, the answer is:\nField, Percentage\nComputer Science, 45.2\nPhysics, 39.8\nChemistry, 35.1\nBiology, 42.7\nMathematics, 52.9\nEngineering, 59.3\nPsychology, 49.4\nSociology, 32.6\nLiterature, 25.9\nMusicology, 18.2" } ], "id_raw": 451 }, { "id": "DIiFW137Mq9F5YwS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nCan you evaluate the overall research output distribution among these academic fields based on the chart?" }, { "from": "gpt", "value": "To evaluate the research output distribution, I will consider the proportions expressed by the bar heights. There's a broad spread, with the highest output ranging at 59.3% for the field 'Engineering'[0.528, 0.181, 0.598, 0.927], and the lowest at 18.2% for 'Musicology'[0.876, 0.698, 0.945, 0.927]. It appears noticeable that most of the fields anchor between 30% and 50%, signifying balanced research output. However, outputs from 'Engineering' and 'Musicology' deviate significantly from the center, suggesting that these fields are relatively specialized or limited in scope in the context of academic research. This results in uneven distribution, leaning the average towards fields sustaining higher research outputs.\nBased on the above thought, the answer is:\nMost academic fields have moderate research output between 30% and 50%, with a few outliers on either end. Therefore, the overall distribution is moderately balanced, and the outliers cause a slight skewness." } ], "id_raw": 451 }, { "id": "lvHt2ZgijfXlVSGF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_1.png", "conversations": [ { "from": "human", "value": "\nWhich academic field is depicted to have the lowest research output?" }, { "from": "gpt", "value": "To answer this, I have to compare the heights of all the bars representing the fields. For instance, the 'Computer Science' bar[0.094, 0.359, 0.164, 0.927] corresponds to the first field and has a research output of 45.2% whereas the 'Musicology' bar[0.876, 0.698, 0.945, 0.927] representing the last field, has a research output of 18.2%. Extending this comparison to the other bars, I can detect that the 'Musicology' field has the lowest height signifying the minimum percentage among all the bars.\nBased on the above thought, the answer is:\nMusicology" } ], "id_raw": 451 }, { "id": "ZJN0FZGWPob1welR", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nExtract data from the bar chart showing \"Usage of Materials in Scientific Research & Technological Innovation\" and create a CSV format table." }, { "from": "gpt", "value": "To create a CSV table, I need to consider the x-axis labels which represent the materials and the y-axis labels that represent the usage percentages according to the chart's title \"[0.241, 0.025, 0.806, 0.055]\". There are six bars, each for a different material where {Copper}[0.137, 0.919, 0.187, 0.942] has the highest bar at 45%, and {Iridium}[0.861, 0.919, 0.909, 0.942] with the shortest bar at 2%. There are labels on top of each bar, e.g., {45% of Copper}[0.146, 0.123, 0.177, 0.146] and {2% of Iridium}[0.874, 0.846, 0.896, 0.869]. Thus the CSV table will look something like this:\nBased on the above thought, the answer is:\nMaterial,Usage (%)\nCopper,45\nAluminium,35\nSilver,10\nGold,5\nPlatinum,3\nIridium,2" } ], "id_raw": 452 }, { "id": "YiCIisebse4ZHIYq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nIs Aluminium used more widely than Gold in Scientific Research and Technological Innovation according to the chart? Explain your answer." }, { "from": "gpt", "value": "On the chart, we can see that '{Aluminium}'[0.269, 0.919, 0.344, 0.942] and '{Gold}'[0.580, 0.919, 0.612, 0.942] are labels on the x-axis. Comparing the bar heights of the two, it's clear {the bar for Aluminium}[0.249, 0.314, 0.364, 0.903] is much taller than {the bar for Gold}[0.538, 0.819, 0.654, 0.903], indicating a greater usage percentage for Aluminium at '35%', compared to Gold's '5%'. Based on these findings, it's clear that Aluminium is used more widely than Gold in scientific research and technological innovation.\nBased on the above thought, the answer is:\nYes, Aluminium is used more widely than Gold. The bar representing Aluminium stands at 35% usage while the bar for Gold indicates a usage of only 5%." } ], "id_raw": 452 }, { "id": "pUbNI7H58ZQBSfwW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, what's the percentage difference in usage between Copper and Aluminium?" }, { "from": "gpt", "value": "The bar graph represents the percentage usage of different materials. The '{Copper}'[0.104, 0.146, 0.220, 0.903] bar corresponds to 45% usage, labeled at '45%'. The '{Aluminium}'[0.249, 0.314, 0.364, 0.903] bar corresponds to 35% usage, labeled at '35%'. The answer will be the difference between the usage of Copper and Aluminium.\nBased on the above thought, the answer is:\n10%" } ], "id_raw": 452 }, { "id": "i5KzR52fvhJrEHjt", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart information into a csv formatted table?" }, { "from": "gpt", "value": "I can see that the chart has been organized into several categories. The x-axis represents six separate years from {2016}[0.118, 0.919, 0.153, 0.942] to {2021}[0.902, 0.919, 0.937, 0.942]. The height of the bars represents the percentage of companies adopting each trend; this is shown on the y-axis with values ranging from {0}[0.052, 0.894, 0.061, 0.917] to {180}[0.035, 0.004, 0.061, 0.027]. A glance at the legend located at [0.078, 0.074, 0.262, 0.185] shows that there are three bars, each color represents a different trend: {blue}[0.122, 0.083, 0.237, 0.106] represents 'Remote Working', {orange}[0.122, 0.118, 0.190, 0.141] represents 'Upskilling', and {green}[0.122, 0.153, 0.256, 0.176] represents 'Employee Wellness'. I can fetch the height of each bar to get the respective percentages, as they relate to each trend, for each year.\nBased on the above thought, the answer is:\nYear, Remote Working Trend, Upskilling Trend, Employee Wellness Trend\n2016, 10.5, 18.3, 8.7\n2017, 15.2, 23.5, 12.3\n2018, 23.7, 29.8, 20.1\n2019, 32.8, 36.5, 28.7\n2020, 50.3, 45.9, 39.3\n2021, 63.7, 52.3, 54.2" } ], "id_raw": 453 }, { "id": "ZUMWiTGeq1OlqCil", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nCan you deduce any corporate shifts over the six-year period?" }, { "from": "gpt", "value": "Looking at the chart, there are a few notable observations that can be made. Initially, examining the trend for {remote working}[0.122, 0.083, 0.237, 0.106] you can see there's a steady increase year by year, indicating a gradual adoption of this policy by more companies. Then looking at the trend for {upskilling}[0.122, 0.118, 0.190, 0.141] there is also a steady increase, indicating more companies are investing resources to upskill their employees. Lastly, the trend for {employee wellness}[0.122, 0.153, 0.256, 0.176] shows a similar steady rise with a sharp increase between 2019 and 2020 which could be due to more companies giving emphasis on employee wellbeing owing to the global pandemic. This conclusion is corroborated by the sharp rise in remote working trends around the same period.\nBased on the above thought, the answer is:\nYes, over this six-year period there has been a discernible shift towards more remote working, upskilling of staff and an increased emphasis on employee wellness. Notably, the trends for remote working and employee wellness saw a significant increase in 2020, likely due to the impact of the global pandemic." } ], "id_raw": 453 }, { "id": "m3lPy7MkVoyEe9uS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_3.png", "conversations": [ { "from": "human", "value": "\nFor which year did the Employee Wellness trend saw the highest increase in adoption?" }, { "from": "gpt", "value": "To find the year with the highest increase in Employee Wellness adoption, I need to compare the heights of the {green bars}Employee Wellness, which represents the Employee Wellness Trend. I can see that between the years 2019 and 2020 there is a significant increase in the height of the Employee Wellness bar from [0.583, 0.418, 0.630, 0.560] to [0.740, 0.233, 0.787, 0.427]. This marks the largest increase among the years compared.\nBased on the above thought, the answer is:\n2020" } ], "id_raw": 453 }, { "id": "uEbbpZRQ3kETh2zd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "From the title which shows {\"Artificial Intelligence and Automation Trends\"}[0.337, 0.025, 0.705, 0.055] and {the x-axis tick labels} with the topics listed as {\"Deep Learning\"}[0.096, 0.919, 0.197, 0.942], {\"Robotic Process Automation\"}[0.156, 0.919, 0.350, 0.942], {\"Neural Networks\"}[0.303, 0.919, 0.418, 0.942], {\"Machine Learning\"}[0.406, 0.919, 0.529, 0.942], {\"Natural Language Processing\"}[0.473, 0.919, 0.676, 0.942], {\"AI Ethics\"}[0.651, 0.919, 0.711, 0.942], {\"Cognitive Computing\"}[0.715, 0.919, 0.861, 0.942] and {\"Automated Reasoning\"}[0.818, 0.919, 0.972, 0.942], it can be seen that each label represents a unique AI topic. The y-axis tick labels show the range from 0% to 60%, representing the prevalence of each AI topic. The individual bars and their height give the specific percentage for each topic, for example, the bar for {\"Deep Learning\"}[0.104, 0.073, 0.189, 0.903] reaches around the 59.2% mark on the y-axis, therefore the value for the Deep Learning topic is 59.2%.\nBased on the above thought, the answer is:\nAI_Topics,Percentages\nDeep Learning,59.2\nRobotic Process Automation,35.7\nNeural Networks,29.1\nMachine Learning,22.4\nNatural Language Processing,11.8\nAI Ethics,6.0\nCognitive Computing,3.5\nAutomated Reasoning,1.2" } ], "id_raw": 454 }, { "id": "gsgp14MtOK0zabZt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nBy observing the chart, could you discuss the trend of Artificial Intelligence and Automation's adoption?" }, { "from": "gpt", "value": "By examining the bars in the chart, the distribution of the adoption of AI and Automation topics is distinctive. The topic with the highest adoption is {\"Deep Learning\"}[0.104, 0.073, 0.189, 0.903] reaching almost 60%, followed by {\"Robotic Process Automation\"}[0.211, 0.403, 0.296, 0.903] which is around 35.7%, indicating that these two areas are leading the trend in AI and Automation. Other areas, including {\"Neural Networks\"}[0.318, 0.495, 0.403, 0.903], {\"Machine Learning\"}[0.424, 0.589, 0.510, 0.903], and {\"Natural Language Processing\"}[0.531, 0.738, 0.617, 0.903], also have a significant percentage of adoption. However, topics like {\"AI Ethics\"}[0.638, 0.819, 0.724, 0.903], {\"Cognitive Computing\"}[0.745, 0.854, 0.831, 0.903], and particularly {\"Automated Reasoning\"}[0.852, 0.886, 0.938, 0.903], are considerably less prevalent. This distribution could suggest a stronger focus and investment in applications related to deep learning and robotic process automation in the field of AI.\nBased on the above thought, the answer is:\nThe trend highlights a high focus on Deep Learning and Robotic Process Automation, with significant attention given to Neural Networks, Machine Learning, and Natural Language Processing. However, AI Ethics, Cognitive Computing, and particularly Automated Reasoning, while still important, see less adoption, indicating they may be less mature or less invested in at the current stage." } ], "id_raw": 454 }, { "id": "BSI508Z1i78XnlGJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what is the percentage gap between the most adopted AI topic and the least adopted?" }, { "from": "gpt", "value": "As per the chart visualization, for finding the percentage gap between the most adopted and least adopted AI topic, we need to find the highest and the lowest value on the y-axis in terms of percentages. The bar representing the adoption of {\"Deep Learning\"}[0.104, 0.073, 0.189, 0.903] has the maximum length reaching almost 60%, so it's the most adopted AI topic. The bar for {\"Automated Reasoning\"}[0.852, 0.886, 0.938, 0.903] is the smallest, reaching only about 1.2% on the y-axis, so it is the least adopted AI topic. The difference between the two is the percentage gap between the most adopted and least adopted AI topic.\nBased on the above thought, the answer is:\n58%" } ], "id_raw": 454 }, { "id": "crhgpdQkRQpol4oO", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nCan you help to convert the data in the bar chart into a structured CSV table?" }, { "from": "gpt", "value": "Based on the bar chart available, {the x-axis}[0.488, 0.952, 0.550, 0.975] represents different charity names and {the y-axis}[0.015, 0.400, 0.029, 0.565] indicates the corresponding donation percentages, both ranging from 0:00 to 70:00. The bars under each charity name reflect the corresponding donation percentages. For instance, the donation percentage of {Red Cross}[0.152, 0.903] is inferred from the height of {the bar}[0.103, 0.182, 0.201, 0.903] at around 60% position, confirmed by {the label \u201c60.0%\u201d}[0.130, 0.159, 0.174, 0.182]. This can be repeated for each charity name to gather our data for structuring them into a table.\nBased on the above thought, the answer is:\nCharity Name, Donations (%)\nRed Cross, 60\nHabitat for Humanity, 50\nUNICEF, 30\nSave the Children, 25\nDoctors Without Borders, 45\nAmnesty International, 10\nMake-A-Wish Foundation, 5" } ], "id_raw": 455 }, { "id": "VeGosmt2RfC6SBIn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nBased on this chart, how can charities increase their donation amounts?" }, { "from": "gpt", "value": "This question requires a deeper understanding of the chart itself. Maintaining high-level visibility and a positive image can contribute to attracting more donations. Charities like the {Red Cross}[0.152, 0.903] that enjoy high visibility and have a long history of humanitarian work, tend to get the most donations, as mentioned in {the bar}[0.103, 0.182, 0.201, 0.903]. However, activities of charities like the {Make-A-Wish Foundation}[0.869, 0.819, 0.904, 0.843], although meaningful and appreciated, may not appeal to a large number of donors as indicated by the lowest donation percentage shown by {the bar}[0.837, 0.843, 0.935, 0.903]. Therefore, broadening the impact of their work and improving their public visibility and transparency can help charities increase their donation amounts.\nBased on the above thought, the answer is:\nCharities can increase their donation amounts by improving their public visibility, demonstrating the impact of their work, maintaining transparency, and aligning their mission and activities with the interests of potential donors." } ], "id_raw": 455 }, { "id": "cRXWP6XmgDJavY0P", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_5.png", "conversations": [ { "from": "human", "value": "\nWhich charity received the least proportion of donations?" }, { "from": "gpt", "value": "To find the charity with the least percentage of donations, I must firstly determine the bar with the lowest height, which is {the bar}[0.837, 0.843, 0.935, 0.903] corresponding to {Make-A-Wish Foundation}[0.869, 0.819, 0.904, 0.843]. The height matched with the y-axis, and also confirmed by the text label on the bar, indicating a value of 5%, which is the lowest among all charities.\nBased on the above thought, the answer is:\nMake-A-Wish Foundation" } ], "id_raw": 455 }, { "id": "8BUyXHeFVgREOzxm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nCan you provide the adoption rate data from the chart in a CSV format?" }, { "from": "gpt", "value": "Drawing from the chart's title at the top {\"Big Data Integration Overview\"}[0.398, 0.025, 0.649, 0.055], we see that this chart compares the adoption rates for various big data integration tools. The x-axis labels suggest different types of integration tools listed as {MuleSoft}[0.123, 0.919, 0.184, 0.942], {Talend DI}[0.245, 0.919, 0.309, 0.942], {Dell Boomi}[0.362, 0.919, 0.438, 0.942], {IBM Infosphere}[0.472, 0.919, 0.575, 0.942], {Informatica PowerCenter}[0.561, 0.919, 0.733, 0.942], {Oracle Data Integrator}[0.692, 0.919, 0.849, 0.942], {Jitterbit}[0.868, 0.919, 0.919, 0.942]. The y-axis, with labels ranging from {0}[0.043, 0.894, 0.052, 0.917] to {40}[0.035, 0.053, 0.052, 0.076], represents the adoption rate in percentage. Using the length of the bars from the bottom of the y-axis to where they stop, we can determine their corresponding adoption rate. For instance, the bar at {MuleSoft}[0.104, 0.405, 0.203, 0.903] ends around 23.7.\nBased on the above thought, the answer is:\nIntegration Tool,Adoption Rate (%)\nMuleSoft,23.7\nTalend DI,28.3\nDell Boomi,19.5\nIBM Infosphere,12.8\nInformatica PowerCenter,25.6\nOracle Data Integrator,36.4\nJitterbit,14.9" } ], "id_raw": 456 }, { "id": "BmelpdaYHbciv02B", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart's information, what could you infer about the trends in the adoption of big data integration tools?" }, { "from": "gpt", "value": "According to the length of bars, we can deduce relative popularity trends. {Oracle Data Integrator}[0.721, 0.138, 0.820, 0.903], with the longest bar, denotes that it is currently the most adopted tool, indicating that it might be highly effective or well-reputed in the industry. Conversely, the tool with the shortest bar, {IBM Infosphere}[0.474, 0.634, 0.573, 0.903], may be less efficient or less popular. Observing the bars of middling length, such as {MuleSoft}[0.104, 0.405, 0.203, 0.903], {Talend DI}[0.227, 0.308, 0.326, 0.903], and {Informatica PowerCenter}[0.597, 0.365, 0.696, 0.903], we can conclude that these tools fall somewhere in between in terms of adoption rate, perhaps due to mixed feedback from users or similar efficacy.\nBased on the above thought, the answer is:\nOracle Data Integrator is currently the most adopted tool in big data integration, which implies that it may be highly efficient or well-known in the industry. On the other hand, IBM Infosphere, with the lowest adoption rate, might be less effective or not as popular. Other tools such as MuleSoft, Talend DI, and Informatica PowerCenter have average adoption rates, which could be due to receive mixed responses from users based on their efficiency." } ], "id_raw": 456 }, { "id": "DIx9Kg3maQbNQLpl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_6.png", "conversations": [ { "from": "human", "value": "\nWhich integration tool has the lowest adoption rate according to the chart?" }, { "from": "gpt", "value": "To find the integration tool with the lowest adoption rate, I need to survey the length of each bar, from {MuleSoft}[0.104, 0.405, 0.203, 0.903] to {Jitterbit}[0.844, 0.590, 0.943, 0.903]. By comparing the bar's length, we can see that the {IBM Infosphere}[0.474, 0.634, 0.573, 0.903] has the shortest bar among the integration tools, which implies it has the lowest adoption rate.\nBased on the above thought, the answer is:\nIBM Infosphere" } ], "id_raw": 456 }, { "id": "grKXLsZgxoNg22QY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart you are observing, could you please transform the data shown into a structured table using the CSV format?" }, { "from": "gpt", "value": "By visualizing the bar chart with the title {\"GIS Uptake in Australian Provinces\"}[0.380, 0.025, 0.667, 0.055], we can see that there's a single bar for every province on {the x-axis}<[0.104, 0.146, 0.220, 0.903],[0.249, 0.310, 0.364, 0.903],[0.393, 0.409, 0.509, 0.903],[0.538, 0.476, 0.654, 0.903],[0.683, 0.593, 0.798, 0.903],[0.827, 0.760, 0.943, 0.903]>. The height of these bars indicate the corresponding percentages mentioned at the top. From left to right, these bars represent the provinces: \"New South Wales\", \"Victoria\", \"Queensland\", \"South Australia\", \"Western Australia\", and \"Tasmania\". By examining the percentages at the top of the bars, I can assign the corresponding percentages for each province group and convert this data into CSV format.\nBased on the above thought, the answer is:\nProvince,Percentage\nNew South Wales,81\nVictoria,63.5\nQueensland,52.9\nSouth Australia,45.7\nWestern Australia,33.2\nTasmania,15.3" } ], "id_raw": 457 }, { "id": "w3FyR6WGsaGO9C0x", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the general trends or patterns you can see in the GIS uptake based on the chart?" }, { "from": "gpt", "value": "From my understanding of the bar chart, it appears that there's a generally decreasing trend in GIS uptake from New South Wales with the highest percentage uptake {\"81%\"}[0.140, 0.114, 0.184, 0.137] to Tasmania with the lowest percentage uptake {\"15.3%\"}[0.863, 0.727, 0.907, 0.751]. This trend seems to show that as we move from the left side of the chart to the right, the height of the bars, hence GIS usage, is reduced. There's a noticeable drop in the bar height going from Western Australia {\"33.2%\"}[0.719, 0.560, 0.762, 0.583] to Tasmania {\"15.3%\"}[0.863, 0.727, 0.907, 0.751]. This might suggest that the less populous states have significantly lower percentage uptake of GIS compared to the more populous states.\nBased on the above thought, the answer is:\nThe general trend in GIS uptake across the provinces of Australia appears to be decreasing, particularly between Western Australia and Tasmania, with less populous states having a significantly lower percentage uptake compared to more populous states." } ], "id_raw": 457 }, { "id": "6ztfo8beCVPnnNLn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the percentage uptakes of GIS between New South Wales and Tasmania?" }, { "from": "gpt", "value": "To find the difference between the percentage uptakes, I need to locate the bars representing New South Wales and Tasmania on the chart. The percentage of GIS uptake for New South Wales is shown by the height of the bar located at {\"New South Wales bar\"}[0.104, 0.146, 0.220, 0.903]. The percentage is provided above this bar, which shows that it's{\"81%\"}[0.140, 0.114, 0.184, 0.137]. Meanwhile, the percentage of GIS uptake for Tasmania is displayed by the height of the bar {\"Tasmania bar\"}[0.827, 0.760, 0.943, 0.903], for which the percentage is{\"15.3%\"}[0.863, 0.727, 0.907, 0.751]. By subtracting the percentage of Tasmania from New South Wales, I can find the difference.\nBased on the above thought, the answer is:\n65.7%" } ], "id_raw": 457 }, { "id": "6CzCYKs7CyVgKj0B", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nTake the bar chart data on Transportation and Logistics Industry trends and transform it into a CSV format table." }, { "from": "gpt", "value": "From the title {Transportation and Logistics Industry Trends}[0.340, 0.025, 0.707, 0.055] and {the x-axis tick labels}[{\"Steep Decline\"}[0.126, 0.919, 0.222, 0.942]; {\"Dramatic Surge\"}[0.294, 0.919, 0.403, 0.942]; {\"Gradual Recovery\"}[0.461, 0.919, 0.585, 0.942]; {\"Upward Trajectory\"}[0.635, 0.919, 0.762, 0.942]; {\"Slight Decrease\"}[0.819, 0.919, 0.928, 0.942]], it can be inferred that the x-axis represents different trends in the industry. The y-axis stands for the frequency of occurrences of these trends in percentages, ranging from 0 to 90, as indicated by {the y-axis tick labels}[0.043, 0.894, 0.052, 0.917] to [0.035, 0.053, 0.052, 0.076]. For each trend, I am given the percentage frequency as depicted by the height of the individual bars and the value annotations at the top of each bar. For instance, for {Steep Decline}[0.126, 0.919, 0.222, 0.942], the bar height falls approximately at the 30 position and it has a value annotation {\"30.0%\"}[0.152, 0.599, 0.196, 0.623], indicating that the occurrence frequency of 'Steep Decline' is 30.0%. I can use this method for each category to extract the corresponding frequencies.\nBased on the above thought, the answer is:\nTrend, Frequency (%)\nSteep Decline,30.0\nDramatic Surge,80.0\nGradual Recovery,50.0\nUpward Trajectory,70.0\nSlight Decrease,55.0" } ], "id_raw": 458 }, { "id": "Q78pPxPrU4tyGqjk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the chart, which trend seems to be the most critical for the Transportation and Logistics Industry? Justify your answer." }, { "from": "gpt", "value": "According to the y-axis values {Occurrence Frequency (%)'[0.015, 0.329, 0.029, 0.636]}, I can understand that the higher the bar is, the more frequent a specific trend is in the industry. With this understanding, I see that {Dramatic Surge}[0.294, 0.919, 0.403, 0.942] is the highest bar among all, and its value is {\"80.0%\"}[0.327, 0.132, 0.371, 0.156]. Based on the height of the rest bars like {Steep Decline}[0.104, 0.623, 0.244, 0.903] or {Gradual Recovery}[0.454, 0.436, 0.593, 0.903], I can conclude that \"Dramatic Surge\" is more critical than other trends, as it happens more frequently.\nBased on the above thought, the answer is:\nThe \"Dramatic Surge\" trend is the most critical for the Transportation and Logistics Industry because it has the highest occurrence frequency of 80.0% based on the given data." } ], "id_raw": 458 }, { "id": "fpetk3jtqe2eTFi2", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of occurrence frequency across the trends?" }, { "from": "gpt", "value": "To find the range of occurrence frequency across the trends, I have to check the lowest and highest values. However, the lowest value would always be 0 according to the y-axis labels starting from {0}[0.043, 0.894, 0.052, 0.917]. To find the highest value, I can simply look for the highest bar. From only looking at each bar, I can see that the value of {Dramatic Surge}[0.294, 0.919, 0.403, 0.942] looks the highest. To confirm it, I look at {the associated value annotation}[0.327, 0.132, 0.371, 0.156] which shows \"80.0%\". Therefore, the range of occurrence frequency across the trends would be 0 to 80.\nBased on the above thought, the answer is:\n0 to 80%" } ], "id_raw": 458 }, { "id": "KgeNS4pkh9vYY1aO", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data on the chart into a tabular format based on your observation?" }, { "from": "gpt", "value": "The chart under analysis is a bar chart as inferred from the x-axis labels representing different kinds of technology and the y-axis labels representing development prospects ranging from {0}[0.036, 0.786, 0.051, 0.804] to {100}[0.029, 0.040, 0.051, 0.057]. Each bar represents the possible growth of a single type of technology, such as {AI Technology}[0.101, 0.196, 0.170, 0.793], {Quantum Computing}[0.187, 0.307, 0.256, 0.793], and others. The chart's layout suggests the table's two columns should be 'Technology' and 'Development Prospect (%)'.\nBased on the above thought, the answer is:\nTechnology,Development Prospect (%)\nAI Technology,80.0\nQuantum Computing,65.1\nNanotechnology,47.5\nBiotechnology,73.4\nBlockchain Technology,41.1\nSpace Exploration,85.0\n5G Technology,59.2\nElectric Cars,78.5\nVR/AR Technology,52.7\nGreen Energy,91.5" } ], "id_raw": 459 }, { "id": "EdwMYYnG2s5NQEyl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhich technology seems to have the best and worst development prospects according to the chart?" }, { "from": "gpt", "value": "To infer the technology having the best and worst prospects, the tallest and shortest bars on the chart should be identified. {Green Energy}[0.876, 0.110, 0.945, 0.793] appears to be the tallest bar indicating the best development prospects at 91.5%. Conversely, {Blockchain Technology}[0.446, 0.486, 0.515, 0.793] seems to be the shortest bar, symbolizing the least development prospect at 41.1%.\nBased on the above thought, the answer is:\nGreen Energy seems to have the best development prospects while Blockchain Technology appears to have the worst development prospects according to the chart." } ], "id_raw": 459 }, { "id": "LffHlOdOUPcfNpeI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage of technology's development prospect as per the chart?" }, { "from": "gpt", "value": "To get the total percentage of technology\u2019s development prospects, I need to add the percentages of all the different types of technologies. This can be determined by identifying and summarizing the development prospects of all the bars including: {AI Technology}[0.101, 0.196, 0.170, 0.793] at 80.0%, {Quantum Computing}[0.187, 0.307, 0.256, 0.793] at 65.1%, {Nanotechnology}[0.273, 0.439, 0.342, 0.793] at 47.5%, {Biotechnology}[0.360, 0.245, 0.428, 0.793] at 73.4%, {Blockchain Technology}[0.446, 0.486, 0.515, 0.793] at 41.1%, {Space Exploration}[0.532, 0.159, 0.601, 0.793] at 85.0%, {5G Technology}[0.618, 0.351, 0.687, 0.793] at 59.2%, {Electric Cars}[0.704, 0.207, 0.773, 0.793] at 78.5%, {VR/AR Technology}[0.790, 0.400, 0.859, 0.793] at 52.7% and {Green Energy}[0.876, 0.110, 0.945, 0.793] at 91.5%.\nBased on the above thought, the answer is:\n723.9%" } ], "id_raw": 459 }, { "id": "ylHnq3fmXDOq02RQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nCan you help me interpret the data on the bar chart for the tourism trends and translate this into a CSV format?" }, { "from": "gpt", "value": "Sure, based on the {title of the chart}[0.334, 0.025, 0.721, 0.055], {\"Tourism and Hospitality Industry Data Analysis\"}, it appears each bar represents a different category of tourism trend. The {x-axis labels}[{\"Sustainable Tourism\"}[0.091, 0.919, 0.231, 0.942]; {\"Culinary Tourism\"}[0.226, 0.919, 0.341, 0.942]; {\"Adventure Tourism\"}[0.341, 0.919, 0.471, 0.942]; {\"Dark Tourism\"}[0.482, 0.919, 0.573, 0.942]; {\"Eco-Tourism\"}[0.609, 0.919, 0.691, 0.942]; {\"Space Tourism\"}[0.722, 0.919, 0.822, 0.942]; {\"Wellness Tourism\"}[0.835, 0.919, 0.954, 0.942]] indicate the type of tourism trend. For each bar, the height corresponds to the value on the y-axis, which is labeled as {\"Percentage (%)\"}[0.015, 0.394, 0.029, 0.571]. \nFor example, the bar for {\"Dark Tourism\"}[0.479, 0.559, 0.577, 0.903] extends approximately halfway up the chart. The figure above that bar indicates a value of {\"40.85%\"}[0.502, 0.528, 0.554, 0.551]. With all this information, I can construct a CSV table that looks like this:\nBased on the above thought, the answer is:\nTourism Trend, Percentage (%)\nSustainable Tourism, 5.50\nCulinary Tourism, 15.25\nAdventure Tourism, 22.30\nDark Tourism, 40.85\nEco-Tourism, 61.35\nSpace Tourism, 82.30\nWellness Tourism, 95.00" } ], "id_raw": 460 }, { "id": "fWGopfXIZulyUIts", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nWhat trend can you discern from the bar chart about the tourism and hospitality industry?" }, { "from": "gpt", "value": "By observing the bar chart, I can infer certain trends regarding the tourism and hospitality industry. Notably, the heights of the bars consistently increase from left to right, suggesting an overall positive trend in the tourism and hospitality industry's growth rates. Starting from a small percentage in the {\"Sustainable Tourism\"}[0.112, 0.857, 0.210, 0.903] sector (5.5%), it multiplies nearly twentyfold by the end of the sequence in the{\"Wellness Tourism\"}[0.846, 0.104, 0.943, 0.903] sector (95.0%). This suggests a rising overall trend in the preference for these tourism trends, with Wellness Tourism showing the most dramatic increase.\nBased on the above thought, the answer is:\nThere is an overall positive trend in the growth rates in the tourism and hospitality industry, with wellness tourism showing the most significant rise in popularity." } ], "id_raw": 460 }, { "id": "iKmbHLEXK9rHz3r1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which sector saw the highest growth in terms of percentage changes?" }, { "from": "gpt", "value": "To find the highest growth rate in terms of percentage changes, I need to examine each bar's height in the chart. From left to right, we can observe that the height of each bar increases. For instance, the bar for {\"Sustainable Tourism\"}[0.112, 0.857, 0.210, 0.903] is close to the bottom, representing a low percentage, while the bar for {\"Wellness Tourism\"}[0.846, 0.104, 0.943, 0.903] is noticeably taller, almost reaching the top of the chart. The data label above this bar indicates that the growth rate of the Wellness Tourism sector is {\"95.00%\"}[0.873, 0.073, 0.916, 0.096]. Hence, Wellness Tourism has the highest growth rate.\nBased on the above thought, the answer is:\nWellness Tourism." } ], "id_raw": 460 }, { "id": "7d0603kh9gBCuW9Y", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nFrom your observations of the bar chart, please convert the profit change data across these companies into a data table." }, { "from": "gpt", "value": "Analysing the bar chart, we have companies as represented on the x axis, with their names given in the x-ticks ranging from {Coinbase}[0.104, 0.939, 0.158, 0.957] to {TransferWise}[0.873, 0.939, 0.948, 0.957]. These companies have associated bars indicating profit change for each. For instance, the bar for {Coinbase}[0.096, 0.157, 0.166, 0.707] shows a profit change of approximately 5% as indicated by the y-axis mapping and the specific annotation {\"5.0%\"}[0.116, 0.139, 0.146, 0.157]. Similar observations are made for all other companies. For instance, we have {Starling}[0.801, 0.939, 0.846, 0.957] with a bar {Starling bar}[0.789, 0.707, 0.858, 0.762] representing a negative profit change of around 0.5% as marked by {\"-0.5%\"}[0.807, 0.745, 0.840, 0.762]. Using this method, we can convert the bar chart data into the following CSV format table.\nBased on the above thought, the answer is:\nCompany,Profit change (%)\nCoinbase,5.0\nRevolut,3.2\nN26,1.2\nSquare,4.3\nRobinhood,2.1\nPaypal,3.5\nStripe,4.1\nMonzo,-1.0\nStarling,-0.5\nTransferWise,2.0" } ], "id_raw": 461 }, { "id": "dMjBHaMMI4mhWFYZ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about the overall growth of the FinTech and Digital Currency industry from the bar chart?" }, { "from": "gpt", "value": "As per the bar chart, the majority of the companies in the FinTech and Digital Currency sector have experienced profit growth over the past fiscal year. This can be inferred from the positive values of the bars for these companies. Companies such as {Square}[0.370, 0.939, 0.411, 0.957], {Stripe}[0.633, 0.939, 0.668, 0.957] , and {Coinbase}[0.104, 0.939, 0.158, 0.957] show considerable growth, with the tallest bar belonging to {Coinbase}[0.096, 0.157, 0.166, 0.707] with a 5% profit increase. There's only a couple of companies namely {Monzo}[0.718, 0.939, 0.756, 0.957] and {Starling}[0.801, 0.939, 0.846, 0.957] that show a decreased profit, observable from their bars - {Monzo bar}[0.703, 0.707, 0.772, 0.817] and {Starling bar}[0.789, 0.707, 0.858, 0.762] respectively being below the 0% line on the y-axis.\nBased on the above thought, the answer is:\nThe majority of the FinTech and Digital Currency companies have seen a rise in profits, suggesting a generally growth-oriented trend in the industry. However, a few companies such as Monzo and Starling experienced a decline, indicating the growth isn't universal." } ], "id_raw": 461 }, { "id": "BpeutGQzsxAYlpvD", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_11.png", "conversations": [ { "from": "human", "value": "\nWhich company experienced the highest increase in profits?" }, { "from": "gpt", "value": "To find the company with the highest profits, I need to identify the tallest bar on the chart which maps to the value on the y-axis. The {Coinbase bar}[0.096, 0.157, 0.166, 0.707] is the tallest among all with its height representing an increase of approximately 5% as marked by the text label {\"5.0%\"}[0.116, 0.139, 0.146, 0.157].\nBased on the above thought, the answer is:\nCoinbase" } ], "id_raw": 461 }, { "id": "QoFSXp0fzuAGJdvh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you saw, could you convert this into a structured table with risk factors and their corresponding percentage risks?" }, { "from": "gpt", "value": "As per the chart's X-axis and title {\"Financial Management and Risk Control\"}[0.383, 0.019, 0.656, 0.041], I see that the risk factors are labeled on the X-axis and the corresponding risk percentages are represented by the height of each bar. For instance, for the \"Inflation\" category, the risk percentage is determined by the height of the {\"Inflation\"}[0.094, 0.780, 0.194, 0.927] bar as well as its annotation {\"10.0%\"}[0.126, 0.756, 0.162, 0.773], which provides the specific value of 10%. I will list down each risk factor and their corresponding risk percentage in a CSV table format. This process will be repeated for each risk factor on the X-axis.\nBased on the above thought, the answer is:\nRisk Factor,Percentage risk\nInflation,10.0\nInterest rate,12.5\nCredit risk,30.0\nTransaction cost,2.5\nLiquidity risk,18.0\nOperational risk,35.0\nMarket risk,50.0" } ], "id_raw": 462 }, { "id": "HXXLdOIxxkJtXBJf", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which two risk factors should be given the most attention based on their percentage risk?" }, { "from": "gpt", "value": "To answer this question, I need to focus on the bars with the highest heights. By analyzing each bar's height, from \"Inflation\" to \"Market risk\", I notice that {\"Operational risk\"}[0.720, 0.414, 0.820, 0.927] and {\"Market risk\"}[0.845, 0.193, 0.945, 0.927] have the longest bars, meaning they represent the highest percentage risks. Hence, they are the risk factors to pay more attention to.\nBased on the above thought, the answer is:\nOperational risk and Market risk. These two risks have the highest percentage risk values according to the chart." } ], "id_raw": 462 }, { "id": "CAeztzCSGjhE0KEc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_12.png", "conversations": [ { "from": "human", "value": "\nWhich risk factor poses the highest percentage risk?" }, { "from": "gpt", "value": "To find the risk factor with the highest percentage risk, I need to observe the height of each bar in the bar chart. From left to right, the bars correspond to the risk factors such as {\"Inflation\"}[0.094, 0.780, 0.194, 0.927], {\"Interest rate\"}[0.219, 0.744, 0.319, 0.927], on to the last one {\"Market risk\"}[0.845, 0.193, 0.945, 0.927]. Among all the bars, {\"Market risk\"}[0.845, 0.193, 0.945, 0.927] bar reaches the lowest point on the Y-axis indicating the highest percentage risk, and it is labeled with {\"50.0%\"}[0.877, 0.169, 0.913, 0.186].\nBased on the above thought, the answer is:\nMarket risk." } ], "id_raw": 462 }, { "id": "DkUkF5ExEzXi0T1D", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nCan you present the data from the bar chart in the form of a CSV table?" }, { "from": "gpt", "value": "Each bar in the chart represents a training method in the Education and Training Industry, and the length of the bar corresponds to the percentage of entities adopting that method. So, starting from the leftmost bar, the first bar represents the {Webinar}[0.099, 0.607, 0.176, 0.918] method, and its percentage is about 32.5%, which I found by referring to its height and the matching y-axis label. Proceeding similarly for the rest of the bars would enable me to format the data in CSV.\nBased on the above thought, the answer is:\nTraining_Industry,Percentage\nWebinar,32.5\nIn-person_Seminars,22.7\nOnline_Courses,74.2\nIn-house_Training,18.6\nBootcamps,11.1\nCertifications,48.9\nE-learning_Platforms,80.5\nBlended_Learning,29.7\nMicrolearning,53.4" } ], "id_raw": 463 }, { "id": "jYeXWDuZ4de3v3Ke", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhich is the least adopted training method in the industry and how might this information affect the industry's expansion in that area?" }, { "from": "gpt", "value": "To determine the least adopted method, I need to locate the shortest bar on the chart. By examining the bars from left to right, {Bootcamps}[0.484, 0.812, 0.561, 0.918] is the shortest bar with an adoption rate of about 11.1%, according to its height and corresponding y-axis label. The relatively low adoption indicates that the industry has not embraced this training method as much as others, which could be due to several factors such as cost, effectiveness, time constraints, or a combination thereof. This could inform industry professionals that there may be an opportunity in this underexplored area.\nBased on the above thought, the answer is:\nThe least adopted method is Bootcamps with an adoption percentage of 11.1%. This could mean underutilization of this training method in the industry, suggestive of room for growth and potential investment in this area." } ], "id_raw": 463 }, { "id": "I3ItOvmgRrCFE1xu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_13.png", "conversations": [ { "from": "human", "value": "\nWhich training method is adopted the most and how much is its adoption in percentage?" }, { "from": "gpt", "value": "To find the most adopted training method, I need to identify the longest bar in the chart. Looking at each bar from left to right, I can see that the longest bar is {E-learning_Platforms}[0.676, 0.148, 0.753, 0.918] with a percentage of about 80.5% as indicated by the bar's height and its corresponding y-axis label.\nBased on the above thought, the answer is:\nE-learning_Platforms is adopted the most with an adoption percentage of 80.5%." } ], "id_raw": 463 }, { "id": "cH20E4vvC1zPSWu7", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nCan you formulate the bar chart data into a structured table?" }, { "from": "gpt", "value": "Looking at the x-axis of the bar chart, there are labels {Solar Power, Hydropower, Wind Energy, Biofuel, Geothermal, Nuclear Energy, Natural Gas, Coal, and Oil} denoted within the x-tick labels in each of their respective coordinates. It is clear that these labels are the names of the different energy resources. Looking at the set of bars, the height of each bar represents the percentage of energy resource consumption. Also, a value annotation on top of each bar gives the exact percentage value, such as {5.6 for Solar Power}[0.132, 0.832, 0.168, 0.856], {15.8 for Hydropower}[0.222, 0.747, 0.266, 0.770], and so forth. Using these values along with the resource names from the x-axis, I can generate a CSV table with two columns: Energy Resource and Percentage.\nBased on the above thought, the answer is:\nEnergy Resource,Percentage\nSolar Power,5.6\nHydropower,15.8\nWind Energy,35.9\nBiofuel,44.3\nGeothermal,52.0\nNuclear Energy,66.6\nNatural Gas,80.0\nCoal,85.0\nOil,92.5" } ], "id_raw": 464 }, { "id": "O7ogpmc3TpdWUVrY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which renewable energy resource has the highest consumption percentage?" }, { "from": "gpt", "value": "The bars in the chart represent different energy resources with their heights representing the corresponding consumption percentages. Amongst the renewable energies, 'Solar Power', 'Hydropower', 'Wind Energy', 'Geothermal', the bar for {Geothermal}[0.490, 0.466, 0.566, 0.903] is the highest, denoting 52.0%, which is labelled above the bar. This indicates that amongst the renewable energy resources, Geothermal has the highest consumption percentage.\nBased on the above thought, the answer is:\nGeothermal." } ], "id_raw": 464 }, { "id": "yVI77Poh1yyXpGyN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_31_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the data on the chart, what's the difference between the percentages of Coal and Solar Power?" }, { "from": "gpt", "value": "From the chart, I can see that the bar for {Coal}[0.773, 0.188, 0.849, 0.903] rises pretty high with a value of 85.0% given in the label above the bar. On the other hand, the bar for {Solar Power}[0.112, 0.856, 0.188, 0.903] is much lower, with an annotation of 5.6% in the label above the bar. To find the difference, simple subtraction of 5.6 from 85.0 can give us the answer.\nBased on the above thought, the answer is:\n79.4" } ], "id_raw": 464 }, { "id": "a4FNhbDrPrnOFwxM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the stacked bar chart you observed as a CSV format data table." }, { "from": "gpt", "value": "By looking at the bar chart title {\"Market Competition Analysis and Comparison\"}[0.271, 0.168, 0.780, 0.197] and comparing it with the x-axis labels {Market Share}[0.247, 0.925, 0.340, 0.945] and {Sales Volume}[0.711, 0.925, 0.804, 0.945], we can determine that the x-axis shows metrics of competition. The y-axis describes the corresponding values, ranging from {0}[0.047, 0.903, 0.056, 0.923] to {80}[0.039, 0.184, 0.056, 0.204]. Here, six stacked bars representing {Product A Sales}[0.294, 0.082, 0.414, 0.104], {Product B Sales}[0.294, 0.115, 0.414, 0.136], {Product C Sales}[0.487, 0.082, 0.606, 0.104], {Product D Sales}[0.487, 0.115, 0.608, 0.136], {Product E Sales}[0.681, 0.082, 0.799, 0.104] and {Product F Sales}[0.681, 0.115, 0.799, 0.136] are visible. For each metric, each product is represented by a colored bar. By examining the heights of the bars and the corresponding coordinates, the sales values of each product for each metric can be extracted.\nBased on the above thought, the answer is:\nMetric,Product A Sales,Product B Sales,Product C Sales,Product D Sales,Product E Sales,Product F Sales\nMarket Share,13,18,17,7,12,8\nSales Volume,11,6,10,16,14,13" } ], "id_raw": 465 }, { "id": "mquZqSs7OGcNqWdw", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which product had the most desirable outcome in terms of the market competition analysis?" }, { "from": "gpt", "value": "The two metrics, market share and sales volume, need to be compared across the six products in order to determine which had the most favorable outcome in terms of market competition. Higher values for both metrics would indicate a superior market position. Concretely, looking at the heights of the bars for Market Share and Sales Volume, values can be established for each product. {Product A}[0.108, 0.794, 0.479, 0.911] and [0.572, 0.812, 0.943, 0.911], {Product B}[0.108, 0.632, 0.479, 0.794] and [0.572, 0.758, 0.943, 0.812], {Product C}[0.108, 0.479, 0.479, 0.632] and [0.572, 0.668, 0.943, 0.758], {Product D}[0.108, 0.417, 0.479, 0.479] and [0.572, 0.524, 0.943, 0.668], {Product E}[0.108, 0.309, 0.479, 0.417] and [0.572, 0.399, 0.943, 0.524], {Product F}[0.108, 0.237, 0.479, 0.309] and [0.572, 0.282, 0.943, 0.399]. Product E appears to be performing reasonably well, coming in the top half based on both measures.\nBased on the above thought, the answer is:\nProduct E. It has a high market share and a good number for sales volume, indicating a strong market position." } ], "id_raw": 465 }, { "id": "RPvPY2aI3Ac32bwi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which product experienced the greatest sales volume increase?" }, { "from": "gpt", "value": "Sales volumes for the products can be found by examining the bar heights for the Sales Volume metric. By comparing the height of the bars relative to the y-axis score listing, observed values for the sales volume of {Product A}[0.572, 0.812, 0.943, 0.911], {Product B}[0.572, 0.758, 0.943, 0.812], {Product C}[0.572, 0.668, 0.943, 0.758], {Product D}[0.572, 0.524, 0.943, 0.668], {Product E}[0.572, 0.399, 0.943, 0.524] and {Product F}[0.572, 0.282, 0.943, 0.399] are 11, 6, 10, 16, 14, and 13 respectively. It is seen that Product D experienced the most considerable increase in sales volume.\nBased on the above thought, the answer is:\nProduct D." } ], "id_raw": 465 }, { "id": "phCEApC0Xk7Dc3P1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nFrom the given bar chart, is it possible for you to generate a structured table in CSV format?" }, { "from": "gpt", "value": "The chart's title {Bar Chart of Media Influence and Public Opinion Guidance}[0.202, 0.091, 0.849, 0.120] and the label on the x-axis {Media Types}[0.474, 0.953, 0.577, 0.979] define the various categories of media presented. The y-axis label {Level of Influence}[0.015, 0.414, 0.033, 0.624] is a scale, offering the corresponding value for each category. To compile the CSV table, I need to extract the value from each bar. This can be achieved by considering the height of the bars with respect to the scale on the y-axis. For instance, the bar for {Newspaper}[0.108, 0.492, 0.247, 0.911] peaks approximately at 23 on the scale, thus, the influence level of Newspaper is 23.\nBased on the above thought, the answer is:\nMedia,Influence Level\nNewspaper,23\nTV,19\nRadio,12\nOnline,35\nSocial Media,41" } ], "id_raw": 467 }, { "id": "nS4V5C2lt26JBtLb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, could you determine which media type has the most significant influence on shaping public opinion? Why?" }, { "from": "gpt", "value": "This would require a comparison of the lengths of various bars. Among all bars, the bar for {Social Media}[0.804, 0.163, 0.943, 0.911] is the tallest, reaching almost 41 on the y-axis. Consequently, it exerts the highest influence on shaping public opinion, according to the chart. This could be attributed to the growing prevalence and accessibility of social media, enabling them to reach and influence a larger audience.\nBased on the above thought, the answer is:\nSocial Media. The reason can be deduced from the chart, as it represents the tallest bar, which signifies the highest level of influence among all media types. This suggests that Social Media holds the most significant influence in shaping public opinion." } ], "id_raw": 467 }, { "id": "E0cjJUdqzKXGRr8x", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_2.png", "conversations": [ { "from": "human", "value": "\nCould you tell me which media type exerts the least influence?" }, { "from": "gpt", "value": "To find the media type with minimum influence, I must compare the heights of all bars. Starting from the left, the first bar is for {Newspaper}[0.108, 0.492, 0.247, 0.911] which reaches almost 23 on the y-axis. The next is the {TV}[0.282, 0.565, 0.421, 0.911] bar at 19. Further right, I observe the bar for {Radio}[0.456, 0.692, 0.595, 0.911], which reaches the 12 mark which is the shortest so far. Examining the next two bars for {Online}[0.630, 0.273, 0.769, 0.911] (35) and {Social Media}[0.804, 0.163, 0.943, 0.911] (41), they are taller than the previous ones. Hence, by comparing the heights of all bars, I find that Radio has the least influence.\nBased on the above thought, the answer is:\nRadio" } ], "id_raw": 467 }, { "id": "TgBs7wu3uPdlGHAM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart you analyzed, can you convert the healthcare performance dataset into a structured table?" }, { "from": "gpt", "value": "The bar chart is segmented into multiple categories, each having three groups, which I can identify by referring to the chart's {legend}[0.237, 0.019, 0.807, 0.095]. The x-axis has {seven indicators}[Medical Staff Availability[0.078, 0.925, 0.249, 0.945]; Clinical Equipment Efficiency[0.182, 0.925, 0.384, 0.945]; Patients' Satisfaction Score[0.308, 0.925, 0.497, 0.945]; Hospital Cleanliness Rating[0.427, 0.925, 0.617, 0.945]; Disease Recovery Rate[0.562, 0.925, 0.721, 0.945]; Healthcare Awareness[0.684, 0.925, 0.838, 0.945]; Medical Research Progress[0.788, 0.925, 0.973, 0.945];]. The y-axis signifies the achieved level or counts for each indicator. From {each group of bars on y-axis}, I can observe different colors representing 'Standard Level'blue, 'Achieved Level'orange, and 'Incremental Level'green. I'll use these values to create the table.\nBased on the above thought, the answer is:\nHealth Indicators,Standard Level,Achieved Level,Incremental Level\nMedical Staff Availability,200,176,5\nClinical Equipment Efficiency,150,138,4\nPatients' Satisfaction Score,90,70,-3\nHospital Cleanliness Rating,85,80,-1\nDisease Recovery Rate,75,68,-1\nHealthcare Awareness,100,120,4\nMedical Research Progress,60,72,2" } ], "id_raw": 468 }, { "id": "hKVg948vibo3SUKs", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nAs per the observed performance indicators, which area might need more focus, and why?" }, { "from": "gpt", "value": "Analyzing the chart, the quality of service seems to have dropped in certain areas. These areas can be identified by any negative increments. Looking at the chart, I can see that the {green bar below the x-axis}[0.354, 0.597, 0.450, 0.603],[0.474, 0.587, 0.570, 0.589],[0.594, 0.630, 0.689, 0.632] representing incremental level for the \"Patients' Satisfaction Score\", \"Hospital Cleanliness Rating\", and \"Disease Recovery Rate\" are negative, which indicates that these areas have declined over time.\nBased on the above thought, the answer is:\nAccording to the observed performance indictors, the areas that might need more focus are \"Patients' Satisfaction Score\", \"Hospital Cleanliness Rating\", and \"Disease Recovery Rate\". These areas have witnessed a decline over time as per the negative incremental levels." } ], "id_raw": 468 }, { "id": "sJeuq7FgDdZFd02h", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_3.png", "conversations": [ { "from": "human", "value": "\nWhich health indicator witnessed the most significant increment?" }, { "from": "gpt", "value": "Finding the health indicator with the most significant increment means identifying the tallest green bar, which represents \"Incremental Level\". As I can see, the {green bars} are distributed on top of the orange bars, and {the bar for \"Healthcare Awareness\"}[0.713, 0.471, 0.809, 0.479] appears to have the largest height.\nBased on the above thought, the answer is:\nHealthcare Awareness" } ], "id_raw": 468 }, { "id": "qj5sqXRaQZZT0sX1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nBased on the visualization, can you convert the market shares of different industries between the two trends into a table?" }, { "from": "gpt", "value": "The bar chart title {Industry Market Share Comparison}[0.336, 0.091, 0.723, 0.120] suggests that the x-axis {Industries}[0.489, 0.953, 0.571, 0.979] represents different industries, whereas the y-axis indicates corresponding {Market Share}[0.015, 0.440, 0.033, 0.597]. Multiple bars of different colors represent different trends as mentioned in the {legend}[0.409, 0.019, 0.650, 0.095]. The red bars represent {Trend 1}[0.458, 0.065, 0.515, 0.086], and the green bars represent {Trend 2}[0.588, 0.065, 0.644, 0.086]. The heights of these bars indicate the market share, with {Trend 1 data}[{bar_coordinates} '[0.116, 0.697, 0.213, 0.911]'] and {Trend 2 data}[{bar_coordinates} '[0.116, 0.505, 0.213, 0.697]'] available for all the industries. The features extracted would then form the CSV table.\nBased on the above thought, the answer is:\nIndustry,Trend 1,Trend 2\nTV industry,50,45\nGame industry,30,28\nMovie industry,50,48\nMusic industry,70,65\nBook industry,50,46\nArt industry,90,85\nToy industry,80,76" } ], "id_raw": 469 }, { "id": "sc3zQ2IjOp3SkO4b", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trends presented in the chart, could you suggest which industry has the most stable market share?" }, { "from": "gpt", "value": "Examining the chart, the stability of the market share for each industry can generally be determined by the smallest range of fluctuations between Trend 1 and Trend 2. In comparing each pair of bars that represent Trend 1 and Trend 2, such as {TV Industry}[{Trend 1 bar}[0.116, 0.697, 0.213, 0.911];{Trend 2 bar}[0.116, 0.505, 0.213, 0.697]] and continuing this comparison for each subsequent industry's bars, it's evident that the {Game industry}[0.238, 0.783, 0.335, 0.911; 0.238, 0.663, 0.335, 0.783] has the least variation in the market share across two trends. The range of change in the Game industry is just 2 points, which is the smallest change among all industries.\nBased on the above thought, the answer is:\nGame industry. The market share in this sector shows the smallest variation between the two trends, indicating it has the most stable market share." } ], "id_raw": 469 }, { "id": "D4JHmeWxUS3pqGzg", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_4.png", "conversations": [ { "from": "human", "value": "\nIn which industry did the market share drop the most from Trend 1 to Trend 2?" }, { "from": "gpt", "value": "To find out the industry with the highest drop in market shares between Trend 1 and Trend 2, I need to check the bar heights of both the trends for each industry. As per the height of the bars, the initial bar {height of TV industry}[{Trend 1 bar}[0.116, 0.697, 0.213, 0.911];{Trend 2 bar}[0.116, 0.505, 0.213, 0.697]] shows a drop of 5 points, and checking the other bars like Game industry, Movie industry etc. shows similar patterns. Finally, by comparing the differences in each industry, we can see that {Music industry}[0.481, 0.612, 0.579, 0.911; 0.481, 0.334, 0.579, 0.612] has seen the greatest decline from Trend 1 to Trend 2 with a drop of 5 points, which is the biggest change among all industries.\nBased on the above thought, the answer is:\nMusic Industry." } ], "id_raw": 469 }, { "id": "zXljm08zyXKzbPHe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nI have analyzed the bar chart data and need to tabulate it. Could you guide me through converting the chart data into a structured table which includes the colors used for each technology trend?" }, { "from": "gpt", "value": "Upon observing the chart, I can see bars for each technology trend from the years 2013 to 2020. Each bar's color represents a technology trend. To create the table, I will pair the height of each bar, indicating growth, with the corresponding color from the legend. For instance, {the first bar in 2013 for Artificial Intelligence}[0.116, 0.899, 0.201, 0.911] has a color code of {#ff4500}[0.249, 0.066, 0.279, 0.082]. Proceeding with this method, all data are matched with their respective technology trend and color code for each year.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Color Code,AI Growth,Internet of Things,Color Code,IoT Growth,Big Data,Color Code,BD Growth,Cloud Computing,Color Code,CC Growth,Blockchain,Color Code,BC Growth,VR/AR,Color Code,VRAR Growth\n2013,5,#ff4500,10,#ff6347,5,#ff8c00,20,#ffd700,1,#ffff00,2,#eeeeee\n2014,10,#ff4500,10,#ff6347,8,#ff8c00,30,#ffd700,2,#ffff00,4,#eeeeee\n2015,15,#ff4500,15,#ff6347,15,#ff8c00,40,#ffd700,4,#ffff00,6,#eeeeee\n2016,20,#ff4500,27,#ff6347,30,#ff8c00,50,#ffd700,8,#ffff00,9,#eeeeee\n2017,27,#ff4500,30,#ff6347,45,#ff8c00,60,#ffd700,12,#ffff00,14,#eeeeee\n2018,33,#ff4500,35,#ff6347,50,#ff8c00,70,#ffd700,16,#ffff00,20,#eeeeee\n2019,40,#ff4500,40,#ff6347,55,#ff8c00,80,#ffd700,20,#ffff00,25,#eeeeee\n2020,46,#ff4500,45,#ff6347,60,#ff8c00,90,#ffd700,33,#ffff00,30,#eeeeee" } ], "id_raw": 470 }, { "id": "AuaCcIHCCRifzvCX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nCan you briefly explain the growth pattern of Artificial Intelligence and VR/AR as per the chart?" }, { "from": "gpt", "value": "To understand the growth patterns, I will observe the heights of the bars corresponding to Artificial Intelligence and VR/AR throughout the years. For Artificial Intelligence, starting at the 2013 bar at [0.116, 0.899, 0.201, 0.911] and ending at the 2020 bar at [0.859, 0.798, 0.944, 0.911], the incremental increase in height shows a generally erratic yet upward trend. When analyzing VR/AR, the bars begin at [0.116, 0.805, 0.201, 0.810] in 2013 and reach [0.859, 0.163, 0.944, 0.237] in 2020, displaying a more rapid and consistent growth pattern compared to Artificial Intelligence.\nBased on the above thought, the answer is:\nArtificial Intelligence shows an increasing but erratic growth pattern over the years, with fluctuations in the rate of growth. On the other hand, VR/AR displays a more consistent and rapid expansion, with a clear upward trajectory and no significant dips or instances of stagnation." } ], "id_raw": 470 }, { "id": "EnZrdOYH0l0ZZUXG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_5.png", "conversations": [ { "from": "human", "value": "\nWhich year did Blockchain experience an abrupt drop compared to the previous year as per the chart?" }, { "from": "gpt", "value": "To determine where Blockchain had an abrupt drop, I will inspect the bars representing Blockchain for each year. For 2013 [0.116, 0.810, 0.201, 0.813], 2014 [0.222, 0.763, 0.307, 0.768], 2015 [0.328, 0.692, 0.413, 0.702], and so on, the bar heights increase until 2019 [0.753, 0.333, 0.838, 0.382]. Then, analyzing the bar for 2020 [0.859, 0.237, 0.944, 0.318], I can see a noticeable decrease in height compared to 2019, implying a drop in Blockchain's growth.\nBased on the above thought, the answer is:\n2020" } ], "id_raw": 470 }, { "id": "qi7xlAPzKc5uy9yt", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nConvert the data represented on the bar chart into a structured table in CSV format." }, { "from": "gpt", "value": "Referring to the title \"Artificial Intelligence and Automation Trends\"[0.283, 0.168, 0.777, 0.197] from the chart, it outlines growth trends for various AI and Automation technologies. The x-axis represents 'Years'[0.508, 0.953, 0.552, 0.979], ranging from '2015'[0.147, 0.925, 0.182, 0.945] to '2021'[0.877, 0.925, 0.913, 0.945]. The y-axis shows the 'Level'[0.015, 0.526, 0.033, 0.588] of growth, ranging from '0'[0.056, 0.903, 0.065, 0.923] to '140'[0.039, 0.136, 0.065, 0.156]. The different technologies have been represented by different coloured bars eg: {Deep Learning}[0.116, 0.845, 0.213, 0.911], {RPA}[0.116, 0.763, 0.213, 0.845], {NLP}[0.116, 0.714, 0.213, 0.763] and {Computer Vision}[0.116, 0.642, 0.213, 0.714]. Each bar's height within a year brackets indicates the level of that technology in the said year. For example, in the year 2015, the level of 'Deep Learning' appears to be 12.\nBased on the above thought, the answer is:\nYear,Deep Learning,RPA,NLP,Computer Vision\n2015,12,15,9,13\n2016,18,15,13,15\n2017,22,16,15,18\n2018,26,18,17,22\n2019,32,26,2,25\n2020,40,26,25,29\n2021,42,25,23,33" } ], "id_raw": 471 }, { "id": "5nCN3vNt9Ux4eAlW", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nFrom the data on this chart, which technology has shown the greatest resistance to growth in recent years?" }, { "from": "gpt", "value": "To answer this question, it is essential to analyze the growth trend of each technology in the recent years from the chart. Looking at the bar heights of the technologies for the years 2019, 2020 and 2021, we can see that {Deep Learning}[0.603, 0.736, 0.700, 0.911; 0.725, 0.692, 0.822, 0.911; 0.846, 0.681, 0.944, 0.911], {Computer Vision}[0.603, 0.445, 0.700, 0.582; 0.725, 0.253, 0.822, 0.412; 0.846, 0.237, 0.944, 0.418] and {NLP}[0.603, 0.582, 0.700, 0.593; 0.725, 0.412, 0.822, 0.549; 0.846, 0.418, 0.944, 0.544] are continuing to show significant growth. However, for {RPA}[0.603, 0.593, 0.700, 0.736; 0.725, 0.549, 0.822, 0.692; 0.846, 0.544, 0.944, 0.681], the progress has been relatively constant, showing the least growth within these years.\nBased on the above thought, the answer is:\nRPA (Robotic Process Automation) has shown the greatest resistance to growth in recent years." } ], "id_raw": 471 }, { "id": "1TYHHauJNPDxHxLc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_6.png", "conversations": [ { "from": "human", "value": "\nWhich technology has experienced the highest level of growth from 2015 through 2021?" }, { "from": "gpt", "value": "To determine which technology has experienced the highest level of growth from 2015 to 2021, I need to measure the height difference of the bars for each technology from 2015 to 2021. {Deep Learning}[0.116, 0.845, 0.213, 0.911; 0.846, 0.681, 0.944, 0.911] increased from level 12 in 2015 to 42 in 2021, a change of 30. {RPA}[0.116, 0.763, 0.213, 0.845; 0.846, 0.544, 0.944, 0.681] increased from 15 to 25, a change of 10. {NLP}[0.116, 0.714, 0.213, 0.763; 0.846, 0.418, 0.944, 0.544] increased from 9 to 23, a change of 14. And {Computer Vision}[0.116, 0.642, 0.213, 0.714; 0.846, 0.237, 0.944, 0.418] increased from 13 to 33, a change of 20. Comparing these changes, it is clear that Deep Learning has experienced the highest growth.\nBased on the above thought, the answer is:\nDeep Learning." } ], "id_raw": 471 }, { "id": "MxCFkgncbRFyLzKe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the bar chart that shows the Technology Entrepreneurship Ecosystem growth from 2015 to 2020 into a structured table format?" }, { "from": "gpt", "value": "To create a structured table, I will analyze the bar chart and extract data for each year and category. The x-axis is labeled with {the years}[0.156, 0.835, 0.191, 0.855]; [0.298, 0.835, 0.333, 0.855]; [0.441, 0.835, 0.476, 0.855]; [0.584, 0.835, 0.619, 0.855]; [0.726, 0.835, 0.761, 0.855]; [0.869, 0.835, 0.904, 0.855], and each metric category, such as {Startup Incubation}[0.225, 0.916, 0.367, 0.937], has a dedicated bar color corresponding to the legend items. By observing the height of each bar, I can determine the value for the corresponding metric in each year. For instance, the bar for {Startup Incubation in 2015}[0.116, 0.769, 0.230, 0.821] begins at y-coordinate 0.769, which correlates with a value of 13 as per the y-axis labels. This process is replicated for all the categories across all the years to fill in the table with accurate data.\nBased on the above thought, the answer is:\nYear,Startup Incubation,Startup Acceleration,Angel Investment,Venture Capital Investment,Patent Application,Product Launches\n2015,13,11,9,24,15,10\n2016,15,12,10,22,20,16\n2017,18,14,15,30,25,19\n2018,23,16,17,32,27,22\n2019,28,18,20,35,30,26\n2020,35,22,22,38,34,30" } ], "id_raw": 472 }, { "id": "Au1K3Rw6Bn5HThtG", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nHow does the ecosystem trend from 2015 to 2020 reflect on the effectiveness of Startup Acceleration compared to other metrics?" }, { "from": "gpt", "value": "By looking at the {bars corresponding to Startup Acceleration}[0.116, 0.725, 0.230, 0.769];[0.259, 0.713, 0.373, 0.761];[0.401, 0.692, 0.516, 0.749];[0.544, 0.664, 0.658, 0.729];[0.687, 0.636, 0.801, 0.709];[0.829, 0.592, 0.944, 0.680] and comparing their height in relation to other categories, I can see a consistent increase over the years, similar to the general upward trend in the ecosystem. This indicates that Startup Acceleration has been effectively contributing to the overall growth of the ecosystem. Moreover, it's important to note that while there is growth in Startup Acceleration, the most significant leaps are seen in {Venture Capital Investment}[0.116, 0.592, 0.230, 0.688];[0.829, 0.350, 0.944, 0.503] and {Patent Applications}[0.116, 0.531, 0.230, 0.592];[0.829, 0.213, 0.944, 0.350], which seem to outpace the growth rate of Startup Acceleration by the year 2020.\nBased on the above thought, the answer is:\nWhile Startup Acceleration has seen consistent and effective growth, its impact is relatively moderate when compared to the explosive increases in the metrics of Venture Capital Investment and Patent Applications, suggesting it is a steady but not the leading driver in the Technology Entrepreneurship Ecosystem's expansion from 2015 to 2020." } ], "id_raw": 472 }, { "id": "rACD9m78FzsOXBX0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat was the total growth in Patent Applications between 2015 and 2020?" }, { "from": "gpt", "value": "To calculate the total growth in {Patent Applications}[0.738, 0.916, 0.878, 0.937], I need to examine the values at the bottom and top of the bars for the years {2015}[0.116, 0.531, 0.230, 0.592] and {2020}[0.829, 0.213, 0.944, 0.350]. The bars indicate starting and ending values leading to a value of 15 for 2015 and 34 for 2020. To compute the growth, the starting value is subtracted from the ending value, giving us 34 - 15.\nBased on the above thought, the answer is:\n19" } ], "id_raw": 472 }, { "id": "IzjDrrYpxTRgnGpK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, could you extract the data and present it in CSV format?" }, { "from": "gpt", "value": "Looking at the chart, I see that the x-axis represents different venture capitalists as evidenced by the x-axis labels {VC A}[0.140, 0.925, 0.173, 0.945], {VC B}[0.263, 0.925, 0.296, 0.945], {VC C}[0.386, 0.925, 0.419, 0.945], {VC D}[0.508, 0.925, 0.543, 0.945], {VC E}[0.632, 0.925, 0.665, 0.945], {VC F}[0.755, 0.925, 0.787, 0.945] and {VC G}[0.877, 0.925, 0.911, 0.945]. The y-axis represents the investments, which can range from zero to 35, as indicated by the y-axis labels. I can also see that there are two bars for each venture capitalist, each representing a different investment series. Based on the bars, I can roughly determine the investment amount for each venture capitalist in each series and map the information to a table. For example, {Venture capitalist A}'s {Series-A bar}[0.108, 0.677, 0.206, 0.911] shows an investment of around 10 million dollars and {Series-B bar}[0.108, 0.374, 0.206, 0.911] shows an investment of about 23 million dollars.\nBased on the above thought, the answer is:\nVenture Capitals,Fintech Series A,Fintech Series B\nVC A,10,23\nVC B,18,32\nVC C,15,25\nVC D,25,15\nVC E,14,30\nVC F,17,19\nVC G,11,22" } ], "id_raw": 473 }, { "id": "KmKv5h0tWWQFs2rj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which venture capitalist shows the most stable behavior between the two series of investments and why?" }, { "from": "gpt", "value": "The question requires an analysis of the changes in investment from Series A to Series B for each venture capitalist. Stability here would mean that the amount invested in Series A and Series B are approximately the same. Observing the heights of bars for each venture capitalist, {VC F} represented by bars {Series-A bar}[0.722, 0.514, 0.820, 0.911] and {Series-B bar}[0.722, 0.467, 0.820, 0.911] shows a Series A investment of 17 million and a Series B investment of 19 million, suggesting a very minor increase, so VC F appears to be the most stable.\nBased on the above thought, the answer is:\nVC F, because the increase in investment from Series A to Series B is minimal compared to other venture capitalists, indicating a more stable investment behavior." } ], "id_raw": 473 }, { "id": "xxAv9jMfuGRacag4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which venture capitalist has the most significant increase in investment from series A to series B?" }, { "from": "gpt", "value": "To answer this question, I need to calculate the difference in investments between series B and series A for each venture capitalist. I can do this by comparing the height of the two corresponding bars for each venture capitalist. For example, for {VC A}, the height of {Series-A bar}[0.108, 0.677, 0.206, 0.911] is around 10 and the height of {Series-B bar}[0.108, 0.374, 0.206, 0.911] is around 23, indicating an increase of 13. Similar calculations for all other venture capitalists will yield the difference. Looking at all the differences, {VC E} with the comparison of {Series-A bar}[0.599, 0.584, 0.697, 0.911] and {Series-B bar}[0.599, 0.210, 0.697, 0.911] see the most significant increase, with an increase from 14 million to 30 million, resulting in a difference of 16 million.\nBased on the above thought, the answer is:\nVC E" } ], "id_raw": 473 }, { "id": "4JLVrSsdc9Jui97P", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you've analyzed, can you represent the data in a structured table representing Volatile Movement and Rapid Growth trends in each academic field?" }, { "from": "gpt", "value": "To convert the data represented in the bar chart to a CSV table, we need to extract the quantities each bar represents. Looking at the x-axis, there are six academic fields represented as distinct bars; {'Computer Science'}[0.108, 0.671, 0.223, 0.806], {'Biotechnology'}[0.252, 0.372, 0.367, 0.701], {'Physics'}[0.396, 0.297, 0.511, 0.716], {'Arts and Humanities'}[0.540, 0.312, 0.655, 0.686], {'Social Science'}[0.684, 0.357, 0.799, 0.791] and {'Economics'}[0.828, 0.237, 0.943, 0.731] respectively. The y-axis reflects the number of topics discussed in these fields, with the range markers, starting at {'0'}[0.047, 0.903, 0.056, 0.923] going up to {'50'}[0.039, 0.154, 0.056, 0.174]. The bars are color-coded and are labeled as {\"Volatile Movement\"}[0.387, 0.082, 0.529, 0.104] and {\"Rapid Growth\"}[0.603, 0.082, 0.706, 0.104] in the legend. Each value can be determined by the height of the respective stack bar. For instance, the RGB value of Computer Science in Volatile Movement is 7 and Rapid Growth is 9.\nBased on the above thought, the answer is:\nField,Volatile Movement,Rapid Growth\nComputer Science,7,9\nBiotechnology,14,22\nPhysics,13,28\nArts and Humanities,15,25\nSocial Science,8,29\nEconomics,12,33" } ], "id_raw": 475 }, { "id": "UzPe3qbfumPPwwpg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nIn terms of engagement in academic fields, what does the bar chart tell us?" }, { "from": "gpt", "value": "This bar chart provides a representation of moving trends across various academic fields over time. By using x-axis tick labels like {'Computer Science'}[0.102, 0.925, 0.229, 0.945], we can see that distinct subjects are addressed. The y-axis, ranging from {'0'}[0.047, 0.903, 0.056, 0.923] to {'50'}[0.039, 0.154, 0.056, 0.174], tracks the number of topics discussed in each of the academic areas. Each stack represents a different type of trend observed: 'Volatile Movement' and 'Rapid Growth'. The lower stack, represented with color label {\"Volatile Movement\"}[0.387, 0.082, 0.529, 0.104] shows fluctuating levels of activity in each field, while the upper stack, with the label of {\"Rapid Growth\"}[0.603, 0.082, 0.706, 0.104], shows a clear increase in activity over time. In general, we can discern that overall engagement in these academic fields is dynamic, with some fields showing more rapid growth than others (for example, Economics), and flucutuates over time.\nBased on the above thought, the answer is:\nThis chart tells us that engagement in academic fields fluctuates over time, yet trends towards overall growth. Some fields, like Economics, show more rapid growth than others, suggesting increased activity and interest over time." } ], "id_raw": 475 }, { "id": "GBn7FMuI2QoyvvZh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_10.png", "conversations": [ { "from": "human", "value": "\nWhich academic field experienced the highest Rapid Growth?" }, { "from": "gpt", "value": "Looking at the upper stack of each pair representing Rapid Growth in the same order of academic's fields as before, observing each respective entry such as in {'Computer Science'}[0.108, 0.806, 0.223, 0.911], {'Biotechnology'}[0.252, 0.701, 0.367, 0.911], {'Physics'}[0.396, 0.716, 0.511, 0.911], {'Arts and Humanities'}[0.540, 0.686, 0.655, 0.911], {'Social Science'}[0.684, 0.791, 0.799, 0.911] and {'Economics'}[0.828, 0.731, 0.943, 0.911]. The academic field that experienced the highest Rapid Growth would be the one for which the y-value is the highest, meaning that the bar reaches the highest point on the y-axis. In this case, it would be 'Economics' since it reaches up to the y-value of 33.\nBased on the above thought, the answer is:\nEconomics" } ], "id_raw": 475 }, { "id": "y4bMYK7P0omLHIMy", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nCan you help me convert the data values shown in the bar chart into a CSV format?" }, { "from": "gpt", "value": "Sure. The diagram's title {\"Emerging Technologies and Trends Outlook\"}[-0.166, 0.031, 0.583, 0.073] informs me that the chart displays Hypothetical Trend values of various emerging technologies from 2018 to 2022. The x-axis represents {the year}[{\"2018\"}[0.111, 0.891, 0.166, 0.920]; {\"2019\"}[0.146, 0.891, 0.201, 0.920]; {\"2020\"}[0.181, 0.891, 0.236, 0.920]; {\"2021\"}[0.216, 0.891, 0.271, 0.920]; {\"2022\"}[0.251, 0.891, 0.306, 0.920]], and the y-axis shows the Trend Value of technologies. The legend in the chart lists the different technologies as {\"AI\"}[0.543, 0.164, 0.566, 0.195],\"Internet of Things\"[0.543, 0.211, 0.755, 0.242], \"Blockchain\"[0.543, 0.258, 0.672, 0.290], \"Quantum Computing\"[0.543, 0.305, 0.793, 0.337], \"AR/VR\"[0.543, 0.353, 0.616, 0.384], \"5G\"[0.543, 0.400, 0.576, 0.431] and \"Cybersecurity\"[0.543, 0.447, 0.709, 0.478]. From the bar chart's height and position, we can extract the corresponding value for each technology in each year. For instance, the value for AI in 2018 can be found at the coordinates of the corresponding bar [0.125, 0.753, 0.153, 0.870] which is 70, and this process will be repeated for each technology for each year.\nBased on the above thought, the answer is:\nYear,AI,Internet of Things,Blockchain,Quantum Computing,AR/VR,5G,Cybersecurity\n2018,70,65,50,10,35,40,60\n2019,75,67,54,20,39,43,64\n2020,79,70,45,30,42,48,68\n2021,85,74,60,40,45,53,72\n2022,90,80,44,50,51,58,76" } ], "id_raw": 476 }, { "id": "cJfX3ha5jHUBdHPC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat inferences can you make about technology trends from 2018 to 2022?" }, { "from": "gpt", "value": "From the year labels on the x-axis [0.111, 0.891, 0.166, 0.920 to 0.251, 0.891, 0.306, 0.920] and the trend lines for each technology, I can infer that most technologies have shown a steady increase. For instance, {AI}[0.543, 0.164, 0.566, 0.195], represented by the series starting at the bar [0.125, 0.753, 0.153, 0.870] and ending at [0.264, 0.720, 0.292, 0.870], increased consistently. The same upward trend is observed for {Internet of Things}[0.543, 0.211, 0.755, 0.242] and {Cybersecurity}[0.543, 0.447, 0.709, 0.478]. However, {Blockchain}[0.543, 0.258, 0.672, 0.290] showed a decrease between 2020 and 2021, and {5G}[0.543, 0.400, 0.576, 0.431] showed marginal decline in 2020. The most dramatic increase occurred in {Quantum Computing}[0.543, 0.305, 0.793, 0.337], which rose sharply from 10 to 50 between 2018 and 2022. Conversely, there is a minor increase each year for {AR/VR}[0.543, 0.353, 0.616, 0.384].\nBased on the above thought, the answer is:\nThe trends indicate that all technologies, including AI, the Internet of Things, Quantum Computing, AR/VR, 5G, and Cybersecurity, experienced an overall increase from 2018 to 2022. Quantum Computing saw the most dramatic rise. There was a slight decrease in the Blockchain trend between 2020 and 2021 and a tiny dip for 5G in 2020. The consistent upward trend globally implies a lively interest and investment in these technologies." } ], "id_raw": 476 }, { "id": "bbsdjxczTnTOTrD7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_11.png", "conversations": [ { "from": "human", "value": "\nWhich technology experienced the biggest change from 2018 to 2022?" }, { "from": "gpt", "value": "To identify which technology experienced the biggest change from 2018 to 2022, I need to compare the heights of the bars representing each technology in 2018 and 2022. If I look at the coordinate for AI [0.125, 0.753, 0.153, 0.870] for 2018 and [0.264, 0.720, 0.292, 0.870] for 2022, the change is 20. By repeating this comparison for all technologies I can see that {Quantum Computing}[0.543, 0.305, 0.793, 0.337] had the highest change from {2018}[0.125, 0.544, 0.153, 0.561] to {2022}[0.264, 0.344, 0.292, 0.512], with a change of 40.\nBased on the above thought, the answer is:\nQuantum Computing." } ], "id_raw": 476 }, { "id": "8jYxrtEXmczc5mcR", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table format for the user activity data for the month of January?" }, { "from": "gpt", "value": "Analyzing the chart, I would need to extract the values of user activity for each social media platform for January. {The blue bar corresponding to Facebook}[0.124, 0.686, 0.180, 0.783] has a value annotation of {1250}[0.135, 0.725, 0.170, 0.745]. Similarly, {the pink bar for Instagram}[0.124, 0.447, 0.180, 0.686], {the light blue bar for Twitter}[0.124, 0.401, 0.180, 0.447], {the dark blue bar for LinkedIn}[0.124, 0.216, 0.180, 0.401], and {the red bar for Pinterest}[0.124, 0.202, 0.180, 0.216] represent their respective user counts to be 3100, 590, 2400, and 180 as annotated above each bar. Based on these observations, the structured table for January could be created.\nBased on the above thought, the answer is:\nPlatform,January Users (in thousands)\nFacebook,1250\nInstagram,3100\nTwitter,590\nLinkedIn,2400\nPinterest,180" } ], "id_raw": 478 }, { "id": "1WVgEIylsyd5PIwa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform showed the greatest increase in number from January to December based on the data?" }, { "from": "gpt", "value": "To determine the platform with the greatest increase, I'd need to look at the rise in bar height from January to December corresponding to each platform. Observing the {January bar for Instagram}[0.124, 0.447, 0.180, 0.686] and the {December bar for Instagram}[0.888, 0.404, 0.944, 0.706], we see an annotation increase from 3100 to 3900. For Facebook, the {January bar}[0.124, 0.686, 0.180, 0.783] shows 1250 and the {December bar}[0.888, 0.706, 0.944, 0.783] shows 1000. LinkedIn starts at {2400 in January}[0.124, 0.216, 0.180, 0.401] and increases to {3150 in December}[0.888, 0.110, 0.944, 0.353]. Twitter and Pinterest both start at lower figures and show slight increments. Upon comparison, Instagram has the largest numerical increase from the beginning to the end of the year.\nBased on the above thought, the answer is:\nInstagram. The platform begins the year with 3100 thousand users in January and grows to 3900 thousand users by December, indicating the greatest absolute increase among the platforms." } ], "id_raw": 478 }, { "id": "xXJX26w0LE3D5THj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of users across all platforms for the month of May?" }, { "from": "gpt", "value": "To calculate the total number of social media users for May, I need to sum the numbers represented by the bars for that month. By locating the bars for May, I can read the values: {Facebook}[0.402, 0.678, 0.458, 0.783] shows 1350, {Instagram}[0.402, 0.431, 0.458, 0.678] shows 3200, {Twitter}[0.402, 0.381, 0.458, 0.431] shows 650, {LinkedIn}[0.402, 0.188, 0.458, 0.381] shows 2500, and {Pinterest}[0.402, 0.172, 0.458, 0.188] shows 200. Adding these values would provide me the total user activity for the month of May.\nBased on the above thought, the answer is:\n7900" } ], "id_raw": 478 }, { "id": "HMG3BBlMeW7Mzlih", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart you've analyzed, could you convert the represented data into a CSV format table?" }, { "from": "gpt", "value": "From the title of the chart {\"Number of Entities in Different Stages of an Entrepreneurship and Innovation Ecosystem\"}[0.022, 0.091, 1.011, 0.120] and the x-axis labels {\"Pre-seed\"}[0.133, 0.925, 0.193, 0.945], {\"Seed\"}[0.287, 0.925, 0.322, 0.945], {\"Series A\"}[0.417, 0.925, 0.474, 0.945], {\"Series B\"}[0.559, 0.925, 0.615, 0.945], {\"Series C\"}[0.700, 0.925, 0.756, 0.945], and {\"Exit\"}[0.856, 0.925, 0.882, 0.945], I can understand these represent the investment stages for the entities. The y-axis labels indicate the number of entities. According to {the legend}[0.110, 0.019, 0.922, 0.095], the {\"Startups\"}[0.428, 0.065, 0.493, 0.086] have an orange bar in the chart and {\"Incubators\"}[0.566, 0.065, 0.647, 0.086] are indicated by a greenish bar. Now, I can understand the color representation and collate the information based on the mention in the legend. The individual bars and their heights would represent the number of startups or incubators in each stage. For example, for \"Pre-seed\", there are approximately {\"8 Startups\"}[0.107, 0.651, 0.220, 0.911] and {\"2 Incubators\"}[0.107, 0.586, 0.220, 0.651]. Taking the heights of the bars as reference, I can extract all numerical values required.\nBased on the above thought, the answer is:\nInvestment Stages,Startups,Incubators\nPre-seed,8,2\nSeed,18,5\nSeries A,13,7\nSeries B,10,8\nSeries C,5,3\nExit,1,0" } ], "id_raw": 479 }, { "id": "NyeYuvWJHn0dewHo", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nConsidering the provided chart, can you assess which entity, startups or incubators, shows a steadier growth across the investment stages? Justify." }, { "from": "gpt", "value": "To answer this question, I should compare the progression of bars for both {\"Startups\"}[0.428, 0.065, 0.493, 0.086] and {\"Incubators\"}[0.566, 0.065, 0.647, 0.086] across the different stages of investment starting from {\"Pre-seed\"}[0.133, 0.925, 0.193, 0.945] up to {\"Exit\"}[0.856, 0.925, 0.882, 0.945]. If I look at the startups, they start at a number of 8 and eventually decrease to 1 at the \"Exit\" stage. For incubators, they start from 2 and peek at the \"Series B\" stage then decrease to 0 at \"Exit\". Though the numbers decrease for both, startups have a more dramatic drop, especially post \"Series B\" stage. Incubators however show a more gradual decrease. Hence, Incubators portray steadier growth, even though they end at 0 at \"Exit\" stage, their decline is more gradual.\nBased on the above thought, the answer is:\nIncubators. Although both Startups and Incubators show a decrease in number as they progress, the decline in the number of Incubators seems to be steadier and less steep compared to that of Startups, hence it indicates a steadier growth." } ], "id_raw": 479 }, { "id": "4a22Gd0hetBp1fUC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_32_14.png", "conversations": [ { "from": "human", "value": "\nAt which stage of investment is the difference in number of startups and incubators the largest?" }, { "from": "gpt", "value": "To arrive at this, I need to compare the difference between the number of startups and incubators at each stage of investment. I start by looking at the bars under {\"Pre-seed\"}[0.133, 0.925, 0.193, 0.945], {\"Seed\"}[0.287, 0.925, 0.322, 0.945], {\"Series A\"}[0.417, 0.925, 0.474, 0.945], {\"Series B\"}[0.559, 0.925, 0.615, 0.945], {\"Series C\"}[0.700, 0.925, 0.756, 0.945], and {\"Exit\"}[0.856, 0.925, 0.882, 0.945]. The difference is calculated by subtracting the number of incubators from that of the startups at each stage. The sequences of bars under each stage such as for \"Seed\" at [0.248, 0.163, 0.361, 0.326] and [0.248, 0.326, 0.361, 0.911] represent the number of Incubators and Startups respectively. After comparing all the heights difference, I find that the difference is largest at the \"Seed\" stage of investment.\nBased on the above thought, the answer is:\nSeed." } ], "id_raw": 479 }, { "id": "xHXMo1rgInAHvqTs", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nEvaluate the data of the status of various environmental indicators and represent them into a CSV format table." }, { "from": "gpt", "value": "The indicators are represented on the x-axis {\"Environmental Indicators\"}[0.423, 0.927, 0.641, 0.963], where the eight indicators are {\"Air quality\"}[0.128, 0.648, 0.203, 0.799], {\"Water quality\"}[0.223, 0.648, 0.318, 0.838], {\"Land use\"}[0.341, 0.648, 0.409, 0.784], {\"Habitat restoration\"}[0.415, 0.648, 0.544, 0.906], {\"Ecosystem services\"}[0.518, 0.648, 0.651, 0.914], {\"Biodiversity\"}[0.647, 0.648, 0.732, 0.818], {\"Pollution control\"}[0.738, 0.648, 0.850, 0.871], {\"Recycling rate\"}[0.849, 0.648, 0.949, 0.848]. The corresponding scores which represents the quality of each indicator are given on the y-axis {\"Score\"}[0.019, 0.289, 0.036, 0.386] , with the actual value noted on each bar; for example, {\"Air quality\"}[0.124, 0.278, 0.207, 0.624] has a score of 0.605 as denoted by {value annotation \"0.605\"}[0.141, 0.243, 0.190, 0.278] and {\"Water quality\"}[0.228, 0.189, 0.312, 0.624] has the score of 0.76 as shown by {value annotation \"0.76\"}[0.251, 0.154, 0.289, 0.189]. Using these, we can formulate the CSV representation denoting each parameter with its corresponding score.\nBased on the above thought, the answer is:\nIndicator,Score\nAir quality,0.605\nWater quality,0.760\nLand use,0.698\nHabitat restoration,0.805\nEcosystem services,0.645\nBiodiversity,0.720\nPollution control,0.685\nRecycling rate,0.825" } ], "id_raw": 480 }, { "id": "1ZxrEpXka0d4XG1X", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what comments can you provide on the status of current environmental indicators?" }, { "from": "gpt", "value": "The chart demonstrates the scores of various environmental indicators, including Air quality, Water quality, Land use, Habitat restoration, Ecosystem services, Biodiversity, Pollution control, and Recycling rate. These scores provide performance review of these indicators. The longest bars represent the areas which are performing the best. The {Recycling rate}[0.857, 0.152, 0.940, 0.624], and {Habitat restoration}[0.438, 0.163, 0.522, 0.624], obtained higher scores than others, indicating better performance in these indicators. However, indicators like {Air quality}[0.124, 0.278, 0.207, 0.624] and {Ecosystem services}[0.543, 0.255, 0.626, 0.624], even though they score above 0.6, their performance is relatively lower than the other indicators. It implies that improvements are potentially needed in these areas to boost their performance.\nBased on the above thought, the answer is:\nThe status of environmental indicators is generally good with scores above 0.6. However, areas like Recycling rate and Habitat restoration appear to have better performance with the highest scores. Yet, certain aspects such as Air quality and Ecosystem services need improvements as their scores are relatively lower." } ], "id_raw": 480 }, { "id": "w67cw9Z6pX0NtACp", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_0.png", "conversations": [ { "from": "human", "value": "\nWhich environmental indicator receives the best score?" }, { "from": "gpt", "value": "Each environmental indicator has a corresponding score represented by the height of the bar and denoted by a text at the top of the bar. By locating each bar and comparing their heights, we can find out that {Recycling rate}[0.857, 0.152, 0.940, 0.624] has the highest bar, with {the value \"0.825\"}[0.874, 0.117, 0.923, 0.152] annotated at the top, meaning that it received the highest score amongst all environmental indicators.\nBased on the above thought, the answer is:\nRecycling rate." } ], "id_raw": 480 }, { "id": "IYjdu8TCrsH7qwe8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nCould you translate the bar chart data into a CSV-style table?" }, { "from": "gpt", "value": "From the chart, it is clear that the 'x-axis' caption is {\"Platforms\"}[0.493, 0.942, 0.559, 0.970], and the 'y-axis' caption is {\"User Engagement\"}[0.015, 0.295, 0.029, 0.544]. The bars [0.108, 0.193, 0.184, 0.799] to [0.867, 0.662, 0.943, 0.799]correspond to specific platforms and their corresponding engagement rate. From this, I can gather that the left axis represents the user engagement rates for various Social Media platforms. The user engagement rate for each platform can be estimated using the height of each bar; for example, the Facebook bar height denotes an engagement rate of 0.8, while the Reddit bar height signifies an engagement rate of 0.18. Thus, converting these visual cues to textual data in CSV format should yield a platform vs engagement rate table.\nBased on the above thought, the answer is:\nPlatform,User Engagement\nFacebook,0.8\nInstagram,0.62\nWhatsApp,0.58\nLinkedIn,0.22\nSnapchat,0.35\nTwitter,0.58\nPinterest,0.45\nTumblr,0.13\nReddit,0.18" } ], "id_raw": 482 }, { "id": "6nLuYPvzbmROhbdO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nGiven the information in the chart, which platform would you recommend for a marketing campaign if the priority is user engagement? And why?" }, { "from": "gpt", "value": "If the priority is user engagement, then the recommendation should be the platform with the highest user engagement rate. Looking at the bar chart, the platform represented by the tallest bar would be recommended, which is Facebook [0.108, 0.193, 0.184, 0.799]. With its significant user engagement rate of 0.8, Facebook is likely to give the marketing campaign much better exposure and make it interact with more active users. So, due to its high user engagement, Facebook is the recommended platform.\nBased on the above thought, the answer is:\nI recommend Facebook. Because it has the highest user engagement rate, so a marketing campaign there is likely to reach a significant number of actively engaged users." } ], "id_raw": 482 }, { "id": "zqbY3BBz2GI95kFE", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_2.png", "conversations": [ { "from": "human", "value": "\nFrom your visual analysis of the graph, which platform had the highest user engagement rate?" }, { "from": "gpt", "value": "To find out which platform had the highest user engagement rate, I will consider the heights of the bars in the bar chart. The taller the bar, the higher the engagement rate. By making a visual comparison, I observe that Facebook [0.108, 0.193, 0.184, 0.799] has the tallest bar among the platforms, suggesting that it has the highest user engagement rate.\nBased on the above thought, the answer is:\nFacebook" } ], "id_raw": 482 }, { "id": "Kj799tipPWew53DG", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data represented by the bar chart into a CSV table?" }, { "from": "gpt", "value": "According to the x-axis labels and the label of each corresponding bar, we can convert the data represented in the bar chart to a CSV. The labels on the x-axis represent the different industries which are: {x-label}[0.494, 0.952, 0.562, 0.975] and the labels on the bars represent the corresponding revenue for each industry: {bar text labels}all the label coordinates above.\nBased on the above thought, the answer is:\nIndustry,Revenue\nVideo Games,80\nOnline Gambling,40\nFilm,200\nMusic,45\nVirtual Reality,35\nSocial Media Entertainment,60\nPodcasts,30\nOnline Sports Betting,55\nSurveillance Entertainment,20\nTheme Parks,115" } ], "id_raw": 483 }, { "id": "PkA2xckLvBQMydsX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart which industries do you think are leading in terms of revenue? And which ones are struggling?" }, { "from": "gpt", "value": "The size of each bar in the chart directly correlates with the revenue of each industry and the larger the bar the more successful the industry. From the bar chart, the industry with the highest revenue is {Film}[0.282, 0.164, 0.350, 0.683], followed by {Theme Parks}[0.876, 0.385, 0.943, 0.683] and {Video Games}[0.112, 0.476, 0.180, 0.683]. Therefore these industries are leading in terms of revenue. On the flip side, the industries with the shortest bars, thus the lower revenues, are {Surveillance Entertainment}[0.791, 0.631, 0.859, 0.683] and {Podcasts}[0.621, 0.605, 0.689, 0.683]. These industries can be considered as struggling compared to the others.\nBased on the above thought, the answer is:\nThe industries leading in terms of revenue are Film, Theme Parks and Video Games. While the industries that are struggling are Surveillance Entertainment and Podcasts." } ], "id_raw": 483 }, { "id": "UyaK70o18ecTHl9W", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the total revenue of all the industries according to the chart?" }, { "from": "gpt", "value": "To answer this question, I need to sum up all the revenue figures represented by the height of each bar in the chart. By looking at the labels displayed on top of each bar<{\"bar text labels\"}all the label coordinates above>, I can easily find out the revenue of each industry and sum them up.\nBased on the above thought, the answer is:\n680" } ], "id_raw": 483 }, { "id": "2Ck3rTUaVIQDYnS9", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nConvert the bar chart data into a CSV table format." }, { "from": "gpt", "value": "The chart title, {x-axis labels}[{\"Raw Materials\"}[0.142, 0.716, 0.241, 0.914]; {\"Production\"}[0.323, 0.716, 0.401, 0.872]; {\"Distribution\"}[0.490, 0.716, 0.574, 0.884]; {\"Marketing\"}[0.665, 0.716, 0.739, 0.864]; {\"After Sales\"}[0.833, 0.716, 0.912, 0.873]], and {y-axis labels}[{\"0.0\"}[0.043, 0.678, 0.071, 0.713]; {\"0.2\"}[0.043, 0.550, 0.071, 0.585]]; {\"0.4\"}[0.043, 0.422, 0.071, 0.457]; {\"0.6\"}[0.043, 0.294, 0.071, 0.329]; {\"0.8\"}[0.043, 0.166, 0.071, 0.201]; {\"1.0\"}[0.043, 0.037, 0.071, 0.073]] in the figure indicate that the x-axis represents stages and y-axis represents the values of the Value Chain. Different bars denote different stages, to extract their corresponding values, I can check the height of the bars and their value annotations at the top. For example, {the \"Raw Materials\" bar}[0.124, 0.436, 0.260, 0.692] reaches around the y-axis 0.4, and its value annotation at the top is {0.4}[0.178, 0.401, 0.205, 0.436], thus, the value for the \"Raw Materials\" stage is 0.4.\nBased on the above thought, the answer is:\nStage,ValueChain\nRaw Materials,0.4\nProduction,0.6\nDistribution,0.95\nMarketing,0.5\nAfter Sales,0.75" } ], "id_raw": 484 }, { "id": "SRMQ9CJr2jfAOgQl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nBased on the given bar chart, in which part of the value chain should one invest to gain maximum value. Explain your answer." }, { "from": "gpt", "value": "By identifying the bar with the greatest height, it can be determined where the maximum value can be gained. The bar indicating the {Distribution}[0.464, 0.083, 0.600, 0.692] is the highest among all bars indicating it has the greatest value on the value chain according to y-axis value{'0.95'}[0.513, 0.048, 0.551, 0.083]. It is considerably higher than the rest of the bars. This suggests investing in the 'Distribution' process of the value chain could yield the maximum value.\nBased on the above thought, the answer is:\nThe 'Distribution' process. The bar corresponding to this process has the highest value on the chart, indicating that it provides the maximum value in the value chain." } ], "id_raw": 484 }, { "id": "I2yyq0a7yoMBCkPw", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the most impactful stage in the value chain as per the bar chart?" }, { "from": "gpt", "value": "To determine the most impactful stage, I need to find the highest bar in the chart. By comparing each bar's ending point on {y-axis}[0.043, 0.678, 0.071, 0.713], I observe that the bar for {the \"Distribution\" stage}[0.464, 0.083, 0.600, 0.692] reaches the highest value, close to {'1.0'}[0.043, 0.037, 0.071, 0.073] on the y-axis. This indicates the \"Distribution\" stage has the most impact in the value chain.\nBased on the above thought, the answer is:\nDistribution" } ], "id_raw": 484 }, { "id": "uIDLQlPdxckUEpN8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nCould you convert the information from the bar chart into a CSV format?" }, { "from": "gpt", "value": "Yes, from the bar chart, I can tell that the x-axis label, {Emerging Market}[0.458, 0.952, 0.606, 0.975], is the category comprising different markets. The y-axis label, {Consumer Behavior}[0.019, 0.309, 0.036, 0.537], correspond with the values each category holds; and it ranges from {0 to 1}[0.043, 0.802, 0.071, 0.826] to [0.043, 0.025, 0.071, 0.048]. The chart uses bars of different heights to denote the values with respect to the respective markets. For instance, the bar at {BRIC}[0.124, 0.306, 0.207, 0.811] represents the 0.65 value. With this, I can generate a CSV table by extracting all values for each market.\nBased on the above thought, the answer is:\nEmerging Market, Consumer Behavior\nBRIC, 0.65\nMINT, 0.75\nVISTA, 0.55\nNext Eleven, 0.80\nFRANK, 0.73\nCIVETS, 0.68\nEAGLEs, 0.82\n3G, 0.70" } ], "id_raw": 485 }, { "id": "clSPVUvhVDPoMQxw", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you observed, which Emerging Market is most likely to experience a significant increase in sales?" }, { "from": "gpt", "value": "From the provided data, the Consumer Behavior metric serves as a reliable indicator of potential sales performance in each Emerging Market. A higher metric signifies a more active consumer behavior, implying higher potential sales. From the chart, we can observe that the bar depicting the Emerging Market {EAGLEs}[0.752, 0.174, 0.836, 0.811] has the highest consumer behavior value at 0.82. This makes it the most promising Market for potential sales increase.\nBased on the above thought, the answer is:\nThe EAGLEs market, because it has the highest Consumer Behavior metric and displays the most active purchasing behaviors, indicating a higher potential for sales." } ], "id_raw": 485 }, { "id": "YZuqR6DrgcPL7QS7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_5.png", "conversations": [ { "from": "human", "value": "\nWhich Emerging Market has the lowest Consumer Behavior metric in the bar chart?" }, { "from": "gpt", "value": "Looking at each bar in the chart indicates the metric value for the associated Emerging Market. For example, the bar at {BRIC}[0.124, 0.306, 0.207, 0.811] is at 0.65 while the bar at {VISTA}[0.333, 0.384, 0.417, 0.811] is at 0.55, which is currently the lowest. Comparing all other bars, it is obvious that no other market has a lower consumer behavior value than 'VISTA'.\nBased on the above thought, the answer is:\nVISTA" } ], "id_raw": 485 }, { "id": "hDZE9B00OabHe6Wj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nPlease convert the displayed bar chart into a tabular format." }, { "from": "gpt", "value": "To convert the bar chart into a table, I will need to extract each exercise on the x-axis and the corresponding participation rate on the y-axis. The {exercise names}[{\"Yoga\"}[0.124, 0.787, 0.166, 0.852], {\"Pilates\"}[0.218, 0.787, 0.270, 0.871], {\"CrossFit\"}[0.315, 0.787, 0.375, 0.883], {\"Running\"}[0.417, 0.787, 0.480, 0.887], {\"Cycling\"}[0.527, 0.787, 0.584, 0.879], {\"Weightlifting\"}[0.599, 0.787, 0.689, 0.931], {\"Swimming\"}[0.717, 0.787, 0.794, 0.909], {\"Zumba\"}[0.843, 0.787, 0.899, 0.876]] are on the x-axis and the {participation rates}[{\"0.23\"}[0.146, 0.405, 0.185, 0.433], {\"0.17\"}[0.251, 0.492, 0.289, 0.520], {\"0.29\"}[0.356, 0.318, 0.394, 0.346], {\"0.47\"}[0.461, 0.057, 0.499, 0.085], {\"0.41\"}[0.565, 0.144, 0.604, 0.172], {\"0.36\"}[0.670, 0.216, 0.708, 0.244], {\"0.39\"}[0.775, 0.173, 0.813, 0.201], {\"0.22\"}[0.879, 0.420, 0.918, 0.448]] are displayed at the top of the corresponding bar. For instance, the bar labeled as {Yoga}[0.124, 0.787, 0.166, 0.852] has a value of {0.23}[0.146, 0.405, 0.185, 0.433] as depicted above the bar. So, I will collect the values of each exercise's participation rate using the information displayed above the bars.\nBased on the above thought, the answer is:\nExercise, Participation_Rate\nYoga, 0.23\nPilates, 0.17\nCrossFit, 0.29\nRunning, 0.47\nCycling, 0.41\nWeightlifting, 0.36\nSwimming, 0.39\nZumba, 0.22" } ], "id_raw": 486 }, { "id": "09FYG9oTFrrsVU25", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nBased on the data presented, what seems to be the most popular form of exercise and why might this be the case?" }, { "from": "gpt", "value": "The given bar chart represents the participation rates for different exercises with the resulting popularity related to a higher participation rate. As seen from the bar for {Running}[0.438, 0.085, 0.522, 0.767], it has the highest value amongst all other bars, with a value of {0.47}[0.461, 0.057, 0.499, 0.085] as specified at the top of the bar, thus indicating it as the most popular form of exercise. The reason for Running being popular might be due to its convenience, as it does not require any distinct equipment or memberships, and can be done anywhere, indoors or outdoors, depending on personal preferences. Also, running is often recommended as a baseline fitness activity which may contribute to its popularity.\nBased on the above thought, the answer is:\nRunning. It has a higher participation rate which may be due to its convenience and universal accessibility." } ], "id_raw": 486 }, { "id": "too5bMbBXTPTsCf6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, what is the maximum participation rate among all exercises?" }, { "from": "gpt", "value": "The participation rate of each exercise is illustrated by the height of the corresponding bar, and the exact rate is written at the top of each bar. I need to compare these values to find the maximum. For example, the bar for {Running}[0.438, 0.085, 0.522, 0.767] has a value of {0.47}[0.461, 0.057, 0.499, 0.085] at the top, which appears to be the highest among all exercise bars. This suggests that Running has the maximum participation rate among all exercises.\nBased on the above thought, the answer is:\n0.47" } ], "id_raw": 486 }, { "id": "Ogej6VKRBD4XZfKB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nBased on the data in the bar chart, please put together a two column CSV table." }, { "from": "gpt", "value": "The title, {\"Funding Allocations for Public Service Sectors\"}[0.235, 0.963, 0.765, 0.992] and the {x-axis labels}[{\"Education\"}[0.166, 0.633, 0.202, 0.804], {\"Healthcare\"}[0.270, 0.633, 0.310, 0.816], {\"Police\"}[0.375, 0.633, 0.399, 0.753], {\"Firefighting\"}[0.480, 0.633, 0.521, 0.820], {\"Public Transport\"}[0.584, 0.633, 0.640, 0.878], {\"Parks & Recreation\"}[0.689, 0.633, 0.753, 0.914], {\"Infrastructure\"}[0.794, 0.633, 0.842, 0.850], {\"Social Services\"}[0.899, 0.633, 0.951, 0.865]] clearly communicate that the x-axis represents the different public service sectors. The y-axis represents the degree of funding each sector receives, ranging from a minimum of {0.0}[0.083, 0.633, 0.071, 0.654] to a maximum of {1.0}[0.083, 0.051, 0.071, 0.073]. The funding values can be determined form the heights of the bars and the respective values annotated at the top of each bar. For example, \"Education\"[0.166, 0.633, 0.202, 0.804] has a value of 0.6 indicated by its bar height and corresponding annotation[0.152, 0.249, 0.179, 0.284]. In the same way we can collate the funding values for all the sectors and represent it in a CSV format table.\nBased on the above thought, the answer is:\nServices,Funding\nEducation,0.6\nHealthcare,0.7\nPolice,0.4\nFirefighting,0.55\nPublic Transport,0.34\nParks & Recreation,0.8\nInfrastructure,0.9\nSocial Services,0.88" } ], "id_raw": 487 }, { "id": "p5681bVRiRImPc2R", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nCan we conclude from the chart that this hypothetical city values Infrastructure the most and Public Transport the least? Provide reasons." }, { "from": "gpt", "value": "Based on the chart, we can make an inference about the city's priorities in terms of public services. The higher the bars in the chart, the greater the allocation of funds, which could be interpreted as a higher level of importance placed on that sector. Given that comparison, we can infer that {Infrastructure}[0.794, 0.633, 0.842, 0.850] with a bar at [0.752, 0.109, 0.836, 0.633] and the maximum fund allocation of 0.9, is probably the most valued. Conversely, {Public Transport}[0.584, 0.633, 0.640, 0.878] with the smallest bar at [0.543, 0.435, 0.626, 0.633] and the least fund allocation of 0.34, is potentially less valued by the city as compared to the other sectors.\nBased on the above thought, the answer is:\nYes, the chart could suggest that Infrastructure is the most valued public service sector within this hypothetical city since it receives the highest allocation of funds. Conversely, Public Transport receives the least funding, suggesting it could be the least valued sector." } ], "id_raw": 487 }, { "id": "8ka1ozUJ2vtwSPe3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_7.png", "conversations": [ { "from": "human", "value": "\nWhich service sector has the maximum funding allocation according to the chart?" }, { "from": "gpt", "value": "By sequentially comparing the heights of the bars for each sector, one by one, we can identify the sector with the maximum allocation. Starting with {\"Education\"}[0.166, 0.633, 0.202, 0.804] represented by the bar at [0.124, 0.284, 0.207, 0.633] with an allocation of 0.6, and moving onto the next, {\"Healthcare\"}[0.270, 0.633, 0.310, 0.816] at [0.228, 0.226, 0.312, 0.633] with an allocation of 0.7, and so on to {\"Infrastructure\"}[0.794, 0.633, 0.842, 0.850] at [0.752, 0.109, 0.836, 0.633] with an allocation of 0.9, which is the highest value compared to all the other sectors.\nBased on the above thought, the answer is:\nInfrastructure" } ], "id_raw": 487 }, { "id": "cUVx3OnOob2wJTwV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the bar chart data in the form of a CSV using the Industry and its respective Growth Rate?" }, { "from": "gpt", "value": "By examining the layout and contents of the graph, it's clear that it's a horizontal bar chart with various sectors of E-commerce and Retail displayed on the Y-axis and their respective growth rates on the X-axis. For instance, the Industry label {Fashion & Apparel}[0.063, 0.685, 0.161, 0.882] corresponds to a bar graph with a growth rate of 0.524, as indicated by its width and the hovered growth rate label above [0.142, 0.261, 0.181, 0.289]. A similar mapping process can be followed for other industries to extract their respective growth rates.\nBased on the above thought, the answer is:\nIndustry, Growth Rate\nFashion & Apparel, 0.524\nElectronics & Media, 0.697\nFood & Personal Care, -0.174\nFurniture & Appliances, 0.385\nToys, Hobby & DIY, 0.455\nHealth & Wellness, 0.615\nAutomotive & Parts, -0.092\nTravel & Accomodation, 0.542" } ], "id_raw": 488 }, { "id": "t4E6mpQRx0nEfwVI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nConsidering the growth trends across industries, which sector might be a risky investment according to the bar chart?" }, { "from": "gpt", "value": "The risk of investment in a sector could be inferred based on its growth rate, with visually shorter bars indicating lower growth rate or negative growth. In this regard, {Food & Personal Care}[0.258, 0.685, 0.373, 0.915] and {Automotive & Parts}[0.691, 0.685, 0.796, 0.895] stand out as they have negative growth rates as visible by their bar[0.330, 0.561, 0.415, 0.652] and[0.753, 0.561, 0.838, 0.609] respectively that extend towards left on the negative x-axis scale. In light of these findings, Food & Personal Care and Automotive & Parts may be considered as risky investments.\nBased on the above thought, the answer is:\nFood & Personal Care and Automotive & Parts might be risky investments due to their negative growth rates." } ], "id_raw": 488 }, { "id": "INIDb1wWS4koBtcR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_8.png", "conversations": [ { "from": "human", "value": "\nCan you determine which sector in the E-commerce and Retail Industry has witnessed the highest growth rate from this bar chart?" }, { "from": "gpt", "value": "By comparing the length of each bar, which corresponds to the growth rate, I need to find the longest one. By observing the chart, it appears that {Electronics & Media}[0.160, 0.685, 0.267, 0.898]'s corresponding bar is the longest, reaching up to a point labeled with the text 0.697[0.247, 0.171, 0.287, 0.199]. Therefore, Electronics & Media has the highest growth rate.\nBased on the above thought, the answer is:\nElectronics & Media" } ], "id_raw": 488 }, { "id": "GsNYOFyV1MvjsEXZ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you observed, could you convert the sales data depicted for each month into a CSV structured table?" }, { "from": "gpt", "value": "Sure, the x-axis of the chart represents the months {\"xlabel\": \"Months\"}[0.509, 0.927, 0.572, 0.963]. The corresponding months are Jan-Dec according to the xticks coordinates. The y-axis of the chart represents sales {\"ylabel\": \"Sales(in thousands)\"}[0.019, 0.249, 0.036, 0.602]. From each rectangle's bar and text coordinates, I can obtain the value of sales for each month. For example, At {\"bar_coordinates\": [0.139, 0.276, 0.194, 0.814]} and {\"text_coordinates\": [0.145, 0.241, 0.188, 0.276]} for January, where the associated text is 4500. By deducing information similarly for each month, I can shape the CSV file.\nBased on the above thought, the answer is:\nMonth,Sales\nJan,4500\nFeb,3500\nMar,3900\nApr,4150\nMay,4600\nJun,4800\nJul,4000\nAug,3200\nSep,4400\nOct,5100\nNov,6000\nDec,4800" } ], "id_raw": 491 }, { "id": "8D0wyJVtjfvv0WuX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nCan you interpret the sales trend for the year 2021 as depicted by the bar chart?" }, { "from": "gpt", "value": "The question requires a comprehensive review and interpretation of the bar chart - in particular, each bar's height that corresponds to sales data for each month. Sales in January {\"bar_coordinates\": [0.139, 0.276, 0.194, 0.814] and its \"text_coordinates\": [0.145, 0.241, 0.188, 0.276]} start at 4500. A decrease is noticeable in sales for February {\"bar_coordinates\": [0.207, 0.396, 0.262, 0.814] and its \"text_coordinates\": [0.213, 0.361, 0.256, 0.396]} with 3500. The sales see a steady rise from March to June, but a drop comes during July {\"bar_coordinates\": [0.547, 0.336, 0.601, 0.814] and its \"text_coordinates\": [0.552, 0.301, 0.596, 0.336]} with sales listed as 4000. Another drop is observed in August {\"bar_coordinates\": [0.615, 0.431, 0.669, 0.814] and its \"text_coordinates\": [0.620, 0.396, 0.664, 0.431]} with the sales figure at 3200 which is the lowest. However, from September sales start rising steadily, with the biggest boost in November {\"bar_coordinates\": [0.819, 0.097, 0.873, 0.814] and its \"text_coordinates\": [0.824, 0.062, 0.868, 0.097]} to 6000, only to drop again in December {\"bar_coordinates\": [0.887, 0.240, 0.941, 0.814] and its \"text_coordinates\": [0.892, 0.205, 0.936, 0.240]} to 4800.\nBased on the above thought, the answer is:\nThe sales seem to fluctuate throughout the year of 2021. While there's a gradual rise in sales from March to June, the sales drastically fall in July and August before an upward trend gets seen from September till November where it peaks. However, the sales figure falls again in December. There seem to be inconsistent growth and abrupt falls making sales seem volatile." } ], "id_raw": 491 }, { "id": "pN49k6Jr6TFwEiEv", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_11.png", "conversations": [ { "from": "human", "value": "\nIn which month, does the sales figure reach its peak for the year 2021?" }, { "from": "gpt", "value": "To answer this question, I would need to compare the heights of all the monthly sales bars. Observing the data from the rectangles of the bars, I find that the Nov {\"bar_coordinates\": [0.819, 0.097, 0.873, 0.814]} and its associated sales {\"text_coordinates\": [0.824, 0.062, 0.868, 0.097]} of 6000 is the highest among all the months.\nBased on the above thought, the answer is:\nNovember." } ], "id_raw": 491 }, { "id": "sXUL0ijaYVOT4n7f", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nCan you aggregate the data from the chart into a CSV format?" }, { "from": "gpt", "value": "Sure, by looking at the {x-label}[0.485, 0.952, 0.579, 0.975] we can determine that the x-axis represents different fields of study. The labels and their corresponding coordinates are as follows: {\"Computer Science\"}[0.148, 0.687], {\"Software Engineering\"}[0.218, 0.687], {\"Electrical Engineering\"}[0.288, 0.687], {\"Mechanical Engineering\"}[0.358, 0.687], {\"Civil Engineering\"}[0.428, 0.687], {\"Environmental Sciences\"}[0.497, 0.687], {\"Active Learning\"}[0.567, 0.687], {\"Language Learning\"}[0.637, 0.687], {\"Online Education Platforms\"}[0.707, 0.687], {\"Practical Training\"}[0.776, 0.687], {\"Overseas Education\"}[0.846, 0.687], {\"Scientific Research\"}[0.916, 0.687]. Meanwhile, the {y-label}[0.015, 0.244, 0.029, 0.477] shows that the y-axis represents the number of students in these fields. From the visible bars and their text labels, I can extract the number of students in each field. For instance, the bar labeled {\"1200\"}[0.131, 0.468, 0.166, 0.491] corresponds to Computer Science field, thus indicating with the number of students in this field is 1200.\nBased on the above thought, the answer is:\nField of Study, Number of Students\nComputer Science, 1200\nSoftware Engineering, 1050\nElectrical Engineering, 980\nMechanical Engineering, 800\nCivil Engineering, 750\nEnvironmental Sciences, 780\nActive Learning, 1500\nLanguage Learning, 2000\nOnline Education Platforms, 3500\nPractical Training, 1800\nOverseas Education, 500\nScientific Research, 2300" } ], "id_raw": 492 }, { "id": "55oIkkM5M8ts2QVx", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nCan you provide some insights regarding the popularity of traditional engineering fields compared to modern education trends based on the chart?" }, { "from": "gpt", "value": "Sure! According to this chart, traditional engineering fields (Mechanical Engineering, Civil Engineering, Electrical Engineering, Software Engineering, and Computer Science) are represented by {\"Mechanical Engineering\"}[0.358, 0.687], {\"Civil Engineering\"}[0.428, 0.687], {\"Electrical Engineering\"}[0.288, 0.687], {\"Software Engineering\"}[0.218, 0.687], {\"Computer Science\"}[0.148, 0.687]. All these traditional fields have fewer students compared to modern education trends such as {\"Online Education Platforms\"}[0.707, 0.687], {\"Language Learning\"}[0.637, 0.687], {\"Active Learning\"}[0.567, 0.687] or {\"Practical Training\"}[0.776, 0.687]. The number of students in Online Education Platforms in particular is remarkably high, indicating a significant shift from traditional engineering fields to these modern trends.\nBased on the above thought, the answer is:\nModern education trends, such as Online Education Platforms, are more popular among students compared to traditional engineering fields. This could be due to the increasing digitalization of education and the appeal of online, flexible learning environments." } ], "id_raw": 492 }, { "id": "q7ImJMhmr1emdaHI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_33_12.png", "conversations": [ { "from": "human", "value": "\nWhich field of study has the highest number of students based on the chart?" }, { "from": "gpt", "value": "To determine this, I have to check the heights of all the bars on the chart. The tallest bar {\"3500\"}[0.689, 0.092, 0.724, 0.116] corresponds to the Online Education Platforms which is represented by the x-axis label {\"Online Education Platforms\"}[0.707, 0.687]. Thus, the field of study with the most number of students is Online Education Platforms.\nBased on the above thought, the answer is:\nOnline Education Platforms" } ], "id_raw": 492 }, { "id": "xtGLDTJhHRldtjpP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nCan you summarize the GDP growth rate data as shown on the bar chart from 2011 to 2020 into a tabular format?" }, { "from": "gpt", "value": "The bar chart is titled {\"GDP growth rate from 2011 to 2020\"}[0.396, 0.021, 0.645, 0.047] and throughout it, there are annual GDP growth rate bars. From 2011 to 2020, the bars corresponding to each year - {2011}[0.096, 0.485, 0.166, 0.629], {2012}[0.183, 0.514, 0.252, 0.629], {2013}[0.270, 0.384, 0.339, 0.629], {2014}[0.356, 0.629, 0.425, 0.701], {2015}[0.443, 0.327, 0.512, 0.629], {2016}[0.529, 0.499, 0.599, 0.629], {2017}[0.616, 0.413, 0.685, 0.629], {2018}[0.703, 0.168, 0.772, 0.629], {2019}[0.789, 0.298, 0.858, 0.629], and {2020}[0.876, 0.629, 0.945, 0.859] - exhibit different heights. These varying heights indicate differences in GDP growth rates, measurable based on the coordinates of each bar's top edge and the scale indicated on the y-axis. By collating this information, we can compile the growth rates into a table.\nBased on the above thought, the answer is:\nYears,GDP growth rate\n2011,1.0\n2012,0.8\n2013,1.7\n2014,-0.5\n2015,2.1\n2016,0.9\n2017,1.5\n2018,3.2\n2019,2.3\n2020,-1.6" } ], "id_raw": 495 }, { "id": "FwlWJKvgwJVOLZ4K", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the trend in the country's GDP growth rate from 2011 to 2020 based on the bar chart." }, { "from": "gpt", "value": "By inspecting the bars representing each year from {2011}[0.096, 0.485, 0.166, 0.629] to {2020}[0.876, 0.629, 0.945, 0.859], I could observe the GDP trend over a decade. The GDP growth rate experienced moderate volatility from 2011 to 2013, followed by a dip into the negative region in 2014. Afterwards, growth resumed and reached a peak in 2018 before declining rapidly in 2019 and dipping into the negative region again in 2020.\nBased on the above thought, the answer is:\nThe GDP growth rate from 2011 to 2020 showed moderate fluctuations before hitting a peak in 2018. After reaching this peak, it declined rapidly and was negative by 2020." } ], "id_raw": 495 }, { "id": "2cF3OkVsHocD6BQQ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, in which year did the country experience the highest GDP growth rate?" }, { "from": "gpt", "value": "To determine the year with the highest GDP growth rate, I need to locate the tallest bar in the chart. I see that all bars span from the x-axis to different heights, determined by their respective GDP growth rates. Comparing the heights of the bars, it is apparent that the bar representing {2018}[0.703, 0.168, 0.772, 0.629] reaches the highest point on the y-axis.\nBased on the above thought, the answer is:\n2018." } ], "id_raw": 495 }, { "id": "dVmk00Nn6zVmoyd0", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nCould you extract the performance data of the community development projects as presented in the bar chart and arrange it into a CSV format?" }, { "from": "gpt", "value": "Looking at the various bars labeled {\"Infrastructure Development\"}[0.197, 0.170, 0.265, 0.582], {\"Youth Development Programs\"}[0.282, 0.115, 0.350, 0.582] and so on, I can see they display the performance of different community development projects. The performance for each project is represented by the height of its bar. For instance, the bar for the project {\"Housing Availability\"}[0.112, 0.142, 0.180, 0.582] reaches an estimated 80 mark on the y-axis, indicating a performance metric of 80. By extracting this data across all bars, we can arrange the performance metrics of all the projects into a CSV format.\nBased on the above thought, the answer is:\nCommunity Development Project,Performance\nHousing Availability,80\nInfrastructure Development,75\nYouth Development Programs,85\nAdult Literacy Programs,70\nEmployment Assistance,65\nVocational Training Programs,73\nHealthcare Services,80\nPension Schemes,90\nChildren's Programs,75\nEnvironmental Protection,70" } ], "id_raw": 496 }, { "id": "wRGXMxM6yaYJiTC8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nRegarding the local government's community development projects, how is the overall situation reflected in the performance data?" }, { "from": "gpt", "value": "The bar chart shows the performance of various community development projects. By observing all the bars, we see that most projects have a performance rate exceeding 70, such as {\"Housing Availability\"}[0.112, 0.142, 0.180, 0.582], {\"Youth Development Programs\"}[0.282, 0.115, 0.350, 0.582], and {\"Healthcare Services\"}[0.621, 0.142, 0.689, 0.582]. This suggests that the government's community development projects are generally successful with a good performance rating. However, two projects stand out - {\"Employment Assistance\"}[0.451, 0.225, 0.519, 0.582], which has the lowest performance, and {\"Pension Schemes\"}[0.706, 0.087, 0.774, 0.582], which has the highest. This indicates that there might be significant room for improvement in some areas like employment assistance, while the pensions schemes project appears to be extremely successful.\nBased on the above thought, the answer is:\nThe overall situation presented by the bar chart is that the government's community development projects are generally successful, with most having a performance rating exceeding 70. However, there's variation as well, with rooms for improvement, particularly in the \"Employment Assistance\" project that has the lowest performance score. The highest performing project is \"Pension Schemes\", suggesting that it is an extremely successful project." } ], "id_raw": 496 }, { "id": "9pyARuaTdCPjmVrk", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_1.png", "conversations": [ { "from": "human", "value": "\nWhich project has the lowest performance rating among all the community development projects?" }, { "from": "gpt", "value": "Inspecting the height of each bar presents the \"Performance\" of each \"Community Development Project\". The bar of lowest height translates into the minimum performance amount. Upon examination, I can specifically see that the project {\"Employment Assistance\"}[0.451, 0.225, 0.519, 0.582] aligns just above the 65 mark on the y-axis. This implies that Employment Assistance has the lowest performance, quantitatively less than any other labeled bars. Thus, we can say that the project which has the least performance is Employment Assistance.\nBased on the above thought, the answer is:\nEmployment Assistance." } ], "id_raw": 496 }, { "id": "kK1KQBDCdcCMBoaF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you are viewing, can you extract the data and arrange it in a CSV format?" }, { "from": "gpt", "value": "To generate the CSV table from the chart, I should first identify both axes in the chart. The {x-axis}[0.472, 0.952, 0.579, 0.975] stands for Research Fields that include AI, Physics, Chemistry, Biotechnology, Astronomy, Data Science, Robotics, Environmental Science(Nanotech), and Genetics. The {y-axis}[0.015, 0.367, 0.029, 0.561] stands for the Innovation Score, which ranges from 0.0 to 1.0. Using the height of the {bars} including {\"AI\"}[0.108, 0.185, 0.176, 0.903], {\"Physics\"}[0.193, 0.280, 0.261, 0.903] and so on, I can determine the Innovation Score for each research field.\nBased on the above thought, the answer is:\nResearch Fields,Innovation Score\nAI,0.90\nPhysics,0.78\nChemistry,0.84\nBiotechnology,0.80\nAstronomy,0.72\nData Science,0.88\nRobotics,0.93\nEnvironmental Science,0.79\nNanotech,0.82\nGenetics,1.00" } ], "id_raw": 497 }, { "id": "prAU5jbBST3P7KGC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which field seems to be the most promising in terms of innovation and why?" }, { "from": "gpt", "value": "According to the chart, the field with the highest Innovation Score is identified by {the tallest bar}. In this chart, {the bar for \"Genetics\"}[0.875, 0.105, 0.943, 0.903] is the tallest indicating that Genetics has received the highest Innovation Score. This suggests that, in terms of innovation, Genetics may be the most promising field because its high Innovation Score indicates that significant scientific discoveries, technology advancement, or influential research are being produced in this area.\nBased on the above thought, the answer is:\nGenetics. It has the highest Innovation Score, indicating significant breakthroughs and advancements in this field." } ], "id_raw": 497 }, { "id": "lScRqSWKflstNDZy", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_2.png", "conversations": [ { "from": "human", "value": "\nWhich field has the lowest Innovation Score according to the chart?" }, { "from": "gpt", "value": "The lowest Innovation Score corresponds to the shortest bar in the chart. I see that {the bar for \"Astronomy\"}[0.449, 0.328, 0.517, 0.903] is the shortest, which means that Astronomy has the lowest Innovation Score among all fields.\nBased on the above thought, the answer is:\nAstronomy." } ], "id_raw": 497 }, { "id": "1XhPDyICkx0whGnZ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nCould you analyze the bar graph and generate a CSV table with the Processes and their corresponding Effectiveness?" }, { "from": "gpt", "value": "Based on the bar chart, it's clear that the x-axis stands for different kinds of processes, as I can tell from the title at {the x-axis}[0.492, 0.952, 0.560, 0.975] which says \"Processes\". The y-axis is labeled with \"Effectiveness\", as I can see from the label at {the y-axis}[0.015, 0.277, 0.029, 0.431]. The value of Effectiveness ranges from {0.0 to 1.0}[0.035, 0.675, 0.057, 0.698]; [0.035, 0.076, 0.057, 0.099]. Each process has one blue bar corresponds to its effectiveness. For example, the value for {process \"Underperforming\"}[0.110, 0.700, 0.204, 0.857] could be interpolated from the height of its bar at [0.108, 0.414, 0.206, 0.684] as approximately 0.45, which aligns with the raw data. The Process and its Effectiveness can thus be enumerated and tabulated.\nBased on the above thought, the answer is:\nProcesses,Effectiveness\nUnderperforming,0.45\nAverage,0.65\nGood,0.75\nExceptional,0.90\nOutstanding,1.0\nProcess of Concern,0.35\nAnother Process of Concern,0.25" } ], "id_raw": 499 }, { "id": "cQRgFjdYy534LXq6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about the overall performance of the processes based on the bar chart?" }, { "from": "gpt", "value": "From the bar chart it\u2019s clear that most processes perform well, with effectiveness values from 0.45 to 1.0. However, there are two processes that fall below these values and are of concern. Both {Process of Concern}[0.720, 0.700, 0.823, 0.872] and {Another Process of Concern}[0.821, 0.700, 0.967, 0.942] have bars that are significantly shorter than the rest, indicating lower effectiveness. These processes, particularly 'Another Process of Concern', stand out in the chart for their low effectiveness, indicating they need serious attention for improvement to meet the standards set by the other processes.\nBased on the above thought, the answer is:\nOverall, most of the processes are highly effective, with 'Outstanding' being the highest. However, 'Process of Concern' and 'Another Process of Concern' are significantly less effective than the other processes, indicating that these are areas requiring improvement." } ], "id_raw": 499 }, { "id": "uRZEu21A1AuLUadG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_4.png", "conversations": [ { "from": "human", "value": "\nWhich process has the lowest effectiveness?" }, { "from": "gpt", "value": "To identify the process that has the lowest effectiveness, I need to examine the chart and find the shortest bar. Among all the bars, the bar at [0.845, 0.534, 0.943, 0.684] is the shortest. This bar corresponds to the process named {Another Process of Concern}[0.821, 0.700, 0.967, 0.942]. Thus, 'Another Process of Concern' appears to have the lowest effectiveness.\nBased on the above thought, the answer is:\nAnother Process of Concern." } ], "id_raw": 499 }, { "id": "LYi4E23wi34DUEw3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nCan you extract the frequency of each social activity in the chart and represent it as a CSV table?" }, { "from": "gpt", "value": "Sure. I can see that the x-axis represents different social activities, evidenced by {\"Various Social Activities\"}[0.443, 0.952, 0.608, 0.975], and there are specific labels corresponding to each bar in chart, such as {\"Gardening\"}[0.112, 0.765, 0.173, 0.867] and {\"Walking\"}[0.203, 0.765, 0.252, 0.846]. The y-axis represents the frequency, which is indicated by {\"Frequency\"}[0.015, 0.327, 0.029, 0.447], and its scale is provided in the form of y-axis tick labels, ranging from {0.0}[0.035, 0.740, 0.057, 0.763] to {1.0}[0.035, 0.082, 0.057, 0.105]. Each bar in the chart, like the bar for {Gardening}[0.108, 0.334, 0.176, 0.749], indicates the frequency of the corresponding social, the value of which can be determined by the height of each bar.\nBased on the above thought, the answer is:\nSocial_Activities, Frequency\nGardening, 0.63\nWalking, 0.79\nRunning, 0.81\nSwimming, 0.88\nReading_Books, 0.70\nDancing, 0.60\nAttending_Concerts, 0.55\nTravel, 0.90\nCooking, 0.75\nWatching_Movies, 0.95" } ], "id_raw": 500 }, { "id": "KY3gr2DB8l8zuVS6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, can you deduce which social activities are most and least preferred by individuals?" }, { "from": "gpt", "value": "This chart displays the popularity or regular participation frequency of each social activity amongst a group of individuals. By examining the bars for each social activity, it is evident that the {Watching_Movies}[0.875, 0.124, 0.943, 0.749] has the highest bar, indicating it is the most adhered social activity. Conversely, {Attending_Concerts}[0.619, 0.387, 0.688, 0.749] bar is the shortest, notifying that it is the social activity least commonly practiced by these individuals. These outcomes infer that watching movies is highly favored by the individuals, while attending concerts is less popular amongst this crowd.\nBased on the above thought, the answer is:\nThe most preferred social activity according to the chart is Watching Movies, while the least preferred one is Attending Concerts." } ], "id_raw": 500 }, { "id": "tzkzrBUjnAJD2mCc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_5.png", "conversations": [ { "from": "human", "value": "\nWhich social activity has the lowest frequency, according to the chart?" }, { "from": "gpt", "value": "To discern the activity with lowest frequency, I need to locate the shortest bar in the chart. Observing each bar from left to right, starting from {Gardening}[0.108, 0.334, 0.176, 0.749], I can see the bar corresponding to {Attending_Concerts}[0.619, 0.387, 0.688, 0.749] is the lowest amongst all. It refers to the social activity with the lowest frequency.\nBased on the above thought, the answer is:\nAttending Concerts." } ], "id_raw": 500 }, { "id": "LvcfJDT0e7CbRTDb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the data represented in the bar chart into a CSV format?" }, { "from": "gpt", "value": "From the horizontal axis, there are ten categories, which are the different investment strategies labeled {\"Investment Strategies\"}[0.459, 0.952, 0.613, 0.975]: Diversification[0.154, 0.586, 0.168, 0.752], Value Investing[0.237, 0.586, 0.251, 0.767], Growth Investing[0.320, 0.586, 0.334, 0.787], Momentum Investing[0.404, 0.586, 0.418, 0.835], Quality Investing[0.487, 0.586, 0.501, 0.786], Socially Responsible Investing[0.570, 0.586, 0.585, 0.942], Index Investing[0.654, 0.586, 0.668, 0.767], Small Cap Investing[0.737, 0.586, 0.751, 0.825], Balanced Investment[0.820, 0.586, 0.835, 0.836] and Inflation Hedging[0.904, 0.586, 0.918, 0.789]. The vertical axis labeled {\"Return Rate\"}[0.015, 0.233, 0.029, 0.370] shows the return rate for each investment strategy. The exact values can be derived by the height of the corresponding bars, such as for Diversification[{corresponding bar}[0.127, 0.229, 0.194, 0.521]], the height from y=0 to the top of the bar indicates a return rate of 0.12.\nBased on the above thought, the answer is:\nInvestment_Strategies,Return_Rate\nDiversification,0.12\nValue_Investing,0.08\nGrowth_Investing,0.1\nMomentum_Investing,0.11\nQuality_Investing,0.14\nSocially_Responsible_Investing,-0.01\nIndex_Investing,0.09\nSmall_Cap_Investing,0.15\nBalanced_Investment,0.1\nInflation_Hedging,0.07" } ], "id_raw": 501 }, { "id": "hfpRRXd2bdaH5vUS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nHow would you assess the performance of the strategies mentioned in the chart and suggest a potential correction strategy?" }, { "from": "gpt", "value": "The strategies' performance can be evaluated using the height of each bar in the bar graph. For instance, Diversification shows a somewhat strong performance with a return rate of 0.12, and the height of the bar[{Diversification}[0.127, 0.229, 0.194, 0.521]] matches this assessment. On the other hand, Socially Responsible Investing strategy shows poor performance because it demonstrates negative returns, which can be seen in the low height of the corresponding bar[{Socially_Responsible_Investing}[0.544, 0.521, 0.611, 0.545]]. Based on this visualization, the Small Cap Investing strategy yields the highest return rate and displays the highest bar[{Small_Cap_Investing}[0.711, 0.156, 0.777, 0.521]], hence is the best performing strategy. As a suggestion, for strategies that have performed poorly such as the Socially Responsible Investing, re-evaluation of this strategy could be done, and maybe a part of the investment fund allocated to it could be diverted to better-performing strategies.\nBased on the above thought, the answer is:\nBased on the chart, Small Cap Investing strategy has the best performance, while the Socially Responsible Investing is under-performing. A potential corrective strategy could be to reduce the investment in Socially Responsible Investing and shift it to Small Cap Investing." } ], "id_raw": 501 }, { "id": "agxcABTXzgQYU8DK", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the return rate of the strategy that has maximum investment return?" }, { "from": "gpt", "value": "To get the strategy that has maximum investment return, I need to find the highest bar in the chart. The highest bar corresponds to the \"Small Cap Investing\" strategy<{bar}[0.711, 0.156, 0.777, 0.521]>. The return rate can be inferred by relating the height of the bar to the y-axis. The top of the bar reaches almost up to the 0.15 point on the y-axis.\nBased on the above thought, the answer is:\n0.15" } ], "id_raw": 501 }, { "id": "1LE1nx6Gl6QLgKJt", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data about the success rate of different AI applications into a structured table in CSV format?" }, { "from": "gpt", "value": "Absolutely! The bar chart shows ten different AI application categories along {the x-axis}[0.055, 0.047, 0.988, 0.684], from {Machine_Vision}[0.098, 0.209, 0.167, 0.684] to {Gaming}[0.876, 0.105, 0.945, 0.684]. The y-axis represents the success rate of each AI application ranging from {0.0}[0.029, 0.677, 0.047, 0.695] to {1.0}[0.029, 0.098, 0.047, 0.115]. The height of the bar determines the value of the success rate. Based on these details, I can structure the data as AI Applications and Success Rates in a CSV table.\nBased on the above thought, the answer is:\nAI_Applications,Success_Rate\nMachine_Vision,0.82\nNatural_Language_Processing,0.91\nRobotics,0.86\nForecasting,0.78\nHealthCare,0.95\nSpeech_Recognition,0.92\nAutonomous_Vehicles,0.70\nCybersecurity,0.95\nEducation,0.88\nGaming,1.0" } ], "id_raw": 502 }, { "id": "g2JdTGRCwSkTdQ0C", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nBased on the observation from the chart, can you tell what could potentially be the reason for gaming having the highest success rate?" }, { "from": "gpt", "value": "The {bar for gaming}[0.876, 0.105, 0.945, 0.684] is the tallest among all, indicating it as the highest success rate. As an AI system assistant, I can't provide subjective reasoning. However, based on popular AI trends, the success rate in gaming might be due to the advancement in AI technologies that are designed for gaming applications. These technologies include machine learning algorithms used for creating more realistic and intelligent Non-Player Characters (NPCs) and the developments in the field of reinforcement learning that are used widely in game simulations.\nBased on the above thought, the answer is:\nThe advancements in AI technologies specifically designed for gaming applications, such as machine learning and reinforcement learning, have likely contributed to the high success rate in the Gaming category." } ], "id_raw": 502 }, { "id": "juIim0fwAaU6lb3R", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_7.png", "conversations": [ { "from": "human", "value": "\nWhich AI application has the lowest success rate according to the chart?" }, { "from": "gpt", "value": "To find the AI application with the lowest success rate, I need to examine the success rate bars' heights. The {y-axis}[0.013, 0.308, 0.024, 0.423] signifies the success rate from {0.0}[0.029, 0.677, 0.047, 0.695] to {1.0}[0.029, 0.098, 0.047, 0.115]. The lowest bar corresponds to the lowest success rate. By comparing each bar's height, it's evident the {Autonomous_Vehicles bar}[0.616, 0.279, 0.686, 0.684] is the shortest, thus indicating it as the AI application with the lowest success rate.\nBased on the above thought, the answer is:\nAutonomous Vehicles." } ], "id_raw": 502 }, { "id": "HTLDsCG4qFw2XZQ3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart in front of you, can you provide the data on the effectiveness rating of top 10 cyber security and data privacy measures in the form of a CSV table?" }, { "from": "gpt", "value": "From the title {\"Cybersecurity and Data Privacy Measures\"}[0.400, 0.952, 0.642, 0.975] and {the x-axis tick labels}[\"Encryption Methods\"[0.126, 0.643, 0.138, 0.873],\"Private Browsing\"[0.213, 0.643, 0.225, 0.837],\"Password Managers\"[0.299, 0.643, 0.311, 0.872],\"Two-factor Authentication\"[0.386, 0.643, 0.397, 0.940],\"Secure Wireless Networks\"[0.472, 0.643, 0.484, 0.942],\"VPN\"[0.559, 0.643, 0.570, 0.690], \"Data Encryption\"[0.645, 0.643, 0.657, 0.830], \"Anonymising Services\"[0.732, 0.643, 0.743, 0.897], \"Biometric Verification\"[0.818, 0.643, 0.830, 0.892], \"Privacy-Focused Browsers\"[0.905, 0.643, 0.916, 0.942], I see that the x-axis represents different categories. The y-axis with labels ranging from {0.0}[0.029, 0.617, 0.047, 0.641] to {1.0}[0.029, 0.071, 0.047, 0.094] represents the corresponding values. For each category, there is a {blue bar}[{bar1}[0.098, 0.200, 0.167, 0.626], {bar2}[0.184, 0.178, 0.253, 0.626], {bar3}[0.271, 0.145, 0.340, 0.626], {bar4}[0.357, 0.134, 0.426, 0.626], {bar5}[0.444, 0.271, 0.513, 0.626], {bar6}[0.530, 0.112, 0.599, 0.626], {bar7}[0.616, 0.134, 0.686, 0.626], {bar8}[0.703, 0.194, 0.772, 0.626], {bar9}[0.789, 0.167, 0.859, 0.626], {bar10}[0.876, 0.129, 0.945, 0.626] representing the corresponding value. I need to extract the values using the height of the bars.\nBased on the above thought, the answer is:\nCategories,Data\nEncryption Methods,0.78\nPrivate Browsing,0.82\nPassword Managers,0.88\nTwo-factor Authentication,0.9\nSecure Wireless Networks,0.65\nVPN,0.94\nData Encryption,0.9\nAnonymising Services,0.79\nBiometric Verification,0.84\nPrivacy-Focused Browsers,0.91" } ], "id_raw": 503 }, { "id": "uf3P7gdJlMb6xuJ2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which measure do you think needs to be added to improve the current cybersecurity strategy considering effectiveness?" }, { "from": "gpt", "value": "By looking at the {bars}[{bar1}[0.098, 0.200, 0.167, 0.626], {bar2}[0.184, 0.178, 0.253, 0.626], {bar3}[0.271, 0.145, 0.340, 0.626], {bar4}[0.357, 0.134, 0.426, 0.626], {bar5}[0.444, 0.271, 0.513, 0.626], {bar6}[0.530, 0.112, 0.599, 0.626], {bar7}[0.616, 0.134, 0.686, 0.626], {bar8}[0.703, 0.194, 0.772, 0.626], {bar9}[0.789, 0.167, 0.859, 0.626], {bar10}[0.876, 0.129, 0.945, 0.626] of the chart, the measure with the lowest effectiveness rating is clearly {the bar representing \"Secure Wireless Networks\"}[0.444, 0.271, 0.513, 0.626]. So, to improve the current cybersecurity strategy, we should add measures that could make \"Secure Wireless Networks\" more effective.\nBased on the above thought, the answer is:\nMeasures to improve the effectiveness of \"Secure Wireless Networks\" should be added." } ], "id_raw": 503 }, { "id": "TV53YKFL6YNWUU2h", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which cybersecurity and data privacy measure has the highest effectiveness rating?" }, { "from": "gpt", "value": "To find out which cybersecurity and data privacy measure has the highest effectiveness rating, I need to find the tallest bar among the 10 bars. By comparing all the bars one by one, I found that {the bar representing \"VPN\"}[0.530, 0.112, 0.599, 0.626] is the tallest, which means \"VPN\" has the highest effectiveness rating.\nBased on the above thought, the answer is:\nVPN" } ], "id_raw": 503 }, { "id": "hFzAsDwBj0z3iXtN", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nPlease construct a structured table of the dataset corresponding to the bar chart using the chart's information." }, { "from": "gpt", "value": "Looking at the bar chart, one can observe that on the x-axis are the names of different academic subjects, {labels of subjects}[{\"Math\"} [0.132, 0.829, 0.161, 0.873], {\"English\"} [0.219, 0.829, 0.256, 0.886], {\"Biology\"} [0.305, 0.829, 0.344, 0.887], {\"Chemistry\"} [0.392, 0.829, 0.442, 0.905], {\"Physics\"} [0.478, 0.829, 0.517, 0.887], {\"Computer Science\"} [0.565, 0.829, 0.648, 0.954], {\"Art\"} [0.651, 0.829, 0.672, 0.860], {\"Music\"} [0.738, 0.829, 0.769, 0.877], {\"Physical Education\"} [0.824, 0.829, 0.909, 0.957], {\"History\"} [0.911, 0.829, 0.948, 0.885]]. On the y-axis are the utilization rates, which range from 0 to 1, according to {tick_labels} [{\"0.0\"} [0.029, 0.810, 0.047, 0.828], {\"0.2\"} [0.029, 0.665, 0.047, 0.683], ..., {\"1.0\"} [0.029, 0.084, 0.047, 0.102]]. Thus, the chart data can be transformed into a structured table by taking each subject and its respective utilization value. The height of each bar on the chart corresponds to its utilization ratio.\nBased on the above thought, the answer is:\nSubject, Utilization\nMath, 0.95\nEnglish, 0.89\nBiology, 0.72\nChemistry, 0.84\nPhysics, 0.79\nComputer Science, 0.99\nArt, 0.68\nMusic, 0.70\nPhysical Education, 0.91\nHistory, 0.94" } ], "id_raw": 504 }, { "id": "DTaG1Z5zyClxDJeo", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart provided, could you identify a potential issue in the utilization of education resources?" }, { "from": "gpt", "value": "Noticing the {label}[0.461, 0.964, 0.581, 0.981] \"Education Resources\" along the x-axis and the {label}[0.013, 0.374, 0.024, 0.462] \"Utilization\" on the y-axis, I can see that the chart shows the utilization ratios of different education resources. The length of each bar represents the utilization of resources in each discipline. Focusing on the shorter bars like {Art}[0.616, 0.324, 0.686, 0.817] or {Biology}[0.271, 0.295, 0.340, 0.817], while compared to {Computer Science}[0.530, 0.099, 0.599, 0.817] and {Math}[0.098, 0.128, 0.167, 0.817] which are longer bars, there indeed is a potential issue. The issue is the uneven distribution of resource utilization across different subjects. The STEM subjects seem to be utilized more than soft sciences and arts. This could lead to an imbalance in academic skills amongst students if not addressed properly.\nBased on the above thought, the answer is:\nThe issue is the uneven distribution of resource utilization across different subjects, with STEM subjects such as Math and Computer Science having higher utilization rates while less scientifically oriented subjects like Arts and Biology demonstrate lower utilization. This could reflect an imbalanced focus on certain academic disciplines over others." } ], "id_raw": 504 }, { "id": "QqhVSL6HtmD9iLbk", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the average utilization of all subjects?" }, { "from": "gpt", "value": "To find the average utilization across all subjects, we can add up the utilization rates for all subjects and divide by the total number of subjects. Here, the utilization values are represented by the height of the bars for each subject. There are 10 bars for 10 subjects {bar heights}[0.98 ,0.171, 0.253, 0.817], [0.184, 0.171, 0.253, 0.817], [0.271, 0.295, 0.340, 0.817], [0.357, 0.207, 0.426, 0.817], [0.444, 0.244, 0.513, 0.817], [0.530, 0.099, 0.599, 0.817], [0.616, 0.324, 0.686, 0.817], [0.703, 0.309, 0.772, 0.817], [0.789, 0.157, 0.859, 0.817], [0.876, 0.135, 0.945, 0.817].\nBased on the above thought, the answer is:\n0.84" } ], "id_raw": 504 }, { "id": "LOLTaWr6qPjm2xei", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart information into a structured table in CSV format according to what you see?" }, { "from": "gpt", "value": "I see a bar chart titled {\"Trade Value of different Industry Sectors\"}[0.386, 0.952, 0.666, 0.975] plotted against various industry sectors on the x-axis and their corresponding trade values on the y-axis. There are ten bars in total, each representing an industry sector. The height of each bar corresponds to the trade value of each industry. Let's observe each bar one by one. The first bar represents the {\"Clothing_Industry\"}[0.094, 0.723, 0.190, 0.884] with a trade value of approximately 0.81 indicated by the height of {the bar}[0.108, 0.205, 0.176, 0.707]. Similarly, we observe other bars and extract the data. This entire process is repeated for every industry to form the structured table in CSV format.\nBased on the above thought, the answer is:\nIndustry,Trade_Value\nClothing_Industry,0.81\nAutomobile_Industry,0.67\nElectronics_Industry,0.76\nPharmaceutical_Industry,0.57\nFood_Industries,0.82\nEnergy_Sector,0.63\nMetal_Industry,0.91\nChemical_Industry,0.87\nLumber_Industry,0.88\nAgriculture,0.52" } ], "id_raw": 505 }, { "id": "PpEQrtiOK5YpKlEq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nDrawing from the graph, which sectors are predicted to have a promising future regarding trade value? Explain why." }, { "from": "gpt", "value": "The future promising sectors regarding trade value can be concluded from the bars that have a trade value near or above the average trade value for all industries. Let's find these bars. The {Food_Industries bar}[0.449, 0.199, 0.517, 0.707], {Metal_Industry bar}[0.619, 0.143, 0.688, 0.707], {Chemical_Industry bar}[0.705, 0.168, 0.773, 0.707], and {Lumber_Industry bar}[0.790, 0.161, 0.858, 0.707] are all relatively high, indicating that these industries have strong trade values. Given that higher trade value generally predicts a more promising future in the context of globalization, the Food Industries, Metal Industry, Chemical Industry, and Lumber Industry sectors therefore appear to have a promising future in trade value.\nBased on the above thought, the answer is:\nFood Industries, Metal Industry, Chemical Industry, and Lumber Industry. Because these sectors have high trade values, suggesting that they may have a promising future in terms of trade value." } ], "id_raw": 505 }, { "id": "RM87GRUhzC90adL9", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which industry has the lowest trade value?" }, { "from": "gpt", "value": "To find out which industry has the lowest trade value, I need to find the shortest bar on the chart. Each of these bars corresponds to a specific industry. By comparing the heights of the bars, it appears that the shortest bar corresponds to the {Agriculture}[0.877, 0.723, 0.941, 0.830] industry, with {the bar for Agriculture}[0.875, 0.385, 0.943, 0.707] corresponding to a trade value of approximately 0.52. This value is lower than any other trade value represented by the other bars in the chart.\nBased on the above thought, the answer is:\nAgriculture" } ], "id_raw": 505 }, { "id": "pqk2ShV2tEsvu6vY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart to a CSV format, including the industry and its corresponding growth score?" }, { "from": "gpt", "value": "After examining the chart, I see that the x-axis represents various sectors of the \"Knowledge and Innovation Industry\" as indicated by the x-label[0.361, 0.952, 0.686, 0.975], while the y-axis represents the corresponding 'Annual Growth Score' using the y-label[0.015, 0.211, 0.029, 0.455]. For example, the bar representing \"AI and Machine Learning\"[0.104, 0.098, 0.172, 0.641] is almost reaching the 10 mark on the y-axis, indicating its growth score is 9.7. I will follow this approach for the rest of the bars to extract their corresponding values.\nBased on the above thought, the answer is:\nIndustry,Annual Growth Score\nAI and Machine Learning,9.7\nBiotechnology,7.6\nData Science,8.5\nGreen Energy,9.5\nSpace Tech,8.2\nTelecommunication,7.8\nNanotechnology,9.9\nInternet Security,7.5\nQuantum Computing,7.85\nIoT,8.7" } ], "id_raw": 506 }, { "id": "kR2m7bKkStvi7tMn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which areas in the Knowledge Economy and Innovation sector would you recommend for investment in terms of growth?" }, { "from": "gpt", "value": "Considering the bar heights, industries with larger values indicate a faster growing industry and hence, more promising for investments. Specifically, the bars representing \"Nanotechnology\"[0.618, 0.087, 0.686, 0.641] and \"Green Energy\"[0.361, 0.109, 0.429, 0.641] are closest to the maximum 10 value on the y-axis, suggesting they are the fast growing fields. Therefore, they may be the most appropriate ones for investment.\nBased on the above thought, the answer is:\nNanotechnology and Green Energy." } ], "id_raw": 506 }, { "id": "MuI5jWL4iH078tNK", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which industry has the highest annual growth score?" }, { "from": "gpt", "value": "Inspecting each bar, I need to identify the one reaching the highest on the y-axis. In particular, the bar for \"Nanotechnology\"[0.618, 0.087, 0.686, 0.641] is closer to the 10 mark, indicating the highest annual growth score among all the industries.\nBased on the above thought, the answer is:\nNanotechnology." } ], "id_raw": 506 }, { "id": "7wxMxRCYmPQ0XBne", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data, as present on the chart, into a table with each item's production quantity being displayed?" }, { "from": "gpt", "value": "The chart 'Food and Agriculture Industry Analysis'[0.386, 0.019, 0.653, 0.041] shows the production quantity of different agriculture commodities. The commodities are listed on the x-axis[0.448, 0.964, 0.591, 0.981], with each bar's height corresponding to its production quantity on the y-axis[0.013, 0.311, 0.024, 0.603]. I'll extract the precise quantity from each bar's y-range. For instance, the first bar corresponds to 'Wheat'[0.094, 0.444, 0.164, 0.867] with a production total of 10.3 million metric tons. Proceeding in similar fashion for remaining bars enables me to prepare the entire table.\nBased on the above thought, the answer is:\nitem,production\nWheat,10.3\nCorn,13.5\nRice,7.6\nSoybeans,9.4\nCoffee,2.6\nCotton,6.0\nSugarcane,19.5\nPotatoes,7.2\nGrains,14.7\nFruits,16.1" } ], "id_raw": 507 }, { "id": "esIJqLkW7KL0zWTj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart tell us about the trend in the food and agriculture industry?" }, { "from": "gpt", "value": "The chart[0.052, 0.047, 0.988, 0.867] gives an overview of the production quantities of various crops in the food and agriculture industry. It seems that sugarcane [0.615, 0.067, 0.685, 0.867] and fruits[0.876, 0.207, 0.945, 0.867] show a significantly higher yield compared to other commodities, indicating they might be more profitable or in higher demand. Wheat[0.094, 0.444, 0.164, 0.867] on the other hand, shows slow growth, possibly indicating less profitability or lower demand. Coffee[0.441, 0.760, 0.511, 0.867] representing the lowest yield, implies that it might be the least lucrative or least highly demanded in comparison to other commodities.\nBased on the above thought, the answer is:\nThe chart shows an increasing trend in the production of sugarcane and fruits, indicating that they are likely the most profitable or highly demanded commodities. However, commodities such as wheat and coffee show relatively low production, indicating less profitability or demand. This data could suggest that farmers and producers might earn more profit if they focus on sugarcane and fruits production." } ], "id_raw": 507 }, { "id": "k6COMZD4npWnPODS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what is the total production quantity of all the commodities combined?" }, { "from": "gpt", "value": "The bars in the chart represent the quantity produced for each agricultural commodity. To find the total production, I need to sum up the heights of all bars. Each rectangle with bar_coordinates represents the quantity produced for a particular commodity, such as 'Wheat'[0.094, 0.444, 0.164, 0.867] for 10.3 million metric tons, and 'Corn'[0.181, 0.313, 0.250, 0.867] for 13.5 million metric tons. If I continue and take the sum of the agricultural production quantities for Rice, Soybeans, Coffee, Cotton, Sugarcane, Potatoes, Grains, Fruits, I'll get the total production quantity.\nBased on the above thought, the answer is:\n106.9" } ], "id_raw": 507 }, { "id": "fnDCnne8tkRyYS1H", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the bar chart, can you generate a structured data table in CSV format that reflects the annual funds allocation for the Culture and Arts industry between 2010 to 2020?" }, { "from": "gpt", "value": "The chart is a bar graph with the {X-axis representing years}[0.052, 0.047, 0.988, 0.927] and the {Y-axis representing the amount of funds}[0.507, 0.964, 0.532, 0.981]. Each bar corresponds to a specific year and its height represents the amount of funding, as clearly shown in the {caption below each bar}[0.882, 0.597, 0.945, 1.588]. By looking at these vertical bars from left to right, I can determine the funding for each year. For example, the {1st bar from the left}[0.094, 0.839, 0.157, 1.588] represents the funding amount for the year 2010, which is 34 million.\nBased on the above thought, the answer is:\nYear,Art_funds_in_Millions\n2010,34\n2011,36\n2012,45\n2013,39\n2014,41\n2015,48\n2016,54\n2017,59\n2018,56\n2019,57\n2020,45" } ], "id_raw": 508 }, { "id": "gjf8FhvlQCpzZ6EV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nJudging by the funding trends in the chart, how would you evaluate the overall growth of the Culture and Arts industry from 2010 to 2020?" }, { "from": "gpt", "value": "As indicated by the bar graph, the funding for the Culture and Arts industry has generally been on the rise from {the year 2010}[0.094, 0.839, 0.157, 1.588] till {the year 2019}[0.803, 0.333, 0.866, 1.588], with a few lower values interspersed in the middle years such as {the year 2013}[0.330, 0.729, 0.393, 1.588]. There were also some standout years like {2012}[0.252, 0.597, 0.315, 1.588], {2015}[0.488, 0.531, 0.551, 1.588], and {2017}[0.646, 0.289, 0.709, 1.588], when the funding was markedly greater. However, in {the year 2020}[0.882, 0.597, 0.945, 1.588], there is a significant drop, bringing the funding closer to the levels of 2012. This abrupt fall could potentially be due to external events impacting the industry, such as the global Coronavirus pandemic. Despite the decrease at the end, the overall ten-year period shows growth and development in the culture and arts industry.\nBased on the above thought, the answer is:\nThe overall growth was positive over the 10-year period from 2010 to 2020, with noticeable development and sporadic spikes in funding. However, the year 2020 experienced a significant drop in funding." } ], "id_raw": 508 }, { "id": "bDKPADETEUwcRPBT", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_13.png", "conversations": [ { "from": "human", "value": "\nWhich year saw the most significant increase in funds compared to the previous year?" }, { "from": "gpt", "value": "To determine which year saw the greatest increase in funding, I should compare the height of each bar with the one before it. Starting from the {2nd bar}[0.173, 0.795, 0.236, 1.588], which represents 2011, and comparing it to the first bar for 2010, there was an increase of 2 million. If I follow this comparison to the end till {the last bar}[0.882, 0.597, 0.945, 1.588] for 2020, the year with the most significant increase from the previous year is 2017, indicated by {the bar}[0.646, 0.289, 0.709, 1.588], where the funds increased by 9 million from 2016's 54 million to 59 million in 2017.\nBased on the above thought, the answer is:\n2017" } ], "id_raw": 508 }, { "id": "3fVFIXbPJRb5ODVI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information presented in the bar chart into a data table in CSV format?" }, { "from": "gpt", "value": "Sure, the bar chart provides leadership scores for various departments. The labels of each bar are evident from the x-axis labels, where we have {'Sales'}[0.120, 0.774, 0.130, 0.821], {'Marketing'}[0.208, 0.774, 0.217, 0.861], {'Human Resources'}[0.295, 0.774, 0.305, 0.931], {'IT'}[0.383, 0.774, 0.392, 0.790], {'Finance'}[0.470, 0.774, 0.479, 0.841], {'Operations'}[0.558, 0.774, 0.567, 0.870], {'Customer Service'}[0.645, 0.774, 0.654, 0.928], {'Product'}[0.733, 0.774, 0.742, 0.841], {'Corporate Strategy'}[0.820, 0.774, 0.829, 0.942] and {'Business Intelligence'}[0.907, 0.774, 0.917, 0.957]. On correlating the heights of these bars, their corresponding {\"Leadership Scores\"} are gained. For instance, the bar representing 'Finance' is at height of 95, consequent to the {label for finance \"[0.440, 0.118, 0.510, 1.867]\"} and correlating to {'the ytick for 95'}[0.029, 0.111, 0.041, 0.128].\nBased on the above thought, the answer is:\nDepartment,Leadership Score\nSales,75\nMarketing,80\nHuman Resources,90\nIT,85\nFinance,95\nOperations,70\nCustomer Service,88\nProduct,72\nCorporate Strategy,78\nBusiness Intelligence,92" } ], "id_raw": 509 }, { "id": "I7STqlRBsvUwhKLP", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which department needs to work on its leadership skills and why?" }, { "from": "gpt", "value": "To answer this, I need to identify the department with the lowest leadership score, which means looking for the shortest bar. I observe that {Operations}[0.527, 0.578, 0.597, 1.867] has the smallest bar height. Hence the department named 'Operations' has got the lowest leadership score of 70 that aligns with {'the label for 70'}[0.029, 0.571, 0.041, 0.589]. This score is the lowest among the scores of all the other departments and lags behind by at least 2 points compared to the other departments.\nBased on the above thought, the answer is:\nOperations. Because it has the lowest leadership score of 70 among all the departments, indicating that its leadership needs further improvement." } ], "id_raw": 509 }, { "id": "7eeEdHdhG8FG8zh7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat's the average leadership score according to the chart?" }, { "from": "gpt", "value": "To find out the average leadership score, I must sum up the scores of all departments and then divide by the total number of departments. Based on {the bar heights} and their correlation to the y-axis labels, I see that the scores are 75('Sales'), 80('Marketing'), 90('Human Resources'), 85('IT'), 95('Finance'), 70('Operations'), 88('Customer Service'), 72('Product'), 78('Corporate Strategy'), and 92('Business Intelligence'). There are 10 departments in total.\nBased on the above thought, the answer is:\n82.5" } ], "id_raw": 509 }, { "id": "kMUvDNhxmmzttovx", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nPlease create a structured table representing the performance data of all companies over the three quarters as observed from the bar chart." }, { "from": "gpt", "value": "I am analyzing the bar chart which consists of three subplots, each signifying a group of companies. Within each subplot, every company has three horizontal bars corresponding to their performance in each quarter. The x-axis of the subplots presents performance values and the y-axis names the companies. To extract the data, I locate the end of each bar which indicates the performance value and the label alongside the y-axis for the company name. For instance, in the first subplot, I can observe the performance of Amgen with {the end of the bar for Quarter 1}[0.389, 0.221, 0.389, 0.286] at approximately 100, the bar for Quarter 2 ending at approximately [0.575, 0.221, 0.575, 0.286] 210, and for Quarter 3, the bar ends at approximately [0.736, 0.221, 0.736, 0.286] 305. This process is continued for each company across all subplots to create the full table.\nBased on the above thought, the answer is:\nCompany, Quarter 1, Quarter 2, Quarter 3\nAmgen, 100, 210, 305\nJohnson & Johnson, 120, 250, 390\nMerck & Co, 130, 270, 430\nGilead Sciences, 140, 290, 450\nPfizer, 150, 310, 490\nRoche, 160, 340, 540\nNovartis, 170, 360, 570\nSanofi, 180, 380, 600\nBristol-Myers Squibb, 190, 400, 630\nRegeneron Pharmaceuticals, 215, 450, 705" } ], "id_raw": 514 }, { "id": "EhbZAqXo6AULyIFd", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nWhich company displays the most consistent quarterly performance growth on the chart?" }, { "from": "gpt", "value": "To deduce the company with the most consistent quarterly performance growth, I am examining the slopes of the bar edges for each company across the quarters. The consistency of growth is indicated by the uniformity of the increase in lengths of the bars across each quarter. {The first subplot}[0.219, 0.047, 0.985, 0.297] contains bars representing Amgen, Johnson & Johnson, and Merck & Co. Among these, {Johnson & Johnson's bars}[0.219, 0.139, 0.422, 0.205];[0.422, 0.139, 0.643, 0.205];[0.643, 0.139, 0.881, 0.205] show a steady and consistent increase with no declines. Similar analysis for the subsequent subplots indicates that Johnson & Johnson, along with Novartis from the third subplot, who's bars are [0.219, 0.892, 0.395, 0.940];[0.395, 0.892, 0.591, 0.940];[0.591, 0.892, 0.809, 0.940], exhibit consistent performance growth with no signs of decline or stagnation.\nBased on the above thought, the answer is:\nBoth Johnson & Johnson and Novartis exhibit the most consistent quarterly performance growth, with a steady increase in their performance values each quarter and no decreases over the three quarters." } ], "id_raw": 514 }, { "id": "Zk0FSglEVHpRq0IB", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_35_4.png", "conversations": [ { "from": "human", "value": "\nHow much has Pfizer's performance increased from Quarter 1 to Quarter 3?" }, { "from": "gpt", "value": "To calculate Pfizer's performance increase from Quarter 1 to Quarter 3, I first locate Pfizer's bars in the relevant subplot. {The bar representing Pfizer's performance in Quarter 1}[0.219, 0.467, 0.422, 0.532] ends at an x-value of approximately 150, and {the bar for Quarter 3}[0.638, 0.467, 0.881, 0.532] ends at an x-value of approximately 490. Subtracting the former from the latter gives the performance increase.\nBased on the above thought, the answer is:\n340" } ], "id_raw": 514 }, { "id": "05NJhgKbeuK24vWC", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the chart on risk metrics across different intervals into a structured table?" }, { "from": "gpt", "value": "By examining the horizontal bar plots in the three sub-charts, I can compile the data into a table. Starting with the first {subplot}[0.106, 0.075, 0.382, 0.923] which represents the Start metrics, each bar such as {Cyber Risk bar}[0.106, 0.805, 0.216, 0.884] corresponds to a risk factor and shows the starting metric. Proceeding to the second {subplot}[0.407, 0.075, 0.682, 0.923] for Interval 1 and the third {subplot}[0.707, 0.075, 0.983, 0.923] for Interval 2, I can identify corresponding bars like the {Operational Risk bar for Interval 1}[0.407, 0.607, 0.503, 0.686] and {Market Risk bar for Interval 2}[0.707, 0.508, 0.914, 0.588] to complete the data for each interval. Using this process, I can transcribe all values for the risk factors at the different intervals into a CSV format.\nBased on the above thought, the answer is:\nRisk Factors,Start,Interval 1,Interval 2\nCyber Risk,0.4,0.6,0.35\nCredit Risk,0.7,0.8,0.5\nOperational Risk,0.3,0.35,0.4\nMarket Risk,0.65,0.7,0.75\nReputation Risk,0.55,0.6,0.6\nStrategic Risk,0.8,0.85,0.85\nLegal Risk,0.35,0.4,0.3\nCompliance Risk,0.6,0.7,0.72" } ], "id_raw": 518 }, { "id": "N4wUse1PoL5QC8Cw", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nWhich risk factor has shown the greatest change from Start to Interval 2?" }, { "from": "gpt", "value": "To determine the risk factor with the greatest change, I examine the bars representing the Start and Interval 2 metrics for each risk factor. Starting with {Cyber Risk}[0.106, 0.075, 0.382, 0.923], comparing its start metric bar [0.106, 0.805, 0.216, 0.884] with Interval 2 bar [0.707, 0.805, 0.803, 0.884], the change is a 0.05 decrease. Following this method, I compare each metric pair such as {Credit Risk Start bar}[0.106, 0.706, 0.299, 0.785] to {Credit Risk Interval 2 bar}[0.707, 0.706, 0.845, 0.785]. After examining all metric pairs, I can determine which risk factor has the largest metric change from Start to Interval 2.\nBased on the above thought, the answer is:\nCredit Risk. There is a change from a risk metric of 0.7 at the Start to 0.5 at Interval 2, which is the greatest reduction among all the risk factors analyzed." } ], "id_raw": 518 }, { "id": "s5XuMilsrrmjXubL", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the average Start metric for all the risk factors represented in the chart?" }, { "from": "gpt", "value": "To calculate the average Start metric, I need to analyze and aggregate the values of the start metrics from the first {subplot}[0.106, 0.075, 0.382, 0.923]. I'll locate each bar corresponding to the Start metrics, such as the {Cyber Risk bar}[0.106, 0.805, 0.216, 0.884] and read off the values as 0.4 for Cyber Risk, 0.7 for Credit Risk, 0.3 for Operational Risk and so on until Compliance Risk which has a value of 0.6. Summing these and dividing by the total number of risk factors, which is 8, I arrive at the average Start metric.\nBased on the above thought, the answer is:\n0.5375" } ], "id_raw": 518 }, { "id": "fLKVcLOhW8RR3zmK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nCan you produce a CSV formatted table from the population data across the different historical ages and time periods from the bar chart?" }, { "from": "gpt", "value": "To create the CSV formatted table, I must examine each subchart's bars, representing different historical ages and time periods. I can see the x-axis labels indicating the time periods as {20 years}[0.139, 0.976, 0.161, 0.988], {40 years}[0.223, 0.976, 0.245, 0.988], going up to {200 years}[0.890, 0.976, 0.923, 0.988], and the y-axis labels indicating population scale starting from {0}[0.052, 0.963, 0.063, 0.975] scaling up to {5000}[0.019, 0.789, 0.063, 0.800]. I will analyze the bars' height in each era, which includes {Stone Age}[0.116, 0.828, 0.183, 0.968], {Bronze Age}[0.116, 0.863, 0.183, 0.968], {Iron Age}[0.116, 0.915, 0.183, 0.968], {Medieval Ages}[0.116, 0.922, 0.183, 0.968], and {Modern Age}[0.116, 0.908, 0.183, 0.968], and cross-reference them with the y-axis to obtain the population numbers.\nBased on the above thought, the answer is:\nTime,Stone Age,Bronze Age,Iron Age,Medieval Age,Modern Age\n20,4000,3000,1500,1300,1700\n40,3800,2800,1400,1250,1650\n60,3700,2700,1275,1200,1550\n80,3550,2650,1250,1100,1450\n100,3400,2600,1225,950,1300\n120,3300,2550,1200,850,1050\n140,3200,2500,1175,800,1000\n160,3100,2450,1150,725,950\n180,3000,2400,1125,650,900\n200,2900,2350,1100,575,850" } ], "id_raw": 523 }, { "id": "iXGQKWTDaKrm4HI0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which historical age shows the most consistent development over time?" }, { "from": "gpt", "value": "To determine which historical age shows the most consistent development, I'll analyze the uniformity of the bars' heights within each age's subchart. A consistent development would mean the bars are of similar height across the different time periods. I will need to examine the bars representing different eras including {Stone Age}[0.116, 0.828, 0.183, 0.968], marked by a decreasing trend in bar heights; {Bronze Age}[0.116, 0.863, 0.183, 0.968], with another clear decline; {Iron Age}[0.116, 0.915, 0.183, 0.968], which shows fluctuations making it inconsistent; {Medieval Ages}[0.116, 0.922, 0.183, 0.968], also showcasing a decline. Lastly, I will look at {Modern Age}[0.116, 0.908, 0.183, 0.968], where I notice population numbers consistently close to each other yet marginally decreasing, showing slight inconsistency.\nBased on the above thought, the answer is:\nUpon analyzing the bars, the Modern Age shows the most consistent development over time despite a small marginal decrease. The bars in the Modern Age's subchart have less drastic changes in height compared to other ages, which show more significant declines or fluctuations." } ], "id_raw": 523 }, { "id": "wT82PunXhvPgSLRn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total decrease in population for the Bronze Age from the earliest to the latest time period?" }, { "from": "gpt", "value": "To calculate the total decrease in population for the Bronze Age from the earliest to the latest time period, I need to subtract the population of the earliest time period from that of the latest time period. I'll observe the bar heights at {Bronze Age at 20 years}[0.116, 0.863, 0.183, 0.968] with a population height near {3000}[0.019, 0.858, 0.063, 0.870] and compare that to the bar at {Bronze Age at 200 years}[0.873, 0.886, 0.940, 0.968] with a height suggesting a population near {2350}[0.019, 0.893, 0.063, 0.905].\nBased on the above thought, the answer is:\n650" } ], "id_raw": 523 }, { "id": "qwGgtlceL0wqaCOO", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nCould you please process the data from the bar chart into a CSV table?" }, { "from": "gpt", "value": "Sure, to convert the data in the bar chart into a CSV format, I need to observe and analyze each bar in the chart. Double bar plots are present in this chart with each bar plot having bars representing the percentage of population in a particular tier in a country. Important details can be extracted from the legend {legend}[0.354, -0.091, 0.646, -0.008] that specify the countries. The two plots are split using tiers mentioned on {yticks}[0.019, 0.603, 0.115, 0.621]; [0.024, 0.832, 0.115, 0.850]. The x-axis of the bar graph indicates the percentage population in that tier, and it is the same for both the subplots. Now to get the values for each bar, I observe the length of the bars corresponding to the scale on x-axis. For example, the bar representing the Middle Tier in USA located at {bar1}[0.127, 0.518, 0.941, 0.702] shows a population percentage of around 70.\nBased on the above thought, the answer is:\nCountry,Upper Tier,Middle Tier\nUSA,30,70\nCanada,45,55\nUK,50,50\nGermany,30,70\nFrance,60,40" } ], "id_raw": 527 }, { "id": "JlwUIqtCeH7s78LX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nFrom the social stratification perspective of these countries, what do the charts imply about the population distribution among the two social tiers?" }, { "from": "gpt", "value": "Based on the bar chart, we can observe the distribution of population among the Upper Tier and Middle Tier in different countries. The bar chart showing the Middle Tier located at {Middle_Tier_chart}[0.127, 0.019, 0.981, 0.473] and the bar chart describing the Upper Tier located at {Upper_Tier_chart}[0.127, 0.498, 0.981, 0.952] provide valuable insights on the percentage of population in both tiers for each country. As per provided data, it can be observed that in most countries, the Middle Tier has a majority of the population, the bars representing Middle Tier are visibly longer than those in Upper Tier. There are exceptions though, for example, Germany situated at {Germany}[0.559, -0.084, 0.639, -0.067] in the legend, has an equal distribution of population in both tiers. The difference in population distribution among these tiers indicates the disparity in social stratification in these countries. Studying these bar heights and comparing them, we can get an understanding of social disparities and the composition of various tiers in different countries.\nBased on the above thought, the answer is:\nThe charts imply that most countries have a higher percentage of population in the Middle Tier than in the Upper Tier. The distribution of population among these two social tiers in different countries can indicate the disparity in social stratification. However, for some countries like Germany, the population is equally distributed between the Upper and Middle Tiers, demonstrating a balanced social structure in the country." } ], "id_raw": 527 }, { "id": "7jThhSTEvOX7CSyu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_2.png", "conversations": [ { "from": "human", "value": "\nWhich country has the biggest difference between the percentage of the upper and middle tiers?" }, { "from": "gpt", "value": "To find out this answer, I need to compare the bars representing different countries across both subplots. Looking at the bars of USA with coordinates {USA_upper}[0.127, 0.748, 0.592, 0.931] and {USA_middle}[0.127, 0.518, 0.941, 0.702], the difference between Upper and Middle Tiers is 40. If I conduct the same analysis for all countries by comparing their bars with respective coordinates. After calculating, it is easy to observe that the country with the biggest difference between the percentage of the upper and middle tiers is USA with a difference of 40.\nBased on the above thought, the answer is:\nUSA" } ], "id_raw": 527 }, { "id": "Vcet7JWNaiYhuVMR", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nCan you provide structured CSV data of the development trends in the Culture and Arts Industry, focusing on each art form and their progression over the four phases?" }, { "from": "gpt", "value": "As per the observation of the grouped bar chart, the Y-axis categorizes art forms including {Music, Visual Arts, Literature, and Cinema}<{yticks}[0.113, 0.413, 0.113, 0.080]>. The X-axis indicates the trend values across four different stages: {steady increase, inexorable rise, marginal improvement, and rapid expansion}<{legend}<[0.305, -0.073, 0.695, -0.027]>. Bars are grouped by art forms and, within each group, a unique color represents each phase. For instance, {the red bar}<[0.113, 0.369, 0.425, 0.457]> under Music corresponds to a steady increase with a value of 150 as indicated by the X-axis range {0 -> 150}. By systematically analyzing each colored bar's width within the grouped art forms, numerical values corresponding to each phase can be determined and structured into CSV format.\nBased on the above thought, the answer is:\nArtform,Steady Increase,Inexorable Rise,Marginal Improvement,Rapid Expansion\nMusic,150,80,95,30\nVisual Arts,230,90,85,220\nLiterature,400,60,70,215\nCinema,260,120,90,280" } ], "id_raw": 528 }, { "id": "aNytIeYI5eTvXYSV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nWhich art form shows the most balanced development across all four phases?" }, { "from": "gpt", "value": "To determine the most balanced development, I will compare the distribution of the stacked bar lengths across the four phases for each art form. {Visual Arts}'s values show a steady increase, a moderate inexorable rise, a marginal improvement, and a significant rise in rapid expansion with the bars located at {[0.113, 0.258, 0.591, 0.346]}, {[0.113, 0.258, 0.300, 0.346]}, {[0.113, 0.258, 0.290, 0.346]}, and {[0.113, 0.258, 0.570, 0.346]}, respectively. Comparing these to other art forms, such as {Literature} that shows a consistent high value in {steady increase}<[0.113, 0.147, 0.943, 0.236]> but lower values in other categories, or {Music} that has less growth in {rapid expansion}<[0.113, 0.369, 0.176, 0.457]>, Visual arts maintains a modest yet more consistent level throughout, rather than peaking or dropping significantly in any single phase.\nBased on the above thought, the answer is:\nVisual Arts. It has consistent values across all four phases with no extreme peaks, indicating balanced development. The moderate increases and improvements suggest that Visual Arts is developing in a stable and balanced manner compared to the other art forms." } ], "id_raw": 528 }, { "id": "zgv3GWaDftSQmSTz", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_3.png", "conversations": [ { "from": "human", "value": "\nHow much higher is Literature's value compared to Cinema under the \"steady increase\" phase?" }, { "from": "gpt", "value": "By examining the lengths of the bars in the steady increase phase, I can see that Literature {bar coordinates for Literature}<[0.113, 0.147, 0.943, 0.236]> has a length that corresponds to a value of 400, while Cinema {bar coordinates for Cinema}<[0.113, 0.036, 0.653, 0.125]> measures a value of 260. The calculation to find the height difference simply involves subtracting Cinema's value from Literature's value.\nBased on the above thought, the answer is:\n140" } ], "id_raw": 528 }, { "id": "35jhVv8Z7Q0ACNjv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data of the \"Unstable Behavior\" and \"Slow Recovery\" indices in the chart and present them in a CSV table format?" }, { "from": "gpt", "value": "The data given is based on the migration trends of four countries: {USA}[0.111, 0.409], {Canada}[0.111, 0.300], {Germany}[0.111, 0.191], and {France}[0.111, 0.083]. According to the color legend, the green color represents {Unstable Behavior}[0.291, -0.083, 0.326, -0.071] and blue represents {Slow Recovery}[0.534, -0.083, 0.569, -0.071]. I can gather this data from two separate parts of the chart: The first subplot with coordinates {subplot_0}[0.111, 0.019, 0.963, 0.473] shows the migration instances under the \"Unstable Behavior\" index, whereas the second subplot with coordinates {subplot_1}[0.111, 0.498, 0.963, 0.952] details the instances under the \"Slow Recovery\" index. \n\nGiven the length of horizontal bars representing the respective indices in the two subplots, I am deducting following data: In subplot_1, for {USA}[0.111, 0.365, 0.781, 0.452], the x_range ends at 2370. Similarly, in subplot_2 for {USA}[0.111, 0.844, 0.922, 0.931], the x_range ends at 2870, and so forth from other bars. By plotting these details in a CSV format, it gives us the following data in a structured table:\nBased on the above thought, the answer is:\nCountry,Unstable Behavior,Slow Recovery\nUSA,2370,2870\nCanada,55,25\nGermany,598,600\nFrance,515,500" } ], "id_raw": 529 }, { "id": "HHkLnmrsaXYlPzcA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nWhich country is recovering the most in terms of migration trends after the period of unstable behaviour? And why?" }, { "from": "gpt", "value": "The recovery is represented by the index - \"Slow Recovery\". Therefore, to answer this, we need to consider the lengths of the bar under the \"Slow Recovery\" index in the second subplot, as they reflect the number of migrations during the recovery period for each country. For {USA}[0.111, 0.844, 0.922, 0.931], the end-point is 2870. Similarly, for {Canada}[0.111, 0.735, 0.118, 0.822], the endpoint is 25. For {Germany}[0.111, 0.627, 0.280, 0.714], it is 600. And finally, for {France}[0.111, 0.518, 0.252, 0.605], the point is 500. Comparing these, It can be inferred that USA has the highest number of migrations during the recovery period.\nBased on the above thought, the answer is:\nUSA. The USA has the most number of migrations during the 'Slow Recovery' period, indicating a higher recovery rate in migration trends after the period of unstable behavior." } ], "id_raw": 529 }, { "id": "dwZHOJ2AQeEh5VT6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which country had the greatest migration difference between \"Unstable Behaviour\" and \"Slow Recovery\"?" }, { "from": "gpt", "value": "To find the greatest difference, it's crucial to subtract the value of 'Unstable Behavior' from 'Slow Recovery' for each country. For instance, in USA, the end points of the bars under 'Unstable Behavior' and 'Slow Recovery' are [0.781, 0.452] and [0.922, 0.931] respectively which represents a difference of 500. Similarly, calculating difference for other countries: Canada[0.118, 0.344] and Canada[0.126, 0.822] have a difference of -30, Germany[0.280, 0.235] and Germany[0.280, 0.714] have a difference of 2, France[0.256, 0.126] and France[0.252, 0.605] have a difference of -15. The country with the greatest migration difference is USA with 500.\nBased on the above thought, the answer is:\nUSA" } ], "id_raw": 529 }, { "id": "k1zBl3kQEjMEfa2P", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information on the bar chart into a CSV table?" }, { "from": "gpt", "value": "Sure! I see that there is a bar chart labeled {State-wise Health Condition Incidence}[0.372, 0.019, 0.767, 0.041] that describes several health conditions across different U.S. States. The y-axis represents the States and the x-axis represents the number of cases. I can see from the chart that there are different bars for each state, where each bar length represents the amount of cases for a specific health condition. The conditions represented in the chart are {Heart Disease, Cancer, Diabetes, and Injury}, according to the legend at {legend}[0.849, 0.534, 0.973, 0.566]. Looking at {California's bar}[0.157, 0.353, 0.942, 0.430] for example, I can see from the length of the red part which represents {Heart Disease}[0.157, 0.353, 0.817, 0.430], that there were 3450 cases. By extracting this information for all states and conditions, I am able to form the table below.\nBased on the above thought, the answer is:\nStates,Heart Diseases,Cancer,Diabetes,Injury\nCalifornia,3450,400,6000,580\nTexas,2750,500,7000,690\nFlorida,2300,750,5500,530\nNew York,2900,600,6400,630\nIllinois,2300,550,4000,390\nOhio,1950,450,2000,880\nGeorgia,2500,530,5700,560\nNorth Carolina,1750,690,3200,3100" } ], "id_raw": 530 }, { "id": "CfqLkHvXwr4ILEGo", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nFrom what can be seen in the chart, which health condition seems to be most prevalent and why?" }, { "from": "gpt", "value": "To determine which health condition is most prevalent, I would need to look at the length of each colored segment in the bars of the various states as the length of each segment represents the number of cases for each health condition. From the chart, the red segment representing {Heart Disease}[0.904, 0.595, 0.966, 0.612] appears consistently long in all states, thus Heart Disease appears to be the most prevalent health condition.\nBased on the above thought, the answer is:\nHeart Disease. This is because the segment representing Heart Disease in the chart is consistently long across all states, indicating that there are many cases of this health condition." } ], "id_raw": 530 }, { "id": "X6fUikbDi2B6GxFx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_5.png", "conversations": [ { "from": "human", "value": "\nWhich state has the highest Cancer cases?" }, { "from": "gpt", "value": "To determine which state has the highest Cancer cases, I need to look at the length of the colored segment representing Cancer in each state's bar. From the legend, I know that {Cancer is represented by the light yellow color} [0.904, 0.541, 0.966, 0.559]. From a glance, {North Carolina's light yellow segment} [0.157, 0.544, 0.314, 0.621] seems to be the longest among all states meaning North Carolina has the highest Cancer cases.\nBased on the above thought, the answer is:\nNorth Carolina." } ], "id_raw": 530 }, { "id": "zQ7pq2MyuFE1imAl", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nBased on the data you've analyzed from the chart, could you please convert the information into a CSV formatted table?" }, { "from": "gpt", "value": "According to the provided chart, there are two wide bars in each subgraph which indicate fields of Total Spending, Growth Trend, E-commerce Growth and Offline Retail Decline for each pair of emerging markets. There are four main categories in both emerging markets. Beginning with the \"total spending\" category, represented by the wide blue bar at the bottom of the stack. The {bar for Emerging Market 1 is at }[0.157, 0.065, 0.817, 0.142]{ and the bar for Emerging Market 2 is at }[0.157, 0.161, 0.680, 0.238]. Moving to \"Growth Trend\", it is represented by the orange portion of the stacked bar. I can see same for \"E-Commerce Growth\" and \"Offline Retail Decline\". Analysing the length of the bars represents various measurements for the related fields. For instance, the length of the \"total spending\" part measures the overall spending in billions. Once I extract the visual observations into raw data, I can arrange them into a CSV format.\nBased on the above thought, the answer is:\nEmerging Market,Total Spending,Growth Trend,E-Commerce Growth,Offline Retail Decline\nEmerging Market 1,250,20,80,-10\nEmerging Market 2,150,30,50,-20" } ], "id_raw": 531 }, { "id": "fXwiDjmv0WeMpsKo", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nConsidering the offline retail decline, which emerging market has let its traditional retail sector struggle more and why?" }, { "from": "gpt", "value": "Both subcharts show that all markets have a decline in the offline retail sector, symbolized by the red section of each bar. However, we can visually observe that {Emerging Market 1}[0.157, 0.065, 0.817, 0.142] has a less extent of the red part, thus a less decline in offline retail. Conversely, we can see that {Emerging Market 2}[0.157, 0.161, 0.680, 0.238] has a more significant decrease in offline retail, suggested by the more extended red section of the bar.\nBased on the above thought, the answer is:\nEmerging Market 2. The portion of the bar representing offline retail decline is larger for Emerging Market 2, suggesting that traditional retail has struggled more in this market. The possible reasons might be a rapid shift to online shopping or more competitive online market setting, not represented in the chart." } ], "id_raw": 531 }, { "id": "bDzenl381yHLMFMf", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_6.png", "conversations": [ { "from": "human", "value": "\nWhich emerging market demonstrated the highest growth in E-Commerce?" }, { "from": "gpt", "value": "The chart represents that each subchart has its own category of E-commerce growth which is shown as a green color. The E-commerce growth for Emerging Market 1 is {bar at }[0.157, 0.065, 0.817, 0.142] and the E-commerce growth for Emerging Market 2 is represented by a {bar at }[0.157, 0.161, 0.680, 0.238]. Visually, Emerging Market 1 has a longer part devoted to E-commerce growth than Emerging Market 2 showing that it had a higher growth in E-commerce.\nBased on the above thought, the answer is:\nEmerging Market 1" } ], "id_raw": 531 }, { "id": "JsiBhi9FcS5qF6sv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "From the legend at the bottom of the chart, I can see that the colors represent different societal demographic aspects: adult literacy rate, youth unemployment, and tertiary education completion. The data in the chart can be parsed by identifying the genders on the y-axis and then detecting the lengths of the bars for each category. The color will indicate which category the data belongs to. For example, the blue bar segment for 'Female' with a length of {0.094, 0.039, 0.573, 0.223} represents the 'Adult Literacy Rate (%)' for females, indicating a value of 88, and the orange bar segment for the same 'Female' category with a length of {0.573, 0.039, 0.654, 0.223} Represents the 'Youth Unemployment Rate (%)' for females, indicating a value of 15.\nBased on the above thought, the answer is:\nEntity,Male,Female\nAdult Literacy Rate,92,88\nYouth Unemployment,11,15\nTertiary Education Completion,45,50" } ], "id_raw": 533 }, { "id": "I9uZG2GcAsKPoGuk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nAnalyze the gender discrepancy for tertiary education completion rate." }, { "from": "gpt", "value": "To analyze the gender discrepancy for tertiary education completion, I need to focus on the green bars in the chart that represent {Tertiary Education Completion Rate (%)}[0.353, -0.082, 0.696, -0.065] in the {legend}[0.297, -0.141, 0.703, -0.058]. If we compare the lengths of the green bars for both {female}[0.654, 0.518, 0.926, 0.702] and {male}[0.654, 0.748, 0.899, 0.931], it is apparent that the women's bar is longer. This denotes a higher rate of tertiary education completion for women than men.\nBased on the above thought, the answer is:\nThe discrepancy in the tertiary education completion rates between the two genders is that women's rates are higher than men's. This suggests that remarkably more women have been accessing and completing higher education." } ], "id_raw": 533 }, { "id": "bfoFbme3hv13l2LV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_8.png", "conversations": [ { "from": "human", "value": "\nWhich gender has a higher unemployment rate according to the graph?" }, { "from": "gpt", "value": "To find out which gender has a higher unemployment rate, I need to look at the orange color bar of {youth unemployment rate}[0.353, -0.108, 0.620, -0.091] according to the {legend}[0.297, -0.141, 0.703, -0.058]. When comparing the lengths of the orange bars for {male}[0.594, 0.269, 0.654, 0.452] and {female}[0.573, 0.039, 0.654, 0.223], it can be noted that the female category's orange bar is longer, indicating a higher unemployment rate.\nBased on the above thought, the answer is:\nFemale" } ], "id_raw": 533 }, { "id": "IZfsuFCh26H0iGaE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nFrom the observation of the chart representing entrepreneurial activities across countries, can you generate a table showing 'High tech startups' and 'New business registrations' for each country?" }, { "from": "gpt", "value": "Upon inspecting the upper portion of the chart, I can identify a group of horizontal bars positioned at {the first bar group}[0.111, 0.019, 0.981, 0.473] for the countries Japan, Australia, Canada, and Germany as stated by {yticks labels}[0.050, 0.076, 0.099, 0.093] to [0.019, 0.402, 0.099, 0.419]. Following this, I see the corresponding data within their respective bars, for instance, the 'High tech startups' for Japan found at {Japan's startup bar}[0.111, 0.039, 0.115, 0.126] and 'New business registrations' bar extended to {Japan's registration bar}[0.115, 0.039, 0.940, 0.126], indicating the values 30 and 5533 respectively. Continuing this process with the other countries in both chart portions will yield a complete data table.\nBased on the above thought, the answer is:\nCountry,High tech startups,New business registrations\nJapan,30,5533\nAustralia,27,3548\nCanada,40,4565\nGermany,51,5122\nItaly,12,3520\nFrance,15,3817\nUK,23,4126\nUSA,32,3233" } ], "id_raw": 534 }, { "id": "btgo0jrE6zbMYbig", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nConsidering the data provided, which country exhibits a significant disparity between high tech startups and new business registrations?" }, { "from": "gpt", "value": "To assess the disparity between the two measures, I need to examine the gap between the lengths of the two color-coded bars for each country. For instance, {Japan's high tech startups bar}[0.111, 0.039, 0.115, 0.126] is significantly shorter than {Japan's new business registrations bar}[0.115, 0.039, 0.940, 0.126]. The respective x-ranges from the JSON structure suggest that Japan has 30 high tech startups (in 000s) but a much higher value of 5533 new business registrations (in 000s), showing a marked disparity. Similar analysis for other countries reveals that this gap is notably larger for Japan compared to others, indicating a distinctive focus on less technology-intensive sectors or traditional businesses in Japan's entrepreneurial market.\nBased on the above thought, the answer is:\nJapan exhibits a significant disparity between high tech startups and new business registrations, which suggests a strong entrepreneurial activity but with a potential focus on traditional or lower-tech industries." } ], "id_raw": 534 }, { "id": "cUJLGJw3kwJlPEoV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the average number of new business registrations for the countries listed in the lower half of the chart?" }, { "from": "gpt", "value": "To calculate the average number of new business registrations for the lower half of the chart, I need to identify and sum up the number of new business registrations for Italy, France, UK, and USA represented by {blue bars}[0.113, 0.518, 0.638, 0.605] to [0.116, 0.844, 0.595, 0.931] and then divide by the number of countries, which is four. Adding the identified values of new business registrations - Italy (3520), France (3817), UK (4126), and USA (3233) - gives a total of 14696. Dividing 14696 by 4 provides the average.\nBased on the above thought, the answer is:\n3674" } ], "id_raw": 534 }, { "id": "hWbdn1jg3g0B6sIX", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data represented by this bar chart and convert the data into a CSV format table for me?" }, { "from": "gpt", "value": "From what I can make out from the bar chart, the x-axis represents the different categories of social media platforms such as {Facebook}[0.120, 0.731, 0.336, 0.909], {Twitter}[0.120, 0.507, 0.303, 0.686], {WhatsApp}[0.120, 0.284, 0.355, 0.463], and {Instagram}[0.120, 0.061, 0.284, 0.240]. The bar lengths for each category represent the corresponding values of social media active users, digital advertising spending, and email users. The color of each bar is in accordance with the legend given which is {'Social Media Active Users (millions)'}[0.156, -0.083, 0.191, -0.071], {'Digital Advertising Spending (US$ billions)'}[0.156, -0.057, 0.191, -0.044] and {'Email Users (millions)'}[0.607, -0.083, 0.642, -0.071]. These values can be extracted by analyzing the width of each bar. To generate the CSV table, I will consider the platform, the number of active users, the digital advertising spending, and the number of email users.\nBased on the above thought, the answer is:\nplatform,social_media_active_users,digital_advertising_spending,email_users\nFacebook,2300,2800,6700\nTwitter,1950,2400,3280\nWhatsApp,2500,3030,8730\nInstagram,1750,2440,5540" } ], "id_raw": 535 }, { "id": "zvemFSahNP6IFU4D", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nAmong the platforms you observed in the chart, which would you recommend for a company aiming to reach a wide audience through digital advertising and why?" }, { "from": "gpt", "value": "To answer this, I need to evaluate both the digital advertising spending and the active users on each platform because a successful advertising campaign should reach a wider audience and also have a higher advertising investment. By looking at the corresponding bars, I observed that {WhatsApp}[0.355, 0.284, 0.404, 0.463] has the highest digital advertising spending and {Facebook}[0.120, 0.731, 0.336, 0.909] has the highest number of social media active users. Comparatively, {WhatsApp}[0.120, 0.284, 0.355, 0.463] also has a higher number of social media active users and the digital advertising spending is highest. Therefore, I would recommend WhatsApp.\nBased on the above thought, the answer is:\nWhatsApp, as it not only has the highest digital advertising spending but also counts for an impressive number of social media active users." } ], "id_raw": 535 }, { "id": "cAxFbpypMnhtPkkQ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_10.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform has the highest number of email users?" }, { "from": "gpt", "value": "To find out which social media platform has the highest number of email users, I need to focus on the width of the blue bars. Each bar corresponds to a platform and its width indicates the number of email users. By comparing, it can be seen that the bar representing {email users for WhatsApp platform}[0.404, 0.284, 0.940, 0.463] has the maximum width, therefore WhatsApp has the highest number of email users among the four platforms.\nBased on the above thought, the answer is:\nWhatsApp" } ], "id_raw": 535 }, { "id": "B9Nnf8OWlbGbuVYf", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nPlease convert the data from the bar chart into a structured CSV format table." }, { "from": "gpt", "value": "To reproduce the data in a CSV table, we will extrapolate the information provided by each bar of the chart for each country. The countries are denoted by the {y-ticks}[{label: 'USA', 'label_coordinates': '[0.019, 0.144, 0.107, 0.161]', 'tick_coordinates': '[0.120, 0.150]'}; {'label: 'UK', 'label_coordinates': '[0.019, 0.367, 0.107, 0.384]', 'tick_coordinates': '[0.120, 0.374]'};{'label: 'France', 'label_coordinates': '[0.049, 0.590, 0.107, 0.607]', 'tick_coordinates': '[0.120, 0.597]'}; {'label: 'Italy', 'label_coordinates': '[0.026, 0.813, 0.107, 0.831]', 'tick_coordinates': '[0.120, 0.820]'}]. For example we can look at USA, the first country. For every statistic, we measure the width of the corresponding segment in the bar. For hotel occupancy rate, which in the legend reads as the color #ff6347 {label: 'Social Media Active Users (millions)', 'color': '#ff6347'}, we look at the leftmost segment in the bar with the corresponding color. Following the same rule, we can find the values for average daily rate, RevPAR, and tourism revenue and proceed similarly for the other countries.\nBased on the above thought, the answer is:\nCountry,Hotel Occupancy Rate (%),Average Daily Rate ($),RevPAR ($),Tourism Revenue ($bn)\nUSA,68.3,123.6,84.4,1572.2\nUK,78.1,112.8,88.1,221.7\nFrance,75.5,117.8,89.0,256.4\nItaly,72.9,109.5,79.9,205.6\nCanada,66.8,115.3,77.0,90.2\nAustralia,74.1,130.4,96.5,45.3\nGermany,72.3,108.6,78.6,154.6\nJapan,85.0,102.3,87.0,441.4" } ], "id_raw": 536 }, { "id": "fHD0X1Y5rI3HX4pY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nBy looking at the strips for hotel occupancy rate on the chart, which country has the potential to increase its tourism revenue the most by only increasing its hotel occupancy rate?" }, { "from": "gpt", "value": "The receipts from the hotel industry are directly proportional to hotel occupancy rate because a higher occupancy rate means more revenue. To decide which country has the most potential to increase its tourism revenue by increasing its hotel occupancy rate, I should find a country with a relatively low hotel occupancy rate, but a high tourism revenue. The hotel occupancy rates can be seen in the leftmost sections of each country's bar, for example {\"USA\"}[{'bar_coordinates': '[0.120, 0.061, 0.158, 0.240]', 'y_label': 'USA', 'x_range': '0.00 -> 400.00'}], and the tourism revenues can be read from the rightmost sections of each bar, like {\"USA\"}[{'bar_coordinates': '[0.395, 0.061, 0.462, 0.240]', 'text_coordinates': None, 'text_params': None, 'y_label': 'USA', 'x_range': '2750.00 -> 3100.00'}]. After comparing, I find that the country that fits the criteria best, a large room for improvement in hotel occupancy rate, and already a high tourism revenue is USA.\nBased on the above thought, the answer is:\nUSA" } ], "id_raw": 536 }, { "id": "A8jF2Nl4tyL1xLSq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which country had the highest tourism revenue?" }, { "from": "gpt", "value": "Reading from the chart, tourism revenue for each country is represented by the rightmost section of each bar. To find the maximum, I will have to compare the values represented by the lengths of these sections. Considering each y-tick label as a country, we start from {\"USA\"}[{'label': 'USA', 'label_coordinates': '[0.019, 0.144, 0.107, 0.161]', 'tick_coordinates': '[0.120, 0.150]'}], and do the same for every country up to {\"Japan\"}[{'label': 'Japan', 'label_coordinates': '[0.749, 0.813, 0.887, 0.831]', 'tick_coordinates': '[0.831, 0.820]'}]. We can note that the bar associated with the tick labeled \u2018USA', {USA}[{\"bar_coordinates\": \"[0.120, 0.061, 0.462, 0.240]\", 'y_label': 'USA', 'x_range': '0.00 - 3100.00'}], extends the furthest out on the right, denoting the largest tourism revenue.\nBased on the above thought, the answer is:\nUSA" } ], "id_raw": 536 }, { "id": "Z9Elldlu3eECtCwh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table denoting the annual growth rate projections for VR and AR technologies as observed from the top subplot?" }, { "from": "gpt", "value": "To analyze the annual growth rate projections for VR and AR technologies, I will look at {the top subplot}[0.075, 0.019, 0.981, 0.473]. The years are listed on {the y-axis}, ranging from {2022}[0.019, 0.067, 0.063, 0.084] to {2026}[0.019, 0.411, 0.063, 0.428]. The VR bars' coordinates are as follows: for {2022}[0.075, 0.039, 0.181, 0.108], {2023}[0.075, 0.125, 0.233, 0.194], {2024}[0.075, 0.211, 0.328, 0.280], {2025}[0.075, 0.297, 0.457, 0.366], and {2026}[0.075, 0.383, 0.516, 0.452]. Similarly, the AR bars' coordinates are listed as {2022}[0.181, 0.039, 0.339, 0.108], {2023}[0.233, 0.125, 0.427, 0.194], {2024}[0.328, 0.211, 0.604, 0.280], {2025}[0.457, 0.297, 0.806, 0.366], {2026}[0.516, 0.383, 0.938, 0.452]. Using the data provided, I first calculate the yearly values for each technology and then compute the growth rates comparing the consecutive years.\nBased on the above thought, the answer is:\nYear,Growth Rate VR (%),Growth Rate AR (%)\n2022, , \n2023,13.79,23.26\n2024,30.30,18.60\n2025,60.47,21.54\n2026,50.72,26.67" } ], "id_raw": 537 }, { "id": "JA2xTyqep9Lzha9c", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nObserving the trends in the chart, which technology, VR or AR, shows a more aggressive growth pattern over the years?" }, { "from": "gpt", "value": "Analyzing the trends, I focus on the length of {VR bars}[0.075, 0.039, 0.181, 0.108]; [0.075, 0.125, 0.233, 0.194]; [0.075, 0.211, 0.328, 0.280]; [0.075, 0.297, 0.457, 0.366]; [0.075, 0.383, 0.516, 0.452] and {AR bars}[0.181, 0.039, 0.339, 0.108]; [0.233, 0.125, 0.427, 0.194]; [0.328, 0.211, 0.604, 0.280]; [0.457, 0.297, 0.806, 0.366]; [0.516, 0.383, 0.938, 0.452] for each year in the provided data. AR technology starts at a higher value in 2022, and the length of AR bars progressively outgrows VR bars each subsequent year, indicating a more steeply sloped growth curve. This is particularly evident in {2025}[0.457, 0.297, 0.806, 0.366]; [0.806, 0.297, 1.078, 0.366] and {2026}[0.516, 0.383, 0.938, 0.452]; [0.938, 0.383, 1.240, 0.452] where AR growth clearly surpasses VR growth despite a significant increase in VR growth during these years. The distances covered by the bars on the x-axis correspond to their growth magnitude, with AR covering more ground than VR by 2026.\nBased on the above thought, the answer is:\nAugmented Reality (AR) shows a more aggressive growth pattern over the years. This is determined by the lengthier bars representing AR in the chart and its consistently larger increases, especially notable in the final years." } ], "id_raw": 537 }, { "id": "jnFjyODKJT8wc0Xw", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage increase in the AR trend from 2022 to 2023 as depicted on the chart?" }, { "from": "gpt", "value": "To determine the percentage increase in the AR trend from 2022 to 2023, I need to look at the AR bars within {the top subplot}[0.075, 0.019, 0.981, 0.473]. The {2022 AR bar}[0.181, 0.039, 0.339, 0.108] shows the value at the start and the {2023 AR bar}[0.233, 0.125, 0.427, 0.194] shows the value at the end. Using the raw data, the value for 2022 is 430 and for 2023 is 530. Therefore, the percentage increase is calculated as ((530 - 430) / 430) * 100.\nBased on the above thought, the answer is:\n23.26%" } ], "id_raw": 537 }, { "id": "RvzFLV2CuA99BB0y", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nCould you provide a structured CSV format table for the socioeconomic factors of the four countries based on the observed bar chart?" }, { "from": "gpt", "value": "Observing the two subplots, I see that the y-axis lists the countries {Brazil and China}[0.075, 0.124, 0.124, 0.142] in the first subplot and {India and South Africa}[0.082, 0.603, 0.124, 0.621] in the second subplot. The bars are grouped by color to represent each socioeconomic factor, as indicated {in the legend}[0.171, -0.091, 0.829, -0.034], such as {Population Growth}[0.137, 0.039, 0.257, 0.223], {GDP per Capita}[0.257, 0.039, 0.262, 0.223], {Unemployment Rate}[0.262, 0.039, 0.269, 0.223], and {Healthcare Expenditure}[0.269, 0.039, 0.271, 0.223]. To form a CSV, I\u2019ll arrange the data by socioeconomic factor and country in a tabular form.\nBased on the above thought, the answer is:\nCountry,Population Growth (M),GDP per Capita (k USD),Unemployment Rate (%),Healthcare Expenditure (% of GDP)\nBrazil,211,8.7,12.8,3.8\nChina,1392,10.1,3.8,3.1\nIndia,1366,2.1,5.5,1.3\nSouth Africa,59,6.4,28.7,8.1" } ], "id_raw": 538 }, { "id": "dx9IAnsmzpDhcDXa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nObserving the chart, which country shows a balance between economic growth and public healthcare expenditure?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the balance between the GDP per capita and healthcare public expenditure for each country. I can see that Brazil has {a GDP per capita bar}[0.257, 0.039, 0.262, 0.223] and {a healthcare expenditure bar}[0.269, 0.039, 0.271, 0.223], China shows {a GDP per capita bar}[0.931, 0.269, 0.937, 0.452] and {a healthcare bar}[0.939, 0.269, 0.941, 0.452], while India and South Africa present their respective data in the second subplot with {GDP per capita and healthcare bars for India}[0.916, 0.518, 0.918, 0.702] [0.921, 0.518, 0.922, 0.702] and {for South Africa}[0.170, 0.748, 0.174, 0.931] [0.190, 0.748, 0.195, 0.931]. Considering the provided data, Brazil and South Africa have higher healthcare expenditures in proportion to their GDP, while China shows higher economic growth. India also indicates lower public healthcare expenditure relative to its GDP.\nBased on the above thought, the answer is:\nChina exhibits a balance between economic growth and public healthcare expenditure. Its GDP per capita is the highest, suggesting strong economic growth, yet its public healthcare expenditure as a percentage of GDP is moderate, not indicating a significant imbalance." } ], "id_raw": 538 }, { "id": "ZqCGXJde7l1FKQ4T", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total of GDP per capita in thousands of USD for all four countries combined, as per the bar chart?" }, { "from": "gpt", "value": "Each country's GDP per capita is visually represented by a shade of blue bar. To calculate the total GDP per capita, I need to sum the values of the bars representing GDP per capita for {Brazil}[0.257, 0.039, 0.262, 0.223], {China}[0.931, 0.269, 0.937, 0.452], {India}[0.916, 0.518, 0.918, 0.702], and {South Africa}[0.170, 0.748, 0.174, 0.931]. According to the data provided, Brazil's GDP per capita is 8.7, China's is 10.1, India's is 2.1, and South Africa's is 6.4.\nBased on the above thought, the answer is:\n27.3" } ], "id_raw": 538 }, { "id": "tKhTDo2J5bkKpdmM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please extract the data into a CSV format table." }, { "from": "gpt", "value": "The chart consists of two group of countries: Germany, Canada, Australia, and Japan, at {the top subplot}[0.111, 0.498, 0.981, 0.952]; USA, China, France, and Italy, at {the bottom subplot}[0.111, 0.019, 0.981, 0.473]. For each group, there are two types of bars: AI Research Funding (in billions USD) and Number of AI Companies, as indicated by {the legend}[0.159, -0.091, 0.841, -0.060]. I can start extracting the values by using the position of the bars according to the x-axis and the associated labels on the y-axis. For instance, {the pink bar for 'Germany'}[0.111, 0.365, 0.111, 0.452] in {top subplot}[0.111, 0.498, 0.981, 0.952] is for the 'AI Research Funding', and the length of the bar indicates that it is 5 billion USD. Similarly, {the blue bar for 'Germany'}[0.111, 0.365, 0.307, 0.452] indicates that the number of AI companies in Germany is 4250. By using the same approach, I can extract the other values from both subplots.\nBased on the above thought, the answer is:\nType,Germany,Canada,Australia,Japan,USA,China,France,Italy\nAI Research Funding in billions USD,5,3.7,4,4.8,20,15,12,10\nNumber of AI Companies,4250,3000,3400,3800,18000,16000,9000,5000" } ], "id_raw": 539 }, { "id": "pCJ0K5RpeknwdwZG", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, how can we observe the growth of technological progress in AI Application Cases across countries?" }, { "from": "gpt", "value": "The progress can be analyzed via the increment in 'AI Research Funding' and 'Number of AI Companies' for each country. The pink and blue bars represented by {the legend}[0.159, -0.091, 0.841, -0.060] respectively show 'AI Research Funding (in billions USD)' and 'Number of AI Companies' for each country. For instance, {USA}[0.063, 0.881, 0.099, 0.898] in {the bottom subplot}[0.111, 0.019, 0.981, 0.473] showing prominent growth in 'AI Research Funding' and 'Number of AI Companies' indicates its rapid technological progress. The chart reveals an overall gradual increase in both research funding and the number of AI companies, displaying the growth of technology in AI application cases in almost all countries.\nBased on the above thought, the answer is:\nThe growth of technological progress can be observed through increasing AI Research Funding and the Number of AI Companies. In general, there is a noticeable growth rate for most countries. However, the USA exhibits the fastest growth, showing the most significant technological progress across the analyzed countries." } ], "id_raw": 539 }, { "id": "WIiwzDtHCFRQHc4W", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_36_14.png", "conversations": [ { "from": "human", "value": "\nWhich country has the largest number of AI companies?" }, { "from": "gpt", "value": "The largest number of AI companies can be found by comparing the lengths of {the blue bars}['#00bfff'] in the chart, as indicated by {the legend}[0.159, -0.091, 0.841, -0.060]. The {USA's blue bar}[0.112, 0.844, 0.940, 0.931] in {the bottom subplot}[0.111, 0.019, 0.981, 0.473] stands out among all the bars, as it extends much further along the x-axis than any other bars for the number of AI companies. Thus, it represents the highest number of AI companies, which according to it is 18000.\nBased on the above thought, the answer is:\nUSA" } ], "id_raw": 539 }, { "id": "OEpAHns5zYuPR0Cd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nPlease extract the data from this Risk Assessment and Management bar chart and compile it into a structured CSV format table." }, { "from": "gpt", "value": "Looking at the chart, the Risk Factors are positioned along the y-axis, with each {Risk Factor}[for example, \"Cyber-security\"[0.068, 0.806, 0.169, 0.834]] accompanied by a corresponding bar extending positively or negatively along the x-axis, with its length or position indicating the relative Risk Score. I can infer the value of each Risk Score by noting the position of the end of each bar relative to the x-axis with ticks ranging from {\u221260}[0.164, 0.903, 0.193, 0.931] to {100}[0.959, 0.903, 0.985, 0.931]. For example, the {Security Breach risk factor}[0.059, 0.731, 0.169, 0.759] has a {bar}[0.476, 0.712, 0.699, 0.772] extending to the 45 position indicating a risk score of 45 while {Cyber-security}[0.068, 0.806, 0.169, 0.834] has a {bar}[0.377, 0.787, 0.476, 0.847] extending negatively along the x-axis to the -20 position, signifying a Risk Score of -20. Diligently following this process, I can extract the Risk Score for each Risk Factor as shown below.\nBased on the above thought, the answer is:\nRisk_Factor, Risk_Score\nCyber-security, -20\nSecurity Breach, 45\nFraud Risk, 30\nOperational Risk, -10\nMarket Volatility, 35\nRegulatory Risk, -50\nEnvironmental Risk, 70\nReputation Risk, -40\nGeo-Political Risk, 90\nFinancial Risk, -30" } ], "id_raw": 540 }, { "id": "GL01GtL1W32eRR1x", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nBased on the data represented in the chart, could you share insights about the overall risk trends within the business entity?" }, { "from": "gpt", "value": "The bar chart showcases the change in risk severity across various risk factors within a business entity. The risk factors with negative Risk Score values, such as {Cyber-security}[0.068, 0.806, 0.169, 0.834] and {Operational Risk}[0.054, 0.581, 0.169, 0.609] show bars extending to the left of the axis, indicating a decrease in risk severity relative to the previous period. Similarly, risk factors with positive scores like {Fraud Risk}[0.097, 0.656, 0.169, 0.684] and {Market Volatility}[0.056, 0.506, 0.169, 0.534] exhibit bars extending to the right, signaling an increase in risk severity. Such variability suggests that while certain risk categories have seen improvements over time, others have increased in severity. The broad range of positive and negative changes could indicate a multi-faceted risk profile and diversified risk management strategies within the organization. Risks such as the {Geo-Political Risk}[0.049, 0.205, 0.169, 0.233] present the most significant recent increase, signaling a potential area where the organization must direct its risk management resources.\nBased on the above thought, the answer is:\nBased on the depicted risk severity changes, it appears that the business entity has mixed results in managing different risk factors. While the severity of some risks, like Geo-political Risk and Environmental Risk, has increased, some others like Cyber-security and Regulatory Risk have seen a reduction. Thus, it may be beneficial for the entity to focus on monitoring and managing the increasing risks while maintaining the improvements seen in the other areas." } ], "id_raw": 540 }, { "id": "SezOz4bpgbCHWewr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the given bar chart, which risk factor has the greatest increase in risk?" }, { "from": "gpt", "value": "To find the risk factor with the greatest increase in risk, it is necessary to locate the bar that extends the farthest to the right, along the x-axis from the neutral {zero}[0.472, 0.903, 0.480, 0.931] point. The bar that extends the furthest would correspond to the risk factor that has seen the greatest increase in risk. Directing my attention to the right half of the x-axis, I find that the bar representing {Geo-Political Risk}[0.476, 0.186, 0.922, 0.246] extends the furthest to the right, reaching the {\"90\"}[0.765, 0.903, 0.782, 0.931]x-coordinate.\nBased on the above thought, the answer is:\nGeo-Political Risk" } ], "id_raw": 540 }, { "id": "bdNAXKU96GC9mCs7", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nCould you translate the data from the chart into a structured table?" }, { "from": "gpt", "value": "The chart is a bar chart with the title {\"Artificial Intelligence and Automation Trends\"}[0.432, 0.030, 0.800, 0.066] at the top, which suggests that the data presented involves trends in various technology areas. The y-axis label is {\"Technology\"}[0.015, 0.401, 0.029, 0.557] and the x-axis label is {\"Trends in percentage (%)\"}[0.529, 0.942, 0.703, 0.970], indicating a relationship between different technologies and their corresponding trends. The y-axis tick labels consists of 12 categories, including {\"Artificial Intelligence\"}[0.095, 0.811, 0.238, 0.839], {\"Robotics\"}[0.179, 0.748, 0.238, 0.776], {\"Machine Learning\"}[0.115, 0.686, 0.238, 0.714], etc., each relating to a different technology domain. The bars extend from an origin at 0%, indicated by {the position 0 on the x-axis tick labels} [0.493, 0.903, 0.510, 0.931], extending either left (negative trend) or right (positive trend). For example, for \"Artificial Intelligence\", {the bar}[0.493, 0.797, 0.739, 0.847] extends to the right up to the 50% position on the x-axis. This suggests that the trend for \"Artificial Intelligence\" is a positive 50%. Conversely, for \"Machine Learning\", {the bar}[0.346, 0.672, 0.493, 0.722] extends to the left down to the -30% position on the x-axis, which suggests a negative trend of -30%. This process is used to read and analyze each bar, leading to the construction of the following table.\nBased on the above thought, the answer is:\nTechnology,Trend(%)\nArtificial Intelligence,50\nRobotics,80\nMachine Learning,-30\nCloud Computing,20\nNatural Language Processing,15\nAutomation,-40\nSelf Driving Cars,70\nBiometrics,90\nNeural Networks,-15\nData Analytics,33\nIntelligent Apps,23\nBlockchain,45" } ], "id_raw": 541 }, { "id": "deqVQyWeLaqXS7kY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nBased on the presented data, can you provide an assessment regarding future investment in these technology areas?" }, { "from": "gpt", "value": "To provide an investment assessment, each technology trend needs to be considered. Starting with {\"Artificial Intelligence\"}[0.095, 0.811, 0.238, 0.839], {the bar}[0.493, 0.797, 0.739, 0.847] shows a positive trend of 50%. This suggests a growing interest and investment potential in the sector, offering potentially good returns. The next technologically significant area is {\"Robotics\"}[0.179, 0.748, 0.238, 0.776], and its {bar}[0.493, 0.735, 0.887, 0.784] shows a positive trend of 80%. This suggests a high growth rate, often indicating a good investment opportunity. For trends that are negative, such as {\"Automation\"}[0.157, 0.499, 0.238, 0.527], {the bar}[0.296, 0.485, 0.493, 0.535] shows a negative trend, indicating that this may not be a prime candidate for investment. Considering all the technology bars, key areas for potential investment would likely be those with a significantly positive trend, while areas exhibiting negative trends may pose more risk.\nBased on the above thought, the answer is:\nThe chart suggests that investing in Robotics, Biometrics, and Self Driving Cars could yield good returns due to their strong positive trends of 80%, 90%, and 70% respectively. Conversely, Automation and Neural Networks are sectors exhibiting negative trends and hence may not be the best choice for investment at this time." } ], "id_raw": 541 }, { "id": "Uay33Y8M4x6byxIp", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest positive trend recorded in technology?" }, { "from": "gpt", "value": "To find the highest positive trend, I need to identify the bar that extends furthest to the right from the origin {0%}[0.493, 0.903, 0.510, 0.931]. The bar that extends the furthest to the right is the one representing {Biometrics}[0.493, 0.361, 0.936, 0.411]. This bar ends at the 90% position on the x-axis, which is the highest positive percentage exhibited amongst all the technologies.\nBased on the above thought, the answer is:\n90%" } ], "id_raw": 541 }, { "id": "gE3dTRfmXMNzcIQ7", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, can you extract the growth trend data of various technologies and convert it into a CSV format table?" }, { "from": "gpt", "value": "From the y-axis at the left of the chart {Technologies}[0.015, 0.390, 0.029, 0.568] and [0.213, 0.075, 0.985, 0.883], I can see that the y-axis represents different technologies. The x-axis tick labels represent the corresponding growth trends, ranging from {-50}[0.244, 0.903, 0.273, 0.931] to {100}[0.940, 0.787, 0.970, 0.847]. Each technology is represented by a single bar. The length of the bar and its position with respect to the center at {0}[0.485, 0.883] represent the value of the growth trend. If the bar is to the right of the center, it indicates a positive trend, and if it's to the left of the center, it indicates a negative trend. To generate the CSV table, I need to extract the value for each technology. I can use the width of the bars to determine the values. For example, the right end of {the bar for \"Blockchain\"}[0.485, 0.787, 0.940, 0.847] is approximately at 100 position, so the value of \"Blockchain\" is 100. Similarly, the left end of {the bar for \"AI/ML\"}[0.258, 0.712, 0.485, 0.772] is approximately at -50 position, so the value of \"AI/ML\" is -50.\nBased on the above thought, the answer is:\nTechnologies,Difference\nBlockchain,100\nAI/ML,-50\nCryptocurrency,75\nPeer-to-Peer Networking,-15\nBig Data,-30\nData Protection,60\nDigital Wallet,30\nCybersecurity,45\nCloud computing,-10\nIoT,20" } ], "id_raw": 542 }, { "id": "iAZMuyjuUM9WQuTR", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what can be inferred about the trends in technologies related to financial technology and digital currency over the last decade?" }, { "from": "gpt", "value": "The chart provides a comprehensive view of growth trends in different technologies. First of all, with a length to the extreme right {bar for Blockchain}[0.485, 0.787, 0.940, 0.847], we can see that Blockchain has seen a significant growth by 100%, indicating that there has been a massive increase in the interest in Blockchain technology over the decade. AI/ML, on the other hand, {shown by its bar}[0.258, 0.712, 0.485, 0.772] on the left of zero on the x-axis, has seen a decrease in interest by 50%. Technologies with bars on the left of zero such as Peer-to-Peer Networking, Big Data and Cloud computing [0.417, 0.562, 0.485, 0.622]; [0.349, 0.487, 0.485, 0.547]; [0.440, 0.186, 0.485, 0.246] also experienced a decline in growth. Those technologies with bars on the right of zero such as Data Protection, Digital Wallet, Cybersecurity, and IoT [0.485, 0.412, 0.758, 0.472]; [0.485, 0.337, 0.622, 0.397]; [0.485, 0.261, 0.690, 0.322]; [0.485, 0.111, 0.576, 0.171] have shown positive growth. This trend indicates that the focus in financial technology and digital currency has shifted significantly over the last decade.\nBased on the above thought, the answer is:\nThese trends indicate a shift in focus in the field of financial technology and digital currency, with a significant increase in interest in technologies such as Blockchain, Data Protection, Digital Wallet, Cybersecurity, and IoT. Meanwhile, there has been a decrease in interest in some technologies like AI/ML, Peer-to-Peer Networking, Big Data, and Cloud computing. This shift might be a reflection of changing market needs, technological advancements, and other socio-economic factors." } ], "id_raw": 542 }, { "id": "THvLbSWOwMqIo20K", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_2.png", "conversations": [ { "from": "human", "value": "\nWhich technology has experienced the greatest growth over the past decade?" }, { "from": "gpt", "value": "To find the technology with the greatest growth, I need to compare the length of each bar on the positive side of the x-axis. From the top, the {length of the bar for the first technology - \"Blockchain\"}[0.485, 0.787, 0.940, 0.847] corresponds to a growth of 100%. Looking down the chart at the {bars for the other technologies}[0.485, 0.637, 0.826, 0.697]; [0.485, 0.412, 0.758, 0.472]; [0.485, 0.337, 0.622, 0.397]; [0.485, 0.261, 0.690, 0.322]; [0.485, 0.111, 0.576, 0.171], none of them extend further to the right than the bar for \"Blockchain\". Therefore, \"Blockchain\" has experienced the greatest growth.\nBased on the above thought, the answer is:\nBlockchain." } ], "id_raw": 542 }, { "id": "MVOyzSokUvchG6JC", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nCan you help me to extract the information from this horizontal bar chart and transform it into a readable csv-format table?" }, { "from": "gpt", "value": "The chart contains the categories \u2018Technological Innovation and Intellectual Property\u2019 and the corresponding value, which is the percentage of increase or decrease from {-60}[0.191, 0.919, 0.220, 0.942] to {100}[0.959, 0.919, 0.985, 0.942] indicated in {green bars}[0.493, 0.802, 0.732, 0.865] and {red bars}[0.302, 0.646, 0.493, 0.709]. The {y-axis}[0.015, 0.420, 0.029, 0.545] represents each category and the {x-axis}[0.518, 0.952, 0.660, 0.975] indicates the value of each category. Each value can be extracted by the lengths and direction of the bars. For example, {\"Technologies\"}[0.107, 0.824, 0.196, 0.848] with a {bar}[0.493, 0.802, 0.732, 0.865] indicates a positive change of 50%. The values are then matched to the respective categories and tabulated.\nBased on the above thought, the answer is:\nCategory,change(%)\nTechnologies,50\nPatents,100\nCopyrights,-40\nTrademarks,30\nTrade Secrets,-60\nLicensing Agreements,80\nRoyalties,-20\nIndustrial Design,10\nResearch Development,60\nInfringement Cases,10" } ], "id_raw": 543 }, { "id": "e7ZCh6NEJgehKBK7", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which field of innovation and intellectual property seems to be losing importance? Can you explain why?" }, { "from": "gpt", "value": "The loss in importance is represented by bars with a negative value pointing towards -60 on the {x-axis}[0.518, 0.952, 0.660, 0.975]. From the chart, I can tell that the category {Trade Secrets}[0.102, 0.512, 0.196, 0.536] has the {longest bar}[0.206, 0.490, 0.493, 0.553] going towards the left, representing an importance decrease of -60%. This is considerably larger than that of the 'Copyrights' and 'Royalties' categories, which each have a bar length only half as long. The decrease in the relative importance of Trade Secrets may be attributed to companies relying more on patents and licensing agreements (as indicated by their positive growth) to protect their innovative technologies and intellectual property in recent years.\nBased on the above thought, the answer is:\nThe category 'Trade Secrets' seems to be losing importance, which may be due to the shift in strategy by companies to protect their technologies and intellectual property using patents and licensing agreements instead." } ], "id_raw": 543 }, { "id": "bhoGSgVgvTiwRQ7l", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_3.png", "conversations": [ { "from": "human", "value": "\nWhich category in the chart has the highest positive change value?" }, { "from": "gpt", "value": "The positive change values in each category are indicated by the length of the {green bars}[0.493, 0.802, 0.732, 0.865] in the direction towards 100 on the {x-axis}[0.518, 0.952, 0.660, 0.975]. By scanning each category starting from the top, we find that the {category 'Patents'}[0.144, 0.746, 0.196, 0.770] with the {bar}[0.493, 0.724, 0.972, 0.787] has the most extended length towards {100}[0.959, 0.919, 0.985, 0.942] on the x-axis. Hence, the category 'Patents' has the highest positive chang value.\nBased on the above thought, the answer is:\nPatents" } ], "id_raw": 543 }, { "id": "vNYYh1UEz60rnWvE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nCould you please identify the aspects of social network presence and interpersonal relationships from the bar chart and list the corresponding percentage changes in ascending order?" }, { "from": "gpt", "value": "To answer this question, I will identify all the y_axis labels, which represent different aspects of social network presence and interpersonal relationships, as well as the range on the x_axis, which indicates the percentage changes. I will list all aspect and the associated percentage changes in ascending order based on the bars' {start and end positions}{\"bars_of_group_0\"}[{\"bar_coordinates\":\"[0.488, 0.797, 0.681, 0.847]\"}, {\"bar_coordinates\":\"[0.391, 0.735, 0.488, 0.784]\"}, {\"bar_coordinates\":\"[0.488, 0.672, 0.778, 0.722]\"}, {\"bar_coordinates\":\"[0.488, 0.610, 0.875, 0.660]\"}, {\"bar_coordinates\":\"[0.439, 0.548, 0.488, 0.597]\"}, {\"bar_coordinates\":\"[0.488, 0.485, 0.633, 0.535]\"}, {\"bar_coordinates\":\"[0.294, 0.423, 0.488, 0.473]\"}, {\"bar_coordinates\":\"[0.488, 0.361, 0.585, 0.411]\"}, {\"bar_coordinates\":\"[0.488, 0.236, 0.730, 0.286]\"}, {\"bar_coordinates\":\"[0.488, 0.174, 0.827, 0.224]\"}, {\"bar_coordinates\":\"[0.391, 0.111, 0.488, 0.161]\"}].\nBased on the above thought, the answer is:\nAspects,Changes(%)\nNumber of Blocks,-100\nShares per post,-50\nNumber of Friends,-50\nNumber of Shares,-25\nMentions per Post,0\nSocial Network,1\nPosts per Day,50\nNumber of Unfollows,75\nLikes per Post,125\nNumber of Followers,100\nNumber of Likes,150\nComments per Post,175\nNumber of Comments,200" } ], "id_raw": 544 }, { "id": "bwA942CmaOfGbuRk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the data in the chart, could you analyze and evaluate the effectiveness of this individual's online interactions and effectiveness in maintaining his/her Social Network?" }, { "from": "gpt", "value": "Analyzing the data from various aspects, it seems that the number of likes({bar}[0.488, 0.672, 0.778, 0.722]) and the number of comments({bar}[0.488, 0.610, 0.875, 0.660]) per post have the most significant increases, it can infer that the individual is effectively engaging their audience. Furthermore, the number of followers({bar}[0.488, 0.797, 0.681, 0.847]) also experienced a substantial increase, indicating the individual's ability to attract new followers. However, the individual's number of friends({bar}[0.391, 0.735, 0.488, 0.784]) has sharply dropped, which might suggest some potential issues in maintaining relationships. The situation gets worse as the number of shares per post({bar}[0.391, 0.111, 0.488, 0.161]) and the friendship({bar}[0.391, 0.735, 0.488, 0.784]) both went negative, which means the individual is possibly experiencing a decrease in popularity or likability. This dichotomy between gaining popularity and maintaining friendships might be something this individual should pay attention to.\nBased on the above thought, the answer is:\nI'd say the individual is effective in engaging with his/her online audience as evidenced by the increase in likes and comments per post. However, they seem to have difficulty in maintaining personal relationships as indicated by the decline in the number of friends and shares per post." } ], "id_raw": 544 }, { "id": "BciwqiDRcD1rMhUD", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the average percentage change of the aspects given in the chart?" }, { "from": "gpt", "value": "To calculate the average percentage change, I have to sum up all the percentage changes and then divide by the number of aspects. As given in {the bars}{\"bars_of_group_0\"}[{\"bar_coordinates\":\"[0.488, 0.797, 0.681, 0.847]\"}, {\"bar_coordinates\":\"[0.391, 0.735, 0.488, 0.784]\"}, {\"bar_coordinates\":\"[0.488, 0.672, 0.778, 0.722]\"}, {\"bar_coordinates\":\"[0.488, 0.610, 0.875, 0.660]\"}, {\"bar_coordinates\":\"[0.439, 0.548, 0.488, 0.597]\"}, {\"bar_coordinates\":\"[0.488, 0.485, 0.633, 0.535]\"}, {\"bar_coordinates\":\"[0.294, 0.423, 0.488, 0.473]\"}, {\"bar_coordinates\":\"[0.488, 0.361, 0.585, 0.411]\"}, {\"bar_coordinates\":\"[0.488, 0.236, 0.730, 0.286]\"}, {\"bar_coordinates\":\"[0.488, 0.174, 0.827, 0.224]\"}, {\"bar_coordinates\":\"[0.391, 0.111, 0.488, 0.161]\"}], percentage changes are -100, -50, -50, -25, 0, 1, 50, 75, 125, 100, 150, 175, and 200 respectively. The total number of aspects is 13. Sum of changes(/100) is -100 + -50 + -50 + -25 + 0 + 1 + 50 + 75 + 125 + 100 + 150 + 175 + 200 which equals to 651. The average can be calculated as the sum of changes(/100) divided by the total number of aspects which is 651 / 13.\nBased on the above thought, the answer is:\n50.07692307692308" } ], "id_raw": 544 }, { "id": "0VdYP4zYPSOhvgzU", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nIn regards to the bar chart that you analyzed, can you perceive data in form of a table with 'Cultural Items' as rows and 'Percent Change' as columns?" }, { "from": "gpt", "value": "From my observation, I can comprehend that the title of the chart {\"Percent Change in Popularity of Cultural Items Post Integration\"}[0.339, 0.030, 0.860, 0.066]. The y-axis of the chart represents 'Cultural Items' which include categories like 'Ethnic Cuisine', 'Religious Practices', 'Folk Dances', 'Traditional Clothing', and so on. The x-axis of the chart specifies the 'Percent Change' or the rate of acceptance or decline of these 'Cultural Items'. For example, the cultural item {Ethnic Cuisine}[0.227, 0.817] shows a {positive change of 30%}[0.387, 0.787, 0.546, 0.847] while the {Religious Practices}[0.227, 0.742] shows a {negative change or decline of 10%}[0.333, 0.712, 0.387, 0.772]. By following the same logic for the remaining bars in the chart, I can transcribe the entire chart into a table.\nBased on the above thought, the answer is:\nCultural Items,Percent Change\nEthnic Cuisine,30\nReligious Practices,-10\nFolk Dances,20\nTraditional Clothing,35\nLanguage and Literature,60\nMarriage Customs,70\nArt and Craft,-15\nMusic and Poetry,80\nArchitectural Styles,-20\nFestivals and Celebrations,100" } ], "id_raw": 545 }, { "id": "pk3dbxfbVjUBxvH4", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which cultural items are experiencing a decline after the integration event?" }, { "from": "gpt", "value": "By observing each row of the chart, the bars that extend towards the negative side of the x-axis indicate the cultural items that are declining after the integration event. For instance, {the bar for 'Religious Practices'}[0.227, 0.742] extends into the negative side of the X-axis from {0}[0.387, 0.883] to {-10}[0.319, 0.903], demonstrating a decline. According to the same analysis method, the cultural items {'Art and Craft'}[0.227, 0.367] with bar extending to {-15}[0.307, 0.337], and {'Architectural Styles'}[0.227, 0.216] extending to {-20}[0.280, 0.186] also showed a decrease in popularity post-integration.\nBased on the above thought, the answer is:\nThe cultural items experiencing a decline are 'Religious Practices', 'Art and Craft', and 'Architectural Styles'." } ], "id_raw": 545 }, { "id": "z2KevKGN6xvDw9pq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_5.png", "conversations": [ { "from": "human", "value": "\nWhich cultural item has the highest percentage change?" }, { "from": "gpt", "value": "In order to figure out the Cultural Item with the highest percentage change, I need to look for the bar extending the furthest in the positive direction on the x-axis. Upon observation, that turns out to be {the bar for 'Festivals and Celebrations'}[0.387, 0.111, 0.919, 0.171], extending to a position marked at {100}[0.905, 0.903, 0.932, 0.931] on the x-axis. By comparing this length with other bars, we observe that no other bar extends this far on the x-axis consequently indicating the highest percentage change.\nBased on the above thought, the answer is:\nFestivals and Celebrations." } ], "id_raw": 545 }, { "id": "EMcnM5RPUx1JyCvr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nCan you list the design elements and their respective creativity scores in a CSV-formatted table according to the graph?" }, { "from": "gpt", "value": "From {the y-axis tick labels}[{\"Color Theory\"}[0.035, 0.852, 0.124, 0.872]; {\"Typography\"}[0.042, 0.808, 0.124, 0.828]; {\"Whitespace\"}[0.042, 0.764, 0.124, 0.784]; {\"Balance\"}[0.068, 0.720, 0.124, 0.740]; {\"Contrast\"}[0.064, 0.676, 0.124, 0.696]; {\"Lines\"}[0.088, 0.632, 0.124, 0.652]; {\"Texture\"}[0.073, 0.587, 0.124, 0.607]; {\"Value\"}[0.085, 0.543, 0.124, 0.563]; {\"Shape\"}[0.080, 0.499, 0.124, 0.519]; {\"Size\"}[0.095, 0.455, 0.124, 0.475]; {\"Unity\"}[0.087, 0.411, 0.124, 0.431]; {\"Variety\"}[0.075, 0.367, 0.124, 0.387]; {\"Proportion\"}[0.052, 0.323, 0.124, 0.343]; {\"Alignment\"}[0.052, 0.279, 0.124, 0.299]; {\"Hierarchy\"}[0.056, 0.235, 0.124, 0.255]; {\"Emphasis\"}[0.057, 0.191, 0.124, 0.211]; {\"Repetition\"}[0.053, 0.146, 0.124, 0.166]; {\"Proximity\"}[0.059, 0.102, 0.124, 0.122]], we can extract all the design elements. For the creativity scores, we can refer to the x-axis and the length of each horizontal bar, for example, the bar for {\"Color Theory\"}[0.447, 0.842, 0.671, 0.878] stretches from 0 to 50 on the x-axis, so the creativity score for \"Color Theory\" is 50. By performing a similar analysis for each design element, we can build the table.\nBased on the above thought, the answer is:\nDesign Element,Creativity Score\nColor Theory,50\nTypography,35\nWhitespace,-65\nBalance,20\nContrast,-30\nLines,45\nTexture,80\nValue,110\nShape,90\nSize,-40\nUnity,100\nVariety,60\nProportion,-25\nAlignment,80\nHierarchy,90\nEmphasis,-15\nRepetition,40\nProximity,70" } ], "id_raw": 546 }, { "id": "JGvn6Evfj2pZx4uF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nIs there a general trend observed in terms of creativity scores being above or below zero?" }, { "from": "gpt", "value": "To identify the general trend, I need to introspect the sign of the creativity scores of each element. Design elements with positive creativity scores include: {\"Color Theory\"}[0.447, 0.842, 0.671, 0.878], {\"Typography\"}[0.447, 0.798, 0.604, 0.833], {\"Balance\"}[0.447, 0.710, 0.537, 0.745], {\"Lines\"}[0.447, 0.622, 0.649, 0.657], {\"Texture\"}[0.447, 0.578, 0.806, 0.613], {\"Value\"}[0.447, 0.534, 0.940, 0.569], {\"Shape\"}[0.447, 0.489, 0.851, 0.525], {\"Unity\"}[0.447, 0.401, 0.895, 0.437], {\"Variety\"}[0.447, 0.357, 0.716, 0.392], {\"Alignment\"}[0.447, 0.269, 0.806, 0.304], {\"Hierarchy\"}[0.447, 0.225, 0.851, 0.260], {\"Repetition\"}[0.447, 0.137, 0.626, 0.172], {\"Proximity\"}[0.447, 0.093, 0.761, 0.128]. Design elements with negative creativity scores include: {\"Whitespace\"}[0.156, 0.754, 0.447, 0.789], {\"Contrast\"}[0.313, 0.666, 0.447, 0.701], {\"Size\"}[0.268, 0.445, 0.447, 0.481], {\"Proportion\"}[0.335, 0.313, 0.447, 0.348], {\"Emphasis\"}[0.380, 0.181, 0.447, 0.216]. By comparing the quantity of positive and negative scores, it is conspicuous that there are more positive scores than negative.\nBased on the above thought, the answer is:\nYes, the general trend of creativity scores tends towards positive, as the number of design elements with a positive creativity score are more than the amount with a negative score." } ], "id_raw": 546 }, { "id": "mseKzHIKTq0fpxhx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the creativity score of the design elements categorized under 'Alignment'?" }, { "from": "gpt", "value": "Based on the labeling of the y-axis, I find the category 'Alignment' is located at its {coordinates}[0.052, 0.279, 0.124, 0.299]. The horizontal bar representing \u2018Alignment\u2019 starts at zero and stretches until the x value of 80, which is depicted by its {coordinates}[0.447, 0.269, 0.806, 0.304]. Therefore, the creativity score for 'Alignment' is 80.\nBased on the above thought, the answer is:\n80" } ], "id_raw": 546 }, { "id": "sJKWemirFUtwH5Rk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nCould you please translate the data of the chart you have observed into a CSV format?" }, { "from": "gpt", "value": "The horizontal bar chart titled {\"Growth Rate (%) of Various Sectors in the Internet and Communication Industry\"}[0.259, 0.030, 0.921, 0.066] visualizes the growth rate of several sectors in the internet and communication industry. The x-axis represents the growth rate in percentage, and the y-axis represents the various sectors in the industry. The bars correspond to different sectors, such as {E-commerce}[0.347, 0.809, 0.484, 0.847], {Social Media}[0.347, 0.763, 0.514, 0.800], and {Online Banking}[0.347, 0.716, 0.614, 0.754] etc. The length of each bar corresponds to the percentage growth rate of the sector, visible on {the x-axis}[0.533, 0.942, 0.648, 0.970]. For example, the growth rate for E-commerce sector can be read off where the E-commerce's bar end intersects with the x-axis. The CSV table can then be generated using these categories and their corresponding growth rates.\nBased on the above thought, the answer is:\nSector, Growth Rate (%)\nEcommerce, 45\nSocial Media, 55\nOnline Banking, 88\nVideo Streaming, 160\nOnline Gaming, 35\nSearch Engines, 44\nOnline Advertisement, 50\nCloud Services, 100\nArtificial Intelligence, 150\nInternet of Things, 200\nVR/AR, 75\nMobile Apps, 140\nWeb Development, 65\nEmail Services, -10\nE-learning, 30\nVoIP, 40" } ], "id_raw": 547 }, { "id": "V8zgaNMBQqivgy2l", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nBased on the data visualised in this chart, which sectors would you recommend for investment considering their growth rate trends?" }, { "from": "gpt", "value": "To answer this question, it is important to focus on bars that display the highest growth rate percentages. The bar for {'Artificial Intelligence'}[0.347, 0.437, 0.803, 0.474] shows a significant increase with 150% rate, and the bar representing {'Internet of Things'} [0.347, 0.391, 0.955, 0.428] growth is the highest among all sectors, indicating 200% growth. The {'Video Streaming'} [0.347, 0.670, 0.833, 0.707] and {'Mobile Apps'}[0.347, 0.298, 0.772, 0.335] are also relatively long with 160% and 140% growth rates respectively. Such impressive growth rates reflect strong developments and good investment potential in these sectors.\nBased on the above thought, the answer is:\nI would recommend investing in the Artificial Intelligence, Internet of Things, Video Streaming, and Mobile Apps sectors due to their pronounced growth rates as visualised on this chart." } ], "id_raw": 547 }, { "id": "1WHcoEb5qB3gwEGu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_7.png", "conversations": [ { "from": "human", "value": "\nWhich Internet and Communication sector as per the chart has shown the maximum growth rate?" }, { "from": "gpt", "value": "In order to find out the Internet and Communication sector that has shown the maximum growth rate, I need to locate the bar with the highest length on the x-axis. Observing the bars, the longest green light bar is {'Internet of Things'}[0.347, 0.391, 0.955, 0.428]. The x-value at the end of this bar displays the growth rate of this sector on the x-axis {'200'}[0.941, 0.903, 0.968, 0.931].\nBased on the above thought, the answer is:\nInternet of Things" } ], "id_raw": 547 }, { "id": "eadOCEKNLqzVeKYw", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nCan you produce a table from the bar chart providing a list of issues related to social inequality and right protection along with their respective prioritizations?" }, { "from": "gpt", "value": "Observing the {xlabel}[0.539, 0.968, 0.655, 0.983], I understand that it represents the prioritization in percentages and the {ylabel}[0.015, 0.465, 0.029, 0.512] signifies the subjects of social inequality and right protection. Truth be told, the main objective here is to locate the length of each bar and interpret the numeric value attached to it. For instance, using the lower left corner of the bar as a reference point, the bar corresponding to {\u201cDiscrimination\u201d}[0.333, 0.858, 0.416, 0.895] reaches the negative part of the x-axis to -30, meaning the rate of prioritization is -30%. Similarly, I will extract the values for all other bars. Lastly, I need to ensure that the negative rates like \"Discrimination\" are captured correctly.\nBased on the above thought, the answer is:\nSubject,Rate\nDiscrimination,-30\nHarassment,-20\nChild Protection,10\nDisability Rights,15\nEducation Rights,35\nHealthcare Rights,40\nEmployment Rights,50\nGender Equality,60\nEconomic Inequality,70\nPolitical Representation,75\nFree Speech,80\nPrivacy Rights,90\nEnvironmental Protection,100\nHomeland Security,-60\nFair Trade,120\nMinorities Representation,140\nImmigration Rights,160\nRacial Equality,180" } ], "id_raw": 548 }, { "id": "YL4RcWn6okGQ9gXX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nBased on the insights from the chart, what is the state of awareness and action towards social inequality and rights protection?" }, { "from": "gpt", "value": "From the chart, it appears there is a wide range of issues related to social inequality and rights protection, with different levels of prioritization. By inspecting the bars corresponding to each issue, it's evident that many matters are receiving positive attention, with bars reaching the positive range of the prioritization scale. For example, {\u201cRacial Equality\u201d bar}[0.416, 0.082, 0.916, 0.119], which indicates high prioritization. However, there are some issues like {\u201cDiscrimination\u201d}[0.333, 0.858, 0.416, 0.895] and {\u201cHarassment\u201d}[0.361, 0.812, 0.416, 0.849] that fall into the negative range indicating less prioritization. A significant drop is identified in {\u201cHomeland Security\u201d}[0.250, 0.265, 0.416, 0.301] which could be a cause of concern. Thus, while there seems to be a positive trend overall in the awareness and action towards social inequality and rights protection, the chart indicates that more efforts are needed in certain areas.\nBased on the above thought, the answer is:\nThe issues of social inequality and rights protection have diverse levels of awareness and action. While many areas show positive progress, issues like Discrimination, Harassment, and especially Homeland Security require more attention and efforts due to their negative rates." } ], "id_raw": 548 }, { "id": "MKPOJwfN4SZdPLiI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_8.png", "conversations": [ { "from": "human", "value": "\nWhich subject related to social inequality and rights protection is the least prioritized according to the chart?" }, { "from": "gpt", "value": "The object of this task is to identify the bar which has the lowest value on the x-axis. I will inspect each bar from top to bottom. Accordingly, with each comparison, whenever I find a bar lower than the current least, I will replace the current least. In the end, I find the {bar of \u201cHomeland Security\u201d}[0.250, 0.265, 0.416, 0.301] is reaching as far as -60, making it the least prioritized subject among all.\nBased on the above thought, the answer is:\nHomeland Security." } ], "id_raw": 548 }, { "id": "THRn0eJE2qhg6LQV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nPlease extract the data of each city project's Local Growth and convert it into structured tabular form in CSV format." }, { "from": "gpt", "value": "Of course. Firstly, the chart is a horizontal bar chart with each bar representing a different city project {such as \"Community Gardens\"[0.080, 0.848, 0.223, 0.868], \"Public Parks\"[0.140, 0.795, 0.223, 0.815], \"Public Transport\"[0.112, 0.742, 0.223, 0.762], etc.} listed on the y-axis, and the length of the bar indicates the growth rate on the x-axis. To tabulate this data, I need to extract the label of each project from the y-axis and its corresponding growth rate based on its bar length on the x-axis. For instance, the project {\u201cCommunity Gardens\u201d}[0.080, 0.848, 0.223, 0.868] shows a growth rate of 15% as determined by its {bar coordinate}[0.580, 0.835, 0.753, 0.877] on the x-axis. Similarly, I'll extract other city projects and their growth rates.\nBased on the above thought, the answer is:\nCity Projects,Local Growth (%)\n\"Community Gardens\",15\n\"Public Parks\",30\n\"Public Transport\",8\n\"Housing Projects\",-10\n\"City Beautification\",10\n\"Water Supply\",5\n\"Internet Connectivity\",20\n\"Waste Management\",-5\n\"Public Libraries\",15\n\"Public Schools\",-25\n\"Bike Lanes\",0\n\"Street Lighting\",7\n\"Renewable Energy Sources\",30\n\"Historic Preservation\",15\n\"Fire Stations\",-5" } ], "id_raw": 549 }, { "id": "PGpdNZDC7dehD2M3", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nCan you infer from the chart which areas the city is prioritizing and investing in?" }, { "from": "gpt", "value": "This bar chart highlights the areas where the city is channeling its resources by showing the relative growth rate of various city projects. Areas where the city is prioritizing can be identified by looking at the projects with the highest positive growth rates as per the {length of the bar represented on the x-axis}. Projects like yet not limited to {\"Public Parks\"[0.580, 0.782, 0.927, 0.824]}, and {\"Renewable Energy Sources\"[0.580, 0.199, 0.927, 0.241]} are displaying impressive growth and it can be inferred that these areas are being prioritized. Conversely, projects with negative growth rates like {\"Public Schools\"[0.290, 0.358, 0.580, 0.400]}, indicating budget cuts or a shift in focus, could suggest areas not currently prioritized.\nBased on the above thought, the answer is:\nThe city is prioritizing and investing more in areas like Public Parks and Renewable Energy Sources given their high positive growth rates. However, areas like Public Schools seem to be less of a priority, as evidenced by their negative growth rates." } ], "id_raw": 549 }, { "id": "ikznTQDPbRmdn3it", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which city project has the highest local growth rate?" }, { "from": "gpt", "value": "The chart displays the local growth rate of city projects on the x-axis. I'll locate the longest bar in the positive direction to identify the project with the highest growth rate. By visually inspecting the graph, the two longest bars are {\"Public Parks\"[0.580, 0.782, 0.927, 0.824]} and {\"Renewable Energy Sources\"[0.580, 0.199, 0.927, 0.241]}, and observing their labels, we can see they both represent a growth rate of 30% as per the {x-axis label \"30\"}[0.918, 0.931, 0.936, 0.951]. Hence, both these projects show the highest local growth rate.\nBased on the above thought, the answer is:\nPublic Parks and Renewable Energy Sources." } ], "id_raw": 549 }, { "id": "bdZNzUOh2Rn0AeNL", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the observed bar chart and structure it into a CSV format table?" }, { "from": "gpt", "value": "I'm looking at the {title}[0.453, 0.030, 0.705, 0.066] and I see that this is a chart showing 'Big Data Integration Overview'. The {x-axis tick labels}[0.172, 0.903, 0.201, 0.931, 0.339, 0.903, 0.348, 0.931, 0.492, 0.903, 0.509, 0.931, 0.645, 0.903, 0.671, 0.931, 0.802, 0.903, 0.828, 0.931, 0.959, 0.903, 0.985, 0.931] represent the differences in percentages, and the {y-axis tick labels}[0.068, 0.806, 0.177, 0.834, 0.069, 0.731, 0.177, 0.759, 0.078, 0.656, 0.177, 0.684, 0.035, 0.581, 0.177, 0.609, 0.062, 0.506, 0.177, 0.534, 0.053, 0.431, 0.177, 0.459, 0.060, 0.356, 0.177, 0.384, 0.039, 0.280, 0.177, 0.308, 0.095, 0.205, 0.177, 0.233, 0.047, 0.130, 0.177, 0.158] represent different stages in the big data integration process. The bars show the differences by their length based on their x coordinate values and differ in color showing whether it is an improvement or a decline. As an example, {Data Extraction}[0.312, 0.787, 0.344, 0.847] indicates that it decreased by -10 and {Data Wrangling}[0.344, 0.712, 0.501, 0.772] signifies an increase by 50. By going through all the bar coordinates, I can determine the respective differences for each stage.\nBased on the above thought, the answer is:\nStage,Difference\nData Extraction,-10\nData Wrangling,50\nData Cleaning,-30\nData Transformation,-5\nData Integration,60\nSchema Matching,20\nEntity Resolution,80\nDuplicate Detection,-40\nData Fusion,130\nConflict Resolution,150" } ], "id_raw": 550 }, { "id": "gxBqLz5Ptd5IFhab", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which stage appears as the most challenging in the Big Data Integration process?" }, { "from": "gpt", "value": "A stage could be considered challenging based on the decline or low improvement recorded. Using that understanding, I analyze the bar sizes in the chart. Initially, I note {Data Extraction}[0.312, 0.787, 0.344, 0.847] with a decline of -10, followed by {Data Cleaning}[0.249, 0.637, 0.344, 0.697] with a decline of -30, then {Data Transformation}[0.328, 0.562, 0.344, 0.622] with a minor decline of -5, and finally {Duplicate Detection}[0.218, 0.261, 0.344, 0.322] with a significant decline of -40. Among these stages, Duplicate Detection shows the greatest decline, indicating it as one of the most challenging stages in the Big Data Integration process.\nBased on the above thought, the answer is:\nDuplicate Detection - it shows the greatest decline, suggesting that it is a challenging area in the Big Data Integration process." } ], "id_raw": 550 }, { "id": "FmcwDkOt66iu7SMu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_10.png", "conversations": [ { "from": "human", "value": "\nPlease find the stage in which the highest improvement was recorded according to the bar chart." }, { "from": "gpt", "value": "The improvement is shown by the positive length of the bars in the chart. Using the {x-axis tick labels}[0.172, 0.903, 0.201, 0.931, 0.339, 0.903, 0.348, 0.931, 0.492, 0.903, 0.509, 0.931, 0.645, 0.903, 0.671, 0.931, 0.802, 0.903, 0.828, 0.931, 0.959, 0.903, 0.985, 0.931], I can determine the percentage change difference for each stage from 0. By comparing the lengths of the bars, I can particularly point out the longest one, which is {Conflict Resolution}[0.344, 0.111, 0.815, 0.171]. Its length starts from 0 and extends till 150 on the x-axis, thereby indicating the highest improvement.\nBased on the above thought, the answer is:\nConflict Resolution" } ], "id_raw": 550 }, { "id": "pHgmcWzZLMkNW1bp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nCan you process the data from the bar chart into a structured CSV format table?" }, { "from": "gpt", "value": "Yes, from what I'm seeing, the bar chart depicts various social welfare and charity activities on the x-axis and the corresponding relative differences from the x-axis {xticks}[0.208, 0.919, 0.237, 0.942]; [0.266, 0.919, 0.295, 0.942]; [0.324, 0.919, 0.353, 0.942]; [0.382, 0.919, 0.411, 0.942]; [0.440, 0.919, 0.469, 0.942]; [0.508, 0.919, 0.517, 0.942]; [0.562, 0.919, 0.579, 0.942]; [0.620, 0.919, 0.637, 0.942]; [0.678, 0.919, 0.695, 0.942]; [0.736, 0.919, 0.753, 0.942]; [0.794, 0.919, 0.811, 0.942]; [0.851, 0.919, 0.869, 0.942]; [0.909, 0.919, 0.927, 0.942]; [0.967, 0.919, 0.985, 0.942]. Let's start by inspecting the first bar: {Home Support bar}[0.483, 0.813, 0.512, 0.865], which represents the Home Support activity. It demonstrates a difference of about -5. We repeat the same process for each of the other bars corresponding to each activity. From these, we can construct a CSV table that lists each activity in one column and the corresponding differences in another column.\nBased on the above thought, the answer is:\nActivities,Differences\nHome Support,-5\nFood Program,10\nMonetary Contribution,-10\nMedical Assistance,5\nEducational Project,25\nClothing Donation,-30\nFree Counselling,40\nCommunity Development,34\nClean Water Project,-20\nEmergency Relief,65\nChild Care,35\nHousing Support,-15" } ], "id_raw": 551 }, { "id": "qy4md7Ty5b12DTeM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred from the chart with respect to the focus or value perceived in different social welfare and charity activities?" }, { "from": "gpt", "value": "Analyzing the chart, we can discover a few notable trends in societal value or focus ascribed to different social welfare and charity activities. First, if we examine the outliers, we can see that {Emergency Relief}[0.512, 0.230, 0.889, 0.282]stands out as the activity with the maximum positive difference, indicating a considerable high attention or value perception. In contrast, {Clothing Donation}[0.338, 0.489, 0.512, 0.541], with a significant negative difference, suggests it is not perceived as favorable or valued as the other activities. Furthermore, the trends observed suggest the society's greater focus and positive value on activities that provide immediate and direct impact such as 'Free Counselling' besides 'Emergency Relief' and 'Community Development'. Policy makers could potentially take these insights into account while devising social welfare initiatives or charities may consider these perceptions while planning fundraising activities.\nBased on the above thought, the answer is:\nThe chart suggests a greater societal focus and positive value perception toward 'Emergency Relief', 'Free Counselling', and 'Community Development' activities. Meanwhile, activities such as 'Clothing Donation' are not as favored or valued. These insights may help in making informed decisions regarding social welfare planning and fundraising efforts." } ], "id_raw": 551 }, { "id": "8LQqCMMTzp5P9nCj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_11.png", "conversations": [ { "from": "human", "value": "\nIn which activity is the difference the greatest?" }, { "from": "gpt", "value": "To determine the activity where the difference is greatest, we need to compare the lengths of the different bars in the bar chart from {Home Support}[0.483, 0.813, 0.512, 0.865] to {Housing Support}[0.425, 0.100, 0.512, 0.152]. Noting that bars can extend either to the left or the right of the origin (representing negative and positive differences, respectively), we find that the longest bar, and hence the greatest absolute difference, occurs for the {Emergency Relief}[0.512, 0.230, 0.889, 0.282]activity.\nBased on the above thought, the answer is:\nEmergency Relief." } ], "id_raw": 551 }, { "id": "7lRDNZf7b3ctzhRb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nFrom the chart you see, please convert the development prospects of future technology into a structured table." }, { "from": "gpt", "value": "With the given bar chart, I can see that it has 11 categories along the y-axis and the length of each bar represents the 'ChangePercent' value associated with each category. Starting from top to bottom, I can see the categories clearly defined from their respective labels: {AI}[0.167, 0.808, 0.180, 0.836], {Quantum Computing}[0.035, 0.740, 0.180, 0.768], {Biotechnology}[0.081, 0.672, 0.180, 0.700], {5G Technology}[0.078, 0.604, 0.180, 0.632], {Green Energy}[0.085, 0.536, 0.180, 0.564], {Blockchain}[0.105, 0.468, 0.180, 0.496], {Space Exploration}[0.054, 0.400, 0.180, 0.428], {Augmented Reality}[0.047, 0.332, 0.180, 0.360], {Cybersecurity}[0.084, 0.264, 0.180, 0.292], {Internet of Things}[0.057, 0.196, 0.180, 0.224], and {Smart Cities}[0.095, 0.128, 0.180, 0.156]. I can make use of the x-axis guideline and the known x-axis values to determine the 'ChangePercent' for each category based on the length of their bar on the chart. For example, the 'ChangePercent' for AI is around 23 based on the length of its bar[0.366, 0.792, 0.570, 0.847] reaching at 23 position on the x-axis. Using this method, I can create a structured table from the chart data.\nBased on the above thought, the answer is:\nTechnology,ChangePercent\nAI,23\nQuantum Computing,65\nBiotechnology,-15\n5G Technology,30\nGreen Energy,53\nBlockchain,47\nSpace Exploration,-5\nAugmented Reality,32\nCybersecurity,49\nInternet of Things,35\nSmart Cities,-10" } ], "id_raw": 552 }, { "id": "TN5UqSdt6gRkJgM2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which technological sector should an investor likely to avoid based on future growth prospects? Can you provide the reasoning behind it?" }, { "from": "gpt", "value": "This question requires a comprehensive comparison of the lengths and direction of all bars in the chart. I consider the bars that extend towards the left, since a negative percentage change indicates potential risk for investment. For such a bar, I need to look for the one with the greatest length as it represents the highest predicted decline. The longest bar of this type belongs to {Biotechnology}[0.081, 0.672, 0.180, 0.700] at position [0.234, 0.656, 0.366, 0.711], with its length reaching the -15 point on the x-axis, suggesting it would have the largest decline in the near future.\nBased on the above thought, the answer is:\nBiotechnology. Because it has the highest decline percentage amongst all the technologies, which indicates it's increasingly challenging or less focused in the sector, making it a higher risk for investment." } ], "id_raw": 552 }, { "id": "Hk35Xkp1yL0QF1qs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_12.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, which technology is projected to have the highest percentage change in the future?" }, { "from": "gpt", "value": "To answer this question, I need to visually scan the lengths of all the bars from the bar-chart. As observed, the longest green bar protrudes the most into the positive side of the x-axis, it belongs to {Quantum Computing}[0.035, 0.740, 0.180, 0.768] at position,r>[0.366, 0.724, 0.941, 0.779]. By referencing its end point with the values on the x-axis, it indicates the highest projected percentage change for the future as it reached the 65 point on the x-axis.\nBased on the above thought, the answer is:\nQuantum Computing." } ], "id_raw": 552 }, { "id": "VnZCKdFMXMqLUSsQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data represented by the bar chart into CSV format?" }, { "from": "gpt", "value": "From the title {\"Years of Protection for Different Types of Intellectual Property\"}[0.319, 0.030, 0.827, 0.066] I gather that the y-axis represents various types of intellectual property and the x-axis represents the protection duration or years. I can see from the {y-axis ticks}[{\"Software Patent\"}[0.040, 0.792, 0.151, 0.820]; {\"Hardware Patent\"}[0.035, 0.684, 0.151, 0.712]; {\"Design Patent\"}[0.054, 0.576, 0.151, 0.604]; {\"Utility Patent\"}[0.061, 0.468, 0.151, 0.496]; {\"Trade Secret\"}[0.064, 0.360, 0.151, 0.388]; {\"Trademark\"}[0.077, 0.252, 0.151, 0.280]; {\"Copyright\"}[0.083, 0.144, 0.151, 0.172]] that these types of intellectual property include Software Patent, Hardware Patent, Design Patent, Utility Patent, Trade Secret, Trademark, and Copyright. Each of them has an associated bar that indicates the number of years the property is protected. For example, {the bar for \"Software Patent\"}[0.161, 0.760, 0.264, 0.847] extends to approximately the 15 position on the x-axis. Therefore, the respective years for each intellectual property type should be Software Patent for 15 years, Hardware Patent for 20 years, Design Patent for 14 years, Utility Patent for 20 years, Trade Secret for an unlimited period, Trademark for 10 years, and Copyright for 70 years. This information can be presented in CSV format.\nBased on the above thought, the answer is:\nInnovation Type,Years of Protection\nSoftware Patent,15\nHardware Patent,20\nDesign Patent,14\nUtility Patent,20\nTrade Secret,Unlimited\nTrademark,10\nCopyright,70" } ], "id_raw": 553 }, { "id": "2FHuqEuHFFf16VVD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you determine which type of intellectual property is most highly valued and why?" }, { "from": "gpt", "value": "In the context of this chart, \"value\" could be determined by the length of protection years granted to different types of intellectual property. The longer the protection years, the more valuable the intellectual property type might be deemed because it suggests its owner can control its use and profit from it for a longer period of time. With this idea, we can conclude that {the bar for \"Trade Secret\"}[0.161, 0.328, 0.848, 0.414] holding the maximum length representing 'Unlimited' years of protection, shows that Trade Secret might be considered the most highly valued type of intellectual property, as their protection period is unlimited, suggesting a high level of esteem for proprietors of such properties.\nBased on the above thought, the answer is:\nTrade Secret could be considered the most valued type of intellectual property because it has an unlimited protection duration." } ], "id_raw": 553 }, { "id": "iUXHAreYGwCIdzZs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_13.png", "conversations": [ { "from": "human", "value": "\nHow many types of intellectual property have protection years more than 15?" }, { "from": "gpt", "value": "To answer this question, I need to look at all the bars in the chart and check which one extends beyond the 15-year mark on the x-axis. Looking from top to bottom, {the bar for \"Software Patent\"}[0.161, 0.760, 0.264, 0.847] almost reaches the 15 position, so it clearly has protection years of 15. Then, {the bar for \"Hardware Patent\"}[0.161, 0.652, 0.298, 0.739] extends beyond 15 and {the bar for \"Utility Patent\"}[0.161, 0.436, 0.298, 0.522] also extends beyond 15. {The bar for \"Trade Secret\"}[0.161, 0.328, 0.848, 0.414] extends significantly to the right, representing unlimited protection years, so it must be counted as well. Lastly, {the bar for \"Copyright\"}[0.161, 0.111, 0.642, 0.198] extends well beyond 15, up to around the 70 position. Therefore, four types of intellectual property have more than 15 years of protection.\nBased on the above thought, the answer is:\nFour." } ], "id_raw": 553 }, { "id": "TU1BbZKvOWGoFOad", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, can you convert the data into a table format with columns denoting 'Sector', 'Q1', 'Q2', 'Q3', and 'Q4'?" }, { "from": "gpt", "value": "I am analyzing the bar chart with the title {\"Building and Real Estate Market Trends\"}[0.408, 0.030, 0.733, 0.066]. The x-axis represents different quarters, which can be found in the legend {\"Q1\"}[0.901, 0.102, 0.929, 0.122], {\"Q2\"}[0.901, 0.144, 0.929, 0.164], {\"Q3\"}[0.901, 0.186, 0.929, 0.205], {\"Q4\"}[0.901, 0.228, 0.929, 0.247]. The y-axis represents different sectors, which are labeled as {\"Residential Sales\"}[0.041, 0.785, 0.159, 0.813], {\"Commercial Sales\"}[0.035, 0.658, 0.159, 0.686], {\"Industrial Sales\"}[0.053, 0.531, 0.159, 0.559], {\"Residential Rent\"}[0.046, 0.405, 0.159, 0.433], {\"Commercial Rent\"}[0.040, 0.278, 0.159, 0.306], {\"Industrial Rent\"}[0.058, 0.151, 0.159, 0.179]. For each sector and each quarter, there is one bar indicating the value. For example, the bar for Q1 of Residential Sales has coordinates of [0.169, 0.745, 0.876, 0.847], corresponding to a value of 220. By doing similar analysis, I can generate the whole table.\nBased on the above thought, the answer is:\nSector,Q1,Q2,Q3,Q4\nResidential Sales,220,180,175,200\nCommercial Sales,120,170,160,160\nIndustrial Sales,150,100,95,160\nResidential Rent,240,230,225,240\nCommercial Rent,130,180,175,180\nIndustrial Rent,100,90,100,90" } ], "id_raw": 554 }, { "id": "ieMqqzbtId6IswtM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector shows consistent growth from Q1 to Q4?" }, { "from": "gpt", "value": "To answer this question, I need to compare the lengths of the bars in each sector from Q1 to Q4. For example, the bars for the Residential Sales are located at {\"Q1\"}[0.169, 0.745, 0.876, 0.847], {\"Q2\"}[0.169, 0.745, 0.747, 0.847], {\"Q3\"}[0.169, 0.745, 0.731, 0.847] and {\"Q4\"}[0.169, 0.745, 0.811, 0.847]. It is clear that there is not a consistent growth from Q1 to Q4. Similar analysis can be done for Commercial Sales, Industrial Sales, Residential Rent, Commercial Rent, and Industrial Rent. By comparing the bar lengths, I find that none of the sectors show consistent growth from Q1 to Q4.\nBased on the above thought, the answer is:\nNone of the sectors show consistent growth from Q1 to Q4." } ], "id_raw": 554 }, { "id": "eE3FqcFbjYlXPURl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_37_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what is the value for Q2 of Residential Sales?" }, { "from": "gpt", "value": "To find the value for Q2 of Residential Sales, I need to locate the bars for Residential Sales located at {\"Residential Sales\"}[0.041, 0.785, 0.159, 0.813], then identify the second bar representing Q2. This bar has coordinates of [0.169, 0.745, 0.747, 0.847], and the value could be estimated using the x-axis grid, which shows output values in the interval from [0.164, 0.903, 0.173, 0.931] to [0.959, 0.903, 0.985, 0.931]. It is clear that the second bar for Residential Sales is about 3 quarters of the total range, which corresponds to a value of 180 units.\nBased on the above thought, the answer is:\n180" } ], "id_raw": 554 }, { "id": "WUQwTJWQBn7y0DVh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, could you list down the technologies and their respective trend values in a CSV format table?" }, { "from": "gpt", "value": "The x-axis tick labels represent the technology names from left to right, including {Solar}[0.116, 0.863, 0.151, 0.906], {Wind}[0.202, 0.863, 0.237, 0.906], {Hydro}[0.286, 0.863, 0.325, 0.912], {Geothermal}[0.358, 0.863, 0.426, 0.947], {Biomass}[0.452, 0.863, 0.504, 0.927], {Nuclear}[0.540, 0.863, 0.588, 0.922], {Carbon Capture}[0.606, 0.863, 0.694, 0.972], {Smart Grid}[0.704, 0.863, 0.768, 0.942], {Energy Storage}[0.779, 0.863, 0.865, 0.970], and {Energy Efficiency}[0.861, 0.863, 0.956, 0.981]. Their respective trend values can be determined by cross-referencing the height of each bar to the y-axis scale, which starts at 0 and goes up to 80. For example, the height of {the bar representing Solar}[0.099, 0.337, 0.168, 0.857] is at level 50 on the y-axis. Therefore, the trend value for the \"solar\" technology is 50. By further observing and comparing the heights of other bars with the y-axis, I can extract each trend value for every technology.\nBased on the above thought, the answer is:\nTechnology,Trend Value\nSolar,50\nWind,60\nHydro,45\nGeothermal,55\nBiomass,50\nNuclear,70\nCarbon Capture,40\nSmart Grid,55\nEnergy Storage,50\nEnergy Efficiency,55" } ], "id_raw": 570 }, { "id": "TW3nJSw0Hg8OnFkp", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nCan you explain what this chart is likely to represent and what conclusions one might be able to draw from it?" }, { "from": "gpt", "value": "This chart appears to be a comparison of different energy technology's development trends through their respective growth trend values, represented by the height of each bar. The x-axis labels {Solar}[0.116, 0.863, 0.151, 0.906], {Wind}[0.202, 0.863, 0.237, 0.906], {Hydro}[0.286, 0.863, 0.325, 0.912], {Geothermal}[0.358, 0.863, 0.426, 0.947], {Biomass}[0.452, 0.863, 0.504, 0.927], {Nuclear}[0.540, 0.863, 0.588, 0.922], {Carbon Capture}[0.606, 0.863, 0.694, 0.972], {Smart Grid}[0.704, 0.863, 0.768, 0.942], {Energy Storage}[0.779, 0.863, 0.865, 0.970], and {Energy Efficiency}[0.861, 0.863, 0.956, 0.981] correspond to the different energy technologies. The y-axis represents the technology development trend values, and the higher the bar, the stronger the growth or more rapid the development. From this chart, one could conclude that generally, all the energy technologies show a positive development trend, with Nuclear being the most rapidly developing technology due to its highest bar, followed by Wind and Geothermal. Interestingly, Carbon Capture displays the slowest growth, signifying it might receive less attention or investment in the coming years.\nBased on the above thought, the answer is:\nThis chart represents the recent development trends in various energy technologies. One might conclude that Nuclear technology presents the fastest growth, followed by Wind and Geothermal. However, Carbon Capture shows slower development compared to other technologies." } ], "id_raw": 570 }, { "id": "DUiSl6aFs0f9OXDL", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_0.png", "conversations": [ { "from": "human", "value": "\nWhich technology has the maximum future growth trend value as depicted in the bar chart?" }, { "from": "gpt", "value": "To answer this question, I need to examine the highest bar on the chart. The y-axis represents the trend values, and the taller the bar, the higher the trend value. By visibly comparing the heights of the bars, I can see that {the bar representing Nuclear}[0.530, 0.129, 0.598, 0.857] is the tallest, suggesting that Nuclear technology has the highest future growth trend value.\nBased on the above thought, the answer is:\nNuclear." } ], "id_raw": 570 }, { "id": "H80NBdp9WZo7h1aS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the given bar graph data into a table format based on your observations?" }, { "from": "gpt", "value": "From the chart, the x-axis corresponds to various lifestyle activities, including: 'Reading'[0.123, 0.469, 0.184, 0.825], 'Exercise'[0.199, 0.351, 0.259, 0.825], 'Travel'[0.274, 0.272, 0.335, 0.825], 'Parties'[0.350, 0.509, 0.411, 0.825], 'Eating_Out'[0.426, 0.232, 0.486, 0.825], 'Music'[0.501, 0.192, 0.562, 0.825], 'Art'[0.577, 0.311, 0.638, 0.825], 'Movies'[0.653, 0.153, 0.713, 0.825], 'Online_Gaming'[0.728, 0.113, 0.789, 0.825], 'Sports'[0.804, 0.272, 0.865, 0.825], and 'Nightlife'[0.880, 0.430, 0.940, 0.825]. The y-axis shows the preference percentage for each activity, and the range of percentage is from {0}[0.065, 0.816, 0.076, 0.839] to {100}[0.043, 0.025, 0.076, 0.048]. Each color bar represents an activity, and the height of the bars corresponds to the popularity of the activity, i.e., the preference percentage. By analyzing the heights of these bars, I can construct a CSV table with the activities as row labels and the preference percentage as values.\nBased on the above thought, the answer is:\nActivities,Preference(%)\nReading,45\nExercise,60\nTravel,70\nParties,40\nEating_Out,75\nMusic,80\nArt,65\nMovies,85\nOnline_Gaming,90\nSports,70\nNightlife,50" } ], "id_raw": 571 }, { "id": "fgp24beSV6GrjErw", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nHow would you analyse the population's preference towards leisure activities based on the bar graph?" }, { "from": "gpt", "value": "Observing the bar chart, it is clear that bars{'Movies'}[0.653, 0.153, 0.713, 0.825] and {'Online_Gaming'}[0.728, 0.113, 0.789, 0.825] are amongst the tallest, indicating a higher preference percentage of individuals towards these activities. This suggests that a large proportion of the surveyed population seem to prefer movies and online games as their leisure activities. On the contrary, the shortest bar {'Reading'}[0.123, 0.469, 0.184, 0.825] implies that reading is not as popular. The bar representing 'Sports'[0.804, 0.272, 0.865, 0.825] and 'Travel'[0.274, 0.272, 0.335, 0.825] show moderate height, indicating a balanced interest in these activities. This analysis highlights a trend where digital activities like online gaming and movies are more popular, while traditional activities like reading are less popular among the surveyed population.\nBased on the above thought, the answer is:\nThere is a clear preference for digital leisure activities such as online gaming and watching movies, while traditional activities like reading are less popular among the population." } ], "id_raw": 571 }, { "id": "M6EOX7T1XNaoU2ZM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_1.png", "conversations": [ { "from": "human", "value": "\nWhich activity has the least preference percentage according to the graph?" }, { "from": "gpt", "value": "To determine which activity has the least preference percentage, I need to compare the heights of bars representing the preference for each activity. By scanning all the bars, it appears that the shortest bar represents the 'Reading' activity with its coordinates at [0.123, 0.469, 0.184, 0.825], suggesting that it has the smallest preference percentage.\nBased on the above thought, the answer is:\nReading." } ], "id_raw": 571 }, { "id": "zyIY3xuQmtur8twF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the data in the bar chart into a CSV format table?" }, { "from": "gpt", "value": "From the chart, it's clear that each bar represents a certain health predictor which I understand are 'Age', 'BMI', 'Blood Pressure', 'Heart Rate', 'Air Quality', 'Diet', 'Exercise Frequency', 'Sleep Quality', 'Smoking', 'Alcohol Consumption'. These health predictors are clearly labeled on the x-axis from the information given in {the x-axis tick labels}[{\"Age\"}[0.127, 0.785, 0.156, 0.834]; {\"BMI\"}[0.213, 0.785, 0.241, 0.832]; {\"Blood Pressure\"}[0.271, 0.785, 0.354, 0.923]; {\"Heart Rate\"}[0.366, 0.785, 0.429, 0.890]; {\"Air Quality\"}[0.452, 0.785, 0.514, 0.888]; {\"Diet\"}[0.553, 0.785, 0.583, 0.836]; {\"Exercise Frequency\"}[0.601, 0.785, 0.706, 0.960]; {\"Sleep Quality\"}[0.701, 0.785, 0.776, 0.911]; {\"Smoking\"}[0.798, 0.785, 0.850, 0.873]; {\"Alcohol Consumption\"}[0.852, 0.785, 0.966, 0.975]. The vertical y-axis denotes the accuracy percentages that these predictors hold, ranging from {0}[0.052, 0.768, 0.061, 0.791] to {100}[0.035, 0.025, 0.061, 0.048]. To generate the CSV table, I need to extract the values for each health predictor. This can be done by reading the heights of these bars. For instance, the height of {the bar for \"Age\"}[0.108, 0.257, 0.176, 0.777] goes up to approximately 70%, meaning the accuracy of Age in predicting health status is 70%.\nBased on the above thought, the answer is:\nHealth Predictor,Accuracy (%)\nAge,70\nBMI,65\nBlood Pressure,78\nHeart Rate,80\nAir Quality,60\nDiet,75\nExercise Frequency,82\nSleep Quality,68\nSmoking,45\nAlcohol Consumption,55" } ], "id_raw": 572 }, { "id": "g7wn8bLclEsKlTwY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nCan you infer which factors significantly contribute to predicting health status, and why so?" }, { "from": "gpt", "value": "Inferring from the data, the factors that significantly predict health status would be the ones with higher accuracy percentages. This come from the taller bars that represents them in the chart. Specifically, the significant contributors include {bar for \"Blood Pressure\"}[0.278, 0.198, 0.346, 0.777] which represents an accuracy of approximately 78%, the {bar for \"Heart Rate\"}[0.363, 0.183, 0.432, 0.777] at around 80% accuracy and the highest one {bar for \"Exercise Frequency\"}[0.619, 0.168, 0.687, 0.777] at approximately 82% accuracy. These three factors significantly predict health status because they offer the greatest accuracy in this context indicating their crucial role in health predictions.\nBased on the above thought, the answer is:\nYes, the factors that significantly contribute to predicting health statuses are Blood Pressure, Heart Rate, and Exercise Frequency. This inference is drawn considering their high accuracies, which maximizes their predicting reliability." } ], "id_raw": 572 }, { "id": "gtAyNIJXWToTc0ec", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_2.png", "conversations": [ { "from": "human", "value": "\nWhich health predictor shows the highest accuracy according to the chart?" }, { "from": "gpt", "value": "In order to determine which health predictor exhibits the highest accuracy, it's crucial to compare the heights of all the bars. Let's take a look from left to right, starting with {the bar for \"Age\"}[0.108, 0.257, 0.176, 0.777] which represents an accuracy of 70%. This process is continued for all health predictors. The maximum height can be seen at {the bar for \"Exercise Frequency\"}[0.619, 0.168, 0.687, 0.777] which corresponds to an accuracy of 82%. Hence, \"Exercise Frequency\" is the health predictor with the highest accuracy.\nBased on the above thought, the answer is:\nExercise Frequency" } ], "id_raw": 572 }, { "id": "3qmNLyUke3OLI6mP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you put together a CSV table that represents the usage rate of different social networks?" }, { "from": "gpt", "value": "Yes, sure. The {X-axis}[{\"Facebook\"}[0.106, 0.906, 0.162, 0.976], {\"Twitter\"}[0.198, 0.906, 0.241, 0.960]; {\"Instagram\"}[0.276, 0.906, 0.336, 0.981]; {\"Snapchat\"}[0.364, 0.906, 0.420, 0.976]; {\"LinkedIn\"}[0.452, 0.906, 0.503, 0.970]; {\"WhatsApp\"}[0.534, 0.906, 0.594, 0.981]; {\"Pinterest\"}[0.623, 0.906, 0.677, 0.973]; {\"Viber\"}[0.718, 0.906, 0.754, 0.951]; {\"Telegram\"}[0.795, 0.906, 0.850, 0.975]; {\"WeChat\"}[0.884, 0.906, 0.932, 0.966]] labels represent different social networking platforms, and the corresponding {bars}[{\"Facebook\"}[0.099, 0.778, 0.168, 0.900], {\"Twitter\"}[0.185, 0.638, 0.254, 0.900], {\"Instagram\"}[0.271, 0.323, 0.340, 0.900], {\"Snapchat\"}[0.357, 0.131, 0.426, 0.900], {\"LinkedIn\"}[0.444, 0.410, 0.512, 0.900], {\"WhatsApp\"}[0.530, 0.498, 0.598, 0.900], {\"Pinterest\"}[0.616, 0.201, 0.685, 0.900], {\"Viber\"}[0.702, 0.533, 0.771, 0.900], {\"Telegram\"}[0.788, 0.708, 0.857, 0.900], {\"WeChat\"}[0.874, 0.078, 0.943, 0.900]] in the chart represent the usage rates of these platforms. The height of each respective bar corresponds to the percentage of total communication. By referencing the y-axis labels range {0}[0.043, 0.893, 0.052, 0.911] to {50}[0.035, 0.019, 0.052, 0.036], we can create a CSV table to record the usage rate for each networking platform.\nBased on the above thought, the answer is:\nSocial Networks,Usage Rate (%)\nFacebook,7\nTwitter,15\nInstagram,33\nSnapchat,44\nLinkedIn,28\nWhatsApp,23\nPinterest,40\nViber,21\nTelegram,11\nWeChat,47" } ], "id_raw": 573 }, { "id": "cRmJ6C1STCGNhc5l", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the overall usage trend of these social networking platforms?" }, { "from": "gpt", "value": "Looking at the data distribution on the chart, there is no obvious constant rise or decline trend among these platforms. However, we can observe that {Snapchat}[0.364, 0.906, 0.420, 0.976] and {WeChat}[0.884, 0.906, 0.932, 0.966] have a significantly higher usage rate than the others, which are represented by their tall bars located at [0.357, 0.131, 0.426, 0.900] and [0.874, 0.078, 0.943, 0.900] respectively. It implies that these two platforms are currently the most preferred for communication. And {Facebook}[0.106, 0.906, 0.162, 0.976] and {Telegram}[0.795, 0.906, 0.850, 0.975] are used relative less by people, which can be inferred from the height of their bars [0.099, 0.778, 0.168, 0.900] and [0.788, 0.708, 0.857, 0.900], respectively. The rest of the platforms fall somewhere in between.\nBased on the above thought, the answer is:\nThe usage rate of these social networking platforms varies significantly, with Snapchat and WeChat being the most used platforms, while Facebook and Telegram are significantly less used. The rest of the platforms have moderate usage rates." } ], "id_raw": 573 }, { "id": "IWvDU0yO2VfLvwAh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_3.png", "conversations": [ { "from": "human", "value": "\nWhich social networking platform has the highest usage rate according to the chart?" }, { "from": "gpt", "value": "To find the platform with the highest usage rate, I need to compare the height of the bars representing each platform. After scanning from left to right, I found that {the WeChat bar}[0.874, 0.078, 0.943, 0.900] is the tallest, which means WeChat has the highest usage rate.\nBased on the above thought, the answer is:\nWeChat." } ], "id_raw": 573 }, { "id": "63AHbNglOKbrQksl", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart observed, please convert the data showing the 'Contribution to Ecosystem' by each category into a CSV format table." }, { "from": "gpt", "value": "Observing the chart, I can locate the bars at the bottom corresponding to ten categories, from left to right: {Intellectual property}[0.085, 0.755, 0.194, 0.892], {Entrepreneurial Education}[0.153, 0.755, 0.291, 0.928], {Government Support}[0.249, 0.755, 0.362, 0.896], {Market Openness}[0.340, 0.755, 0.436, 0.874], {Investment}[0.438, 0.755, 0.503, 0.837], {Innovation Output}[0.504, 0.755, 0.603, 0.879], {Infrastructure}[0.598, 0.755, 0.675, 0.851], {Cultural Support}[0.674, 0.755, 0.764, 0.868], {Business Angel Activity}[0.740, 0.755, 0.864, 0.910], {Successful Entrepreneurial Activity}[0.794, 0.755, 0.975, 0.981]. For each category, there is a bar that represents the 'Contribution to Ecosystem'. I'll map the height of the bar to the numerical value using the y-axis, which has its min label {0}[0.052, 0.742, 0.061, 0.760] and max label {100}[0.035, 0.019, 0.061, 0.036]. For example, the bar of {Intellectual property}[0.106, 0.279, 0.173, 0.749] has a height that matches the y-value '65'. Therefore, the value for 'Intellectual property' is 65.\nBased on the above thought, the answer is:\nCategory,Contribution to Ecosystem\nIntellectual property,65\nEntrepreneurial Education,60\nGovernment Support,75\nMarket Openness,45\nInvestment,85\nInnovation Output,50\nInfrastructure,70\nCultural Support,55\nBusiness Angel Activity,80\nSuccessful Entrepreneurial Activity,75" } ], "id_raw": 574 }, { "id": "3DomOq4w7g8QwpPb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what measures could be taken to improve the Innovation and Technology Entrepreneurship Ecosystem?" }, { "from": "gpt", "value": "From the chart, it can be seen that the 'Investment'[0.438, 134, 0.504, 0.749] and 'Business Angel Activity'[0.769, 0.170, 0.835, 0.749] have the highest bar heights, indicating strong performance. Meanwhile, the bar for 'Market Openness'[0.355, 0.423, 0.421, 0.749] is noticeably shorter compared to other categories, indicating a weaker performance. As 'Market Openness' contributes to the ecosystem, increasing the market openness may improve the overall performance of the Innovation and Technology Entrepreneurship Ecosystem. For instance, the government or related authorities can take measures to ease regulations, promote fair trade, and encourage global partnership, which could potentially increase 'Market Openness' and thus enhance the Innovation and Technology Entrepreneurship Ecosystem.\nBased on the above thought, the answer is:\nIncreasing the 'Market Openness' by easing regulations, promoting fair trade, and encouraging global partnerships could potentially improve the Innovation and Technology Entrepreneurship Ecosystem." } ], "id_raw": 574 }, { "id": "LGZo48IpXBzfLDI6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_4.png", "conversations": [ { "from": "human", "value": "\nWhich category exhibits the maximum 'Contribution to Ecosystem'?" }, { "from": "gpt", "value": "To find out which category contributes the most to the ecosystem, I need to locate the bar with the highest y-value. Scanning through the bars from the left over to the right, I can see that the {Investment}[0.438, 0.755, 0.503, 0.837] and {Business Angel Activity}[0.740, 0.755, 0.864, 0.910] bars stand taller than the rest. Upon comparing their heights, I find that the height of the {Investment}[0.438, 0.134, 0.504, 0.749] bar appears to reach the highest point on the y-axis. Hence, 'Investment' appears to have the highest contribution to the ecosystem.\nBased on the above thought, the answer is:\nInvestment" } ], "id_raw": 574 }, { "id": "0t5tY829AyrSo9CD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart you observed into a table format?" }, { "from": "gpt", "value": "The bar chart has a description of the title - 'Business Management and Organizational Leadership', which is not shown in the chart but in the description - for each bar, represented by x-axis tick labels such as {Communication}[0.098, 0.837, 0.185, 0.946] and {Strategic Planning}[0.859, 0.837, 0.959, 0.962]. The heights of bars correspond to the values represented on the y-axis, for example, {Communication bar}[0.108, 0.267, 0.176, 0.831] has a value of 70 as it matches with the label marked {70}[0.043, 0.267, 0.061, 0.285] on y-axis. This can be converted into rows of data for a structured table.\nBased on the above thought, the answer is:\nCategory,Success Percentage (%)\nCommunication,70\nDecision Making,45\nProblem Solving,55\nLeadership,75\nMotivation,65\nConflict Management,40\nDelegation,50\nTime Management,80\nTeam Building,60\nStrategic Planning,85" } ], "id_raw": 575 }, { "id": "JzPasoh0qvurZkds", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nConsidering all the aspects presented in the chart, where should a company focus to improve the overall performance of their management team?" }, { "from": "gpt", "value": "The bar chart presents various aspects of management with their success percentages. For a company to improve the overall performance, they should focus on the areas that have the least success. From the chart, the category with the lowest success percentage is {Conflict Management}[0.534, 0.509, 0.602, 0.831] which aligns approximately to the 40% mark. It is followed by the second lowest, {Decision Making}[0.193, 0.469, 0.261, 0.831], which is slightly below the 50% mark. Therefore, performance improvements should focus on these two areas to enhance overall team performance.\nBased on the above thought, the answer is:\nThe company should focus on improving Conflict Management and Decision Making as they have the least success percentages." } ], "id_raw": 575 }, { "id": "dzkdusRlLvTU6iDZ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_5.png", "conversations": [ { "from": "human", "value": "\nIdentify the management aspect with the least success percentage according to the chart?" }, { "from": "gpt", "value": "To find the management aspect with the least success percentage, I must examine the height of each bar. For example, the bar for {Conflict Management}[0.534, 0.509, 0.602, 0.831] is the lowest compared to others, aligning with the {40%}[0.043, 0.502, 0.061, 0.520] mark on the y-axis. Therefore, Conflict Management is the category with the least success percentage.\nBased on the above thought, the answer is:\nConflict Management" } ], "id_raw": 575 }, { "id": "EfNI1Q29zwlTUrCV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nYou see a bar chart detailing the scores of different aspects of innovation project management. Could you please report the data from this chart in a CSV format?" }, { "from": "gpt", "value": "Sure, from {the y-axis}[{'ylabel': 'Score', 'color': 'black', 'rotation': 90.0}, '[0.015, 0.467, 0.029, 0.516]'], I can see that the y-axis represents the score, ranging from 0 to 100. The x-axis includes labels representating different aspects of innovation project management such as {Idea Generation}[0.107, 0.380, 0.175, 0.958], {Time Management}[0.192, 0.631, 0.259, 0.958], and so on. The score is represented by the height of the corresponding bars. For example, {the bar for \"Idea Generation\"}[0.107, 0.380, 0.175, 0.958] reaches up to approximately the 60 position on the y-axis, indicating that the score for Idea Generation is 62. I'll pull all the data together in a similar way.\nBased on the above thought, the answer is:\nAspect,Score\nIdea Generation,62\nTime Management,35\nBudget Management,50\nRisk Management,38\nQuality Control,66\nCommunication,44\nProject Closure,55\nHardware Innovation,47\nSoftware Innovation,70\nMarketing Innovation,33" } ], "id_raw": 576 }, { "id": "6LLY1tq70TQNqOTy", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, can you tell me about the project management aspects that need to be improved?" }, { "from": "gpt", "value": "Based on the chart, the aspects that need improvement are those with the lower scores. These include {Time Management}[0.192, 0.631, 0.259, 0.958] and {Marketing Innovation}[0.866, 0.650, 0.934, 0.958]. Specifically, the Time Management aspect has a low score of 35 and Marketing Innovation has a score of 33, indicating these are areas where performance is weaker.\nBased on the above thought, the answer is:\nThe aspects that need to be improved are Time Management and Marketing Innovation as they have the lowest scores." } ], "id_raw": 576 }, { "id": "pwFEIrffB4YMwH5N", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest scored aspect in the innovation project management according to the bar chart?" }, { "from": "gpt", "value": "I need to find the tallest bar in the chart. Scanning the heights of the bars, I find that {Software Innovation}[{'bar_coordinates': '[0.782, 0.305, 0.850, 0.958]', 'x_label': 'Software Innovation', 'y_range': '0.00 -> 70.00'}] has the tallest bar, indicating it has the highest score.\nBased on the above thought, the answer is:\nSoftware Innovation." } ], "id_raw": 576 }, { "id": "0NviWnzDpCBSejOV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nCan you extract data from the bar chart and transform it into a CSV format table?" }, { "from": "gpt", "value": "I can see that {the x-axis tick labels}[{\"Food Bank\"}[0.100, 0.824, 0.151, 0.901]; {\"Medical Aid\"}[0.185, 0.824, 0.240, 0.908]; {\"Disaster Relief\"}[0.266, 0.824, 0.333, 0.926]; {\"Elderly Care\"}[0.358, 0.824, 0.416, 0.912]; {\"Disabled Support\"}[0.434, 0.824, 0.513, 0.943]; {\"Child Welfare\"}[0.530, 0.824, 0.592, 0.919]; {\"Education\"}[0.624, 0.824, 0.673, 0.898]; {\"Homeless Support\"}[0.694, 0.824, 0.777, 0.949]; {\"Environment Protection\"}[0.770, 0.824, 0.875, 0.981]; {\"Animal Welfare\"}[0.875, 0.824, 0.945, 0.930]] are named after different social welfare and charity activities. The {y-axis tick labels}[{\"10\"}[0.029, 0.698, 0.043, 0.716]; {\"20\"}[0.029, 0.585, 0.043, 0.603]; {\"30\"}[0.029, 0.472, 0.043, 0.489]; {\"40\"}[0.029, 0.359, 0.043, 0.376]; {\"50\"}[0.029, 0.245, 0.043, 0.263]; {\"60\"}[0.029, 0.132, 0.043, 0.150]; {\"70\"}[0.029, 0.019, 0.043, 0.036]] represent the corresponding funding amounts in millions of dollars. Therefore, the height of each bar corresponds to the amount of funding received by the respective activity. To generate the CSV table, I need to calculate the fund value for each activity according to the height of its bar. For example, the bar for {Food Bank}[0.090, 0.196, 0.160, 0.818] is approximately at the 55 position on the y-axis, so the funding for the Food Bank activity is 55 million dollars.\nBased on the above thought, the answer is:\nCharity Activity,Funding\nFood Bank,55\nMedical Aid,50\nDisaster Relief,60\nElderly Care,52\nDisabled Support,45\nChild Welfare,38\nEducation,43\nHomeless Support,55\nEnvironment Protection,50\nAnimal Welfare,48" } ], "id_raw": 577 }, { "id": "eLbWhrWkUFY96rWC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which area of charity activities requires more focus and funding?" }, { "from": "gpt", "value": "Looking at the bar heights, we can assess which area needs more funding. The height of {Child Welfare}[0.526, 0.388, 0.596, 0.818] and {Education}[0.613, 0.331, 0.683, 0.818] bars are relatively low, at approximately the 38 and 43 positions on the Y-axis respectively. This suggests that these two areas receive lower amounts compared to most other charitable activities, therefore may require more focus and funding for their development.\nBased on the above thought, the answer is:\nChild Welfare and Education require more focus and funding based on the funding trend portrayed in this chart." } ], "id_raw": 577 }, { "id": "Nb8uMqkZwg6ynlo0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_7.png", "conversations": [ { "from": "human", "value": "\nWhich charity activity received the highest funding?" }, { "from": "gpt", "value": "Looking at all the bars, the one with the highest position on the Y-axis would represent the charity activity which received the highest funding. There is a bar for {Disaster Relief}[0.265, 0.139, 0.334, 0.818] reaching approximately the 60 position on the y-axis, which is higher than all the other bars.\nBased on the above thought, the answer is:\nDisaster Relief." } ], "id_raw": 577 }, { "id": "bDFey3LX8F9CHkVD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bars in the chart, can you structure this information into a simple table presenting Career and Workplace Development Trends and their respective corresponding Company Dedication percentages?" }, { "from": "gpt", "value": "The bar labels which are seen as the x-axis labels consist of a number of categories related to Career and Workplace Development Trends. They range from {\"Workplace Flexibility\"}[0.079, 0.813, 0.172, 0.953] to {\"Mental Health Awareness\"}[0.854, 0.813, 0.966, 0.981]. The y-axis is defined by {a parameter labeled \"Company Dedication (%)\"}[0.012, 0.308, 0.024, 0.525] with its labels lying between {10}[0.029, 0.703, 0.043, 0.720] and {80}[0.029, 0.019, 0.043, 0.036]. Each category has a corresponding bar such as the {\"Emotional Intelligence\"}[0.613, 0.319, 0.683, 0.807] bar, indicating its percentage value. By translating these particulars into a table, we obtain a table that represents Career and Workplace Development Trends and the corresponding Company Dedication percentages.\nBased on the above thought, the answer is:\nTrend,Percentage\nWorkplace Flexibility,40\nVirtual Collaboration,55\nDiversity & Inclusion,45\nLeadership Development,60\nContinuous Learning,70\nWork-Life Balance,65\nEmotional Intelligence,50\nDigital Literacy,35\nArtificial Intelligence,55\nMental Health Awareness,75" } ], "id_raw": 578 }, { "id": "fFxJ19bEpJjJF8eq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer from the graph regarding Career and Workplace Development Trends?" }, { "from": "gpt", "value": "With the graphical representation of bars, where each distinct bar corresponds to a trend within Career and Workplace Development, I can identify that some trends receive more attention than others based on the associating bar heights. On the higher spectrum are {\"Leadership Development\"}[0.352, 0.221, 0.422, 0.807] and {\"Mental Health Awareness\"}[0.875, 0.074, 0.945, 0.807]. Interestingly,{\"Digital Literacy\"}[0.701, 0.465, 0.770, 0.807] has a far less comparative bar height, implying it gets lesser focus in the current environment. Judging by the trends positioned towards the higher end of the percentage scale, it is clear that workplaces are putting a strong emphasis on individual growth, both in terms of knowledge skills and on the personal mental health front.\nBased on the above thought, the answer is:\nThe graph suggests that companies are focusing greatly on the personal development of their employees, making 'Leadership Development' and 'Mental Health Awareness' a priority. However, the skill-based trend 'Digital Literacy' seems to be getting less attention comparatively." } ], "id_raw": 578 }, { "id": "2B0j5HE5xmRAnO0t", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the graph you're observing, which Career and Workplace Development Trend do companies dedicate the most to?" }, { "from": "gpt", "value": "By glancing the vertical span of the bars on the chart from {\"Workplace Flexibility\"}[0.090, 0.416, 0.160, 0.807] to {\"Mental Health Awareness\"}[0.875, 0.074, 0.945, 0.807], each measuring the percentage of respective company dedication, it's evident that one bar in particular extends more vertically than the others. This bar corresponds to {\"Mental Health Awareness\"}[0.875, 0.074, 0.945, 0.807], which reaches the highest point on the chart, thus suggesting that companies dedicate more towards 'Mental Health Awareness' compared to other trends.\nBased on the above thought, the answer is:\nMental Health Awareness." } ], "id_raw": 578 }, { "id": "BKfuZ6dhGNAFqDUA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the bar chart, could you please convert the data representation into a structured CSV format table?" }, { "from": "gpt", "value": "From the chart I observed, it denotes categories of environmental aspects on the X-axis labels [0.111, 0.846, 0.939, 0.921] and their corresponding sustainability scores on the y-axis. The bars and their heights represent the sustainability scores for each category such as {Air Quality}[0.108, 0.270, 0.176, 0.840], {Biodiversity}[0.193, 0.189, 0.261, 0.840], {Clean Energy}[0.278, 0.351, 0.346, 0.840] and so on up to {Resources}[0.875, 0.270, 0.943, 0.840]. By comparing the heights of these bars to the y-axis ticks, I can extract the sustainability score for each category.\nBased on the above thought, the answer is:\nCategory,Sustainability Score \nAir Quality,70\nBiodiversity,80\nClean Energy,60\nClimate Change,85\nWaste Management,55\nWater Quality,65\nLand Use,85\nPollution,90\nPopulation,75\nResources,70" } ], "id_raw": 579 }, { "id": "w1hIH0ehap5kGq5N", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the chart, which area seems to need the most improvement in sustainability?" }, { "from": "gpt", "value": "From my observation of the bar chart, I can figure out that the area that needs the most improvement is the one with the lowest sustainability score. This corresponds to the bar with the minimum height. Looking at the chart, the minimum height is for the {Waste Management}[0.449, 0.392, 0.517, 0.840] category, which is around 55 on the y-axis. Therefore, Waste Management needs the most improvement in sustainability.\nBased on the above thought, the answer is:\nWaste Management." } ], "id_raw": 579 }, { "id": "PNQI4lHuuLTC01un", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest sustainability score and which category does it belong to?" }, { "from": "gpt", "value": "By examining the heights of the bars in the chart, I can determine the highest value. Here, the bar with maximum height corresponds to the {Pollution}[0.704, 0.107, 0.773, 0.840] category, which is closer to 90 on the y-axis ticks. Hence, the highest sustainability score is 90 and it belongs to the Pollution category.\nBased on the above thought, the answer is:\nPollution." } ], "id_raw": 579 }, { "id": "5fSTAYprBGo89D1L", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nCould you provide the data representing the innovation rate of the various sectors in a structured table format?" }, { "from": "gpt", "value": "The bar chart provides data for several different sectors in the knowledge economy, represented as vertical bars. The chart includes bars for sectors such as {BioTech}[0.099, 0.344, 0.168, 1.513], {EdTech}[0.185, 0.578, 0.254, 1.513], {FinTech}[0.271, 0.132, 0.340, 1.513], {AgriTech}[0.357, 0.302, 0.426, 1.513], {MediaTech}[0.444, 0.663, 0.512, 1.513], {InsuranceTech}[0.530, 0.705, 0.598, 1.513], {HealthTech}[0.616, 0.281, 0.685, 1.513], {GreenTech}[0.702, 0.387, 0.771, 1.513], {NanoTech}[0.788, 0.535, 0.857, 1.513] and {GeoTech}[0.874, 0.196, 0.943, 1.513]. The height of each bar on the y-axis denotes the innovation rate of each sector. To provide this data in a structured table format, I will take each sector as a distinct category and list the corresponding innovation rate as a value with respect to that category.\nBased on the above thought, the answer is:\nIndustries,Rate\nBioTech,55\nEdTech,44\nFinTech,65\nAgriTech,57\nMediaTech,40\nInsuranceTech,38\nHealthTech,58\nGreenTech,53\nNanoTech,46\nGeoTech,62" } ], "id_raw": 580 }, { "id": "NsSQHWGI1OEKpwng", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector needs to improve their innovation efforts the most? Why?" }, { "from": "gpt", "value": "To determine which sector need to improve their innovation efforts the most, I will look for the bar representing the lowest value on the y-axis, which represents the innovation rate. The lower the bar, the less innovative the sector is. From my observation, {the InsuranceTech bar}[0.530, 0.705, 0.598, 1.513] is the shortest, which means it has the lowest value among all sectors. Therefore, InsuranceTech appears to be the least innovative and needs to improve their innovation efforts the most.\nBased on the above thought, the answer is:\nInsuranceTech. Because according to the chart, it has the lowest innovation rate among all the sectors." } ], "id_raw": 580 }, { "id": "dTlBMiwSIA3Y42tC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which industry has the highest innovation rate?" }, { "from": "gpt", "value": "The bar chart is used to represent the innovation rate in several sectors of the knowledge economy. The height of each bar corresponds to the innovation rate. The taller a bar is, the higher the rate. To determine the industry with the highest innovation rate, I will need to identify the tallest bar on the chart. Skimming through the heights, {the FinTech bar}[0.271, 0.132, 0.340, 1.513] appears to be the tallest, thus indicating that FinTech is the industry with the highest innovation rate.\nBased on the above thought, the answer is:\nFinTech" } ], "id_raw": 580 }, { "id": "c7Tp0P0wzwBkdmIh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nPlease convert the bar chart's data into a structured CSV format." }, { "from": "gpt", "value": "From the chart, I can see that the x-axis contains different trend categories from {\"GradualDecline\"}[0.052, 0.846, 0.138, 0.953], {\"SteadyDecline\"}[0.143, 0.846, 0.224, 0.947], {\"SuddenDecrease\"}[0.216, 0.846, 0.309, 0.963], to {\"SignificantIncrease\"}[0.805, 0.846, 0.909, 0.976]. Each bar's height corresponds to a value on the y-axis, labeled as {\"Values (%)\"}[0.015, 0.384, 0.029, 0.476], ranging from {0}[0.043, 0.827, 0.052, 0.845] to {80+}[0.035, 0.019, 0.052, 0.036]. Outlier1 and Outlier2 are represented as two bars in the middle of the chart. By referring to the height of each bar, and the labels on the y-axis, I can infer the values for each trend to generate a structured CSV format table.\nBased on the above thought, the answer is:\nTrend,Value\nGradualDecline,55\nSteadyDecline,50\nSuddenDecrease,40\nSignificantDecrease,30\nOutlier1,65\nOutlier2,25\nSteadyIncrease,50\nGradualIncrease,55\nSuddenIncrease,60\nSignificantIncrease,70" } ], "id_raw": 581 }, { "id": "JqSRYYccD8vUjkv9", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nCan you infer some specific patterns or irregularities in the trends based on the bar chart data?" }, { "from": "gpt", "value": "The chart's bar patterns indicate a gradual decline from 55% to 30% in values, starting from {\"GradualDecline\"}[0.052, 0.846, 0.138, 0.953] to {\"SignificantDecrease\"}[0.287, 0.846, 0.395, 0.981]. Then, there are two outliers, {\"Outlier1\"}[0.431, 0.846, 0.481, 0.908] at 65%, which is higher than the previous trend, and {\"Outlier2\"}[0.517, 0.846, 0.566, 0.908] at 25%, which is lower than the previous trends. After the outliers, the values steadily increase from 50% to 70%, starting from {\"SteadyIncrease\"}[0.566, 0.846, 0.652, 0.953] to {\"SignificantIncrease\"}[0.805, 0.846, 0.909, 0.976]. This indicates some abnormal behavior of the two outliers which do not follow the general decrease and increase pattern.\nBased on the above thought, the answer is:\nYes, the chart shows a general decrease and increase pattern with some irregularity. While the values generally decrease from 55% to 30% and then increase from 50% to 70%, two outliers are present in the middle of the chart which do not follow this pattern: Outlier1 at 65% and Outlier2 at 25%." } ], "id_raw": 581 }, { "id": "8si0RMUiysqGeJ3t", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which trend category saw a significant increase in values?" }, { "from": "gpt", "value": "I can infer the trend category by looking at the bar heights. Starting from the left, the bars generally decrease until the {\"Outlier2\"}[0.517, 0.846, 0.566, 0.908] trend. The height of the bars then begins to increase. Therefore, the trend category that saw a significant increase in values begins with {\"SteadyIncrease\"}[0.566, 0.846, 0.652, 0.953].\nBased on the above thought, the answer is:\nSteadyIncrease." } ], "id_raw": 581 }, { "id": "WbZ9sej3eGIeL94z", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart of the companies' revenue change, can you create a CSV format table of this data?" }, { "from": "gpt", "value": "Sure. I notice from the chart's x-axis that the labels {\"Apple\"}[0.115, 0.911, 0.153, 0.958], {\"Google\"}[0.197, 0.911, 0.242, 0.967], {\"Facebook\"}[0.278, 0.911, 0.334, 0.981], {\"Microsoft\"}[0.365, 0.911, 0.419, 0.979], {\"Amazon\"}[0.453, 0.911, 0.503, 0.973], {\"Netflix\"}[0.543, 0.911, 0.585, 0.963], {\"Tesla\"}[0.633, 0.911, 0.667, 0.954], {\"SpaceX\"}[0.713, 0.911, 0.760, 0.969], {\"Twitter\"}[0.801, 0.911, 0.844, 0.965], and {\"LinkedIn\"}[0.883, 0.911, 0.934, 0.975] correspond to various companies. On the y-axis, figures from {0}[0.043, 0.898, 0.052, 0.916] to {35}[0.035, 0.019, 0.052, 0.036] denote the revenue change in percentage terms. The bar heights for each company determine the revenue change values. For instance, the company {\"Apple\"}[0.099, 0.528, 0.168, 0.905] has a bar height that aligns with {15}[0.035, 0.521, 0.052, 0.539] on the y-axis, indicating a 15% change in revenue.\nBased on the above thought, the answer is:\nCompany,Revenue Change (%)\nApple,15\nGoogle,25\nFacebook,18\nMicrosoft,22\nAmazon,12\nNetflix,24\nTesla,30\nSpaceX,28\nTwitter,8\nLinkedIn,10" } ], "id_raw": 583 }, { "id": "EP82CrSb5fZ4LD58", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about the technology sector's performance in terms of revenue change based on the bar chart?" }, { "from": "gpt", "value": "The question asks for an overall understanding of the technology sector's performance in terms of revenue. To answer, I'll assess the bars representing different companies. Noticing that most of the companies' bars, such as {\"Apple\"}[0.099, 0.528, 0.168, 0.905], {\"Google\"}[0.185, 0.277, 0.254, 0.905], and {\"Facebook\"}[0.271, 0.453, 0.340, 0.905], fall within the higher y-axis values, I deduce that the majority of these tech companies experienced significant revenue growth. However,{\"Twitter\"}[0.788, 0.704, 0.857, 0.905] and {\"LinkedIn\"}[0.874, 0.654, 0.943, 0.905] exhibit a relatively small increase in revenue, which means not all companies in the technology sector show a positive trend\nBased on the above thought, the answer is:\nOverall, a considerable growth in revenue is demonstrated across the majority of technology companies. However, there were variations, and not all tech firms showed equally robust growth." } ], "id_raw": 583 }, { "id": "wB4EU47yipB1sHEs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_13.png", "conversations": [ { "from": "human", "value": "\nWhich company had the highest percentage change in revenues according to the graph?" }, { "from": "gpt", "value": "Judging by the heights of the bars, which correspond to the Revenue Change (%) for each company, I need to find the bar with the greatest height. This can be done by comparing the top coordinates of all bars: {\"Apple\"}[0.099, 0.528, 0.168, 0.905], {\"Google\"}[0.185, 0.277, 0.254, 0.905], {\"Facebook\"}[0.271, 0.453, 0.340, 0.905], {\"Microsoft\"}[0.357, 0.352, 0.426, 0.905], {\"Amazon\"}[0.444, 0.603, 0.512, 0.905], {\"Netflix\"}[0.530, 0.302, 0.598, 0.905], {\"Tesla\"}[0.616, 0.151, 0.685, 0.905], {\"SpaceX\"}[0.702, 0.201, 0.771, 0.905], {\"Twitter\"}[0.788, 0.704, 0.857, 0.905], {\"LinkedIn\"}[0.874, 0.654, 0.943, 0.905]. Among these, the {\"Tesla\"}[0.616, 0.151, 0.685, 0.905] bar reaches the highest level on the y-axis, aligning with {30}[0.035, 0.144, 0.052, 0.162], indicating that Tesla had the highest percentage change in revenues.\nBased on the above thought, the answer is:\nTesla." } ], "id_raw": 583 }, { "id": "hlefMPEO0l5Xdwes", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nBased on the information extracted from the bars in the spectrogram, please convert the corresponding topic and influence data into a CSV format table." }, { "from": "gpt", "value": "For this bar chart at {the coordinates}[0.066, 0.026, 0.985, 0.864], it conveys the public interest level in various social and political trends. The X-axis of the chart contains different political/social topics, represented by the labels. The Y-axis, labeled {Active Public Interest Level}[0.015, 0.328, 0.029, 0.562], represents the value of interest level against each topic. To transform the data into a CSV file, I will need to extract the name of each topic from the X-axis, its coordinate given, and the corresponding value on the Y-axis, which can be deduced from the height of each bar. For instance, {Climate Change}[0.106, 0.235, 0.193, 0.864] bar shows approximately 75, and {Election}[0.215, 0.151, 0.300, 0.864] shows approximately 85, and so forth.\nBased on the above thought, the answer is:\nTopic,Influence\nClimate Change,75\nElection,85\nProtest,65\nTrade War,60\nSocial Media,55\nImmigration,70\nUnemployment,35\nHealthcare,80" } ], "id_raw": 584 }, { "id": "p7jqZS98qiDBn3V1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you describe the trend of public interest level in these topics?" }, { "from": "gpt", "value": "The trend of public interest level could be determined by analyzing the heights of the bars. Starting from {Climate Change}[0.108, 0.235, 0.193, 0.864] bar with a high public interest level of about 75 and moving to {Election}[0.215, 0.151, 0.300, 0.864] bar, a prominent topic, sees the pinnacle of interest at 85. After this, there is a gradual decline as we move on to {Protest}[0.322, 0.319, 0.408, 0.864], {Trade War}[0.429, 0.361, 0.515, 0.864], {Social Media}[0.536, 0.403, 0.622, 0.864] and finally {Unemployment}[0.750, 0.571, 0.836, 0.864] has the lowest interest level at 35. However, this decreasing trend reverses as we move towards {Immigration}[0.643, 0.277, 0.729, 0.864] and with {Healthcare}[0.858, 0.193, 0.943, 0.864], the interest level rises sharply again to reach around 80. Thus, the interest level trend fluctuates over these topics and doesn't strictly follow a monotonous increase or decrease.\nBased on the above thought, the answer is:\nThe trend of public interest level first increases, then undergoes a substantial decrease, followed by a significant increase at the end." } ], "id_raw": 584 }, { "id": "FcRk0NwlwOAK2TLu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_39_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which topic has the least public interest level?" }, { "from": "gpt", "value": "To find the topic with the least public interest level, I need to look for the shortest bar in the chart. Each bar in the chart corresponds to a specific topic. By visually examining the bars, it's clear that the shortest bar corresponds to {Unemployment}[0.750, 0.571, 0.836, 0.864], which spans approximately from 0 to 35 according to the y-axis.\nBased on the above thought, the answer is:\nUnemployment" } ], "id_raw": 584 }, { "id": "6AD5efRpCbEvFy5t", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nCould you translate the data from the bar graph of \"Risk complications associated with EdTech Innovations over the years\" to a table CSV format?" }, { "from": "gpt", "value": "This horizontal bar chart plots 'Risk Complication Score' on the x-axis and 'EdTech Innovations' on the y-axis. Each bar represents an 'EdTech Innovation' and each of the same color group represents a different time period: {3 Years}[0.297, 0.083, 0.347, 0.106], {5 Years}[0.297, 0.118, 0.347, 0.141], {7 Years}[0.297, 0.153, 0.347, 0.176], {10 Years}[0.297, 0.188, 0.356, 0.211]. The x-coordinates of the bars represent the scores. I can convert the bars into CSV format by first identifying the 'EdTech Innovations', then determining the length of the corresponding bars to get the scores for each time period. For example, for 'Virtual_Reality_Training'[0.074, 0.172, 0.236, 0.195], it has 4 bars, and by finding the x-coordinates of each bar's right side from the given JSON data, the risk complication scores for different time can be obtained.\nBased on the above thought, the answer is:\nRisk_Complication,3_Years,5_Years,7_Years,10_Years\nVirtual_Reality_Training,-1.23,-1.73,-2.67,-3.24\nArtificial_Intelligence_Education,-2.3,-2.63,-2.0,-3.1\nInnovative_Learning_Methods,-1.43,-2.47,-2.1,-3.05\nE-Learning_Platform,-1.98,-1.96,-2.9,-3.45" } ], "id_raw": 585 }, { "id": "uFuc1KroaAtweews", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the possible reasons for the increase in the risk complication score for 'Virtual_Reality_Training' over the years?" }, { "from": "gpt", "value": "Possible reasons for increased risk complications for Virtual Reality Training could include increasing complexity of the technology, lack of adequate professional training materials, or issues related to the ergonomic and psychosocial risks associated with extended use of these technologies. An increase in the cost associated with deploying VR in education or a rise in legal implications of the VR tech could also lead to an escalation in the risks. Since the study lacks specific underlying factors causing the increasing risk in complication score, these potential reasons are hypothesized based on general knowledge about VR training. The bar chart provided indicates these feeding through an increase in risk complication scores over the years, seen in the increasing length of the bars from {3 Years}[0.727, 0.100, 0.974, 0.141] to {10 Years}[0.323, 0.221, 0.974, 0.261] for 'Virtual_Reality_Training'[0.074, 0.172, 0.236, 0.195].\nBased on the above thought, the answer is:\nPotential reasons could include the increasing complexity of VR technology, inadequate professional training materials, ergonomic and psychosocial risks, increasing costs, and possible legal implications. However, this chart does not provide specific details on these factors." } ], "id_raw": 585 }, { "id": "nPmraRcVZGmgPCo1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_0.png", "conversations": [ { "from": "human", "value": "\nWhich EdTech innovation has the smallest risk complication score in total over the years?" }, { "from": "gpt", "value": "To find the EdTech innovation with the smallest total risk complication score, I would need to sum up the scores for each innovation and then compare them. First calculate the total risk complication score for each innovation, such as 'Virtual_Reality_Training', for 3 Years, 5 Years, 7 Years and 10 Years, the bars are located at [0.727, 0.100, 0.974, 0.141], [0.626, 0.141, 0.974, 0.181], [0.438, 0.181, 0.974, 0.221] and [0.323, 0.221, 0.974, 0.261] respectively. The x-values of the left sides of these four bars are -1.23, -1.73, -2.67 and -3.24 respectively, and the sum is -8.87. Do the similar calculation for 'Artificial_Intelligence_Education', 'Innovative_Learning_Methods', and 'E-Learning_Platform'. Then we can find that 'E-Learning_Platform' has the smallest total risk complication score over the years.\nBased on the above thought, the answer is:\nE-Learning_Platform." } ], "id_raw": 585 }, { "id": "LXrf1TuU9HrctZ2Z", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nWhen observing the bar chart, how would you translate the data present into a structured CSV table?" }, { "from": "gpt", "value": "The chart {subtitle}[0.371, 0.025, 0.800, 0.055] shows the development of the Internet and Communication Industry over a decade. The x-axis scores, represent the development curve of each sector, with its labels ranging from {-4.0}[0.111, 0.919, 0.145, 0.942] to {0.0}[0.963, 0.919, 0.985, 0.942]. On the y-axis, we see various sectors on which this development is based - {Ethernet-Cable provision}[0.197, 0.145], {Optic-fiber provision}[0.197, 0.370], {Wi-Fi Hotspots}[0.197, 0.595], {5G Infrastructure}[0.197, 0.820]. According to the legend[0.204, 0.815, 0.289, 0.891], the salmon pink bars represent the status of these sectors in {2009}[0.248, 0.824, 0.283, 0.847] starting at x=0 and stretching left to their respective values, while the sky blue bars represent the progress up to {2019}[0.248, 0.859, 0.283, 0.882] starting at x=0 and stretching left to their respective values.\nBased on the above thought, the answer is:\nSectors,2009,2019\nEthernet-Cable provision,-3.5,-0.7\nOptic-fiber provision,-3.3,-0.3\nWi-Fi Hotspots,-0.6,-2.7\n5G Infrastructure,-1.2,-1.1" } ], "id_raw": 586 }, { "id": "zMTREoH5k2bG44lK", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nWhich sector needs to prioritize its development, based on the overall trend observed in the chart?" }, { "from": "gpt", "value": "Considering the overall development trend, the {Internet and Communication Industry Development}[0.371, 0.025, 0.800, 0.055] shown on the graph implies the change in each sector from {2009}[0.248, 0.824, 0.283, 0.847] to {2019}[0.248, 0.859, 0.283, 0.882]. By analyzing the {Wi-Fi Hotspots}[0.197, 0.595] bars of 2009 and 2019, we can see this sector has the biggest negative change over the decade, thus needing more attention for improvement.\nBased on the above thought, the answer is:\nWi-Fi Hotspots. Even though other sectors also show negative trends, the decline of Wi-Fi Hotspots from 2009 to 2019 represents the largest negative change and needs urgent prioritization for development." } ], "id_raw": 586 }, { "id": "MR6KHmAE0FVS6nVV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_1.png", "conversations": [ { "from": "human", "value": "\nConsidering the data on the chart, which sector saw the most improvement from 2009 to 2019?" }, { "from": "gpt", "value": "To identify the sector with the maximum progress, I should analyze the pink and the blue bars for each sector on the y-axis. From the {legend}[0.204, 0.815, 0.289, 0.891], pink bars represent the sector's status in {2009}[0.248, 0.824, 0.283, 0.847] and the blue bar for {2019}[0.248, 0.859, 0.283, 0.882]. From this, the difference between the status of each sector in 2009 and 2019 needs to be calculated. Comparing the differences in scores, the maximum development should be identified which would hint at the sector with the most progress.\nBased on the above thought, the answer is:\nOptic-fiber provision." } ], "id_raw": 586 }, { "id": "WNzyv8TjQdFkRmMY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data represented in the bar chart into a CSV format table?" }, { "from": "gpt", "value": "The bar chart shows the scores of four different global trends (Global Warming, Resource Scarcity, Population Growth, Technology Advancement) over four years (2017-2020). {The x-axis}[0.199, 0.919, 1.031, 0.942] represents 'Score' with values from 0 to 80 while {the y-axis}[0.065, 0.172, 0.193, 0.798] represents the four categories of global issues. To derive each value, I can use the corresponding bar length and the annotations inside them, for instance, {the first bar of Global warming in 2017}[0.203, 0.100, 0.513, 0.141] has an annotation of \"30.0\" and the corresponding length indicates a similar value, so the value of Global Warming in 2017 is 30. In the same manner, I can extract values for each year in all categories.\nBased on the above thought, the answer is:\nCategory,2017,2018,2019,2020\nGlobal Warming,30.0,35.8,37.3,32.9\nResource Scarcity,20.0,21.9,23.7,24.3\nPopulation Growth,60.0,62.3,67.2,72.0\nTechnology Advancement,40.0,44.0,49.0,65.0" } ], "id_raw": 587 }, { "id": "axdJbGhnNSpym0Uj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about the trend of technological advancement from the chart?" }, { "from": "gpt", "value": "As for {Technology Advancement}[0.203, 0.704, 0.875, 0.865], it is evident that the bar lengths steadily increase from 2017 to 2020 showing that there has been a gradual increase in the scores. Starting from a score of 40.0 in 2017 to 65.0 in 2020, it can be inferred that technological advancement has shown steady and significant improvement over these years.\nBased on the above thought, the answer is:\nThe Technology Advancement score has seen a consistent increase from 2017 to 2020 indicating a positive trend suggesting substantial progress in this area during these years." } ], "id_raw": 587 }, { "id": "fQ5BslOjfKEBLpmW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_2.png", "conversations": [ { "from": "human", "value": "\nFrom 2017 to 2020, which category has the biggest fluctuation in trend?" }, { "from": "gpt", "value": "Going through each of the four categories to find out the one with the largest variation, I'd subtract the smallest score from the largest for each category. Looking at {Global Warming}[0.203, 0.100, 0.589, 0.221], the scores range from 30.0 in 2017 to 37.3 in 2019, so the fluctuation is 7.3; for {Resource Scarcity}[0.203, 0.302, 0.454, 0.422], the fluctuation is between 20.0 in 2017 and 24.3 in 2020, so the fluctuation is 4.3; similarly for {Population Growth}[0.203, 0.503, 0.948, 0.664] the fluctuation from 60.0 in 2017 to 72.0 in 2020 makes it 12.0; As for {Technology Advancement}[0.203, 0.704, 0.875, 0.865], the range is from 40.0 in 2017 to 65.0 in 2020, hence the fluctuation is 25.0 which is the maximum among all the categories.\nBased on the above thought, the answer is:\nTechnology Advancement." } ], "id_raw": 587 }, { "id": "x1ZKkpUl2NGihpqI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nCould you output the data in the chart you just analyzed in CSV format?" }, { "from": "gpt", "value": "Sure. The chart is a grouped horizontal bar chart, indicating scores for four aspects of CyberSecurity and Data Privacy categories. Each of the four categories has a series of bars representing different years from 2011 to 2014. I can read the scores for each year by measuring the bar length in a category. From these bar lengths, I can assign these score values to each category and year combination. For an instance, considering the {Two-Factor Authentication category}[0.015, 0.775, 0.195, 0.798], the set of horizontal bars representing the years 2011 to 2014 are located : {{2011}[0.414, 0.704, 0.974, 0.744], {2012}[0.457, 0.744, 0.974, 0.784], {2013}[0.242, 0.784, 0.974, 0.824], {2014}[0.285, 0.824, 0.974, 0.865]}. The coordinated represent the lengths of the bars and therefore these can be converted into scores. This process can be repeated for remaining categories to tabulate the data into CSV format.\nBased on the above thought, the answer is:\nCategories,2011,2012,2013,2014\nEncryption Standard,-3.0,-2.8,-3.2,-2.5\nFirewall Protection,-3.1,-2.7,-3.0,-2.4\nVPN Security,-2.8,-2.5,-3.1,-3.0\nTwo-Factor Authentication,-2.6,-2.4,-3.4,-3.2" } ], "id_raw": 588 }, { "id": "9SjNMyPwZy97Iv2D", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nWhich aspect of Cybersecurity and Data Privacy seems to have improved the most during the period 2011 to 2014, according to the chart?" }, { "from": "gpt", "value": "The most improved aspect would be the one with the samllest(variance or) difference between the starting and ending points for the period under consideration. Carefully examining the lengths of the bars assigned to each category for the year 2011 to 2014, one can see: {Encryption Standard}[0.328, 0.100, 0.974, 0.141] to [0.436, 0.221, 0.974, 0.261] (i.e. 2011 to 2014), {Firewall Protection}[0.306, 0.302, 0.974, 0.342] to [0.457, 0.422, 0.974, 0.462], {VPN Security}[0.371, 0.503, 0.974, 0.543] to [0.328, 0.623, 0.974, 0.664], and {Two-Factor Authentication}[0.414, 0.704, 0.974, 0.744] to [0.285, 0.824, 0.974, 0.865]. Therefore, after making a comparison among these four aspects over the designated years, it is clear that the Firewall Protection seems to have made the most improvement.\nBased on the above thought, the answer is:\nFirewall Protection." } ], "id_raw": 588 }, { "id": "LFUHHzTUkDNFtSCB", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the mean score for VPN Security across the four years?" }, { "from": "gpt", "value": "To find the mean score for VPN Security across the years 2011 to 2014, I need to look at the lengths of the bars assigned to VPN Security, located at [0.106, 0.574, 0.195, 0.597]. According to the lengths of the bars, the scores for the years 2011 through 2014 are respectively located at [0.371, 0.503, 0.974, 0.543], [0.436, 0.543, 0.974, 0.583], [0.306, 0.583, 0.974, 0.623], and [0.328, 0.623, 0.974, 0.664]. Add these four scores and then divide the total by 4 to calculate the mean value.\nBased on the above thought, the answer is:\n-2.85" } ], "id_raw": 588 }, { "id": "RFqdNBC4VdyiAhYh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nCould you convert the bar chart data into a table format using the data you have?" }, { "from": "gpt", "value": "From the labels on the yticks {'yticks': [{'label': 'Electric Vehicle Sales', 'label_coordinates': '[0.061, 0.172, 0.207, 0.195]', 'tick_coordinates': '[0.217, 0.181]'}, {'label': 'AI Technology Stocks', 'label_coordinates': '[0.062, 0.373, 0.207, 0.396]', 'tick_coordinates': '[0.217, 0.382]'}, {'label': 'Cyber Security Investments', 'label_coordinates': '[0.015, 0.574, 0.207, 0.597]', 'tick_coordinates': '[0.217, 0.583]'}, {'label': 'Green Energy Consumption', 'label_coordinates': '[0.016, 0.775, 0.207, 0.798]', 'tick_coordinates': '[0.217, 0.784]'}], I can understand that the four categories are \"Electric Vehicle Sales\", \"AI Technology Stocks\", \"Cyber Security Investments\", and \"Green Energy Consumption\" respectively. The x-axis {\"xlabel\"}[0.582, 0.952, 0.620, 0.975] describes the values for each category. Each year, represented by different bars grouped together for each category, form the columns of the table. I can check the values indicated in each bar for the years 2020 to 2023 of these categories to create the CSV table.\nBased on the above thought, the answer is:\nCategories,Year 2020,Year 2021,Year 2022,Year 2023\nElectric Vehicles Sales,2000,2500,3250,4000\nAI Technology Stocks,5000,4500,4300,4100\nCyber Security Investments,3000,3200,2960,3100\nGreen Energy Consumption,1000,1500,2250,3000" } ], "id_raw": 589 }, { "id": "K6uhP5TnMpycnB7u", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nJudging by the chart, which industry is expected to see the least growth over the years according to the predictions?" }, { "from": "gpt", "value": "This requires comparing the overall trend of the bars for each category from 2020 to 2023. Each color bar represents the values for a different year for each category. Looking at the bars for 'AI Technology Stocks'[{'bar_coordinates': '[0.217, 0.302, 0.948, 0.342]'} {'bar_coordinates': '[0.217, 0.382, 0.817, 0.422]'}], there's a clear decline over the years. This suggests that 'AI Technology Stocks' is expected to see the least growth.\nBased on the above thought, the answer is:\nAI Technology Stocks is expected to see the least growth over the years." } ], "id_raw": 589 }, { "id": "2W7xCEG7r0DjhLr7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_4.png", "conversations": [ { "from": "human", "value": "\nWhich sector showed the maximum increase in projections from the year 2020 to 2023?" }, { "from": "gpt", "value": "To find which sector had the maximum increase from 2020 to 2023, I need to consider the first and last values of each bar group. The first bars in the groups represent the year 2020 and the last bars represent the year 2023. Looking at the values, it seems that 'Green Energy Consumption' sector{bar_year_2020}[0.217, 0.824, 0.363, 0.865]{bar_year_2023}[0.217, 0.824, 0.656, 0.865] shows the most substantial increase. Looking at the exact values, 'Green Energy Consumption' grows from 1000 in 2020 to 3000 in 2023, which is an increase of 2000, which is the maximum increase of all.\nBased on the above thought, the answer is:\nGreen Energy Consumption" } ], "id_raw": 589 }, { "id": "ymZTzXnaJ9Pl0g4D", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart you see, generate a table that captures the popularity scores for each fashion trend." }, { "from": "gpt", "value": "From the chart subplot with coordinates 'subplot_0'[0.104, 0.062, 0.985, 0.903], it is clear that the different stories (Emo, Grunge, Steampunk, Boho) represent the y-axis and the 'Scores'[0.522, 0.952, 0.568, 0.975] represent the x-axis. Each of the four bars within each fashion trend represents a separate value. Using the lengths of the bars, I can identify the corresponding values on the x-axis. For instance, the Emo bar's length reaches around 5.1 on the x-axis[0.104, 0.100, 0.626, 0.132]. Similarly, for Grunge, it's about 4.7[0.104, 0.313, 0.585, 0.345], for Steampunk, it's about 5.9[0.104, 0.525, 0.708, 0.557] and for Boho, it's about 6.3[0.104, 0.737, 0.749, 0.769]. By repeating this operation for each bar, I can create a CSV formatted table of the data.\nBased on the above thought, the answer is:\nFashion_Trend,Emo,Grunge,Steampunk,Boho\nEmo,5.1,3.2,2.1,1.5\nGrunge,4.7,5.8,6.0,4.5\nSteampunk,5.9,6.7,7.8,5.7\nBoho,6.3,7.0,8.2,6.5" } ], "id_raw": 590 }, { "id": "CTZGQW8LwI1Evcqa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nWhich fashion trend has a fairly stable score and shows less fluctuation in the graph?" }, { "from": "gpt", "value": "To determine the fashion trend with the least fluctuation in scores, I would examine the difference in lengths of the bars representing each trend. For instance, the 'Emo' trend represented by the pink bar[0.104, 0.100, 0.626, 0.132] shows considerable differences in bar lengths over time, indicating changing scores. Similarly, 'Grunge' represented by the green bar[0.104, 0.345, 0.698, 0.376] and 'Steampunk' represented by the blue bar[0.104, 0.589, 0.902, 0.621] exhibit noticeable differences in bar lengths. In contrast, the 'Boho' trend, represented by the brown bar, has bars that are relatively equivalent in length, indicating small changes in the score over time[0.104, 0.833, 0.943, 0.865]. Thus, the 'Boho' fashion trend shows less fluctuation and is more stable.\nBased on the above thought, the answer is:\nBoho" } ], "id_raw": 590 }, { "id": "BYECpFAYUlRQHwwd", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_5.png", "conversations": [ { "from": "human", "value": "\nIn which fashion trend category is there the highest popularity score?" }, { "from": "gpt", "value": "To find the category with the highest popularity score, I need to locate and compare the furthest ends of each category's relevant bars. Focusing first on 'Emo', represented by the pink bar, the highest score is at 5.1[0.104, 0.100, 0.626, 0.132]. For 'Grunge', the green bar reaches up to 5.8[0.104, 0.345, 0.698, 0.376]. 'Steampunk', represented by the blue bar, even further to 7.8[0.104, 0.589, 0.902, 0.621]. Lastly, 'Boho', the brown bar, has the highest score of 8.2[0.104, 0.833, 0.943, 0.865]. Thus, the Boho category has the highest popularity score.\nBased on the above thought, the answer is:\nBoho" } ], "id_raw": 590 }, { "id": "m9sAoirUcX39BncE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nCan you transform the information given in the bar chart into a structured CSV format?" }, { "from": "gpt", "value": "I can tell from the {chart title}[0.361, 0.025, 0.752, 0.055], 'Education Resource Distribution and Utilization', the four {y-axis labels}[{'LLAMA-Default'}[0.015, 0.164, 0.118, 0.187]; {'LLAMA-HAG'}[0.036, 0.370, 0.118, 0.393]; {'Vicuna-Default'}[0.015, 0.577, 0.118, 0.600]; {'Vicuna-HAG'}[0.036, 0.783, 0.118, 0.807]] and the x-axis labels which range from {0}[0.123, 0.919, 0.132, 0.942] to {160}[1.015, 0.919, 1.042, 0.942] that there are four groupings that represent educational resource utilization scores. Each group has two bars, corresponding to two cycles. The value of each measurement can be read from the labels against the bars such as the {'135'}[0.861, 0.125, 0.888, 0.148] of cycle 1 in the LLAMA-Default category.\nBased on the above thought, the answer is:\nEducation Resource Utilization,Cycle 1,Cycle 2\nLLAMA-Default,135,118\nLLAMA-HAG,112,137\nVicuna-Default,145,112\nVicuna-HAG,107,120" } ], "id_raw": 591 }, { "id": "w491Uh6aj1fsZAgP", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which kind of education resource allocation seems to show a better improvement from Cycle 1 to Cycle 2? And explain why that is the case." }, { "from": "gpt", "value": "To answer this question, I would need to thoroughly analyze the group bars on the chart. Specifically, the bars for {'Cycle 1'}[0.921, 0.824, 0.973, 0.847] and {'Cycle 2'}[0.921, 0.859, 0.972, 0.882] for each category. Looking at categories like {'LLAMA-Default'}[0.015, 0.164, 0.118, 0.187], we see that the bar for Cycle 2 is shorter than Cycle 1, which means utilization decreased. In contrast, when looking at {'LLAMA-HAG'}[0.036, 0.370, 0.118, 0.393], there was an increase in utilization from Cycle 1 to Cycle 2. Therefore, by comparing these bars for each category, a determination can be made.\nBased on the above thought, the answer is:\nThe LLAMA-HAG education resource allocation showed better improvement from Cycle 1 to Cycle 2, as indicated by an increase in resource utilization." } ], "id_raw": 591 }, { "id": "wSyHGNgciUs8wAj0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_6.png", "conversations": [ { "from": "human", "value": "\nWhich category had the highest increase in resource utilization from Cycle 1 to Cycle 2?" }, { "from": "gpt", "value": "To find the category with the highest increase in resource utilization from Cycle 1 to Cycle 2, I need to subtract the score of Cycle 1 from the score of Cycle 2 for each of the four categories. By comparing the scores labelled at the ends of the bars like {'Cycle 1 of LLAMA-Default'}[0.861, 0.125, 0.888, 0.148], {'Cycle 2 of LLAMA-Default'}[0.918, 0.538, 0.944, 0.561], and others, I can calculate the difference.\nBased on the above thought, the answer is:\nLLAMA-HAG" } ], "id_raw": 591 }, { "id": "bQvLnejK2WHctgfi", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nPlease provide a structured table extracted from the provided chart information associated with the various trends in social activities." }, { "from": "gpt", "value": "Observing the layout of the chart with segments dedicated to different activities such as 'Recreation', 'Socialization', 'Outdoor', and 'Lifestyle' represented by colored bars. Each category is associated with trends like Sharp decrease, Gradual recovery, and so forth. The bars' lengths indicate the activity frequency for each category and trend. To compile the data accurately into a structured table, I reference the bars and their corresponding values across different trend categories. Each category's values across different trends are comprehensively observed and recorded for accurate data representation.\nBased on the above thought, the answer is:\nTrend,Recreation,Socialization,Outdoor,Lifestyle\nSharp-Decrease,85,90,75,80\nGradual-Recovery,60,64,70,72\nModerate-Growth,62,65,70,73\nFluctuating-Decline,70,74,75,76\nErratic-Decline,50,52,53,54\nSteady-Recovery,55,58,60,64\nModerate-Increase,65,67,70,73\nPronounced-Increase,75,77,80,85" } ], "id_raw": 592 }, { "id": "rZwnXEXDeBhzP1uZ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nWhich trend seems to be showing a consistent recovery across all social activities?" }, { "from": "gpt", "value": "To analyze which trend shows a consistent recovery across all activities, I examine each bar associated with the specified trends. I observe bars labeled with trends indicative of recovery, such as 'Gradual-Recovery' and 'Steady-Recovery'. By comparing the bars' lengths and their annotations for each activity, I aim to identify a trend where each activity shows an upward or recovering trend length. I confirm the recovery trends by looking at their respective values across all four categories.\nBased on the above thought, the answer is:\nSteady-Recovery, as it shows consistent and notable increases in frequency across all categories when compared to more erratic or fluctuating trends." } ], "id_raw": 592 }, { "id": "y1BHa7VPXx5d7ZyQ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest frequency noted for the 'Outdoor' category?" }, { "from": "gpt", "value": "To determine the highest frequency for the 'Outdoor' category, I will look at the lengths of the bars representing the outdoor activities. By locating each bar tied to the Outdoor section and reading off the value annotations present at the bar ends, I can compare and find the highest value. Each of these bars and their corresponding values are noted, and by comparing them, the maximum can be determined.\nBased on the above thought, the answer is:\n80" } ], "id_raw": 592 }, { "id": "g31T9piwIUiV04KD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nAnalyze the bar chart and produce a table of scores for each technology over the indicated periods of 1, 2, 5, and 10 years." }, { "from": "gpt", "value": "To derive the table, we can examine each group of bars. Each set of bars corresponds to a particular technology as depicted by the y-axis labels: {Robotic Automation's bars}[0.579, 0.087, 0.708, 0.255], {Artificial Intelligence's bars}[0.499, 0.297, 0.933, 0.466], {Blockchain's bars}[0.467, 0.550, 0.917, 0.676], and {Nano Technology's bars}[0.226, 0.719, 0.949, 0.887]. Inside each bar, there's a numeric value, which is the score for each technology at different future phases: 1 year, 2 years, 5 years, and 10 years. This is also supported by the color-coded legend: [{1-year legend}[0.894, 0.064, 0.918, 0.076], {2-years legend}[0.894, 0.090, 0.918, 0.102], {5-years legend}[0.894, 0.116, 0.918, 0.128], {10-years legend}[0.894, 0.142, 0.918, 0.155]]. For instance, under Robotic Automation, the 1-year bar value is 0.8, the 2-year bar value is 1, the 5-year bar value is -0.5, and the 10-year bar value is -2.5. Doing the same for the rest would give us the scores for each technology across the different periods.\nBased on the above thought, the answer is:\nTechnology,1 year,2 years,5 years,10 years\nRobotic Automation,0.8,1,-0.5,-2.5\nArtificial Intelligence,-0.5,1.5,2.2,-1\nBlockchain,2.1,-0.7,1.4,0.7\nNano Technology,-2.2,0.6,2.3,1.5" } ], "id_raw": 593 }, { "id": "6wVZsFBLSiBuFNPs", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nBased on each technology's score progression over the years, which technology seems to be the riskiest investment in the next 10 years?" }, { "from": "gpt", "value": "A risky investment would be one where the scores are highly variable and tend to decrease. Measuring the score variations across the four periods can give us a hint. It appears {Robotic Automation}[0.579, 0.087, 0.708, 0.255] has the most significant decline from 1 score at the 2-year mark{x=0.740, y=0.171} to -2.5 score at the 10-year period{x=0.268, y=0.255}, indicating significant volatility and an overall declining trend. Thus, Robotic Automation could be the riskiest investment for the next 10 years.\nBased on the above thought, the answer is:\nRobotic Automation" } ], "id_raw": 593 }, { "id": "POsRlA68TvgExlRT", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which technology has the highest score in the 5-year span?" }, { "from": "gpt", "value": "The 5-year period is represented by the {orange bars}[0.894, 0.116, 0.918, 0.128] as per the legend. To find the technology with the highest score in this timeline, we need to compare these orange bars across the different technologies. Looking at the numeric values within each orange bar, we can see that the highest figure appears in the {Nano Technology group}[0.579, 0.803, 0.949, 0.845], with a score of 2.3.\nBased on the above thought, the answer is:\nNano Technology" } ], "id_raw": 593 }, { "id": "GtfO71nYw0GQRwDh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nCould you please transform the chart information into a CSV format table?" }, { "from": "gpt", "value": "This bar chart consists of several elements. It has a title {\"Charity Activities by Support Type\"}[0.442, 0.025, 0.723, 0.055], which indicates the main focus of the chart. Then, the x-axis represents scores, which are numerical data obtained based on the length of the bars. {The x-axis tick labels}[{\"\u22122\"}[0.039, 0.919, 0.060, 0.942]; {\"\u22121\"}[0.206, 0.919, 0.226, 0.942]; {\"0\"}[0.378, 0.919, 0.387, 0.942]; {\"1\"}[0.545, 0.919, 0.553, 0.942]; {\"2\"}[0.711, 0.919, 0.720, 0.942]; {\"3\"}[0.877, 0.919, 0.886, 0.942]; {\"4\"}[1.044, 0.919, 1.053, 0.942]] vary from -2 to 4, offering data ranges for interpreting the length of the bars. The y-axis represents categories of charity activities, with {the y-axis tick labels}<{\"Food_Banks\"}[0.087, 0.155, 0.170, 0.179]; {\"Homeless_Shelters\"}[0.039, 0.367, 0.170, 0.391]; {\"Educational_Programs\"}[0.015, 0.580, 0.170, 0.603]; {\"Animal_Welfare\"}[0.062, 0.792, 0.170, 0.815]>. Each category has three bars referring to different types of support labeled within {the legend}<[0.805, 0.074, 0.978, 0.185]>. By analysing the bars' length and the corresponding number, I can then generate the CSV table. For instance, in the category of \"Food Banks\", the length of {the bar for private donations}[0.383, 0.100, 0.632, 0.143] extends approximately to 1.5 as indicated by the label \"1.5\", the length of {the bar for state funding}[0.383, 0.143, 0.782, 0.185] extends to 2.4, {the bar for volunteer work)[0.383, 0.185, 0.516, 0.228] extends to 0.8. Using this same approach, I can determine the values for other types of charity activities.\nBased on the above thought, the answer is:\nCharity_Type,Private_Donations,State_Funding,Volunteer_Work\nFood_Banks,1.5,2.4,0.8\nHomeless_Shelters,2.2,3.1,-0.5\nEducational_Programs,3.4,1.8,0.9\nAnimal_Welfare,0.9,2.7,-1.0" } ], "id_raw": 595 }, { "id": "Bb3ITIzMsLEAVfk1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nWhich charity activity has the highest net score across all types of support and why?" }, { "from": "gpt", "value": "To find the charity activity with the highest net score, I need to calculate the net scores of all charity activities and then compare them. Starting with 'Food Banks', I determine the net score by adding up scores of each type of support under this category, these scores are labeled at the end of {the corresponding bars}[{\"Private Donations\"}[0.383, 0.100, 0.632, 0.143] {\"State Funding\"}[0.383, 0.143, 0.782, 0.185] {\"Volunteer Work\"}[0.383, 0.185, 0.516, 0.228]]. The same procedure is put up on 'Homeless Shelters', 'Educational Programs' and 'Animal Welfare'. Then I can find out that among the four activities, 'Educational Programs' has the highest net score, which is contributed by the relatively high score in 'Private Donations' and balanced support from other two types.\nBased on the above thought, the answer is:\nEducational Programs has the highest net score across all types of support. This is mainly due to the relatively high score in Private Donations and balanced support from State Funding and Volunteer Work." } ], "id_raw": 595 }, { "id": "SX3uSDChlyeX9i4F", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the net scores across all three types of support for 'Homeless Shelters'?" }, { "from": "gpt", "value": "To calculate the net scores for 'Homeless Shelters', we need to add up the scores of each type of support under this category. Firstly, the score for 'Private Donations' can be found on the corresponding bar chart with coordinates [0.383, 0.313, 0.749, 0.355], and the labeled score at coordinates [0.760, 0.152, 0.782, 0.176] is 2.2. Secondly, the score for 'State Funding' can be found on the corresponding bar chart with coordinates [0.383, 0.355, 0.898, 0.398], and the labeled score at coordinates [0.876, 0.365, 0.898, 0.388] is 3.1. Lastly, the score for 'Volunteer Work' can be found on the corresponding bar chart with coordinates [0.299, 0.398, 0.383, 0.440], and the score labeled at coordinates [0.322, 0.407, 0.344, 0.431] is -0.5. Adding these scores up, we can derive the net scores for 'Homeless Shelters'.\nBased on the above thought, the answer is:\n4.8" } ], "id_raw": 595 }, { "id": "phVIPnrO73tTg1Tr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the information provided in the horizontal bar chart into a structured table in CSV format?" }, { "from": "gpt", "value": "The bar chart title is {\"Consumer Index Scores by Region\"}[0.408, 0.025, 0.692, 0.055] from the subtitle, which provides a generalized idea of the data being represented. The chart data is visible through numerical text represented on the top side of each bar, along with color-coded bar comparisons representing the numerical differences. \nFrom the yticks, I have located the labels {\"North America\"}[0.015, 0.164, 0.116, 0.187], {\"Europe\"}[0.067, 0.370, 0.116, 0.393], {\"Asia\"}[0.087, 0.577, 0.116, 0.600] and {\"Africa\"}[0.076, 0.783, 0.116, 0.807] representing the four different regions.\nFrom the 'sublegend', I learned that we have two indices: {\"OffP_Consumer_Index\"}[0.177, 0.083, 0.329, 0.107] color-coded as green, and {\"OnP_Consumer_Index\"}[0.177, 0.118, 0.327, 0.142] color-coded as blue.\nBy locating the positioning and length of each bar from the 'bars' of the chart data, and combining this with previously retrieved tick labels and bar text, I am able to derive the exact values represented in the chart for each region's offline and online consumer indices.\nBased on the above thought, the answer is:\nRegion,OffP_Consumer_Index,OnP_Consumer_Index\nNorth America,-1.2,-1.5\nEurope,-1.8,-2.0\nAsia,-1.5,-1.6\nAfrica,-3.0,-2.5" } ], "id_raw": 596 }, { "id": "VbqbryOtixzuN1mv", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nCan you assert which region has been affected the most by the shift to online consumption, according to the chart?" }, { "from": "gpt", "value": "To answer this question, I will assess the relative changes in the OnP_Consumer_Index and OffP_Consumer_Index within each region. This involves comparing the lengths of the corresponding bars for each region. The four regions are {\"North America\"}[0.015, 0.164, 0.116, 0.187], {\"Europe\"}[0.067, 0.370, 0.116, 0.393], {\"Asia\"}[0.087, 0.577, 0.116, 0.600], and {\"Africa\"}[0.076, 0.783, 0.116, 0.807]. From the comparison of the changes in their bars, it can be observed that {\"Africa\"}[0.076, 0.783, 0.116, 0.807] exhibits the steepest shift, as indicated by the significant length difference between its {\"OffP_Consumer_Index bar\"}[0.166, 0.720, 0.974, 0.792] and {\"OnP_Consumer_Index bar\"}[0.300, 0.792, 0.974, 0.865]. Therefore, Africa appears to have been most affected by a shift to online consumption.\nBased on the above thought, the answer is:\nAfrica, as the length of the bar representing the OnP_Consumer_Index score is markedly less negative than the bar representing the OffP_Consumer_Index score, indicating a considerable shift to online consumption in the region." } ], "id_raw": 596 }, { "id": "KnxQqojcFnFTIfTt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_11.png", "conversations": [ { "from": "human", "value": "\nWhich region has the least difference between OffP_Consumer_Index and OnP_Consumer_Index score?" }, { "from": "gpt", "value": "To determine which region has the least difference between the OffP_Consumer_Index and OnP_Consumer_Index score, we can first observe from the graph that {\"North America\"}[0.015, 0.164, 0.116, 0.187], {\"Europe\"}[0.067, 0.370, 0.116, 0.393], {\"Asia\"}[0.087, 0.577, 0.116, 0.600] and {\"Africa\"}[0.076, 0.783, 0.116, 0.807] are the regions with different scores conveyed by the bars' lengths. Comparing the lengths of bars for both online and offline purchases within each region, we can see that in {\"North America\"}[0.015, 0.164, 0.116, 0.187], where the {\"OffP_Consumer_Index bar\"}[0.651, 0.100, 0.974, 0.173] and {\"OnP_Consumer_Index bar\"}[0.570, 0.173, 0.974, 0.245] are quite close, the difference in score is the smallest.\nBased on the above thought, the answer is:\nNorth America." } ], "id_raw": 596 }, { "id": "IJOp7JuKAomqzDwc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nGiven the bar chart you observed, can you help form a structured table?" }, { "from": "gpt", "value": "Based on the title of the graph {\"Local Government and Community Development Performance\"}[0.322, 0.025, 0.840, 0.055] and the data located on {the y-axis tick labels}[{\"Slow Increase\"}[0.072, 0.148, 0.168, 0.172]; {\"Uneven Development\"}[0.017, 0.311, 0.168, 0.334]; {\"Gradual Improvement\"}[0.015, 0.473, 0.168, 0.497]; {\"Steep Increase\"}[0.065, 0.636, 0.168, 0.659]; {\"Rapid Recovery\"}[0.060, 0.799, 0.168, 0.822]], I can infer the y-axis corresponds to the performance parameters. The x-axis represents the corresponding values that range from {0}[0.167, 0.919, 0.189, 0.942] to {1.2}[1.005, 0.919, 1.027, 0.942]. For each of the performance parameters, there are two bars of different colors. The {legend}[0.750, 0.074, 0.978, 0.151] indicates that the {blue bars}<{\"Local Government\"}[0.794, 0.083, 0.921, 0.106]> represents Local Government performance, and the {orange bars}<{\"Community Development\"}[0.794, 0.118, 0.973, 0.141]> show Community Development performance. For generating the corresponding table, the length of each bar needs to be examined and the x-axis referred to determine the corresponding value.\nBased on the above thought, the answer is:\nPerformance Measurement,Local Government,Community Development\nSlow Increase,0.8,0.6\nUneven Development,0.6,0.5\nGradual Improvement,0.65,0.6\nSteep Increase,0.95,0.8\nRapid Recovery,1.1,1.0" } ], "id_raw": 597 }, { "id": "kh7NhtEIrvhj8Gm9", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which organ do you think performs better in terms of Rapid Recovery? Can you explain why?" }, { "from": "gpt", "value": "This question asks for a comparison between the {blue bar}<{\"Local Government\"}[0.794, 0.083, 0.921, 0.106]> and the {orange bar}<{\"Community Development\"}[0.794, 0.118, 0.973, 0.141]> at the Rapid Recovery performance parameter. The length of {Local Government's bar for Rapid Recovery}[0.178, 0.751, 0.947, 0.808] appears to be longer than {Community Development's bar}[0.178, 0.808, 0.877, 0.865] by referring to the x-axis, we can confirm that Local Government's performance value for Rapid Recovery is approximately 1.1 while the Community Development's performance value is approximately 1.0. Thus, it indicates that Local Government performs better in terms of Rapid Recovery.\nBased on the above thought, the answer is:\nLocal Government. Since the performance value of Local Government for Rapid Recovery is higher than that of Community Development, we can conclude that it performs better." } ], "id_raw": 597 }, { "id": "won1XJWUxuFo6Kvc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in performance value regarding Uneven Development between Local Government and Community Development?" }, { "from": "gpt", "value": "To ascertain the difference in performance value regarding Uneven Development between Local Government and Community Development, it's essential to compare the two bar lengths for that performance parameter. Looking at {the bar of Uneven Development for Local Government}[0.178, 0.263, 0.597, 0.320] and for {Community Development}[0.178, 0.320, 0.527, 0.377], by referring to the x-axis values, it can be determined that the performance value of Local Government for Uneven Development is approximately 0.6 while that for Community Development is approximately 0.5. Hence, the difference becomes 0.6 - 0.5 = 0.1.\nBased on the above thought, the answer is:\n0.1" } ], "id_raw": 597 }, { "id": "yZbcnoUtyWRkUXJe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the bar chart into a structured CSV table?" }, { "from": "gpt", "value": "To convert the data into a CSV table, I need to look at various parts of the chart. From {the x-axis label \"Number of Visitors\"}[0.517, 0.952, 0.646, 0.975] and {the four y-axis labels}[{\"Marginal Growth\"}[0.054, 0.164, 0.169, 0.187]; {\"Steep Increase\"}[0.066, 0.370, 0.169, 0.393]; {\"Rapid Growth\"}[0.074, 0.577, 0.169, 0.600]; {\"Unpredictable Decline\"}[0.015, 0.783, 0.169, 0.807]], I can see that the y-axis represents different visitor trends and the x-axis represents the number of visitors under each trend. Now looking at the {legend}[0.816, 0.074, 0.978, 0.151], we have two categories: \"National Gallery\" (represented by {light red bars})[0.821, 0.085, 0.849, 0.101] and \"Historic Site\" (represented by {light blue bars})[0.821, 0.120, 0.849, 0.136]. Together, this tells me that each group of bars represents the number of visitors at these two destinations under different trends. From this, I can extract and construct each row of the CSV table by locating each rectanguler bar and the corresponding text annotations that denote the number of visitors. For instance, {the light red bar of \"marginal growth\"}[0.178, 0.100, 0.343, 0.173] and its {text annotation \"30\"}[0.343, 0.125, 0.361, 0.148] indicates that there were 30 visitors at the National Gallery under this trend.\nBased on the above thought, the answer is:\nTrend,National Gallery,Historic Site\nMarginal Growth,30,50\nSteep Increase,67,88\nRapid Growth,100,130\nUnpredictable Decline,97,140" } ], "id_raw": 598 }, { "id": "xegIv2Xz64Nx8EMS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which site do you think experienced a more positive growth in the number of visitors? Please explain your reasoning." }, { "from": "gpt", "value": "To answer this, I need to examine {the bars associated with \"National Gallery\"}[0.821, 0.085, 0.849, 0.101] and {those associated with \"Historic Site\"}[0.821, 0.120, 0.849, 0.136] across all the visitor trends. In the \"Marginal Growth\" trend, the Historic Site has more visitors as compared to the National Gallery. This observation is based on comparing {the length of the two bars}[0.178, 0.100, 0.343, 0.173] and [0.178, 0.173, 0.453, 0.245] in this category. Similarly, in the \"Steep Increase\" and \"Rapid Growth\", the Historic Site still attracted more visitors. This can be verified by comparing {the respective bar lengths}[0.178, 0.307, 0.546, 0.379] and [0.178, 0.379, 0.661, 0.452] for \"Steep Increase\", and [0.178, 0.514, 0.727, 0.586] and [0.178, 0.586, 0.892, 0.658] for \"Rapid Growth\". In the \"Unpredictable Decline\" trend, although it represented a decrease, the Historic Site still had more viewership. I can observe this by looking at {the pair of bars}[0.178, 0.720, 0.711, 0.792] and [0.178, 0.792, 0.947, 0.865] in this category. Thus, the Historic Site seems to have a more positive growth overall.\nBased on the above thought, the answer is:\nThe Historic Site. This conclusion is based on the observation that across all the visitor trends, the Historic Site consistently attracted more visitors than the National Gallery." } ], "id_raw": 598 }, { "id": "MkGw3h9OG3Od4lR9", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_13.png", "conversations": [ { "from": "human", "value": "\nUnder which visitors' trend did the National Gallery and the Historic Site attract the highest number of combined visitors?" }, { "from": "gpt", "value": "To answer this question, I need to sum up the number of visitors for each trend and identify the one with the highest total. Looking at each pair of bars from bottom to top {the bars in the \u201cMarginal Growth\u201d category}[0.178, 0.100, 0.343, 0.173] and [0.178, 0.173, 0.453, 0.245] have text annotations of 30 and 50, totaling 80 visitors. Similarly, {the bars in the \u201cSteep Increase\u201d category}[0.178, 0.307, 0.546, 0.379] and [0.178, 0.379, 0.661, 0.452] represent 67 and 88 visitors, totaling 155. For the \u201cRapid Growth\u201d category, {the bars}[0.178, 0.514, 0.727, 0.586] and [0.178, 0.586, 0.892, 0.658] indicate 100 and 130 visitors, thus totaling 230. Finally, in the \u201cUnpredictable Decline\u201d category, {the bars}[0.178, 0.720, 0.711, 0.792] and [0.178, 0.792, 0.947, 0.865] represent 97 and 140 visitors, which sums up to 237 visitors, which is the highest combined visitors number among all trends.\nBased on the above thought, the answer is:\nUnpredictable Decline." } ], "id_raw": 598 }, { "id": "UkyLIid8cOdb5hsK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV table to record the scores of each fashion trend under the Gothic and Punk subcultures?" }, { "from": "gpt", "value": "Yes, I can gather this information by observing each bar's starting point in relation to the x-axis {x-ticks}[{-4.0}[-0.027, 0.919, 0.006, 0.942]; {-3.5}[0.096, 0.919, 0.130, 0.942]; {-3.0}[0.219, 0.919, 0.253, 0.942]; {-2.5}[0.342, 0.919, 0.376, 0.942]; {-2.0}[0.465, 0.919, 0.499, 0.942]; {-1.5}[0.588, 0.919, 0.622, 0.942]; {-1.0}[0.711, 0.919, 0.745, 0.942]; {-0.5}[0.834, 0.919, 0.868, 0.942]; {0.0}[0.963, 0.919, 0.985, 0.942]]. For instance, I noticed that in the Gothic subculture, the Fashion Trend 1 bar begins at the -3.5 position of the x-axis, hence the score for Gothic subculture for Fashion Trend 1 is -3.5. I need to carry out a similar observation for other fashion trends of each subculture to generate the CSV table.\nBased on the above thought, the answer is:\nSubculture,Fashion Trend 1,Fashion Trend 2,Fashion Trend 3,Fashion Trend 4\nGothic,-3.5,-1.5,-2.0,-1.0\nPunk,-1.0,-2.0,-0.5,-3.5" } ], "id_raw": 599 }, { "id": "0UaNgfeDXPHB3lVc", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nWhich subculture seems to have a more varied impact on the different fashion trends, based on your observation of the bar chart?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the range in scores within each subculture. The Gothic subculture has all trends trending negatively with a difference of -3.5 to -1.0 showing a steady downward trend. Meanwhile, under the Punk subculture, {the values}[{-1.0}[0.728, 0.525, 0.974, 0.610]; {-2.0}[0.482, 0.610, 0.974, 0.695]; {-0.5}[0.851, 0.695, 0.974, 0.780]; {-3.5}[0.113, 0.780, 0.974, 0.865];] range from -3.5 to -0.5 indicating a more varied impact across the fashion trends.\nBased on the above thought, the answer is:\nThe Punk subculture has a more varied impact on the different fashion trends." } ], "id_raw": 599 }, { "id": "SfTizk2wP0Cfw6zq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_40_14.png", "conversations": [ { "from": "human", "value": "\nWhich fashion trend under the Gothic subculture has the greatest impact based on the score?" }, { "from": "gpt", "value": "To determine the fashion trend with the greatest impact under the Gothic subculture, I need to locate the bar with the least negative value or the largest bar, since a smaller negative value equates to greater impact. Observing the Gothic subculture-related bars, I found that {the Fashion trend 4 bar}[0.728, 0.355, 0.974, 0.440] extends to -1.0 on the x-axis, which is higher than the other three fashion trends under the Gothic subculture. Hence, the Fashion Trend 4 shows the greatest impact under the Gothic subculture.\nBased on the above thought, the answer is:\nFashion Trend 4." } ], "id_raw": 599 }, { "id": "th0cJstYiJmvGStV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart you observed, can you represent the data in a CSV format?" }, { "from": "gpt", "value": "This horizontal bar chart depicts the market share percentages of several industries over four years. The industries, and their respective bars listed from top to bottom are: {Telecommunication}[0.199, 0.784, 0.810, 0.887], {ECommerce}[0.199, 0.578, 0.578, 0.681], {Software}[0.199, 0.373, 0.944, 0.476], and {Manufacturing}[0.199, 0.168, 0.382, 0.270]. For each industry, there are four bars with different colors, representing percentages for the years 2017-2020, as it can be seen from the {legend}[0.352, 0.027, 0.828, 0.119]. To convert this into a CSV table, I'll use the x-axis labels {0}[0.193, 0.942, 0.204, 0.970] to {90}[0.589, 0.942, 0.611, 0.970] to gauge the lengths of the bars and extract the percentages for each year for every industry. For example, the first bar for Telecommunication represents the year 2017 and extends to about the 30% mark on the x-axis.\nBased on the above thought, the answer is:\nIndustry,Market Share 2017,Market Share 2018,Market Share 2019,Market Share 2020\nTelecommunication,30,33,39,37\nECommerce,12,20,25,28\nSoftware,45,47,40,35\nManufacturing,15,11,8,7" } ], "id_raw": 600 }, { "id": "2XH7dHGhFGYj0b0x", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nBased on the information given in the chart, which industry dominated the market throughout the 2017-2020 period?" }, { "from": "gpt", "value": "The industry that dominated the market throughout the given period can be identified by which of them consistently had the longest bars in the chart. Upon scanning the chart, I see that the {Software} industry[0.199, 0.373, 0.944, 0.476] consistently had the longest length of bars for all the listed years 2017 to 2020. Thus, it can be inferred that the Software industry had the highest market share, and therefore dominated the market during this period.\nBased on the above thought, the answer is:\nSoftware" } ], "id_raw": 600 }, { "id": "JvxLwBIocKyZWvoc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_0.png", "conversations": [ { "from": "human", "value": "\nWhich industry shows the greatest increase in market share from 2017 to 2020?" }, { "from": "gpt", "value": "To answer this question, I'll compare the lengths of the bars for each industry from 2017 to 2020. Starting with the {Telecommunication} industry[0.199, 0.784, 0.810, 0.887], I see that the bars extend from approximately the 30% mark to the 35% mark on the x-axis. This represents an increase of about 7%. The next industry is {ECommerce}[0.199, 0.578, 0.578, 0.681], where the bars range from roughly 10% to over 25%, indicating an increase of about 15%. The {Software} industry[0.199, 0.373, 0.944, 0.476] shows a decrease in market share, while the {Manufacturing} industry[0.199, 0.168, 0.382, 0.270] also shows a decrease. Therefore, the industry with the highest increase in market share from 2017 to 2020 is the ECommerce industry.\nBased on the above thought, the answer is:\nECommerce" } ], "id_raw": 600 }, { "id": "Pu5XfNguI26l16Zn", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nFrom the horizontal stacked bar chart, please construct a CSV table that illustrates the manufacturing efficiency for each sector and includes the measures for Raw Material Efficiency, Production Speed, and Quality Control." }, { "from": "gpt", "value": "From the bar chart, I notice four industries' efficiency measures based on the y-axis labels - {\"Electronics\"}[0.068, 0.828, 0.162, 0.856], {\"Automobiles\"}[0.054, 0.633, 0.162, 0.661], {\"Pharmaceuticals\"}[0.019, 0.438, 0.162, 0.466], and {\"Textiles\"}[0.098, 0.242, 0.162, 0.270]. There are three colors of bars in each category, corresponding of the measures - {Raw Material Efficiency}[0.273, 0.045, 0.475, 0.073], {Production Speed}[0.559, 0.045, 0.711, 0.073] and {Quality Control}[0.794, 0.045, 0.925, 0.073] from the legend created by stack-ups of horizontal rectangular bars for which we can use to extract values for the table. \nBy analyzing the x-axis ticks and the corresponding values of each stacked bars, for example, {the Electronics manufacturing efficiency for Raw Material Efficiency}[0.174, 0.791, 0.511, 0.888], I can estimate and confirm the values from the right edge of the colored bars, which is equal to 3000 as shown on the x-axis.\nBased on the above thought, the answer is:\nIndustry,Raw Material Efficiency,Production Speed,Quality Control\nElectronics,3000,2000,1800\nAutomobiles,2500,2200,1700\nPharmaceuticals,2600,1500,2000\nTextiles,2300,1800,1300" } ], "id_raw": 603 }, { "id": "RNHWUPOp6XiglPfS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what could you infer about the Automobiles industry's efficiency compared to other industries?" }, { "from": "gpt", "value": "Observing the lengths of the different colored bars, we can infer that the Automobiles sector represented by {the bars for Automobiles}[0.174, 0.595, 0.892, 0.693] performs well overall in all areas, but doesn't lead in any of the three efficiency measures. Focusing on the darker green section which refers to {Production Speed}[0.559, 0.045, 0.711, 0.073], the Automobiles sector has the longest bar, meaning that it has the highest production speed among the four industries. Moreover, focusing on the blue section which refers to {Quality Control}[0.794, 0.045, 0.925, 0.073], the Automobile sector looks like it's performing above average, which suggests that it's also efficient in maintaining high quality in its products.\nBased on the above thought, the answer is:\nThe Automobiles industry demonstrates strong overall performance in all efficiency measures - Raw Material Efficiency, Production Speed, and Quality Control. It has the highest production speed among all industries. It also has a good quality control, performing above average compared to others." } ], "id_raw": 603 }, { "id": "33LFYrXBqxxHDN2S", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_3.png", "conversations": [ { "from": "human", "value": "\nWhich sector has the greatest total manufacturing efficiency when summing up all three measures?" }, { "from": "gpt", "value": "To determine this, I will analyse the distance from the right end of the {Quality Control bars}[{Electronics}[0.735, 0.791, 0.936, 0.888]; {Automobiles}[0.701, 0.595, 0.892, 0.693]; {Pharmaceuticals}[0.634, 0.400, 0.858, 0.497]; {Textiles}[0.634, 0.204, 0.780, 0.302]] to the chart's y-axis for every industry. The sector with the longest bar will have the highest total efficiency.\nBased on the above thought, the answer is:\nElectronics" } ], "id_raw": 603 }, { "id": "RCZPeIJRNikel94w", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart, could you reformat the data into a table in CSV format?" }, { "from": "gpt", "value": "From the bar chart, we can gather that the x-axis represents the distinct influence areas, specifically {High_Influence_Areas}[0.225, 0.660, 0.945, 0.888] and {Weak_Influence_Areas}[0.225, 0.204, 0.601, 0.432]. Each bar within these two categories is marked with different colors corresponding to different financial market indicators as stated in {the sublegend}[0.603, 0.153, 0.826, 0.126]: {Stock Market}[0.436, 0.045, 0.549, 0.073] with the color code #d66555, which represents the Stock Market index points; {Interest Rates}[0.436, 0.087, 0.557, 0.115] in the color code #88a27d, which corresponds to the percentage of Interest Rates; {Inflation Rate}[0.640, 0.045, 0.757, 0.073] in the color code #a1b5ce, indicating the percentage of Inflation Rate; and finally, {Unemployment Rate}[0.640, 0.087, 0.819, 0.115] in the color code #34495e, symbolizing the percentage of Unemployment Rate. According to the x-axis tick labels, the origin is at {0}[0.219, 0.942, 0.230, 0.970] and the maximum x value is at {6500}[0.822, 0.942, 0.866, 0.970], I need to calculate the corresponding x value for each sub bar by calculating the ratio and then multiply it by the total range (6500-0). Each cell in the table can be represented by measuring the length of the designated colored segment in each stacked bar.\nBased on the above thought, the answer is:\nInfluence_Areas,Stock_Market,Interest_Rates,Inflation_Rate,Unemployment_Rate\nHigh_Influence_Areas,5000,1800,700,60\nWeak_Influence_Areas,2500,950,400,100" } ], "id_raw": 604 }, { "id": "g8XkhfchirbNUDB2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which area of influence should receive more attention for improvement? Reason your answer." }, { "from": "gpt", "value": "Given the four financial market indicators, we can clearly see that the {Weak_Influence_Areas}[0.225, 0.204, 0.601, 0.432] contain the alarming rate for the Unemployment Rate, which has an abrupt increase as observed from the {dark blue segment}[0.592, 0.204, 0.601, 0.432] as per the {sublegend}[0.603, 0.153, 0.826, 0.126]. The 'Stock Market' value, 'Interest Rates', and 'Inflation Rate', represented by {red segment}[0.225, 0.204, 0.463, 0.432], {green segment}[0.463, 0.204, 0.554, 0.432], and {light blue segment}[0.554, 0.204, 0.592, 0.432] respectively, are also relatively high in comparison to the other area of influence. These indicators are crucial to the overall economic health and well-being in that area. Hence, by deducing from the values of these indicators, Weak_Influence_Areas demand more attention for improvement.\nBased on the above thought, the answer is:\nWeak_Influence_Areas. The factors of a high Unemployment Rate, coupled with a fair contribution to Stock Market Index points, Interest Rates, and Inflation Rate, all indicate a certain level of economic distress, hence requiring more attention for overall improvement." } ], "id_raw": 604 }, { "id": "vB6lZqU47fDuHv0A", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_4.png", "conversations": [ { "from": "human", "value": "\nWhich area of influence has the largest value in the 'Stock Market' according to the chart?" }, { "from": "gpt", "value": "To determine the area of influence with the highest 'Stock Market' value, I need to compare the corresponding segments of the Stock Market in each stacked bar, which are represented by {the red segments}<{'red segments'}[{'High_Influence_Areas'}[0.225, 0.660, 0.701, 0.888]; {'Weak_Influence_Areas'}[0.225, 0.204, 0.463, 0.432]]> and distinguishable by their distinct #d66555 color. It is clear that the red segment in {High_Influence_Areas}[0.225, 0.660, 0.701, 0.888] is significantly larger than that in {Weak_Influence_Areas}[0.225, 0.204, 0.463, 0.432], indicating that the 'Stock Market' value is indeed larger in High_Influence_Areas.\nBased on the above thought, the answer is:\nHigh_Influence_Areas" } ], "id_raw": 604 }, { "id": "UzKgHflRXiQMMFRF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nFrom your observation in the chart, can you please represent the data in a CSV format?" }, { "from": "gpt", "value": "The chart shows the different investments' return rate in horizontal bars. The vertical axis orders investments types: {low risk bonds}[0.186, 0.791, 0.249, 0.888], {mutual funds}[0.186, 0.595, 0.249, 0.693], {real estate}[0.186, 0.400, 0.249, 0.497], and {equity}[0.186, 0.204, 0.249, 0.302]. The horizontal axis represents the return rates, ranging from {0}[0.181, 0.942, 0.192, 0.970] to {20}[0.490, 0.942, 0.512, 0.970]. The bars are color-coded to distinguish between the different investment types, as indicated by {the legend items}[{'Low Risk Bonds Return (%)'}[0.001, 0.045, 0.234, 0.073]; {'Mutual Funds Return (%)'}[0.317, 0.045, 0.531, 0.073]; {'Real Estate Return (%)'}[0.615, 0.045, 0.811, 0.073]; {'Equity Return (%)'}[0.894, 0.045, 1.048, 0.073]]. The length of each bar corresponds to the return rate for each type of investment. For instance, the length of the {bar for 'Low Risk Bonds'}[0.186, 0.791, 0.249, 0.888] corresponds to a return rate of 4.\nBased on the above thought, the answer is:\nInvestment Type,Return Rate\nLow Risk Bonds,4\nMutual Funds,8\nReal Estate,6\nEquity,20" } ], "id_raw": 605 }, { "id": "AaW31IDHj9BhijpO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what type of investment would you recommend for someone seeking to maximize returns on their investment? And, why?" }, { "from": "gpt", "value": "In terms of achieving maximum return on investment, the length of the bar is critical as it represents return rate. The longest bar in the chart, indicating a higher return rate, is {Equity}[0.186, 0.204, 0.249, 0.302], reaching the 20 mark on the x-axis, far exceeding the other investment types. Therefore, Equity would provide the highest return. However, it's worth noting that this doesn't take into account the risk associated with each type of investment.\nBased on the above thought, the answer is:\nI would recommend Equity. This is because Equity has the highest return rate (20%) among all represented investment types. But it's crucial to also consider the risk levels associated with equity investments." } ], "id_raw": 605 }, { "id": "gJUhEpcDwKoUzeY0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in return rates between Real Estate and Equity?" }, { "from": "gpt", "value": "To determine the difference in return rates between Real Estate and Equity, I have to compare the lengths of their corresponding bars. The {real estate bar}[0.186, 0.400, 0.249, 0.497] stretches to about the 6 position on the x-axis, while {the equity bar}[0.186, 0.204, 0.249, 0.302] extends roughly to the 20 position. The difference can be calculated by subtracting these two values.\nBased on the above thought, the answer is:\n14" } ], "id_raw": 605 }, { "id": "3xBHjxgKdRjJKGNB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data presented in the chart to a CSV table?" }, { "from": "gpt", "value": "Based on the x-axis labels, which range from {\"0\"}[0.274, 0.942, 0.285, 0.970] to {\"90\"}[0.447, 0.942, 0.469, 0.970], and the grouped bars under each category ({\"Rights Protection\"}[0.119, 0.763, 0.267, 0.791] and {\"Social Inequality\"}[0.124, 0.307, 0.267, 0.335]), we can understand the percentages for each indicator. Each group consists of four bars representing different parameters. They are {\"Gender Wage Gap (%)\"}[-0.093, 0.045, 0.102, 0.073], {\"Maternity Leave Duration (weeks)\"}[0.185, 0.045, 0.480, 0.073], {\"Employment Rate Women (%)\"}[0.563, 0.045, 0.825, 0.073], and {\"Employment Rate Men (%)\"}[0.908, 0.045, 1.141, 0.073]. I extract the end coordinates of each bar against the y-axis labels to obtain the percentage of each parameter. Based on this analysis, we can create the CSV table.\nBased on the above thought, the answer is:\nCategories,Gender Wage Gap,Maternity Leave Duration,Employment Rate Women, Employment Rate Men\nRights Protection,25,52,60,75\nSocial Inequality,35,42,52,77" } ], "id_raw": 606 }, { "id": "Aj0pKnipJKY17Cla", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, in which situation is social inequality more significant?" }, { "from": "gpt", "value": "When comparing the situation of social inequality, we need to consider all four indicators. This includes the {\"Gender Wage Gap\"}[-0.093, 0.045, 0.102, 0.073], {\"Maternity Leave Duration\"}[0.185, 0.045, 0.480, 0.073], and the employment rates of {\"women\"}[0.563, 0.045, 0.825, 0.073] and {\"men\"}[0.908, 0.045, 1.141, 0.073]. If we look at the bar charts, we find the Gender Wage Gap is larger in the field of social inequality [0.279, 0.204, 0.349, 0.432] than in rights protection [0.279, 0.660, 0.329, 0.888]. The Maternity Leave Duration is less in social inequality [0.349, 0.204, 0.432, 0.432] than in rights protection [0.329, 0.660, 0.432, 0.888]. Also, the employment rates for both women [0.432, 0.204, 0.535, 0.432] and men [0.535, 0.204, 0.688, 0.432] are also less under social inequality.\nBased on the above thought, the answer is:\nAccording to the chart, social inequality is more significant in terms of wage gap, maternity leave duration, and employment rates of both genders. All these parameters indicate greater disparities under the field of social inequality compared to rights protection." } ], "id_raw": 606 }, { "id": "Ji4TWpXO2vUz1f64", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_6.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, how much less is the percentage of the employment rate of women in the field of social inequality than in the field of rights protection?" }, { "from": "gpt", "value": "To answer the question, I need to compare the percentage of the employment rate of women in the field of social inequality with that of rights protection. The corresponding bar for employment rate of women under rights protection is located at [0.432, 0.660, 0.551, 0.888], and it ends around \"60\" on the X-axis. Looking at the bar for employment rate of women under social inequality, i.e. [0.432, 0.204, 0.535, 0.432], it ends around \"52\" on the X-axis. Therefore, the difference can be calculated.\nBased on the above thought, the answer is:\n8%" } ], "id_raw": 606 }, { "id": "D12l3XalyNgvXZFb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nCan you extract the sales data of Television, Mobile Phones, and Video Game Consoles from the chart and present it in a structured table format?" }, { "from": "gpt", "value": "Observing the sales data represented by {horizontal bars}[0.217, 0.550, 0.867, 0.673] for Television, {horizontal bars}[0.217, 0.305, 0.945, 0.428] for Mobile Phones, and {horizontal bars}[0.217, 0.061, 0.591, 0.183] for Video Game Consoles, each bar group corresponds to a month from January to December. The left edge of each bar starts at the y-axis, indicating zero sales, and the right edge corresponds to the total sales value, according to {the x-axis labels and tick marks}[0.212, 0.723, 0.503, 0.751]. The data can be extracted according to the end coordinates of each month's bars to represent sales figures for corresponding product categories.\nBased on the above thought, the answer is:\nMonth,Television,Mobile Phones,Video Game Consoles\nJan,150,180,80\nFeb,155,185,80\nMar,150,190,80\nApr,155,200,80\nMay,160,205,90\nJun,210,220,100\nJul,225,225,110\nAug,220,240,120\nSep,225,245,130\nOct,240,260,150\nNov,300,320,200\nDec,310,330,220" } ], "id_raw": 607 }, { "id": "tvWNsRMaRhuQgkgF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhich product category shows the most significant sales increase from January to December, and how is that visualized in the chart?" }, { "from": "gpt", "value": "To determine which product category shows the most significant sales increase, I need to analyze the starting position and ending position of the bars for each category. Starting from {the left edge of January sales}[0.217, 0.550, 0.256, 0.673]; [0.217, 0.305, 0.264, 0.428]; [0.217, 0.061, 0.238, 0.183] and comparing it to {the rightmost edge of December sales}[0.832, 0.550, 0.867, 0.673]; [0.890, 0.305, 0.945, 0.428]; [0.557, 0.061, 0.591, 0.183] for Television, Mobile Phones, and Video Game Consoles respectively, it becomes evident that Video Game Consoles show the largest relative increase. This is visualized by the larger distance between the January and December bars for Video Game Consoles {Video Game Consoles bars}[0.217, 0.061, 0.238, 0.183]; [0.557, 0.061, 0.591, 0.183] in relation to its initial bar length.\nBased on the above thought, the answer is:\nVideo Game Consoles. It shows the most significant sales increase from January to December, starting and ending at 80 and 220 units, respectively. This increase is visualized in the chart by the larger proportional lengthening of the {Video Game Consoles bar}[0.217, 0.061, 0.591, 0.183] compared to the other product categories." } ], "id_raw": 607 }, { "id": "kNQiyebnl1euxj3v", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the sum of December sales for all product categories?" }, { "from": "gpt", "value": "To calculate the total December sales, I must add the sales figures of each product category. Utilizing the chart, I locate the {rightmost edge of the Television bar}[0.832, 0.550, 0.867, 0.673] for December that indicates 310 units, the {rightmost edge of the Mobile Phones bar}[0.890, 0.305, 0.945, 0.428] indicating 330 units, and the {rightmost edge of the Video Game Consoles bar}[0.557, 0.061, 0.591, 0.183] indicating 220 units. Summing these values gives us the total sales for December.\nBased on the above thought, the answer is:\n860" } ], "id_raw": 607 }, { "id": "iRbKe4tgCih4HIk8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the numbers from the bar chart and arrange them in a tabular format?" }, { "from": "gpt", "value": "By observing the horizontal bar chart, and taking into account {the x-axis tick labels}[{\"0\"}[0.244, 0.942, 0.255, 0.970], {\"250\"}[0.351, 0.942, 0.384, 0.970], {\"500\"}[0.469, 0.942, 0.502, 0.970], {\"750\"}[0.588, 0.942, 0.621, 0.970], {\"1000\"}[0.701, 0.942, 0.745, 0.970], {\"1250\"}[0.819, 0.942, 0.863, 0.970]], it appears that the chart is indicating amounts in millions of dollars donated towards different social and charity events over the past 5 and 10 years. The amounts are represented on the x-axis, while {the four categories shown on the y-axis}<{\"Donation to Health\"}[0.072, 0.828, 0.237, 0.856]; {\"Donation to Education\"}[0.043, 0.633, 0.237, 0.661]; {\"Donation to Environment\"}[0.019, 0.438, 0.237, 0.466]; {\"Charity Concerts\"}[0.092, 0.242, 0.237, 0.270]> are the beneficiaries of these donations. We see that each category contains two parts, designated by {legend}[0.326, 0.034, 0.883, 0.084] that indicates the past 5 and 10 years. Let's construct a table of the donation amounts towards these categories for 5 and 10 years. I can calculate these amounts by comparing the lengths of giant bars such as {\"Donation to Health\"}[0.249, 0.791, 0.831, 0.888], {\"Donation to Education\"}[0.249, 0.595, 0.443, 0.693], {\"Donation to Environment\"}[0.249, 0.400, 0.732, 0.497], {\"Charity Concerts\"}[0.249, 0.204, 0.472, 0.302] to the x-axis tick labels.\nBased on the above thought, the answer is:\nPhilanthropic Activity,Donations - Past 5 Year,Donations - Past 10 Year\nDonation to Health,650,580\nDonation to Education,230,180\nDonation to Environment,670,350\nCharity Concerts,320,150" } ], "id_raw": 608 }, { "id": "R0LdeJnxHPuBF8Nh", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat overall trend do you see in donations to social welfare and charity activities over the last 5 years and 10 years?" }, { "from": "gpt", "value": "The chart shows two distinct periods of donations for four categories. The two periods are distinguished by {legend}[0.326, 0.034, 0.883, 0.084] - the dark red sections represent the amounts donated in the last 5 years, and the light green sections are the amounts from 5 to 10 years ago. Analysing the lengths of each category's bar: {\"Donation to Health\"}[0.249, 0.791, 0.557, 0.888] vs [0.557, 0.791, 0.831, 0.888], {\"Donation to Education\"}[0.249, 0.595, 0.358, 0.693] vs [0.358, 0.595, 0.443, 0.693], {\"Donation to Environment\"}[0.249, 0.400, 0.566, 0.497] vs [0.566, 0.400, 0.732, 0.497], {\"Charity Concerts\"}[0.249, 0.204, 0.401, 0.302] vs [0.401, 0.204, 0.472, 0.302], it can be seen that donations to 'health' and 'environment' sectors have shown a substantial increase over the 5-year period, demonstrating a positive trend. Donations to 'education' have shown a slight increase over the same period. However, donations to 'charity concerts' have decreased, indicating a negative trend. Overall, the trend shows that more donations have been directed towards health and environmental activities over the recent 5 years as compared to before.\nBased on the above thought, the answer is:\nThe overall trend over the past 5 years shows a significant increase in donations towards the health and environment sectors, slight increase in education, but a decline in the donations towards charity concerts. This suggests a shift towards issues of health and environment in the social welfare and charity landscape." } ], "id_raw": 608 }, { "id": "tpoMuac9kgLHUBz0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_8.png", "conversations": [ { "from": "human", "value": "\nIn which category has the greatest increase in donations been seen over the past 10 years?" }, { "from": "gpt", "value": "Based on the color differentiation in {the legend}[0.326, 0.034, 0.883, 0.084], the dark red sections represent the donations over the past 5 years and the light green sections indicate the donations over the past 10 years. To compare the increases in the past 10 years, I will subtract the value of each dark red section from the value of each light green section in the same category. By calculating and comparing: {\"Donation to Health\"}[0.557, 0.791, 0.831, 0.888], {\"Donation to Education\"}[0.358, 0.595, 0.443, 0.693], {\"Donation to Environment\"}[0.566, 0.400, 0.732, 0.497], {\"Charity Concerts\"}[0.401, 0.204, 0.472, 0.302], it's clear that the {\"Donation to Environment\"}[0.566, 0.400, 0.732, 0.497] section \u2014 light green(670) minus dark red(350) \u2014 has seen the highest increase in the past 10 years.\nBased on the above thought, the answer is:\nDonation to Environment." } ], "id_raw": 608 }, { "id": "PhIlc0y8l4D1gy03", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nConsidering the chart info you observed, demonstrate the data in a table using the CSV format." }, { "from": "gpt", "value": "The chart is a horizontal stacked bar chart depicting two tiers of data from 2012 to 2020. According to {the legend}[0.213, 0.027, 0.827, 0.077], the red bars represent 'Global Trade (% of GDP)' and the green bars represent 'Supply Chain Reliability Index'. The width of each colored segment corresponds to their respective values in each year. By observing {the x-axis tick labels}[0.069, 0.942, 0.981, 0.970] and the coordinates of these bars, I can estimate the values for each year and category. For example, the {bar for 2012}[0.075, 0.168, 0.679, 0.210] shows that 'Global Trade (% of GDP)' ends at about 40% on the x-axis, and 'Supply Chain Reliability Index' starts from there and ends around 90% on the x-axis. By repeating this process for every year, I can extract the data for the table.\nBased on the above thought, the answer is:\nYear,Global Trade (% of GDP),Supply Chain Reliability Index\n2012,40,49\n2013,32,55\n2014,37,53\n2015,44,51\n2016,48,38\n2017,52,32\n2018,54,31\n2019,58,32\n2020,64,30" } ], "id_raw": 609 }, { "id": "dRqarijTynSIIE7G", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how would you describe the correlation between Global Trade (% of GDP) and the Supply Chain Reliability Index during the period 2012 to 2020?" }, { "from": "gpt", "value": "From the chart's bar widths, one can see the progression of both Global Trade (% of GDP) represented by {the red bars}['params': {'label': 'Global Trade (% of GDP)', 'color': '#d66555'}] and the Supply Chain Reliability Index represented by {the green bars}['params': {'label': 'Supply Chain Reliability Index', 'color': '#88a27d'}] from 2012 to 2020. A general observation shows that while the width of the red bars {Global Trade (% of GDP)} consistently increases throughout the years, the width of the green bars {Supply Chain Reliability Index} varies but generally stays within the same range. Therefore, one might speculate that even though Global Trade (% of GDP) has been continuously increasing, the Supply Chain Reliability Index has remained relatively stable.\nBased on the above thought, the answer is:\nThere appears to be a weak negative correlation between the rate of Global Trade (% of GDP) and the Supply Chain Reliability Index from 2012 to 2020. An increase in global trade does not necessitate an improvement in supply chain reliability." } ], "id_raw": 609 }, { "id": "J6qBs3jmjfdRZWJx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_9.png", "conversations": [ { "from": "human", "value": "\nIn what year is the Supply Chain Reliability Index highest?" }, { "from": "gpt", "value": "To find the year with the highest Supply Chain Reliability Index, I need to compare the ending points on the x-axis of the green bar segments for each year, as {the legend}[0.213, 0.027, 0.827, 0.077] reveals that the green bars represent the Supply Chain Reliability Index. For example, in 2012, the {green bar segment}[0.262, 0.168, 0.679, 0.210] ends around 90% on the x-axis. By comparing all the years, it\u2019s clear that {2020}[0.019, 0.854, 0.063, 0.882] with its {green bar segment}[0.375, 0.844, 0.796, 0.887] ends furthest on the x-axis, at approximately 94%.\nBased on the above thought, the answer is:\n2020." } ], "id_raw": 609 }, { "id": "Jf2rnpcz2W6uJZZM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart information on Medical and Biotechnology Trends into CSV format data?" }, { "from": "gpt", "value": "From {the x-axis tick labels}<{'0'}[0.243, 0.723, 0.254, 0.751];{'250'}[0.329, 0.723, 0.362, 0.751];{'500'}[0.426, 0.723, 0.459, 0.751];{'750'}[0.523, 0.723, 0.556, 0.751];{'1000'}[0.614, 0.723, 0.658, 0.751];{'1250'}[0.711, 0.723, 0.755, 0.751];{'1500'}[0.808, 0.723, 0.852, 0.751];{'1750'}[0.905, 0.723, 0.949, 0.751]>, I can see that the x-axis represents different index values, ranging from 0 to 1750. The y-axis represents the different Industry Categories. Additionally, {the color coded legend of the bar chart}<[0.376, 0.784, 0.853, 0.960]> indicates the kind of trend each industry category represents. To generate a CSV table, I will list each Industry Category, the trend it shows, and the index it has reached. I can infer the index values from the length of each bar.\nBased on the above thought, the answer is:\nIndustry,Pattern,Index\nFlu Vaccine Production,Steady decline,1000\nAntibiotics Production,Rapid decline,800\nRobotic Surgeries,Periodic fluctuations,1200\nStem Cell Research,Pronounced increase,1800\nGeneral Pharma Industry,Gradual recovery,1500\nGene Therapy,Sluggish growth,1350\nBiological Implants,Declining pattern,1150" } ], "id_raw": 610 }, { "id": "mFYNRNRF99V8e343", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what industry pattern should investors be careful of? Why?" }, { "from": "gpt", "value": "From the bar chart, the length of the bars shows the index values for various industry patterns. {The 'Rapid decline'[0.248, 0.531, 0.559, 0.578]} and {'Declining pattern'[0.248, 0.061, 0.694, 0.108]} trends have noticeably short bars, indicating that these industries have low index values. These can be risky areas for investors as industries under these patterns are in a downturn and may not provide favorable returns. Furthermore, {the color coded legend of the bar chart}<[0.376, 0.784, 0.853, 0.960]> indicates that the trends showing these patterns are Antibiotics Production and Biological Implants. Investors should be cautious when investing in these industries.\nBased on the above thought, the answer is:\nInvestors should be cautious of the industries following the 'Rapid decline' and 'Declining pattern' trends, namely Antibiotics Production and Biological Implants. This is because their index values are low, indicating that these industries are not doing well currently and might not provide promising returns." } ], "id_raw": 610 }, { "id": "stHvsUbRbH8dussh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which industry has the greatest index value?" }, { "from": "gpt", "value": "To find the industry with the greatest index value, I need to look for the longest bar on the bar chart. Looking at {the length of each bar for the respective industries}<{'Steady decline'}[0.248, 0.626, 0.636, 0.673];{'Rapid decline'}[0.248, 0.531, 0.559, 0.578];{'Periodic fluctuations'}[0.248, 0.437, 0.714, 0.484];{'Pronounced increase'}[0.248, 0.343, 0.946, 0.390];{'Gradual recovery'}[0.248, 0.249, 0.830, 0.296];{'Sluggish growth'}[0.248, 0.155, 0.772, 0.202];{'Declining pattern'}[0.248, 0.061, 0.694, 0.108]>, it is easy to note that the \"Pronounced increase\" category has the longest bar, representing the greatest index value. Correspondingly, this relates to the industry category of 'Stem Cell Research'.\nBased on the above thought, the answer is:\nStem Cell Research." } ], "id_raw": 610 }, { "id": "CJLGlppI4eTQXtQd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you are viewing, can you extract the values for each indicator and arrange them in a CSV formatted table?" }, { "from": "gpt", "value": "As I observe the bar chart titled {\"Health and Medical Field indicators\"}[0.323, 0.961, 0.647, 0.981], two groups are presented, which are {\"US\"}[0.080, 0.756, 0.104, 0.784] and {\"EU\"}[0.080, 0.276, 0.104, 0.304]. Three different indicators are represented per group, which according to the legend {\"Public Health Awareness (%)\"}[0.071, 0.038, 0.319, 0.066], {\"Pharmaceutical Research (%)\"}[0.402, 0.038, 0.658, 0.066], {\"Medical Equipment Patents\"}[0.741, 0.038, 0.977, 0.066] are Public Health Awareness, Pharmaceutical Research and Medical Equipment Patents respectively. Each indicator is represented as a bar. The bars corresponding to the U.S and EU are distinctly colored. Therefore, the first category, Public Health Awareness, is represented by the bars {\"US\"}[0.116, 0.647, 0.343, 0.887] and {\"EU\"}[0.116, 0.168, 0.327, 0.407]. The second category, Pharmaceutical Research, is represented by {\"US\"}[0.343, 0.647, 0.604, 0.887] and {\"EU\"}[0.327, 0.168, 0.571, 0.407]. The third category, Medical Equipment Patents, is represented by the bars {\"US\"}[0.604, 0.647, 0.799, 0.887] and {\"EU\"}[0.571, 0.168, 0.848, 0.407]. So, I can look at the end of each bar to determine its corresponding value on the x-axis.\nBased on the above thought, the answer is:\n,Public Health Awareness,Pharmaceutical Research,Medical Equipment Patents\nUS,700,800,600\nEU,650,750,850" } ], "id_raw": 611 }, { "id": "V5ePHEqx9c0uGpgQ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nBased on your understanding of the chart, which geographical entity - US or EU - has a better handle on the health and medical field in totality?" }, { "from": "gpt", "value": "To answer this question, it's important to summarize the differences in the health and medical indicators between the two geographical entities - the {\"US\"}[0.080, 0.756, 0.104, 0.784] and the {\"EU\"}[0.080, 0.276, 0.104, 0.304]. For Public Health Awareness, the US has a higher percentage, as indicated by the longer {\"US\"}[0.116, 0.647, 0.343, 0.887] bar in comparison to the {\"EU\"}[0.116, 0.168, 0.327, 0.407] bar. Similarly, with Pharmaceutical Research, the US has also a higher percentage, {\"US\"}[0.343, 0.647, 0.604, 0.887] bar is longer than the {\"EU\"}[0.327, 0.168, 0.571, 0.407] bar. However, when it comes to the total number of Medical Equipment Patents, the EU surpasses the US, as represented by the EU's longer bar {\"EU\"}[0.571, 0.168, 0.848, 0.407] vs the US bar {\"US\"}[0.604, 0.647, 0.799, 0.887]. Considering these categories represent different aspects of the medical and health field, it might not be sufficient to determine who is handling it better based on a singular aspect. But, based on this particular data, the US is performing better in terms of Public Health Awareness and Pharmaceutical Research percentages, and the EU is leading in Medical Equipment Patents.\nBased on the above thought, the answer is:\nIt's difficult to determine definitively as it depends on the priority given to each indicator. However, going by two out of three categories presented, the US appears to have a slight edge in Public Health Awareness and Pharmaceutical Research. Therefore, one could argue that the US is handling the health and medical field better in totality based on these specific indicators." } ], "id_raw": 611 }, { "id": "xNSeeZObsicuX9TJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_11.png", "conversations": [ { "from": "human", "value": "\nConsidering the bar chart, can you tell me in which category does the EU surpass the US in terms of percentages and numbers?" }, { "from": "gpt", "value": "To answer this question, I need to compare the lengths of the bars comparing the US and EU for each of the three categories. Looking at the category of Public Health Awareness, the bar representing {\"US\"}[0.116, 0.647, 0.343, 0.887] is longer than the bar representing {\"EU\"}[0.116, 0.168, 0.327, 0.407]; for the category of Pharmaceutical Research, the bar representing {\"US\"}[0.343, 0.647, 0.604, 0.887] is also longer than the bar representing {\"EU\"}[0.327, 0.168, 0.571, 0.407], meaning EU does not surpass US in these two categories. But, in the third category, Medical Equipment Patents, the bar representing {\"EU\"}[0.571, 0.168, 0.848, 0.407] is longer than the bar representing {\"US\"}[0.604, 0.647, 0.799, 0.887]. So, the EU surpasses the US in terms of the number of Medical Equipment Patents.\nBased on the above thought, the answer is:\nMedical Equipment Patents" } ], "id_raw": 611 }, { "id": "yhd6yfIWEKOD7NNS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you've observed, could you convert the data into a structured table format?" }, { "from": "gpt", "value": "The bar chart shows two major categories: {Environmental Protection}[0.211, 0.660, 0.671, 0.888]; [0.211, 0.204, 0.729, 0.432] and {Community Investment}[0.671, 0.660, 0.767, 0.888]; [0.729, 0.204, 0.882, 0.432]. For each category, there are two bars indicating the commitments in 2009 and achievements in 2020, respectively. According to the x-axis tick labels, the commitments for environmental protection are measured at 1200 and the achievements in 2020 are at 1350; for community investment, the commitments in 2009 are at 250 and the achievements in 2020 are at 400. All these findings can be converted into a table.\nBased on the above thought, the answer is:\nCategory,Commitments(2009),Achievements(2020)\nEnvironmental Protection,1200,1350\nCommunity Investment,250,400" } ], "id_raw": 612 }, { "id": "OPfscZrnEWxDePNf", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, how well has the business done in terms of accomplishing its 2009 commitments by 2020?" }, { "from": "gpt", "value": "To answer this question comprehensively, I need to inspect how the commitments for the respective categories of Environmental Protection and Community Investment translated into achievements by 2020. From the chart, I observe that the {finish point of the achievements in 2020}[0.729, 0.204, 0.729, 0.432] is beyond the {expected commitments made back in 2009}[0.211, 0.660, 0.671, 0.888] in both categories. This trend denotes that despite commitments being set at a certain target, the actual achievements have exceeded them by quite a margin. Thus, the business has not only met but surpassed their commitments.\nBased on the above thought, the answer is:\nThe business has done very well. It has not only met but exceeded its 2009 commitments by 2020 in both the Environmental Protection and Community Investment categories." } ], "id_raw": 612 }, { "id": "jLbwZjV2CnC7UYEH", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_12.png", "conversations": [ { "from": "human", "value": "\nIn which category is the increase from 2009 to 2020 greater?" }, { "from": "gpt", "value": "To evaluate which category has the greater increase from 2009 to 2020, I'd compare the length of each bar representing achievement to its corresponding bar representing commitment. {The Environmental Protection bar for commitment}[0.211, 0.660, 0.671, 0.888] stretches till 1200, and its corresponding {achievement bar}[0.211, 0.204, 0.729, 0.432] till 1350, indicating an increase of 150. Comparatively, the {Community Investment bar for commitment}[0.671, 0.660, 0.767, 0.888] is at 250 units, and its corresponding {achievement bar}[0.729, 0.204, 0.882, 0.432] ends at 400, reflecting an increase of 150 as well. Hence, the increase is equal in both categories.\nBased on the above thought, the answer is:\nBoth categories have the same increase." } ], "id_raw": 612 }, { "id": "wfEnoSiZF8rGZuD2", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the horizontal bar graph into CSV format?" }, { "from": "gpt", "value": "Looking at {the x-axis tick labels}<[{\"0\"}[0.268, 0.942, 0.279, 0.970]; {\"100\"}[0.305, 0.942, 0.338, 0.970]; {\"200\"}[0.353, 0.942, 0.386, 0.970]; {\"300\"}[0.401, 0.942, 0.434, 0.970]; {\"400\"}[0.450, 0.942, 0.482, 0.970]; {\"500\"}[0.498, 0.942, 0.531, 0.970]; {\"600\"}[0.546, 0.942, 0.579, 0.970]; {\"700\"}[0.594, 0.942, 0.627, 0.970]; {\"800\"}[0.642, 0.942, 0.675, 0.970]; {\"900\"}[0.690, 0.942, 0.723, 0.970]; {\"1000\"}[0.733, 0.942, 0.777, 0.970]; {\"1100\"}[0.781, 0.942, 0.825, 0.970]; {\"1200\"}[0.829, 0.942, 0.873, 0.970]; {\"1300\"}[0.877, 0.942, 0.921, 0.970]; {\"1400\"}[0.926, 0.942, 0.969, 0.970]], I can see that it scales up to 1400. The y-axis tick labels represent the categories of data. For each category, there are two bars indicating trends at two points in time. The legends at the bottom show that {the red bars}<[{\"Initial Data\"}[0.516, 0.045, 0.611, 0.073]]> represent the initial data and {the green bars}<[{\"Later Data\"}[0.695, 0.045, 0.787, 0.073]]> represent the later data. Each category has both a red and green bar associated with it, the width of the bars directly linked to their values. For example, looking at the category \"E-Learning Adoption\", the width of the {red bar}[0.273, 0.791, 0.293, 0.888] ends at approximately 40 and the {green bar}[0.293, 0.791, 0.331, 0.888] ends at approximately 120 on the x-axis.\nBased on the above thought, the answer is:\nCategories,Initial Data,Later Data\nE-Learning Adoption,40,120\nOnline Certification Courses,60,180\nE-Books Downloaded,20,130\nDigital Training Investment,500,1400" } ], "id_raw": 613 }, { "id": "79De40HLBCansnJh", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nJudging from the bar graph, which sector has made the most significant progress in the adoption of education and training technology?" }, { "from": "gpt", "value": "{The bar graph}<[0.273, 0.170, 0.981, 0.923]> reflects the progress made in the adoption of education and training technology in different sectors. Looking at the y-axis, we can see four categories: 'E-Learning Adoption', 'Online Certification Courses', 'E-Books Downloaded', and 'Digital Training Investment'. These categories are represented by horizontal bars, and each category has two bars: {one in red}<[0.273, 0.791, 0.293, 0.888]; [0.273, 0.595, 0.302, 0.693]; [0.273, 0.400, 0.283, 0.497]; [0.273, 0.204, 0.514, 0.302]> showing initial data and {one in green}<[0.293, 0.791, 0.331, 0.888]; [0.302, 0.595, 0.360, 0.693]; [0.283, 0.400, 0.336, 0.497]; [0.514, 0.204, 0.948, 0.302]> showing later data. The most significant progress would be indicated by the greatest growth from initial data (red bar) to later data (green bar). Evidently, the \"Digital Training Investment\" category shows a major increase from the initial 500 to the later 1400, demonstrating the most significant progress among the categories.\nBased on the above thought, the answer is:\nDigital Training Investment has made the most significant progress in the adoption of education and training technology." } ], "id_raw": 613 }, { "id": "aqSuEecSvkPGxn8h", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_13.png", "conversations": [ { "from": "human", "value": "\nWhich category shows the greatest increase in measurements from initial data to later data?" }, { "from": "gpt", "value": "To identify the category with the greatest increase, I'll compare the lengths of the red and green bars in each category. Starting with \"E-Learning Adoption\", I can see that the {green bar}[0.293, 0.791, 0.331, 0.888] extends approximately 80 units more than the {red bar}[0.273, 0.791, 0.293, 0.888] on the x-axis. Likewise, I can observe a growth of 120 units in the category \"Online Certification Courses\". However, the category \"E-Books Downloaded\" showed an impressive growth of 110 units. The category \"Digital Training Investment\" surpassed all with a huge increase of 900 units comparing {the red bar}[0.273, 0.204, 0.514, 0.302] and {the green bar}[0.514, 0.204, 0.948, 0.302].\nBased on the above thought, the answer is:\nDigital Training Investment." } ], "id_raw": 613 }, { "id": "RhXka40ydZ8Zac2I", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, could you please convert the chart data into a CSV format table?" }, { "from": "gpt", "value": "The chart title {\"Healthy Living Indicators\"}[0.447, 0.941, 0.580, 0.968] states that it measures various indicators related to health. The y-axis holds the different categories such as {healthy food intake}[0.175, 0.889, 0.223, 0.953], {smoking rate}[0.284, 0.448, 0.333, 0.512], and {sports activity participation}[0.071, 0.232, 0.120, 0.294]. The x-axis data ranges from {0}[0.175, 0.942, 0.186, 0.970] to {90}[0.549, 0.942, 0.571, 0.970] representing the percentage of each category. For instance, the healthy food intake of the {general population}[0.180, 0.660, 0.391, 0.888] is around 50 and of {urban population}[0.180, 0.204, 0.450, 0.432] is approximately 64. Translating these plots and the remaining data, a CSV table can be created.\nBased on the above thought, the answer is:\nCategory,Population,Urban Population\nHealthy Food Intake (%),50,64\nSmoking Rate (%),44,33\nSports Activity Participation (%),60,73" } ], "id_raw": 614 }, { "id": "312lTTcIlc4Xdaqm", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nBased on the presented chart, what conclusion can you draw about an urban lifestyle concerning these three health indicators?" }, { "from": "gpt", "value": "From the look of the chart, every category shows a difference when comparing the general population and the urban population. For each category, the urban population bars are either on the right or at the same position as the general population bars. In the case of {healthy food intake}[0.180, 0.204, 0.450, 0.432] and {sports activity participation}[0.590, 0.204, 0.897, 0.432], it's seen that the urban population displays higher percentages compared to the general population. Conversely, for {smoking rates}[0.450, 0.204, 0.590, 0.432], the percentage is less in urban populations compared to general population. Therefore, it can be deduced that urbanization leads to improvement for all three indicators of a healthy lifestyle.\nBased on the above thought, the answer is:\nAn urban lifestyle tends to have a higher healthy food intake and sports activity participation rate, and a lower smoking rate, than the general population. This suggests that living in urban areas may be beneficial for these aspects of health." } ], "id_raw": 614 }, { "id": "6BhbXLJgZsgyvJEn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_41_14.png", "conversations": [ { "from": "human", "value": "\nWhich lifestyle behaviour shows the most improvement when comparing general and urban populations?" }, { "from": "gpt", "value": "To answer this, I would need to calculate the differences between general and urban populations for each category. For Healthy Food Intake (%), the bars for the {general population}[0.180, 0.660, 0.391, 0.888] and the {urban population}[0.180, 0.204, 0.450, 0.432] show a difference of approximately 14%. For the Smoking Rate (%), the bars for the {general population}[0.391, 0.660, 0.577, 0.888] and the {urban population}[0.450, 0.204, 0.590, 0.432] show a decrease of about 11%. Lastly, for Sports Activity Participation (%), the bars for the {general population}[0.577, 0.660, 0.830, 0.888] and the {urban population}[0.590, 0.204, 0.897, 0.432] show an increase of 13%. Therefore, the maximum improvement is observed in the Healthy Food Intake.\nBased on the above thought, the answer is:\nHealthy Food Intake (%)" } ], "id_raw": 614 }, { "id": "8cVZkpzb3NhLFkxC", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nGiven your analysis of the bar chart, please convert the data into a structured CSV format table." }, { "from": "gpt", "value": "The data in the chart presents changes in synthetic and human-authored data in various categories of the Culture and Arts Industry. These categories include {Visual Arts}[0.166, 0.862,0.291,0.877], {Cinema}[0.166, 0.801,0.220,0.816], {Music}[0.166, 0.739,0.207,0.754], {Theatre}[0.166, 0.677,0.229,0.693], {Dance}[0.166, 0.616,0.211,0.631], {Literature}[0.166, 0.555,0.245,0.570], {Digital Arts}[0.166, 0.493,0.262,0.508], and {Architecture}[0.166, 0.431,0.264,0.447]. To produce a CSV table, I map the length of the colored bars opposite to each category to the numerical value of synthetic and human-authored data. For instance, the {synthetic data bar}[0.125, 0.857, 0.186, 0.883] and {human-authored data bar}[0.186, 0.857, 0.305, 0.883] for the Visual Arts category suggests the values -15 and 18, respectively. I'll follow a similar process for the remaining categories.\nBased on the above thought, the answer is:\nCategory,Synthetic Data,Human-Authored Data\nVisual Arts,-15,18\nCinema,-30,22\nMusic,-28,16\nTheatre,-18,26\nDance,-19,18\nLiterature,-16,13\nDigital Arts,-22,24\nArchitecture,-20,15" } ], "id_raw": 623 }, { "id": "3KdUO3rKEEM1Yx5k", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what can you infer about the change in the Culture and Arts Industry?" }, { "from": "gpt", "value": "By analyzing the bar chart, I can obtain various pieces of information about the synthetic data and human-authored data of different domains in the Culture and Arts Industry. For each category, synthetic data is represented by bars extending towards the left represented by negative values {[0.125, 0.431, 0.801, 0.862]}, while human-authored data is represented by bars extending towards the right positive direction {[0.125, 0.431, 0.801, 0.862]}. This phenomenon suggests that human participation across all domains is increasing over time, especially in categories like Theatre and Digital Arts where the human-authored values are noticeably high. Conversely, synthetic interactions seem to be decreasing for all domains, suggesting that human interactions are being favored.\nBased on the above thought, the answer is:\nHuman-authored data in the Culture and Arts industry across all domains show increased activity while synthetic interactions are on the decline, highlighting the shift towards more human involvement in the industry." } ], "id_raw": 623 }, { "id": "hgYbgOb6VUQ78Ls7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value of Synthetic Data in the chart?" }, { "from": "gpt", "value": "To find the highest value of Synthetic Data, I need to compare the lengths of the {colored bars on the left}[0.125, 0.431, 0.801, 0.862]. The longer the bar, the higher the value. By closely inspecting the chart, I found that the Cinema category {[0.166, 0.801,0.220,0.816]} has the longest synthetic data bar {[0.125, 0.795, 0.062, 0.822]} corresponding to a value of -30. Despite being a negative value, it's the highest when considering the absolute value.\nBased on the above thought, the answer is:\n-30" } ], "id_raw": 623 }, { "id": "ko5hkukUvVGJa5Sx", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data representing the influence of different media platforms on public opinion into a CSV formatted table." }, { "from": "gpt", "value": "The chart's Y-axis consists of different media types, and the X-axis contains a range of values indicating influence. Both the Y-axis labels and the corresponding bar length indicating influence are needed to be extracted to form the dataset. The types of media are {Traditional Media}[0.072, 0.104, 0.221, 0.122], {Social-media}[0.109, 0.229, 0.221, 0.246], {Independent Blogs}[0.058, 0.354, 0.221, 0.371], {Public Forums}[0.100, 0.478, 0.221, 0.496], {Mainstream News Sites}[0.019, 0.603, 0.221, 0.621], {Underground Media}[0.050, 0.728, 0.221, 0.745], and {TV and Radio}[0.106, 0.852, 0.221, 0.870]. We can take bar lengths from the graphics referred to in the dataset, for example, {Traditional Media}[0.233, 0.061, 0.782, 0.161] has a length that extends roughly to the 90 mark on the X-axis, as indicated by the {text annotation}[0.782, 0.102, 0.804, 0.120]. This procedure will be replicated for all media types to gather their respective influence values.\nBased on the above thought, the answer is:\nMedia,Influence\nTraditional Media,90\nSocial-media,110\nIndependent Blogs,70\nPublic Forums,85\nMainstream News Sites,110\nUnderground Media,65\nTV and Radio,100" } ], "id_raw": 625 }, { "id": "BJLUuBu0lpPWUcKd", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nBased on the observed data, given the growing role of the internet, what likely change in influence could we expect for all media types if we were to revisit this data in the future?" }, { "from": "gpt", "value": "This question requires foresight based on current data and trends. The data shows that {Social-media}[0.109, 0.229, 0.221, 0.246] currently has a significant influence, second only to {Mainstream News Sites}[0.019, 0.603, 0.221, 0.621]. Based on the expanding impact of the Internet and digital media, we might expect Social-media to continue growing in influence. At the same time, the influence of {Independent Blogs}[0.058, 0.354, 0.221, 0.371] and {Public Forums}[0.100, 0.478, 0.221, 0.496], also online platforms, may likely increase. Traditional Media and TV and Radio might lose some influence due to a shift from conventional channels to digital media. Also, with the rise of digital journalism, the influence of Mainstream News Sites could go down.\nBased on the above thought, the answer is:\nThe influence of Internet-related media types such as Social-media, Independent Blogs, and Public Forums is likely to grow due to increased access and use of the internet. Traditional platforms like TV and Radio and Traditional Media may see a decrease in influence given the shift towards digital sources of information. Mainstream News Sites might also face a decrease in influence due to the rise of independent digital journalism platforms." } ], "id_raw": 625 }, { "id": "HcPZCWbdhaKtgfLt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the average influence value across all types of media platforms?" }, { "from": "gpt", "value": "To find the average influence value, I will sum up the influence value for each media type, as indicated by the length of the corresponding bars, and then divide by the number of media types. For example, for {Traditional Media}[0.233, 0.061, 0.782, 0.161] the influence value is given by the label {90}[0.782, 0.102, 0.804, 0.120]. We have to add up the values of all platforms and then divide by the total number of platforms which is 7.\nBased on the above thought, the answer is:\n90" } ], "id_raw": 625 }, { "id": "MCM3fkW59bURARYB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat specific integration levels do the instances depict in the bar chart? Please summarize this information as structured table data in CSV format." }, { "from": "gpt", "value": "The legend at the top of the chart defines the instances, with each instance represented by a different color. I notice that the chart consists of two categories: {Historical Monuments}[0.204, 0.927, 0.329, 0.963] and {Folk Lore}[0.736, 0.927, 0.788, 0.963] shown on the x-axis. Each category has seven bar instances. By evaluating the height of each instance's bar and cross-referencing it with the y-axis titled {Integration Level}[0.012, 0.330, 0.028, 0.682], I can identify the exact integration level values for each instance. For instance, within the Historical Monuments category, the {bar representing first instance}[0.093, 0.588, 0.143, 0.915] has an integration level of 4, as indicated by the {annotation above the bar}[0.114, 0.545, 0.121, 0.580].\nBased on the above thought, the answer is:\nHeritage,Instance 1,Instance 2,Instance 3,Instance 4,Instance 5,Instance 6,Instance 7\nHistorical Monuments,4,1,2.3,0.9,2.6,1.1,2.8\nFolk Lore,7,2.3,3.9,1.7,3,1.5,4" } ], "id_raw": 630 }, { "id": "nlt6k1cyuZaqnDQn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nBased on the distribution of cultural integration levels for 'Historical Monuments' and 'Folk Lore', what can we interpret about the acceptance of these types of cultural heritage in society?" }, { "from": "gpt", "value": "To answer this question, we need to look at the overall pattern of cultural integration levels for both {Historical Monuments}[0.204, 0.927, 0.329, 0.963] and {Folk Lore}[0.736, 0.927, 0.788, 0.963] across all instances. The varying bar heights within each category indicate the diversity of cultural integration levels. For Historical Monuments, the pattern suggests a predominantly lower cultural integration level, with most instances hover around 1 to 3, apart from the {first instance}[0.093, 0.588, 0.143, 0.915] which reaches level 4, indicating these artifacts are not widely integrated into modern culture in most cases. Conversely, the pattern for Folk Lore shows a few instances reaching higher integration levels, particularly the {first instance}[0.588, 0.343, 0.638, 0.915] which reaches level 7, suggesting that this type of cultural heritage is more incorporated into society.\nBased on the above thought, the answer is:\nHistorical Monuments generally have a lower degree of cultural integration, suggesting these forms of cultural heritage are not widely assimilated in society. On the other hand, Folk Lore demonstrates higher levels of cultural integration in some instances, indicating more societal acceptance and assimilation of these cultural elements." } ], "id_raw": 630 }, { "id": "h1DcPBB2Fy78OhRB", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_0.png", "conversations": [ { "from": "human", "value": "\nFor the Heritage 'Historical Monuments', what is the maximum integration level depicted in the instances?" }, { "from": "gpt", "value": "The maximum integration level is obtained by finding the tallest bar under the category {Historical Monuments}[0.204, 0.927, 0.329, 0.963]. Looking across all the instances, the {highest bar}[0.390, 0.686, 0.440, 0.915] corresponds to the seventh instance and reaches an integration level of {2.8}[0.406, 0.643, 0.424, 0.678].\nBased on the above thought, the answer is:\n2.8" } ], "id_raw": 630 }, { "id": "nZsMZt3inAC2mNT4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data on project issues in a CSV format?" }, { "from": "gpt", "value": "To answer this question, I need to refer to the {bar graph}[0.044, 0.090, 0.988, 0.944] representing different projects. Each project has a set of bars, with each bar representing a different metric as indicated by {the legend}[0.187, 0.017, 0.845, 0.093]. Focusing on the {Issues metric}[0.502, 0.063, 0.525, 0.079], I see bars corresponding to this metric for each project. I will then use the height of the {Issues bars}[0.143, 0.688, 0.162, 0.944] and their {top value annotations}[0.145, 0.654, 0.160, 0.671] to extract the data.\nBased on the above thought, the answer is:\nProjects,Issues\nUrban Regeneration,1.5\nParks & Open Spaces,1.7\nPublic Transportation,1.3\nHousing Development,1.4\nInfrastructure renewal,1.8" } ], "id_raw": 631 }, { "id": "g9S3sPfkbTmoH7tK", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nWhich project had the highest stakeholder involvement and how does it compare to that project's completion metric?" }, { "from": "gpt", "value": "I will analyze the {Stake Involve bars}[0.162, 0.517, 0.180, 0.944] to identify which project has the highest bar, indicating the highest stakeholder involvement. It appears that {Infrastructure renewal}[0.907, 0.398, 0.926, 0.944] has the highest stakeholder involvement with a value annotation of {3.2}[0.909, 0.364, 0.924, 0.380]. Now I will compare this with the {Completion metric}[0.380, 0.063, 0.403, 0.079] for the same project to observe the relationship. The Completion bar for Infrastructure renewal has a value annotation of {2.9}[0.872, 0.415, 0.887, 0.432], which is slightly less than the stakeholder involvement.\nBased on the above thought, the answer is:\nInfrastructure renewal had the highest stakeholder involvement at 3.2. This figure slightly surpasses the project's completion metric of 2.9, indicating robust engagement despite the project not being the furthest along in completion." } ], "id_raw": 631 }, { "id": "CStBbP766xRJ8izJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of Good Reviews for all projects combined?" }, { "from": "gpt", "value": "To calculate the total number of Good Reviews, I will sum up the values indicated at the top of each {Good Reviews bar}[0.180, 0.346, 0.199, 0.944]. The numbers are {3.5 for Urban Regeneration}[0.182, 0.313, 0.197, 0.329], {4.0 for Parks & Open Spaces}[0.369, 0.227, 0.384, 0.244], {2.8 for Public Transportation}[0.555, 0.432, 0.570, 0.449], {3.1 for Housing Development}[0.742, 0.381, 0.756, 0.398], and {4.1 for Infrastructure renewal}[0.928, 0.210, 0.943, 0.227]. Adding these values will give me the total.\nBased on the above thought, the answer is:\n18.5" } ], "id_raw": 631 }, { "id": "zFR1mFS8d59QJbwO", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart you see, could you please convert the data into a CSV format table?" }, { "from": "gpt", "value": "The chart is showing trends in user count numbers on three major social media platforms over different years. The x-axis {the x-axis tick labels}[{\"Facebook\"}[0.182, 0.927, 0.236, 0.963]; {\"Twitter\"}[0.496, 0.927, 0.535, 0.963]; {\"Instagram\"}[0.793, 0.927, 0.851, 0.963]] details the platforms, while the y-axis ranges from {0}[0.032, 0.902, 0.040, 0.937] to {4 billions}[0.032, 0.160, 0.040, 0.195] representing user counts. According to the legend {legend}[0.353, 0.043, 0.678, 0.158], each color represents a different year: {purple}[0.357, 0.113, 0.381, 0.137] for 2018, {pink}[0.442, 0.113, 0.465, 0.137] for 2019, {green}[0.527, 0.113, 0.550, 0.137] for 2020, and {orange}[0.612, 0.113, 0.635, 0.137] for 2021. The values can be inferred from the height of the bars and the data labels above them. For example, in 2018, {the purple bar for Facebook}[0.086, 0.508, 0.148, 0.915] corresponds to {2.2 billions users}[0.108, 0.454, 0.126, 0.489].\nBased on the above thought, the answer is:\nYear,Facebook,Twitter,Instagram\n2018,2.2,1.0,1.5\n2019,2.7,1.1,1.7\n2020,2.8,1.0,2.0\n2021,3.0,0.9,2.3" } ], "id_raw": 633 }, { "id": "I3VPF4SjgbaN9lTl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which social media platform showed a more consistent growth in user base over the years?" }, { "from": "gpt", "value": "To answer this question, I need to observe the variations in the bars' heights for each platform from year to year. If we look at the Facebook bars, there is a consistent rise in their heights from {2018}[0.086, 0.508, 0.148, 0.915], {2019}[0.148, 0.415, 0.209, 0.915], {2020}[0.209, 0.397, 0.270, 0.915], to {2021}[0.270, 0.359, 0.332, 0.915]. For Twitter, it fluctuated and then decreased from {2018}[0.393, 0.730, 0.454, 0.915], {2019}[0.454, 0.712, 0.516, 0.915], {2020}[0.516, 0.730, 0.577, 0.915], and {2021}[0.577, 0.749, 0.638, 0.915]. Lastly, for Instagram, it also showed a consistent rise in user base from {2018}[0.699, 0.637, 0.761, 0.915], {2019}[0.761, 0.600, 0.822, 0.915], {2020}[0.822, 0.545, 0.883, 0.915], to {2021}[0.883, 0.489, 0.945, 0.915].\nBased on the above thought, the answer is:\nFacebook and Instagram. They both showed a consistent rise in their user base over the years from 2018 to 2021." } ], "id_raw": 633 }, { "id": "6rQjkRc3bpUjb6F7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which social media platform experienced the highest user growth from 2018 to 2021?" }, { "from": "gpt", "value": "To determine the platform with the highest user growth from 2018 to 2021, I need to calculate the difference in user count for each platform between these years. For Facebook, the user count rose from {2.2 billion in 2018}[0.108, 0.454, 0.126, 0.489] to {3.0 billion in 2021}[0.292, 0.306, 0.310, 0.341] resulting in a growth of 0.8 billion users. For Twitter, the user count changed from {1.0 billion in 2018}[0.414, 0.677, 0.433, 0.712] to {0.9 billion in 2021}[0.598, 0.695, 0.617, 0.730], therefore it actually lost users. For Instagram, the user count increased from {1.5 billion in 2018}[0.721, 0.584, 0.739, 0.619] to {2.3 billion in 2021}[0.905, 0.436, 0.923, 0.471] resulting in a growth of 0.8 billion users.\nBased on the above thought, the answer is:\nFacebook and Instagram." } ], "id_raw": 633 }, { "id": "ib08gICxgKpn3AqV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nAs per the data visualization you can see, extract the multi-bar chart data into a CSV table with policies and impacts." }, { "from": "gpt", "value": "From the bar chart title {\"Impact vs Policy\"}[0.516, 0.970, 0.737, 1.014] at the top and the categorical labels on {the x-axis}[{\"No Lockdown\"}[0.188, 0.792, 0.250, 0.809]; {\"Partial Lockdown\"}[0.477, 0.792, 0.556, 0.809]; {\"Complete Lockdown\"}[0.766, 0.792, 0.860, 0.809]], I can see that the x-axis represents different government policies. The y-axis labels {\"Impact\"}[0.010, 0.370, 0.022, 0.441] signifies the level of impact, ranging from -4 to 3. Each bar corresponds to the impact of a specific policy. {The legend}[0.381, 0.870, 0.652, 0.980] informs that each colored bar represents a different impact factor. Therefore, I need to map each colored bar's height to its corresponding policy and impact. This can be accurately quantified using the numerical annotations at the top of each bar. For instance, the {\"Covid Cases Decline\"}[0.086, 0.243, 0.130, 0.351] bar coordinates for the \"No Lockdown\" policy denotes a 1.0 impact, as stated by {the value annotation \"1.0\"}[0.101, 0.214, 0.115, 0.232] at the top of the bar.\nBased on the above thought, the answer is:\nPolicy, Covid Cases Decline, Economic Impact, Public Response, Healthcare Capacity, Mental Health Impact, School Disruptions\nNo Lockdown, 1.0, -2.0, 0.3, 0.6, -0.4, -0.2\nPartial Lockdown, 1.2, -1.7, 0.9, 1.6, -0.2, -0.9\nComplete Lockdown, 2.7, -3.8, -0.8, 2.3, -1.8, -2.4" } ], "id_raw": 635 }, { "id": "nOjo6VnpVKl6RyVH", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_5.png", "conversations": [ { "from": "human", "value": "\nWhich policy achieved the largest 'Healthcare Capacity' impact according to the chart?" }, { "from": "gpt", "value": "To determine which policy achieved the highest 'Healthcare Capacity' impact, I need to compare the bar heights under each policy for 'Healthcare Capacity'. On examining the bars for 'Healthcare Capacity', I observe that {\"the bar for 'No Lockdown'\"}[0.219, 0.286, 0.264, 0.351] has an impact of 0.6, {\"the bar for 'Partial Lockdown'\"}[0.516, 0.178, 0.561, 0.351] has an impact of 1.6, and {\"the bar for 'Complete Lockdown'\"}[0.813, 0.102, 0.858, 0.351] has the highest impact of 2.3 which also corresponds to {the value annotation \"2.3\"}[0.828, 0.073, 0.843, 0.091] at the top of the bar.\nBased on the above thought, the answer is:\nComplete Lockdown." } ], "id_raw": 635 }, { "id": "xNKDsNhsCyavu3Rz", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nPlease provide the movement trends information displayed in the bar chart as a CSV format." }, { "from": "gpt", "value": "The bar chart embodies two trends: {Internal Mobility}[0.087, 0.071, 0.168, 0.091] represented with blue bars and {International Mobility}[0.087, 0.101, 0.193, 0.121] represented with red bars for different years. The years range from {2010}[0.127, 0.931, 0.152, 0.951] to {2019}[0.919, 0.931, 0.944, 0.951], spreading across the x-axis. The y-axis represents the population in thousands, and we have our range from {0}[0.037, 0.909, 0.044, 0.929] to {600}[0.025, -0.022, 0.044, -0.002]. The data can be extracted by referencing the height of these bars under each year. For example, under 2010, the {bar for Internal Mobility}[0.093, 0.219, 0.124, 0.917] measures to approximately 450 and the {bar for International Mobility}[0.124, 0.389, 0.155, 0.917] measures to about 340. Similar assessments can be made to get values for subsequent years.\nBased on the above thought, the answer is:\nYear,Internal Mobility,International Mobility\n2010,450,340\n2011,470,350\n2012,465,365\n2013,469,370\n2014,525,375\n2015,530,390\n2016,520,400\n2017,495,405\n2018,500,410\n2019,515,420" } ], "id_raw": 636 }, { "id": "8RqOKwnxcYbH1g2c", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_6.png", "conversations": [ { "from": "human", "value": "\nWhich year had the most significant difference between Internal Mobility and International Mobility?" }, { "from": "gpt", "value": "To figure out the year having the most significant difference between Internal Mobility and International Mobility, I need to compare the difference between the heights of the two bars under each year. Starting with 2010 and moving right, the difference between {the bar for Internal Mobility}[0.093, 0.219, 0.124, 0.917] and {the bar for International Mobility}[0.124, 0.389, 0.155, 0.917] is 110. Through this process, I identify the bars under each subsequent year and identify their height difference. Finally, I find out that the year 2015 shows the highest difference, with values of {Internal Mobility and International Mobility}[0.533, 0.094, 0.564, 0.917; 0.564, 0.312, 0.595, 0.917] being 530 and 390 respectively, having a difference of 140.\nBased on the above thought, the answer is:\n2015" } ], "id_raw": 636 }, { "id": "ROlfI7RIvRr4NeTt", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you're seeing, could you convert the respective returns of the different market conditions in CSV format sorted by investment strategy?" }, { "from": "gpt", "value": "Firstly, I notice that there are three major groupings on the x-axis displaying the three investment strategies: one for {Conservative}[0.282, 0.952, 0.358, 0.975], one for {Moderate}[0.472, 0.952, 0.528, 0.975], and one for {Aggressive}[0.648, 0.952, 0.712, 0.975]. In each of these groupings, there are seven bars representing the seven market conditions based on the {legend}[-0.039, 0.017, 1.039, 0.093], including Stable Growth, Substantial Decrease, Erratic Behavior, Accelerated Decline, Steady Decline, Sudden Growth, and Mixed Performance. I need to check the height of each of these bars and cross-reference it with the y-axis, which ranges from {0.0}[0.141, 0.934, 0.160, 0.958] to {2.5}[0.141, 0.081, 0.160, 0.105], to determine the corresponding returns under each market condition for the investment strategies. For example, under the {Conservative} strategy, the {Stable Growth} label corresponds to the bar with coordinates [0.194, 0.602, 0.230, 0.944] and has a height that aligns with {1.0}[0.141, 0.593, 0.160, 0.616] on the y-axis meaning the return under the Stable Growth condition for the Conservative strategy is 1.0.\nBased on the above thought, the answer is:\nInvestment_Strategies,Stable_Growth,Substantial_Decrease,Erratic_Behavior,Accelerated_Decline,Steady_Decline,Sudden_Growth,Mixed_Performance\nConservative,1.0,0.8,1.2,0.6,0.9,1.4,1.0\nModerate,1.3,0.6,1.0,0.4,0.7,1.7,1.1\nAggressive,1.8,0.4,1.6,0.2,0.5,2.2,1.5" } ], "id_raw": 637 }, { "id": "FjfbtzkkH1JVZg44", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which investment strategy would you consider the safest and why?" }, { "from": "gpt", "value": "To answer this question, I'd need to look at the sets of bars representing the three investment strategies: {Conservative}[0.282, 0.952, 0.358, 0.975], {Moderate}[0.472, 0.952, 0.528, 0.975], and {Aggressive}[0.648, 0.952, 0.712, 0.975]. Each of these groups contains seven bars, which represent the returns under different market conditions as outlined in the {legend}[-0.039, 0.017, 1.039, 0.093]. A safer investment strategy would likely have more moderate and consistent returns across various market conditions. By comparing the heights of the bars within each strategy group, it seems that the {Conservative} strategy generally has moderate heights that do not fluctuate as much as the other strategies, especially in more volatile market conditions such as \"Erratic Behavior\" and \"Sudden Growth\". For example, the bar representing \"Sudden Growth\" for the Conservative strategy {[0.374, 0.466, 0.410, 0.944]} is noticeably shorter than that of the other strategies, indicating a lower return but potentially lower risk.\nBased on the above thought, the answer is:\nThe Conservative strategy. This is because it provides more steady and less fluctuating returns across various market conditions, potentially indicating lower risk compared to the Moderate and Aggressive strategies." } ], "id_raw": 637 }, { "id": "nuipylpdh3UYhKM4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_7.png", "conversations": [ { "from": "human", "value": "\nUnder which market condition, the Moderate strategy has the highest returns?" }, { "from": "gpt", "value": "In order to figure this out, I need to specifically focus on the group of bars that correspond to the {Moderate}[0.472, 0.952, 0.528, 0.975] investment strategy. There are seven bars within this group, representing different market conditions as per the {legend}[-0.039, 0.017, 1.039, 0.093]. By comparing the heights of these bars and cross-referencing them with the y-axis that ranges from {0.0}[0.141, 0.934, 0.160, 0.958] to {2.5}[0.141, 0.081, 0.160, 0.105], I can determine the returns under each condition. The tallest bar within this group corresponds to the {Sudden Growth}[0.554, 0.363, 0.590, 0.944] label and reaches a height that aligns with {1.7}[0.141, 0.363, 0.160, 0.386] on the y-axis. Therefore, the Moderate strategy has the highest returns under the Sudden Growth market condition.\nBased on the above thought, the answer is:\nSudden Growth." } ], "id_raw": 637 }, { "id": "q54fyGkdPHOIQ4Ng", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nCould you please transform the bar chart data into a CSV format?" }, { "from": "gpt", "value": "Given the layout of the chart, I can identify three distinct categories: {Online Education}[0.226, 0.818, 0.311, 0.841], {Training Services}[0.478, 0.818, 0.562, 0.841], and {Edtech Startups}[0.731, 0.818, 0.811, 0.841]. These represent the x-axis labels. The y-axis, with labels ranging from 0[0.040, 0.800, 0.046, 0.824] to 130[0.028, 0.025, 0.046, 0.048], measures the y values corresponding to each category for each year. The years, represented from 2010 to 2016 are identified in the {legend}[0.271, 0.899, 0.768, 0.976], through color coding. Each bar corresponds to the value of a category in a particular year. For example, the {2010 bar for Online Education}[0.093, 0.338, 0.143, 0.810] has a height reaching approximately 79, as indicated by the {text annotation \"79\"}[0.111, 0.303, 0.124, 0.326].\nBased on the above thought, the answer is:\nYear,Online Education,Training Services,Edtech Startups\n2010,79,45,5\n2011,85,40,10\n2012,84,41,20\n2013,90,44,30\n2014,109,47,45\n2015,95,50,60\n2016,120,60,90" } ], "id_raw": 638 }, { "id": "mF3UEWG3It7io3aN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you list the key trends in the industry from 2010 to 2016?" }, { "from": "gpt", "value": "The key trends in the industry can be determined from the bar height changes of three categories. In terms of {Online Education}[0.226, 0.818, 0.311, 0.841], the bar heights have generally increased over the years, indicating a consistent and steady growth through the period, with a major leap between 2013 and 2014 and a minor fall between 2014 and 2015. For {Training Services}[0.478, 0.818, 0.562, 0.841], the growth was slow with fluctuations at the beginning, but it started to increase steadily from 2012. Lastly, for {Edtech Startups}[0.731, 0.818, 0.811, 0.841], the bar heights display the fastest growth rate among all categories, particularly after 2012, indicating an exponential growth throughout the period.\nBased on the above thought, the answer is:\nThere are three main visible trends: \n1. A steady growth of Online Education with a major leap in 2014 and a minor fall in 2015.\n2. A slow start with steady growth after 2012 in Training Services.\n3. A rapid growth rate in Edtech Startups, especially after 2012." } ], "id_raw": 638 }, { "id": "fTstC4O6edyGt5gT", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, in what year did the Edtech Startups have the biggest growth?" }, { "from": "gpt", "value": "To answer this, I need to compare the changes in the bars for Edtech Startups from year to year. Comparing the height difference of {2011's bar of Edtech Startups}[0.645, 0.750, 0.695, 0.810] and {2012's bar of Edtech Startups}[0.695, 0.690, 0.746, 0.810], I can see that these bars represent 10 and 20 respectively, which means that Edtech Startups grew by 10 within this one year. After comparing the bars of other years in the same way, it's clear that the largest growth happened between 2015 and 2016 where the bar of {2015's Edtech Startups}[0.846, 0.452, 0.896, 0.810] represents 60 and the bar of {2016's Edtech Startups}[0.896, 0.273, 0.947, 0.810] represents 90, indicating a increase of 30, which is the largest growth.\nBased on the above thought, the answer is:\n2016" } ], "id_raw": 638 }, { "id": "IKaTqTvutQ2yjcpn", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nCan you create a table that lists the severity of each environmental stressor per decade?" }, { "from": "gpt", "value": "To organize the data into a table, I need to assess the information provided by every bar in the chart. I'll begin with the {first group of bars}[0.116, 0.400, 0.140, 0.944] representing the year 2010 and cover each category horizontally. For example, the {first bar in the group}[0.116, 0.400, 0.140, 0.944] represents Deforestation, with a severity of 100, as indicated by the {label}[0.119, 0.372, 0.138, 0.395]. I\u2019ll proceed similarly with the subsequent bars for {Water pollution}[0.273, 0.672, 0.297, 0.944], {Ocean acidification}[0.430, 0.753, 0.453, 0.944], and other environmental stressors, noting their severities. Then I'll move on to the {second group of bars}[0.140, 0.509, 0.163, 0.944] for the year 2020, and repeat the process for the {third}[0.163, 0.536, 0.187, 0.944] and {fourth group}[0.187, 0.547, 0.210, 0.944] representing 2030 and 2040, respectively.\nBased on the above thought, the answer is:\nYear,Deforestation,Water pollution,Ocean acidification,Overfishing,Air pollution,Soil erosion\n2010,100,50,35,70,120,50\n2020,80,55,40,60,110,45\n2030,75,58,38,50,105,42\n2040,73,60,36,45,102,40" } ], "id_raw": 640 }, { "id": "XC66ZNhN9iPD5WfI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nBased on the trend observed, which environmental stressor has shown the most improvement over the decades?" }, { "from": "gpt", "value": "To determine the most improved stressor, I begin by examining the proportional changes over time for each stressor. I'll focus on the start and end values for a clear comparison. For example, {Deforestation has decreased}[0.116, 0.400, 0.140, 0.944] from 100 in 2010 to {73 in 2040}[0.187, 0.547, 0.210, 0.944]. Next, I observe {Air pollution}[0.743, 0.292, 0.766, 0.944], which shows a significant reduction from 120 to {102}[0.813, 0.390, 0.837, 0.944]. Analyzing all stressors in a similar fashion, Air pollution shows the highest numerical decrease but considering the severity scale and initial values, Overfishing shows the most percentage-wise improvement from 70 to {45}[0.657, 0.699, 0.680, 0.944].\nBased on the above thought, the answer is:\nOverfishing has shown the most improvement as its severity has decreased from 70 to 45, which is a higher percentage decrease compared to others." } ], "id_raw": 640 }, { "id": "HVCbiJ4BiKEPVeRc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease in deforestation from 2010 to 2040?" }, { "from": "gpt", "value": "To calculate the percentage decrease, I first identify the bar representing Deforestation in 2010, located at [0.116, 0.400, 0.140, 0.944] with a severity value of 100. Then, I find the bar for deforestation in 2040 at [0.187, 0.547, 0.210, 0.944] with a severity value of 73. Using the values, the calculation for percentage decrease is done by subtracting the final value from the initial value, dividing by the initial value, and multiplying by 100.\nBased on the above thought, the answer is:\n27%" } ], "id_raw": 640 }, { "id": "kzRVIpQXZtekKeOz", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nCan you construct a table showing the trend values for Blockchain technology from 2019 to 2025 as visualized in the bar chart?" }, { "from": "gpt", "value": "To create a table for Blockchain technology, I must look at each bar pertaining to Blockchain on the chart. Starting with the year 2019, {the first bar for Blockchain}[0.210, 0.676, 0.223, 0.771] represents a value of 0.9, denoted by {the text above the bar}[0.208, 0.643, 0.224, 0.666]. Proceeding to the subsequent years, I identify the rest of the bars representing the values for Blockchain from 2020 to 2025 at the coordinates [0.223, 0.666, 0.236, 0.771], [0.236, 0.634, 0.249, 0.771], [0.249, 0.603, 0.261, 0.771], [0.261, 0.582, 0.274, 0.771], [0.274, 0.571, 0.287, 0.771], and [0.287, 0.561, 0.300, 0.771] with their respective top text annotations providing the values for each year indicated on the bars' corresponding legend [0.265, 0.893, 0.762, 0.970].\nBased on the above thought, the answer is:\nYear,Trend Value\n2019,0.9\n2020,1.0\n2021,1.3\n2022,1.6\n2023,1.8\n2024,1.9\n2025,2.0" } ], "id_raw": 641 }, { "id": "Ks15ZkdTsGClabYB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which technology has shown the most significant growth from 2019 to 2025?" }, { "from": "gpt", "value": "To answer this, I must compare the growth of each technology by examining the height of the bars from 2019 and 2025 for each technology. For example, {AI's 2019 bar}[0.081, 0.550, 0.094, 0.771] and {AI's 2025 bar}[0.158, 0.097, 0.171, 0.771] show a significant increase. Following this method for each technology, I observe that {Quantum Computing's 2019 bar}[0.726, 0.750, 0.739, 0.771] indicates a small initial value, while {Quantum Computing's 2025 bar}[0.804, 0.455, 0.817, 0.771] shows the largest relative increase in height among all technologies, indicating the most significant growth. This correlates with the outlier information identified in the trend, marking a major technological breakthrough for Quantum Computing.\nBased on the above thought, the answer is:\nQuantum Computing. It has shown the most significant growth, starting with a very small value in 2019, and growing much larger by 2025, which is evident from the vertical growth of the representative bars on the bar chart." } ], "id_raw": 641 }, { "id": "g81f1ob4RO9c6oXV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_11.png", "conversations": [ { "from": "human", "value": "\nWhat was the trend value for Cloud Computing in 2022 based on the chart?" }, { "from": "gpt", "value": "To find the trend value for Cloud Computing in 2022, I must look for the bar corresponding to Cloud Computing labeled '2022'. The {bar for Cloud Computing in 2022}[0.378, 0.455, 0.391, 0.771] is accompanied by {a text annotation}[0.376, 0.421, 0.392, 0.445] that indicates the value, which upon inspection reads '3.0'.\nBased on the above thought, the answer is:\n3.0" } ], "id_raw": 641 }, { "id": "MnukuUwaaGgRwNXE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nCould you provide the prevalence rates for Asthma across all the age groups in a structured table format?" }, { "from": "gpt", "value": "To create the table, I will look for the bars representing Asthma, each marked with a specific color on the chart. I see that {the Asthma bars}[0.101, 0.822, 0.116, 0.944]; [0.252, 0.736, 0.267, 0.944]; [0.402, 0.627, 0.417, 0.944]; [0.552, 0.541, 0.567, 0.944]; [0.702, 0.456, 0.717, 0.944]; [0.853, 0.298, 0.868, 0.944] are associated with the age groups on the x-axis with coordinates {Under 30}[0.112, 0.952, 0.166, 0.975], {30-40}[0.273, 0.952, 0.306, 0.975], {40-50}[0.423, 0.952, 0.456, 0.975], {50-60}[0.573, 0.952, 0.606, 0.975], {60-70}[0.723, 0.952, 0.757, 0.975], {Over 70}[0.867, 0.952, 0.913, 0.975]. The values labelled on top of each bar will give us the prevalence rate for Asthma, such as {1.0}[0.100, 0.786, 0.118, 0.810] for Under 30, and so forth.\nBased on the above thought, the answer is:\nAge Group,Asthma\nUnder 30,1.0\n30-40,1.7\n40-50,2.6\n50-60,3.3\n60-70,4.0\nOver 70,5.3" } ], "id_raw": 643 }, { "id": "2lSiQXKn7uPkkLIk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nWhich health condition showed the most significant increase in prevalence rate from the under 30 age group to the over 70 age group?" }, { "from": "gpt", "value": "To analyze the increment in prevalence rates, I will compare the starting and ending values of each health condition. Focusing first on the {Heart Disease bars}[0.086, 0.822, 0.101, 0.944]; [0.838, 0.188, 0.853, 0.944], the first bar has the label {1.0}[0.085, 0.786, 0.103, 0.810] and the last one {6.2}[0.836, 0.152, 0.854, 0.176], showing a significant increase. I will perform this same process for each condition. After scanning all the health conditions, the increase from the under 30 to the over 70 age group for each health condition is evident. From my analysis, {Heart Disease}[0.162, 0.061, 0.244, 0.084] shows the highest increase in prevalence rate from 1.0 to 6.2.\nBased on the above thought, the answer is:\nHeart Disease. By comparing the label values on top of the bars representing Under 30 and Over 70 age groups for each health condition, Heart Disease displays the highest increase in prevalence rate, going from 1.0 to 6.2 according to the data provided." } ], "id_raw": 643 }, { "id": "DJZLVfIc7BUuaWVE", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the prevalence rate of Diabetes for the 40-50 age group?" }, { "from": "gpt", "value": "To find the prevalence rate of Diabetes for the 40-50 age group, I need to locate the corresponding bar on the chart. The bar for Diabetes is color-coded and for the age group of 40-50, {the bar}[0.432, 0.651, 0.447, 0.944] is clearly visible. Above this bar, there is a text label that provides the prevalence rate. Here, I can see the label {2.4}[0.430, 0.616, 0.449, 0.639] on top of the bar.\nBased on the above thought, the answer is:\n2.4" } ], "id_raw": 643 }, { "id": "ufhNUOXt8eSiUjX5", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, what were the revenues (in million dollars) of all six industries in the years 2008 and 2018?" }, { "from": "gpt", "value": "The data that is represented by the bar chart consist of the revenues of six different industries in the years 2008 and 2018. The y axis of the chart represents the revenues, which range from {0}[0.054, 0.934, 0.061, 0.958] to {7000}[0.032, 0.158, 0.061, 0.182], in million dollars. The industries are represented by the 'Industry' {legend}[0.222, 0.024, 0.831, 0.101], where each colored rectangle corresponds to a different industry - 'Oil' with color code '#009b77', 'Gas' with '#f26419', 'Clean Energy' with '#b2473e', 'Water' with '#0072b5', 'Electricity' with '#33a1fd' and 'Biotechnology' with '#ffdb5c'. The two bars for each industry represent the revenues for the years 2008 and 2018. For instance, for the industry 'Oil', the revenues in 2008 and 2018 can be determined by the heights of the two bars '{bar 2008}'[0.107, 0.611, 0.160, 0.944] and '{bar 2018}'[0.631, 0.633, 0.684, 0.944], respectively. Similarly, the values for each of the remaining industries can be inferred by their respective bars.\nBased on the above thought, the answer is:\nIndustry,2008,2018\nOil,3000,2800\nGas,2800,2900\nClean Energy,1000,2300\nWater,3500,3600\nElectricity,4000,5000\nBiotechnology,3600,4000" } ], "id_raw": 644 }, { "id": "uf1ZISxWxPgKQGnS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which industry has seen the most growth from 2008 to 2018?" }, { "from": "gpt", "value": "Looking at the 'Industry' {legend}[0.222, 0.024, 0.831, 0.101] and the bars in the chart, we can see the revenues for various industries in 2008 and 2018. It seems that the height difference between the pair of bars for each industry will enable us to determine the industry that experienced the highest growth in its revenues. For instance, the bars for the 'Oil' industry {'2008 bar'}[0.107, 0.611, 0.160, 0.944] and {'2018 bar'}[0.631, 0.633, 0.684, 0.944] exhibit a decrease showing its revenue dwindled during this period. In contrast, the bars for the 'Clean Energy' industry {'2008 bar'}[0.212, 0.833, 0.265, 0.944] and {'2018 bar'}[0.736, 0.689, 0.788, 0.944] show a remarkable increase which indicates a substantial growth in revenues. Comparing the pairs of bars for all industries, it appears that the 'Clean Energy' industry shows the most growth.\nBased on the above thought, the answer is:\nThe Clean Energy industry has seen the most growth from 2008 to 2018 because it has more than doubled its revenue during this period." } ], "id_raw": 644 }, { "id": "vWzy7Yiat7doGwt0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_43_14.png", "conversations": [ { "from": "human", "value": "\nWhich industry has the highest revenue in 2018?" }, { "from": "gpt", "value": "The 'Industry' {legend}[0.222, 0.024, 0.831, 0.101] and the bars in the chart represent the revenues for the various industries in 2008 and 2018. We are interested in the revenues in 2018, which are represented by the second bar for each industry. I'll compare the heights of these bars to find out which one has the highest value. For example, the 2018 revenue for the 'Oil' industry is represented by the {bar}[0.631, 0.633, 0.684, 0.944]. After comparing all the bars for 2018, we can see that {the highest bar belongs to 'Electricity'}[0.841, 0.389, 0.893, 0.944], indicating that it has the highest revenue in 2018.\nBased on the above thought, the answer is:\nElectricity." } ], "id_raw": 644 }, { "id": "FOzBXXZfIBkqGx32", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nCan you divide the data from the chart into categories and list them in a CSV format?" }, { "from": "gpt", "value": "From the title of the x-axis \"{Time Scale}\"[0.469, 0.942, 0.595, 0.970] I can see that the x-axis represents the two categories for the time scale: {Years}[0.214, 0.903, 0.274, 0.931] and {Decades}[0.770, 0.903, 0.870, 0.931]. The y-axis shows the impact percentages from {0}[0.058, 0.872, 0.072, 0.900] to {6}[0.058, 0.030, 0.072, 0.058]. There are two types of bars to represent the amount of impact due to different reasons: {Economic Growth}[0.129, 0.350, 0.244, 0.883] and {Policy Impact}[0.244, 0.153, 0.359, 0.883]. I will extract the necessary values from the bars representing each reason on both categories and list them in a CSV table.\nBased on the above thought, the answer is:\nTime Scale,Economic Growth,Policy Impact\nYears,3.8,5.2\nDecades,2.5,1.0" } ], "id_raw": 645 }, { "id": "QnN3v6oa0v20lOIb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which time scale had a more impactful economic growth and policy-making scenario?" }, { "from": "gpt", "value": "To determine this, I need to look at the heights of the bars under each time scale. For the 'Years' time scale, the heights are {Economic Growth - Years}[0.129, 0.350, 0.244, 0.883] and {Policy Impact - Years}[0.244, 0.153, 0.359, 0.883], indicating 3.8 and 5.2 respectively. For 'Decades', the heights are {Economic Growth - Decades}[0.704, 0.532, 0.820, 0.883] and {Policy Impact - Decades}[0.820, 0.743, 0.935, 0.883], indicating 2.5 and 1.0 respectively. Comparing the numbers, the 'Years' time scale has higher values for both 'Economic Growth' and 'Policy Impact'.\nBased on the above thought, the answer is:\nThe 'Years' time scale had a more impactful economic growth and policy-making scenario, as both economic growth and policy impact figures are higher than those of the 'Decades' time scale." } ], "id_raw": 645 }, { "id": "NSxRwAMMkuXcL0Hb", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar graph, what is the difference in economic growth between the two time scales?" }, { "from": "gpt", "value": "To find the difference in economic growth between the two-time scales, I should subtract the economic growth of the 'Decades' time scale(which is located at {Decades - Economic Growth}[0.704, 0.532, 0.820, 0.883] and registered as 2.5) from the economic growth of the 'Years' time scale (which is located at {Years - Economic Growth}[0.129, 0.350, 0.244, 0.883] and is registered as 3.8). Therefore, the difference is 3.8 - 2.5.\nBased on the above thought, the answer is:\n1.3" } ], "id_raw": 645 }, { "id": "uVYCzwvTZ1v9K8Or", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nHaving observed the bar chart corresponding to governmental policies and their associated effectiveness and implementation scores, could you convert the relevant data into a structured table in CSV format?" }, { "from": "gpt", "value": "Observing the x-axis in the chart, {xlabel}[0.419, 0.942, 0.637, 0.970], we see that it represents the types of policies: 'Environment Policy' and 'Economic Policy'. This is reinforced by the individual labels found at {[0.189, 0.903, 0.411, 0.931]} and {[0.778, 0.903, 0.964, 0.931]}, respectively. On the other hand, the y-axis represents the 'levels' of effectiveness or implementation of these policies which can be observed from {ylabel}[0.025, 0.418, 0.048, 0.506]. The bars represent the values for both 'Effectiveness' and 'Implementation for each policy'. As indicated by the chart legend at {sublegend}[0.100, 0.055, 0.441, 0.147], the {first group of bars}[0.129, 0.294, 0.243, 0.883] and [0.699, 0.546, 0.814, 0.883] represent 'Policy Effectiveness', while the {second group of bars}[0.243, 0.209, 0.357, 0.883] and [0.814, 0.597, 0.928, 0.883] represent 'Policy Implementation'. The value for each bar can be determined by observing the height of the bar in correlation with the y-axis. For instance, the first bar for 'Policy Effectiveness' under the 'Environment Policy' category has a height that corresponds to a level of approximately '3.5' on the y-axis.\nBased on the above thought, the answer is:\nPolicy Type,Policy Effectiveness,Policy Implementation\nEnvironment Policy,3.5,4.0\nEconomic Policy,2.0,1.7" } ], "id_raw": 646 }, { "id": "DgYqya1DVhcZng50", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, evaluate the status of these two government policies. Can you infer which policy is more successful and why?" }, { "from": "gpt", "value": "Based on the bar chart, the 'Effectiveness' and 'Implementation' of 'Environment' and 'Economic' policies are being compared. Looking at the 'Environment Policy' bars at coordinates {[0.129, 0.294, 0.243, 0.883]} and {[0.243, 0.209, 0.357, 0.883]} for 'Effectiveness' and 'Implementation' respectively, the 'Implementation' seems to be slightly higher than the 'Effectiveness'. Now observing the 'Economic Policy' bars located at {[0.699, 0.546, 0.814, 0.883]} and {[0.814, 0.597, 0.928, 0.883]}, it's clear that the 'Effectiveness' is higher than 'Implementation'. Ultimately, a policy can only be as good as its implementation and hence while the 'Environment Policy' is high on both constructs, it seems to be doing a better job since its implementation is relatively higher. The 'Economic Policy' while having a higher effectiveness, seems to be lacking in its implementation.\nBased on the above thought, the answer is:\nThe 'Environment Policy' seems to be more successful as it exhibits both higher effectiveness and implementation levels when compared to 'Economic Policy'. This indicates that not only are the policies effective in theory, but their practical execution has also been successful." } ], "id_raw": 646 }, { "id": "5YuGeZInzHRJDDYD", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_1.png", "conversations": [ { "from": "human", "value": "\nFor which policy type is the difference between 'Policy Effectiveness' and 'Policy Implementation' the largest?" }, { "from": "gpt", "value": "To find the policy with the largest difference between 'Policy Effectiveness' and 'Policy Implementation' levels, I need to compare the heights of the two corresponding bars under each policy. For 'Environment Policy', the two bars are located at {[0.129, 0.294, 0.243, 0.883]} and {[0.243, 0.209, 0.357, 0.883]} respectively, indicating levels of '3.5' and '4.0' based on their heights. Thus, the difference is '0.5'. For 'Economic Policy', the bars are located at {[0.699, 0.546, 0.814, 0.883]} and {[0.814, 0.597, 0.928, 0.883]} respectively, indicating levels of '2.0' and '1.7'. Thus, the difference here is '0.3'. Comparing the two, we can tell that 'Environment Policy' has the larger difference.\nBased on the above thought, the answer is:\nEnvironment Policy" } ], "id_raw": 646 }, { "id": "TaLCEqm7H5B9ZFWp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nIn the chart, can you extract and convert the raw data for each scenario over the four quarters in 2020 into a CSV table?" }, { "from": "gpt", "value": "The bar chart presents data from three scenarios titled as 'Moderate Growth', 'Steady Decline', and 'Stable Growth' over four quarters in 2020. The {X-axis labels}[\"Quarters\"][0.502, 0.952, 0.578, 0.975] indicates the four quarters and the {Y-axis labels}[\"Units\"][0.019, 0.439, 0.036, 0.498] tells us the numerical value associated with each bar. The bar's height represents the data value in Units. To get the values to be put in the table we can observe the height of each respective bar. For instance, looking at the 'Moderate Growth' bar for '2020 Q1', its {bar coordinate}[0.139, 0.555, 0.184, 0.903] reaches approximately halfway between 500 and 1000 on the Y-axis, giving us the value of 1000 Units. By following the same process for the other bars under each scenario for every quarter, a structured CSV table can be generated.\nBased on the above thought, the answer is:\nTime,Moderate Growth,Steady Decline,Stable Growth\n2020 Q1,1000,2000,1500\n2020 Q2,1100,1900,1500\n2020 Q3,1150,1800,1500\n2020 Q4,1200,1600,1500" } ], "id_raw": 647 }, { "id": "L1z6iuDGWT35UQiV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart trends, what scenario seems to have the best performance over all quarters of 2020? Please explain your reasoning." }, { "from": "gpt", "value": "To determine the \"best\" scenario, one could hypothesize that growth is the preferred outcomes. The ideal scenario would therefore represent growth or at least stable values. When looking at the chart, the bar heights reflect the performance of each scenario for each quarter. For instance, the 'Moderate Growth' scenario depicted by [0.752, 0.115, 0.894, 0.137] shows gradual ascension of bars from the first [0.139, 0.555, 0.184, 0.903] to the last quarter [0.808, 0.486, 0.852, 0.903]. In contrast, the 'Steady Decline' scenario [0.752, 0.150, 0.883, 0.172] has bars showing a progressive decline from the first [0.184, 0.208, 0.228, 0.903] to the last quarter [0.852, 0.347, 0.897, 0.903]. The final 'Stable Growth' scenario [0.752, 0.185, 0.876, 0.207] maintains the same height throughout all quarter bars such as [0.228, 0.382, 0.273, 0.903] and [0.897, 0.382, 0.941, 0.903]. Thus, the 'Moderate Growth' scenario performed the best, as it demonstrated a positive change across all quarters.\nBased on the above thought, the answer is:\nThe 'Moderate Growth' scenario performed the best as demonstrated by the upward trend over the quarters." } ], "id_raw": 647 }, { "id": "vxDmfccWNS8lg440", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total combined growth for all scenarios over all quarters?" }, { "from": "gpt", "value": "To find the total combined growth, I would need to add up the values from all the bars on the chart. Each group of bars represents a quarter with each bar representing a unique scenario. For example, in \"2020 Q1\" the total growth would be the sum of the values of the {first set of bars}[\"2020 Q1\"] representing Moderate Growth [0.139, 0.555, 0.184, 0.903], Steady Decline [0.184, 0.208, 0.228, 0.903] and Stable Growth [0.228, 0.382, 0.273, 0.903]. By observing the chart, it can be seen they approximately reach 1000, 2000, and 1500 units respectively on the Y-axis. The same process should be done for the other three quarters to get the total combined growth for all the quarters.\nBased on the above thought, the answer is:\n19,350." } ], "id_raw": 647 }, { "id": "lrljw20J9YpsMdSq", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured data table from the bar chart visual you have examined?" }, { "from": "gpt", "value": "From the label at the bottom of the chart {Application Type}[0.416, 0.942, 0.607, 0.970] and {the x-axis tick labels}[{Chatbot}[0.257, 0.903, 0.349, 0.931]; {Recommendation System}[0.675, 0.903, 0.972, 0.931]], I can ascertain that the x-axis represents the types of applications. The y-axis scale shows scores from {0.0}[0.058, 0.872, 0.094, 0.900] to {2.5}[0.058, 0.030, 0.094, 0.058], which represents the performance scores of the AI models. According to {the legend}[0.122, 0.055, 0.410, 0.147], the bars are grouped by the colour which represents two different AI methods Deep Learning{[0.131, 0.069, 0.178, 0.088]} and Machine Learning{[0.131, 0.110, 0.178, 0.130]}. The height of each bar for each respective method and the application indicate the performance score. For example, {the bar for Chatbot in Deep Learning}[0.147, 0.142, 0.251, 0.883] has a height that extends roughly to y=2.2 line and hence has a performance score of 2.2.\nBased on the above thought, the answer is:\nApplication Type,Deep Learning,Machine Learning\nChatbot,2.2,1.1\nRecommendation System,1.3,2.1" } ], "id_raw": 648 }, { "id": "OtWehhlOIWGqlQtk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which application performs better when using Deep Learning methods and why?" }, { "from": "gpt", "value": "To answer the question, I need to compare the performance scores of Deep Learning on different applications. From the chatbot application, {the bar for Deep Learning}[0.147, 0.142, 0.251, 0.883] extends to y=2.2 indicating a performance score of 2.2. On the other hand, the {bar for Deep Learning in Recommendation System}[0.668, 0.445, 0.772, 0.883] only extends to y=1.3, indicating a performance score of 1.3. Based on the performance scores, higher values mean better performance, therefore, Deep Learning has got a higher score in Chatbot application and performed better.\nBased on the above thought, the answer is:\nChatbot application. This is because the performance score for Deep Learning is higher in Chatbot application compared to Recommendation System application." } ], "id_raw": 648 }, { "id": "JInjc9TF0sVaGgTi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_3.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which Machine Learning method gave a higher performance score in Chatbot applications?" }, { "from": "gpt", "value": "First of all, I need to identify the bars that represent the performance score of the two Machine Learning methods - Deep Learning and Machine Learning, in Chatbot application. {The bar for Chatbot for Deep Learning}[0.147, 0.142, 0.251, 0.883] extends roughly to y=2.2 whereas {the bar for Chatbot for Machine Learning}[0.251, 0.513, 0.355, 0.883] extends roughly to y=1.1, indicating that performance score of Deep Learning is higher in Chatbot application.\nBased on the above thought, the answer is:\nDeep Learning" } ], "id_raw": 648 }, { "id": "z1PdVCDnes8XWGAS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the bar chart's data into a table with a CSV format?" }, { "from": "gpt", "value": "First, looking at {the x-axis}[0.234, 0.903, 0.851, 0.931], we see that there are two companies {H&M}[0.234, 0.903, 0.290, 0.931] and {Zara}[0.796, 0.903, 0.851, 0.931]. On the y-axis, numbers range from {0.0}[0.058, 0.872, 0.094, 0.900] up to near {4.5}[0.058, -0.021, 0.094, 0.007], representing the supply chain cost in billion USD. The year is indicated by colors of bars. From the legend we can see {the blue colored bar}[0.831, 0.058, 0.877, 0.077] represents 2020 and {orange bars}[0.831, 0.099, 0.877, 0.119] represents 2021. Under H&M there are 2 colored bars, {blue}[0.150, 0.268, 0.262, 0.883] and {orange}[0.262, 0.586, 0.374, 0.883]. The top of the blue bar is around 3.1 and for orange bar it is around 1.5. Similar can be deduced for Zara's bars where {blue}[0.711, 0.586, 0.823, 0.883] stands for around 1.5 and {orange}[0.823, 0.387, 0.936, 0.883] roughly indicates 2.5. Hence each row of the CSV will be the year, followed by the supply chain cost of H&M and then Zara.\nBased on the above thought, the answer is:\nYear,H&M,Zara \n2020,3.1,1.5\n2021,1.5,2.5" } ], "id_raw": 649 }, { "id": "1Vvq4N9DF4EfGMWB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart provided, which company seems to have had a more favorable supply chain cost progression from 2020 to 2021?" }, { "from": "gpt", "value": "To analyze the progression of supply chain costs, I'll refer to the heights of the bars representing each company in 2020 and 2021. {H&M's bars}[{2020}[0.150, 0.268, 0.262, 0.883];{2021}[0.262, 0.586, 0.374, 0.883]] suggest a trend of decreasing cost from 3.1 to 1.5 billion USD. Meanwhile, {Zara's bars}[{2020}[0.711, 0.586, 0.823, 0.883];{2021}[0.823, 0.387, 0.936, 0.883]] show an increasing trend from 1.5 to 2.5 billion USD. Lower supply chain costs are generally more favorable for a company.\nBased on the above thought, the answer is:\nH&M. The company has shown a decrease in supply chain costs from 2020 to 2021, which is typically considered favorable progress." } ], "id_raw": 649 }, { "id": "xTWLhhiBx64LzFrV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_4.png", "conversations": [ { "from": "human", "value": "\nWhich company has a steeper increase or decrease in supply chain cost from 2020 to 2021?" }, { "from": "gpt", "value": "To see which company had a steeper change in supply chain cost, I need compare the difference between the 2020 and 2021 bars for H&M and Zara. The height difference for {H&M bars}[{2020}[0.150, 0.268, 0.262, 0.883];{2021}[0.262, 0.586, 0.374, 0.883]] is around 1.6 and {Zara bars}[{2020}[0.711, 0.586, 0.823, 0.883];{2021}[0.823, 0.387, 0.936, 0.883]] is around 1. Hence, the supply chain cost of H&M has changed more steeply.\nBased on the above thought, the answer is:\nH&M" } ], "id_raw": 649 }, { "id": "hJnxD5HGO3nryC0c", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the chart into structured data in the form of a table?" }, { "from": "gpt", "value": "I see from the {chart}[0.110, 0.041, 0.975, 0.883] that we have data in the form of bars for two media outlets - {FOX News}[0.204, 0.903, 0.320, 0.931] and {CNN}[0.798, 0.903, 0.849, 0.931], and for two categories of influence - {Strong Influence}[0.764, 0.066, 0.954, 0.094] and {Slight Influence}[0.764, 0.108, 0.944, 0.136]. By examining the corresponding bars, I can find out the level of influence these outlets have. For instance, the {bar}[0.150, 0.715, 0.262, 0.883] representing Strong Influence for FOX News reaches approximately 0.6 on the y-axis, and the {bar for Slight Influence}[0.262, 0.209, 0.374, 0.883] reaches 2.4. For CNN, the corresponding bars reach 1.8 and 0.8 respectively. Using these data, I can construct the table.\nBased on the above thought, the answer is:\nMedia Outlet,Strong Influence,Slight Influence\nFOX News,0.6,2.4\nCNN,1.8,0.8" } ], "id_raw": 651 }, { "id": "nRDuFGsLEgCk5kze", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the chart, which media outlet can potentially have a more consistent influence?" }, { "from": "gpt", "value": "Looking at the graph, when considering the bars representing the level of influence for both categories, it's visible that {FOX News}[0.204, 0.903, 0.320, 0.931] displays a more considerable difference between strong and slight influence with bars at {0.6}[0.150, 0.715, 0.262, 0.883] and {2.4}[0.262, 0.209, 0.374, 0.883] rspectively. {CNN}[0.798, 0.903, 0.849, 0.931], on the other hand, shows lesser variation between strong and slight influence with bars at {1.8}[0.711, 0.378, 0.823, 0.883] and {0.8}[0.823, 0.659, 0.936, 0.883]. Lower variation suggests a more consistent level of influence.\nBased on the above thought, the answer is:\nCNN. Because it shows less variation between strong and slight influence, it suggests a more consistent level of influence." } ], "id_raw": 651 }, { "id": "NsuLMt19pymnCqPd", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_6.png", "conversations": [ { "from": "human", "value": "\nWhich media outlet has a higher total influence, considering both strong and slight influences combined?" }, { "from": "gpt", "value": "To find the total influence of each media outlet, I need to sum up the height of the bars representing Strong Influence and Slight Influence for each outlet. {FOX News}[0.204, 0.903, 0.320, 0.931] has a strong influence of {0.6}[0.058, 0.732, 0.094, 0.760] and slight influence of {2.4}[0.058, 0.170, 0.094, 0.198], resulting in a total of 3.0. {CNN}[0.798, 0.903, 0.849, 0.931] has a strong influence of {1.8}[0.058, 0.378, 0.094, 0.406] and slight influence of {0.8}[0.058, 0.659, 0.094, 0.687], which sums up to 2.6. Hence, the total influence from each media outlet can be calculated.\nBased on the above thought, the answer is:\nFOX News." } ], "id_raw": 651 }, { "id": "IbzUA0UtjQ7BqDfc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nFrom your analysis of the provided bar chart, please assist in structuring the data into a CSV format table." }, { "from": "gpt", "value": "Based on the ylabel {\"Influence Level\"}[0.025, 0.357, 0.048, 0.567], the y-axis represents the influence level. The x-axis, which is labeled by {\"Media Sources\"}[0.459, 0.942, 0.627, 0.970], shows the media sources which are {Test 1}[0.284, 0.903, 0.352, 0.931] and {Test 2}[0.846, 0.903, 0.913, 0.931]. There are two groups of bars represented by {Media 1}[0.797, 0.069, 0.844, 0.088] and {Media 2}[0.797, 0.110, 0.844, 0.130] in {legend}[0.788, 0.055, 0.963, 0.147]. For Test 1 in Media 1, the influence level is represented by the height of {the first white bar}[0.150, 0.146, 0.262, 0.883], which is around 3.5. So, by comparing the height of each bar to the tick marks on the y-axis and repeated these steps for each media source, I can derive the corresponding CSV table.\nBased on the above thought, the answer is:\nMedia Source, Media 1, Media 2\nTest 1, 3.5, 2.0\nTest 2, 2.5, 1.0" } ], "id_raw": 652 }, { "id": "SehWoGTOEzjpD5il", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what can you infer about the credibility or effectiveness of the two media sources over time?" }, { "from": "gpt", "value": "The two sets of bars represent the influence levels of {Media 1}[0.797, 0.069, 0.844, 0.088] and {Media 2}[0.797, 0.110, 0.844, 0.130] at two different time periods (Test 1 and Test 2 from the x-axis). If I look at the height of the bars, both media sources have decreased in influence from {Test 1}[0.284, 0.903, 0.352, 0.931] to {Test 2}[0.846, 0.903, 0.913, 0.931]. This suggests that over this time frame, both media sources have seen a decline in the level of influence they have over their audience. This might suggest that their credibility or effectiveness in guiding public opinion may be waning, but it could also be due to other external factors such as changing audience preferences, the emergence of new competing media sources, etc.\nBased on the above thought, the answer is:\nBoth Media 1 and Media 2 have seen a decrease in their effectiveness or credibility over time, as indicated by the decrease in their respective influence levels." } ], "id_raw": 652 }, { "id": "IvZGfnyM1Lb3YlTI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_7.png", "conversations": [ { "from": "human", "value": "\nWhich media source saw a larger drop in influence level between Test 1 and Test 2?" }, { "from": "gpt", "value": "If I want to compare the change in influence level for each media source, I need to consider the change in height of the bars between the two tests. In {Media 1}, the {test 1 bar}[0.150, 0.146, 0.262, 0.883] is taller than the {test 2 bar}[0.711, 0.357, 0.823, 0.883]. The heights represent influence levels of 3.5 and 2.5 respectively, indicating a drop of 1.0. Similarly, in {Media 2}, the {test 1 bar}[0.262, 0.462, 0.374, 0.883] is taller than the {test 2 bar}[0.823, 0.673, 0.936, 0.883]. The heights represent influence levels of 2.0 and 1.0 respectively, indicating a drop of 1.0 as well.\nBased on the above thought, the answer is:\nNeither. Both Media 1 and Media 2 showed the same drop in influence level of 1.0." } ], "id_raw": 652 }, { "id": "DmgDnmA90KbIHX4L", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nFrom the bar charts you observe, can you extract the exact values represented by the bars for California and Florida for each factor - Forest Coverage, Population Density, and Public Park Coverage, and present them in a CSV format table?" }, { "from": "gpt", "value": "Looking at the bar chart, I can see that there are three different series indicated by different hatching styles as per the {legend}[0.086, 0.046, 0.332, 0.157]. The first series of bars represent {Forest Coverage}[{\"California\"}[0.118, 0.738, 0.221, 0.903], {\"Florida\"}[0.632, 0.799, 0.735, 0.903]]. We can see the second series as {Population Density}[{\"California\"}[0.221, 0.164, 0.324, 0.903], {\"Florida\"}[0.735, 0.251, 0.837, 0.903]], and the third series as {Public Park Coverage}[{\"California\"}[0.324, 0.842, 0.427, 0.903], {\"Florida\"}[0.837, 0.825, 0.940, 0.903]]. The x-axis tick labels represent the states under evaluation, which are {California}[0.231, 0.919, 0.314, 0.942] and {Florida}[0.756, 0.919, 0.816, 0.942].\nWhile the y-axis ranges from {0}[0.054, 0.894, 0.065, 0.917] to {10}[0.043, 0.025, 0.065, 0.048] and is used to represent the values of each of these factors. The length of each of these bars represents the value of the factor it is representing.\nFor example, for {Forest Coverage in California}[0.118, 0.738, 0.221, 0.903], the height of the bar reaches approximately 1.9 on the y-axis. Similarly, other values for the factors for each state can be determined using the length of their corresponding bars.\nBased on the above thought, the answer is:\nState,Forest Coverage,Population Density,Public Park Coverage\nCalifornia,1.9,8.5,0.7\nFlorida,1.2,7.5,0.9" } ], "id_raw": 653 }, { "id": "nuIDzVzV74qwhki8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, how can Florida improve its Forest Coverage to match up with California? Also, what can California learn from Florida according to the bar chart?" }, { "from": "gpt", "value": "According to the chart, {Florida}[0.756, 0.919, 0.816, 0.942] has significantly less {Forest Coverage}[0.632, 0.799, 0.735, 0.903] than {California}[0.231, 0.919, 0.314, 0.942]. To improve its Forest Coverage, Florida could consider implementing reforestation programs, protecting existing natural forests, and increasing awareness and education around the importance of forests. \nConversely, {California}[0.231, 0.919, 0.314, 0.942] has less {Public Park Coverage}[0.324, 0.842, 0.427, 0.903] in comparison to {Florida}[0.756, 0.919, 0.816, 0.942]. California can learn from Florida by implementing more public parks and open spaces for its residents. This could improve the quality of life for residents and increase tourism appeal.\nBased on the above thought, the answer is:\nFlorida can improve its forest coverage by implementing reforestation programs, protecting existing natural forests, and increasing awareness and education around the importance of forests. California can learn from Florida how to improve its public park coverage by creating more public parks and green spaces. This can make living environments more pleasant and potentially attract more tourists." } ], "id_raw": 653 }, { "id": "wBkxgQOgxwPFPCha", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which state has a higher cumulative average across all three factors, Forest Coverage, Population Density, and Public Park Coverage?" }, { "from": "gpt", "value": "To determine the state with a higher cumulative average across the three factors, I need to calculate the average for each state. This can be done by adding the representative values for each factor under each state and dividing the total by the number of factors. For {California}[0.231, 0.919, 0.314, 0.942], the factors {Forest Coverage}[0.118, 0.738, 0.221, 0.903], {Population Density}[0.221, 0.164, 0.324, 0.903] and {Public Park Coverage}[0.324, 0.842, 0.427, 0.903] yield values of 1.9, 8.5, and 0.7 respectively, resulting in a cumulative average of approximately (1.9 + 8.5 + 0.7) / 3 = 3.7. Similarly, for {Florida}[0.756, 0.919, 0.816, 0.942], I get values of 1.2 for {Forest Coverage}[0.632, 0.799, 0.735, 0.903], 7.5 for {Population Density}[0.735, 0.251, 0.837, 0.903] and 0.9 for {Public Park Coverage}[0.837, 0.825, 0.940, 0.903], taking an average of (1.2 + 7.5 + 0.9) / 3 = 3.2.\nBased on the above thought, the answer is:\nCalifornia." } ], "id_raw": 653 }, { "id": "zrXweyX6ZxyjXfyM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nCan you help me convert the data from the bar chart into a structured CSV formatted table?" }, { "from": "gpt", "value": "Based on the chart figure presented, I am observing three bars at two different positions on the x-axis. The x-axis represents the platforms {Email}[0.230, 0.903, 0.294, 0.931] and {SMS}[0.799, 0.903, 0.848, 0.931]. On the y-axis labeled as 'Average Time (s)'[0.025, 0.344, 0.048, 0.580], the values range from 0.0 to 4.5. The height of the bars corresponds directly to this value range. The bars are divided into two colours, each representing a platform, as clarified by {the legend}[0.762, 0.055, 0.963, 0.147] - {Instagram}[0.836, 0.066, 0.954, 0.094] and {Twitter}[0.836, 0.108, 0.914, 0.136]. For example, at Email's x-axis position, the {Instagram bar}[0.150, 0.228, 0.262, 0.883] reaches approximately 3.5 while the {Twitter bar}[0.262, 0.135, 0.374, 0.883] occupies about 4.0 units of average time, which can be verified by the bar's position on the y-axis.\nBased on the above thought, the answer is:\nPlatform,Instagram,Twitter\nEmail,3.5,4.0\nSMS,1.2,1.4" } ], "id_raw": 654 }, { "id": "tuWEDUc1tV48q9jk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, could you explain the differences in message sending times across different platforms and methods?" }, { "from": "gpt", "value": "Certainly, based on the provided bar chart, it presents the average time taken in seconds to send a message on different platforms (Email and SMS) and through different methods (Instagram and Twitter). For {Email}[0.230, 0.903, 0.294, 0.931], it takes more time to send a message than through {SMS}[0.799, 0.903, 0.848, 0.931], no matter whether the platform is Instagram or Twitter. If we look at individual platforms, on {Twitter}[0.836, 0.108, 0.914, 0.136] it takes longer on average to send a message than on {Instagram}[0.836, 0.066, 0.954, 0.094], both for SMS and Email. The longest average sending time is on Twitter via Email, and the shortest is on Instagram via SMS.\nBased on the above thought, the answer is:\nEmail generally takes more time to send messages compared to SMS, regardless of whether the platform is Instagram or Twitter. The messaging time for Twitter is generally longer than Instagram on both Email and SMS. The fastest way to send a message is via SMS on Instagram and the slowest is via Email on Twitter." } ], "id_raw": 654 }, { "id": "QLlK2oOlCtA0h7PL", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me on which platform Instagram takes more time to send a message than Twitter?" }, { "from": "gpt", "value": "To answer this, I need to compare the heights of the Instagram and Twitter bars for each platform. The {Instagram bar}[0.150, 0.228, 0.262, 0.883] for Email is approximately at 3.5 on the y-axis, while the {Twitter bar}[0.262, 0.135, 0.374, 0.883] is closer to 4.0. So for the Email platform, Twitter takes more time. Then looking at SMS, the {Instagram bar}[0.711, 0.659, 0.823, 0.883] is lower, around 1.2 whereas the {Twitter bar}[0.823, 0.621, 0.936, 0.883] is taller, around 1.4. So it seems for both platforms, Twitter takes more time to send a message.\nBased on the above thought, the answer is:\nNeither" } ], "id_raw": 654 }, { "id": "o5BmYN9O7dfwtK8c", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nPlease help me translate the data from the chart you observed into a CSV format table." }, { "from": "gpt", "value": "Based on the xlabel of the chart that indicates {Healthcare Tech}[0.450, 0.942, 0.635, 0.970], I can see that the x-axis represents two categories: {CRISPR}[0.276, 0.903, 0.360, 0.931] and {Gene Therapy}[0.798, 0.903, 0.961, 0.931]. The y-axis represents the 'Transmission Delay (ms)', as indicated by the ylabel {at the side}[0.025, 0.287, 0.048, 0.626], and the two types of bars indicate the transmission delay in milliseconds for each technology according to {the legend}[0.122, 0.044, 0.549, 0.136]. By combining the height of the bars and the y-axis scales, I can determine that the delay amounts for each healthcare tech in relation to telemedicine and machine learning in medicine. For instance, {the white bar for \"CRISPR\"}[0.150, 0.526, 0.262, 0.883] reaches the y-value at around 1.8 and the {the white bar for \"Gene Therapy\"}[0.711, 0.745, 0.823, 0.883] ends around the y-value of 0.7, which reveals the delay amounts for Telemedicine.\nBased on the above thought, the answer is:\nHealthcare Tech,Telemedicine,Machine Learning in Medicine\nCRISPR,1.8,1.0\nGene Therapy,0.7,2.1" } ], "id_raw": 655 }, { "id": "ELSAfoeaIUXqMvNh", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which medical technology has a higher transmission delay and why?" }, { "from": "gpt", "value": "To answer this question, I need to compare the medical technology bars at each Healthcare Tech. At {CRISPR}[0.276, 0.903, 0.360, 0.931], {Telemedicine bar}[0.150, 0.526, 0.262, 0.883] has a higher transmission delay than {Machine Learning in Medicine bar}[0.262, 0.685, 0.374, 0.883]; the situation is reversed at {Gene Therapy}[0.798, 0.903, 0.961, 0.931], with the bar of {Machine Learning in Medicine}[0.823, 0.467, 0.936, 0.883] being taller than that of {Telemedicine}[0.711, 0.745, 0.823, 0.883]. Therefore, it can be deduced that the overall transmission delays of the two technologies are close, with Machine Learning in Medicine having a marginally higher delay due to its significant delay at \"Gene Therapy\".\nBased on the above thought, the answer is:\nMachine Learning in Medicine. Although Telemedicine has a higher transmission delay at CRISPR, the delay of Machine Learning in Medicine significantly exceeds Telemedicine at Gene Therapy, making the overall delay of Machine Learning in Medicine slightly higher." } ], "id_raw": 655 }, { "id": "OXB6SGKHyvUcpI8Y", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_10.png", "conversations": [ { "from": "human", "value": "\nIn the chart, which healthcare tech has a higher transmission delay for Telemedicine?" }, { "from": "gpt", "value": "The transmission delays for Telemedicine are represented by white bars. Hence, by comparing the heights of the {Telemedicine bar at \"CRISPR\"}[0.150, 0.526, 0.262, 0.883] and the {Telemedicine bar at \"Gene Therapy\"}[0.711, 0.745, 0.823, 0.883], it can be observed that the bar for \"CRISPR\" is taller, indicating a higher delay.\nBased on the above thought, the answer is:\nCRISPR." } ], "id_raw": 655 }, { "id": "xILJ4XXhMlkdopFe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nAfter analyzing the bar graph, could you convert the information into a tabular format?" }, { "from": "gpt", "value": "The bar graph's title isn't presented explicitly but the xlabel {Risk Factors}[0.484, 0.964, 0.567, 0.981] and ylabel {Risk Level}[0.015, 0.433, 0.029, 0.520], along with the legend with labels \"Probability\", \"Impact\", and \"Manageability\" at [0.073, 0.034, 0.221, 0.118], give an understanding of the chart. It represents the risk factors with their corresponding Probability, Impact, and Manageability. The x-axis represents two categories: {Economic Conditions}[0.192, 0.939, 0.337, 0.957] and {Regulatory Changes}[0.716, 0.939, 0.857, 0.957]. The y-axis represents the risk level from {0}[0.035, 0.920, 0.057, 0.938] to {1}[0.035, 0.019, 0.057, 0.036]. Different bars represent different parameters of every risk factor. For example, the bar representing the 'Probability' for 'Economic Conditions' is in the coordinates [0.108, 0.206, 0.212, 0.927]. The y-coordinate of the top of this bar is 0.927, aligns with 0.8 on the y-axis. This similar approach can be used to assess all data points and thus I am able to build a table.\nBased on the above thought, the answer is:\nRisk Factors, Probability, Impact, Manageability\nEconomic Conditions, 0.8, 0.9, 0.7\nRegulatory Changes, 0.6, 0.85, 0.5" } ], "id_raw": 656 }, { "id": "njYuHPlVY9rpWAMS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nWhat observations can we make about the two risk factors and their management in the company?" }, { "from": "gpt", "value": "We will base our analysis on the bar graphs for quantities 'Probability', 'Impact', and 'Manageability' for the two risk factors: {Economic Conditions}[0.192, 0.939, 0.337, 0.957] and {Regulatory Changes}[0.716, 0.939, 0.857, 0.957]. Economic Conditions shows a high risk level for both Probability and Impact, as denoted by the bars at [0.108, 0.206, 0.212, 0.927] and [0.212, 0.116, 0.317, 0.927] respectively. However, the manageability of these risks at [0.317, 0.296, 0.421, 0.927] is relatively high. This suggests that the company is well-equipped to manage these risks despite their high probability and impact. On the other hand, Regulatory Changes shows a medium level of Probability and high level of Impact, and the associated bars are at [0.630, 0.386, 0.734, 0.927] and [0.734, 0.161, 0.839, 0.927]. However, Manageability, indicated by the bar at [0.839, 0.476, 0.943, 0.927], is comparatively lower, indicating that the company faces challenges in managing these types of risks.\nBased on the above thought, the answer is:\nEconomic conditions present a high risk but are manageable for the company. In contrast, Regulatory Changes, while less likely, are harder for the company to manage despite having a high impact." } ], "id_raw": 656 }, { "id": "5c5ovyVybQcKLmuq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_11.png", "conversations": [ { "from": "human", "value": "\nWhich risk factor shows a lower level of manageability?" }, { "from": "gpt", "value": "To identify the risk factor having a lower level of manageability, I will go through both the manageability bars for each risk factor. These are identified as the third bar for every risk category. In this bar graph, there are two risk factors, being {Economic Conditions}[0.192, 0.939, 0.337, 0.957] and {Regulatory Changes}[0.716, 0.939, 0.857, 0.957] with their manageability levels represented by the bars at [0.317, 0.296, 0.421, 0.927] and [0.839, 0.476, 0.943, 0.927] respectively. It can be observed that the bar corresponding to Regulatory Changes is seen lower on the \"Risk Level\" y-axis than Economic Conditions. Therefore, Regulatory Changes has a lower level of manageability.\nBased on the above thought, the answer is:\nRegulatory Changes." } ], "id_raw": 656 }, { "id": "z3oU4lfhynSdmPwI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart you see, can you extract the data and provide it in a structured table format?" }, { "from": "gpt", "value": "Sure, I see a bar chart that has Year 1, Year 2, and Year 3 on the x-axis {\"Years\"}[0.512, 0.942, 0.573, 0.970]. The y-axis {\"Advancements\"}[0.025, 0.353, 0.048, 0.560] scale starts at 0 and ends at 4.5, and is used to measure the advancements of Health Technology and Medical Technology. There are two sets of bars, the first set representing Health Technology {\"Health Technology\"}[0.728, 0.055, 0.941, 0.083], and the second set representing Medical Technology {\"Medical Technology\"}[0.728, 0.097, 0.954, 0.125]. Each set contains three bars corresponding to each year. The height of the bars corresponds to the value of advancements. For instance, for Year 1, the height of the first bar {\"Health Technology Year 1\"}[0.150, 0.224, 0.215, 0.883] is about 3.4, and the height of the second bar {\"Medical Technology Year 1\"}[0.215, 0.243, 0.281, 0.883] is about 3.3. By utilizing the coordinates attached to each bar, I can extract the value of advancements for each technology in each year and organize them into a structured table.\nBased on the above thought, the answer is:\nYears,Health Technology,Medical Technology\nYear 1,3.4,3.3\nYear 2,2.4,1.5\nYear 3,1.6,1.7" } ], "id_raw": 657 }, { "id": "2QnZzaLJQ3ubVy6E", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which technology seems to experience a substantial decrease in advancements and can you interpret the reasons why?" }, { "from": "gpt", "value": "Looking at the graph, on the data associated with each bar for both {\"Health Technology\"}[0.728, 0.055, 0.941, 0.083] and {\"Medical Technology\"}[0.728, 0.097, 0.954, 0.125], I notice that both types of technology have experienced decreases in advancements over the three years. But the decline is more pronounced for Medical Technology, especially from Year 1 to Year 2, as evidenced by the substantial drop in height of the bars {\"Medical Technology Year 1\"}[0.215, 0.243, 0.281, 0.883] to {\"Medical Technology Year 2\"}[0.543, 0.592, 0.608, 0.883]. This change suggests that there may have been challenges that affected the medical technology field more significantly compared to health technology. Possible reasons could be decreases in research funding, loss of key personnel, policy changes, regulatory hurdles or other external constraints that could have hampered advancements.\nBased on the above thought, the answer is:\nThe Medical Technology has experienced a substantial decrease. Possible reasons could be changes in funding, loss of key personnel, policy or regulatory changes, or other external factors." } ], "id_raw": 657 }, { "id": "1UwbSKO61YJhiwhE", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_12.png", "conversations": [ { "from": "human", "value": "\nWhich year has the highest sum of advancements in both Health and Medical Technologies?" }, { "from": "gpt", "value": "To find the year with the highest total advancements, I need to compare the sum of the advancements of both technologies for each year. For Year 1, the sum of advancements is the height of {\"Health Technology Year 1\"}[0.150, 0.224, 0.215, 0.883], which is approximately 3.4, added to the height of {\"Medical Technology Year 1\"}[0.215, 0.243, 0.281, 0.883] which is almost 3.3, giving a total of 6.7. I perform the same calculation for Year 2 and Year 3 by summing the heights of their respective bars. Finally, I can determine which year has the highest total advancements.\nBased on the above thought, the answer is:\nYear 1" } ], "id_raw": 657 }, { "id": "lCdIA6PjTFGxEU2B", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data presented in the bar chart into a CSV formatted table?" }, { "from": "gpt", "value": "From the bar chart, there are two groups of bars representing the performance ratings for {'Deep Learning Performance'}[0.749, 0.055, 0.918, 0.083] and {'Machine Learning Performance'}[0.749, 0.097, 0.954, 0.125] of the two AI algorithms. The x-axis {AI Algorithms}[0.454, 0.942, 0.609, 0.970] represents the AI algorithms {'FastAI'}[0.229, 0.903, 0.298, 0.931] and {'TensorFlow'}[0.737, 0.903, 0.864, 0.931]. The y-axis represents the performance rating values ranging from {0}[0.058, 0.872, 0.072, 0.900] to {6}[0.058, -0.094, 0.072, -0.066]. The height of the bars corresponds to the performance rating value. Thus, the 'Deep Learning Performance' rating of FastAI can be read from {the first bar of group_0}[0.129, 0.513, 0.263, 0.883] as 2.3 and of TensorFlow as 2.9 from {the second bar of group_0}[0.666, 0.416, 0.800, 0.883]. For 'Machine Learning Performance', FastAI got a rating of 1.8 from {the first bar of group_1}[0.263, 0.594, 0.397, 0.883], whereas TensorFlow got a rating of 4.9 from {the second bar of group_1}[0.800, 0.094, 0.935, 0.883].\nBased on the above thought, the answer is:\nAI Algorithms,Deep Learning Performance,Machine Learning Performance\nFastAI,2.3,1.8\nTensorFlow,2.9,4.9" } ], "id_raw": 658 }, { "id": "76Tgn5pUYpKRZqmH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nInterprete the discrepancy in performance ratings between the two AI algorithms across the two domains." }, { "from": "gpt", "value": "Reviewing the chart, it can be seen that the performance rating for both algorithms in the two domains, namely {Deep Learning}[0.749, 0.055, 0.918, 0.083] and {Machine Learning}[0.749, 0.097, 0.954, 0.125], are represented by a collection of bars. In both domains, the {bars representing 'TensorFlow'}[0.666, 0.416, 0.800, 0.883] for Deep Learning and [0.800, 0.094, 0.935, 0.883] for Machine Learning, are taller than those representing 'FastAI', both< r>[0.129, 0.513, 0.263, 0.883] for Deep Learning and [0.263, 0.594, 0.397, 0.883] for Machine Learning, indicating a higher performance rating. Moreover, the margin of dominance of 'TensorFlow' over 'FastAI' is significantly pronounced in the Machine Learning domain, suggesting that TensorFlow vastly outperforms FastAI in machine learning tasks in terms of several factors examined including model training speed, model performance, and handling complex tasks.\nBased on the above thought, the answer is:\nTensorFlow outperforms FastAI in both domains, with a significantly greater margin in the Machine Learning domain. This suggests that TensorFlow could be the more efficient choice for tasks requiring machine learning capabilities." } ], "id_raw": 658 }, { "id": "dZBKItFagezNPhNK", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_13.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the Machine Learning performance rating of TensorFlow than FastAI?" }, { "from": "gpt", "value": "To answer this question, I need to look at the comparison of the bars under the 'Machine Learning Performance' group. The {bar for 'FastAI'}[0.263, 0.594, 0.397, 0.883] represents a performance rating of 1.8 whereas {the bar for 'TensorFlow'}[0.800, 0.094, 0.935, 0.883] has a considerably longer height, corresponding to a rating of 4.9. Subtracting the rating of FastAI from that of TensorFlow gives the difference in performance ratings.\nBased on the above thought, the answer is:\n3.1" } ], "id_raw": 658 }, { "id": "bTpktQDcNBD7tAwT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nPlease convert the data shown in this gaming industry development chart to a CSV format table." }, { "from": "gpt", "value": "To convert this chart into a CSV format, I need to identify the categories, which are given by the legend including {PC Gaming}[0.189, 0.066, 0.317, 0.094], {Console Gaming}[0.189, 0.108, 0.378, 0.136], and {Mobile Gaming}[0.189, 0.150, 0.363, 0.178]. Two years, {2014}[0.237, 0.903, 0.295, 0.931] and {2020}[0.783, 0.903, 0.842, 0.931] in the x-axis are the time points at which data was collected. Y-values, representing the percentage contributions to the overall industry, can be calculated based on the y-axis scale ranged from {0}[0.072, 0.872, 0.087, 0.900] to {60}[0.058, 0.030, 0.087, 0.058] and the height of each bar. The heights of the bars indicate the proportions; for example, the PC Gaming bar for 2014[0.143, 0.392, 0.225, 0.883] stands approximately at the 35 mark on the y-axis, and the PC Gaming bar for 2020[0.689, 0.322, 0.771, 0.883] is approximately on the 40 mark.\nBased on the above thought, the answer is:\nYear,PC Gaming,Console Gaming,Mobile Gaming\n2014,35,40,25\n2020,40,35,55" } ], "id_raw": 659 }, { "id": "kM66eOgbrCH3JH6n", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nFrom the gaming industry trends represented in the chart, what future predictions can you make about the contributions of PC, Console and Mobile Gaming to the gaming industry by 2026?" }, { "from": "gpt", "value": "To make a prediction about the future of the gaming industry based on the chart, note down the changing trends from 2014 to 2020 first. Looking at the {PC Gaming}[0.189, 0.066, 0.317, 0.094] bars in 2014[0.143, 0.392, 0.225, 0.883] and 2020[0.689, 0.322, 0.771, 0.883], the proportion has only slightly increased. So, we can predict that PC Gaming will likely maintain a steady contribution in the future, perhaps with slight increases. For {Console Gaming}[0.189, 0.108, 0.378, 0.136], the bars have shown a decrease[0.225, 0.322, 0.307, 0.883]; [0.771, 0.392, 0.853, 0.883], so it may continue to decrease in popularity by 2026. For {Mobile Gaming}[0.189, 0.150, 0.363, 0.178], the bars have shown a stark increase in popularity, rising from about 25[0.307, 0.532, 0.389, 0.883] in 2014 to about 55[0.853, 0.111, 0.935, 0.883] in 2020. Therefore, if the trend continues, Mobile Gaming may become even more popular and may dominate the gaming industry by 2026.\nBased on the above thought, the answer is:\nThe predictions would be that PC Gaming would maintain a steady contribution in the future with slight increases. However, the popularity of Console Gaming may continue to decrease by 2026. Mobile Gaming is predicted to become even more popular and could take the dominant position in the gaming industry by 2026." } ], "id_raw": 659 }, { "id": "UJ0kCbk9SGHTEZtQ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_44_14.png", "conversations": [ { "from": "human", "value": "\nWhich type of gaming has observed the greatest increase in popularity from 2014 to 2020?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height of bars in 2014 and 2020 for each category. The height of bars represents the popularity. For {PC Gaming}[0.189, 0.066, 0.317, 0.094] the bars in 2014[0.143, 0.392, 0.225, 0.883] and 2020[0.689, 0.322, 0.771, 0.883] appear to have a minimal difference of about 5 points on the y-axis. For {Console Gaming}[0.189, 0.108, 0.378, 0.136], the bars[0.225, 0.322, 0.307, 0.883]; [0.771, 0.392, 0.853, 0.883] have rather decreased than increased. The bars for {Mobile Gaming}[0.189, 0.150, 0.363, 0.178], however, show the biggest increase among all, from about 25[0.307, 0.532, 0.389, 0.883] in 2014 to about 55[0.853, 0.111, 0.935, 0.883] in 2020, showing a clear push of 30 points.\nBased on the above thought, the answer is:\nMobile Gaming." } ], "id_raw": 659 }, { "id": "P0GNRSAXkE4jaNHp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nPlease convert the bar chart data into a CSV format table." }, { "from": "gpt", "value": "Let's first identify the categories. According to {the x-axis labels}[0.285, 0.907, 0.384, 0.934],[0.729, 0.907, 0.836, 0.934], the x-axis represents the platform, \"Facebook\" and \"Instagram\". Now let's focus on the data. The y-axis represents the count, ranging from {0}[0.119, 0.887, 0.132, 0.914] to {250000}[0.052, 0.029, 0.132, 0.055]. We have two types of bars for each platform, blue for post count and orange for user activity, identified by {the legend}[0.758, 0.052, 0.967, 0.141]. Let's examine the bars for each platform. For example, {the first blue bar}[0.177, 0.726, 0.334, 0.898] which represents the post count for Facebook is approximately at the position of 50000, and there is also {a value label \"50000\"}[0.223, 0.699, 0.289, 0.726] at the top of the bar. Thus, I can identify the value in each category using the height of the bars and the value annotations above them.\nBased on the above thought, the answer is:\nPlatform,Post_Count,User_Activity\nFacebook,50000,200000\nInstagram,40000,150000" } ], "id_raw": 660 }, { "id": "cJQRRZXdRL64owW0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which platform do you think has a more active user base? Provide reasoning behind your answer." }, { "from": "gpt", "value": "The user base activity can be determined by comparing the {orange bars}[0.334, 0.383, 0.491, 0.898],[0.782, 0.554, 0.939, 0.898] which represent user activity for each platform. As the height of the orange bar for \"Facebook\", positioned at [0.334, 0.383, 0.491, 0.898] is taller than that of \"Instagram\", located at [0.782, 0.554, 0.939, 0.898], it can be inferred that Facebook has a higher amount of user activity. In addition, despite having a similar post count, as represented by {the blue bars}[0.177, 0.726, 0.334, 0.898],[0.626, 0.760, 0.782, 0.898], Facebook yields significantly higher user activity, suggesting more user engagement per post. Hence, Facebook appears to have a more active user base.\nBased on the above thought, the answer is:\nFacebook, because it has a higher user activity count and higher user engagement per post." } ], "id_raw": 660 }, { "id": "JMW7rOMV0vGu7O8x", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_45_0.png", "conversations": [ { "from": "human", "value": "\nWhich platform shows a higher number for user activity?" }, { "from": "gpt", "value": "To determine which platform shows a higher number for user activity, I need to compare the height of the {orange bars}[0.334, 0.383, 0.491, 0.898],[0.782, 0.554, 0.939, 0.898] that represent user activity for each platform. As we can observe, the orange bar for \"Facebook\", positioned at [0.334, 0.383, 0.491, 0.898], is taller than the orange bar for \"Instagram\", located at [0.782, 0.554, 0.939, 0.898]. Hence, Facebook shows a higher number for user activity.\nBased on the above thought, the answer is:\nFacebook." } ], "id_raw": 660 }, { "id": "R5HG9CLawDd3pTOm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nBased on your understanding of the different bar charts, please convert the data they represent into a CSV format table." }, { "from": "gpt", "value": "From the label on the x-axis {\"Year\"}[0.510, 0.945, 0.554, 0.971] and {the x-axis tick labels}[{\"2010\"}[0.267, 0.907, 0.320, 0.934]; {\"2020\"}[0.744, 0.907, 0.797, 0.934]], it can be inferred that the x-axis represents the different years. The y-axis, labelled as {\"Percentage (%)\"}[0.023, 0.361, 0.044, 0.565], signifies the success and failure rates of startups for that year. The different colored bars represent different parameters. According to {the legend}[0.722, 0.042, 0.967, 0.130], {the blue bars}<{\"Startup Success\"}[0.790, 0.053, 0.958, 0.079]> are the success rates and {the orange bars}<{\"Startup Failure\"}[0.790, 0.093, 0.944, 0.119]> specify the failure rates. The text on each bar indicates the exact percentage value the bar represents, for example, the {blue bar for the year 2010}[0.127, 0.663, 0.294, 0.898] has an annotation {\"22.10\"}[0.180, 0.636, 0.240, 0.663] on top of it implying that the success rate of startups in 2010 was 22.10%.\nBased on the above thought, the answer is:\nYear,Startup_Success,Startup_Failure\n2010,22.1,77.9\n2020,33.5,66.5" } ], "id_raw": 661 }, { "id": "RWQg6Q9uqXb4F2xf", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, could you analyze the overall trend of startup success and failure rates from 2010 to 2020?" }, { "from": "gpt", "value": "Yes, by analyzing and comparing the heights of the {blue bars}[0.127, 0.663, 0.294, 0.898; 0.603, 0.542, 0.770, 0.898] and {orange bars}[0.294, 0.070, 0.460, 0.898; 0.770, 0.191, 0.937, 0.898] represented in the graph, along with their corresponding {value annotations}[\"22.10\", \"33.50\", \"77.90\", \"66.50\"], it can be seen that the startup success rate has shown a moderate incline from 2010 to 2020. In 2010, the success rate was 22.1% and in 2020 it increased to 33.5%. Correspondingly, the failure rate for startups, which was 77.9% in 2010, dropped to 66.5% in 2020. Hence, overall, there seems to be a growth in the success rate and a decline in the failure rate of startups from 2010 to 2020.\nBased on the above thought, the answer is:\nThe startup success rate has been growing moderately and the failure rate has been dropping moderately from 2010 to 2020." } ], "id_raw": 661 }, { "id": "8NAGglS6yzAuphXJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_45_1.png", "conversations": [ { "from": "human", "value": "\nBetween which two years did startups experience the highest growth in success rates?" }, { "from": "gpt", "value": "To find the years in which startups saw the highest growth in success rates, I need to compare {the blue bars}<{\"Startup Success\"}[0.790, 0.053, 0.958, 0.079]> associated with different years. Comparing {the blue bar for the year 2010}[0.127, 0.663, 0.294, 0.898] with {the blue bar for the year 2020}[0.603, 0.542, 0.770, 0.898], we can see an increase in the success rate from 22.10% in 2010 to 33.50% in 2020 as indicated by the {value annotations}[{\"2010 - 22.10\"}[0.180, 0.636, 0.240, 0.663]; {\"2020 - 33.50\"}[0.657, 0.515, 0.716, 0.542]]. This represents the highest growth in success rates in the given data set.\nBased on the above thought, the answer is:\n2010 to 2020" } ], "id_raw": 661 }, { "id": "sfxYDtRL7tG0TvLi", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the bar chart into a CSV formatted table?" }, { "from": "gpt", "value": "From the title of the x-axis at the bottom of the chart {Cuisine}[0.494, 0.945, 0.570, 0.971], and {the x-axis tick labels}[{\"Average Dish Price\"}[0.195, 0.907, 0.392, 0.934]; {\"Restaurant Popularity\"}[0.656, 0.907, 0.884, 0.934]], it is clear that the x-axis represents two categories: the average dish price and the popularity of the restaurant. The y-axis tick labels represent the corresponding values, ranging from {0}[0.066, 0.887, 0.079, 0.914] to almost {90} (as this is a scaled graph). For each category, there are two bars according to {the legend}[0.796, 0.042, 0.967, 0.130] which represents Italian and Japanese cuisines. I can use the value annotations at the top of the bars to determine the values. For instance, {the Italian bar for \"Average Dish Price\"}[0.127, 0.523, 0.294, 0.898] has an annotation of {\"35.40\"}[0.180, 0.496, 0.240, 0.523] at the top, so the average price of the Italian Dish is $35.40.\nBased on the above thought, the answer is:\nCuisine,Average Dish Price,Restaurant Popularity\nItalian,35.40,75.30\nJapanese,27.60,78.12" } ], "id_raw": 669 }, { "id": "jamGPECTeBznqZ1l", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what is the commonality and differences of these two cuisines?" }, { "from": "gpt", "value": "This question requires a comprehensive comparison between {the bar for Italian Cuisine}<{\"Italian\"}[0.863,0.053, 0.929, 0.079]> and {the bar for Japanese Cuisine}<{\"Japanese\"}[0.863, 0.093, 0.958, 0.119]>. In terms of similarities, both cuisines {the two bars in \"Restaurant Popularity\"}[{Italian bar}[0.603, 0.100, 0.770, 0.898]; {Japanese bar}[0.770, 0.070, 0.937, 0.898]] have a popularity rating over 70, indicating both cuisines are very popular. However, the differences lie in the average dish price wherein the {Italian bar at \"Average Dish Price\"}[0.127, 0.523, 0.294, 0.898] is higher than the {Japanese bar at \"Average Dish Price\"}[0.294, 0.605, 0.460, 0.898], which shows Italian dishes are generally more expensive than Japanese dishes.\nBased on the above thought, the answer is:\nThe commonality between Italian and Japanese cuisine is they are both highly popular among the population, with popularity ratings over 70%. The key difference lies in the average dish prices, Italian food is generally more expensive than Japanese food." } ], "id_raw": 669 }, { "id": "7yM7DsadC0pDAEh2", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_45_9.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine has a higher difference in the average dish price and restaurant popularity?" }, { "from": "gpt", "value": "To find the cuisine with a higher difference between the average dish price and restaurant popularity, I need to subtract the dish price from the restaurant popularity for each cuisine. Firstly, for the Italian cuisine, {the bar for the average dish price}[0.127, 0.523, 0.294, 0.898] indicates a value of $35.40 and {the bar for the restaurant popularity}[0.603, 0.100, 0.770, 0.898] indicates a value of 75.30%. The difference is around 39.90. Similarly, for the Japanese cuisine, {the bar for average dish price}[0.294, 0.605, 0.460, 0.898] shows a value of $27.60 and {the bar for restaurant popularity}[0.770, 0.070, 0.937, 0.898] shows the value of 78.12%. The difference is around 50.52. Hence, the Japanese cuisine has a higher difference in average dish price and restaurant popularity.\nBased on the above thought, the answer is:\nJapanese." } ], "id_raw": 669 }, { "id": "pLGmtw9AGKpOLVbq", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nCould you please create a table that summarizes the outlook scores for all the technologies as represented in the bar chart?" }, { "from": "gpt", "value": "The bar chart represents the outlook score of various technologies. On the x-axis, we have the different technologies named such as {5G}[0.137, 0.919, 0.157, 0.942], {Blockchain}[0.263, 0.919, 0.339, 0.942], {Virtual Reality}[0.406, 0.919, 0.504, 0.942], {Augmented Reality}[0.543, 0.919, 0.675, 0.942], {Artificial Intelligence}[0.692, 0.919, 0.834, 0.942], and {IoT}[0.906, 0.919, 0.927, 0.942]. On the y-axis, we have the corresponding outlook scores ranging from 80 to 100 as given by the {y-axis labels}[0.043, 0.902, 0.074, 0.925]; [0.043, 0.792, 0.074, 0.816]; [0.043, 0.683, 0.074, 0.706]; [0.043, 0.573, 0.074, 0.596]; [0.043, 0.463, 0.074, 0.487]; [0.043, 0.354, 0.074, 0.377]; [0.043, 0.244, 0.074, 0.268]; [0.043, 0.135, 0.074, 0.158]; [0.035, 0.025, 0.074, 0.048]. The height of each bar and the value annotation at the top of it depict the outlook score for the respective technology. For instance, the height of the bar for {5G}[0.120, 0.363, 0.174, 4.418] corresponds to an outlook score of 92.5 as per the value annotation.\nBased on the above thought, the answer is:\nTechnology,Outlook Score\n5G,92.5\nBlockchain,86.9\nVirtual Reality,89.3\nAugmented Reality,87.5\nArtificial Intelligence,95.3\nIoT,90.2" } ], "id_raw": 671 }, { "id": "DFusNcCG0wCV54b8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, which technologies seem to have a promising future based on their outlook scores?" }, { "from": "gpt", "value": "To answer this question, I have to look at the height of the bars that represent the outlook score for each technology. For instance, the bar for {5G}[0.120, 0.363, 0.174, 4.418] has a height corresponding to a score of 92.5, which suggests a positive outlook. Similarly, the bar for {Artificial Intelligence}[0.736, 0.240, 0.790, 4.418] has the highest height, corresponding to a score of 95.3, indicating the most promising future among the technologies. The bar for {IoT}[0.890, 0.464, 0.944, 4.418], corresponding to a score of 90.2, also suggests a positive outlook. On the other hand, the bar for {Blockchain}[0.274, 0.608, 0.328, 4.418] is the smallest, indicating a slightly less optimistic outlook compared to the others but still higher than 80.\nBased on the above thought, the answer is:\nThe technologies that seem to have a promising future based on their outlook scores are 5G, Artificial Intelligence, and IoT as they all have outlook scores above 90. Although the Blockchain has the lowest score, it is still a promising technology as it has a score greater than 80 which indicates substantial potential." } ], "id_raw": 671 }, { "id": "en0Jsx3NVd7J5I4u", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_45_11.png", "conversations": [ { "from": "human", "value": "\nWhich technology has the lowest outlook score according to the chart?" }, { "from": "gpt", "value": "To find the technology with the lowest outlook score, I need to compare the height of the bars for each technology. Looking from left to right, the first bar for {5G}[0.120, 0.363, 0.174, 4.418] shows a score of approximately 92.5. The second bar for {Blockchain}[0.274, 0.608, 0.328, 4.418] shows a score of around 86.9, and so on. Comparing all the bars and their corresponding scores, it can be observed that the {Blockchain}[0.274, 0.608, 0.328, 4.418] has the smallest bar, representing the lowest outlook score.\nBased on the above thought, the answer is:\nBlockchain." } ], "id_raw": 671 }, { "id": "72J1uQzafcGTPBaR", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nCould you convert the bar chart data into a CSV format table?" }, { "from": "gpt", "value": "I will identify the relevant elements in the chart and then transform them into a structured table. The x-axis of the chart represents the 'Number of Issue Points' at {[0.421, 0.945, 0.662, 0.971]}, with distinct values of {100}[0.289, 0.907, 0.329, 0.934] and {1000}[0.748, 0.907, 0.801, 0.934]. The y-axis represents 'Points' at {[0.023, 0.429, 0.044, 0.508]}, ranging from {40.0}[0.052, 0.887, 0.099, 0.914] to {60.0}[0.052, 0.029, 0.099, 0.055]. The bar height and the value annotations at the top can determine the values for each group. For 'Equal Rights', there are two bars at [{100}[0.146, 0.484, 0.309, 2.615], {1000}[0.612, 0.420, 0.775, 2.615]], with values 49.63 and 51.12 respectively. For 'Social Welfare', there are two bars at [{100}[0.309, 0.522, 0.472, 2.615], {1000}[0.775, 0.169, 0.938, 2.615]], with values 48.75 and 56.97 respectively. By using this information, I can form the CSV table.\nBased on the above thought, the answer is:\nNumber of Issue Points,Equal Rights,Social Welfare\n100,49.63,48.75\n1000,51.12,56.97" } ], "id_raw": 673 }, { "id": "DJnZtUJzXyM6pHut", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nBased on the given chart, how do you interpret the policy changes as the number of issue points increase?" }, { "from": "gpt", "value": "To interpret policy changes reflected in the chart, we need to compare the change in 'Equal Rights' and 'Social Welfare' bars from 100 to 1000 issue points. The 'Equal Rights' value at 100 issue points {[0.146, 0.484, 0.309, 2.615]}is slightly higher than the 'Social Welfare' value {[0.309, 0.522, 0.472, 2.615]}. However, when the issue points increase to 1000, the 'Social Welfare' value {[0.775, 0.169, 0.938, 2.615]} significantly surpasses the 'Equal Rights' value {[0.612, 0.420, 0.775, 2.615]}.\nBased on the above thought, the answer is:\nAs the number of issue points increase from 100 to 1000, there's a proportional increase in the emphasis on 'Social Welfare' compared to 'Equal Rights'. This could suggest that the policy focus shifts more towards social welfare issues as social protests intensify." } ], "id_raw": 673 }, { "id": "adbgJgh8HoVXFuQh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_45_13.png", "conversations": [ { "from": "human", "value": "\nFor how many issue points, the score for 'Equal Rights' is higher than that for 'Social Welfare'?" }, { "from": "gpt", "value": "To determine this, I need to compare the heights of each pair of 'Equal Rights' and 'Social Welfare' bars for the same number of issue points. For the first pair at issue points of {100}[0.289, 0.907, 0.329, 0.934], the 'Equal Rights' bar at {[0.146, 0.484, 0.309, 2.615]} is higher than the 'Social Welfare' bar at {[0.309, 0.522, 0.472, 2.615]}. For the second pair at issue points of {1000}[0.748, 0.907, 0.801, 0.934], the 'Equal Rights' bar at {[0.612, 0.420, 0.775, 2.615]} is lower than the 'Social Welfare' bar at {[0.775, 0.169, 0.938, 2.615]}. Hence, for only one number of issue points, the score for 'Equal Rights' is higher than that for 'Social Welfare'.\nBased on the above thought, the answer is:\n1" } ], "id_raw": 673 }, { "id": "SV3Vv9f1EN30aHFU", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nCould you transform the depicted bar chart into a structured table format?" }, { "from": "gpt", "value": "I can infer from the chart that the axes are labeled with 'Health Indicators' as given by the {xlabel}[0.442, 0.945, 0.622, 0.971] and 'Score' presented by the {ylabel}[0.023, 0.431, 0.044, 0.506]. The x-axis represents the two categories - {'Physical_Activity'}[0.206, 0.907, 0.381, 0.934] and {'Balanced_Diet'}[0.694, 0.907, 0.846, 0.934]. There are two respective bars for each category distinguished by their color where the {'Healthy Individuals'}[0.757, 0.063, 0.958, 0.090] have blue coloring and the {'Patients'}[0.757, 0.103, 0.841, 0.130] are depicted in orange. I can determine the values of each category by examining the height of these bars and the annotations given alongside each. For instance, the score for 'Physical_Activity' under 'Healthy Individuals' is around 75 due to the colored bar height[0.127, 0.361, 0.294, 1.971] and corresponding annotation[0.180, 0.334, 0.240, 0.361].\nBased on the above thought, the answer is:\nHealth Indicator,Score_Healthy_Individuals,Score_Patients\nPhysical_Activity,75,60\nBalanced_Diet,85,62" } ], "id_raw": 674 }, { "id": "NRRp3l46u4PzmH8e", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nHow well is the health indicator 'Balanced Diet' maintained by patients in comparison to healthy individuals based on this chart?" }, { "from": "gpt", "value": "I can compare the bars representing \u2018Healthy Individuals\u2019 and \u2018Patients\u2019 under the category 'Balanced Diet'. The bar for 'Healthy Individuals'[0.603, 0.146, 0.770, 1.971] has an associated score of 85[0.657, 0.120, 0.717, 0.146]. On the other hand, the bar for 'Patients'[0.770, 0.640, 0.937, 1.971] is a bit lower but still fairly high with a score of 62[0.824, 0.613, 0.883, 0.640]. These observations indicate that patients have managed to maintain a reasonably high score for this indicator, although it's slightly lower than that of healthy individuals.\nBased on the above thought, the answer is:\nThe patients have reasonably maintained the health indicator 'Balanced Diet' because their score of 62 is not significantly lower than the score of 85 for the healthy individuals." } ], "id_raw": 674 }, { "id": "sRNXMzEVujo3rNe1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_45_14.png", "conversations": [ { "from": "human", "value": "\nHow much more is the score for Healthy Individuals in the Physical Activity indicator than Patients in the same indicator?" }, { "from": "gpt", "value": "To compute the difference, I need to find and compare the values of 'Physical Activity' for both 'Healthy Individuals' and 'Patients'. The score for 'Healthy Individuals' is 75 as noted by the score annotation [0.180, 0.334, 0.240, 0.361] at the top of the bar [0.127, 0.361, 0.294, 1.971]. Now, the 'Patients' have a score of 60 annotated at [0.347, 0.656, 0.407, 0.683]. I can then subtract the 'Patients' score from that of the 'Healthy Individuals'.\nBased on the above thought, the answer is:\n15" } ], "id_raw": 674 }, { "id": "h29q9AKBqBzFhIY6", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nCan you provide the ROI data for Bitcoin and Blockchain over the next 5, 10, and 20 years in tabular CSV format as depicted in the chart?" }, { "from": "gpt", "value": "Examining the individual bars within the bar chart and noting their heights relative to the y-axis labels, I can deduce the ROI values for each duration. Each subgroup of bars corresponds to different time frames: the first group portrays the ROI for the next 5 years, the second group for 10 years, and the third group for 20 years. The {blue bars}[0.046, 0.191, 0.075, 0.543] depict Bitcoin's ROI across the different durations, while the {red bars}[0.046, 0.543, 0.075, 0.580] represent Blockchain's ROI. For each group of bars, the ROI value is displayed at the top as a number within the bar chart. For instance, the {Bitcoin 5-year ROI bar}[0.046, 0.191, 0.075, 0.543] has a value of {78.3}[0.049, 0.195, 0.071, 0.230] marked at its peak. By repeating this process for each bar, I can construct the complete table.\nBased on the above thought, the answer is:\nDuration,Bitcoin ROI,Blockchain ROI\n5 years,78.3,12.1\n10 years,33.7,35.7\n20 years,45.9,17.7" } ], "id_raw": 675 }, { "id": "PQvwjLZ335tqHgcs", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nWhich investment, Bitcoin or Blockchain, seems to offer a higher return after 20 years according to the data presented in the chart?" }, { "from": "gpt", "value": "To answer this question, I must compare the 20-year ROI values of Bitcoin and Blockchain. I focus my attention on the last bars in each subgroup within the chart. For the 20-year forecast, the {Bitcoin bar}[0.557, 0.438, 0.586, 0.543] shows a value of {23.4}[0.503, 0.388, 0.525, 0.423], while the {Blockchain bar}[0.557, 0.543, 0.586, 0.690] has a marked value of {32.6}[0.561, 0.640, 0.583, 0.675]. These values indicate the projected ROI after 20 years for Bitcoin and Blockchain, respectively. By comparing the two numbers, I can determine which investment offers a higher return.\nBased on the above thought, the answer is:\nBlockchain. After 20 years, the projected ROI for Blockchain is higher than that of Bitcoin according to the chart data presented, with Blockchain showing a projected ROI of 32.6% compared to Bitcoin's 23.4%." } ], "id_raw": 675 }, { "id": "ZnET2tuIx3BS2e0d", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the average ROI for Blockchain across all three durations?" }, { "from": "gpt", "value": "To calculate Blockchain's average ROI, I first need to locate the ROIs for each of the durations. By examining {Blockchain's 5-year ROI bar}[0.046, 0.543, 0.075, 0.580], I find a value of {12.1}[0.107, 0.548, 0.129, 0.583]. For the {10-year duration}[0.302, 0.543, 0.331, 0.598], Blockchain has an ROI value of {35.7}[0.305, 0.654, 0.327, 0.689]. And, finally, the {20-year ROI}[0.442, 0.543, 0.470, 0.717] for Blockchain is {17.7}[0.503, 0.701, 0.525, 0.736]. I will add these three numbers and divide by 3 to find the average.\nBased on the above thought, the answer is:\n21.83" } ], "id_raw": 675 }, { "id": "r8GAOVOn7tnx8uRd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data of the \"Great Recession\" and \"Stagnation\" in a structured table format based on your observation of the bar chart?" }, { "from": "gpt", "value": "Analyzing the subchart titled {Great Recession}[0.070, 0.037, 0.166, 0.083], I see bars at intervals {5}[0.046, 0.245, 0.075, 0.498], {10}[0.104, 0.220, 0.133, 0.498], and {20}[0.162, 0.169, 0.191, 0.498]. Each bar is annotated with their respective values, such as {50}[0.054, 0.253, 0.067, 0.288] at interval 5. Similarly, in the {Stagnation}[0.049, 0.771, 0.193, 0.886] subplot, there are bars at intervals {5}[0.046, 0.498, 0.075, 0.853], {10}[0.104, 0.498, 0.133, 0.802], and {20}[0.162, 0.498, 0.191, 0.751], annotated with values such as {60}[0.112, 0.749, 0.124, 0.784] at interval 5. Based on this visual data, a structured table can be formed.\nBased on the above thought, the answer is:\nEvent,5,10,20\nGreat Recession,50,55,65\nStagnation,70,60,50" } ], "id_raw": 676 }, { "id": "AOUXKy2ONHYnbmB1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nWhich economic carrier shown in the bar chart demonstrates consistent negative growth across the observed intervals?" }, { "from": "gpt", "value": "To discern which economic carrier exhibits consistent negative growth, I evaluate the bars' trends over the three intervals. In the {Great Recession}[0.070, 0.037, 0.166, 0.083] subplot, the values increase from {50}[0.046, 0.245, 0.075, 0.498] at interval {5} to {65}[0.162, 0.169, 0.191, 0.498] at interval {20}. For {Hyperinflation}[0.049, 0.771, 0.193, 0.886], values decrease from {60}[0.046, 0.498, 0.075, 0.853] at {5} to {20}[0.162, 0.498, 0.191, 0.777] at {20}. Reviewing all carriers and their bar trends, I look for consistent downward slopes across the intervals from the annotated numerical values.\nBased on the above thought, the answer is:\nHyperinflation. Values consistently decrease from intervals 5 to 20, indicating a steady negative growth trend as per bar graphs represented in its subplot." } ], "id_raw": 676 }, { "id": "E55Ggw33ZqVrLHBj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_46_1.png", "conversations": [ { "from": "human", "value": "\nConsidering the \"EU Debt Crisis\" subplot, what was the percentage change in the \"Stock Market Crashes\" category from interval 5 to 20?" }, { "from": "gpt", "value": "To calculate the percentage change in the {Stock Market Crashes}[0.837, 0.346, 0.866, 0.498] category within the {EU Debt Crisis}[0.867, 0.037, 0.952, 0.083] subplot, I examine the annotated values for the bars at intervals {5}[0.837, 0.346, 0.866, 0.498], specified as {30}[0.845, 0.354, 0.858, 0.389] and {20}[0.953, 0.397, 0.982, 0.498], specified as {20}[0.903, 0.380, 0.916, 0.415]. The percentage change is found by subtracting the later value from the earlier value and dividing by the earlier value, then multiplying by 100.\nBased on the above thought, the answer is:\n-33.33%" } ], "id_raw": 676 }, { "id": "rjIozb7l8ViWtn4N", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nCould you create a structured table from the given bar chart which represents yield trends for automotive and non-automotive sectors across three years?" }, { "from": "gpt", "value": "By analyzing the {subplots}[0.030, 0.037, 0.198, 0.961], I can see that each subplot corresponds to a different company. Each subplot contains {two groups of bars}[0.038, 0.116, 0.063, 0.499] and [0.038, 0.499, 0.063, 0.558], indicating automotive and non-automotive yields, respectively. The x-axis has {ticks for three years}[0.039, 0.971, 0.062, 0.985]; [0.102, 0.971, 0.126, 0.985]; [0.166, 0.971, 0.190, 0.985]. The value for each bar is provided as text above or below the bar, e.g., for Toyota in Year 1, {automotive yield is \"66.3\"}[0.042, 0.146, 0.059, 0.160] and non-automotive yield is \"{-10.1}\"[0.042, 0.512, 0.059, 0.526]. I'll continue this process for each company and year to construct the table.\nBased on the above thought, the answer is:\nCompany,Year 1 Automotive,Year 1 Non-Automotive,Year 2 Automotive,Year 2 Non-Automotive,Year 3 Automotive,Year 3 Non-Automotive\nToyota,66.3,-10.1,61.9,-13.6,53.8,-13.7\nFord,49.3,-36.3,45.7,-41.0,41.6,-31.5\nTesla,48.0,-40.5,42.6,-49.6,36.5,-36.5\nNissan,43.8,-49.5,40.2,-56.8,30.6,-42.8\nGeneral Motors,22.3,-64.7,25.9,-59.6,22.5,-43.4" } ], "id_raw": 682 }, { "id": "MAitNiPCjfheMjot", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nDo non-automotive sectors show a growth trend across the years for any of the companies based on the bar chart?" }, { "from": "gpt", "value": "To analyze growth trends for the non-automotive sector, I need to evaluate the {red bars}[0.038, 0.499, 0.063, 0.558]; [0.038, 0.499, 0.063, 0.558] for each company in {each year}[0.039, 0.971, 0.062, 0.985]; [0.102, 0.971, 0.126, 0.985]; [0.166, 0.971, 0.190, 0.985]. I compare the non-automotive yields across years to see if there are increases. Observing the values above mentioned red bars, it's clear that no company shows a consistent increase over three years - some increase and then decrease or fluctuate. Hence from this bar chart, it is not evident that there is a significant growth trend for any company's non-automotive sector.\nBased on the above thought, the answer is:\nNo consistent growth trend. Yields fluctuate without a clear upward trajectory for all companies." } ], "id_raw": 682 }, { "id": "jWunEfnl3r7qIkxY", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the average automotive yield for General Motors over the three-year span?" }, { "from": "gpt", "value": "I will look at the {subplot for General Motors}[0.823, 0.037, 0.992, 0.961] which contains {three blue bars}[0.831, 0.371, 0.856, 0.499]; [0.895, 0.350, 0.920, 0.499]; [0.959, 0.369, 0.984, 0.499], representing three years of automotive yield. Reading off the values, I find {Year 1 as \"22.3\"}[0.835, 0.400, 0.852, 0.414], {Year 2 as \"25.9\"}[0.899, 0.379, 0.916, 0.393], and {Year 3 as \"22.5\"}[0.959, 0.369, 0.984, 0.499]. To find the average, I add these values and divide by three.\nBased on the above thought, the answer is:\n23.6" } ], "id_raw": 682 }, { "id": "0rnOxXxRZ6LmzZvo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nHow do the productivity values change over the three-week period in the bar chart data?" }, { "from": "gpt", "value": "To ascertain the productivity values' changes, I need to inspect the first subplot titled {Productivity}[0.024, 0.093, 0.165, 0.903]. It consists of three vertical bars representing {Week 1}[0.030, 0.294, 0.056, 0.903], {Week 2}[0.082, 0.238, 0.107, 0.903], and {Week 3}[0.133, 0.192, 0.159, 0.903]. Each of these bars is annotated with a value at the top, which indicates the productivity for that week. By examining these coordinates, I can extract the relevant productivity values for all three weeks.\nBased on the above thought, the answer is:\nWeek,Productivity\nWeek 1,60.2\nWeek 2,65.7\nWeek 3,70.3" } ], "id_raw": 683 }, { "id": "kheWDphsJzigLzet", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nWhich of the KPIs has shown a consistent trend over the three weeks?" }, { "from": "gpt", "value": "To identify KPIs with a consistent trend, I review each subplot and observe the direction of the value changes across all weeks. Consistency in trends appears as an uninterrupted increase or decrease in heights of the consecutive bars. By examining the bar heights chronologically, I can determine whether a KPI shows a positive, negative, or volatile trend. The KPIs are respectively located in their subplots: {Productivity}[0.024, 0.093, 0.165, 0.903], {Sales}[0.189, 0.093, 0.331, 0.903], {Customer Satisfaction}[0.354, 0.093, 0.496, 0.903], {Product Quality}[0.519, 0.093, 0.661, 0.903], {Operational Efficiency}[0.685, 0.093, 0.826, 0.903], and {Profitability}[0.850, 0.093, 0.992, 0.903]. Closer inspection reveals a consistent upward trend for Sales and Operational Efficiency.\nBased on the above thought, the answer is:\nSales and Operational Efficiency. Each shows a steady increase over the three weeks, without any interruption or reversal in the trend." } ], "id_raw": 683 }, { "id": "b8JUPAp9wzkyIffr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_46_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in operational efficiency from Week 1 to Week 3?" }, { "from": "gpt", "value": "To find the percentage increase in operational efficiency from Week 1 to Week 3, I need to look at the fifth subplot titled {Operational Efficiency}[0.685, 0.093, 0.826, 0.903] and compare the height of the bars for {Week 1}[0.691, 0.699, 0.717, 0.903] and {Week 3}[0.794, 0.644, 0.820, 0.903]. The annotations on the bars reveal values of 20.2 for Week 1 and 25.6 for Week 3. The percentage increase is calculated by the formula [(Week 3 value - Week 1 value) / Week 1 value * 100].\nBased on the above thought, the answer is:\n26.7%" } ], "id_raw": 683 }, { "id": "JLYCtWqb88yQvVI2", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you observed, can you convert the data representation into table form, ideally in CSV format?" }, { "from": "gpt", "value": "By observing the displayed chart labels {\"Trade Equilibrium\"}[0.098, 0.118, 0.199, 0.141] and {\"Globalization\"}[0.098, 0.153, 0.173, 0.176] from {the legend}[0.061, 0.074, 0.203, 0.185], and chart trends on {x-axis}[0.074, 0.744, 0.949, 0.880], I can note that each trend is represented by two bars, reflecting the scores of trade equilibrium and globalization respectively. To convert these data into a CSV table, I extract the labels of the x-axis tick labels representing the trade trends, then calculate the values for \"Trade Equilibrium\" and \"Globalization\" by estimating the height of the bars based on the y-axis scale from {0}[0.043, 0.727, 0.051, 0.750] to {100}[0.029, 0.053, 0.051, 0.076]. This height combined with the exact data displayed at the top of each bar, for example {\"25.15\"}[0.076, 0.536, 0.109, 0.560] for the \"Slow Recovery\" trend, allows for accurate data extraction.\nBased on the above thought, the answer is:\nLabels,Trade Equilibrium,Globalization\nSlow Recovery,25.15,20.21\nModerate Growth,60.17,40.19\nSteady Recovery,10.78,12.87\nSustained Growth,5.66,2.45\nRapid Decline,18.55,26.78\nSlow Recovery - outlier,80.85,33.90\nModerate Growth - outlier,15.21,42.10\nSteady Recovery - outlier,5.22,3.26\nSustained Growth - outlier,30.15,35.22\nRapid Decline - outlier,90.19,77.28" } ], "id_raw": 705 }, { "id": "N7mTiyFmsElUW2n8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what can you infer about the relationship between international trade equilibrium and globalization?" }, { "from": "gpt", "value": "The comparison and analysis of {Globalization bars}<{\"Globalization\"}[0.098, 0.153, 0.173, 0.176]> and {Trade Equilibrium bars}<{\"Trade Equilibrium\"}[0.098, 0.118, 0.199, 0.141]> under each trend label on the {x-axis}[0.074, 0.744, 0.949, 0.880] can infer the relationship between international trade equilibrium and globalization. The bars generally show that for a specific trend, if trade equilibrium value is higher, globalization value is also higher, and vice versa. This suggests a positive correlation between trade equilibrium and globalization. However, it's worth pointing out that there are outliers, for example, the \"Slow Recovery - outlier\"[0.496, 0.744, 0.599, 0.890] with high trade equilibrium but relatively low globalization. This indicates that the correlation is not absolute and can be influenced by other variables or unique circumstances.\nBased on the above thought, the answer is:\nIt can be inferred that international trade equilibrium and globalization are generally positively correlated. However, due to some outliers, the relationship is not always consistent which implies the influence of other variables or unique circumstances." } ], "id_raw": 705 }, { "id": "NpR0F12ohTFOoywS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what is the trend with the highest Globalization value?" }, { "from": "gpt", "value": "To answer this question, I should focus on the {orange bars}<{\"Globalization\"}[0.098, 0.153, 0.173, 0.176]> representing the Globalization value for each trend. By comparing the heights of these bars and considering the y-axis labels, I can find the trend with the highest Globalization value. Detailed observation indicates that the bar within {\"Rapid Decline - outlier\"}[0.850, 0.744, 0.949, 0.890] coordinates[x0.900,y0.215,x0.930,y0.736] is the highest, going up to nearly 100 on the y-axis. Its exact value {\"77.28\"}[ 0.898, 0.185, 0.931, 0.208] is displayed on the top of the bar. Therefore, the trend with the highest Globalization value is \"Rapid Decline - outlier\".\nBased on the above thought, the answer is:\nRapid Decline - outlier" } ], "id_raw": 705 }, { "id": "aNzk7zQXEdSGWjE5", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the investment amounts of Venture Capital Market in CSV format?" }, { "from": "gpt", "value": "The x-axis is labeled with the company names: {SeedFund}[0.096, 0.803, 0.144, 0.900], {VenturePromotions}[0.185, 0.803, 0.271, 0.975], {QuickCapital}[0.307, 0.803, 0.367, 0.924], {SharpInvestors}[0.410, 0.803, 0.480, 0.943], {GoldenEggs}[0.525, 0.803, 0.582, 0.917], {RapidGrowers}[0.629, 0.803, 0.695, 0.934], {CashCows}[0.746, 0.803, 0.796, 0.903] and {DiamondMiners}[0.843, 0.803, 0.915, 0.948]. The y-axis includes the range of values from {0}[0.043, 0.786, 0.051, 0.809] to {100}[0.029, 0.053, 0.051, 0.076]. There are two types of investments: {Non_Aggregation}[0.877, 0.118, 0.977, 0.142] represented by blue bars and {Aggregation}[0.877, 0.153, 0.948, 0.176] represented by orange bars. Each investment type has labeled values such as {75.3}[0.085, 0.213, 0.111, 0.236] and {30.2}[0.129, 0.544, 0.154, 0.567] enabling the creation of the CSV table.\nBased on the above thought, the answer is:\nCompanies,Non_Aggregation,Aggregation\nSeedFund,75.3,30.2\nVenturePromotions,90.4,45.6\nQuickCapital,20.0,80.7\nSharpInvestors,10.0,70.0\nGoldenEggs,60.6,0.0\nRapidGrowers,70.7,47.1\nCashCows,30.1,90.7\nDiamondMiners,40.2,80.3" } ], "id_raw": 707 }, { "id": "ACo9BmPKYicW6Lgp", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nWhich company had consistent investment size between Non_Aggregation and Aggregation?" }, { "from": "gpt", "value": "The consistency of investment size can be measured by how close Non_Aggregation and Aggregation investments are for each company. The gap between {RapidGrowers Non_Aggregation investment}[0.628, 0.247, 0.653, 0.270] and {its Aggregation investment}[0.671, 0.420, 0.697, 0.443] seems to be the smallest among all. The differences for other companies appear to be relatively larger, such as {SeedFund}[0.077, 0.243, 0.120, 0.795] and {0.120, 0.574, 0.163, 0.795], and {QuickCapital}[0.293, 0.649, 0.337, 0.795] and {0.337, 0.204, 0.380, 0.795], where differences in heights of the bars are substantially larger.\nBased on the above thought, the answer is:\nRapidGrowers" } ], "id_raw": 707 }, { "id": "GlN3P3JRrMz6sQcH", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_2.png", "conversations": [ { "from": "human", "value": "\nWhich company had the highest increase from Non_Aggregation to Aggregation investment?" }, { "from": "gpt", "value": "To answer this question, I need to find the difference between the investment amounts of Non_Aggregation and Aggregation for each company. For example, {SeedFund}[0.096, 0.803, 0.144, 0.900] has an investment of {75.3}[0.085, 0.213, 0.111, 0.236] for Non_Aggregation which increased to {30.2}[0.129, 0.544, 0.154, 0.567] in Aggregation. The same calculation applies to the rest of the companies. It can be observed that {QuickCapital}[0.307, 0.803, 0.367, 0.924] has Non_Aggregation investment of {20.0}[0.302, 0.618, 0.328, 0.642] and the Aggregation investment increased to {80.7}[0.346, 0.173, 0.371, 0.197], presenting the greatest increment.\nBased on the above thought, the answer is:\nQuickCapital" } ], "id_raw": 707 }, { "id": "nncAtptiljCcFg4z", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nFrom the bar graph details you\u2019ve seen, how would you structure the two trends, 'Non-aggregation' and 'Aggregation,' into a readable table?" }, { "from": "gpt", "value": "Starting with the chart's title {\u201cCulture and Arts Industry Development\u201d}[0.396, 0.025, 0.668, 0.055], I observe a clear categorization along the {x-axis}[0.018, 0.695, 0.949, 0.890] with different sectors in the culture and arts industry. The {y-axis}[0.035, 0.262, 0.047, 0.486] denotes development level in values from {0 to 100}[0.066, 0.677, 0.073, 0.701; 0.051, 0.053, 0.073, 0.076]. There are two sets of bars for each sector, one for each trend. Aligning the {value labels}[0.099, 0.370, 0.132, 0.393; 0.808, 0.597, 0.834, 0.621] with each bar will help provide the exact figures for the table\u2014e.g., 'Theatre and Puppetry' has {60.5 for Non-aggregation}[0.093, 0.309, 0.138, 0.687] and {45.89 for Aggregation}[0.093, 0.400, 0.138, 0.687].\nBased on the above thought, the answer is:\nSector,Non-aggregation,Aggregation\nTheatre and Puppetry,60.5,45.89\nPainting and Digital Art,38.6,55.22\nClassical Music,15.9,32.43\nBallet and Contemporary Dance,75.55,22.18\nLiterature,42.3,12.67\nPhotography,50.25,25.16\nFilm and Video,13.55,70.89\nSculpture,32.11,26.98\nArchitecture,22.08,68.56\nPerforming Arts Festivals,40.9,15.43\nCeramics,60.19,32.13\nFashion,85.9,9.45\nGraphic Design,25.51,38.2\nPop Music,8.82,77.29" } ], "id_raw": 708 }, { "id": "chmjsXjUjxW8OaL8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nConsidering the data observed on the chart, which culture and arts industry sector seems to have adapted most significantly to aggregation?" }, { "from": "gpt", "value": "To determine the sector with the most significant adaptation to aggregation, I shall look for the biggest positive shift from {Non-aggregation}[0.121, 0.118, 0.219, 0.141] to {Aggregation}[0.121, 0.153, 0.192, 0.176] by analyzing the differences in bar heights for both trends. For instance, {Pop Music's Aggregation}[0.927, 0.204, 0.971, 0.687] bar shows a large growth when compared to its {Non-aggregation}[0.927, 0.631, 0.971, 0.687] counterpart. This suggests a substantial shift, further corroborated by the value annotations indicating {8.82 for Non-aggregation}[0.927, 0.631, 0.971, 0.687] and {77.29 for Aggregation}[0.927, 0.204, 0.971, 0.687], showing that Pop Music has adapted the most significantly to aggregated methods within the culture and arts industry.\nBased on the above thought, the answer is:\nPop Music" } ], "id_raw": 708 }, { "id": "weoBB9nl0ioPf7ay", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_3.png", "conversations": [ { "from": "human", "value": "\nWhich sector shows the highest level of development under the Non-aggregation trend as depicted by the graph?" }, { "from": "gpt", "value": "To identify the highest level of Non-aggregation, I need to assess all the value annotations above the corresponding bars for this trend. Starting with Fashion, which has a very high development level of {85.9}[0.798, 0.150, 0.843, 0.687], I will compare it with other sectors. Proceeding through the graph, none of the {value annotations}[0.103, 0.279, 0.129, 0.302; 0.936, 0.601, 0.962, 0.625] indicate a value higher than 85.9. Therefore, Fashion represents the highest Non-aggregation development level among the sectors evaluated.\nBased on the above thought, the answer is:\nFashion" } ], "id_raw": 708 }, { "id": "O9GwCWCozQuTgXvC", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nCan you help me convert the bar chart data representing the 'Non-Volatile' and 'Trending' series over months into a structured CSV format table?" }, { "from": "gpt", "value": "Based on the chart entitled {\"Time Series Data Comparison\"}[0.398, 0.030, 0.644, 0.066], I can see that it illustrates two series, 'Non-Volatile' and 'Trending', with monthly data. Looking at the chart's x-axis, I note markers labeled with the months from January to December, found in {\"xticks\"}<{element list}>[{list of dictionaries}]. On the chart's y-axis, in {\"yticks\"}, data values range from 0 to 60. This axis provides the magnitude of each month's data. Each month has two bars with different colors, representing 'Non-Volatile' and 'Trending' series respectively. According to the legend at {the top}[0.064, 0.089, 0.197, 0.223], the green bars correspond to the 'Non-Volatile' series and the orange bars represent the 'Trending' series. The height of each bar and the annotated value at the top of each bar indicate the actual data. Thus, for the 'Non-Volatile' series in January, the value would be 10 as indicated by the green bar found at {\"bar_coordinates\"}<[0.107, 0.789, 0.128, 0.932]>, and the text labeled '10' found at {\"text_coordinates\"}<[0.109, 0.753, 0.127, 0.781]>. Repeating this process for each month and for both 'Trending' and 'Non-Volatile' series allows me to extract all the values necessary to create a structured table.\nBased on the above thought, the answer is:\nMonth,Non_Volatile,Trending\nJan,10,5\nFeb,12,8\nMar,10,10\nApr,11,15\nMay,15,19\nJun,20,25\nJul,19,29\nAug,17,32\nSep,20,37\nOct,18,41\nNov,22,46\nDec,25,52" } ], "id_raw": 709 }, { "id": "YvXRLL2bzwlnD10N", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, which of the two series does the chart suggest has shown a more significant increase over the year, and what led you to this interpretation?" }, { "from": "gpt", "value": "The chart illustrates data for 'Non-Volatile' and 'Trending' series over time, and we are tasked to analyze which saw a more significant growth over the course of the year. The green bars represent the 'Non-Volatile' series and the orange bars the 'Trending' series, as per the {chart legend}[0.064, 0.089, 0.197, 0.223]. By comparing {the bar from 'Non-Volatile' in January}[0.107, 0.789, 0.128, 0.932] with {its bar in December}[0.892, 0.575, 0.914, 0.932], and {the bar from 'Trending' in January}[0.128, 0.861, 0.150, 0.932] with {its bar in December}[0.914, 0.189, 0.935, 0.932], one can see that though both series have seen an increase, the 'Trending' series appears to change more significantly. This is visible in the rise of the 'Trending' bars' heights, which goes from approximately 5 to 52, compared to the 'Non-Volatile' bars' heights which rise from 10 to 25. Therefore, on basing it on the bar heights and their corresponding data values, the 'Trending' series shows a more significant increase over the year.\nBased on the above thought, the answer is:\nThe 'Trending' series showed a more significant increase over the year. This conclusion is based on the evident increase in the height of the 'Trending' bars, from a data value of 5 to 52, which is more pronounced than the rise from 10 to 25 in the 'Non-Volatile' series." } ], "id_raw": 709 }, { "id": "z6Y9RCY88EDh5INR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_4.png", "conversations": [ { "from": "human", "value": "\nWhich month recorded the highest data value for the 'Trending' series according to the bar chart?" }, { "from": "gpt", "value": "To identify the month with the highest data value for the 'Trending' series, I need to check the height of each orange bar corresponding to the 'Trending' series in the chart and find the tallest one. This highest bar I found is {\"bar_coordinates\"}<[0.914, 0.189, 0.935, 0.932]>, with its top reaching up to roughly data value 52 on the y-axis. The corresponding x-axis label indicates the month this highest value was recorded, which is for the month of {December}[0.900, 0.942, 0.927, 0.970].\nBased on the above thought, the answer is:\nDecember." } ], "id_raw": 709 }, { "id": "SvjYNFu5Wc56rLzc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV table representing the percentage implementation in both Non-Aggressive and Aggressive scenarios?" }, { "from": "gpt", "value": "To create a CSV table from the bar chart, labeled as \"Impact of Environmental Protection and Sustainable Development Policies, 2022\"{subtitle}[0.186, 0.025, 0.856, 0.055], I must consider the x-axis labels {Air_Pollution, CO2_Emissions, ...}[0.025, 0.785, 0.095, 0.902]; [0.074, 0.785, 0.159, 0.925]; ... ; [0.809, 0.785, 0.921, 0.971] that represent environmental fields and the y-axis titles {0, 10, ..., 80}[0.043, 0.767, 0.052, 0.791]; ... [0.035, 0.053, 0.052, 0.076] which indicates percentage implementation. The value of each bar for both scenarios can be discovered by analyzing their respective heights and corresponding text labels. For instance, {the bar for Air_Pollution in Non-Aggressive}[0.073, 0.208, 0.117, 0.776] is approximately at a position correlating with 63.71% and {the bar for Air_Pollution in Aggressive}[0.073, 0.776, 0.117, 0.776] is at a position correlating with 0%.\nBased on the above thought, the answer is:\nField,Non-Aggressive,Aggressive\nAir_Pollution,63.71,0.00\nCO2_Emissions,74.61,0.00\nEnergy_Consumption,55.14,23.77\nGreen_Buildings,18.29,17.94\nWildlife_Protection,0.00,28.84\nForest_Area,39.84,0.00\nOcean_Protection,47.38,0.00\nRecycling,35.85,23.12\nCarbon_Footprint,0.00,33.92\nGreen_Policies,53.29,12.72\nRenewable_Energy,0.00,32.89\nEcological_Footprint,0.00,22.59\nBiodiversity,15.86,16.16\nConservation_Efforts,0.00,18.05" } ], "id_raw": 710 }, { "id": "OJCemsaJfljXCiSC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the focus on Wildlife Protection efforts between the Non-Aggressive and Aggressive scenarios?" }, { "from": "gpt", "value": "To infer the focus on Wildlife Protection efforts between the two scenarios, it is necessary to compare the values of {the Wildlife Protection bars}[0.327, 0.776, 0.372, 0.776]; [0.327, 0.519, 0.372, 0.776]. In the Non-Aggressive scenario, the Wildlife Protection effort is non-existent, with a value of 0, as per the height of its bar {Non-Aggressive Wildlife Protection bar}[0.327, 0.776, 0.372, 0.776]. Conversely, in the Aggressive scenario, the bar suggests a value of 28.84% effort is made towards Wildlife Protection {Aggressive Wildlife Protection bar}[0.327, 0.519, 0.372, 0.776]. This stark difference indicates a significant focus and investment in Wildlife Protection only in the Aggressive scenario.\nBased on the above thought, the answer is:\nThe chart reveals that while there is no effort towards Wildlife Protection in the Non-Aggressive scenario, a substantial effort of 28.84% implementation is recognized in the Aggressive scenario. This suggests that actionable changes towards Wildlife Protection are being considered only under aggressive environmental measures." } ], "id_raw": 710 }, { "id": "H7rsboHXakK9xZZP", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_5.png", "conversations": [ { "from": "human", "value": "\nHow much did the Green Building percentage change between the two scenarios?" }, { "from": "gpt", "value": "To calculate the change in Green Building percentages between the Non-Aggressive and Aggressive scenarios, I must examine the height of {the bar representing Green_Buildings in the Non-Aggressive scenario}[0.264, 0.613, 0.308, 0.776] which shows a value of 18.29%, and compare it to {the bar representing Green_Buildings in the Aggressive scenario}[0.264, 0.616, 0.308, 0.776] which indicates 17.94%. Subtracting the Aggressive scenario percentage from the Non-Aggressive scenario percentage will give me the change.\nBased on the above thought, the answer is:\n0.35" } ], "id_raw": 710 }, { "id": "dTBFHb0gp23WaYHP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nAnalyze the bar chart and provide the differences in consumer behavior for each category in a CSV format table." }, { "from": "gpt", "value": "Observing the bar chart, I see pairs of bars representing Non-Aggregation and Aggregation consumer behaviors across several product categories. The pairs are distinguished by different shades, with {Non-Aggregation}[0.082, 0.480, 0.126, 0.769] in a light green shade and {Aggregation}[0.207, 0.451, 0.252, 0.769] in a light red shade. The y-axis, labeled \"Consumer Behaviour\", stretches from a value of {0}[0.052, 0.758, 0.061, 0.786] upwards to {120}[0.035, 0.064, 0.061, 0.092], indicating the level of consumption. For each category, I will determine the difference by subtracting the Aggregation bar value from the corresponding Non-Aggregation bar value, using the bounding box information for precise location.\nBased on the above thought, the answer is:\nCategory,Difference (Non-Aggregation - Aggregation)\nFast Food,20\nE-Commerce,15\nGrocery,15\nCar Sales,-10\nHousehold Goods,35\nAttire,-20\nLuxury Goods,-15\nFootwear,-10\nAccessories,-5\nMakeup,-15\nFragrances,-10\nElectronics,-20\nGourmet,-20\nWines & Spirits,-10" } ], "id_raw": 712 }, { "id": "9NkpmMJjmN5hnsxj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nCan we infer which categories are leading the shift towards aggregation in consumer behavior?" }, { "from": "gpt", "value": "To infer which categories are leading the shift towards Aggregation consumer behavior, I shall compare the respective heights of bars depicting Aggregation and Non-Aggregation behaviors. Categories where the {Aggregation bar}[0.207, 0.451, 0.252, 0.769] surpasses the height of the {Non-Aggregation bar}[0.082, 0.480, 0.126, 0.769] suggest a shift towards Aggregation. Notably, the categories of {Makeup}[0.648, 0.422, 0.692, 0.769], {Fragrances}[0.711, 0.364, 0.755, 0.769] and {Electronics}[0.774, 0.480, 0.818, 0.769] display a significantly higher Aggregation bar, indicating these categories are at the forefront of this consumer shift.\nBased on the above thought, the answer is:\nThe categories leading the shift towards aggregation are Makeup, Fragrances, and Electronics as indicated by their respective Aggregation bars being taller than the Non-Aggregation ones, signifying a stronger inclination towards E-commerce in these areas." } ], "id_raw": 712 }, { "id": "nSMZHcq6k8tn6LOs", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_7.png", "conversations": [ { "from": "human", "value": "\nWhich product category shows the highest level of traditional Non-Aggregation buying behavior compared to Aggregation?" }, { "from": "gpt", "value": "To identify the category with the highest level of Non-Aggregation behavior, I must scan through each {product category's bars}[0.082, 0.480, 0.126, 0.769] and discern which Non-Aggregation bar is the tallest when compared to its Aggregation counterpart. I notice that the bar representing Non-Aggregation for {Household Goods}[0.333, 0.306, 0.377, 0.769] has the highest disparity above the corresponding Aggregation bar, with labels {Non-Aggregation \"80\"}[0.347, 0.270, 0.364, 0.298] and {Aggregation \"45\"}[0.333, 0.509, 0.377, 0.769].\nBased on the above thought, the answer is:\nHousehold Goods" } ], "id_raw": 712 }, { "id": "dt1glka7zANgdkqE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nProvide the data depicted by the bar chart in CSV table format." }, { "from": "gpt", "value": "The chart's title {\"Tech Companies Patent Application Comparison\"}[0.323, 0.030, 0.719, 0.066] indicates that the data relates to the comparison of patenting applications across various tech companies for their products. The x-axis labels represent the tech companies. For example, {\"Microsoft\"}[0.050, 0.818, 0.105, 0.928] and {\"Google\"}[0.140, 0.818, 0.185, 0.908]. The y-axis {\"Number of Products\"}[0.015, 0.304, 0.029, 0.580] indicates the product counts we are comparing. There are two bars for each company, one with {\"No Patent\"}[0.866, 0.144, 0.894, 0.164] and one with {\"Patent\"}[0.866, 0.186, 0.894, 0.205]. For example, {Microsoft's bars}[0.077, 0.646, 0.105, 0.809] [0.105, 0.440, 0.133, 0.809] suggest that Microsoft had 15.5 products without a patent and 35.2 products with a patent. The same process is repeated for all the other tech companies.\nBased on the above thought, the answer is:\nCompany,No Patent,Patent\nMicrosoft,15.5,35.2\nGoogle,21.7,45.8\nTesla,12.56,28.9\nApple,28.33,52.14\nIBM,37.68,64.33\nAmazon,41.7,59.2\nFacebook,24.39,42.11\nOracle,33.42,53.17\nAlibaba,7.8,23.9\nUber,6.89,18.22\nSnap,5.12,12.45" } ], "id_raw": 714 }, { "id": "ULLMVH0a62qlnT0w", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nBased on the data, which company is placing the most emphasis on securing patents for its products and why?" }, { "from": "gpt", "value": "This question aims to identify the company filing for the most patents. I'll need to compare the heights of the light brown bars for each company, which represent the quantity of products with {\"Patent\"}[0.866, 0.186, 0.894, 0.205]. For instance, the {patented products bar for Microsoft}[0.105, 0.440, 0.133, 0.809] corresponds to 35.2 products, while the {one for Google}[0.185, 0.328, 0.213, 0.809] corresponds to 45.8 products. By continuing this comparison across all the companies, it becomes clear that {IBM}[0.397, 0.818, 0.425, 0.874] with the {highest \"Patent\" bar}[0.425, 0.134, 0.453, 0.809] has the most products with patents, totaling 64.33. This suggests IBM places the most emphasis on securing patents for its products, possibly as a means to protect its intellectual property and potentially maximize revenue.\nBased on the above thought, the answer is:\nIBM, because it has the highest number of patented products at 64.33." } ], "id_raw": 714 }, { "id": "J2zYnDrZrwSkULzn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_9.png", "conversations": [ { "from": "human", "value": "\nWhich company has the most products without any patents?" }, { "from": "gpt", "value": "To locate the company with the most products non-protected by patents, I'll need to examine the heights of the light green bars representing the number of products with {\"No Patent\"}[0.866, 0.144, 0.894, 0.164]. I must look at each bar in turn. For instance, the {bar for Microsoft}[0.077, 0.646, 0.105, 0.809] corresponds to 15.5 products and the {bar for Google}[0.157, 0.581, 0.185, 0.809] corresponds to 21.7 products. Repeating this process for all, it is clear that {Amazon}[0.455, 0.818, 0.505, 0.918] with the {highest \"No Patent\" bar}[0.477, 0.371, 0.505, 0.809] implies it has the most products with no patents, the exact figure can be read as 41.7 from the chart.\nBased on the above thought, the answer is:\nAmazon" } ], "id_raw": 714 }, { "id": "0ODA4k0rGZbC0mnF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data showing growth and decline rates for different cuisines into a structured table format displaying the numerical values?" }, { "from": "gpt", "value": "By observing the bar chart, I identified the cuisines on the x-axis with labels such as {Italian}[0.059, 0.881, 0.086, 0.939] through to {Lebanese}[0.856, 0.881, 0.894, 0.963]. For each cuisine, there are two bars representing {Growth}[0.917, 0.101, 0.985, 0.121] and {Decline}[0.917, 0.131, 0.985, 0.151] rates, color-coded for differentiation. The values for each bar are annotated at the top of the bars, such as for {Italian cuisine's Growth rate}[0.042, 0.393, 0.063, 0.413] being 30.5 and {Decline rate}[0.082, 0.692, 0.103, 0.712] being 10.5. I will extract the values for each rate and cuisine to construct the table.\nBased on the above thought, the answer is:\nCuisine,Growth Rate,Decline Rate\nItalian,30.5,10.5\nFrench,40.0,15.2\nChinese,35.3,8.6\nThai,48.2,5.3\nIndian,41.9,14.5\nMexican,38.7,12.0\nGreek,45.3,7.5\nJapanese,37.2,19.5\nVietnamese,42.2,20.3\nTurkish,32.1,17.6\nKorean,46.5,23.4\nGerman,33.6,8.8\nAustrian,29.5,3.2\nArmenian,28.4,11.5\nLebanese,41.8,15.4" } ], "id_raw": 716 }, { "id": "nHr87YOF2usH1bYD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine has the most significant growth relative to its decline, according to the bar chart?" }, { "from": "gpt", "value": "To evaluate the cuisines with significant growth relative to decline, I will compare the difference between the heights of {Growth}[0.917, 0.101, 0.985, 0.121] and {Decline}[0.917, 0.131, 0.985, 0.151] bars for each cuisine. For example, the {Thai cuisine Growth bar}[0.204, 0.155, 0.244, 0.875] indicates the growth rate is substantially higher than its {Decline bar}[0.244, 0.795, 0.285, 0.875], observable through the vertical distances from the x-axis to the top of each bar. After analyzing all cuisines, Thai cuisine exhibits the most significant discrepancy favoring growth over decline.\nBased on the above thought, the answer is:\nThai cuisine has the most significant growth relative to its decline. This is deduced by observing the large disparity between the growth and decline bar heights in the bar chart, with the Growth rate bar being markedly taller than its corresponding Decline rate bar, indicating a more substantial positive difference." } ], "id_raw": 716 }, { "id": "54MGQ5qtf4mbiRz5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest decline rate observed among the cuisines?" }, { "from": "gpt", "value": "To find the highest decline rate, I compared the values annotated at the top of the {Decline rate bars}[0.531, 0.545, 0.561, 0.565]. By examining each value, I found that the {bar for Korean cuisine}[0.646, 0.499, 0.676, 0.519] has the highest annotated value, which is 23.4%.\nBased on the above thought, the answer is:\n23.4" } ], "id_raw": 716 }, { "id": "BXMwzyHNukxznu8k", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nAs shown in the bar chart, can you convert the provided data into a structured table with CSV format?" }, { "from": "gpt", "value": "From the subtitle of the chart {Comparison of Energy Output and Power Efficiency using different Energy Resources}[0.282, 0.030, 0.751, 0.066], it's clear that the purpose is to compare different energy resources. The energy resources are presented on the x-axis, which can be determined by analyzing {the x-axis tick labels}<{\"Coal\"}[0.023, 0.787, 0.044, 0.850]; {\"Petroleum\"}[0.090, 0.787, 0.130, 0.907]; {\"NaturalGas\"}[0.172, 0.787, 0.216, 0.917].... {\"OtherRenewable\"}[0.928, 0.787, 0.989, 0.970]>. The bar heights correspond to values for two parameters - Energymax and Powerefficiency. After inspecting the {sublegend}<{\"Parameters\"}[0.905, 0.100, 0.958, 0.128]> items, it's clear {Energymax bars}< {\"Energymax\"}[0.908, 0.142, 0.961, 0.170]> are represented in one color and the {Powerefficiency bars}< {\"Powerefficiency\"}[0.908, 0.183, 0.981, 0.211]> are represented in the other. The y-axis values range from {0}[0.035, 0.766, 0.041, 0.794] to {100}[0.023, 0.064, 0.041, 0.092]. I can extract the values by approximating the height of the bars from the y-axis. For example, the {Energymax bar for \"Coal\"}[0.014, 0.108, 0.044, 0.777] extends up to about 95 which corresponds to 95.32% as shown by {the value annotation \"95.32\"}[0.000, 0.998, 0.001, 1.000]. Hence, the \"Energymax\" value for Coal is 95.32.\nBased on the above thought, the answer is:\nResource,Energymax,Powerefficiency\nCoal,95.32,19.45\nPetroleum,90.65,24.96\nNaturalGas,77.88,28.43\nHydro,69.04,33.37\nWind,32.67,37.32\nSolar,41.79,60.44\nNuclear,82.34,73.95\nBioEnergy,70.46,26.08\nGeoThermal,58.22,49.69\nHydrogen,13.38,72.83\nAluminum,10.25,89.54\nOtherRenewable,20.64,50.22" } ], "id_raw": 717 }, { "id": "w5mRchDaf9TJlgz6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nOn reviewing the chart, which kind of energy resources do you think is the most promising for the future and why?" }, { "from": "gpt", "value": "To answer this question, there's a need to consider both maximum energy output {Energymax}<{\"Energymax\"}[0.908, 0.142, 0.961, 0.170]> and power efficiency {Powerefficiency}<{\"Powerefficiency\"}[0.908, 0.183, 0.981, 0.211]>. While it's important to have high energy output, the efficiency in converting this energy to power is also key. Arbitrating between the two parameters might boil down to the trade-offs that are acceptable. That being said, if we favor power efficiency over maximum output, based on the height of {the bars for 'Hydrogen'}[{Energymax bar}[0.787, 0.683, 0.817, 0.777]; {Powerefficiency bar}[0.817, 0.266, 0.847, 0.777]], 'Hydrogen' emerges as a good option. It has a significantly high power efficiency {72.83}[0.819, 0.229, 0.845, 0.257], but its maximum output {13.38}[0.789, 0.647, 0.815, 0.675] is relatively lower. However, with technological advancements, we might be able to leverage its efficiency and better its energy output in the future.\nBased on the above thought, the answer is:\nHydrogen. Though it currently has a relatively lower maximum energy output, its high power efficiency makes it a promising energy resource for the future, hoping that technological advancement would increase its energy output." } ], "id_raw": 717 }, { "id": "y4QftoH1Hqi0os6e", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_12.png", "conversations": [ { "from": "human", "value": "\nWhich resource has the highest power efficiency as indicated on the chart?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the {Powerefficiency bars}<{\"Powerefficiency\"}[0.908, 0.183, 0.981, 0.211]> across the different resource categories. The highest bar represents the resource with the highest power efficiency. By comparing the heights of the bars, it can be seen that the {bar for 'Aluminum'}[0.903, 0.148, 0.933, 0.777] is the tallest. Subsequently, the textual annotation {89.54}[0.905, 0.112, 0.931, 0.140] confirms that 'Aluminum' has the highest power efficiency of 89.54.\nBased on the above thought, the answer is:\nAluminum" } ], "id_raw": 717 }, { "id": "xWsgGoQq58sOrt2P", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, could you convert the data expressed into a structured table with 'Category', 'Non-aggregation', and 'Aggregation' columns?" }, { "from": "gpt", "value": "The graph represents a range of categories associated with building and the real estate market. Each category has two corresponding bars, with blue indicating \"Non-aggregation\" and orange \"Aggregation\". Labeled ticks on the x-axis signify each category's starting point. For example, '\"Apartment Sales\"'[0.051, 0.685, 0.143, 0.868] and '\"Construction Permits\"'[0.115, 0.685, 0.227, 0.909] are the first and second categories. Correspondingly, each category's bars are positioned, and their height represents the respective values. For example, the height of the 'Non-aggregation' bar for the 'Apartment Sales' category< r>[0.113, 0.222, 0.143, 0.675] aligns approximately with the 98000 mark on the y-axis. Combining these observations allows me to generate a CSV-style table representation of the data in question.\nBased on the above thought, the answer is:\nCategory,Non-aggregation,Aggregation\n'Apartment Sales',98000,85000\n'Construction Permits',15000,12500\n'Foreclosures',5000,6000\n'Real Estate Investments',80000,95000\n'Commercial Property Sales',96000,102000\n'Residential Property Sales',110000,120000\n'Mortgage Approvals',85000,90000\n'Rental Market',50000,52000\n'Real Estate Agents',3000,3800\n'Property Auctions',11000,15000" } ], "id_raw": 719 }, { "id": "1EBYjLDx4hHdLA2r", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nFrom the data represented on the chart, which aspect of the building and real estate market had the most significant increase when moved from non-aggregation to aggregation?" }, { "from": "gpt", "value": "In order to determine which category experienced the most significant increase when transitioning from non-aggregation to aggregation, I need to compare the differences between the two corresponding bars within each category. Their colors, as identified by {the legend}[0.810, 0.089, 0.978, 0.223], are blue for non-aggregation and orange for aggregation. Upon comparison, it appears that the category with label '\"Real Estate Investments\"'[0.266, 0.685, 0.395, 0.942] has the most significant increase, going from {the non-aggregation bar}[0.366, 0.305, 0.395, 0.675], approximately at 80000 on the y-axis< r>[0.043, 0.294, 0.087, 0.322], to {the aggregation bar}[0.395, 0.236, 0.425, 0.675], approximately at 95000 on the y-axis< r>[0.035, 0.202, 0.087, 0.230].\nBased on the above thought, the answer is:\nThe 'Real Estate Investments' category had the most significant increase when moved from non-aggregation to aggregation." } ], "id_raw": 719 }, { "id": "YyJBbfXpKNogivlC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in number between non-aggregation and aggregation for the 'Construction Permits' category?" }, { "from": "gpt", "value": "The non-aggregation and aggregation bars for the category labeled '\"Construction Permits\"'[0.115, 0.685, 0.227, 0.909] correspond to the values of 15000 and 12500 respectively. To find the difference, subtract the two values: 15000 - 12500. Further deductions can be made using the heights of the bars associated with 'Construction Permits', with the non-aggregation bar< r>[0.197, 0.606, 0.227, 0.675] higher than the aggregation bar< r>[0.227, 0.617, 0.256, 0.675], emphasizing the difference in value determined earlier.\nBased on the above thought, the answer is:\n2500" } ], "id_raw": 719 }, { "id": "olveKvpaOnfjMIM0", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nConvert the information represented on the horizontal bar chart into a CSV format table." }, { "from": "gpt", "value": "The Y-axis of the chart {y-axis labels}[{\"Equity Market\"}[0.015, 0.710, 0.111, 0.756]; {\"Bond Market\"}[0.023, 0.471, 0.111, 0.518]] shows two markets: Equity Market and Bond Market. Each market has a stacked bar with different segments representing stability levels from {the legend items}<{\"Extremely unstable\"}[0.254, 0.261, 0.281, 0.293]; {\"Significantly unstable\"}[0.254, 0.331, 0.281, 0.363]; {\"Moderately unstable\"}[0.470, 0.261, 0.498, 0.293]; {\"Stable\"}[0.470, 0.331, 0.498, 0.363]; {\"Significantly Stable\"}[0.680, 0.261, 0.707, 0.293]; {\"Extremely Stable\"}[0.680, 0.331, 0.707, 0.363]>. By comparing the lengths of these segments, I can extract the corresponding values. For instance, for the Equity Market, the {bar segment representing \"Extremely unstable\"}[0.121, 0.668, 0.450, 0.788] has a length of 0.4 as the x-value extends from 0.00 -> 0.40. By identifying such values for each segment in both markets, I can construct the CSV table.\nBased on the above thought, the answer is:\nMarket,Extremely unstable,Significantly unstable,Moderately unstable,Stable,Significantly stable,Extremely stable\nEquity Market,0.4,0.1,0.15,0.05,0.2,0.1\nBond Market,0.35,0.05,0.1,0.1,0.25,0.15" } ], "id_raw": 720 }, { "id": "kyE65911isN25uDC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which market seems to be more unstable overall? And provide your reasoning." }, { "from": "gpt", "value": "To determine the overall instability of the markets, I need to consider the sum of the segments representing unstable levels: 'Extremely unstable', 'Significantly unstable', and 'Moderately unstable'. For the Equity Market, the lengths of these bar segments are {extremely unstable}[0.121, 0.668, 0.450, 0.788] (0.4), {significantly unstable}[0.450, 0.668, 0.532, 0.788] (0.1), and {moderately unstable}[0.532, 0.668, 0.656, 0.788] (0.15). The sum is 0.65. Similarly, for the Bond Market, the lengths of these bar segments are {extremely unstable}[0.121, 0.430, 0.409, 0.549] (0.35), {significantly unstable}[0.409, 0.430, 0.450, 0.549] (0.05), and {moderately unstable}[0.450, 0.430, 0.532, 0.549] (0.1). The sum is 0.5. Therefore, the Equity Market seems to be more unstable overall.\nBased on the above thought, the answer is:\nEquity Market. Because the combined length of the segments representing 'Extremely Unstable', 'Significantly Unstable', and 'Moderately Unstable' is larger in the Equity Market (0.65) compared to the Bond Market (0.5)." } ], "id_raw": 720 }, { "id": "7t6VU8iQM1IXM9TH", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_0.png", "conversations": [ { "from": "human", "value": "\nIn which market is the extremely unstable sector larger?" }, { "from": "gpt", "value": "To find the market with a larger extremely unstable sector, I can compare the lengths of the corresponding bar segments. The {bar segment for \"extremely unstable\" in the Equity Market}[0.121, 0.668, 0.450, 0.788] extends from x-values 0.00 -> 0.40, meaning it has a value of 0.4. Similarly, the {bar segment for \"extremely unstable\" in the Bond Market}[0.121, 0.430, 0.409, 0.549] extends from x-values 0.00 -> 0.35, meaning it has a value of 0.35. Thus, the extremely unstable sector is larger in the Equity Market.\nBased on the above thought, the answer is:\nEquity Market." } ], "id_raw": 720 }, { "id": "9n0c0tee2O4gY4YE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nPlease collate the information shown on the chart into an easy-to-read CSV table for future reference." }, { "from": "gpt", "value": "Based on the legend information provided in the chart, I can see that there are five categories to be considered, namely: {Success}[0.377, 0.257, 0.432, 0.303], {Logistical Errors}[0.377, 0.326, 0.488, 0.373], {Delivery Delay}[0.554, 0.257, 0.656, 0.303], {Package Damage}[0.554, 0.326, 0.675, 0.373] and {Safety Incidents}[0.742, 0.257, 0.854, 0.303]. I will need to find these categories for each type of delivery. Looking at the y-axis labels, there are two types of delivery: {Driverless Delivery}[0.066, 0.710, 0.197, 0.756] and {Unmanned Aerial Delivery}[0.015, 0.471, 0.197, 0.518]}. Each bar for the 2 delivery types is sectioned off into various categories, with the proportion of each represented by the bar length along the x-axis. Starting from the left bar, from left to right, the values are represented as such: success, logistical errors, delivery delay, package damage, and safety incidents.\nBased on the above thought, the answer is:\nCategories, Driverless Delivery, Unmanned Aerial Delivery\nSuccess, 0.8, 0.6\nLogistical Errors, 0.1, 0.2\nDelivery Delay, 0.05, 0.05\nPackage Damage, 0.03, 0.05\nSafety Incidents, 0.02, 0.1" } ], "id_raw": 721 }, { "id": "4XAmv7VelqHTIF9W", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nFrom the perspective of safety, which delivery method would you recommend and why?" }, { "from": "gpt", "value": "To answer this question, it is important to analyze the blue segment that represents {Safety Incidents}[0.742, 0.257, 0.854, 0.303]. Examining the {Safety Incidents section for Driverless Delivery}[0.933, 0.668, 0.948, 0.788], which appears to have a length corresponding to a rate of 2%. Meanwhile, {Safety Incidents for Unmanned Aerial Delivery}[0.874, 0.430, 0.948, 0.549] have a higher rate of 10%. Comparing these percentages, it is logical to opt for the one with the lowest Safety Incidents percentage.\nBased on the above thought, the answer is:\nI recommend opting for the Driverless Delivery method, as it has a lower Safety Incidents rate at only 2%, compared to the 10% for the Unmanned Aerial Delivery method." } ], "id_raw": 721 }, { "id": "JE8yOsQMfRDy6Nt3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which delivery type has the highest success rate?" }, { "from": "gpt", "value": "To identify which delivery has the highest success rate, I need to compare the length of the green bar under each delivery method in the chart. For {Driverless Delivery}[0.066, 0.710, 0.197, 0.756], the length of the green bar starts from {[0.207, 0.668]} to {[0.800, 0.788]}, indicating a success rate of 80%. While for {Unmanned Aerial Delivery}[0.015, 0.471, 0.197, 0.518], the length of the green bar starts from {[0.207, 0.430]} to {[0.652, 0.549]}, indicating a success rate of 60%.\nBased on the above thought, the answer is:\nDriverless Delivery." } ], "id_raw": 721 }, { "id": "xkTrf9M6nRDXv2zM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nBased on the horizontal bar chart observed, could you translate the data into a structured table format?" }, { "from": "gpt", "value": "This is a group bar graph with two AI apps on the y-axis and five different performance levels represented by different colors on the x-axis, which are {Success Prediction}[0.251, 0.656, 0.342, 0.703], {Failure Analysis}[0.251, 0.725, 0.333, 0.772], {Security Risk}[0.407, 0.656, 0.452, 0.703], {Capacity Utilization}[0.407, 0.725, 0.461, 0.772] and {Load Forecasting}[0.593, 0.656, 0.667, 0.703]. The values for each performance level are calculated using the lengths of the corresponding segments. Looking at the bars, {AI App1}[0.079, 0.688, 0.942, 0.828] has a Success Prediction of 0.65, Failure Analysis of 0.25, Security Risk of 0.05, Capacity Utilization of 0.03 and Load Forecasting of 0.02, whereas {AI App2}[0.079, 0.452, 0.942, 0.589] has a Success Prediction of 0.75, Failure Analysis of 0.15, Security Risk of 0.07, Capacity Utilization of 0.02 and Load Forecasting of 0.01. Using these values I can create a CSV table.\nBased on the above thought, the answer is:\nCategory,AI App1,AI App2\nSuccess Prediction,0.65,0.75\nFailure Analysis,0.25,0.15\nSecurity Risk,0.05,0.07\nCapacity Utilization,0.03,0.02\nLoad Forecasting,0.02,0.01" } ], "id_raw": 722 }, { "id": "yUenGWyNwYJorRYg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which AI application has shown better performance in terms of Success Prediction? And why?" }, { "from": "gpt", "value": "The performance of Success Prediction for each app can be analyzed by comparing the lengths of the first segment in each bar. The bar for {AI App1}[0.079, 0.688, 0.640, 0.828] shows a lower value of Success Prediction with only 65% whereas the segment for {AI App2}[0.079, 0.452, 0.726, 0.589] shows a higher value at 75% in terms of Success Prediction. Therefore, comparing the length of both segments, it can be inferred that AI App2 has executed a better performance in the aspect of Success Prediction.\nBased on the above thought, the answer is:\nAI App2, because it has a higher Success Prediction rate of 75%, compared to AI App1 which stands at 65%." } ], "id_raw": 722 }, { "id": "o1R01YBVuAibV3EU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_2.png", "conversations": [ { "from": "human", "value": "\nWhich AI App has the lowest combined performance across all categories?" }, { "from": "gpt", "value": "By comparing the total lengths of {AI App1's bar}[0.079, 0.688, 0.942, 0.828] and {AI App2's bar}[0.079, 0.452, 0.942, 0.589], the total performance of each app can be calculated. Looking at the x-axis [0.068, 0.852, 1.125, 0.898], AI App1 has a total performance level of 1.0, as each segment from Success Prediction to Load Forecasting sums up to fill the entire length of the bar. In comparison, AI App2 also fills the entire length of the bar which indicates a total performance level of 1.0 as well. Therefore, both AI App1 and AI App2 have the same total performance level.\nBased on the above thought, the answer is:\nNeither, both AI App1 and AI App2 have equal combined performance level." } ], "id_raw": 722 }, { "id": "QVmCPaE4HCNidhVM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nCan you please generate a CSV table from the bar chart information?" }, { "from": "gpt", "value": "The bar chart shows percentage of total efforts distributed among various factors for two platforms, Facebook and Instagram, as per their respective bar lengths. For example, {the influence collaboration for Facebook} [0.621, 0.575, 0.765, 0.776] lies at approximately 0.16 (0.79 - 0.62) way through 0-1.0 on the x-axis, and {for Instagram} [0.604, 0.173, 0.799, 0.374] lies at approximately 0.23 (0.83 - 0.6) way through. I'll follow similar way for each category and for each platform to collect the data to build the table.\nBased on the above thought, the answer is:\nPlatform,Post Engagement Rate,Advertising Cost,Organic Reach,Influencer Collaboration,Community Engagement,Algorithm Change\nFacebook,0.27,0.23,0.12,0.16,0.15,0.03\nInstagram,0.35,0.17,0.08,0.23,0.14,0.03" } ], "id_raw": 723 }, { "id": "Bg8KJ3qISLBOEWU4", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nWhich platform would you recommend for implementing an advertising campaign based on the chart figures, and why?" }, { "from": "gpt", "value": "In terms of advertising, I need to focus on factors 'Advertising Cost' and 'Post Engagement Rate' which are key for an advertising campaign. For Instagram, the 'Advertising Cost' [0.392, 0.173, 0.536, 0.374] is slightly lower and the 'Post Engagement Rate' [0.095, 0.173, 0.392, 0.374] is definitely higher than for Facebook where the 'Advertising Cost' [0.324, 0.575, 0.519, 0.776]r is higher, and the 'Post Engagement Rate' [0.095, 0.575, 0.324, 0.776] is lower. In addition, Instagram has a higher 'Influencer Collaboration' rate [0.604, 0.173, 0.799, 0.374], which is beneficial for advertising efforts. So, considering these three factors, I would recommend Instagram.\nBased on the above thought, the answer is:\nInstagram, because it has a higher post engagement rate and influencer collaboration rate, and a lower advertising cost, making it more cost-effective and potentially impactful for an advertising campaign." } ], "id_raw": 723 }, { "id": "Jf4jNoByZE1ZbMiU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_3.png", "conversations": [ { "from": "human", "value": "\nWhich factor has the highest percentage of total efforts on Instagram?" }, { "from": "gpt", "value": "To find the factor with the highest percentage of total efforts on Instagram, I need to look at the lengths of the bars associated with Instagram. Looking from left to right, the first bar is for 'Post Engagement Rate' [0.095, 0.173, 0.392, 0.374] with a length of approximately 0.35 (0.392 - 0.095), the second bar is for 'Advertising Cost' [0.392, 0.173, 0.536, 0.374] with a length of approximately 0.17, and so on. It appears that the 'Post Engagement Rate' has the longest length and hence, the highest percentage of total efforts on Instagram.\nBased on the above thought, the answer is:\nPost Engagement Rate" } ], "id_raw": 723 }, { "id": "hDVlClFlabuK3cTT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nCould you provide the raw data used to create the bar chart in a CSV table format?" }, { "from": "gpt", "value": "I can see that it's a stacked bar chart displaying performance scores for two technologies {TechProjectX}[0.015, 0.710, 0.103, 0.756] and {TechProjectY}[0.016, 0.471, 0.103, 0.518] along multiple parameters. Parameters {Economic Viability}[0.293, 0.257, 0.421, 0.303], {Technological Feasibility}[0.293, 0.326, 0.461, 0.373], {Business Need}[0.527, 0.257, 0.629, 0.303], {Market Demand}[0.527, 0.326, 0.639, 0.373], {Strategic Alignment}[0.705, 0.257, 0.844, 0.303], {End Score}[0.705, 0.326, 0.775, 0.373] are denoted with different colors. These parameters correspond to x-values, ranging from {0.0}[0.102, 0.838, 0.124, 0.885] to {1.2}[1.099, 0.838, 1.121, 0.885]. Combining these details alongside the exact markings of each category under corresponding tech projects, the CSV table can be generated.\nBased on the above thought, the answer is:\nInnovation Name,Economic Viability,Technological Feasibility,Business Need,Market Demand,Strategic Alignment,End Score\nTechProjectX,0.7,0.15,0.05,0.06,0.02,0.02\nTechProjectY,0.6,0.02,0.1,0.12,0.1,0.06" } ], "id_raw": 724 }, { "id": "aQFGawRVtl8bmaQb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which innovation project would you recommend for investment and why?" }, { "from": "gpt", "value": "When looking at the bars, I can clearly see that {TechProjectX}[0.113, 0.668, 0.943, 0.788] has significantly higher scores in imperative areas such as {Economic Viability}[0.113, 0.668, 0.694, 0.788] and {Technological Feasibility}[0.694, 0.668, 0.819, 0.788]. These areas are crucial for the success and sustainability of an innovative project. {TechProjectX}[0.113, 0.668, 0.943, 0.788] also has respectable scores in the other measured areas. All this implies that this project has a balanced and high-performance profile across the evaluated metrics.\nBased on the above thought, the answer is:\nTechProjectX. It performs significantly better in crucial areas like Economic Viability and Technological Feasibility, indicating a higher probability of success for this project." } ], "id_raw": 724 }, { "id": "YCMqtx2vUyj87M10", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_4.png", "conversations": [ { "from": "human", "value": "\nWhich technology project obtained the highest total score?" }, { "from": "gpt", "value": "By comparing the total widths of the bars for {TechProjectX}[0.113, 0.668, 0.943, 0.788] and {TechProjectY}[0.113, 0.430, 0.943, 0.549], it's clear that TechProjectX spans a wider range on the x-axis and therefore has a higher total score.\nBased on the above thought, the answer is:\nTechProjectX" } ], "id_raw": 724 }, { "id": "0kR1o88gjG3anZ67", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the information from the stacked bar chart, could you map the performance for each factor in the Efficiency_check and Workflow_analysis processes? Provide this in a structural CSV table format." }, { "from": "gpt", "value": "The chart's x-axis records the effect of different factors on business processes as seen from its label {\"Effect of factors on business processes\"}[0.433, 0.952, 0.704, 0.975]. The y-axis distinguishes the two processes as specified by the y-axis labels {\"Efficiency_check\"}[0.028, 0.752, 0.142, 0.775] and {\"Workflow_analysis\"}[0.015, 0.315, 0.142, 0.339]. For each business process, there are segments of bars each corresponding to a factor color coded as per {legend}[0.264, 0.036, 0.873, 0.113]. When creating a CSV table, I should list each factor and their corresponding value in the Efficiency_check process and then Workflow_analysis process. The length of each colored segment corresponds to the value of that factor. For instance, in the Efficiency_check process, the green (representing Success) segment is from [0.152, 0.652] to [0.311, 0.870] indicating a corresponding factor value of approximately 0.2.\nBased on the above thought, the answer is:\nFactor, Efficiency_check, Workflow_analysis\nSuccess, 0.2, 0.7\nGrounding_error, 0.35, 0.1\nOccupancy_error, 0.1, 0.05\nGripper_collision, 0.15, 0.05\nTrajectory_optimization_error, 0.05, 0.05\nGrasping_failed, 0.15, 0.05" } ], "id_raw": 725 }, { "id": "fNbYqklHASBKYvZx", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which business process is more successful, and why?" }, { "from": "gpt", "value": "To determine the more successful business process, I need to compare the lengths of the green segments, which represent Success, in both Efficiency_check and Workflow_analysis processes. For the Efficiency_check process, the green bar is located at {[0.152, 0.652, 0.311, 0.870]}, implying it spans approximately 0.2 on the x-axis. For Workflow_analysis, the green segment's coordinates are {[0.152, 0.216, 0.707, 0.434]}, indicating it spans roughly 0.7 on the x-axis. Therefore, the Workflow_analysis process is more successful since it has a longer green segment, representing a higher success rate.\nBased on the above thought, the answer is:\nWorkflow_analysis, because it has a longer green segment indicating a higher success rate." } ], "id_raw": 725 }, { "id": "9MByq7pAQjnTD8s3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_5.png", "conversations": [ { "from": "human", "value": "\nIn which of the business processes did the Occupancy Error have a larger effect?" }, { "from": "gpt", "value": "In order to confirm which business process had a more significant Occupancy Error effect, I can compare the lengths of the orange bars, {representing Occupancy Error} in the Efficiency_check and Workflow_analysis processes. For Efficiency_check, the orange segment is located at the coordinates [0.588, 0.652, 0.668, 0.870] and spans approximately 0.1 on x-axis. For the Workflow_analysis, the orange bar's coordinates {[0.787, 0.216, 0.826, 0.434]} reveal that it spans roughly 0.05 on the x-axis. Therefore, the Occupancy Error was larger in the Efficiency_check process.\nBased on the above thought, the answer is:\nEfficiency_check" } ], "id_raw": 725 }, { "id": "UGFUn22H7i2NZBS4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the bar chart depict in terms of percentages for each category across the years 2017 and 2022? Can you chart the information obtained into a CSV format?" }, { "from": "gpt", "value": "The bar chart describes respondents' distributions between two major categories: 'Healthy Lifestyle' and 'Digital Detachment'. It presents data for two different years - 2017 and 2022. The bars are colored differently to represent each category, as indicated in {the legend}[0.337, 0.238, 0.706, 0.322]. The blue bars represent 'Healthy Lifestyle' and the orange bars represent 'Digital Detachment'. The lengths of the bars for each year reflect the respective percentages. For example, in 2017, {the healthy lifestyle bar}[0.060, 0.668, 0.451, 0.788] represents 60%, and {the digital detachment bar}[0.451, 0.668, 0.874, 0.788] represents 65%. In 2022, {the healthy lifestyle bar}[0.060, 0.430, 0.483, 0.549] extends to 65%, whereas {the digital detachment bar}[0.483, 0.430, 0.940, 0.549] reaches 70%. This information can be arranged into a CSV table as follows:\nBased on the above thought, the answer is:\n\"Year\",\"Healthy Lifestyle\",\"Digital Detachment\"\n\"2017\",0.6,0.65\n\"2022\",0.65,0.7" } ], "id_raw": 726 }, { "id": "JAqpRisTHhiZu2et", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nAnalyze the trends observed in the social and cultural poll depicted in the chart and elaborate what it means for the preferences of the respondents." }, { "from": "gpt", "value": "Looking at the chart and considering the colored segments for 'Healthy Lifestyle' and 'Digital Detachment' in {the legend}[0.337, 0.238, 0.706, 0.322], it is evident that there is a shift in the respondents' preferences between 2017 and 2022. \nThe blue bars for 'Healthy Lifestyle' in {2017}[0.060, 0.668, 0.451, 0.788] and {2022}[0.060, 0.430, 0.483, 0.549] demonstrate a rise from 60% to 65%, indicating a growing preference for a healthier living approach. \nLikewise, a similar trend can be observed in the orange bars for 'Digital Detachment' for {2017}[0.451, 0.668, 0.874, 0.788] and {2022}[0.483, 0.430, 0.940, 0.549], representing an increase from 65% to 70%. A potential interpretation could be an increasing tendency for respondents to retreat from digital engagement over the years.\nBased on the above thought, the answer is:\nThe data suggests that from 2017 to 2022, respondents displayed an increasing preference towards 'Healthy Lifestyle' and 'Digital Detachment'. This shift could be a sign of growing awareness about the importance of health and well-being, and the tendency toward less digital engagement or more balanced technology use." } ], "id_raw": 726 }, { "id": "JrlRsTnGzqS3Azre", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_6.png", "conversations": [ { "from": "human", "value": "\nBetween 2017 and 2022, by what percentage did the preference for a healthy lifestyle increase?" }, { "from": "gpt", "value": "To determine the increase rate of preference for a healthy lifestyle from 2017 to 2022, I can compare the proportions of the blue bars corresponding to these years. By examining the bars in {2017}[0.060, 0.668, 0.451, 0.788] and {2022}[0.060, 0.430, 0.483, 0.549], I can conclude that the rate increases from 60% to 65%. The difference between these values gives the required percentage increase.\nBased on the above thought, the answer is:\n5%" } ], "id_raw": 726 }, { "id": "UJ1MAiKAgUTUCMLm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart visualization you've seen, could you please build a structured data table in CSV format?" }, { "from": "gpt", "value": "The charts x-axis provides the percentage of total trials ranging from {0.0}[0.084, 0.838, 0.106, 0.885] to {1.0}[1.026, 0.838, 1.048, 0.885]. The individual segments based on the chart data fall under two sectors {NewTech}[0.024, 0.710, 0.086, 0.756] and {FutureEdu}[0.015, 0.471, 0.086, 0.518], with the horizontal bars representing two different parameters \u2014 {Innovation}[0.442, 0.257, 0.515, 0.303] and {Infrastructure}[0.582, 0.257, 0.677, 0.303], as indicated in the chart's legend. To construct a CSV table, the lengths of the bars for each sector and parameter need to be identified. For NewTech, the length of the green bar (Innovation) extends from [0.095, 0.668] to [0.660, 0.788], which indicates a value of 0.6, and the length of the grey bar (Infrastructure) extends from [0.660, 0.668] to [0.896, 0.788], representing a value of 0.25. For FutureEdu, the length of the green bar (Innovation) extends from [0.095, 0.430] to [0.613, 0.549], indicating a value of 0.55, and the grey bar (Infrastructure) stretches from [0.613, 0.430] to [0.943, 0.549], which equates to 0.35.\nBased on the above thought, the answer is:\nSectors,Innovation,Infrastructure\nNewTech,0.6,0.25\nFutureEdu,0.55,0.35" } ], "id_raw": 727 }, { "id": "fxGrq7Ix1H2x2FIu", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nBased on the visualization, which sector has a better balance between innovation and infrastructure development?" }, { "from": "gpt", "value": "This question requires comparing the lengths of the innovation and infrastructure bars for both sectors. In {NewTech}[0.024, 0.710, 0.086, 0.756], the {Innovation}[0.442, 0.257, 0.515, 0.303] bar extends to 0.6, while the {Infrastructure}[0.582, 0.257, 0.677, 0.303] bar extends only till 0.25. This indicates a large disparity, meaning there's much innovation but a lack of infrastructure support. In the case of {FutureEdu}[0.015, 0.471, 0.086, 0.518], the Innovation bar extends until 0.55 and the Infrastructure bar extends until 0.35, showing a smaller degree of disparity and therefore, a better balance between innovation and infrastructure.\nBased on the above thought, the answer is:\nFutureEdu has a better balance between innovation and infrastructure development as it shows a smaller degree of disparity between the two." } ], "id_raw": 727 }, { "id": "prYUQBkd1qdfWSAe", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector has a higher innovation score?" }, { "from": "gpt", "value": "To determine the sector with a higher innovation score, the length of the green bars representing innovation for both sectors needs to be compared. The {NewTech innovation score}[0.095, 0.668, 0.660, 0.788] extends till 0.60 on the x-axis, while the {FutureEdu innovation score}[0.095, 0.430, 0.613, 0.549] extends till 0.55. It's apparent that NewTech has a higher Innovation score.\nBased on the above thought, the answer is:\nNewTech" } ], "id_raw": 727 }, { "id": "2fwUZjYtG90dC6jG", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nPlease convert the information present in the bar chart into a CSV format table." }, { "from": "gpt", "value": "The given chart is a comparison of the success and error types between Urban Planning and Community Development trials. The x-axis displayed at {x-axis}[0.507, 0.903, 0.681, 0.950] represents the percentage of total trials. The y-axis displayed at {y-axis labels}[{\"Urban Planning\"}[0.087, 0.710, 0.193, 0.756]; {\"Community Development\"}[0.015, 0.471, 0.193, 0.518]] represents the category: Urban Planning and Community Development. The different parts of the bar correspond to Success, Grounding Error, Occupancy Error, Gripper Collision, Trajectory Optimization Error, and Grasping Failed as indicated in the legend at legend location[0.289, 0.238, 0.899, 0.392]. The length of each part, along the x-axis of percentages, gives the respective values. Therefore, the same information forms the following CSV table.\nBased on the above thought, the answer is:\nCategory,Success,Grounding Error,Occupancy Error,Gripper Collision,Trajectory Optimization Error,Grasping Failed\nUrban Planning,0.5,0.1,0.1,0.05,0.1,0.15\nCommunity Development,0.6,0.05,0.15,0.05,0.1,0.05" } ], "id_raw": 728 }, { "id": "nrJDXoD8xysoIfHS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, which category had a better performance overall? Explain your reasoning." }, { "from": "gpt", "value": "To determine which category had a better performance overall, I need to consider both the ratio of success and the presence of errors. Looking at the green bar marked 'Success' at {legend}[0.333, 0.257, 0.389, 0.303], for Urban Planning at {Urban Planning bar}[0.203, 0.668, 0.575, 0.788] and Community Development at {Community Development bar}[0.203, 0.430, 0.650, 0.549], we see that Urban Planning had a 0.5 success rate and Community Development had 0.6. This points towards Community Development being more successful. To conclude, we should also compare the error rates. Analyzing each error type, we find that except for 'Grasping Failed', all other errors like Grounding Error, Occupancy Error, Gripper Collision, Trajectory Optimization Error are less in Community Development compared to Urban Planning. This further strengthens the case for Community Development having an overall better performance.\nBased on the above thought, the answer is:\nCommunity Development. This is due to its higher success rate and comparatively lower error rates." } ], "id_raw": 728 }, { "id": "C9uEcgoR7OwOF5Kb", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which category had the most 'Grounding Error'?" }, { "from": "gpt", "value": "To identify the category with the most number of 'Grounding Error', I will check the lengths of the grey colored bars labelled 'Grounding Error' at {legend}[0.333, 0.326, 0.444, 0.373]. When cross-checking the chart at {bars}[{\"Urban Planning\"}[0.575, 0.668, 0.650, 0.788]; {\"Community Planning\"}[0.650, 0.430, 0.687, 0.549]], it can be seen that The 'Grounding Error' for Urban Planning spans from 0.5 to 0.6 on the x-axis which is 0.1 in length, while for Community Development it spans from 0.6 to 0.65 which is 0.05 in length.\nBased on the above thought, the answer is:\nUrban Planning." } ], "id_raw": 728 }, { "id": "YKoCP9U8uDvH6tLe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nBased on the dual horizontal stacked bar chart, can you organize the sector-wise contributions to Global trends and Asia into a CSV formatted table?" }, { "from": "gpt", "value": "Yes, I can see from {the Y-tick labels}[0.015, 0.710, 0.108, 0.756]; [0.079, 0.471, 0.108, 0.518] that 'Global trends' and 'Asia', are the two categories of data represented in the chart. The horizontal stacked bars within each category are reflecting the respective sectoral contributions. These contributions are represented as percentage values on {the X-axis}[0.107, 0.838, 0.129, 0.885]; [0.257, 0.838, 0.279, 0.885]; [0.407, 0.838, 0.429, 0.885]; [0.557, 0.838, 0.579, 0.885]; [0.707, 0.838, 0.729, 0.885]; [0.858, 0.838, 0.880, 0.885]; [1.008, 0.838, 1.030, 0.885]. Each colored section of the stacked bar is labeled in {the legend}[0.336, 0.238, 0.767, 0.392] as 'Manufacturing', 'Services', 'Imports', 'Exports', 'GDP Increase', and 'FDI'. For instance, the contribution of Manufacturing in Global trends {indicated by the red bar}[0.118, 0.668, 0.441, 0.788] extends up to 0.43 or 43% as shown on the x-axis. In the same way, I can determine the contributions of each sector in both categories.\nBased on the above thought, the answer is:\n,Manufacturing,Services,Imports,Exports,GDP_Increase,FDI\nGlobal_trends,0.43,0.33,0.2,0.09,0.02,0.03\nAsia,0.47,0.27,0.17,0.11,0.06,0.02" } ], "id_raw": 729 }, { "id": "wnQl4m4TjqtTFZjf", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nLooking at the 'Global trends' data, which do you think is the most influential sector contributing to the economic output and why?" }, { "from": "gpt", "value": "The horizontal stacked bars for 'Global trends' show the size of each sector's contribution as the length of their respective colored segments. A longer segment implies a larger contribution. Therefore, to decide the sector with the most influence on the economic output, we should look for the longest segment in the 'Global trends' category. From observation, we can see that {the 'Manufacturing' sector}[0.118, 0.668, 0.441, 0.788] is the longest. So, 'Manufacturing' contributes the most and can therefore be deemed the most influential in terms of economic output.\nBased on the above thought, the answer is:\nThe 'Manufacturing' sector is the most influential as it contributes the most to the total economic output based on 'Global trends'." } ], "id_raw": 729 }, { "id": "llcMbsjuLrkYL7kI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_9.png", "conversations": [ { "from": "human", "value": "\nIn the 'Asia' category, which sector has shown an overall decrease in percentage contribution as compared to 'Global trends'?" }, { "from": "gpt", "value": "To answer this, I would compare each sector's contribution in the 'Asia' category with its corresponding contribution in 'Global trends'. For example, for the 'Manufacturing' sector, I would compare the length of {the red section in the 'Asia' bar}[0.118, 0.430, 0.471, 0.549] with {the red section in the 'Global trends' bar}[0.118, 0.668, 0.441, 0.788]. Doing this for all sectors reveals that the 'Services' sector, represented by {the blue section of the 'Asia' bar}[0.471, 0.430, 0.673, 0.549] is lesser than {the blue section in the 'Global trends' bar}[0.441, 0.668, 0.688, 0.788], indicating a decrease in the 'Services' sector contribution in the 'Asia' category as compared to 'Global trends'.\nBased on the above thought, the answer is:\nServices." } ], "id_raw": 729 }, { "id": "Etqhx98mHg9D9zp3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nCan you analyze the chart and extract the data representation into a CSV format?" }, { "from": "gpt", "value": "The bar chart represents two categories {Self-driving Vehicles}[0.015, 0.710, 0.154, 0.756] and {Drone Delivery}[0.050, 0.471, 0.154, 0.518]. Each bar represents the success rate and error rate of the two technologies. The x-axis {Percentage of Total Trials}[0.488, 0.903, 0.661, 0.950] represents the range from {0.0}[0.153, 0.838, 0.175, 0.885] to {1.0}[0.935, 0.838, 0.957, 0.885]. With the help of the legend section {legend}[0.271, 0.238, 0.879, 0.392], I could understand each bar's color corresponds to the performance parameters. For example, from the coordinates of the green section of the bar for {Self-driving Vehicles}[0.164, 0.668, 0.711, 0.788], it can be inferred that the success rate is 0.7. Correspondingly, I can derive the performance values for the entire chart. The following CSV format info could be obtained.\nBased on the above thought, the answer is:\nCategories,Errors,Value\nSelf-driving Vehicles,Success,0.7\nSelf-driving Vehicles,Grounding Error,0.1\nSelf-driving Vehicles,Occupancy Error,0.05\nSelf-driving Vehicles,Gripper Collision,0.1\nSelf-driving Vehicles,Trajectory Optimization Error,0.03\nSelf-driving Vehicles,Grasping Failed,0.02\nDrone Delivery,Success,0.5\nDrone Delivery,Grounding Error,0.2\nDrone Delivery,Occupancy Error,0.1\nDrone Delivery,Gripper Collision,0.05\nDrone Delivery,Trajectory Optimization Error,0.1\nDrone Delivery,Grasping Failed,0.05" } ], "id_raw": 730 }, { "id": "k3E1G6qigzdupmAJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which technology seems to be more efficient and why?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the error rates and the success rates of both technologies {Self-driving Vehicles}[0.015, 0.710, 0.154, 0.756] and {Drone Delivery}[0.050, 0.471, 0.154, 0.518]. Starting with the {green}[0.276, 0.261, 0.304, 0.293] section in each bar which represents successful operations, we can see that Self-Driving Vehicles have a higher success rate than Drone Delivery. Looking at the various error categories represented by the rest of the colors in the bar, we can see that Self-driving Vehicles have lower error rates in almost all categories except for Grounding Errors. Therefore, considering the success rate and the error rates, Self-Driving Vehicles seem more efficient.\nBased on the above thought, the answer is:\nSelf-driving Vehicles seems to be more efficient because they have a higher success rate and lower error rates in most categories." } ], "id_raw": 730 }, { "id": "1eQANB81OuHIo7h2", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_10.png", "conversations": [ { "from": "human", "value": "\nWhich technology has a higher success rate?" }, { "from": "gpt", "value": "In order to find out which technology has a higher success rate, I need to compare the section identified as {Success}[0.315, 0.257, 0.370, 0.303]. The green bar segment depicting success for {Self-driving Vehicles}[0.164, 0.668, 0.711, 0.788] extends to approximately 0.7 on the x-axis. Meanwhile, the green bar segment denoting success for {Drone Delivery}[0.164, 0.430, 0.555, 0.549] extends to nearly 0.5 on the x-axis. Comparing the two, it's clear that the Self-driving Vehicles technology has a higher success rate.\nBased on the above thought, the answer is:\nSelf-driving Vehicles" } ], "id_raw": 730 }, { "id": "2lzy0GSf5HOfzOx9", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data corresponding to the bar chart into a CSV table for both policies - Tax Reform and Drugs Control?" }, { "from": "gpt", "value": "The chart includes two horizontal bars, each representing one of the two policies: {Tax Reform}[0.121, 0.668, 0.920, 0.788] and {Drugs Control}[0.121, 0.430, 0.944, 0.549]. Each bar is divided into six differently colored segments representing six different results categories according to {the legend}[0.235, 0.238, 0.871, 0.392]. For {Tax Reform}, I see that the segment for {Success}[0.121, 0.668, 0.600, 0.788] extends up to a value of approximately 0.6 on the x-axis, as denoted by the tick mark (\u201c0.6\u201d)[0.589, 0.838, 0.611, 0.885]. Moving rightward, each segment represents a different result category, with the {Policy Ineffectiveness}[0.600, 0.668, 0.760, 0.788] extending up to 0.8, and thus indicating a value of 0.2. By following the same reasoning for the entire length, I can construct each category's percentage for both policies.\nBased on the above thought, the answer is:\nResults Categories,Tax Reform,Drugs Control\nSuccess,0.6,0.65\nPolicy Ineffectiveness,0.2,0.15\nUnderfunding,0.1,0.05\nLegal Challenges,0.05,0.05\nImplementation Errors,0.03,0.08\nUnforeseen Consequences,0.02,0.05" } ], "id_raw": 731 }, { "id": "hmIOxZwWBRTNbsYt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, can you infer which policy is considered more effective based on reviews?" }, { "from": "gpt", "value": "To answer this question, I will primarily look at the length of the {Success}[0.280, 0.257, 0.335, 0.303] segment under each policy as it indicates positive reviews. For {Tax Reform}[0.121, 0.668, 0.920, 0.788] this segment is located between 0.00 and 0.60 according to the {x axis}[0.121, 0.806, 1.080, 0.806]. For {Drugs Control}[0.121, 0.430, 0.944, 0.549], this segment extends between 0.00 and 0.65. Therefore Drugs Control has a slightly higher success rate. However, a thorough evaluation would also consider the length of the negative categories such as implementation errors, legal challenges etc. From a cursory glance, it appears that the Drugs Control policy has lesser negative impacts than Tax Reform except for Unforeseen Consequences. Thus, Drugs Control seems more effective overall.\nBased on the above thought, the answer is:\nDrugs Control, as per the reviews, it has a larger success segment value and lesser negative impacts, making it seem more effective overall." } ], "id_raw": 731 }, { "id": "hFwGziAHpmlXLRK4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which policy has a higher rate of unforeseen consequences?" }, { "from": "gpt", "value": "I will need to compare the segment of {Unforeseen Consequences}[0.642, 0.331, 0.670, 0.363] for both the policies. For {Tax Reform}[0.121, 0.668, 0.920, 0.788] the segment is located at [0.904, 0.668, 0.920, 0.788], which corresponds to 0.02, as read from the {x axis}[0.121, 0.806, 1.080, 0.806]. For {Drugs Control}[0.121, 0.430, 0.944, 0.549] it is located at [0.904, 0.430, 0.944, 0.549], which corresponds to a value of 0.05. Thus, by comparing both values, it's clear that the policy with the higher rate of unforeseen consequences is Drugs Control.\nBased on the above thought, the answer is:\nDrugs Control." } ], "id_raw": 731 }, { "id": "85lgXVBolrOfrjzV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the data from the stacked bar chart into a CSV format table?" }, { "from": "gpt", "value": "On the chart, I notice two horizontal bars at {Clean Energy}[0.117, 0.668, 0.935, 0.788] and {Fossil Fuels}[0.117, 0.430, 0.944, 0.549] labeled, which seem to represent two types of energy use. Each of these bars is divided into segments with different colors and lengths. The colors correspond to different impacts on the environment, according to {the legend}[0.283, 0.238, 0.819, 0.392]. The lengths of the sections correspond to the percentage values given on the x-axis as {the x-axis labels}[0.106, 0.838, 0.946, 0.885] dictate. For example, {the green part of the \"Clean Energy\" bar}[0.117, 0.668, 0.608, 0.788] indicates a success rate of about 60%; the same success rate for {Fossil Fuels}[0.117, 0.430, 0.445, 0.549] are 40%. Assessing the rest of the values in such a way will allow me to convert the data into a CSV format.\nBased on the above thought, the answer is:\nCategory, Clean Energy, Fossil Fuel\nSuccess,0.6,0.4\nCarbon footprint,0.1,0.4\nWater Usage,0.1,0.05\nLand Degradation,0.05,0.05\nBiodiversity loss,0.1,0.04\nAir pollution,0.05,0.07" } ], "id_raw": 734 }, { "id": "IZsYrOHjxNtHbk8O", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nAs an AI analyzing this horizontal stacked bar chart, which energy source do you think has a more positive impact on the environment?" }, { "from": "gpt", "value": "For judging which energy source has a more positive impact on the environment, consider each {coloured segment}[0.283, 0.238, 0.819, 0.392] in the bars for Clean Energy and Fossil Fuels. It can be seen that Clean Energy has a higher success rate and leaves a smaller amount of carbon footprint, results in less water usage and land degradation, slightly more biodiversity loss, but less air pollution. All these can be inferred by comparing the lengths of each segment of the two bars which are better in {Clean Energy Bar}[0.117, 0.668, 0.935, 0.788] than {Fossil Fuels Bar}[0.117, 0.430, 0.944, 0.549].\nBased on the above thought, the answer is:\nClean Energy. This is due to its higher success rate and lesser environmental damages such as carbon footprint, water usage, land degradation, and air pollution than those of Fossil Fuels." } ], "id_raw": 734 }, { "id": "ShMa7Fja9cFrwgvq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_49_14.png", "conversations": [ { "from": "human", "value": "\nHow much more successful is Clean Energy as compared to Fossil Fuel?" }, { "from": "gpt", "value": "As per the chart, the success rate of Clean Energy and Fossil Fuels can be seen in the green part of the bars at {Clean Energy success segment}[0.117, 0.668, 0.608, 0.788] and {Fossil Fuels success segment}[0.117, 0.430, 0.445, 0.549]. The former seems to extend up to 0.6 on the x-axis, and the latter up to 0.4. By calculating the difference between these two, I can find the answer.\nBased on the above thought, the answer is:\n0.2" } ], "id_raw": 734 }, { "id": "rmxhwJtwqmpVEuUe", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you evaluated, can you generate a CSV table containing the data for each social media platform and their corresponding average daily user count?" }, { "from": "gpt", "value": "Certainly, I can derive this information. From the chart's title {\"Social Media Data Analysis: Average Daily User Count\"}[0.265, 0.025, 0.824, 0.055], I understand that it is a comparison of the average daily user count of various social media platforms. There are 7 bars in total on the chart, denoting 7 different social media platforms. Using the {the y-axis tick labels}[{\"Twitter\"}[0.049, 0.818, 0.107, 0.841]; {\"Facebook\"}[0.025, 0.704, 0.107, 0.728]; {\"Instagram\"}[0.019, 0.591, 0.107, 0.614]; {\"LinkedIn\"}[0.035, 0.477, 0.107, 0.501]; {\"Pinterest\"}[0.030, 0.364, 0.107, 0.387]; {\"Snapchat\"}[0.025, 0.250, 0.107, 0.274]; {\"Reddit\"}[0.051, 0.137, 0.107, 0.160]] and bar lengths along with data annotations, I can obtain the user count for each platform.\nBased on the above thought, the answer is:\nPlatform,AvgDailyUserCount\nTwitter,5.0\nFacebook,4.8\nInstagram,4.63\nLinkedIn,4.1\nPinterest,3.8\nSnapchat,3.52\nReddit,2.38" } ], "id_raw": 735 }, { "id": "Q7481ys359p4h7M4", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which social media platform would likely have the most reach for a worldwide ad campaign?" }, { "from": "gpt", "value": "This requires identifying the social media platform with the highest daily user count. Looking at the chart, we can see that the longest bar corresponds to Twitter {[0.113, 0.782, 0.832, 0.872]}, with an average daily user count of 5.0 billion. This number is higher than the user counts of all other social media platforms identified in the chart. Therefore, Twitter would likely have the most reach for a worldwide ad campaign as it has the highest global daily engagement.\nBased on the above thought, the answer is:\nTwitter" } ], "id_raw": 735 }, { "id": "94CsFTlXbZ4Gg8S8", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in average daily user count between Twitter and Reddit according to the chart?" }, { "from": "gpt", "value": "To find the difference, I need to note the user counts of Twitter and Reddit. From the bar chart, we can see that the bar representing Twitter {[0.113, 0.782, 0.832, 0.872], 'Twitter'} has a length signifying an average daily user count of 5.0 billion. Similarly, Reddit's bar {[0.113, 0.101, 0.455, 0.192], 'Reddit'} shows an average user count of 2.38 billion. Subtracting Reddit's number from Twitter's gives us the difference in their respective average daily user counts.\nBased on the above thought, the answer is:\n2.62 billion" } ], "id_raw": 735 }, { "id": "gn0O9fRzCc0bETbi", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis, could you please convert the bar chart data into a CSV format?" }, { "from": "gpt", "value": "Certainly. It can be observed from the chart that we have several economic indicators as categories {economic categories}[0.059, 0.759, 0.198, 0.794]; [0.084, 0.613, 0.198, 0.648]; [0.022, 0.466, 0.198, 0.501]; [0.081, 0.320, 0.198, 0.355]; [0.019, 0.173, 0.198, 0.208], represented along the y-axis and their respective values on the x-axis ranging from {0.1}[0.268, 0.879, 0.295, 0.914] to {0.9}[0.890, 0.879, 0.917, 0.914]. The value of each economic indicator is represented as the length of the bar. For instance, the bar for 'New Businesses'[0.204, 0.714, 0.833, 0.831] ends at around 0.81, which is also confirmed by the text next to the bar[0.833, 0.755, 0.878, 0.790]. Similarly, the values for each economic indicator can be inferred.\nBased on the above thought, the answer is:\nEconomic Indicator,Value\nNew Businesses,0.81\nStock Market,0.89\nConsumer Spending,0.68\nInflation Rate,0.55\nUnemployment Rate,0.25" } ], "id_raw": 736 }, { "id": "pGI0Mafjmuvd0dpj", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nIf an investor wishes to invest in a sector where the economic indicator is performing the best, which sector should they consider?" }, { "from": "gpt", "value": "To identify the sector where the economic indicator is performing the best, I need to focus on the bar with the highest value. By analyzing the bars of `New Businesses`[0.204, 0.714, 0.833, 0.831], `Stock Market`[0.204, 0.568, 0.896, 0.685], `Consumer Spending`[0.204, 0.421, 0.732, 0.538], `Inflation Rate`[0.204, 0.275, 0.631, 0.392], and `Unemployment Rate`[0.204, 0.128, 0.398, 0.246], it can be observed that the `Stock Market` bar extends the furthest along the x-axis, indicating that it has the highest value. Therefore, `Stock Market` would be the most rewarding investment sector based on the provided graph.\nBased on the above thought, the answer is:\nThe investor should consider investing in the Stock Market sector as it currently has the highest economic indicator value." } ], "id_raw": 736 }, { "id": "r0kpkjYWbU2nRt0p", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_1.png", "conversations": [ { "from": "human", "value": "\nWhich economic indicator has the highest value in the bar chart?" }, { "from": "gpt", "value": "To answer this question, the lengths of all the bars in the chart must be compared. By examining the bars of 'New Businesses'[0.204, 0.714, 0.833, 0.831], 'Stock Market'[0.204, 0.568, 0.896, 0.685], 'Consumer Spending'[0.204, 0.421, 0.732, 0.538], 'Inflation Rate'[0.204, 0.275, 0.631, 0.392], and 'Unemployment Rate'[0.204, 0.128, 0.398, 0.246], we find that the bar for 'Stock Market' has the longest length.\nBased on the above thought, the answer is:\nStock Market." } ], "id_raw": 736 }, { "id": "5DuIOw0d5TYF0IFb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you see, convert the data into a structured table in CSV format." }, { "from": "gpt", "value": "The chart's {subtitle}[0.311, 0.075, 0.907, 0.165] indicates that it represents the Environmental and Climate Change Impact in various categories. The categories, read from top to bottom, are {Deforestation Rate}[0.035, 0.639, 0.253, 0.709], {Global Temperature}[0.025, 0.536, 0.253, 0.606], {Air Pollution}[0.115, 0.432, 0.253, 0.502], {Water Availability}[0.050, 0.329, 0.253, 0.399] and {Biodiversity Loss}[0.059, 0.225, 0.253, 0.295]. Impact values for each category are obtained from the text annotations at the end of the bars. For instance, {the Deforestation Rate bar's text annotation}[0.692, 0.632, 0.751, 0.702] shows a value of 0.62.\nBased on the above thought, the answer is:\nCategory,Impact\nDeforestation Rate,0.62\nGlobal Temperature,0.91\nAir Pollution,0.82\nWater Availability,0.73\nBiodiversity Loss,0.85" } ], "id_raw": 737 }, { "id": "IiCSNzuMonvlUxXZ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nBased on the environmental and climate change impact, which category is the most pressing to address and why?" }, { "from": "gpt", "value": "To determine the most pressing category, I need to look at the one with the highest impact value. Reviewing the bar lengths and the text annotations at the end of each bar, it's clear that the {Global Temperature bar}[0.261, 0.522, 0.894, 0.605] has the largest length with an {impact annotation}[0.894, 0.528, 0.953, 0.598] of 0.91, making it the category with the highest environmental and climate change impact.\nBased on the above thought, the answer is:\nGlobal Temperature. This is due to its highest environmental and climate change impact value of 0.91 compared to the other categories." } ], "id_raw": 737 }, { "id": "EFDRB6AEFtCwuLIB", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the average impact across all categories?" }, { "from": "gpt", "value": "To find the average impact, I need to determine the values of each category: {Deforestation Rate}[0.692, 0.632, 0.751, 0.702], {Global Temperature}[0.894, 0.528, 0.953, 0.598], {Air Pollution}[0.832, 0.425, 0.890, 0.495], {Water Availability}[0.769, 0.321, 0.827, 0.391], and {Biodiversity Loss}[0.852, 0.218, 0.911, 0.288]. Sum up these values and divide by the total number of categories, which is 5.\nBased on the above thought, the answer is:\n0.786" } ], "id_raw": 737 }, { "id": "tPGjZcGrQilBOxyP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the bar chart data into a CSV format for me?" }, { "from": "gpt", "value": "From the chart title {\"Happiness Index in Different Areas\"}[0.386, 0.037, 0.675, 0.083], I find this chart is about the happiness index in different areas. The y-axis represents different areas including {Suburban}[0.015, 0.717, 0.082, 0.752], {Urban}[0.040, 0.466, 0.082, 0.501], and {Rural}[0.046, 0.215, 0.082, 0.250]. The x-axis ticks with labels ranging from {0.0}[0.076, 0.879, 0.098, 0.914] to {1.0}[0.963, 0.879, 0.985, 0.914] indicate the Happiness Index. The bars' lengths correspond to the index values. For example, for {Suburban}[0.087, 0.631, 0.814, 0.831], I see a value {\"0.82\"}[0.814, 0.713, 0.849, 0.748] at the end of the bar. By following the same approach, I can convert the chart into a CSV format.\nBased on the above thought, the answer is:\nArea,Happiness Index\nSuburban,0.82\nUrban,0.76\nRural,0.64" } ], "id_raw": 738 }, { "id": "7G2mP5kovTik45dl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which area needs the most improvement in terms of increasing the happiness index, and why?" }, { "from": "gpt", "value": "By looking at the lengths of the bars, I can see that the shortest is for the {Rural area}[0.087, 0.128, 0.655, 0.329] with a happiness index of {\"0.64\"}[0.655, 0.211, 0.690, 0.246]. Compared to {Suburban}[0.087, 0.631, 0.814, 0.831] and {Urban}[0.087, 0.379, 0.761, 0.580], Rural has the lowest index value. Therefore, to increase the overall happiness index, improvements should mainly focus on Rural areas.\nBased on the above thought, the answer is:\nThe Rural area. Because its happiness index is the lowest among Suburban, Urban, and Rural areas, according to the chart." } ], "id_raw": 738 }, { "id": "8ztmaBVOGuwJ86P8", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in happiness index between Suburban and Rural areas?" }, { "from": "gpt", "value": "The endpoint of the bar for {Suburban}[0.087, 0.631, 0.814, 0.831] represents a happiness index of {\"0.82\"}[0.814, 0.713, 0.849, 0.748], and for {Rural}[0.087, 0.128, 0.655, 0.329] it is {\"0.64\"}[0.655, 0.211, 0.690, 0.246]. I can calculate the difference by subtracting the index of Rural from Suburban.\nBased on the above thought, the answer is:\n0.18" } ], "id_raw": 738 }, { "id": "q3BgLfsBzZXC0w3Z", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the chart data into a CSV format table?" }, { "from": "gpt", "value": "To translate the bar chart into a CSV table, I'll go through each bar, correlate it with its tick label in the y-axis and measure its length up to the value annotated in the x-axis. The chart's title {\"Probability of Market Increase for Various Financial Instruments\"}[0.306, 0.030, 0.831, 0.066] indicates a comparative analysis of the increase probability for different financial instruments. The x-axis {\"Probability of Increase\"}[0.492, 0.942, 0.646, 0.970] represents the rate of increase in percentage, ranging from 0.6 to 0.9, while the y-axis lists the various financial instruments. Starting with the bar at the top, representing {\"National Bonds\"}[0.054, 0.783, 0.159, 0.811], I can see that its end corresponds to an annotated value of {\"0.65\"}[0.749, 0.780, 0.784, 0.808] in the x-axis. I can carry on this process for all bars on the chart, noting the corresponding instrument and its increase probability.\nBased on the above thought, the answer is:\nFinancial_Instrument, Increase_Probability\nNational_Bonds, 0.65\nInternational_Bonds, 0.72\nStock_Market_Indices, 0.70\nMutual_Funds, 0.67\nCryptocurrencies, 0.78" } ], "id_raw": 739 }, { "id": "vPVAEiFZenP4FFTf", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhat insight can you draw about the market for the next quarter, considering this chart?" }, { "from": "gpt", "value": "The bars in the chart are representations of the probability of market increase for various financial instruments. From {\"National Bonds\"}[0.054, 0.783, 0.159, 0.811] to {\"Cryptocurrencies\"}[0.041, 0.163, 0.159, 0.191], the length of the bars generally increase. That indicate that the predicted probability of market increase is higher for more volatile assets like {\"Cryptocurrencies\"}[0.041, 0.163, 0.159, 0.191] and lower for more stable assets like {\"National Bonds\"}[0.054, 0.783, 0.159, 0.811]. So, the market, in general, appears to be bullish (increasing) for the coming quarter but the degree of increase varies across different types of financial instruments.\nBased on the above thought, the answer is:\nThe market seems to be in a bullish state for the next quarter. However, the degree of increase varies across different financial instruments. High-risk instruments like Cryptocurrencies are expected to have a higher increase than more stable ones like National Bonds." } ], "id_raw": 739 }, { "id": "Mq3PEFpIqiNQncWv", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_4.png", "conversations": [ { "from": "human", "value": "\nWhich financial instrument has the highest probability of market increase?" }, { "from": "gpt", "value": "To find out which financial instrument has the highest probability of market increase, I need to compare the length of each bar on the chart. The longer the bar, the higher the probability. I can see that the bar for {\"Cryptocurrencies\"}[0.041, 0.163, 0.159, 0.191] stretches out the furthest on the x-axis with an annotated value of {\"0.78\"}[0.866, 0.160, 0.901, 0.188], which is the highest among all bars, hence Cryptocurrencies have the highest probability.\nBased on the above thought, the answer is:\nCryptocurrencies." } ], "id_raw": 739 }, { "id": "hN8snsyy0xcFfe1T", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from this chart and present it in a CSV format table, stating each 'Technology' and its corresponding 'Trend value'?" }, { "from": "gpt", "value": "To answer this question, I'm observing the y-axis labels providing the 'Technologies' and the data labels at the end of each bar specifying the 'Trend values'. For instance, the data label '0.88' corresponds to the technology labelled 'AI-driven Development' as seen at [0.180, 0.809, 0.861, 0.872], indicating a trend value of 0.88 for 'AI-driven Development'. By following a similar process for each bar on the chart, I can extract the relevant data and present it in a tabulated CSV format.\nBased on the above thought, the answer is:\nTechnologies,Trend Value\nAI-driven Development,0.88\nBlockchain,0.79\nEdge Computing,0.91\nQuantum Computing,0.74\nHuman Augmentation,0.96\nBiotechnology,0.84\n5G Networks,0.99\nAugmented Reality,0.83\nVirtual Reality,0.79\nDistributed Cloud,0.92" } ], "id_raw": 740 }, { "id": "bFvE7p6Etj0daC6t", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, What can you infer about the trend values of the two technologies 'Edge Computing' and 'Augmented Reality'?" }, { "from": "gpt", "value": "To infer the trend values of 'Edge Computing' and 'Augmented Reality', I'm locating their respective bars and data labels. 'Edge Computing' has a bar found at [0.180, 0.652, 0.885, 0.715] with a data label showing '0.91', suggesting a predicted strong upward trend value. On the other hand, 'Augmented Reality' at [0.180, 0.258, 0.823, 0.321] shows a trend value of '0.83', indicating a noticeably positive trend but not as strong as 'Edge Computing'. Therefore, both technologies are trending positively, though 'Edge Computing' is expected to trend upwards more rapidly than 'Augmented Reality'.\nBased on the above thought, the answer is:\nBoth 'Edge Computing' and 'Augmented Reality' are expected to trend positively. However, 'Edge Computing' with a trend value of 0.91 has a stronger positive trend compared to 'Augmented Reality' with a trend value of 0.83." } ], "id_raw": 740 }, { "id": "65lRgdaBBSkZEMms", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_5.png", "conversations": [ { "from": "human", "value": "\nWhat technology according to the chart is anticipating the most pronounced increase in trend value?" }, { "from": "gpt", "value": "To answer this question, I need to locate the bar with the longest length, implying the highest trend value. Observing each bar, I notice '5G Networks' situated at [0.180, 0.337, 0.947, 0.400] has its bar extending to nearly 1 on the x-axis, indicating the highest trend value among other technologies. This implies '5G Networks' is anticipated to experience the most pronounced increase.\nBased on the above thought, the answer is:\n5G Networks." } ], "id_raw": 740 }, { "id": "lepXOXBGM3SDq1D3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into a CSV table?" }, { "from": "gpt", "value": "The chart is a horizontal bar chart with categories shown on the Y-axis and percentage increase over a decade on the X-axis. The title { \"Financial Market Fluctuations and Trends\" }[0.370, 0.037, 0.791, 0.083] suggests that the chart represents the financial market trends. The y-axis tick labels include: {\"Stock Market\"}[0.074, 0.744, 0.187, 0.779], {\"Gold Price\"}[0.100, 0.559, 0.187, 0.594] {\"Real Estate\"}[0.089, 0.374, 0.187, 0.409] and {\"Currency Exchange\"}[0.019, 0.189, 0.187, 0.224]. These labels correspond to different categories. The X-axis shows the percentage increase over a decade, which can be obtained from the length of each bar for each category. For example, the length of {\"the bar corresponding to the Stock Market\"}[0.193, 0.683, 0.774, 0.831] extends approximately to the 4.5% point on the x-axis, suggesting that the value associated with the \"Stock Market\" category is 4.5%.\nBased on the above thought, the answer is:\nCategories,Values\nStock Market,4.5\nGold Price,4.7\nReal Estate,5.2\nCurrency Exchange,4.8" } ], "id_raw": 742 }, { "id": "pac1ItjjH1v6FuCF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nBased on the data in the chart, can you identify which market trend is considered an outlier and why?" }, { "from": "gpt", "value": "An outlier is a data point that is significantly different from other similar data points. In the context of this chart, a market trend that shows significantly more or less growth compared to other similar market trends might be considered an outlie. I find that {\"Real Estate\"}[0.089, 0.374, 0.187, 0.409] stands distinctly separate from the rest of the bars since its bar extends longest on x-axis reaching the 5.2% point. It's visually clear that {\"the bar of Real Estate\"}[0.193, 0.313, 0.864, 0.461] is longer than other bars, and the percentage increase for real estate is higher than any other sectors' increase by quite a margin. Therefore, \"Real Estate\" can be considered an outlier.\nBased on the above thought, the answer is:\nYes, the Real Estate market is considered an outlier as it has shown the highest growth rate of 5.2%, which is significantly higher than the other categories." } ], "id_raw": 742 }, { "id": "DGXjfs2POtJAcxnG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_7.png", "conversations": [ { "from": "human", "value": "\nWhich category had the highest percentage increase over a decade?" }, { "from": "gpt", "value": "To find out which category had the highest percentage increase, I need to look at which bar extends the furthest on the x-axis. Looking at all the bars, it's clear that {\"the bar of Real Estate\"}[0.193, 0.313, 0.864, 0.461] extends the furthest, all the way to 5.2% point. The corresponding label on the x-axis confirms this observation.\nBased on the above thought, the answer is:\nReal Estate" } ], "id_raw": 742 }, { "id": "G6oB1Lub96ax8KTr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nCan you arrange the details of the bar chart into a CSV format?" }, { "from": "gpt", "value": "From the title at the top with coordinates span {\"Average Return Rates of Different Investment Strategies\"}[0.222, 0.05, 1.006, 0.11] I understand that this is a horizontal bar chart showing the average return rates of different investment strategies. The x-axis represents the average annual return rate and has the coordinates span {\"Average Annual Return Rate\"}[0.45, 0.903, 0.778, 0.95]. The y-axis displays different investment strategies, the bars of each strategy with coordinates [0.286, 0.685, 0.844, 0.790] and so on are tagged with their corresponding percentage performance along the x-axis. Using this information, I can generate the CSV table by mapping each category (Investment Strategies) to their corresponding values (Return Rate).\nBased on the above thought, the answer is:\nInvestment Strategies,Return Rate\nHigh Risk High Return,85%\nStable Low Yield,10%\nBalanced Portfolio,50%\nAggressive Growth,70%\nConservative Mix,30%" } ], "id_raw": 743 }, { "id": "60bcxsnVNSfyZYd5", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nWhich investment strategy would you recommend to the investors based on the chart?" }, { "from": "gpt", "value": "To answer this question, I need to carefully look at each bar on the chart and the corresponding investment strategy it represents. The bar for {\"High Risk High Return\"}[0.286, 0.685, 0.844, 0.790] is the longest, indicating the highest return rate. However, suggeting this strategy will depend on the risk appetite of the investors. For conservative or risk averse investors, I would recommend {\"Conservative Mix\"}[0.286, 0.156, 0.483, 0.262] which has a smaller but relatively safer return. For those seeking a balance of risk and return, the answer would be {\"Balanced Portfolio\"}[0.286, 0.420, 0.614, 0.526]. For aggressive investors who are willing to take on more risk for potential higher returns, I would suggest {\"High Risk High Return\"}[0.286, 0.685, 0.844, 0.790].\nBased on the above thought, the answer is:\nThe recommendation would depend on the risk appetite of the individual investor - conservative investors may prefer the Conservative Mix, balanced investors the Balanced Portfolio, and aggressive investors the High Risk High Return strategy." } ], "id_raw": 743 }, { "id": "ckHIDNm49zWUyhcC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_8.png", "conversations": [ { "from": "human", "value": "\nCan you name the investment strategy that offers the highest return rate?" }, { "from": "gpt", "value": "I'll need to figure out which bar is longest, using their coordinates. Going through all the bars in turn, it turns out that the bar at {\"High Risk High Return\"}[0.286, 0.685, 0.844, 0.790] is the longest. So the investment strategy that offers the highest return rate is High Risk High Return.\nBased on the above thought, the answer is:\nHigh Risk High Return." } ], "id_raw": 743 }, { "id": "oKQl8EaNwYPZdp8u", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the bar chart data into a structured table using CSV format?" }, { "from": "gpt", "value": "Observing the {chart title}[0.225, 0.037, 0.877, 0.083] and the labels on the axes, {x-axis label}[0.484, 0.927, 0.618, 0.963] and {y-axis labels}[0.053, 0.759, 0.128, 0.794; 0.020, 0.613, 0.128, 0.648; 0.019, 0.466, 0.128, 0.501; 0.027, 0.320, 0.128, 0.355; 0.030, 0.173, 0.128, 0.208], we can identify that the two columns in the table should be 'Media Channels' and 'Influence Score'. The media channels are represented by individual bars, and the corresponding influence scores are represented by the length of those bars. Checking the length of the bars and the annotations attached to them, we can gather the precise scores for each media channel. For example, the {TV News bar}[0.134, 0.714, 0.784, 0.831] extends up to 0.78 on the x-axis, as confirmed by the {annotation}[0.784, 0.755, 0.828, 0.790].\nBased on the above thought, the answer is:\nMedia Channels,Influence Score\nTV News,0.78\nOnline News,0.85\nSocial Media,0.92\nRadio News,0.65\nPrint Media,0.7" } ], "id_raw": 744 }, { "id": "OKq8Pmnip3GqCloa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, how does the influence of traditional media compare to that of contemporary media in guiding public opinion?" }, { "from": "gpt", "value": "To answer this question, I need to compare {the influence scores of traditional media channels}[{TV News bar}[0.134, 0.714, 0.784, 0.831]; {Radio News bar}[0.134, 0.275, 0.676, 0.392]; {Print Media bar}[0.134, 0.128, 0.718, 0.246]] with {the influence scores of contemporary media channels}[{Online News bar}[0.134, 0.568, 0.843, 0.685]; {Social Media bar}[0.134, 0.421, 0.901, 0.538]]. Based on the length of the bars, TV News and Print Media have moderate influence scores, while Radio News has the lowest. Conversely, Online News and Social Media, have the highest influence scores, with Social Media having the highest of all. Therefore, traditional media still holds a reasonable influence, but contemporary media channels appear to be significantly more influential in guiding public opinion.\nBased on the above thought, the answer is:\nContemporary media, specifically Online News and Social Media, are significantly more influential in guiding public opinion than traditional media like TV News, Radio News, and Print Media. While traditional media still holds influence, the rise of online platforms has resulted in their dominant role in shaping public opinion." } ], "id_raw": 744 }, { "id": "eWwFKbSuMCSNihaP", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_9.png", "conversations": [ { "from": "human", "value": "\nWhich media channel has the lowest influence score?" }, { "from": "gpt", "value": "To find the media channel with the lowest influence score, I need to examine the length of each bar on the chart. The bars are located at these coordinates {TV News bar}[0.134, 0.714, 0.784, 0.831], {Online News bar}[0.134, 0.568, 0.843, 0.685], {Social Media bar}[0.134, 0.421, 0.901, 0.538], {Radio News bar}[0.134, 0.275, 0.676, 0.392], and {Print Media bar}[0.134, 0.128, 0.718, 0.246]. By comparing their lengths, I can see that {the Radio News bar}[0.134, 0.275, 0.676, 0.392] is the shortest, indicating that Radio News has the lowest influence score.\nBased on the above thought, the answer is:\nRadio News" } ], "id_raw": 744 }, { "id": "zSunc8JaB2DckorS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart for 'Efficiency Improvement Potential Assessment', please construct a table containing different tasks and their corresponding efficiency improvement potential." }, { "from": "gpt", "value": "Looking at the bar graph with title {Efficiency Improvement Potential Assessment}[0.309, 0.037, 0.943, 0.083], there are several bars with labels on the y-axis which suggests different types of tasks. When we move from the top to bottom, we have {Automation}[0.296, 0.734, 0.904, 0.831], {Delegation}[0.296, 0.613, 0.858, 0.710], {Process Improvement}[0.296, 0.492, 0.811, 0.589], {Technological Upgrade}[0.296, 0.371, 0.752, 0.468], {Remote Working}[0.296, 0.250, 0.725, 0.347] and finally {Outsourcing}[0.296, 0.128, 0.679, 0.225]. The length of each bar on the x-axis represents the efficiency improvement potential from 0 to 1, as indicated by {the x-axis labels}[0.434, 0.927, 0.818, 0.963]. The efficiency improvement potentials for each task can be obtained by looking at the values next to the bars on the right, for example, for Automation, {the improvement potential is 0.92}[0.904, 0.765, 0.962, 0.800].\nBased on the above thought, the answer is:\nTasks,Efficiency Improvement Potential\nAutomation,0.92\nDelegation,0.85\nProcess Improvement,0.78\nTechnological Upgrade,0.69\nRemote Working,0.65\nOutsourcing,0.58" } ], "id_raw": 745 }, { "id": "xXSZNFCxB01NNwDz", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nWhich task appears to present an outlier in the decreasing trend of efficiency improvement potential?" }, { "from": "gpt", "value": "An outlier in this context would be a value that deviates significantly from the overall downward trend. Looking at the lengths of the bars from Automation to Outsourcing, the trend generally decreases. However, there is a noticeable decrease in the length of the bar from {Delegation}[0.296, 0.613, 0.858, 0.710] to {Process Improvement}[0.296, 0.492, 0.811, 0.589], which represents a relatively sharp drop from {an improvement potential of 0.85}[0.858, 0.644, 0.916, 0.679] to {0.78}[0.811, 0.523, 0.870, 0.558]. The graph then restores to the decreasing trend from Process Improvement to Technological Upgrade. Hence, Process Improvement appears to be an outlier in this context.\nBased on the above thought, the answer is:\nProcess Improvement. It showed a significant drop in efficiency improvement potential compared to the previous task (Delegation) and is not fully represented of the overall downward trend." } ], "id_raw": 745 }, { "id": "mExDBRrzLzEfPMfL", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which task shows the least potential for efficiency improvement?" }, { "from": "gpt", "value": "To identify the task with the least potential for efficiency improvement, I need to find the shortest bar in the graph. Looking at the chart from the top to the bottom, the lengths of the bars decrease generally, with the shortest bar representing {Outsourcing}[0.296, 0.128, 0.679, 0.225], with {an improvement potential of 0.58}[0.679, 0.159, 0.738, 0.194]. Hence, the task with the least potential for improvement is Outsourcing.\nBased on the above thought, the answer is:\nOutsourcing." } ], "id_raw": 745 }, { "id": "jQcuHdo03BPkMN4c", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nCan you compile the information in the bar chart into a detailed CSV data table?" }, { "from": "gpt", "value": "Initially, the chart's title \"Comparing Media Influence & Public Opinion Guidance\" at coordinates [0.197, 0.037, 0.953, 0.083] reveals the data's theme. The x-axis represents the influence index, with values ranging from {0.0}[0.164, 0.879, 0.200, 0.914] to {1.0}[0.995, 0.879, 1.032, 0.914]. The y-axis displays different media outlets like {BBC}[0.126, 0.786, 0.174, 0.821] and {Fox News}[0.064, 0.626, 0.174, 0.661]. Each bar's length, such as {BBC's bar}[0.182, 0.767, 0.931, 0.831], corresponds to the media outlet's influence index. The influence indices are even labeled at the end of each bar, with BBC's as {0.9}[0.931, 0.782, 0.974, 0.817], for instance. Therefore, the CSV formatted data can be generated by matching each media outlet's name against their influence index.\nBased on the above thought, the answer is:\nMedia Outlet,Influence Index\nBBC,0.9\nCNN,0.88\nFox News,0.61\nAl Jazeera,0.84\nRussia Today,0.67\nSky News,0.8\nABC News,0.75\nNBC News,0.78\nCBC News,0.85" } ], "id_raw": 746 }, { "id": "C6zPazz0V6UX9zPa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, how would you compare the influence of BBC and CNN on public opinion?" }, { "from": "gpt", "value": "From the chart, I can see that the length of {BBC's bar}[0.182, 0.767, 0.931, 0.831] extends further than the length of {CNN's bar}[0.182, 0.688, 0.914, 0.751] on the x-axis. This indicates that the Influence Index of BBC is higher (with a noted Influence Index of {0.9}[0.931, 0.782, 0.974, 0.817]) than CNN's Influence Index of {0.88}[0.914, 0.702, 0.973, 0.737]. As a result, it can be inferred that according to the chart, BBC has a stronger influence on public opinion in comparison to CNN.\nBased on the above thought, the answer is:\nBBC has a stronger influence on public opinion than CNN, given its higher Influence Index." } ], "id_raw": 746 }, { "id": "owyTZen46UAJOCeP", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the media outlet with the highest Influence Index according to the bar chart?" }, { "from": "gpt", "value": "Assuming that the bar chart's length represents the influence index, the media outlet with the highest Influence Index would be represented by the longest bar. Looking at {BBC's bar}[0.182, 0.767, 0.931, 0.831], the length extends to 0.9 on the x-axis, the highest among {all the bars}[0.182, 0.128, 0.931, 0.831]. As such, BBC has the highest Influence Index.\nBased on the above thought, the answer is:\nBBC" } ], "id_raw": 746 }, { "id": "OjQ6S4kaX7DOFo0Y", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the chart into a table format?" }, { "from": "gpt", "value": "By examining the chart's title {\"Trend of Public Interest in Cultural Heritage and Cultural Integration\"}[0.140, 0.075, 1.076, 0.165] I understand this is a trend chart. Our data divided into two categories: {Cultural Heritage}[0.260, 0.487, 0.873, 0.708] and {Cultural Integration}[0.260, 0.211, 0.748, 0.432], which represents the labels on the y-axis. The x-axis {\"Level of Public Interest\"}[0.477, 0.855, 0.740, 0.925] has a label ranging from {0.0}[0.241, 0.757, 0.278, 0.827] to {1.0}[0.938, 0.757, 0.975, 0.827]. In order to transform the data into a table format, I can obtain the relevant values for each category by examining the length of each bar and the corresponding value annotation at the end of each bar e.g. for Cultural Heritage, {the bar's length}[0.260, 0.487, 0.873, 0.708] and its {value annotation \"0.88\"}[0.873, 0.563, 0.932, 0.633] confirm that the value of public interest in Cultural Heritage is 0.88.\nBased on the above thought, the answer is:\nCategory,Level of Public Interest\nCultural Heritage,0.88\nCultural Integration,0.70" } ], "id_raw": 747 }, { "id": "T9lSzDxyg5V7NIbM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nBased on the trends observed in the chart, which area \u2014 Cultural Heritage or Cultural Integration \u2014 do you believe has shown more consistent public interest over the years and why?" }, { "from": "gpt", "value": "To answer this question, I need to compare both the {bar of Cultural Heritage}[0.260, 0.487, 0.873, 0.708] and the {bar of Cultural Integration}[0.260, 0.211, 0.748, 0.432] on the chart. Since the x-axis represents the level of public interest, we can infer from the bar lengths and the {value annotations \"0.88\" and \"0.70\"}[0.873, 0.563, 0.932, 0.633; 0.748, 0.287, 0.791, 0.357] that Cultural Heritage fluctuates quite significantly, with high peaks and sharp falls. In contrast, Cultural Integration shows a gradual and steady growth, and is slower in pace, indicating a more consistent level of public interest over time.\nBased on the above thought, the answer is:\nCultural Integration. Despite its slower pace, it shows a more consistent and steady growth in public interest over time compared to the erratic and sharp fluctuations seen in Cultural Heritage." } ], "id_raw": 747 }, { "id": "ao4eLBJfmxuAZ01Q", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_12.png", "conversations": [ { "from": "human", "value": "\nWhich category has a higher level of public interest according to the chart?" }, { "from": "gpt", "value": "In each category, there is a bar that represents the level of public interest. For {Cultural Heritage}[0.260, 0.487, 0.873, 0.708], the length of the bar and the {value annotation \"0.88\"}[0.873, 0.563, 0.932, 0.633] indicates that the public interest level is 0.88. On the other hand, for {Cultural Integration}[0.260, 0.211, 0.748, 0.432], the corresponding length of the bar and the {value annotation \"0.70\"}[0.748, 0.287, 0.791, 0.357] reveal that the public interest level is 0.70. By comparing these two values, we can conclude which category has a higher level of public interest.\nBased on the above thought, the answer is:\nCultural Heritage." } ], "id_raw": 747 }, { "id": "h7GDKRuGqZN6TlSP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nPlease convert the data illustrated in the horizontal bar chart into a CSV format." }, { "from": "gpt", "value": "To generate a CSV table, I need to extract the values of each category. The y-axis is represented by categories and the x-axis by their concerning values. The y-axis tick labels, yticks, show the categories: {Monetary Policy}[0.061, 0.810, 0.149, 0.830], {Quantitative Easing}[0.039, 0.735, 0.149, 0.755], {Interest Rates}[0.072, 0.660, 0.149, 0.680], {Fiscal Stimulus}[0.067, 0.585, 0.149, 0.605], {Banking Regulations}[0.035, 0.510, 0.149, 0.530], {Trade Policies}[0.074, 0.435, 0.149, 0.455], {Foreign Exchange Rates}[0.015, 0.360, 0.149, 0.380], {Subprime Mortgages}[0.033, 0.285, 0.149, 0.305], {Commodities}[0.076, 0.210, 0.149, 0.230], and {Exchange-traded Fund}[0.023, 0.135, 0.149, 0.155]. The x-values for these categories can be collected from the bar lengths and the corresponding values {0.89}[0.884, 0.804, 0.919, 0.832], {0.67}[0.703, 0.729, 0.739, 0.757], {0.59}[0.638, 0.654, 0.673, 0.682], {0.82}[0.826, 0.579, 0.862, 0.607], {0.70}[0.728, 0.504, 0.763, 0.532], {0.55}[0.605, 0.429, 0.640, 0.457], {0.76}[0.777, 0.354, 0.812, 0.382], {0.52}[0.581, 0.279, 0.616, 0.307], {0.69}[0.720, 0.204, 0.755, 0.232], and {0.62}[0.662, 0.129, 0.698, 0.157].\nBased on the above thought, the answer is:\nCategory,Value\nMonetary Policy,0.89\nQuantitative Easing,0.67\nInterest Rates,0.59\nFiscal Stimulus,0.82\nBanking Regulations,0.70\nTrade Policies,0.55\nForeign Exchange Rates,0.76\nSubprime Mortgages,0.52\nCommodities,0.69\nExchange-traded Fund,0.62" } ], "id_raw": 748 }, { "id": "nLCi7lZRBM7wr34Y", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, what can we interpret about the status of subprime mortgages in relation to other parameters of global financial systems and policies?" }, { "from": "gpt", "value": "From the chart, the length of the {bar representing 'Subprime Mortgages'}[0.154, 0.263, 0.581, 0.323] appears to be shorter than most of the other bars, which represents lower value {0.52}[0.581, 0.279, 0.616, 0.307]. In comparison to most of the other parameters, Subprime Mortgages has a lower associated value, suggesting that the key parameters of Subprime Mortgages are less influential or present in the global financial system and policies compared to other factors.\nBased on the above thought, the answer is:\nSubprime Mortgages seem less influential or less present in the global financial system and policies compared to the other parameters given the lower associated value." } ], "id_raw": 748 }, { "id": "XqkQmUlFyEPnSTSz", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_13.png", "conversations": [ { "from": "human", "value": "\nWhat value indicates the maximum key parameter in the various global financial systems and policies according to the chart?" }, { "from": "gpt", "value": "I need to look for the longest bar on the chart. According to the coordinates, the longest bar is {Monetary Policy}[0.154, 0.788, 0.884, 0.848] with a value of {0.89}[0.884, 0.804, 0.919, 0.832]. Therefore, Monetary Policy has the highest associated value.\nBased on the above thought, the answer is:\n0.89" } ], "id_raw": 748 }, { "id": "I74JOn5IodWpJdgN", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you arrange the social responsibility indicators in order of increasing adoption rate?" }, { "from": "gpt", "value": "To answer this, I will examine the position of the x-coordinates of {the right-hand edge of each bar}[0.400, 0.715, 0.400, 0.831];[0.661, 0.569, 0.661, 0.685];[0.841, 0.423, 0.841, 0.540];[0.893, 0.277, 0.893, 0.394];[0.542, 0.131, 0.542, 0.248], which represents the proportion of companies that have adopted each indicator. Each bar's right end corresponds to the value of each social responsibility indicator: Biodiversity Protection, Emission Reduction, Fair Trade, Charitable Giving, and Sustainable Packaging. I will list them in ascending order.\nBased on the above thought, the answer is:\nSocial Responsibility Indicator,Proportion of Sustainable Business Practice\nBiodiversity Protection,0.24\nSustainable Packaging,0.43\nEmission Reduction,0.59\nFair Trade,0.83\nCharitable Giving,0.9" } ], "id_raw": 749 }, { "id": "xOSoFfFadYYdqX5i", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which social responsibility indicator is most popular among companies?" }, { "from": "gpt", "value": "This is determined by the length of {the bars}[0.221, 0.715, 0.400, 0.831];[0.221, 0.569, 0.661, 0.685];[0.221, 0.423, 0.841, 0.540];[0.221, 0.277, 0.893, 0.394];[0.221, 0.131, 0.542, 0.248]. The bar representing Charitable Giving appears to be the longest, reaching furthest across the x-axis, meaning it has the highest proportion of companies that have adopted it.\nBased on the above thought, the answer is:\nCharitable Giving is the most popular social responsibility indicator among companies." } ], "id_raw": 749 }, { "id": "WtDRFFvcpwVrmdP3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_50_14.png", "conversations": [ { "from": "human", "value": "\nWhat proportion of companies have adopted Fair Trade as a social responsibility indicator?" }, { "from": "gpt", "value": "To find this proportion, I need to locate {the bar representing Fair Trade}[0.221, 0.423, 0.841, 0.540]. The right-hand coordinate of this bar suggests the proportion of companies that have adopted Fair Trade. I can read it directly as 0.83.\nBased on the above thought, the answer is:\n0.83" } ], "id_raw": 749 }, { "id": "x9MIpWOT3HSMDe8z", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nCan you translate the data from the bar graphs for chunk sizes 1 to 4 into a CSV format?" }, { "from": "gpt", "value": "Observing the first subplot within the coordinates {subplot}[0.123, 0.021, 0.975, 0.285], we see the bar charts for chunk sizes {1}[0.138, 0.100, 0.161, 0.126] to {4}[0.371, 0.047, 0.394, 0.126] for the 'Ours' technique. Correspondingly, for 'Saliency-based Sampling', the chunks sizes range from {1}[0.161, 0.126, 0.185, 0.206] to {4}[0.394, 0.126, 0.417, 0.232]. Extracting values from the rate of gain change on the y-axis tick labels, we can convert them into a structured table.\nBased on the above thought, the answer is:\nChunk Size,Ours,Saliency-based Sampling\n1,5,-15\n2,3,-10\n3,8,-25\n4,15,-20" } ], "id_raw": 751 }, { "id": "Ae0X2hsP4yU27kTh", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nConsidering the overall trend, which big data integration technique demonstrates a more consistent performance based on the provided chart?" }, { "from": "gpt", "value": "To determine the consistency of performance for each technique, we examine the fluctuation of bars in each subplot. Consistency can be interpreted as fewer deviations in bar height within their respective y-ranges. For 'Ours', we observe fluctuating bar heights within {subplot 0}[0.123, 0.021, 0.975, 0.285], {subplot 1}[0.123, 0.349, 0.975, 0.613], and {subplot 2}[0.123, 0.677, 0.975, 0.942]. There are notable changes such as the dip in chunk size {5}[0.448, 0.433, 0.471, 0.455]. 'Saliency-based Sampling', despite experiencing a general decrease, shows less variation between adjacent chunk sizes in each subplot, suggesting more consistency compared to 'Ours'.\nBased on the above thought, the answer is:\nSaliency-based Sampling. Despite showing a general decrease, it experiences smaller fluctuations between chunk sizes, which could be interpreted as more consistent compared to the variability observed in 'Ours'." } ], "id_raw": 751 }, { "id": "McUELr3Fcv8JvdaI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_51_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest rate of gain change observed for 'Ours' in the dataset provided?" }, { "from": "gpt", "value": "To find the highest rate of gain change for 'Ours', I must examine the height of bars corresponding to 'Ours' across all subplots. The value on the y-axis where the top of the bar resides denotes the rate of gain change for each chunk size. For 'Ours', I am looking at the bar segments represented within {subplot 0}[0.123, 0.021, 0.975, 0.285], {subplot 1}[0.123, 0.349, 0.975, 0.613], and {subplot 2}[0.123, 0.677, 0.975, 0.942]. Comparing the coordinate points of the bars' upper edges, and the corresponding y-value labels such as {20}[0.077, 0.015, 0.106, 0.029] and {18}[0.077, 0.025, 0.106, 0.039] to determine the highest point, we see that the highest bar is for chunk size {10}[0.836, 0.677, 0.859, 0.783] with a y-range of '0.00 -> 20.00'.\nBased on the above thought, the answer is:\n20" } ], "id_raw": 751 }, { "id": "FnKigWkPMmJNrD8t", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nCan you provide a comparison of Series A and Series B across the different metrics in a CSV format table, based on your observation of the bar chart?" }, { "from": "gpt", "value": "Observing the bar chart, there are three subplots with {dark blue bars}[0.147, 0.101, 0.260, 0.115] and {light blue bars}[0.260, 0.115, 0.373, 0.215] representing Series A and Series B respectively. The metric labels are visible along the x-axis with the metrics {Data Cleanliness}[0.163, 0.314, 0.357, 0.328], {Data Accuracy}[0.457, 0.314, 0.626, 0.328], {Data Completeness}[0.709, 0.314, 0.937, 0.328], and so on for the remaining metrics. By looking at the y-axis, I can see numeric labels indicating the value for each Series, with negative values on the upper y-axis and positive on the lower, plotting different ranges such as [-8, 0], [-7, 0], [-6, 0], etc. Utilizing the bar's height, I will match each metric's value for Series A and Series B from the bar chart.\nBased on the above thought, the answer is:\nMetric,Series A, Series B\nData Cleanliness,0.6,-4.2\nData Accuracy,0.9,-7.5\nData Completeness,2.4,-6.3\nData Consistency,3.2,-4.2\nData Timeliness,0.1,1.2\nData Relevance,0.8,-2.3\nData Quantity,3.9,-7.4" } ], "id_raw": 752 }, { "id": "TcnDmeE1mnAaF7Ta", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nBased on the data analysis observed in the bar chart, which metric shows the greatest disparity between Series A and Series B, and could you explain how you determined this?" }, { "from": "gpt", "value": "To assess the greatest disparity between Series A and Series B for a metric, I look at the individual bars representing both series and compare their heights. Inspecting the bar chart, I identify each metric and their corresponding {dark blue bar}[0.147, 0.101, 0.260, 0.115] for Series A, and {light blue bar}[0.260, 0.115, 0.373, 0.215] for Series B. The y-axis helps me estimate the numeric value for each bar. By visually comparing the bar heights such as those of {Data Accuracy}[0.429, 0.094, 0.541, 0.115]; [0.541, 0.115, 0.654, 0.293] and noting the difference between the two series, I can spot the metric with the greatest disparity.\nBased on the above thought, the answer is:\nData Accuracy. By comparing the heights of the bars for each metric, the disparity in Data Accuracy is visibly the most significant, with Series A having a positive value of 0.9, and Series B a negative value of -7.5, which results in a disparity of 8.4." } ], "id_raw": 752 }, { "id": "l0LzGxRZaZz5lPHi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_51_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the sum of the positive values Series A shows across all metrics?" }, { "from": "gpt", "value": "To calculate the sum of the positive values for Series A, I am observing the height of {the dark blue bars}[0.147, 0.101, 0.260, 0.115]; [0.429, 0.094, 0.541, 0.115]; [0.710, 0.058, 0.823, 0.115] depicting Series A across different metrics. The y-axis shows the numeric values, aiding me to interpret the values of each bar. By correctly identifying the ends representing {positives values}[0.058, 0.015, 0.092, 0.029] and adding them up, I can reach the sum for Series A across all metrics.\nBased on the above thought, the answer is:\n9.9" } ], "id_raw": 752 }, { "id": "3KBdVdUd2GKOIeqy", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nWould you please convert the bar charts observed into a table with CSV format for the sales data of Product X and Product Y?" }, { "from": "gpt", "value": "The sales data of Product X and Product Y is presented in two subcharts that handle the sales trend respectively. For Product X, let's start at {\"2020 Q1\"}[0.129, 0.459, 0.227, 0.473]. The sales value can be obtained by looking at the height of {\"the bar\"}[0.157, 0.275, 0.199, 0.449], which corresponds approximately to 102 on the y-axis. Similarly, for {\"2020 Q2\"}[0.234, 0.459, 0.332, 0.473], the bar [0.262, 0.193, 0.304, 0.449] suggests the sales value is 150. The process is repeated for the other quarters of 2020 and 2021. \nAs for Product Y, let's start at {\"2020 Q1\"}[0.129, 0.951, 0.227, 0.965]. The sales value can be obtained by looking at the height of {\"the bar\"}[0.157, 0.702, 0.199, 0.942], which corresponds approximately to 150 on the y-axis. The same process will be repeated for the remaining quarters of 2020 and 2021 in the same manner.\nBased on the above thought, the answer is:\nYear,Quarter,X-Product,Y-Product\n2020,Q1,102,150\n2020,Q2,150,175\n2020,Q3,180,190\n2020,Q4,200,210\n2021,Q1,220,245\n2021,Q2,230,245\n2021,Q3,210,245\n2021,Q4,150,240" } ], "id_raw": 755 }, { "id": "9vstrPWbbc1W6LtW", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nGiven the sales trends, which product would you say performed better over the two-year period?" }, { "from": "gpt", "value": "To answer this question, I need to consider the sales trend of the two products independently. For Product X, as shown in the first sub-chart, the sales figure starts at 102 in {\"2020 Q1\"}[0.129, 0.459, 0.227, 0.473] and gradually increases to its peak in {\"2021 Q2\"}[0.655, 0.459, 0.754, 0.473] with a value of 230. However, sales then drop significantly to 150 by {\"2021 Q4\"}[0.866, 0.459, 0.964, 0.473]. In contrast, for Product Y, according to the second sub-chart, the sales figure starts at 150 in {\"2020 Q1\"}[0.129, 0.951, 0.227, 0.965] and witnesses a steady increase to 245 by {\"2021 Q1\"}[0.550, 0.951, 0.648, 0.965], if further compared with other quarters, the sales of Product Y does not have a drop like Product X.\nBased on the above thought, the answer is:\nProduct Y performed better. Mainly because over the two-year period, it revealed a steady growth trend and there were no notable dips like in the case of Product X." } ], "id_raw": 755 }, { "id": "DBN15EvbSq6Il3F1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_51_5.png", "conversations": [ { "from": "human", "value": "\nDuring which quarter did the sales for product Y reach its maximum?" }, { "from": "gpt", "value": "In the second subchart for product Y, I can see that the sales starts at {\"2020 Q1\"}[0.129, 0.951, 0.227, 0.965] with a value of 150. The sales figures increase gradually with the highest {\"bar\"}[0.578, 0.549, 0.620, 0.942] reached at {\"2021 Q1\"}[0.550, 0.951, 0.648, 0.965], further comparing with other qaurters, it is confirmed that 2021 Q1 has the highest sales for product Y.\nBased on the above thought, the answer is:\n2021 Q1" } ], "id_raw": 755 }, { "id": "4WsWY2Y6E0T4d9sx", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, generate a table summarizing the rate of change in gain for each parameter for both \"Ours\" and \"Risk-based\" methods." }, { "from": "gpt", "value": "From the position of the bar chart labels, the {x-axis}[0.505, 0.940, 0.586, 0.969] indicates the 'Methodology' and the {y-axis}[0.023, 0.303, 0.045, 0.653] represents 'Rate of Gain Change[%]'. There are six groups of bars, and each group corresponds to a parameter listed in {the legend}[0.638, 0.092, 0.966, 0.363], including 'Tools Changed', 'Tools Used', 'Network Security', 'Physical Security', 'Awareness Programme', and 'Policy Review'. For each group, there are two bars representing the 'Ours' and 'Risk-based' methods, as indicated by {the x-axis labels}[{\"Ours\"}[0.307, 0.899, 0.357, 0.928], {\"Risk-based\"}[0.629, 0.899, 0.747, 0.928]]. The height of each bar represents the rate of change. For example, for the 'Tools Changed' parameter, the rate of change for the 'Ours' method is approximately 15% based on {the height of the corresponding bar}[0.154, 0.587, 0.225, 0.697], and for the 'Risk-based' method, the rate is about 20% according to {the height of its bar}[0.510, 0.551, 0.581, 0.697].\nBased on the above thought, the answer is:\nParameter,Ours,Risk-based\nTools Changed,15,20\nTools Used,80,60\nNetwork Security,-10,-20\nPhysical Security,15,35\nAwareness Programme,25,30\nPolicy Review,40,45" } ], "id_raw": 756 }, { "id": "EIF9EUi4Hhn7V3yP", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, could you describe how the \"Risk-based\" method performs in terms of risk management compared to the \"Ours\" method?" }, { "from": "gpt", "value": "Sure, by comparing the bar heights for both methods, we can generally say that the \"Risk-based\" method underperforms in {tool usage}[{\"Ours\"}[0.225, 0.114, 0.296, 0.697], {\"Risk-based\"}[0.581, 0.260, 0.652, 0.697] as it has a lower rate of gain change. However, it outperforms in areas like {network security}[{\"Ours\"}[0.296, 0.697, 0.368, 0.769], {\"Risk-based\"}[0.652, 0.697, 0.724, 0.842], {physical security}[{\"Ours\"}[0.368, 0.587, 0.439, 0.697], {\"Risk-based\"}[0.724, 0.442, 0.795, 0.697], and {policy review}[{\"Ours\"}[0.510, 0.405, 0.581, 0.697], {\"Risk-based\"}[0.866, 0.369, 0.937, 0.697]. There's no significant difference for {awareness programme}[{\"Ours\"}[0.439, 0.515, 0.510, 0.697], {\"Risk-based\"}[0.795, 0.478, 0.866, 0.697], and the \"Risk-based\" method only slightly outperforms the \"Ours\" method in this regard. For the {tools changed}[{\"Ours\"}[0.154, 0.587, 0.225, 0.697],{\"Risk-based\"}[0.510, 0.551, 0.581, 0.697] parameter, the\"Risk-based\" approaches it with a greater rate than the\"Ours\" method.\nBased on the above thought, the answer is:\nThe \"Risk-based\" method performs better in areas of Network Security, Physical Security and Policy Review, but underperforms in Tool Usage compared to the \"Ours\" method. The two methods are quite comparable on the parameter of Awareness Programme. The \"Risk-based\" method changes tools at a higher rate as compared to the \"Ours\" method." } ], "id_raw": 756 }, { "id": "4vuhEPSeQ9b9mnEO", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_51_6.png", "conversations": [ { "from": "human", "value": "\nWhich parameter has experienced the highest rate of gain change under the \"Risk-based\" method?" }, { "from": "gpt", "value": "To find the parameter with the highest change under the \"Risk-based\" method, I need to consider the height of bars corresponding to the 'Risk-based' method for each parameter. By comparing the height of these bars, I can see that the bar corresponding to 'Tools Used' which is located at {this position}[0.581, 0.260, 0.652, 0.697] is the highest, its top reaches approximately the 60% line on the y-axis, which indicates the highest rate of gain change under the \"Risk-based\" method.\nBased on the above thought, the answer is:\nTools Used." } ], "id_raw": 756 }, { "id": "vG1FmLNZHWj3x4ob", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the stacked bar chart into a structured table format?" }, { "from": "gpt", "value": "Observing the chart, I see there are three subplots indicating different scales for CO2 emissions and mean temperatures over the years. To construct a table, I'll consider {subplot_0}[0.103, 0.018, 0.975, 0.287] which represents CO2 emissions in billion tons. The years are indicated along {the x-axis}[0.040, 0.316, 0.564, 0.330], and the corresponding emissions values are indicated by the height of the blue bars present on this subplot. Similarly, {subplot_1}[0.103, 0.345, 0.975, 0.614] contains bars depicting mean temperatures, with the orange bars on this chart. Each pair of {blue}[0.143, 0.063, 0.173, 0.287] and {orange}[0.143, 0.369, 0.173, 0.614] bars represent the values for CO2 emissions and mean temperature for a corresponding year, from \"2001\" [0.144, 0.296, 0.203, 0.310] to \"2011\" [0.906, 0.296, 0.965, 0.310]. By aligning this information with the y-axis data on each subplot, I can extract the associated values and convert them into a CSV format table.\nBased on the above thought, the answer is:\nYear,CO2 Emissions (Billion Tons),Mean Temperature (\u00b0C)\n2001,5.06,10.52\n2002,5.11,9.85\n2003,5.21,9.92\n2004,5.48,10.35\n2005,5.61,10.68\n2006,5.77,11.01\n2007,5.77,10.09\n2008,5.79,9.99\n2009,5.60,10.63\n2010,5.69,10.64\n2011,5.48,10.85" } ], "id_raw": 757 }, { "id": "hPTHa3XDALLPwu5T", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between CO2 emissions and mean global temperatures as depicted in the chart?" }, { "from": "gpt", "value": "To understand the relationship between CO2 emissions and mean global temperatures according to the chart, I will analyze the patterns of {CO2 emissions bars}[0.143, 0.824, 0.172, 0.942] alongside the {mean temperature bars}[0.172, 0.697, 0.202, 0.942] over the years indicated by {x-axis ticks}[0.143, 0.951, 0.201, 0.965]. By closely examining the height of the bars every year, I can correlate rises and falls in CO2 emissions with the respective changes in mean global temperatures. This comparison helps determine if there's a pattern indicating a direct or inverse correlation, or if the relationship is more complex, perhaps marked by lags or other factors.\nBased on the above thought, the answer is:\nThe chart suggests a general correlation where years with higher CO2 emissions also tend to show higher mean global temperatures. Although the relationship is not perfectly linear, as seen by some fluctuations, there is noticeable alignment between increases in CO2 emissions and higher temperature values\u2014indicative of a positive correlation, which is consistent with the understanding that higher CO2 emissions contribute to global warming." } ], "id_raw": 757 }, { "id": "9gsz43V6tCBI58V0", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_51_7.png", "conversations": [ { "from": "human", "value": "\nWhich year saw the highest increase in CO2 emissions compared to the previous year?" }, { "from": "gpt", "value": "To determine the year with the highest increase in CO2 emissions, I have to calculate the year-over-year change. Starting with \"2001\" {bar for 2001 CO2 Emissions}[0.143, 0.063, 0.173, 0.287], I observe the heights of subsequent bars relative to the previous one. For example, comparing the {bar for 2002 CO2 Emissions}[0.219, 0.060, 0.249, 0.287] to {the bar for 2001}[0.143, 0.063, 0.173, 0.287], the difference in height is small. Progressing to {the bar for 2004 CO2 Emissions}[0.371, 0.044, 0.402, 0.287], there's a notable jump from {the bar for 2003}[0.295, 0.056, 0.326, 0.287], suggesting a greater increase. This process is repeated for each year until \"2011\" {bar for 2011 CO2 Emissions}[0.905, 0.044, 0.935, 0.287]. After examining all year-to-year changes, the year with the highest rise can be identified.\nBased on the above thought, the answer is:\n2004" } ], "id_raw": 757 }, { "id": "AGoR4Lle5Jda5BBd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nConvert the data from the bar chart comparing 'Substantial Increase' and 'Erratic Increase' over time into a CSV table." }, { "from": "gpt", "value": "By analyzing the {subplot_2}[0.103, 0.672, 0.975, 0.942] which includes the comparison of 'Substantial Increase' and 'Erratic Increase', I first observe the {xticks}[0.143, 0.951, 0.201, 0.965] and [0.877, 0.951, 0.935, 0.965] which correspond to the '2008' and '2010' labels at the bottom of the chart. Then I identify the corresponding bars for 'Substantial Increase' marked by {red color}[0.124, 0.685, 0.170, 0.695] and 'Erratic Increase' marked by {blue color}[0.124, 0.706, 0.170, 0.716]. Using the height of {bars}[0.143, 0.761, 0.202, 0.942] and [0.877, 0.685, 0.935, 0.942] for 'Substantial Increase' and {bars}[0.143, 0.799, 0.202, 0.942] and [0.877, 0.758, 0.935, 0.942] for 'Erratic Increase', I can extract the rate of growth for the years 2008 and 2010. These include '30.50 -> 50.20' for 'Substantial Increase' and '27.80 -> 35.80' for 'Erratic Increase', allowing me to tabulate the data.\nBased on the above thought, the answer is:\nYear,Substantial Increase,Erratic Increase\n2008,35.20,27.80\n2010,50.20,35.80" } ], "id_raw": 758 }, { "id": "QUXht7dPpSQFOXkF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nWhich growth trend category showed more stability over the period?" }, { "from": "gpt", "value": "To determine stability, I look for smaller changes in growth rates over time for each category. By examining the height differences between the years 2008 and 2010 for each category, I am looking for the ones with less variation. Starting with {Persistent Growth}[0.124, 0.029, 0.170, 0.038], I can see that there is a clear increase from the bar heights {2008}[0.143, 0.197, 0.202, 0.285] to {2010}[0.877, 0.028, 0.935, 0.285]. However, when I analyze {Marginal Improvement}[0.124, 0.378, 0.170, 0.388], the increase in the bar heights from {2008}[0.143, 0.491, 0.202, 0.613] to {2010}[0.877, 0.480, 0.935, 0.613] is much smaller, indicating more stability. Categories like {Abrupt Increase}[0.124, 0.357, 0.170, 0.367] and {Erratic Increase}[0.124, 0.706, 0.170, 0.716] are ruled out due to the visible higher variation in their growth rates.\nBased on the above thought, the answer is:\nMarginal Improvement. The 'Marginal Improvement' category exhibited more stability over the period as it showed a smaller change in growth rate compared to the other categories." } ], "id_raw": 758 }, { "id": "yrn7Ch44ezo1fUJ4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_51_8.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage increase in 'Persistent Growth' from 2008 to 2010?" }, { "from": "gpt", "value": "To calculate the percentage increase in 'Persistent Growth' between the years 2008 and 2010, I will reference the bars for 'Persistent Growth' identified by the {blue color}[0.124, 0.029, 0.170, 0.038]. First, locating the bars for the years {2008}[0.143, 0.197, 0.202, 0.285] and {2010}[0.877, 0.028, 0.935, 0.285], I can see that the {initial value}[0.143, 0.197, 0.202, 0.285] is 10.5% and the {final value}[0.877, 0.028, 0.935, 0.285] is 30.5%. The percentage increase can be calculated by the formula ((final - initial) / initial) * 100.\nBased on the above thought, the answer is:\n190.476%" } ], "id_raw": 758 }, { "id": "nnyXOmgkIamkoKUc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nCan you create a table showing the rates of technological breakthroughs and innovation introduction for each year as presented in the bar charts?" }, { "from": "gpt", "value": "Observing the two subplots within the chart, I see that technological breakthroughs are shown in subplot_0 with {the bars}[0.161, 0.434, 0.214, 0.446] for {the year 2008}[0.093, 0.456, 0.151, 0.470], extending to {the 2020 bar}[0.884, 0.036, 0.936, 0.446]. Each bar represents the growth rate for the respective year. Similarly, innovation introduction rates are shown in subplot_1 with {the bars}[0.161, 0.726, 0.214, 0.780] for {the year 2008}[0.093, 0.951, 0.151, 0.965], continuing to {the 2020 bar}[0.884, 0.694, 0.936, 0.726]. To construct a table, the year labels and bar heights are compared against the {y-axis labels}[0.058, 0.482, 0.106, 0.496] and [0.058, 1.044, 0.106, 1.058] to determine the growth rate percentages for both the technological breakthroughs and innovation introduction.\nBased on the above thought, the answer is:\nYear,Technological Breakthrough,Innovation Introduction\n2009,3,-5\n2010,7,-6\n2011,12,-9\n2012,22,-15\n2013,17,-7\n2014,35,-12\n2015,50,-14\n2016,38,-9\n2017,68,-4\n2018,80,-6\n2019,92,-2\n2020,100,3" } ], "id_raw": 762 }, { "id": "iMMyjRDjBzTPkwqr", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nWhich chart contained more variance in its growth rates, and what conclusions can be drawn from that?" }, { "from": "gpt", "value": "To determine variance, I need to assess the variability in the heights of the bars representing each year. The chart for {technological breakthroughs is in subplot_0}[0.123, 0.015, 0.975, 0.446] shows growth with minor fluctuations, seen in the bar heights like {the bar for year 2013}[0.424, 0.377, 0.477, 0.446] being smaller than its preceding year {2012}[0.358, 0.356, 0.411, 0.446]. Conversely, the chart for {innovation introduction in subplot_1}[0.123, 0.510, 0.975, 0.942] demonstrates a major variance, as evidenced by significant fluctuations like the sizeable drop from {the year 2011 bar}[0.358, 0.726, 0.411, 0.888] to the subsequent rise in {2020}[0.884, 0.694, 0.936, 0.726]. While technological breakthroughs exhibit steady growth with some exceptions, innovation introduction rates fluctuate greatly, suggesting that breakthroughs faced steadier progress whereas the introduction of innovations saw inconsistent shifts over the same period.\nBased on the above thought, the answer is:\nThe chart for innovation introduction contained more variance. This suggests that while technological breakthroughs were steadily increasing, the rate at which innovations were introduced to the market was highly erratic, indicating possible challenges in transitioning from breakthroughs to practical, market-ready innovations." } ], "id_raw": 762 }, { "id": "ECemmXtrsBlFU96C", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_51_12.png", "conversations": [ { "from": "human", "value": "\nOver the observed period, how many years saw a growth in technological breakthroughs but a decline in innovation introduction?" }, { "from": "gpt", "value": "I am analyzing each year's growth rate by looking at the bars in subplot_0 for {technological breakthroughs}[0.161, 0.434, 0.214, 0.446] and subplot_1 for {innovation introduction}[0.161, 0.726, 0.214, 0.780]. The years where technological breakthrough rates are above the {0% line}[0.123, 0.446] while innovation introduction rates are below the {0% line}[0.123, 0.942] would indicate the described condition. I proceed to count these occurrences for each year.\nBased on the above thought, the answer is:\n10" } ], "id_raw": 762 }, { "id": "kDBFULl5QWpe4Eih", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data represented by the stacked bar chart into a table?" }, { "from": "gpt", "value": "Sure. The x-axis represents the different project phases {Conceptualization}[0.137, 0.919, 0.262, 0.942], {Development}[0.372, 0.919, 0.465, 0.942], {Implementation}[0.582, 0.919, 0.692, 0.942] and {Evaluation}[0.819, 0.919, 0.893, 0.942]. Each phase has a bar stacked with 4 segments, each denoting a measure of project evaluation and management according to the {legend}[0.078, 0.074, 0.254, 0.220]. The data values are represented by the height of the bar segments. To determine specific values, I'll analyze the height of the segments and the corresponding value annotations. For instance, the segment representing the {Lightbulb Moment} in the {Conceptualization} phase has a height corresponding to 25, as confirmed by {the value annotation \"25\"}[0.191, 0.863, 0.208, 0.886].\nBased on the above thought, the answer is:\nProject Phase,Lightbulb Moment,R&D Spend,User Acceptance,Market Response\nConceptualization,25,33,40,50\nDevelopment,50,55,85,65\nImplementation,84,43,105,90\nEvaluation,20,110,50,171" } ], "id_raw": 765 }, { "id": "qjkqL0qQDlTjqFyq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, during which project phase does the 'R&D Spend' seem to have been most effectively used, given the corresponding 'Market Response'?" }, { "from": "gpt", "value": "In the chart, {R&D Spend}[0.122, 0.118, 0.201, 0.141] and {Market Response}[0.122, 0.188, 0.241, 0.211] are two measure segments that we need to focus on. I will analyze the segment heights and value annotations to compare the performance during different project phases. For {Conceptualization}[0.112, 0.846, 0.287, 0.903] and {Development}[0.331, 0.789, 0.506, 0.903], both R&D Spend and Market Response have gradually risen, which shows effective R&D spending. However, during {Implementation}[0.550, 0.711, 0.725, 0.903], R&D Spend has a decrease, but Market Response continues to grow, which might indicate a good return on previous spending. In the last phase, {Evaluation}[0.768, 0.857, 0.943, 0.903], both R&D Spend and Market Response skyrocket, showing a high spending and a matching high market response.\nBased on the above thought, the answer is:\nThe Implementation phase. Although the R&D spending dropped during this phase, the market response continues to grow, indicating that the spending in the previous phases was effectively used to produce results." } ], "id_raw": 765 }, { "id": "hoPL9GI8nQWAE4nt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the result of subtracting the Lightbulb Moment's value of the Implementation phase from that of the Development phase?" }, { "from": "gpt", "value": "We need to locate the corresponding bars of the Lightbulb Moment for the Development and Implementation phases. The bar for the {Development} phase [0.331, 0.789, 0.506, 0.903] has a value as per the annotation {50}[0.410, 0.834, 0.427, 0.858]. Similarly, the bar for the {Implementation} phase [0.550, 0.711, 0.725, 0.903] has a value of {84}[0.628, 0.795, 0.646, 0.819]. Thus, to answer the question, we subtract the value of the Lightbulb Moment in the Implementation phase from its value in the Development phase.\nBased on the above thought, the answer is:\n34" } ], "id_raw": 765 }, { "id": "kd2aXxr1ueo7rLhm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nBased on the information from the stacked bar chart, can you prepare a CSV data table?" }, { "from": "gpt", "value": "Sure, each stack on the chart represents a separate category of AI and ML tools application. The blue and light blue bars represent the 'Deep Learning' and 'Machine Learning' trends respectively, as per {the legend}[0.805, 0.074, 0.978, 0.151]. These bars are segmented by bars for each category: {Computer Vision}[0.112, 0.521, 0.251, 0.903], {Natural Language Processing}[0.285, 0.673, 0.424, 0.737], {Statistical Analysis}[0.459, 0.665, 0.597, 0.805], {Mathematical Modelling}[0.632, 0.102, 0.770, 0.385], and {Anomaly detection}[0.805, 0.514, 0.943, 0.748]. The layers of the bars represent the usage of both tools in that sector. The CSV data table can be generated by identifying the specific values labeled within each bar corresponding to Deep Learning and Machine Learning.\nBased on the above thought, the answer is:\nApplications, Deep Learning, Machine Learning\nComputer Vision, 68, 33\nNatural Language Processing, 44, 17\nStatistical Analysis, 26, 37\nMathematical Modelling, 137, 75\nAnomaly Detection, 41, 62" } ], "id_raw": 766 }, { "id": "O84dOhnaDwqnOyC4", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which tool appears to have a broader range of applications in AI & ML and why?" }, { "from": "gpt", "value": "By analyzing the bar segments corresponding to {Deep Learning}[0.805, 0.074, 0.978, 0.151] and {Machine Learning}[0.805, 0.074, 0.978, 0.151], we can visually establish which tool displays a wider range of applications. Observing the Statistical Analysis category, the blue bar (Deep Learning) extends from the base (Y-axis) to 26, and for Machine Learning, the light blue segment begins from 26 and extends to 63 {Total segment}[0.459, 0.665, 0.597, 0.805]. The same analysis is carried out on other categories, such as Computer Vision [0.112, 0.521, 0.251, 0.903], Natural Language Processing [0.285, 0.673, 0.424, 0.737], Mathematical Modelling [0.632, 0.102, 0.770, 0.385], and Anomaly Detection [0.805, 0.514, 0.943, 0.748]. We find that Machine Learning appears to be widely used across most applications, especially evident in Anomaly Detection, where the difference in usage is significantly higher than Deep Learning.\nBased on the above thought, the answer is:\nMachine Learning. It shows consistent and broad application across the AI & ML fields, and significantly higher usage in Anomaly Detection compares to Deep Learning." } ], "id_raw": 766 }, { "id": "VC5Pz5GyAOAZIklK", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_1.png", "conversations": [ { "from": "human", "value": "\nIn which AI & ML application, the difference between the usage of Deep Learning and Machine Learning is the smallest?" }, { "from": "gpt", "value": "Identifying the minimal difference between the use of Deep Learning and Machine Learning requires a comparison of the height differences (usage difference) between each procedure's bars for each category. Starting from Computer Vision, I'm observing the usage difference between {Deep Learning and Machine Learning}[0.112, 0.646, 0.251, 0.903]. Same way, I compare for Natural Language Processing[0.285, 0.673, 0.424, 0.737], Statistical Analysis[0.459, 0.665, 0.597, 0.805], Mathematical Modelling[0.632, 0.102, 0.770, 0.385] and Anomaly Detection[0.805, 0.514, 0.943, 0.748]. Upon measuring and comparing the usage differences from each AI & ML tool, it appears that the category with the smallest difference between the usage of Deep Learning and Machine Learning is Natural Language Processing.\nBased on the above thought, the answer is:\nNatural Language Processing." } ], "id_raw": 766 }, { "id": "fC9br3tr1pgwp2SS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart that you're currently looking at, could you please convert the chart data into a structured table in CSV format?" }, { "from": "gpt", "value": "By interpreting the content of {the chart title}[0.279, 0.025, 0.776, 0.055] which is \"Stacked Bar Chart of Social Inequality and Rights Protection\" and using the values represented by {the y-axis tick labels}[{\"0\"}[0.052, 0.894, 0.061, 0.917], {\"20\"}[0.043, 0.779, 0.061, 0.802], {\"40\"}[0.043, 0.663, 0.061, 0.687], {\"60\"}[0.043, 0.548, 0.061, 0.571], {\"80\"}[0.043, 0.433, 0.061, 0.456], {\"100\"}[0.035, 0.318, 0.061, 0.341], {\"120\"}[0.035, 0.203, 0.061, 0.226], {\"140\"}[0.035, 0.087, 0.061, 0.111], {\"160\"}[0.034, -0.028, 0.061, -0.005]], we can see that the vertical axis represents the values. Looking at {the x-axis tick labels}[{\"Right Protection\"}[0.144, 0.919, 0.255, 0.942], {\"Economic Inequality\"}[0.348, 0.919, 0.489, 0.942], {\"Social Inequality\"}[0.580, 0.919, 0.695, 0.942], {\"Civil Rights\"}[0.817, 0.919, 0.895, 0.942]] and we can tell that the horizontal axis represents different categories. Each category has two stacks in their bar, respectively {Routine Monitoring}[{\"Routine Monitoring\"}[0.122, 0.083, 0.254, 0.106]] and {Reinforcement}[{\"Reinforcement\"}[0.122, 0.118, 0.224, 0.141]]. To obtain these values, I need to calculate the sum of the heights of each stack. For example, under the category of Right Protection, Routine Monitoring and Reinforcement are represented by {two segments of bars}[{\"Routine Monitoring\"}[0.112, 0.701, 0.287, 0.903], {\"Reinforcement\"}[0.112, 0.413, 0.287, 0.701]] which sum up to \"35 + 50 =85\".\nBased on the above thought, the answer is:\nCategories,Routine Monitoring,Reinforcement\nRight Protection,35,50\nEconomic Inequality,45,30\nSocial Inequality,23,35\nCivil Rights,67,72" } ], "id_raw": 767 }, { "id": "m2DRQdRXcnvEgah3", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nBased on the information in the chart, which measure seems to be more effective in addressing Social Inequality and why?" }, { "from": "gpt", "value": "For the category of Social Inequality, we have two measures represented by {two segments of bars}[{\"Routine Monitoring\"}[0.550, 0.770, 0.725, 0.903], {\"Reinforcement\"}[0.550, 0.569, 0.725, 0.770]]. The {Routine Monitoring}[0.550, 0.770, 0.725, 0.903] represented by the top segment has a value of 23. On the other side, the {Reinforcement}[0.550, 0.569, 0.725, 0.770] represented by the bottom segment has a higher value of 35. Since a higher value indicates more effectiveness in addressing social inequality, it seems that Reinforcement is more effective than Routine Monitoring in this category.\nBased on the above thought, the answer is:\nReinforcement. Since it has a higher value, it appears to be more effective in addressing Social Inequality." } ], "id_raw": 767 }, { "id": "zsnzMdW546G4Sv6h", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total value spent on Civil Rights?" }, { "from": "gpt", "value": "Both {Routine Monitoring}[0.848, 0.698, 0.865, 0.722] and {Reinforcement}[0.848, 0.298, 0.865, 0.321] involve expenses on Civil Rights. The value of expenses by Routine Monitoring on Civil rights is 67, and that of Reinforcement on Civil rights is 72. I will add these two values together to get the total value spent on Civil Rights.\nBased on the above thought, the answer is:\n139." } ], "id_raw": 767 }, { "id": "kbgYBdihtY12ZWTp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nCould you summarize the data shown in the stacked bar chart as a CSV table?" }, { "from": "gpt", "value": "The stacked bar chart in front of me is indicating five categories of Entrepreneurial actions {\"Innovation\"}[0.145, 0.919, 0.218, 0.942], {\"Experiment\"}[0.315, 0.919, 0.395, 0.942], {\"Exploit\"}[0.504, 0.919, 0.552, 0.942], {\"Expand\"}[0.675, 0.919, 0.727, 0.942], {\"Exceed\"}[0.849, 0.919, 0.899, 0.942] against two predictive factors named {\"Motivation\"}[0.895, 0.083, 0.968, 0.106] and {\"Risk-taking\"}[0.895, 0.118, 0.972, 0.141] as per the legend provided. By looking at each bar and the text on it, I can extract the values. For example, the value of Motivation for Innovation category can be deduced from the height and the annotation {30}[0.173, 0.805, 0.190, 0.829] from the bar with coordinates [0.112, 0.731, 0.251, 0.903].\nBased on the above thought, the answer is:\nCategories, Motivation, Risk-taking\nInnovation, 30, 70\nExperiment, 50, 50\nExploit, 80, 60\nExpand, 70, 40\nExceed, 90, 20" } ], "id_raw": 768 }, { "id": "ukIqsF3V89chcgzE", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, how would you explain the trends in entrepreneurial actions with regard to motivation and risk-taking?" }, { "from": "gpt", "value": "From the bar chart, it is clear that \"Motivation\" and \"Risk-taking\" have contrasting trends across the five categories of entrepreneurial actions. The bars corresponding to \"Motivation\" {Motivation bars}<{\"Innovation\"}[0.112, 0.731, 0.251, 0.903]; {\"Experiment\"}[0.285, 0.617, 0.424, 0.903]; {\"Exploit\"}[0.459, 0.445, 0.597, 0.903]; {\"Expand\"}[0.632, 0.503, 0.770, 0.903]; {\"Exceed\"}[0.805, 0.388, 0.943, 0.903]>, show a slight increase with a dip in the \"Expand\" category. This could suggest that motivation to innovate and exploit entrepreneurial opportunities rises over time, notwithstanding the slight dip in motivation to expand. On the contrary, bars belonging to \"Risk-taking\" {Risk-taking bars}<{\"Innovation\"}[0.112, 0.331, 0.251, 0.731]; {\"Experiment\"}[0.285, 0.331, 0.424, 0.617]; {\"Exploit\"}[0.459, 0.102, 0.597, 0.445]; {\"Expand\"}[0.632, 0.274, 0.770, 0.503]; {\"Exceed\"}[0.805, 0.274, 0.943, 0.388]>, follow a decreasing trend. This could signify that as entrepreneurial actions progress from \"Innovation\" to \"Exceed\", the inclination to take risks decreases.\nBased on the above thought, the answer is:\nAcross the five entrepreneurial actions, \"Motivation\" generally increases and \"Risk-taking\" generally decreases. Exceptionally, there's a slight dip in Motivation in the \"Expand\" category, which is to be worthy of attention. However, the general trends suggest that as entrepreneurial actions evolve, they are characterised by increased motivation and reduced risk-taking." } ], "id_raw": 768 }, { "id": "olsAsC6ahTjQ72Tc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_3.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which category shows the smallest difference between \"Motivation\" and \"Risk-taking\" values?" }, { "from": "gpt", "value": "To determine the category with the smallest difference between \"Motivation\" and \"Risk-taking\", I should subtract the two values from each other and find the smallest difference. For instance, the difference in \"Innovation\" category values is observed by calculating the difference between {\"Motivation\"}[30][0.173, 0.805, 0.190, 0.829] and {\"Risk-taking\"}[70][0.173, 0.519, 0.190, 0.543]. This process should be repeated for all five categories and the one with the smallest difference can be identified.\nBased on the above thought, the answer is:\nExperiment." } ], "id_raw": 768 }, { "id": "z5Nq4nuREVYmN0kO", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV format data table for this Stacked Bar Chart describing Design Quality and the Level of Creativity?" }, { "from": "gpt", "value": "Sure, I can create the CSV data table by extracting values from the bars segmented for 'Design Quality' and 'Creativity Levels' for each parameter. For example, under the parameter 'Brainstorming Use', there are two segments for the bars respectively {Design Quality}[0.112, 0.600, 0.287, 0.903] with value \"70\" and {Creativity Levels}[0.112, 0.340, 0.287, 0.600] with value \"60\". Therefore, for Brainstorming Use, Design Quality is 70 and Creativity Level is 60. Similarly other parameters can be evaluated.\nBased on the above thought, the answer is:\nInnovation Parameters, Design Quality, Creativity Levels\nBrainstorming Use, 70, 60\nDesign Thinking Process, 80, 85\nPrototyping and Simulation, 90, 95\nCross-Disciplinary Collaboration, 70, 80" } ], "id_raw": 769 }, { "id": "mQs8qklhBhXgrLpl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which innovation parameter seems to be most effective for enhancing the creativity levels among all?" }, { "from": "gpt", "value": "By observing the segment colours according to the legend[0.811, 0.074, 0.977, 0.151] ('Design Quality' as '#097138' and 'Creativity Levels' as '#74c476'), the 'Creativity Levels', represented by the lighter segment of each bar, can be analyzed. Looking at the Creativity Levels across the four parameters: \n- The segment for Creativity Levels under Brainstorming Use[0.112, 0.340, 0.287, 0.600] has the lower half filled, suggesting moderate levels of creativity.\n- The Creativity Levels under Design Thinking Process[0.331, 0.189, 0.505, 0.557] fills most of the top half of the bar, indicating higher creativity levels than Brainstorming Use. \n- The Creativity Levels in Prototyping and Simulation[0.549, 0.102, 0.724, 0.513] fill an even larger portion of the bar, indicating higher creativity levels than the previous two parameters.\n- The Creativity Levels under Cross-Disciplinary Collaboration[0.767, 0.254, 0.942, 0.600] fills most of the bar but is lower than that for Prototyping and Simulation.\nSo, the 'Prototyping and Simulation' parameter seems to have been most effective in enhancing creativity levels.\nBased on the above thought, the answer is:\n'Prototyping and Simulation' - It shows the highest level of creativity among all the parameters according to the chart." } ], "id_raw": 769 }, { "id": "nbosqn2GigmvSVve", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which innovation parameter had the highest combined value of Design Quality and Creativity Level?" }, { "from": "gpt", "value": "To find the innovation parameter with the highest combined value of Design Quality and Creativity Level, I can look at where the top edge of the highest bar is. In this chart, that is represented by the top edge of the Creativity Level segment under each parameter. From left to right:\n- For Brainstorming use, the combined value is at the top edge of the corresponding Creativity Level segment[0.112, 0.340, 0.287, 0.600], which is positioned near the y-axis label \"125\".\n- For Design Thinking Process, the top edge of the Creativity Level segment[0.331, 0.189, 0.505, 0.557] is near the y-axis label \"175\".\n- The Prototyping and Simulation parameter Creativity Level segment[0.549, 0.102, 0.724, 0.513] top edge is slightly above the y-axis label \"175\".\n- Cross-Disciplinary Collaboration Creativity Level segment[0.767, 0.254, 0.942, 0.600] top edge is above the y-axis label \"150\".\nTherefore, the Prototyping and Simulation parameter had the highest combined value of Design Quality and Creativity Level.\nBased on the above thought, the answer is:\nPrototyping and Simulation" } ], "id_raw": 769 }, { "id": "1exsTkyIzAAD6mFm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the stacked bar chart you observed, can you arrange the data in a structured CSV format?" }, { "from": "gpt", "value": "From the chart's title {\"Stacked Bar Chart of Human Life and Living Environment Data\"}[0.268, 0.025, 0.787, 0.055] and the x-axis tick labels {[{\"Population Growth\"}[0.136, 0.919, 0.264, 0.942]; {\"Green Areas\"}[0.376, 0.919, 0.461, 0.942]; {\"Urbanization level\"}[0.574, 0.919, 0.700, 0.942]; {\"Carbon Footprint\"}[0.797, 0.919, 0.915, 0.942]],} I understand that these categories are compared on the x-axis. The y-axis {[{0}[0.052, 0.894, 0.061, 0.917] to {200}[0.035, 0.004, 0.061, 0.027]]} represents the corresponding values for each category in numerical terms. There are two layers for each data point in the categories according to {the legend}[0.078, 0.074, 0.262, 0.151] where {Dark blue bars}<{\"Human Life\"}[0.122, 0.083, 0.202, 0.106]> and {light blue bars}<{\"Living Environment\"}[0.122, 0.118, 0.256, 0.141]> represent different data layers. The values for both Human Life and Living Environment are needed for each category that can be determined by the height of each bar and the value labels on them. For instance, for \"Population Growth\", {the dark blue bar}[0.112, 0.680, 0.287, 0.903] is at the 50 position, marked by {a value label \"50\"}[0.191, 0.780, 0.208, 0.803] and {the light blue bar}[0.112, 0.391, 0.287, 0.680] constitutes the remaining 65, marked by {a value label \"65\"}[0.191, 0.524, 0.209, 0.548]. The CSV table therefore would contain these values for all categories and for each Human Life and Living Environment.\nBased on the above thought, the answer is:\nCategories, Human Life, Living Environment\nPopulation Growth, 50, 65\nGreen Areas, 30, 55\nUrbanization level, 75, 45\nCarbon Footprint, 95, 85" } ], "id_raw": 770 }, { "id": "EAyBUnVaxlbMhPev", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which aspect of human life and living environment require more conscious efforts for improvement?" }, { "from": "gpt", "value": "Based on the chart, the aspects requiring more conscious efforts for improvement can be determined by the larger value in each category. Looking at {Population Growth bar group}[0.112, 0.391, 0.287, 0.903], we can see that Living Environment needs greater improvement as it is higher with a value of 65 in comparison with Human Life at 50. Next, examining {Green Areas bar group}[0.331, 0.525, 0.506, 0.769] shows the same thing, where Living Environment is higher at 55 and Human Life at 30. On the contrary, {Urbanization Level bar group}[0.550, 0.369, 0.725, 0.569] and {Carbon Footprint bar group}[0.768, 0.102, 0.943, 0.480], indicate that Human Life needs more improvement as they are higher with values of 75 and 95 respectively than Living Environment with 45 and 85. Summing up these values gives an understanding that Human Life might require more collective efforts overall for improvement as it has greater needs in more categories than Living Environment.\nBased on the above thought, the answer is:\nHuman Life. This is because it displays higher values in two fundamental categories (Urbanization Level and Carbon Footprint) when compared with the living environment, which indicates more improvement is needed." } ], "id_raw": 770 }, { "id": "hVxGQTUrOyWYHB5R", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_5.png", "conversations": [ { "from": "human", "value": "\nWhich category has the least total value when considering both 'Human Life' and 'Living Environment'?" }, { "from": "gpt", "value": "I will analyze the total heights of all the columns in the chart, which are determined by the sum of the human life bars and living environment bars. Firstly, {Population Growth bar group}<[{\"Human Life Bar\"}[0.112, 0.680, 0.287, 0.903]; {\"Living Environment Bar\"}[0.112, 0.391, 0.287, 0.680]]> has a total value of 115. Next, {Green Areas bar group}<[{\"Human Life Bar\"}[0.331, 0.769, 0.506, 0.903]; {\"Living Environment Bar\"}[0.331, 0.525, 0.506, 0.769]]> has a total value of 85. Similarly, Urbanization level totals 120 as shown in {Urbanization Level bar group}<[{\"Human Life Bar\"}[0.550, 0.569, 0.725, 0.903]; {\"Living Environment Bar\"}[0.550, 0.369, 0.725, 0.569]]> and lastly, {Carbon Footprint bar group}<[{\"Human Life Bar\"}[0.768, 0.480, 0.943, 0.903]; {\"Living Environment Bar\"}[0.768, 0.102, 0.943, 0.480]]> totals 180. Hence amongst all these, Green Areas category has the least total value.\nBased on the above thought, the answer is:\nGreen Areas." } ], "id_raw": 770 }, { "id": "F2nycHfRe3rQcz1F", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the stacked bar chart you observed and present it in a structured table format?" }, { "from": "gpt", "value": "By referring to the given chart, I can recognize the title {\"Stacked Bar Chart of Social Welfare\"}[0.381, 0.025, 0.675, 0.055] and understand that the chart presents information about different categories of social welfare. From {the x-axis tick labels}[{\"Schooling\"}[0.166, 0.919, 0.233, 0.942]; {\"Healthcare\"}[0.381, 0.919, 0.456, 0.942]; {\"Food Stamps\"}[0.592, 0.919, 0.682, 0.942]; {\"Housing\"}[0.828, 0.919, 0.884, 0.942]], it's clear that these are the categories of public services. The y-axis is {the values}[{\"0\"}[0.052, 0.894, 0.061, 0.917]; {\"20\"}[0.043, 0.734, 0.061, 0.757]; {\"40\"}[0.043, 0.573, 0.061, 0.597]; {\"60\"}[0.043, 0.413, 0.061, 0.437]; {\"80\"}[0.043, 0.253, 0.061, 0.277]; {\"100\"}[0.035, 0.093, 0.061, 0.116]; {\"120\"}[0.035, -0.067, 0.061, -0.044]] being used to represent the count of people in each trend. The {legend}[0.857, 0.074, 0.978, 0.151] shows that the two different segments of each bar represent {Expansion}[0.901, 0.083, 0.972, 0.106] and {Decline}[0.901, 0.118, 0.953, 0.141] segments. I can identify the count for expansion and decline in each category by the height of each segment and the texts embedded in the segment. For instance, the {segment for Expansion in Schooling}[0.112, 0.422, 0.287, 0.903] has a {text of \"60\"}[0.191, 0.651, 0.209, 0.674] aligned with it. By adopting the same approach to others, I can present the information in a table.\nBased on the above thought, the answer is:\nCategories,Expansion,Decline\nSchooling,60,40\nHealthcare,70,30\nFood Stamps,50,50\nHousing,40,60" } ], "id_raw": 771 }, { "id": "pS7TQxXtThC80EcP", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nBased on the visualization you are observing, which type of services are preferably growing and which are unfortunately declining?" }, { "from": "gpt", "value": "Analyzing from the stacked bar chart, we can observe that the {Expansion bars}[0.901, 0.083, 0.972, 0.106] are representing the growing trend of services. The services that are showing growth are {Schooling}[0.112, 0.422, 0.287, 0.903] and {Healthcare}[0.331, 0.342, 0.506, 0.903], which have higher values in the \"Expansion\" segment than in the \"Decline\" segment. On the other hand, the {Decline bars}[0.901, 0.118, 0.953, 0.141] are representing the services that are unfortunately declining. The services that are showing decline are {Food Stamps}[0.550, 0.102, 0.725, 0.503] and {Housing}[0.768, 0.102, 0.943, 0.583], which have higher values in the \"Decline\" segment than in the \"Expansion\" segment.\nBased on the above thought, the answer is:\nSchooling and Healthcare are the services that are growing, while Food Stamps and Housing are unfortunately declining." } ], "id_raw": 771 }, { "id": "omnsCp1gz2EkwPsx", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_6.png", "conversations": [ { "from": "human", "value": "\nWhich social welfare category has the smallest difference in usage trends between Expansion and Decline?" }, { "from": "gpt", "value": "To determine the category with the smallest difference, I will observe and compare the height difference between the Expansion and Decline segments in each bar. For example, the first pair, {the \"Expansion\" section for \"Schooling\"}[0.112, 0.422, 0.287, 0.903] and {the \"Decline\" section for \"Schooling\"}[0.112, 0.102, 0.287, 0.422], the difference between 60 and 40 is 20. By observing and comparing the difference in all categories in the same manner, I can see that the {\"Food Stamps\"}[0.550, 0.102, 0.725, 0.903] category has the smallest difference, their values are both 50, so the difference is 0.\nBased on the above thought, the answer is:\nFood Stamps." } ], "id_raw": 771 }, { "id": "HDCedPvTzkmNP7KA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nCan you turn the market share percentages from the stacked bar chart into a structured table with CSV format?" }, { "from": "gpt", "value": "Analyzing the stacked bar chart starting with the x-axis labels for years which range from {2015}[0.152, 0.919, 0.187, 0.942] to {2020}[0.869, 0.919, 0.904, 0.942], the y-axis labels indicate percentages from {0%}[0.052, 0.894, 0.061, 0.917] to {120%}[0.035, 0.028, 0.061, 0.052], showing the total market shares stacked for each year. The companies are differentiated by the colors in the bars, according to the legend: {Apple}[0.083, 0.085, 0.111, 0.101], {Samsung}[0.083, 0.120, 0.111, 0.136], {Google}[0.083, 0.155, 0.111, 0.171], {Microsoft}[0.083, 0.190, 0.111, 0.206], and {Others}[0.083, 0.225, 0.111, 0.241]. Each bar segment's height, such as {Apple's market share for 2015}[0.112, 0.795, 0.227, 0.903], corresponds to the market share percentage indicated. To generate the CSV table, I will compile each company's market share per year as displayed in the chart.\nBased on the above thought, the answer is:\nYear,Apple,Samsung,Google,Microsoft,Others\n2015,15,30,10,5,40\n2016,20,35,15,7,23\n2017,22,40,20,8,10\n2018,24,42,30,10,5\n2019,21,40,25,8,6\n2020,20,43,23,7,7" } ], "id_raw": 772 }, { "id": "IrX7ygnLNArkzIYM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nWhich company has shown the most notable decline in market share over the period from 2015 to 2020?" }, { "from": "gpt", "value": "A notable decline in market share will be evident by the shrinking size of the color segment for the company across the bars from {2015}[0.152, 0.919, 0.187, 0.942] to {2020}[0.869, 0.919, 0.904, 0.942]. Looking at the segments for {Others in 2015}[0.112, 0.182, 0.227, 0.470] showing 40% down to {Others in 2020}[0.829, 0.182, 0.943, 0.232] showing 7%, there is a dramatic decrease. This is the most significant reduction when compared to other companies like {Apple}[0.083, 0.085, 0.111, 0.101], {Samsung}[0.083, 0.120, 0.111, 0.136], {Google}[0.083, 0.155, 0.111, 0.171], and {Microsoft}[0.083, 0.190, 0.111, 0.206] over the same time frame.\nBased on the above thought, the answer is:\nThe \"Others\" category. It experienced a sharp decline from 40% in 2015 to 7% in 2020, drastically reducing its portion in the market share." } ], "id_raw": 772 }, { "id": "0ZJipxX5nMMEZNta", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat was the total market share percentage for Google from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the total market share percentage for Google over the given years, I will look at the segments of bars representing Google, starting from {Google in 2015}[0.112, 0.506, 0.227, 0.578] with 10%, and continue with the following years: {Google in 2016}[0.256, 0.398, 0.370, 0.506] has 15%, {Google in 2017}[0.399, 0.311, 0.513, 0.456] has 20%, {Google in 2018}[0.542, 0.210, 0.657, 0.427] has 30%, {Google in 2019}[0.685, 0.283, 0.800, 0.463] has 25%, and finally, {Google in 2020}[0.829, 0.283, 0.943, 0.448] has 23%. Summing these values gives Google's combined market share over the six years.\nBased on the above thought, the answer is:\n123" } ], "id_raw": 772 }, { "id": "mN9THKi4Ov09s4yS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nPlease convert the data from the stacked bar chart into a CSV format table." }, { "from": "gpt", "value": "To generate a CSV table from the bar chart, I need to label the data categories and values. The title is {Stacked Bar Chart of Scientific Research and Technological Innovation}[0.242, 0.025, 0.823, 0.055]. The x-axis represents different research fields, indicated by {the x-axis tick labels}[{\"Physics Research\"}[0.148, 0.919, 0.267, 0.942]; {\"Chemistry Research\"}[0.354, 0.919, 0.493, 0.942]; {\"Biology Research\"}[0.581, 0.919, 0.700, 0.942]; {\"Robotics Innovation\"}[0.789, 0.919, 0.926, 0.942]]. The y-axis represents counts, ranging from {0}[0.061, 0.894, 0.070, 0.917] to {8000}[0.035, -0.035, 0.070, -0.011]. The {legend}[0.086, 0.074, 0.254, 0.151] indicates that the green bars represent the papers published and the orange ones represent citations received. Therefore, for each research field, we have values for both papers published and citations, which can be extracted from the bars and text values within {the bars}[{bars in first group}[0.121, 0.856, 0.294, 0.903; 0.337, 0.851, 0.511, 0.903; 0.554, 0.845, 0.727, 0.903; 0.771, 0.833, 0.944, 0.903]; {bars in second group}[0.121, 0.276, 0.294, 0.856; 0.337, 0.363, 0.511, 0.851; 0.554, 0.195, 0.727, 0.845; 0.771, 0.102, 0.944, 0.833]].\nBased on the above thought, the answer is:\nResearch Field,Papers Published,Citations\nPhysics Research,400,5000\nChemistry Research,450,4200\nBiology Research,500,5600\nRobotics Innovation,600,6300" } ], "id_raw": 773 }, { "id": "j9PrJSexTBykngdb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, in which field do the academic works seem to have the most influence? And how did you reach this conclusion?" }, { "from": "gpt", "value": "The influence of academic works is typically indicated by the number of citations their papers have received. In the chart, {Citations}[0.086, 0.074, 0.254, 0.151] are represented by orange bars. Looking at their distributions in different fields - {Physics Research}[0.148, 0.919, 0.267, 0.942], {Chemistry Research}[0.354, 0.919, 0.493, 0.942], {Biology Research}[0.581, 0.919, 0.700, 0.942], and {Robotics Innovation}[0.789, 0.919, 0.926, 0.942] depicted along the x-axis, we can estimate which field's works have the most influence based on the height of the orange bars (the count of citations). The tallest orange bar corresponds to the {Robotics Innovation} field[0.771, 0.102, 0.944, 0.833], indicating the highest count of citations in that area. This suggests that works in the Robotics Innovation field are possibly the most influential.\nBased on the above thought, the answer is:\nThe academic works in the Robotics Innovation field seem to have the most influence, as they have received the most citations, a common measure of scholarly impact." } ], "id_raw": 773 }, { "id": "ddgSEAsaa38XBJ1S", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_8.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart, which research field has the highest number of citations?" }, { "from": "gpt", "value": "The citations are represented by the orange bars of the stacked bar chart, with indications located within the bar areas showing the exact number of citations({'Citations'} plot labeled in {the legend}[0.086, 0.074, 0.254, 0.151]). The fields are placed along the x-axis and indicated by their tick labels {Physics Research}[0.148, 0.919, 0.267, 0.942], {Chemistry Research}[0.354, 0.919, 0.493, 0.942], {Biology Research}[0.581, 0.919, 0.700, 0.942], and {Robotics Innovation}[0.789, 0.919, 0.926, 0.942]. I can visually compare the bar heights of different research fields to find out which one has the highest count of citations. The tallest orange bar corresponds to the field of {Robotics Innovation} with coordinates [0.771, 0.102, 0.944, 0.833], and hence, Robotics Innovation has the highest number of citations.\nBased on the above thought, the answer is:\nRobotics Innovation" } ], "id_raw": 773 }, { "id": "5gwteX9jCDePtiUv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart, please translate the data into a CSV format." }, { "from": "gpt", "value": "From the stacked bar chart's subtitle {\"Stacked Bar Chart of Social Networking\"}[0.365, 0.025, 0.691, 0.055] and the x-axis labels [\"Continuous Decrease\"[0.223, 0.919, 0.371, 0.942]; \"Steep Decline\"[0.711, 0.919, 0.807, 0.942]], I can see that the x-axis represents different trends. The y-axis values range from {0}[0.052, 0.894, 0.061, 0.917] to {400}[0.035, -0.048, 0.061, -0.025]. There are four stacked bars representing different social platforms according to {the legend}[0.857, 0.074, 0.978, 0.220]. To translate this data into a CSV format, I would record the user counts found in the stack bars text annotations under each trend for the four social platforms. For instance, under \"Continuous Decrease\", the user count for Facebook is seen at {\"100\"}[0.284, 0.773, 0.310, 0.797]. The count for Twitter is notified at {\"90\"}[0.288, 0.550, 0.306, 0.573] under the same trend category.\nBased on the above thought, the answer is:\nTrends,Facebook,Twitter,Instagram,LinkedIn\nContinuous Decrease,100,90,80,70\nSteep Decline,80,60,40,20" } ], "id_raw": 774 }, { "id": "RXNvnA37tPN6Bdr6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which social networking platform had the biggest fall in users across both trends and why?" }, { "from": "gpt", "value": "This question requires a comprehensive comparison of the heights of the bars representing each social networking platform under both trend categories. Under \"Continuous Decrease\", we can observe from the rectangles and text annotations that Facebook, Twitter, Instagram, LinkedIn lost 100-70, 90-80, 80-70, 70-70 = 30, 10, 10, 0 users respectively. Under \"Steep Decline\", we can observe that Facebook, Twitter, Instagram, LinkedIn lost 80-80, 60-60, 40-40, 20-20 = 0, 0, 0, 0 users respectively. By summing the numbers of lost users in both trends, we see that Facebook lost the most users.\nBased on the above thought, the answer is:\nFacebook. Because it lost a total of 30 users across both trends, which is the most significant loss if we compare it with other social networking platforms." } ], "id_raw": 774 }, { "id": "OghnujmxINzj4rOX", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_9.png", "conversations": [ { "from": "human", "value": "\nUnder which trend did Facebook lose more users?" }, { "from": "gpt", "value": "To answer this question, I will compare the height of the Facebook bars under the two trend categories. The bar representing Facebook's user loss under \"Continuous Decrease\" is located at [0.112, 0.667, 0.482, 0.903], and the annotation {\"100\"}[0.284, 0.773, 0.310, 0.797] places Facebook's initial user count at 100 for this trend. However, under \"Steep Decline\", the corresponding bar is at [0.574, 0.714, 0.943, 0.903], and from the annotation {\"80\"}[0.750, 0.797, 0.768, 0.820], we can see Facebook's initial user count dropped to 80. So, Facebook lost more users under the \"Continuous Decrease\" trend.\nBased on the above thought, the answer is:\nContinuous Decrease." } ], "id_raw": 774 }, { "id": "BqOY9VxilnrZtO3k", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table that reflects the data represented in the bar chart?" }, { "from": "gpt", "value": "Let me translate the stacked bars into a CSV table. The bars are stacked into two groups, one for each scenario, as shown in the x-axis labels: {Policy Initiatives}[0.241, 0.919, 0.353, 0.942] and {Political Unrest}[0.707, 0.919, 0.810, 0.942]. Each group is divided into four sections, each labeled by {the legend}[0.078, 0.074, 0.304, 0.220] as: 'Increase in National Debt', 'Consumer Price Index', 'Tax Revenue', and 'Government Spending'. The values of each sector correspond to the y-axis values which range from {0}[0.052, 0.894, 0.061, 0.917] to {350}[0.035, -0.025, 0.061, -0.002]. Taking the 'Policy Initiatives' stack as an example, the 'Increase in National Debt' bar at the bottom {[0.112, 0.732, 0.482, 0.903]} is the largest, thus indicating a value of 65. The pattern repeats with the 'Consumer Price Index', 'Tax Revenue', and ' Government Spending' bars, with respective values of 70, 75, and 50 displaying a sequential stack pattern.\nBased on the above thought, the answer is:\nScenarios,Increase in National Debt,Consumer Price Index,Tax Revenue,Government Spending\nPolicy Initiatives,65,70,75,50\nPolitical Unrest,70,90,85,60" } ], "id_raw": 775 }, { "id": "dzpWpykFke6roGyT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, which scenario requires the government to spend more? Please explain your reasoning." }, { "from": "gpt", "value": "This question is asking for the scenario where 'Government Spending' is highest. 'Government Spending' is represented by the top-most section of each stack. Comparing {Government Spending bar in the 'Policy Initiatives'}[0.112, 0.220, 0.482, 0.352] to {Government Spending bar in the 'Political Unrest'}[0.574, 0.102, 0.943, 0.260], the latter is visibly smaller than the former, and this is confirmed by the values {50}[0.288, 0.274, 0.306, 0.298] and {60}[0.750, 0.169, 0.768, 0.193] respectively. So, the government spends more during 'Policy Initiatives'.\nBased on the above thought, the answer is:\nPolicy Initiatives, because the 'Government Spending' section during 'Policy Initiatives' is larger than that during 'Political Unrest'." } ], "id_raw": 775 }, { "id": "VW31md3jGVepOXEl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_10.png", "conversations": [ { "from": "human", "value": "\nCompared to 'Policy Initiatives', how much more increase in the 'National Debt' is expected during 'Political Unrest'?" }, { "from": "gpt", "value": "To answer this question, I need to compare the 'Increase in National Debt' section of both the 'Policy Initiatives' and 'Political Unrest' stacks. This is represented by the bottom section of each group of bars {Policy Initiatives National Debt Bar}[0.112, 0.732, 0.482, 0.903] and {Political Unrest National Debt Bar}[0.574, 0.719, 0.943, 0.903]. The value annotation on top tells me the height of 'Increase in National Debt' in each scenario. For 'Policy Initiative', the value is {65}[0.288, 0.806, 0.306, 0.829] and for 'Political Unrest', it is {70}[0.750, 0.799, 0.767, 0.823]. The difference can be calculated by subtracting the 'Policy Initiative' value from the 'Political Unrest' value.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 775 }, { "id": "BwyC0PiuuqecgXJc", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you've analyzed, can you convert the data into a structured table?" }, { "from": "gpt", "value": "The title at the top of the chart from the coordinates segements includes {\"Progress Chart of AI and ML Concepts\"}[0.356, 0.083, 0.669, 0.113]. The x-axis labels or categories include {Neural Networks, Deep Learning, Machine Learning, Robotics, Speech Recognition} and were located using the coordinates [0.161, 0.906, 0.277, 0.930] till [0.737, 0.906, 0.875, 0.930] respectively. The y-axis labels denote the prominence of these concepts. The concepts are divided in two trends: {Trend 2015}[0.810, 0.141, 0.887, 0.164] and {Trend 2020}[0.810, 0.176, 0.887, 0.199]. I can use the height of the bars to determine the values, for example, the prominence of Neural Networks in 2015 from the code {\"Trend 2015\"}[0.810, 0.141, 0.887, 0.164] and the corresponding bar located at [0.160, 0.680, 0.278, 0.890] was 50. This process will be repeated for each category under both trends.\nBased on the above thought, the answer is:\nConcepts,Trend 2015,Trend 2020\nNeural Networks,50,35\nDeep Learning,75,100\nMachine Learning,65,90\nRobotics,40,55\nSpeech Recognition,55,50" } ], "id_raw": 776 }, { "id": "jfrN3r4VrPFvPvYO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart's data, which concept in AI has seen consistent popularity?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the gap of both bars (Trend 2015 and Trend 2020) for each AI concept. The closer the two values for a specific concept, the more consistent its popularity between 2015 to 2020. After analyzing the bars, it appears that the bar heights for 'Robotics' {\"Trend 2015\"}[0.601, 0.722, 0.718, 0.890] and {\"Trend 2020\"}[0.601, 0.492, 0.718, 0.722] have a relatively smaller gap compared to the other categories. Thus, it seems like the concept of Robotics has retained consistent popularity over the years.\nBased on the above thought, the answer is:\nRobotics, it has shown consistent popularity from 2015 to 2020." } ], "id_raw": 776 }, { "id": "gM7uosYtwuMXG5Zj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_11.png", "conversations": [ { "from": "human", "value": "\nWhich field of AI has shown the most growth from 2015 to 2020?" }, { "from": "gpt", "value": "To extract this information, I have to compare the difference between the heights of bars in the year 2015 and 2020 for every concept. Looking at {'Neural Networks'}[0.160, 0.680, 0.278, 0.890] and its corresponding bar in 2020>r>[0.160, 0.534, 0.278, 0.680], the difference is -15. Going through the rest of the concepts, I found that {'Deep Learning'}[0.307, 0.576, 0.424, 0.890] and its 2020's counterpart [0.307, 0.157, 0.424, 0.576] has a difference of +25. With this in mind, the field with the most growth between 2015 to 2020 is Deep Learning.\nBased on the above thought, the answer is:\nDeep Learning." } ], "id_raw": 776 }, { "id": "9qug5BJMPvLbSHo3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nKindly convert the stacked bar chart information into a table in CSV format." }, { "from": "gpt", "value": "Judging by the chart title located at {'Stacked Bar Chart of Human Life and Living Environment'}[0.291, 0.025, 0.765, 0.055] and the x-axis{'Indexes'}[0.501, 0.952, 0.555, 0.975] and y-axis labels {\"Indicators' values\"}[0.015, 0.381, 0.029, 0.584], each category on the x-axis is associated with stacked bars representing the City Quality of Life Index and People Satisfaction Index. The number on top of each bar indicates the values of the corresponding indicator for both Indexes. For instance, the stacked bar at {the economic indicator}[0.201, 0.265, 0.287, 0.903] reads 70 on top, which belongs to the City Quality of Life Index and 75 which belongs to the People Satisfaction Index. By extracting these values for each indicator, a CSV can be created.\nBased on the above thought, the answer is:\nIndicators,City Quality of Life Index,People Satisfaction Index\nEconomic Indicator,70,75\nSocial Indicator,65,85\nEnvironmental Indicator,86,78\nHealth Indicator,90,92" } ], "id_raw": 777 }, { "id": "usJnxYs8LKEbtFg0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which Index has overall better values across all the indicators?" }, { "from": "gpt", "value": "To answer this question, I will analyze the height of the stack corresponding to each Index for each Indicator category. For example, in the case of the Economic Indicator, the values for the City Quality of Life Index and the People Satisfaction Index are 70 and 75, respectively {Economic Indicator}[0.201, 0.265, 0.287, 0.903]. By comparing these values for all categories {City Quality of Life Index}[0.112, 0.595, 0.287, 0.903];[0.331, 0.617, 0.506, 0.903];[0.550, 0.525, 0.725, 0.903];[0.768, 0.507, 0.943, 0.903] and {People Satisfaction Index}[0.112, 0.265, 0.287, 0.595];[0.331, 0.243, 0.506, 0.617];[0.550, 0.181, 0.725, 0.525];[0.768, 0.102, 0.943, 0.507], I can determine which Index generally achieves better values across all indicators.\nBased on the above thought, the answer is:\nPeople Satisfaction Index. This Index generally achieves better values across all indicators compared to the City Quality of Life Index." } ], "id_raw": 777 }, { "id": "ZIXqpHC5apBGE2hK", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_12.png", "conversations": [ { "from": "human", "value": "\nWhich Indicator has the largest total values among the City Quality of Life Index and People Satisfaction Index?" }, { "from": "gpt", "value": "To calculate this, I will gather the total value of both the City Quality of Life Index and People Satisfaction Index for each Indicator. Using the coordinates of each bar and the value listed on top of the bars, I can establish that, for instance, the total value for the Economic Indicator is 70 (City Quality of Life Index) + 75 (People Satisfaction Index){Economic Indicator}[0.201, 0.265, 0.287, 0.903]. Applying this to every Indicator, I can compare these totals.\nBased on the above thought, the answer is:\nHealth Indicator." } ], "id_raw": 777 }, { "id": "1lzvd588vICPuLvV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart you observed, could you please convert the viewership data into a CSV format table?" }, { "from": "gpt", "value": "Given the information from the X-axis {Streaming Platforms label}[0.461, 0.952, 0.603, 0.975] and bars, it is clear that this axis represents the different streaming platforms. The Y-axis represents viewership in millions, as indicated by the {ylabel}[0.015, 0.348, 0.029, 0.617]. The viewership for each streaming platform is split into two sections according to the {legend}[0.893, 0.074, 0.978, 0.151], {2019 viewership}[0.937, 0.083, 0.973, 0.106] and {2020 viewership}[0.937, 0.118, 0.972, 0.141]. Thus, to create a CSV table, the platform names from the x-axis are required, as well as the two viewership values corresponding to each platform from the bars. For example, for Netflix the viewership figures come from {the 2019 bar}[0.121, 0.567, 0.294, 0.903] and {the 2020 bar}[0.121, 0.148, 0.294, 0.567], which can be read as approximately 550 million and 685 million respectively.\nBased on the above thought, the answer is:\nStreaming_Platform,2019_Viewership,2020_Viewership\nNetflix,550,685\nHBO,620,690\nAmazon_Prime,220,300\nDisney+,130,230" } ], "id_raw": 778 }, { "id": "QOdzoyKPl2vJ9UCZ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which streaming platform showed the most significant year-on-year growth from 2019 to 2020? And provide your reasoning." }, { "from": "gpt", "value": "This question requires a comparison of the growth rates of the four platforms from 2019 and 2020. Growth rate can be calculated as the increase in viewership from 2019 to 2020 divided by the viewership for 2019. Using Netflix as an example, the growth rate would be (685-550)/550, based on the {orange bar for 2020}[0.121, 0.567, 0.294, 0.903] and the {blue bar for 2019}[0.121, 0.148, 0.294, 0.567] which represent the 2020 and 2019 viewership values respectively. The same calculation can be done for the other platforms using the values from their respective pairs of bars. Upon calculation, we see that {Disney+'s bars}[0.771, 0.823, 0.944, 0.903]; [0.771, 0.683, 0.944, 0.823], which indicate viewership values of about 130 million and 230 million for 2019 and 2020 respectively, yield the largest growth rate, indicating the most significant growth.\nBased on the above thought, the answer is:\nDisney+. This is calculated by comparing viewership growth rates from 2019 to 2020 of all platforms. Disney+'s increase in viewership represents a growth rate of nearly 77%, the highest among the platforms." } ], "id_raw": 778 }, { "id": "IOJPVN7NpH9sC3q7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_13.png", "conversations": [ { "from": "human", "value": "\nWhich streaming platform had the greatest increase in viewership from 2019 to 2020?" }, { "from": "gpt", "value": "To answer this question, I need to determine the viewership difference for each platform between 2019 and 2020. This can be obtained by subtracting the 2019 viewership from the 2020 viewership for each platform. For example, for Netflix, I would subtract the {2019 value}[0.121, 0.567, 0.294, 0.903] of about 550 million from the {2020 value}[0.121, 0.148, 0.294, 0.567] of about 685 million to get approximately 135 million. Following similar procedures for HBO, Amazon Prime and Disney+, finally Disney+ has such {two bars}[0.771, 0.823, 0.944, 0.903]; [0.771, 0.683, 0.944, 0.823] indicates that it had the greatest increase of approximately 100 million.\nBased on the above thought, the answer is:\nDisney+" } ], "id_raw": 778 }, { "id": "Knf1q2D0SHQBsj6A", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart for government policy effectiveness, can you convert the dataset into a structured CSV format?" }, { "from": "gpt", "value": "Sure, let's start by looking at the x-axis ticks: {Subdued Expansion}[0.101, 0.919, 0.238, 0.942], {Gradual Increase}[0.254, 0.919, 0.372, 0.942], {Substantial Increase}[0.386, 0.919, 0.527, 0.942], {Rapid Expansion}[0.542, 0.919, 0.657, 0.942], {Unstable Behavior}[0.679, 0.919, 0.807, 0.942], and {Sharp Decrease}[0.831, 0.919, 0.941, 0.942], which represent different government policies. The y-axis represents the impacts, which range from 0 to 200. To create the CSV table, we need to consider the four different stacks within each bar which signify impacts on Economy, Health, Environment, and Tourism, as shown in the {legend}[0.839, 0.074, 0.978, 0.220]. Thus, for each policy, we need to estimate the impact on these four sectors based on the length of the corresponding section in the bar. For example, let's take the policy {Subdued Expansion}: starting from the lowest stack to the top, the impact on Economy is 70 (from the annotation inside the bar), Health is 30, Environment is 45, and Tourism is 30. Applying this method to all bars, we can create the following CSV structure.\nBased on the above thought, the answer is:\nPolicies,Economy,Health,Environment,Tourism\nSubdued Expansion,70,30,45,30\nGradual Increase,50,24,32,45\nSubstantial Increase,30,15,25,40\nRapid Expansion,60,80,65,60\nUnstable Behavior,45,60,20,75\nSharp Decrease,20,70,35,20" } ], "id_raw": 779 }, { "id": "lBIpIJIsAA6mP4i7", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which sector seems to benefit the most from government policies overall and why do you think so?" }, { "from": "gpt", "value": "To find the sector that benefits the most from all policies, I need to consider the combined length of the same-colored sections in all bars. Looking at the colors assigned to each sector in the {legend}[0.839, 0.074, 0.978, 0.220], I can see the darkest color, representing {Economy}[0.884, 0.083, 0.948, 0.106] seems to be taking a large chunk in all the bars. Across all policy bars, the blue sections of the bars are noticeably longer for the majority of policies. Hence, it seems that the Economy sector benefits the most from the government policies.\nBased on the above thought, the answer is:\nThe Economy sector, because it has the longest combined section length in the bars, indicating it benefits the most across all government policies." } ], "id_raw": 779 }, { "id": "BqVsiIokodsGvRkA", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_52_14.png", "conversations": [ { "from": "human", "value": "\nWhich policy has the highest cumulative impact across all sectors?" }, { "from": "gpt", "value": "To find the policy with the highest cumulative impact, I need to consider the total length of each bar. That represents the sum of the impacts in Economy, Health, Environment, and Tourism sectors. {Rapid Expansion}[0.542, 0.102, 0.657, 0.903] is the tallest bar with a height reaching up to 265 as per the y-ticks that means it has the highest cumulative impact across all sectors.\nBased on the above thought, the answer is:\nRapid Expansion." } ], "id_raw": 779 }, { "id": "fVD1E9NLFeobQFR9", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you have observe, can you convert the data into a CSV formatted table?" }, { "from": "gpt", "value": "To convert the data into a CSV formatted table, I first look at the y-tick labels to determine the categories, which are {GDP}[0.077, 0.852, 0.103, 0.891], {Unemployment rate}[0.012, 0.705, 0.103, 0.840], {Inflation rate}[0.042, 0.557, 0.103, 0.649], {Retail sales}[0.048, 0.409, 0.103, 0.491], {Trade balance}[0.037, 0.261, 0.103, 0.360], and {Interest rates}[0.040, 0.114, 0.103, 0.208]. I also notice that the x-tick labels represent the corresponding values based on the length of {the blue bars}, which shows the values from {0}[0.103, 0.939, 0.111, 0.957] to {40000}[0.951, 0.939, 0.988, 0.957]. Now we need to extract the lengths of {the blue bars}[0.107, 0.800, 0.876, 0.918] corresponding to each category using the boundary coordinates and the x-tick labels to convert it into actual values.\nBased on the above thought, the answer is:\nIndicators,Values\nGDP,35689\nUnemployment rate,14.2\nInflation rate,8.3\nRetail sales,25648\nTrade balance,1724\nInterest rates,0.5" } ], "id_raw": 787 }, { "id": "iMmKC4JigeV2yaDu", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which economic indicator has the worst performance? And can you explain why do you think so?" }, { "from": "gpt", "value": "This question requires me to compare the lengths of {the blue bars} of each economic indicator. Based on the length and the x-axis labels, GDP has the highest value around 35689 which is generally a good performance; Unemployment Rate and Inflation Rate are around 14.2 and 8.3 respectively, which are not ideal as lower values are prefered here; Retail Sales has a decent performance with the value about 25648, higher value is generally better; Trade Balance reaches to 1724 which is good as we prefer a positive balance; Interest Rate is around 0.5 which can be considered as normal. Therefore, compared to the others, the Unemployment Rate performs the worst given its relatively high value although lower values are preferred.\nBased on the above thought, the answer is:\nUnemployment Rate. Because it has a high value compared to the normal range and unemployment rate is an indicator where lower values are more favorable." } ], "id_raw": 787 }, { "id": "H92tpTkRC8yixDtd", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat's the value difference between the Employment rate and the Inflation rate according to the chart?" }, { "from": "gpt", "value": "To find the difference between the Employment rate and the Inflation rate, first locate the corresponding horizontal bars. {The Unemployment Rate bar}[0.107, 0.652, 0.224, 0.771] extends to around 14.2 on the x-axis, while {The Inflation Rate bar}[0.107, 0.505, 0.189, 0.623] extends to around 8.3 on the x-axis. Take the larger one and subtract the smaller one to calculate the difference.\nBased on the above thought, the answer is:\n5.9" } ], "id_raw": 787 }, { "id": "d1kKNcRcf1VNfRbU", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a CSV table as per the periods?" }, { "from": "gpt", "value": "Firstly, from the chart, I see that the {x-axis}[0.488, 0.964, 0.569, 0.981] represents the \"Index Value\" and the {y-axis}[0.015, 0.871, 0.072, 0.204] represents different \"Periods\" from 1-8. The plotted bars signify the two indices: namely \"{Creativity Index}\"[0.795, 0.062, 0.906, 0.080] and \"{Design Innovation Index}\"[0.795, 0.088, 0.964, 0.106] according to the legend. Each period has two bars adjacent to each other. For example, {Period 1}[0.015, 0.871, 0.072, 0.889] has bars represented by [0.081, 0.849, 0.148, 0.878] and [0.081, 0.878, 0.202, 0.908] for Creativity and Design Innovation Indices respectively. The width of the bar represents the index value, which could be calculated proportionally to the width of the x-axis. Using similar process for other periods, I can extract data from the chart and subsequently arrange it for the CSV table.\nBased on the above thought, the answer is:\nPeriods,Creativity Index,Design Innovation Index\nPeriod 1,0.75,1.35\nPeriod 2,2.45,0.74\nPeriod 3,4.32,1.95\nPeriod 4,5.35,1.24\nPeriod 5,3.80,4.20\nPeriod 6,6.55,2.80\nPeriod 7,7.60,3.95\nPeriod 8,8.01,4.68" } ], "id_raw": 790 }, { "id": "WX9KzmF9eHsDUJtI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nBy analysing the chart, which index depicts a more consistent trend over the progressing periods?" }, { "from": "gpt", "value": "Analyzing the chart, I observe that the {Creativity index bars}[0.081, 0.849, 0.798, 0.193] have quite a steady increment despite a few fluctuations. In contrast, {Design Innovation Index bars}[0.081, 0.878, 0.500, 0.223] present a pattern with much more abrupt changes over the periods, for example a peak value of 4.20 achieved at period 5 against the regular slow progression. Hence, it could be reasoned that the Creativity Index maintains a more consistent trend compared to Design Innovation Index.\nBased on the above thought, the answer is:\nCreativity Index shows a more consistent trend." } ], "id_raw": 790 }, { "id": "ix73ROTTJxsp6b3M", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_53_10.png", "conversations": [ { "from": "human", "value": "\nWhich period has the highest Creativity Index?" }, { "from": "gpt", "value": "To answer this, I need to look at each Creativity Index bar for all periods. For example, {the Creativity Index bar for Period 1}[0.081, 0.849, 0.148, 0.878] represents a Creativity Index of 0.75. By comparing it with all the other Creativity Index bars that are associated with different periods such as {Period 2}[0.081, 0.751, 0.301, 0.780], {Period 3}[0.081, 0.653, 0.468, 0.683] and so on, I can determine that {the Creativity Index bar for Period 8}[0.081, 0.164, 0.798, 0.193] has the most significant x-coordinate, signifying the highest value of 8.01.\nBased on the above thought, the answer is:\nPeriod 8." } ], "id_raw": 790 }, { "id": "vB3BIFixqEAKMWy2", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nFrom what you observed, could you convert the bar chart data to CSV format table?" }, { "from": "gpt", "value": "From the chart title that shows \"Region Migration\" I can see that the y-axis represents the different regions which include { North America }[0.019, 0.839, 0.145, 0.867], { Europe }[0.083, 0.687, 0.145, 0.715], { Asia }[0.108, 0.535, 0.145, 0.563], { Africa }[0.095, 0.383, 0.145, 0.411] and { Australia }[0.068, 0.231, 0.145, 0.259] . The x-axis displays the scale rounding from the minimum of 0 to the maximum of 24. From the sublegend [0.350, 0.034, 0.788, 0.084], it is clear that there are two types of movements: urban [0.405, 0.045, 0.555, 0.073] and rural [0.639, 0.045, 0.781, 0.073]. The length of the bars shows the proportion of urban and rural movement in each region. For example, for North America, the length of the {Urban Movement bar}[0.157, 0.812, 0.498, 0.888] corresponds to 10.2 and the length of {Rural Movement bar}[0.498, 0.812, 0.661, 0.888] corresponds to 4.9. Similarly, I figured out the rest of the values from the length of the bars for each respective regions.\nBased on the above thought, the answer is:\nRegion, Urban Movement, Rural Movement\nNorth America, 10.2, 4.9\nEurope, 7.1, 5.3\nAsia, 14.3, 5.9\nAfrica, 18.7, 4.8\nAustralia, 2.5, 2.3" } ], "id_raw": 795 }, { "id": "RFpzsUOZYebVWXXV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, which region has the highest balance between urban and rural migration, and why would you say so?" }, { "from": "gpt", "value": "The balance between urban and rural migration in a region implies that the population migrating within urban spots and rural spots are almost equal. To find out this, I need to observe the bar sizes, where the lengths of the Urban Movement and Rural Movement bars almost the same. This is {Australia}[0.068, 0.231, 0.145, 0.259]. The respective bars {[0.157, 0.204, 0.240, 0.280]; [0.240, 0.204, 0.317, 0.280]} show that the urban instigated population movement accounts for ~2.5 and the rural movement accounts for ~2.3. The difference between these values is minimal compared to other regions indicating a balance of population migration between urban and rural areas.\nBased on the above thought, the answer is:\nAustralia. It's due to the fact they have the minimal difference between urban and rural population movement compared to other regions." } ], "id_raw": 795 }, { "id": "oy9RMTRd2RayM8Wi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the Urban Movement and Rural Movement in Asia?" }, { "from": "gpt", "value": "Using the bar lengths, I can determine the values. First, I observe {The Urban Movement bar for Asia}[0.157, 0.508, 0.635, 0.584], which approximately lines up at the 14.3 position on the x-axis. Then I observe {The Rural Movement bar for Asia}[0.635, 0.508, 0.832, 0.584], approximately at the 20.2 position on the x-axis. Now, to calculate the difference between these two values, I subtract the lesser value from the larger value (20.2 - 14.3).\nBased on the above thought, the answer is:\n5.9" } ], "id_raw": 795 }, { "id": "OZhXo3GAmOiLN582", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the detailed data utilization for \"Cloud Storage\" by all sectors as recorded in the horizontal stacked bar chart?" }, { "from": "gpt", "value": "In the given structured chart data, to extract the \"Cloud Storage\" information, I observe the horizontal bars for {Cloud Storage}[0.222, 0.298, 0.368, 0.364]. Each horizontal bar corresponds to one sector, showing how much of the \"Cloud Storage\" stage is utilized proportionally. I need to look at the x-range values at the end of each corresponding {bar}[x0, y0, x1, y1] to see the exact usage percentage. For {Retail}[0.222, 0.298, 0.286, 0.364], the x-range starts at 0.00 and ends at 4.70 suggesting a usage of 4.70%. The same process is repeated for each sector to fill in the table with correct values for \"Cloud Storage\" data utilization.\nBased on the above thought, the answer is:\nSector,Cloud Storage\nRetail,5.7\nTransport,7.2\nEducation,3.2\nHealthcare,8.9\nFinance,5.1\nGovernment,9.7" } ], "id_raw": 796 }, { "id": "KIv0tukYWd18rb3v", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nWhich sector has integrated Big Data the most evenly across all stages, according to the chart?" }, { "from": "gpt", "value": "To identify the sector with the most even distribution of Big Data utilization across all stages, I examine the lengths of each color segment within the horizontal bars dedicated to different sectors and stages. The {sector bars}[x0, 0.168, x1, yt] extend from \"Big Data from IoT\" to \"AI with Big Data\". The most even distribution would be indicated by bars of comparable lengths across all stages. For example, the {Retail bar}[0.222, 0.168, 0.319, 0.233] shows a significant decline, which is steeper than other sectors'. By visually comparing the length of colored segments for each sector, I can determine that {Government}[0.222, 0.821, 0.463, 0.887] shows less variance in the lengths from {IoT}[0.222, 0.168, 0.945, 0.233] to {AI}[0.405, 0.821, 0.463, 0.887], suggesting a relatively even distribution compared to all others.\nBased on the above thought, the answer is:\nThe Government sector. Its horizontal bar segments from Big Data through IoT to AI with Big Data show the least variance in length, indicating a more even integration of Big Data across all stages." } ], "id_raw": 796 }, { "id": "5IRgUDHAOhlc4nFM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_1.png", "conversations": [ { "from": "human", "value": "\nHow much does the \"Finance\" sector contribute to the total utilization percentage for \"Big Data from IoT\" compared to other sectors?" }, { "from": "gpt", "value": "To ascertain the contribution of the Finance sector to \"Big Data from IoT\" utilization, I look at the structured information regarding each horizontal bar that corresponds to this stage. The {Finance} sector bar for \"Big Data from IoT\" is represented as [0.222, 0.168, 0.759, 0.233]. The x-range provided (0.00 -> 47.50) indicates the percentage usage by {Finance}[0.222, 0.168, 0.759, 0.233]. Adding up similar values for each sector gives the total: {Retail}[0.222, 0.168, 0.319, 0.233] contributes 8.6%, {Transport}[0.222, 0.168, 0.438, 0.233] contributes 10.5%, {Education}[0.222, 0.168, 0.499, 0.233] contributes 5.4%, {Healthcare}[0.222, 0.168, 0.672, 0.233] contributes 15.3%, {Finance}[0.222, 0.168, 0.759, 0.233] contributes 7.7%, and {Government}[0.222, 0.168, 0.945, 0.233] contributes 16.4%. The percentages should then be calculated individually to derive {Finance}'s contribution.\nBased on the above thought, the answer is:\n15.76%" } ], "id_raw": 796 }, { "id": "0Dy6clZtR0n2YHWz", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data presented in the bar chart to a CSV formatted table?" }, { "from": "gpt", "value": "The x-axis of the chart represents the number of studies as indicated by {\"Number of Studies\"}[0.517, 0.942, 0.679, 0.970] on the x-axis. The y-axis represents different research fields, as indicated by the labels {\"Physics\"}[0.161, 0.134, 0.225, 0.162], {\"Chemistry\"}[0.136, 0.268, 0.225, 0.296], {\"Biology\"}[0.161, 0.401, 0.225, 0.429], {\"Computer Science\"}[0.066, 0.535, 0.225, 0.563], {\"Artificial Intelligence\"}[0.047, 0.669, 0.225, 0.697], and {\"Quantum Computing\"}[0.043, 0.802, 0.225, 0.830]. I need to check the width of each bar for each research field to obtain the research details. For instance, the bar representing Physics is located at [0.237, 0.111, 0.839, 0.178] with the X-coordinate of the right end around 0.839. Scaling this to the tick marks on the x-axis, we can see that the number of studies is approximately 5000.\nBased on the above thought, the answer is:\nResearch Field, Studies Published\nPhysics, 5000\nChemistry, 4580\nBiology, 5100\nComputer Science, 3600\nArtificial Intelligence, 5700\nQuantum Computing, 2600" } ], "id_raw": 797 }, { "id": "0HtS6dvBUl6oNdLh", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, could you identify a trend or make a conclusion about these research fields?" }, { "from": "gpt", "value": "The number of studies published in each field is represented by the length of the bars. From the {\"Physics\"}[0.237, 0.111, 0.839, 0.178], {\"Chemistry\"}[0.237, 0.245, 0.788, 0.312], {\"Biology\"}[0.237, 0.379, 0.851, 0.446], {\"Computer Science\"}[0.237, 0.512, 0.670, 0.579], {\"Artificial Intelligence\"}[0.237, 0.646, 0.923, 0.713], and {\"Quantum Computing\"}[0.237, 0.780, 0.550, 0.847] bars we observe that the Artificial Intelligence field has seen the highest amount of published studies, indicating increased interest and research activity. While Quantum Computing is an emerging field with fewer studies published, it shows great potential and is rapidly growing.\nBased on the above thought, the answer is:\nThere is a rising interest in the field of Artificial Intelligence which has the most number of published studies, whereas Quantum Computing, while currently having fewer studies, seems a potential area of interest in scientific research due to its rapid growth." } ], "id_raw": 797 }, { "id": "bOKmUfRkyyAYZXLN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_2.png", "conversations": [ { "from": "human", "value": "\nWhich research field has the highest number of studies published according to the chart?" }, { "from": "gpt", "value": "The number of studies published in each research field is represented by the length of the bars on the x-axis. The longest bar indicates the research field with the most studies published. By comparing the length of the bars {\"Physics\"}[0.237, 0.111, 0.839, 0.178], {\"Chemistry\"}[0.237, 0.245, 0.788, 0.312], {\"Biology\"}[0.237, 0.379, 0.851, 0.446], {\"Computer Science\"}[0.237, 0.512, 0.670, 0.579], {\"Artificial Intelligence\"}[0.237, 0.646, 0.923, 0.713], and {\"Quantum Computing\"}[0.237, 0.780, 0.550, 0.847], we find that the bar for {\"Artificial Intelligence\"}[0.237, 0.646, 0.923, 0.713] is the longest, indicating that it has the highest number of published studies.\nBased on the above thought, the answer is:\nArtificial Intelligence" } ], "id_raw": 797 }, { "id": "DOBvNIlYFkTxm1jh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nCan you please provide a structured breakdown of the economic data shown on the bar chart with the categories and socio-economic factors in a CSV format?" }, { "from": "gpt", "value": "From the information given by the chart legend located at [0.291, 0.879, 1.026, 0.971], there are six categories: {Fluctuating Decline}[0.347, 0.890, 0.498, 0.918], {Gradual Downturn}[0.347, 0.932, 0.506, 0.960], {Marginal Improvement}[0.589, 0.890, 0.787, 0.918], {Inexorable Rise}[0.589, 0.932, 0.722, 0.960], {Stable Growth}[0.870, 0.890, 0.994, 0.918], {Declining Pattern}[0.870, 0.932, 1.019, 0.960]. We also have 5 different socio-economical factors as shown on the y-axis: {Online Education Participation}[0.062, 0.738, 0.324, 0.766], {Telecommunication Advancements}[0.019, 0.577, 0.324, 0.605], {Alternative Energy Consumption}[0.042, 0.417, 0.324, 0.445], {AI Adoption}[0.224, 0.256, 0.324, 0.284], {Smart Home Usage}[0.155, 0.095, 0.324, 0.123]. The value for each category in each socio-economic factor can be estimated by the length of the corresponding bar on the x-axis and comparing it with the scale from {0}[0.331, 0.845, 0.342, 0.873] to {50}[0.670, 0.845, 0.692, 0.873].\nBased on the above thought, the answer is:\nCategories,SmartHomeUsage,AIAdoption,AltEnergyConsumption,TeleCommunicationAdvancements,OnlineEducationParticipation\nFluctuating Decline,8.7,6.5,7.8,15.8,10.5\nGradual Downturn,7.3,5.2,7.2,10.1,9.5\nMarginal Improvement,8.7,8.0,7.9,17.0,11.1\nInexorable Rise,9.8,9.2,8.5,18.8,12.0\nStable Growth,8.5,8.6,7.7,17.1,11.3\nDeclining Pattern,7.0,5.1,6.2,10.2,8.6" } ], "id_raw": 799 }, { "id": "ahURSwE5EODBxISv", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which category scenarios can we expect stability in AI Adoption?" }, { "from": "gpt", "value": "We need to focus on the 'AI Adoption'[0.224, 0.256, 0.324, 0.284] rows depicted by the bars on the y-axis. The six bars that correspond to the six existing categories in this row are at coordinates [0.381, 0.227, 0.417, 0.307][0.417, 0.227, 0.472, 0.307][0.472, 0.227, 0.536, 0.307][0.536, 0.227, 0.595, 0.307][0.595, 0.227, 0.630, 0.307][0.630, 0.227, 0.665, 0.307]. Based on the length of these bars, the 'AI Adoption' scenario does not show drastic fluctuations for the category scenarios {Stable Growth}[0.870, 0.890, 0.994, 0.918] and {Marginal improvement}[0.589, 0.890, 0.787, 0.918]. So they depict relative stability.\nBased on the above thought, the answer is:\nStable Growth and Marginal improvement scenarios indicate stability in AI Adoption." } ], "id_raw": 799 }, { "id": "5l8R3bcfvmOPRlVC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_4.png", "conversations": [ { "from": "human", "value": "\nWhich category indicates the highest increase in TeleCommunication Advancements?" }, { "from": "gpt", "value": "We need to focus on the TeleCommunication Advancements rows depicted by the bars on the y-axis at coordinates [0.019, 0.577, 0.324, 0.605]. The six bars that correspond to the six categories in this row are at coordinates [0.445, 0.548, 0.515, 0.629],[0.515, 0.548, 0.632, 0.629],[0.632, 0.548, 0.762, 0.629],[0.762, 0.548, 0.880, 0.629],[0.880, 0.548, 0.951, 0.629]. By comparing these bars' length, we can see that the longest bar, indicating the highest value, is for the category {Inexorable Rise}[0.589, 0.932, 0.722, 0.960].\nBased on the above thought, the answer is:\nInexorable Rise." } ], "id_raw": 799 }, { "id": "YSf76oyI6N1mK03J", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nBased on the horizontal bar chart received, could you put the data it represents into a table in CSV format?" }, { "from": "gpt", "value": "From the six labels visible on the Y-axis indicating {\"Internet Users\"}[0.147, 0.149, 0.270, 0.177], {\"Podcast Listeners\"}[0.118, 0.265, 0.270, 0.293], {\"Online Ad Spend\"}[0.125, 0.381, 0.270, 0.409], {\"Social Media Engagement\"}[0.044, 0.497, 0.270, 0.525], {\"Video Streaming Subscribers\"}[0.019, 0.613, 0.270, 0.641], {\"Third-Party Content Creators\"}[0.020, 0.729, 0.270, 0.757], and {\"OTT Platforms\"}[0.147, 0.845, 0.270, 0.873], I can determine that there are seven different categories in the data. There are two bars grouped together for each category, with {\"2019 values\"}[0.570, 0.035, 0.614, 0.063] represented by the color \"#20b2aa\", and {\"2020 values\"}[0.698, 0.035, 0.741, 0.063] represented by the color \"#da70d6\", as indicated by {the chart's legend}[0.515, 0.024, 0.748, 0.074]. For each category, I need to decode the corresponding 2019 and 2020 values by using the width of the bars and the labels on the X-axis. For example, for \"Internet Users\", the widths of the {2019 bar}[0.282, 0.131, 0.327, 0.189] and the {2020 bar}[0.327, 0.131, 0.577, 0.189] correspond to the X-axis labels at around 22 and 120 respectively. Therefore, the 2019 value for \"Internet Users\" is 22 million and the 2020 value is 120 million.\nBased on the above thought, the answer is:\nCategory,2019,2020\nInternet Users,22,120\nPodcast Listeners,40,80\nOnline Ad Spend,35,130\nSocial Media Engagement,50,200\nVideo Streaming Subscribers,60,260\nThird-Party Content Creators,20,150\nOTT Platforms,15,70" } ], "id_raw": 800 }, { "id": "iSRuKgNCxBg9FvM2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nJudging from the chart, which category of new media and digital content has achieved the fastest growth in 2020 compared to 2019 and can explain what factors might contribute to its rapid growth?" }, { "from": "gpt", "value": "By comparing the widths of the 2019 and 2020 bars for each category, it can be observed that the 'Video Streaming Subscribers' category has seen the most growth. The width of {the 2019 bar}[0.282, 0.595, 0.407, 0.653] and {the 2020 bar}[0.407, 0.595, 0.948, 0.653] correspond to the X-axis labels of approximately 60 and 260, indicating a growth difference of 200. This rapid growth can be attributed to several factors. The year 2020 saw a global pandemic, which could have led to more people staying at home and consequently more people subscribing to video streaming services. Besides, the substantial improvement and wide adoption of internet technology have made video streaming services more user-friendly and accessible, thus attracting a larger population of subscribers.\nBased on the above thought, the answer is:\nThe 'Video Streaming Subscribers' category has achieved the fastest growth. Possible factors for this growth include increased home time due to the global pandemic and the advancement and wider adoption of internet technology making these services more accessible." } ], "id_raw": 800 }, { "id": "Jz8tlwAMTvVFmnPF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which category showed the most significant growth from 2019 to 2020?" }, { "from": "gpt", "value": "To determine the category with the most significant growth from 2019 to 2020, I need to calculate the difference between the 2019 and 2020 bars for each category. Using the width of the bars and the labels on the X-axis, I can derive the value for each year. For example, in the 'Video Streaming Subscribers' category, the width of the {2019 bar}[0.282, 0.595, 0.407, 0.653] and the {2020 bar}[0.407, 0.595, 0.948, 0.653] correspond to the X-axis labels at around 60 and 260 respectively. So, the growth in this category is 260-60=200. By applying the same method to the rest of the categories, it can be concluded that the largest difference is in the 'Video Streaming Subscribers' category, with a growth of 200.\nBased on the above thought, the answer is:\nVideo Streaming Subscribers" } ], "id_raw": 800 }, { "id": "lIlonY0hO6kypDCI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nThe bar chart before me depicts Scientific Research and Technological Innovation in different nations. Could you help me represent this as a CSV table?" }, { "from": "gpt", "value": "Looking at this bar chart, I see that the {x-axis}[0.245, 0.923, 0.970, 0.942] displays values ranging from {0}[0.240, 0.942, 0.251, 0.970] to {50}[0.959, 0.942, 0.981, 0.970], which represent the value of scientific research or technological innovation. The {y-axis}[0.245, 0.923, 0.245, 0.170] contains labels for two categories {Scientific Research}[0.069, 0.307, 0.233, 0.335] and {Technological Innovation}[0.019, 0.763, 0.233, 0.791]. Also visible is a {legend}[0.432, 0.034, 0.783, 0.126] that identifies five nations- US, EU, China, India, and Brazil. They are associated with the bars corresponding to their data on the chart. For example, for US' Scientific Research the bar[0.245, 0.204, 0.281, 0.432] aligns with the value 2.5 and for Technological Innovation, the bar[0.245, 0.660, 0.297, 0.888] aligns with the value 3.6. This process repeats for each bar, enabling me to confidently construct a CSV table as per the data displayed in the chart.\nBased on the above thought, the answer is:\n,Nation,Scientific Research,Technological Innovation\n0,US,2.5,3.6\n1,EU,5.1,7.2\n2,China,1.1,1.5\n3,India,5.3,6.4\n4,Brazil,2.0,3.0" } ], "id_raw": 801 }, { "id": "DkSFcT7sy06Y6gA2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which country do you think is doing better in terms of progressing in Scientific Research and Technological Innovation, and why?" }, { "from": "gpt", "value": "This requires analysis of both Scientific Research and Technological Innovation data. Taking the first parameter, Scientific Research, the largest {bar}[0.281, 0.204, 0.355, 0.432] represents {EU}[0.488, 0.087, 0.511, 0.115], indicating that the EU is leading in scientific research. On the other hand, for Technological Innovation, {India}[0.595, 0.087, 0.637, 0.115] is leading with the largest {bar}[0.424, 0.660, 0.516, 0.888]. Both categories are equally important so it is not possible to determine which country is performing better overall without a more in-depth analysis. However, from the chart, EU leads in Scientific Research and India in Technological Innovation.\nBased on the above thought, the answer is:\nFrom an overall high-level perspective, it's tough to determine as EU is leading in Scientific Research and India in Technological Innovation. Both categories play a critical role and the importance of one over the other depends on specific contexts and goals of these countries." } ], "id_raw": 801 }, { "id": "eO5nwHzY3HbcYIGR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which nation shows the largest difference between Scientific Research and Technological Innovation?" }, { "from": "gpt", "value": "By comparing the {bars}[0.245, 0.660, 0.560, 0.888] representing each nation's Scientific Research and Technological Innovation, I can calculate the difference. For instance, in the case of the {US}[0.488, 0.045, 0.511, 0.073], the bars are at {2.5}[0.245, 0.204, 0.281, 0.432] for Scientific Research, and {3.6}[0.245, 0.660, 0.297, 0.888] for Technological Innovation. I observe similar positions for EU, China, India, and Brazil respectively. By calculating the difference for each, I find that {India}[0.595, 0.087, 0.637, 0.115] has a difference of {1.1}[0.371, 0.204, 0.448, 0.432], [0.424, 0.660, 0.516, 0.888] which is the largest amongst all nations.\nBased on the above thought, the answer is:\nIndia" } ], "id_raw": 801 }, { "id": "CXBUWyrzBXNh10G9", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nPlease convert the bar chart data into a CSV format considering each regional segment within the technology categories." }, { "from": "gpt", "value": "This is a horizontal bar chart showcasing technological development trends across regions. These regions are represented by different colors and divided into segments within each technology category. Taking {\"Internet Speed\"}[0.061, 0.853, 0.175, 0.876] as an example, the segments are divided into five categories, each of which represents a region {\"US\"}[0.186, 0.827, 0.303, 0.897], {\"EU\"}[0.303, 0.827, 0.487, 0.897], {\"China\"}[0.487, 0.827, 0.748, 0.897], {\"India\"}[0.748, 0.827, 0.897, 0.897], {\"Brazil\"}[0.897, 0.827, 0.945, 0.897]. The length of each segment gives us the value for the corresponding category and region. By applying the same process to all technology categories, a CSV table can be generated.\nBased on the above thought, the answer is:\nCategory,US,EU,China,India,Brazil\nInternet Speed,29.3,45.7,65.2,37.1,12.1\nArtificial Intelligence,8.2,12.4,16.4,18.3,14.1\nCybersecurity,22.9,30.2,43.1,33.5,28.7\nBig Data,16.4,21.3,28.7,31.4,24.1\nCloud Computing,12.6,18.5,25.4,23.3,15.2\n5G Connectivity,10.6,16.2,24.3,17.4,10.8" } ], "id_raw": 802 }, { "id": "zoLTYV2kJDaHxKej", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you identify the region that has the most balanced development across all technology categories?" }, { "from": "gpt", "value": "To find out the region with the most balanced development, we need to check the lengths of the segments representing each region inside all categories. By comparing the segment lengths for all regions within the categories - {\"US\"}[0.478, 0.027, 0.499, 0.050], {\"EU\"}[0.478, 0.062, 0.499, 0.085], {\"China\"}[0.573, 0.027, 0.617, 0.050], {\"India\"}[0.573, 0.062, 0.611, 0.085], {\"Brazil\"}[0.691, 0.027, 0.735, 0.050] - we can observe that EU has the most consistent segment lengths, indicating a balanced development across all categories.\nBased on the above thought, the answer is:\nEU. This region has the most balanced development across all technology categories as the length of the segments, representing EU in every category, is relatively consistent." } ], "id_raw": 802 }, { "id": "BljWDHkui8nH9c3K", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_7.png", "conversations": [ { "from": "human", "value": "\nWhich category showed the highest development in the EU region?" }, { "from": "gpt", "value": "The bars in the chart represent different categories and the length of the EU segments inside these bars, represented by {\"EU\"}[0.573, 0.062, 0.611, 0.085] in the legend, represents the EU's development within each category. Looking at the various EU segments - {\"Internet Speed\"}[0.303, 0.827, 0.487, 0.897], {\"Artificial Intelligence\"}[0.219, 0.688, 0.268, 0.757], {\"Cybersecurity\"}[0.278, 0.548, 0.399, 0.618], {\"Big Data\"}[0.252, 0.408, 0.337, 0.478], {\"Cloud Computing\"}[0.236, 0.269, 0.311, 0.338], and {\"5G Connectivity\"}[0.228, 0.129, 0.293, 0.199], we can calculate that the \"Internet Speed\" category has the longest length and thus highest development.\nBased on the above thought, the answer is:\nInternet Speed." } ], "id_raw": 802 }, { "id": "QjzgFHejZBuw4yuK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV table based on the observation of the bar chart depicting the total number of patents issued each year from 2016 to 2020?" }, { "from": "gpt", "value": "To create a CSV table, I will analyze the horizontal bars corresponding to the \"Patents Issued\" category. I notice that for each year, the horizontal bars extend from the y-axis and are color-coded to represent each year. Utilizing the {x-axis tick marks}[0.270, 0.841, 1.102, 0.861] and {the horizontal bars for Patents Issued}[0.274, 0.283, 0.949, 0.340], I can estimate the length of each bar for respective years 2016 to 2020 to convert into numerical values as follows: for {2016}[0.274, 0.283, 0.403, 0.340], the value is approximately 5610, for {2017}[0.403, 0.283, 0.535, 0.340], the value is 5715, and so on.\nBased on the above thought, the answer is:\nYear,Patents Issued\n2016,5610\n2017,5715\n2018,5800\n2019,6000\n2020,6200" } ], "id_raw": 803 }, { "id": "KS3YprjCKNqM6BEp", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which area of the Medical and Biotechnology industry shows the least variation over the 5 years?" }, { "from": "gpt", "value": "Analyzing the variations in bar lengths for each category over the 5 years, I will identify the one with the least change. For example, {Pharmaceutical R&D Spent}[0.274, 0.058, 0.294, 0.114] shows a consistent increase. Observing the {bars representing Medical Devices Manufacturing Revenue}[0.274, 0.171, 0.289, 0.227], which vary from a minimum of $120 billion to a maximum of $130 billion over the period, compared to the others, shows the least variation in terms of absolute value difference and visually presents the smallest changes in bar lengths year over year.\nBased on the above thought, the answer is:\nThe Medical Devices Manufacturing Revenue shows the least variation over the observed 5 years, as indicated by the small changes in bar lengths on the chart." } ], "id_raw": 803 }, { "id": "rFFAa4OTRh1LM3Ti", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage increase in the Private Investments in Biotech from 2016 to 2020?" }, { "from": "gpt", "value": "Reviewing the length of the horizontal bars for Private Investments in Biotech, the actual values can be deciphered. Starting at {2016}[0.274, 0.734, 0.275, 0.790] with a value of $35 billion, and ending at {2020}[0.278, 0.734, 0.279, 0.790] with a value of $44 billion, I can calculate the percentage increase as (44 - 35) / 35 * 100.\nBased on the above thought, the answer is:\n25.71%" } ], "id_raw": 803 }, { "id": "0i61G9lgNe1oq7wS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nFrom the chart displaying technological industry trends, can you provide a CSV formatted table of the data for 5G Coverage across the five markets?" }, { "from": "gpt", "value": "To create a table of 5G Coverage data represented in the chart, I will examine and extract the horizontal bar data for each market. First, the chart's horizontal bars for {5G Coverage}[0.213, 0.204, 0.323, 0.257] and [0.323, 0.204, 0.451, 0.257] and so on show the extent of growth for the US, China, EU, India, and Brazil. From the x-axis tick marks, I can see the bar ending points correspond to values. For {US}[0.213, 0.204, 0.323, 0.257], it appears to end at 56.7, for {China}[0.323, 0.204, 0.451, 0.257] it extends to 75.8, and so on for the rest of the markets by addressing their respective rectangles.\nBased on the above thought, the answer is:\nMarket,5G Coverage\nUS,56.7\nChina,75.8\nEU,50.1\nIndia,48.0\nBrazil,25.6" } ], "id_raw": 805 }, { "id": "h9SxZTrO9T0jCjoC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nWhich market consistently places above average across all technological sectors?" }, { "from": "gpt", "value": "Consistency above average can be determined by analyzing the horizontal bars for each market across all the technology sectors. I'll locate each market's bar for every sector using the coordinate information given for each bar start and end point. For example, {US bars}[0.213, 0.836, 0.323, 0.888] starting with Blockchain, {China bars}[0.323, 0.836, 0.451, 0.888] through to {India bars}[0.548, 0.836, 0.631, 0.888] and {Brazil bars}[0.631, 0.836, 0.686, 0.888]. With 'average' meaning the midpoint along the x-axis, one market's bars are consistently extending beyond the middle, indicating it is above average. Comparing all bars across all sectors, {China}[0.323, 0.204, 0.451, 0.257] shows a clear dominance with its bars extending farther than all other markets, which places it above the average in every sector.\nBased on the above thought, the answer is:\nChina consistently places above average across all technological sectors. Each of China's sector-specific bars extends beyond the midpoint on the x-axis of the chart, which delineates the average value." } ], "id_raw": 805 }, { "id": "MTDudZ3WXqsEzJ3n", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_10.png", "conversations": [ { "from": "human", "value": "\nWhich market shows the least development in Robotics compared to others?" }, { "from": "gpt", "value": "To determine the market with the least development in Robotics, I will compare the lengths of the bar representing Robotics for each market\u2014beginning with the bar ends closest to the y-axis and ending at the recorded values on the x-axis. The corresponding bars' coordinates for {US}[0.213, 0.730, 0.367, 0.783], {China}[0.367, 0.730, 0.548, 0.783], {EU}[0.548, 0.730, 0.690, 0.783], {India}[0.690, 0.730, 0.813, 0.783], and {Brazil}[0.813, 0.730, 0.912, 0.783] are assessed, with Brazil displaying the shortest bar, indicating the lowest development values.\nBased on the above thought, the answer is:\nBrazil." } ], "id_raw": 805 }, { "id": "x2VhWlLgUvl8pFQl", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the data you observed from the bar chart into a CSV format table?" }, { "from": "gpt", "value": "The vertical category labels on the y-axis of the chart include six types of gastronomy related activities namely: {Local Product Consumption}[0.113, 0.189, 0.303, 0.217], {Local Cuisine Uniqueness}[0.127, 0.320, 0.303, 0.348], {Gourmet Festival Attendance}[0.100, 0.451, 0.303, 0.479], {Michelin Starred Restaurant}[0.110, 0.581, 0.303, 0.609], {Number_of_Gastronomy_Books}[0.111, 0.712, 0.303, 0.740], and {Number_of_TV_Shows_Talk_About_Gastronomy}[0.015, 0.843, 0.303, 0.871]. \n\nThe data for each category, per continent, is represented by a series of horizontal bars each having a unique color based on the continent. For example, the {blue bar}[0.313, 0.168, 0.399, 0.233] represents the Local Product Consumption of America, and I can estimate its value by comparing the position of its end with the values in the x-axis. \n\nLooking at {the legend}[0.473, 0.027, 0.816, 0.119], there are 5 different categories represented by unique colors viz, {America}[0.518, 0.038, 0.575, 0.066], {Europe}[0.518, 0.080, 0.567, 0.108], {Asia}[0.642, 0.038, 0.671, 0.066], {Africa}[0.642, 0.080, 0.682, 0.108], and {Australia}[0.748, 0.038, 0.810, 0.066]. \n\nI can then compile each category data for each continent and create a CSV representation.\nBased on the above thought, the answer is:\nCuisine,Local Product Consumption,Local Cuisine Uniqueness,Gourmet Festival Attendance,Michelin Starred Restaurant,num_of_Gastronomy_Books,num_of_TVShows_TalkAbout_Gastronomy\nAmerica,6.5,7.6,5.2,4.4,3.8,6.0\nEurope,8.7,8.3,6.7,5.8,5.2,7.6\nAsia,9.2,9.0,7.6,6.7,6.6,9.0\nAfrica,5.2,6.6,3.5,1.5,1.2,3.0\nAustralia,6.8,6.0,4.3,2.5,2.0,3.8" } ], "id_raw": 806 }, { "id": "O5QYVUkWlPSgUr2P", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which region has the most potential in terms of Gastronomy and why?" }, { "from": "gpt", "value": "The chart represents the level of Gastronomy across all regions, defined by the length of the bars. The higher the bar length for a certain region, the higher the level of Gastronomy in that region across the categories.\nI can observe that {Asia}[0.642, 0.038, 0.671, 0.066] has consistently high values across all categories which are noticeably higher compared to other regions. Based on this, it can be concluded that Asia has high potential owing to the high numbers across the board, notably the {Number of TV Shows Talking About Gastronomy} [0.493, 0.821, 0.613, 0.887] and the {Number of Gastronomy Books} [0.432, 0.691, 0.520, 0.756]. It's apparent that Gastronomy is thriving in Asia in terms of content production and the adoption of local foods and customs.\nBased on the above thought, the answer is:\nAsia shows the most potential. With high levels across all categories, specifically Number of TV Shows talking about gastronomy and Number of Gastronomy books, it signifies an interest and a thriving environment for gastronomy." } ], "id_raw": 806 }, { "id": "Kcp2uYHLflD8lKKF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_11.png", "conversations": [ { "from": "human", "value": "\nIrispect to which cuisine category, does Asia show the highest difference in score compared to America?" }, { "from": "gpt", "value": "To calculate the category with the largest difference in score between Asia and America, I need to compare the lengths of the corresponding bars for each category. The categories from top to bottom on the y-axis are {num_of_TVShows_TalkAbout_Gastronomy}[0.015, 0.843, 0.303, 0.871], {num_of_Gastronomy_Books}[0.111, 0.712, 0.303, 0.740], {Michelin Starred Restaurant}[0.110, 0.581, 0.303, 0.609], {Gourmet Festival Attendance}[0.100, 0.451, 0.303, 0.479], {Local Cuisine Uniqueness}[0.127, 0.320, 0.303, 0.348], and {Local Product Consumption}[0.113, 0.189, 0.303, 0.217]. By examining each pair of bars horizontally, such as the bars length at {num_of_TVShows_TalkAbout_Gastronomy for America and Asia}[0.313, 0.821, 0.392, 0.887] [0.493, 0.821, 0.613, 0.887], it can be concluded that {Local Cuisine Uniqueness}[0.127, 0.320, 0.303, 0.348], with bars at [0.313, 0.298, 0.414, 0.364] for America and [0.524, 0.298, 0.643, 0.364] for Asia, shows the largest difference.\nBased on the above thought, the answer is:\nLocal Cuisine Uniqueness" } ], "id_raw": 806 }, { "id": "zwvy1Ky1HJd30JfD", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nCan you sort the emerging markets and consumer behavior data in a CSV table?" }, { "from": "gpt", "value": "Considering the information from the chart, including the bar positions and the associated market labels, I can create a CSV data table. From examining the chart, I notice the labels {'US'}<[0.472, 0.045, 0.495, 0.073]>, {'EU'}<[0.472, 0.087, 0.495, 0.115]>, {'China'}<[0.578, 0.045, 0.628, 0.073]>, {'India'}<[0.578, 0.087, 0.621, 0.115]>, and {'Brazil'}<[0.711, 0.045, 0.760, 0.073]> on the legend and the corresponding color coded bars {'US'}<[0.202, 0.660, 0.346, 0.888], [0.202, 0.204, 0.346, 0.432]>, {'EU'}<[0.346, 0.660, 0.476, 0.888], [0.346, 0.204, 0.476, 0.432]>, {'China'}<[0.476, 0.660, 0.664, 0.888], [0.476, 0.204, 0.664, 0.432]>, {'India'}<[0.664, 0.660, 0.828, 0.888], [0.664, 0.204, 0.828, 0.432]> and {'Brazil'}<[0.828, 0.660, 0.944, 0.888], [0.828, 0.204, 0.944, 0.432]>. The horizontal bar length determines the numerical value for an emerging market, with '0' at the far left side {'0'}<[0.197, 0.942, 0.207, 0.970]> and '55' on the far right {'55'}<[0.379, 0.942, 0.401, 0.970]>. Thus, by matching the bars' color code bars with the corresponding market names in the legend, then measuring the length of the bars, the CSV format of the data can be generated.\nBased on the above thought, the answer is:\nEmerging Markets,Consumer Behavior\nUS,42\nEU,38\nChina,55\nIndia,48\nBrazil,34" } ], "id_raw": 807 }, { "id": "E8ssU7wPbSz5kdAr", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nBased on the observed bar chart, what is the overall trend of consumer behavior across the emerging markets?" }, { "from": "gpt", "value": "To address this question, I must study the size and direction of the bars across different markets. Looking at the five bars in the chart, including those found at
{'US'}<[0.202, 0.660, 0.346, 0.888]>,
{'EU'}<[0.346, 0.660, 0.476, 0.888]>,
{'China'}<[0.476, 0.660, 0.664, 0.888]>,
{'India'}<[0.664, 0.660, 0.828, 0.888]>, and
{'Brazil'}<[0.828, 0.660, 0.944, 0.888]>, it is clear each bar represents a different length, which parallels different consumer behavior indices. The bars representing China and India extend quite far, indicating strong consumer behavior in those markets. The bars for US show a moderate level of consumer behavior, while EU and Brazil represent the least. Therefore, the dominant trend suggests a higher consumer behavior index in Asian markets- specifically China and India.\nBased on the above thought, the answer is:\nThe overall trend displays strong consumer behavior in the Asian markets, particularly in China and India, followed by more moderate levels in the US. The European and Brazilian markets seem to show weaker consumer behavior." } ], "id_raw": 807 }, { "id": "ift3it07PyKcrk6f", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_12.png", "conversations": [ { "from": "human", "value": "\nWhat emerging market has the most significant consumer behavior index as presented in the chart?" }, { "from": "gpt", "value": "To identify the markets with the highest consumer behavior index, I need to find the bar extending furthest to the right in the chart. In this case, it is the bar positioned at {'China'}<[0.476, 0.660, 0.664, 0.888]>. By comparing its length to the x-axis index I see it extends almost to '55' {[0.379, 0.942, 0.401, 0.970]}. As such, China corroborates with the raw data with the highest consumer behavior index.\nBased on the above thought, the answer is:\nChina." } ], "id_raw": 807 }, { "id": "aP0equ074BFGoRYx", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data you observed from this bar chart about Education Resource Distribution and Utilization into a CSV format table?" }, { "from": "gpt", "value": "To convert this chart into a CSV table, we need to identify the x-axis and y-axis and the segmentation in each bar that shows different trends. The y-axis shows {different categories}[{\"Online Education\"}[0.068, 0.224, 0.216, 0.252]; {\"Classroom Education\"}[0.033, 0.349, 0.216, 0.377]; {\"Homework Assistance\"}[0.025, 0.473, 0.216, 0.501]; {\"Student-Teacher Ratio\"}[0.025, 0.598, 0.216, 0.626]; {\"Education Expenditure\"}[0.019, 0.722, 0.216, 0.750]; {\"College Education\"}[0.059, 0.846, 0.216, 0.874]], while the x-axis shows the quantity for each category and trend. The legend {\"sublegend\"}[0.238, 0.034, 0.971, 0.126] has different colors for trends. For each category, there are five bars with different colors. \nFor instance, let's take the category {\"Online Education\"}[0.068, 0.224, 0.216, 0.252]. There are five bars each representing a different trend. The leftmost bar {\"Continuous decline\"}[0.228, 0.204, 0.365, 0.266] is colored in red and ends around 9.2 on the x-axis. Similarly, the second bar {\"Marginal improvement\"}[0.365, 0.204, 0.445, 0.266] is colored in yellow, and ends around 5.4. I can continue this for all other bars under Online Education, and then repeat with the same method for all other categories.\nBased on the above thought, the answer is:\nCategories,Continuous decline,Marginal improvement,Slight decrease,Slow growth,Inconsistent decline\nOnline Education,9.2,5.4,7.8,4.1,2.2\nClassroom Education,12.1,7.5,10.1,6.5,3.8\nHomework Assistance,7.4,6.3,6.2,7.1,1.5\nStudent-Teacher Ratio,13.6,7.2,8.9,6.2,4.5\nEducation Expenditure,15.7,8.7,10.7,8.1,5.1\nCollege Education,8.2,7.1,6.9,5.9,3.1" } ], "id_raw": 808 }, { "id": "b4geqhQK0Y1x9U7M", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nCan you interpret the trend of 'Slow growth' across all categories in the chart?" }, { "from": "gpt", "value": "To interpret the 'Slow growth' trend across all categories, I need to analyze the length of the bar representing 'slow growth' for each category. The category with the highest quantity would have the best slow growth trend.\nTaking a look at the 'Slow growth' trend (represented in green bars), the quantity for each category are as follows: {\"Online Education\"}[0.561, 0.204, 0.622, 0.266] is about 4.1, {\"Classroom Education\"}[0.669, 0.329, 0.766, 0.391] is about 6.5, {\"Homework Assistance\"}[0.629, 0.453, 0.654, 0.515] is about 7.1, {\"Student-Teacher Ratio\"}[0.669, 0.577, 0.761, 0.640] is about 6.2, {\"Education Expenditure\"}[0.749, 0.702, 0.870, 0.764] is about 8.1, and finally, {\"College Education\"}[0.558, 0.826, 0.645,0.888] has around 5.9. \nBased on this, 'Education Expenditure' category shows the greatest 'Slow growth' trend. And, 'Online Education', despite its prominence, shows the lowest 'Slow growth' trend which could imply a concern in shifting learning modes.\nBased on the above thought, the answer is:\nThe trend 'Slow growth' is fairly diverse across different categories. 'Education Expenditure' shows the most significant slow growth with a quantity of about 8.1. It implies that the investment in education has been continuously increasing, but at a slow rate. In contrast, 'Online Education' shows the least slow growth with a quantity of about 4.1, suggesting that the online education sector has much potential for further expansion. However, all sectors indicated a slow, continued growth, stating an overall improving state in education efforts." } ], "id_raw": 808 }, { "id": "1wB50VCH1dzaujgv", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_54_13.png", "conversations": [ { "from": "human", "value": "\nAs per the bar chart's depiction, which category does receive the least assistance in the 'marginal improvement' trend?" }, { "from": "gpt", "value": "First, I need to locate each category for the trend of 'Marginal improvement'. Then, by comparing the bar lengths of all the categories, I can find the one with the shortest length which receives the least assistance.\nIn the 'Marginal improvement' trend, the height of {\"Online Education\"}[0.365, 0.204, 0.445, 0.266] is around 5.4, {\"Classroom Education\"}[0.460, 0.329, 0.519, 0.391] is around 7.5, {\"Homework Assistance\"}[0.382, 0.453, 0.477, 0.515] is about 6.3, {\"Student-Teacher Ratio\"}[0.486, 0.577, 0.583, 0.640] is around 7.2, {\"Education Expenditure\"}[0.542, 0.702, 0.672, 0.764] is approximately 8.7 and {\"College Education\"}[0.492, 0.826, 0.583, 0.888] is about 7.1. After comparison, the segment for 'Online Education' has the shortest length in the 'Marginal improvement' category making it receive the least assistance.\nBased on the above thought, the answer is:\nOnline Education" } ], "id_raw": 808 }, { "id": "1E8fjtUN4JVSN6wj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nBased on the horizontal bar chart you're seeing, can you extract the data and present it in a table format?" }, { "from": "gpt", "value": "The chart represents ten different economic statistics on the y-axis, with corresponding trend values on the x-axis. Taking the y-axis, I'll collect each y-label from the bar coordinates. For instance, {the bar for \"Gross Domestic Product (GDP)\"}[0.319, 0.818, 0.894, 0.886] shows the name of the statistic and has a text annotation {\"8.3\"}[0.894, 0.843, 0.927, 0.861] at the end, signifying that the trend value for \"Gross Domestic Product (GDP)\" is 8.3. I will repeat this process for all the bars in the chart.\nBased on the above thought, the answer is:\nStatistic,Trend\nGross Domestic Product (GDP),8.3\nSpending,6.7\nEmployment,9.1\nInflation,5.6\nInterest Rates,2.8\nIncome,7.5\nExports,3.4\nImports,4.8\nFactory Orders,7.2\nUnemployment,1.2" } ], "id_raw": 810 }, { "id": "8Onga4qs167HzosZ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nBased on the trend values, what can you infer about the relative performance of the economy?" }, { "from": "gpt", "value": "The length of the bars in the chart provides an idea about the trend of different economic indicators. For instance, the {bar for \"Unemployment\"}[0.319, 0.060, 0.402, 0.127] is very short, indicating that unemployment rates (which are unfavorable when high) are low. Conversely, the {bar for \"Employment\"}[0.319, 0.650, 0.950, 0.717] is significantly long, indicating that the employment rates (which are favorable when high) are high. Also, the high value depicted by the {bar for \"Gross Domestic Product (GDP)\"}[0.319, 0.818, 0.894, 0.886] signifies an increasing economy. Thus, these trends suggest that the economic performance is quite positive and strong in terms of employment and GDP, although there might be areas that require further improvement.\nBased on the above thought, the answer is:\nThe economy is doing well overall, especially in terms of low unemployment and high employment. The Gross Domestic Product is also showing a positive trend, which suggests economic growth. However, some indicators such as Interest Rates may need attention to boost the economic performance further." } ], "id_raw": 810 }, { "id": "462BJAsZ25bJgeNW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_0.png", "conversations": [ { "from": "human", "value": "\nWhich economic indicator has the highest trend value according to the chart?" }, { "from": "gpt", "value": "To find out which economic indicator has the highest trend value, I can compare the length of the bars. For example, {the bar for \"Gross Domestic Product (GDP)\"}[0.319, 0.818, 0.894, 0.886] extends up to an x-coordinate of 0.894. But if I examine {the bar for \"Employment\"}[0.319, 0.650, 0.950, 0.717], it goes up to x=0.950, which suggests that Employment has a higher trend value.\nBased on the above thought, the answer is:\nEmployment." } ], "id_raw": 810 }, { "id": "BKkk3pYbMfhahSzm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, can you construct a table displaying the Innovation Indicators and their corresponding percentages in CSV format?" }, { "from": "gpt", "value": "The input data consists of various Innovation Indicators mentioned on the y-axis {yticks}[{\"Patents\"}[0.196, 0.824, 0.248, 0.848]; {\"Scientific Publications\"}[0.097, 0.746, 0.248, 0.770]; {\"R&D Personnel\"}[0.145, 0.668, 0.248, 0.692]; {\"Startup Creation\"}[0.133, 0.590, 0.248, 0.614]; {\"Venture Capital Investment\"}[0.058, 0.512, 0.248, 0.536]; {\"Collaboration in Innovation\"}[0.060, 0.434, 0.248, 0.458]; {\"Knowledge Transfer\"}[0.112, 0.356, 0.248, 0.380]; {\"Business Expenditures on R&D\"}[0.035, 0.278, 0.248, 0.302]; {\"Entrepreneurial Culture\"}[0.085, 0.200, 0.248, 0.224]; {\"Technological Infrastructure\"}[0.055, 0.122, 0.248, 0.146]] and their corresponding percentages in the {bars_of_group_0} [{'percentage for Patents'}[0.687, 0.822, 0.722, 0.845], {'percentage for Scientific Publications'}[0.467, 0.744, 0.502, 0.767], {'percentage for R&D Personnel'}[0.783, 0.666, 0.818, 0.689], {'percentage for Startup Creation'}[0.812, 0.588, 0.848, 0.611], {'percentage for Venture Capital Investment'}[0.669, 0.510, 0.705, 0.533], {'percentage for Collaboration in Innovation'}[0.538, 0.432, 0.573, 0.455], {'percentage for Knowledge Transfer'}[732, 0.354, 0.776, 0.377], {'percentage for Business Expenditures on R&D'}[0.944, 0.276, 0.988, 0.299], {'percentage for Entrepreneurial Culture'}[0.604, 0.198, 0.639, 0.221], and {'percentage for Technological Infrastructure'}[0.419, 0.120, 0.454, 0.143]]. I can extract these values and construct the CSV data using the labels denoting the innovation indicators as rows and the percentages as the matching values.\nBased on the above thought, the answer is:\nInnovation Indicator, Percentage (%)\nPatents,7.2\nScientific Publications,3.5\nR&D Personnel,8.8\nStartup Creation,9.3\nVenture Capital Investment,6.9\nCollaboration in Innovation,4.7\nKnowledge Transfer,8.1\nBusiness Expenditures on R&D,11.5\nEntrepreneurial Culture,5.8\nTechnological Infrastructure,2.7" } ], "id_raw": 811 }, { "id": "VM5VYPqCl3B9nOHU", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the displayed chart, which area in the entrepreneurship ecosystem needs the most attention and improvement?" }, { "from": "gpt", "value": "The areas needing the most attention in the entrepreneurship ecosystem would be the ones with lower percentages, as these percentages represent their contribution to the ecosystem. By examining the {bars_of_group_0} corresponding to the y-labels, the smallest bar corresponds to 'Technological Infrastructure', which is placed at {'Technological Infrastructure'}[0.055, 0.122, 0.248, 0.146] with its length indicating a percentage of {'2.7%'}[0.419, 0.120, 0.454, 0.143]. Therefore, 'Technological Infrastructure' is the area that requires the most improvement.\nBased on the above thought, the answer is:\nTechnological Infrastructure. As the percentage indicates that it contributes only 2.7% to the Entrepreneurship and Innovation Ecosystem, which is the smallest proportion as compared to all other Innovation Indicators." } ], "id_raw": 811 }, { "id": "AxGT64OywPE4oJLt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage of the highest contribution towards the Entrepreneurship and Innovation Ecosystem?" }, { "from": "gpt", "value": "To answer this, I need to identify the bar with the longest x-axis value. Looking at all the {labels with percentages}, I can see that the longest one is {'Business Expenditures on R&D'}[0.944, 0.276, 0.988, 0.299] which shows '11.5%', and this is the indicator with the highest contribution to the Entrepreneurship and Innovation Ecosystem.\nBased on the above thought, the answer is:\n11.5%" } ], "id_raw": 811 }, { "id": "epWBN11SW7q3oNaj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nConvert the perceived data from the horizontal bar chart into a structured table." }, { "from": "gpt", "value": "Following the labels on the Y-Axis which represents various social issues in order from \"{Climate Change}\"[0.080, 0.844, 0.191, 0.861] to \"{Human Rights}\"[0.093, 0.116, 0.191, 0.134], we can observe that there are 9 social issues being discussed in the data. In the horizontal bar chart, each bar's length corresponds to the frequency of discussion on the respective issue which I can observe from the X-Axis range from \"{0}\"[0.196, 0.939, 0.205, 0.957] to \"{10}\"[1.001, 0.939, 1.019, 0.957]. The frequency percentage for each issue can be derived as per the length of respective bars associated with the social issues. For instance, the bar of \"{Climate Change}\"[0.201, 0.814, 0.565, 0.887] occupies an area from 0 to around 4.5 implying a frequency of 4.5% against climate change. I would follow similar analysis for all issues to extract the frequency data.\nBased on the above thought, the answer is:\nSocial_Issue,Frequency(%)\nClimate_Change,4.5\nWaste_Management,5.2\nFair_Trade,5.7\nSustainable_Packaging,6.3\nRenewable_Energies,6.8\nDiversity_Inclusion,7.0\nGender_Equality,7.6\nAnimal_Welfare,8.5\nHuman_Rights,9.2" } ], "id_raw": 812 }, { "id": "0lRQd749ewnSFQqx", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you interpret from the distribution of frequency pertaining to discussions on different social issues?" }, { "from": "gpt", "value": "The distribution of frequencies against all social issues indicates that the discussion on social issues gradually intensifies from \"{Climate Change}\"[0.201, 0.814, 0.565, 0.887] to \"{Human Rights}\"[0.201, 0.087, 0.945, 0.159]. This can be observed from the increase in bar lengths for each issue from Climate Change (4.5%) to Human Rights (9.2%). For instance, issues such as \"{Gender_Equality}\"[0.201, 0.269, 0.816, 0.341] and \"{Animal_Welfare}\"[0.201, 0.178, 0.889, 0.250] occupy more space on the X-Axis, indicating a higher discussion frequency about them as compared to issues like Climate Change and \"{Waste_Management}\"[0.201, 0.723, 0.622, 0.796]. This could mean that while all issues are important, some issues including Human Rights and Animal Welfare might currently be more pressing or in-focus in discussions around social responsibility and sustainable business than others.\nBased on the above thought, the answer is:\nThe distribution of frequencies suggests growing importance on issues related to Human Rights, Animal Welfare and Gender Equality over other issues. However, given the nature and importance of all these issues, it also suggests a need for more balanced discussions across all issues rather than a few specific ones." } ], "id_raw": 812 }, { "id": "i31FapBDADaEfByn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the largest percentage difference between consecutive social issues discussed in terms of frequency?" }, { "from": "gpt", "value": "This question is about the difference in frequency percentages between every two successive social issues. Starting from the first issue \"{Climate Change}\"[0.201, 0.814, 0.565, 0.887], with a frequency of 4.5%, followed by \"{Waste Management}\"[0.201, 0.723, 0.622, 0.796] that shows a difference of 0.7% (5.2-4.5) and so on. By comparing the differences between consecutive issues one by one, we can find that the difference between \"{Gender Equality}\"[0.201, 0.269, 0.816, 0.341] (7.6%) and \"{Animal Welfare}\"[0.201, 0.178, 0.889, 0.250] (8.5%) is 0.9%, which is the largest difference among all consecutively mentioned social issues.\nBased on the above thought, the answer is:\n0.9%" } ], "id_raw": 812 }, { "id": "gywDQezKOKxuqUuP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nBased on the bar graph figure you see, can you input the data into a structured table using CSV format." }, { "from": "gpt", "value": "I comprehend that the chart represents the main causes of US deaths and their percentile contribution to the total deaths. It is possible to ascertain information using different elements of the chart. For instance, the title of the chart {\"Main Causes of Death in US Population\"}[0.455, 0.019, 0.858, 0.041] clearly describes what the chart is about. The x-axis label {\"Percentage of Total Deaths (%)\"}[0.522, 0.964, 0.791, 0.981] represents the percentage contribution to the total deaths, and the y-axis label {\"Cause\"}[0.019, 0.460, 0.036, 0.514] displays the different causes of death. Different bars in the chart represent different causes of death like {\"Heart Disease\"}[0.355, 0.822, 0.929, 0.887], {\"Cancer\"}[0.355, 0.740, 0.914, 0.805], {\"Accidents\"}[0.355, 0.658, 0.512, 0.724] and so on. Lastly, the values on top of each bar show the corresponding percentage value, for instance \" 23.1%\"[0.929, 0.846, 0.989, 0.863] represents the percentage of total deaths caused by Heart Disease.\nBased on the above thought, the answer is:\nDisease,Percentage\nHeart Disease,23.1\nCancer,22.5\nAccidents,6.3\nChronic lower respiratory diseases,5.6\nStroke,5.1\nAlzheimer's disease,4.5\nDiabetes,3.3\nPneumonia,2.6\nKidney Disease,1.8\nSuicide,1.7" } ], "id_raw": 813 }, { "id": "qcvqCx8lZLp2jm1l", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nIf you were going to reduce death rates based on the chart data, which two causes would you focus on first?" }, { "from": "gpt", "value": "If I were going to reduce death rates based on this chart, I would concentrate on lowering the rates of the two causes of death with the highest percentages. These are {\"Heart Disease\"}[0.355, 0.822, 0.929, 0.887] and {\"Cancer\"}[0.355, 0.740, 0.914, 0.805], which are responsible for \"23.1%\"[0.929, 0.846, 0.989, 0.863] and \"22.5%\"[0.914, 0.764, 0.974, 0.782] of total deaths respectively. This strategy would have the largest impact on total death rates, as these two categories together make up almost half of all deaths.\nBased on the above thought, the answer is:\nI would focus on Heart Disease and Cancer as they are the leading causes of death and therefore addressing these two would have the biggest impact on the total death rate." } ], "id_raw": 813 }, { "id": "EOcT1wBLXNFaGPyR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what is the difference between the percentage values of the two leading causes of death?" }, { "from": "gpt", "value": "By comparing the data from {\"Heart Disease\"}[0.355, 0.822, 0.929, 0.887] with that of {\"Cancer\"}[0.355, 0.740, 0.914, 0.805], it's visible that Heart Disease leads with \"23.1%\"[0.929, 0.846, 0.989, 0.863] and Cancer follows with \"22.5%\"[0.914, 0.764, 0.974, 0.782]. So, the difference between them can be calculated.\nBased on the above thought, the answer is:\n0.6" } ], "id_raw": 813 }, { "id": "XA4M7ucnbZeLA5Jm", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "From the title saying {\"Internet and Communication Services\"}[0.019, 0.309, 0.036, 0.637], I can infer that different internet services are being measured. The y-axis is labeled with different internet and communication services such as {e-Library}[0.146, 0.087, 0.224, 0.104]. The x-axis is labeled {\"Utilization rate (%)\"}[0.523, 0.964, 0.687, 0.981], which means the bars represent the percentage of how widely each service is used. The length of each bar also corresponds to the actual percentage value that is annotated at the end of each bar. For example, the {e-Library bar}[0.236, 0.060, 0.447, 0.127] ends around the number 6 on the x-axis, and there is an accompanying label of {\"6%\"}[0.447, 0.085, 0.480, 0.102] at the end of the bar, so the utilization rate of e-Library is 6.\nBased on the above thought, the answer is:\nServices,Utilization rate (%)\nInternet Applications,20\nChat Room,20\nEmail,19\ne-Business,17\nVoIP,17\nInternet Games,12\nVideo Conferencing,11\nInternet TV,10\nTelecommuting,8\ne-Library,6" } ], "id_raw": 814 }, { "id": "00njyyDg6s3zL4yN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you infer which services are more often used by people and thus might be vital to them?" }, { "from": "gpt", "value": "When I look at this bar chart, I can infer from the chart that the longer the bar of a service, the higher the adoption rate, which might indicate its relative importance to users. According to the bar lengths, {Internet Applications}[0.236, 0.818, 0.939, 0.886] and {Chat Room}[0.236, 0.734, 0.939, 0.802] bars extend further along the x-axis, touching the 20 level, which suggests that these two services are used most frequently by people. Conversely, services like {e-Library}[0.236, 0.060, 0.447, 0.127] and {Telecommuting}[0.236, 0.144, 0.517, 0.212] have bars ending at lower values on the x-axis, indicating that these services are less frequently used by people.\nBased on the above thought, the answer is:\nThe services that are more often used by people and thus might be vital to them are Internet Applications and Chat Room, as they have the highest usage rates according to the chart. Conversely, services like e-Library and Telecommuting have lower adoption rates." } ], "id_raw": 814 }, { "id": "kIpNA9uNClu94j5z", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_4.png", "conversations": [ { "from": "human", "value": "\nWhich service has the highest usage rate?" }, { "from": "gpt", "value": "To find the service with the highest usage rate, I need to locate the bar with the highest value on the x-axis. Moving from bottom to top, I see the initial bar for {e-Library}[0.236, 0.060, 0.447, 0.127] ends at 6, next is {Telecommuting}[0.236, 0.144, 0.517, 0.212] at 8, and so on. The longest bars are for {Internet Applications}[0.236, 0.818, 0.939, 0.886] and {Chat Room}[0.236, 0.734, 0.939, 0.802], both ending around the 20 level on the x-axis.\nBased on the above thought, the answer is:\nInternet Applications and Chat Room." } ], "id_raw": 814 }, { "id": "g57TEvZdkqKFLjPC", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, extract and present the sales data in table format." }, { "from": "gpt", "value": "The bar chart lists the sales of various industries in thousands of dollars. The industries are plotted along the y-axis and are represented by rectangles whose x-coordinates represent sales data. For example, the sales data for the {industry: 'Information Technology'}[0.301, 0.811, 0.831, 0.886] can be extracted to be 25000 by correlating the end x-coordinate of the rectangle to the 'Sales' label on the x-axis. Repeating this operation for each rectangle in the bars list will generate table data for all the industries.\nBased on the above thought, the answer is:\nIndustry, Sales\nInformation Technology, 25000\nBiotechnology, 22000\nSemiconductors, 20000\nSoftware, 30000\nCommunications Equipment, 18000\nTelecommunications, 21000\nMedia, 23000\nPharmaceuticals, 27000\nData processing, 28000" } ], "id_raw": 815 }, { "id": "cnaxe40QQ2ohVDSM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which industry may need more investment based on sales?" }, { "from": "gpt", "value": "Typically, industries with lower sales may need more investment for improvement. By looking at the bar chart, it is evident that the industry with the lowest sales volume, represented by the shortest bar, is {'Communications Equipment'}[0.301, 0.435, 0.682, 0.510]. Its end x-coordinate relates to a 'Sales' value of 18000 which is lower than other industries in the chart. Therefore, the 'Communications Equipment' industry may need more investment.\nBased on the above thought, the answer is:\nCommunications Equipment." } ], "id_raw": 815 }, { "id": "CxnktMoEozfPawDz", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_5.png", "conversations": [ { "from": "human", "value": "\nWhich industry has the highest sales volume?" }, { "from": "gpt", "value": "Through the bar chart, I can detect sales data by examining the x-coordinates of each rectangle in the bar list. The industry with the highest sales overflows from the x-plot that the rectangle with longest length. That is the bar of {'Software'}[0.301, 0.529, 0.937, 0.604] reached the most right position in the x-axis which corresponds to about 30000 in 'Sales'. Therefore, the industry with the highest sales volume is 'Software'.\nBased on the above thought, the answer is:\nSoftware" } ], "id_raw": 815 }, { "id": "khh85OKGueD869yZ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nIn a CSV format table, can you extract and display the data regarding the probability of future scenarios depicted in the bar chart?" }, { "from": "gpt", "value": "Based on what I can notice on the chart, the title {\"Probability of Future Scenarios\"}[0.507, 0.015, 0.760, 0.033] signifies what the chart is about. The y-axis ticks are labeled with future scenarios and the x-axis ticks with probabilities. By calculating the length of each bar, I can precisely determine the probability associated with each scenario. For instance, the length of {the bar for \"Telework\"}[0.281, 0.826, 0.951, 0.901] equals to 25.0 according to the annotation {\"25.0\"}[0.951, 0.856, 0.982, 0.870] placed at the end of this bar. Thus, the probability for \"Telework\" is 25.0.\nBased on the above thought, the answer is:\nScenario,Probability(%)\nTelework,25.0\nE-learning,22.8\nVirtual Reality Entertainment,20.6\nAutomated Transport,18.3\nArtificial Intelligence in Household,14.2\nRemote Healthcare,12.0\nRobot Companions,8.7\nTime Travel,3.5\nLiving on Mars,2.1" } ], "id_raw": 816 }, { "id": "OO9ipWBY810Boee1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nJudging from the probabilities represented in the chart, which future scenarios do you think people believe are most likely to happen?" }, { "from": "gpt", "value": "As per the chart, the bars with the longest lengths represent the scenarios that people think are most likely to happen. The bar for {Telework}[0.281, 0.826, 0.951, 0.901] has an approximate length of 25.0, the longest of all, indicating that it's considered the most probable scenario. Following that, the bar for {E-Learning}[0.281, 0.732, 0.893, 0.807] has the second-longest length, approximate 22.8, leading us to believe it's the next most probable scenario. Lastly, the third most probable scenario is {Virtual Reality Entertainment}[0.281, 0.639, 0.834, 0.714], with an approximate length of 20.6.\nBased on the above thought, the answer is:\nPeople believe that 'Telework', 'E-learning', and 'Virtual Reality Entertainment' are the most probable future scenarios." } ], "id_raw": 816 }, { "id": "PKs0vcvq1K4nYsq3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_6.png", "conversations": [ { "from": "human", "value": "\nBased on the data represented in the bar chart, what is the probability difference between the highest probable and lowest probable future scenario?" }, { "from": "gpt", "value": "To answer this question, I need to compare the lengths of all the bars. The longest bar is representing \"Telework\" - {[0.281, 0.826, 0.951, 0.901]} with a value of 25.0 % according to the annotation {\"25.0\"}[0.951, 0.856, 0.982, 0.870]. The shortest bar corresponds to \"Living on Mars\" - {[0.281, 0.078, 0.338, 0.153]} with a value of 2.1 % according to the annotation {\"2.1\"}[0.338, 0.109, 0.360, 0.123]. Subtracting the shortest scenario's probability from the longest one would give me the difference.\nBased on the above thought, the answer is:\n22.9%" } ], "id_raw": 816 }, { "id": "R0gSDCXDhIHhsiW4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nCan you lay out the data from the horizontal bar chart into a CSV structured table?" }, { "from": "gpt", "value": "There are 10 bars on the chart, each representing a different social element with a particular value attached to it. Each value can be found beside the corresponding bar. For example, the {\"Number of Friends\"}[0.102, 0.848, 0.262, 0.865] bar begins near the y-axis and extends to a point that corresponds to a value of 20, which is confirmed by the associated text {\" 20\"}[0.723, 0.846, 0.751, 0.863]. Similarly, the {\"Number of Active Friends\"}[0.043, 0.766, 0.262, 0.783] bar extends to a corresponding value of 15.5 and so on. Accordingly, I can construct the CSV table.\nBased on the above thought, the answer is:\nSocial Element,Value\nNumber of Friends,20\nNumber of Active Friends,15.5\nMessages Sent,25.7\nMessages Received,28.6\nVideo Calls,30\nText Conversations,18.3\nGroup Chats Participated,22.2\nSocial Events Attended,14.4\nRelationship Initiation,9.6\nRelationship Termination,6.1" } ], "id_raw": 817 }, { "id": "DUMDpn3sSI4U4sFv", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nWhich social element, from the ones depicted in the chart, is not being adequately addressed and needs more attention?" }, { "from": "gpt", "value": "Looking at the bar chart from bottom to top, I am reasoning the inverse length of each bar with its corresponding social element. The bar with the least length corresponds to the aspect that is being the least represented and hence, requires more focus. I see the bar representing {\"Relationship Termination\"}[0.048, 0.112, 0.262, 0.130] ends before the 10 mark on the x-axis, and the corresponding value is {\" 6.1\"}[0.411, 0.111, 0.444, 0.128]. I also cross-verified with other bars, all the other bars extend beyond the 10 mark. The \"Relationship Termination\" is not being adequately represented and may need more attention.\nBased on the above thought, the answer is:\nRelationship Termination." } ], "id_raw": 817 }, { "id": "CYWOc6KzyeitlIGF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value represented by the social elements, according to the bar chart?" }, { "from": "gpt", "value": "For the highest value, my aim is to identify the social element bar that extends the furthest to the right. The {\"Video Calls\"}[0.166, 0.521, 0.262, 0.538] bar reaches the furthest, going beyond the 25 mark along the x-axis. With the value text {\" 30\"}[0.948, 0.519, 0.975, 0.536], I determine that \"Video Calls\" has the highest value, and it is 30.\nBased on the above thought, the answer is:\n30" } ], "id_raw": 817 }, { "id": "0rPndQ2ZZnCgyXHM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nConvert the data represented by the bar chart into a structured CSV format." }, { "from": "gpt", "value": "The horizontal bars on the chart represent various economic indicators, with their lengths proportional to the respective indicator's value. Looking at {GDP}[0.321, 0.811, 0.656, 0.886], the corresponding numerical value shows {\"4.1\"}[0.656, 0.840, 0.684, 0.857]. Similarly, we can observe for {Inflation}[0.321, 0.717, 0.501, 0.792] with {\"2.2\"}[0.501, 0.746, 0.528, 0.763] and so on for all the indicators including Unemployment, Interest Rates, Stock Market, Trade Balance, Currency, Housing, and Consumer Confidence. Now, by using the y-axis labels and corresponding annotations, we can tabulate these visual data into a CSV format.\nBased on the above thought, the answer is:\nIndicator,Value\nGDP,4.1\nInflation,2.2\nUnemployment,6.1\nInterest Rates,0.25\nStock Market,7.6\nTrade Balance,-0.5\nCurrency,3.4\nHousing,1.8\nConsumer Confidence,1.2" } ], "id_raw": 818 }, { "id": "LYPC7AiheJWwSPX5", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the chart, which economic indicator appears to be in a promising state, and why?" }, { "from": "gpt", "value": "Analyzing the chart requires consideration of both, the type of indicator and associated values. Positive indicators like {GDP}[0.321, 0.811, 0.656, 0.886], {Stock Market}[0.321, 0.435, 0.943, 0.510], and {Housing}[0.321, 0.154, 0.468, 0.229] are desirable when they are high. On the other hand, lower values of negative indicators, such as {Unemployment}[0.321, 0.623, 0.820, 0.698] and {Inflation}[0.321, 0.717, 0.501, 0.792], signify a promising state. Looking at these in specific, the Stock Market with a score of {\"7.6\"}[0.943, 0.464, 0.970, 0.482] performs significantly well among the positive indicators, and Unemployment is fairly controlled with a score of {\"6.1\"}[0.820, 0.652, 0.848, 0.669] among the negative indicators.\nBased on the above thought, the answer is:\nThe Stock Market appears to be in a promising state, given its high value of 7.6, being consistent with the positive nature of the indicator. A robust stock market typically signifies healthy investor confidence and economic growth." } ], "id_raw": 818 }, { "id": "bOxvs66ggHYZNLIN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_8.png", "conversations": [ { "from": "human", "value": "\nWhich economic indicator has shown the highest value, as per the graph?" }, { "from": "gpt", "value": "To locate the highest value from the graph, we need to compare the lengths of horizontal bars under each category. {GDP}[0.321, 0.811, 0.656, 0.886] extends up to 4.1 as per annotation {\"4.1\"}[0.656, 0.840, 0.684, 0.857], {Unemployment}[0.321, 0.623, 0.820, 0.698] extends up to 6.1, and {Stock Market}[0.321, 0.435, 0.943, 0.510] extends up to 7.6 as indicated by {\"7.6\"}[0.943, 0.464, 0.970, 0.482]. Similarly for other indicators. By comparing these, we can find the highest value.\nBased on the above thought, the answer is:\nStock Market" } ], "id_raw": 818 }, { "id": "UTB0Dl03ySH6Tvbk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nBased on the information you've observed on the horizontal bar chart, can you convert this data into a structured CSV format table?" }, { "from": "gpt", "value": "The chart comprises of a horizontal bar plot with differing lengths representing different percentages associated with several activities. From the {xlabel}[0.504, 0.964, 0.637, 0.981] and the {ylabel}[0.019, 0.440, 0.036, 0.506], I understand that the x-axis represents 'Percentage (%)' and y-axis represents 'Activity'. The activities ranging from 'Exercise' to 'Doing_Nothing' are listed on the Y-axis as indicated by the {yticks}[{\"Exercise\"}[0.099, 0.845, 0.171, 0.863]; {\"Doing_Nothing\"}[0.043, 0.087, 0.171, 0.105]]. The percentage corresponding to these activities, can be obtained from the information embedded within each {bar}[{\"Exercise\"}[0.183, 0.818, 0.921, 0.886]; {\"Doing_Nothing\"}[0.183, 0.060, 0.258, 0.127]] and by reading the value from the {text_coordinates}[{\"Exercise\"}[0.921, 0.843, 0.981, 0.861]; {\"Doing_Nothing\"}[0.258, 0.085, 0.307, 0.102]].\nBased on the above thought, the answer is:\nActivity,Percentage (%)\nExercise,32.5\nSocializing,28.0\nReading,22.1\nGaming,17.8\nCooking,14.4\nTraveling,12.0\nWorking,10.4\nStudying,8.5\nWatching_TV,4.6\nDoing_Nothing,3.3" } ], "id_raw": 819 }, { "id": "GdoXUVVF5eXpbj65", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide a broad analysis of the general lifestyle trends based on the bar chart data?" }, { "from": "gpt", "value": "The chart is a depiction of the lifestyle trends of individuals based on percentage participation in diverse activities. Leveraging the information from the {bars}[{\"Exercise\"}[0.183, 0.818, 0.921, 0.886]; {\"Socializing\"}[0.183, 0.734, 0.819, 0.802]; {\"Watching_TV\"}[0.183, 0.144, 0.288, 0.212]; {\"Doing_Nothing\"}[0.183, 0.060, 0.258, 0.127]] it's clear that activities like 'Exercise' and 'Socializing' have the highest percentages, signifying high engagement levels and an inclination towards health and social activities. Intermediate level activities include 'Reading', 'Gaming', and 'Cooking' which signify a balanced engagement in solitary activities. Lower level activities such as 'Watching_TV' and 'Doing_Nothing' depict a lower percentage of engagement indicating a lesser inclination towards passive activities. The exception in this trend is 'Working', which is surprisingly less engaged-in as compared to 'Traveling' and 'Cooking', indicating a possible trend towards work-life balance or predominance of non-working population in the survey.\nBased on the above thought, the answer is:\nThe general lifestyle trends indicate a high engagement in physical activities and socializing, a balanced engagement in solitary pursuits like reading, gaming, and cooking, and a lower inclination towards passive activities such as watching TV and doing nothing. There is also a surprising lesser engagement in work compared to traveling and cooking, suggesting a trend towards work-life balance or a predominance of non-working population in the survey." } ], "id_raw": 819 }, { "id": "vYFMpOrqZKW0jnqW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_9.png", "conversations": [ { "from": "human", "value": "\nWhich activity has the lowest participation percentage according to the bar chart?" }, { "from": "gpt", "value": "In order to answer this question, I need to examine the length of each horizontal bar, which represents the corresponding participation percentage for each activity. Visually comparing the {bars}[{\"Exercise\"}[0.183, 0.818, 0.921, 0.886]; {\"Doing_Nothing\"}[0.183, 0.060, 0.258, 0.127]], I see that the smallest bar corresponds with the 'Doing_Nothing' activity at the bottom of the graph. Additionally, this is supported by observing the {text_coordinates}[{\"Doing_Nothing\"}[0.258, 0.085, 0.307, 0.102]] which shows a value of 3.3%. Hence, the activity with the lowest participation percentage is 'Doing_Nothing'.\nBased on the above thought, the answer is:\nDoing Nothing." } ], "id_raw": 819 }, { "id": "yKBiySC8EmeQikzi", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data into a CSV format table?" }, { "from": "gpt", "value": "Judging from the chart's title {\"Investment Trends in Venture Capital Markets\"}[0.394, 0.015, 0.773, 0.033], and based on the x-axis labels ranging from 0 to 900 and the y-axis labels, it seems that the x-axis represents the millon dollars investment made in different industries. Each bar in the chart corresponds to a specific investment amount on the x-axis for a certain industry on the y-axis. For example, the bar for {the industry \"Fintech\"}[0.122, 0.862, 0.172, 0.876] extends to approximately 780 on the x-axis. Thereby, by checking each bar's coordinate and its corresponding label, we can succesfully convert the chart data into a CSV table.\nBased on the above thought, the answer is:\nIndustry,Investment_Million_Dollars\nFintech,780\nAI/ML,600\nHealthCare,550\nEcommerce,500\nMobility,375\nEnterprise Software,320\nManufacturing,280\nEducation,230\nAR/VR,130\nGaming,80" } ], "id_raw": 820 }, { "id": "9vj5SQ0BCoIVA34t", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nWhich industries seem to be the most and the least attractive for investors, according to the bar chart? Justify your answer." }, { "from": "gpt", "value": "To answer this question, I need to look at the bar lengths. Bars with greater lengths {like Fintech}[0.182, 0.833, 0.947, 0.901] and {AI/ML}[0.182, 0.750, 0.770, 0.817] suggest more investment, meaning that these industries are more attractive for investors. Fintech has the greatest investment, around 780 million dollars, suggesting it is the most attractive industry for investors. Conversely, shorter bars {like AR/VR}[0.182, 0.162, 0.309, 0.229] and {Gaming}[0.182, 0.078, 0.260, 0.146] indicate less investment. Among all, Gaming is with the smallest investment, around 80 million dollars, which suggests it to be the least attractive industry for investors.\nBased on the above thought, the answer is:\nThe most attractive industry for investors seems to be Fintech due to the highest investment it receives, and the least attractive seems to be Gaming as it receives the lowest investment." } ], "id_raw": 820 }, { "id": "xUwGWZhDIhnULgFi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_10.png", "conversations": [ { "from": "human", "value": "\nWhich industry has received the least amount of investment?" }, { "from": "gpt", "value": "Looking at the bar chart, I need to identify which bar is the shortest, indicating the smallest investment. Starting from the top bar, which represents {the Fintech industry}[0.122, 0.862, 0.172, 0.876] and has an investment of about 780 million dollars, and moving downwards, I can see that the bar's length gradually decreases until I get to the very last bar, representing {the Gaming industry}[0.117, 0.106, 0.172, 0.120]. The length of this bar indicates that the investment was about 80 million dollars, which seems to be the lowest investment of all industries.\nBased on the above thought, the answer is:\nGaming" } ], "id_raw": 820 }, { "id": "fpJ5SwkqRAFAxlxr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nCould you please extract and layout the provided data in a structured CSV format?" }, { "from": "gpt", "value": "Based on the bar chart, I can see that each data point represents an office location and its corresponding visitation percentage. The location of each office is marked on the y-axis, and the corresponding visitation rate is displayed as the length of a horizontal bar starting from the left side of the chart and extending to the right. For instance, the {Shanghai Office}[0.185, 0.831, 0.936, 0.900] has the longest bar ending at approximately 75.6%, which matches with the text {75.6%}[0.936, 0.858, 0.984, 0.872] to the right side of the bar. Following the same process, I will convert all the office locations and their corresponding visitation percentage into a structured CSV format.\nBased on the above thought, the answer is:\nLocation,Visitation(%)\nShanghai Office,75.6\nBeijing Office,62.4\nTokyo Office,48.2\nSeoul Office,44.3\nSingapore Office,37.5\nBangkok Office,32.8\nJakarta Office,29.1\nManila Office,26.3\nKuala Lumpur Office,22.5\nHanoi Office,15.9" } ], "id_raw": 821 }, { "id": "XpFHc2z2TCp8PzNr", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which office has shown a significant outlier in terms of visitation percentage?" }, { "from": "gpt", "value": "To identify an outlier, we need to assess how significantly any given bar deviates from the other bars in the chart. Visually, the {Beijing Office}[0.185, 0.745, 0.805, 0.814] shows as an outlier with its visitation rate significantly higher than those offices ranked after it, and fairly comparable to the {Shanghai Office}[0.185, 0.831, 0.936, 0.900] which has the highest visitation. This discrepancy in visitation among similar ranked offices suggests that the Beijing Office could be an outlier in terms of the visitation pattern observed here.\nBased on the above thought, the answer is:\nBeijing Office." } ], "id_raw": 821 }, { "id": "uVChs9GuamwDgsjC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference in visitation between the most visited and the least visited office?" }, { "from": "gpt", "value": "To find this, I need to identify the most visited office and the least visited office and then compute the difference between their visitation percentages. The {bar for the Shanghai Office}[0.185, 0.831, 0.936, 0.900] extends all the way to the level marked as 75.6% while the {bar for the Hanoi Office}[0.185, 0.057, 0.343, 0.126] ends close to the level marked as 15.9%. Extracting these values from the bars, I can compute the difference between these two percentages.\nBased on the above thought, the answer is:\n59.7%" } ], "id_raw": 821 }, { "id": "jiymWBVJ0WEj7AxW", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_12.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data showed in the chart in the format of a table, with each row contains an investment strategy and the corresponding return rate?" }, { "from": "gpt", "value": "From the {ylabel}[0.019, 0.398, 0.036, 0.576] and {xticks}[{\"label\": \"0\", \"label_coordinates\": \"[0.223, 0.939, 0.234, 0.957]\", \"tick_coordinates\": \"[0.229, 0.927]\"}, {\"label\": \"2\", \"label_coordinates\": \"[0.333, 0.939, 0.344, 0.957]\", \"tick_coordinates\": \"[0.338, 0.927]\"}, {\"label\": \"4\", \"label_coordinates\": \"[0.442, 0.939, 0.453, 0.957]\", \"tick_coordinates\": \"[0.447, 0.927]\"}, {\"label\": \"6\", \"label_coordinates\": \"[0.551, 0.939, 0.562, 0.957]\", \"tick_coordinates\": \"[0.557, 0.927]\"}, {\"label\": \"8\", \"label_coordinates\": \"[0.661, 0.939, 0.672, 0.957]\", \"tick_coordinates\": \"[0.666, 0.927]\"}, {\"label\": \"10\", \"label_coordinates\": \"[0.764, 0.939, 0.787, 0.957]\", \"tick_coordinates\": \"[0.776, 0.927]\"}, {\"label\": \"12\", \"label_coordinates\": \"[0.874, 0.939, 0.896, 0.957]\", \"tick_coordinates\": \"[0.885, 0.927]\"}, {\"label\": \"14\", \"label_coordinates\": \"[0.983, 0.939, 1.005, 0.957]\", \"tick_coordinates\": \"[0.994, 0.927]\"}], I know the vertical axis represents the investment strategies and the horizontal axis represents the return rates. The {bar chart}[{\"bar_coordinates\": \"[0.229, 0.822, 0.923, 0.887]\", \"text_coordinates\": \"[0.923, 0.846, 0.983, 0.863]\", \"text_params\": {\"s\": \" 12.7%\", \"color\": \"black\"}, \"y_label\": \"High Risk\", \"x_range\": \"0.00 -> 12.70\"}, {\"bar_coordinates\": \"[0.229, 0.740, 0.699, 0.805]\", \"text_coordinates\": \"[0.699, 0.764, 0.748, 0.782]\", \"text_params\": {\"s\": \" 8.6%\", \"color\": \"black\"}, \"y_label\": \"Low Risk\", \"x_range\": \"0.00 -> 8.60\"}, {\"bar_coordinates\": \"[0.229, 0.658, 0.573, 0.724]\", \"text_coordinates\": \"[0.573, 0.682, 0.623, 0.700]\", \"text_params\": {\"s\": \" 6.3%\", \"color\": \"black\"}, \"y_label\": \"Hedging\", \"x_range\": \"0.00 -> 6.30\"}, {\"bar_coordinates\": \"[0.229, 0.577, 0.524, 0.642]\", \"text_coordinates\": \"[0.524, 0.601, 0.573, 0.618]\", \"text_params\": {\"s\": \" 5.4%\", \"color\": \"black\"}, \"y_label\": \"Diversification\", \"x_range\": \"0.00 -> 5.40\"}, {\"bar_coordinates\": \"[0.229, 0.495, 0.672, 0.560]\", \"text_coordinates\": \"[0.672, 0.519, 0.721, 0.536]\", \"text_params\": {\"s\": \" 8.1%\", \"color\": \"black\"}, \"y_label\": \"Growth Investing\", \"x_range\": \"0.00 -> 8.10\"}, {\"bar_coordinates\": \"[0.229, 0.413, 0.661, 0.479]\", \"text_coordinates\": \"[0.661, 0.437, 0.710, 0.455]\", \"text_params\": {\"s\": \" 7.9%\", \"color\": \"black\"}, \"y_label\": \"Value Investing\", \"x_range\": \"0.00 -> 7.90\"}, {\"bar_coordinates\": \"[0.229, 0.332, 0.726, 0.397]\", \"text_coordinates\": \"[0.726, 0.356, 0.776, 0.373]\", \"text_params\": {\"s\": \" 9.1%\", \"color\": \"black\"}, \"y_label\": \"Income Investing\", \"x_range\": \"0.00 -> 9.10\"}, {\"bar_coordinates\": \"[0.229, 0.250, 0.644, 0.315]\", \"text_coordinates\": \"[0.644, 0.274, 0.694, 0.291]\", \"text_params\": {\"s\": \" 7.6%\", \"color\": \"black\"}, \"y_label\": \"Small-Cap Investing\", \"x_range\": \"0.00 -> 7.60\"}, {\"bar_coordinates\": \"[0.229, 0.168, 0.519, 0.234]\", \"text_coordinates\": \"[0.519, 0.192, 0.568, 0.210]\", \"text_params\": {\"s\": \" 5.3%\", \"color\": \"black\"}, \"y_label\": \"Market Timing\", \"x_range\": \"0.00 -> 5.30\"}, {\"bar_coordinates\": \"[0.229, 0.087, 0.431, 0.152]\", \"text_coordinates\": \"[0.431, 0.111, 0.480, 0.128]\", \"text_params\": {\"s\": \" 3.7%\", \"color\": \"black\"}, \"y_label\": \"Bond Investing\", \"x_range\": \"0.00 -> 3.70\"}] contains the exact return rates of each investment strategy. By using the labels and data of each bar in the chart, I can convert the chart into a table.\nBased on the above thought, the answer is:\nInvestment Strategy,Return Rate(%)\nHigh Risk,12.7\nLow Risk,8.6\nHedging,6.3\nDiversification,5.4\nGrowth Investing,8.1\nValue Investing,7.9\nIncome Investing,9.1\nSmall-Cap Investing,7.6\nMarket Timing,5.3\nBond Investing,3.7" } ], "id_raw": 822 }, { "id": "5UMZ3C5nF3lqrFdf", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nCould you reconstruct the data displayed in the bar chart into a CSV format file?" }, { "from": "gpt", "value": "From {the title}[0.556, 0.008, 0.556, 0.008] at the top of the chart, and x-axis tick labels from {0}[0.139, 0.939, 0.150, 0.957] to {10}[1.036, 0.939, 1.058, 0.957], as well as the y-axis tick labels from {\"Facebook\"}[0.050, 0.845, 0.132, 0.863] to {\"Tumblr\"}[0.073, 0.087, 0.132, 0.104], I can conclude that the y-axis represents different social networks and the x-axis represents their respective engagement performances. The values can be obtained by observing the lengths of the horizontal bars and the textual annotations at the end of each bar. For instance, the bar for Facebook from [0.144, 0.818, 0.930, 0.886] ends roughly at the position of 8.7 on the x-axis, and there is also a text annotation \"8.7%\" at the end of the bar [0.930, 0.843, 0.979, 0.861], so the performance of Facebook is 8.7%.\nBased on the above thought, the answer is:\nSocial_Network,Performance\nFacebook,8.7\nTwitter,7.5\nInstagram,7.3\nLinkedIn,6.8\nSnapchat,6.5\nReddit,5.0\nPinterest,4.3\nWhatsApp,4.1\nWeChat,4.0\nTumblr,2.5" } ], "id_raw": 823 }, { "id": "ojKz2azvC31M8Piq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which social network is winning at user engagement and which one is losing? Provide your analysis." }, { "from": "gpt", "value": "This question requires interpreting the chart and comparing the lengths of the bars associated with each social network. Identifying the social network with the best user engagement is straightforward as it has the longest bar. In this case, {Facebook}[0.932, 0.881, 0.981, 0.899] stands out because it has the longest bar, almost spanning the length of the x-axis, indicating that it has the highest performance. On the other hand, {Tumblr}[0.366, 0.062, 0.415, 0.080] has the shortest bar, indicating the least user engagement among all the social networks listed.\nBased on the above thought, the answer is:\nFacebook is winning at user engagement because it has the longest bar, indicating the highest performance. Conversely, Tumblr is losing as it has the shortest bar, indicating the lowest performance." } ], "id_raw": 823 }, { "id": "rx2MSsTtyLxfcAKt", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_13.png", "conversations": [ { "from": "human", "value": "\nWhich social network has the lowest performance?" }, { "from": "gpt", "value": "To find the social network with the least performance, I need to compare the length of all the bars. As the smaller the bar length, the lower the performance. Looking from top to bottom, first is {Facebook}[0.144, 0.818, 0.930, 0.886] with a length roughly at 8.7, then {Twitter}[0.144, 0.734, 0.821, 0.802] roughly at 7.5, and so on until {Tumblr}[0.144, 0.060, 0.370, 0.127], which has a length roughly at 2.5 on the x-axis, and thus has the minimum performance among all social networks.\nBased on the above thought, the answer is:\nTumblr" } ], "id_raw": 823 }, { "id": "TCVFHwapP6zK6Hij", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart given, can you create a table in CSV format that reflects the share percentages of different platforms?" }, { "from": "gpt", "value": "From observing the chart, the `Platforms` are placed on the y-axis which is apparent from labels present at the {left of the chart} [0.040, 0.848, 0.106, 0.865]; [0.035, 0.766, 0.106, 0.783]; [0.040, 0.658, 0.106, 0.702]; [0.047, 0.603, 0.106, 0.620]; [0.059, 0.521, 0.106, 0.538]; [0.061, 0.439, 0.106, 0.457]; [0.035, 0.358, 0.106, 0.375]; [0.042, 0.276, 0.106, 0.293]; [0.063, 0.194, 0.106, 0.212]; [0.067, 0.112, 0.106, 0.130]. Similarly, `shares per USER (%)` can be derived from the X-axis labeled at the {bottom} [0.471, 0.964, 0.609, 0.981]. The bars represent the share of different platforms like `Facebook, Instagram, Snapchat, LinkedIn, Twitter, Reddit, WhatsApp, Telegram, TikTok, Email`. The length of each bar from the y-axis to the right represents the `Shares per USER (%)`. CSV format can be derived by making a column for each variable.\nBased on the above thought, the answer is:\nPlatform, Shares per user (%)\nFacebook, 62.5\nInstagram, 67.25\nSnapchat, 54\nLinkedIn, 29.5\nTwitter, 42.25\nReddit, 35.5\nWhatsApp, 59\nTelegram, 29.75\nTikTok, 51.25\nEmail, 22.75" } ], "id_raw": 824 }, { "id": "rubyMlpeY3DkC7GJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat can we infer about the user sharing behavior across different social media platforms based on the bar chart?" }, { "from": "gpt", "value": "Looking at the chart with bars representing the sharing rate, we can get insights on the sharing behavior for different platforms. For instance, the {bar for Instagram}[0.115, 0.740, 0.924, 0.805] is the longest, which means it has the most content sharing. More traditional communication methods like {Email}[0.115, 0.087, 0.389, 0.152] and {LinkedIn}[0.115, 0.577, 0.470, 0.642] have shorter bars, indicating lesser sharing rates. So it can be inferred that users tend to share more content on social media platforms over traditional ones.\nBased on the above thought, the answer is:\nThe bar chart represents that social platforms like Instagram, Facebook and WhatsApp have higher shares per user, which indicates users are more active and engaging on these platforms. In contrast, platforms like LinkedIn, Telegram and Email have lesser sharing rates which suggest that these platforms could be used more for professional and more specific communications rather than frequent content sharing." } ], "id_raw": 824 }, { "id": "NAJ8UVw8hoBXcSUr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_55_14.png", "conversations": [ { "from": "human", "value": "\nWhich platform has the least sharing rate based on my observation?" }, { "from": "gpt", "value": "To find the platform with the least share rate, I need to compare the lengths of all the bars. The smallest bar on the chart is {the bar at the bottom of the chart}[0.115, 0.087, 0.389, 0.152] which refers to {the label \"Email\"}[0.067, 0.112, 0.106, 0.130] with a share rate of 22.75%.\nBased on the above thought, the answer is:\nEmail" } ], "id_raw": 824 }, { "id": "M8npdWGr53YGsyLF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV formatted table with the volume of items transported by airways for each of the seven years?" }, { "from": "gpt", "value": "The chart with the title {\"Transportation and Logistics Industry Trends Over Time\"}[0.214, 0.020, 0.786, 0.050] includes a subplot for airways {\"Airways\"}[0.493, 0.672, 0.575, 0.702], which has horizontal bars representing volumes for each year. To extract the raw data, I analyzed the length of the bars and correlated them to the x-axis labels showing the volume (in thousands). The y-axis shows each year represented by labeled ticks. For instance, the bar for Year 1 ends at [0.874] on the x-axis, which corresponds to a volume of 900 {\"Year 1\"}[0.043, 0.719, 0.096, 0.742][0.109, 0.718, 0.874, 0.738]. Repeating this process for all years gives the full data set.\nBased on the above thought, the answer is:\nYear,Volume\nYear 1,900\nYear 2,670\nYear 3,800\nYear 4,500\nYear 5,730\nYear 6,130\nYear 7,620" } ], "id_raw": 825 }, { "id": "7dOZZFsdOVlDuPPN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nWhich mode of transportation showed the most consistent growth over the seven years?" }, { "from": "gpt", "value": "To assess consistency in growth, I am observing all bars within the subplots for each mode of transportation: {Shipping}[0.109, 0.117, 0.959, 0.311], {Rail}[0.109, 0.413, 0.959, 0.607], and {Airways}[0.109, 0.709, 0.959, 0.903]. For consistent growth, we're looking for a regular increase in the length of the bars from year to year without significant fluctuations. By analyzing the bar charts, it's clear that {Airways}[0.109, 0.709, 0.959, 0.903] depicted the fewest deviations in bar length increase, except for the decrease visible in the 6th year. Since the datasets for Shipping and Rail show a larger variance including noticeable downturns in volumes in some years, it is evident that Airways demonstrated the most consistent growth despite its Year 6 drop.\nBased on the above thought, the answer is:\nAirways showed the most consistent growth over the seven years, with the exception of a drop in Year 6. However, its general trend appears steadier compared to the other modes of transportation." } ], "id_raw": 825 }, { "id": "uXSd4d4HsjyWYHW4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat was the lowest volume transported by rail over the seven-year period?" }, { "from": "gpt", "value": "To determine the lowest volume transported by rail, I am examining each bar in the subplot for rail {Rail}[0.515, 0.376, 0.553, 0.406], and cross-referencing the end of each horizontal bar with the volume marks on the x-axis. By visually analyzing the bars, I identified that the shortest one corresponds to Year 6, with the end located approximately at [0.279] on the x-axis {Year 6}[0.109, 0.552, 0.279, 0.572], which represents a volume of 200 according to the x-axis labels.\nBased on the above thought, the answer is:\n200" } ], "id_raw": 825 }, { "id": "Ai3iNQTHrxqGk9sF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nCan you arrange the investments in each technology from the years 2021 to 2025 into a CSV format table using the bar chart data?" }, { "from": "gpt", "value": "To generate the table, I examine each bar chart corresponding to the respective year. For the 2021 bar chart visible in the area {axes}[0.213, 0.062, 0.981, 0.181], I note that there are three bars corresponding to Blockchain, AI, and Quantum Computing. I read the investment counts from the lengths of the bars for Blockchain {Blockchain 2021 bar}[0.213, 0.068, 0.823, 0.098], AI {AI 2021 bar}[0.213, 0.106, 0.701, 0.137], and Quantum Computing {Quantum Computing 2021 bar}[0.213, 0.144, 0.945, 0.175]. I continue this process for each subsequent year: 2022 in {axes}[0.213, 0.251, 0.981, 0.369], 2023 in {axes}[0.213, 0.440, 0.981, 0.558], 2024 in {axes}[0.213, 0.628, 0.981, 0.747], and 2025 in {axes}[0.213, 0.817, 0.981, 0.935], extracting the investment data accordingly.\nBased on the above thought, the answer is:\nYear,Blockchain,AI,Quantum Computing\n2021,500,400,600\n2022,480,420,580\n2023,460,430,570\n2024,430,440,500\n2025,400,450,480" } ], "id_raw": 835 }, { "id": "xJQ2hAxgvOCabqlF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nBased on the progression observed in the chart, which technology shows the most consistent upward trend over the years?" }, { "from": "gpt", "value": "I analyze the trend for each technology by locating the bars for Blockchain {Blockchain bars}[0.213, 0.068, 0.823, 0.098]; [0.213, 0.256, 0.798, 0.287]; [0.213, 0.445, 0.774, 0.476]; [0.213, 0.634, 0.737, 0.664]; [0.213, 0.822, 0.701, 0.853], AI {AI bars}[0.213, 0.106, 0.701, 0.137]; [0.213, 0.295, 0.725, 0.326]; [0.213, 0.483, 0.737, 0.514]; [0.213, 0.672, 0.749, 0.703]; [0.213, 0.861, 0.762, 0.892], and Quantum Computing {Quantum Computing bars}[0.213, 0.144, 0.945, 0.175]; [0.213, 0.333, 0.920, 0.364]; [0.213, 0.522, 0.908, 0.553]; [0.213, 0.711, 0.823, 0.741]; [0.213, 0.899, 0.798, 0.930]. Blockchain shows a declining trend, while Quantum Computing fluctuates. AI, however, indicates a minor increase from 2021 to 2025. This is deduced by comparing the relative lengths of the bars for each technology across the years, which reflect investment values.\nBased on the above thought, the answer is:\nAI shows the most consistent upward trend over the years. This positive trajectory is identified by the slight incremental growth in the length of the AI bars each year, despite the overall variances seen in the other technologies." } ], "id_raw": 835 }, { "id": "HzDIERwPy596yy3z", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_56_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the average yearly investment value for Quantum Computing over the observed period?" }, { "from": "gpt", "value": "To calculate the average investment value for Quantum Computing, I will sum the investment values from each year, read from the length of {Quantum Computing bars}[0.213, 0.144, 0.945, 0.175]; [0.213, 0.333, 0.920, 0.364]; [0.213, 0.522, 0.908, 0.553]; [0.213, 0.711, 0.823, 0.741]; [0.213, 0.899, 0.798, 0.930] and divide by the total number of years, which is 5. This yields the values 600, 580, 570, 500, and 480 for each respective year.\nBased on the above thought, the answer is:\n546" } ], "id_raw": 835 }, { "id": "13SljagbxTtnbUis", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data represented in the bar chart into a structured table in CSV format?" }, { "from": "gpt", "value": "Sure, based on what I have observed in the chart, I can see that it is mainly about the progress in Environmental Protection and Sustainable Development. Now I will start to convert the chart into a CSV format table. The x-axis corresponds to the year displayed from {2015}[0.098, 0.939, 0.133, 0.957] to {2020}[0.718, 0.939, 0.753, 0.957], and the y-axis value ranges from {0}[0.052, 0.920, 0.061, 0.938] to {250}[0.035, 0.040, 0.061, 0.057]. The vertical bars represent the development of four different areas. The {legend}[0.787, 0.055, 0.985, 0.165] tells us that the blue color represents 'Carbon Footprint', the light green 'Forestation', the orange 'Green Energy', and the light orange 'Wildlife Conservation'. The length of these bars in the image corresponds to the value on the y-axis. For instance, the {Carbon Footprint in 2015}[0.103, 0.575, 0.128, 0.927] is about 100, by reading the y-axis based on the height of the bar.\nBased on the above thought, the answer is:\nYear,CarbonFootprint,Forestation,GreenEnergy,WildlifeConservation\n2015,100,15,22,35\n2016,95,20,25,33\n2017,90,25,30,32\n2018,92,30,35,30\n2019,85,34,45,31\n2020,70,42,60,28" } ], "id_raw": 841 }, { "id": "YNrxNnXn4Cq8O4X6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which area had the least positive change over the years? Please explain your answer." }, { "from": "gpt", "value": "Comparing the improvements in the four areas, I notice that the vertical bars that correspond to the different years show the different performances of each area. Let's look at the heights for each area from 2015 to 2020. From the {Carbon Footprint in 2015}[0.103, 0.575, 0.128, 0.927] to the {Carbon Footprint in 2020}[0.723, 0.681, 0.748, 0.927], represented by the blue sections of the bars, we see a significant decrease in Carbon Footprint. In contrast, the heights of the light green sections, representing Forestation, show a noticeable increase from the {Forestation in 2015}[0.103, 0.522, 0.128, 0.575] to the {Forestation in 2020}[0.723, 0.533, 0.748, 0.681]. The increase in the size of the colored sections representing Green Energy from the {Green Energy section for 2015}[0.103, 0.445, 0.128, 0.522] to the {Green Energy section for 2020}[0.723, 0.321, 0.748, 0.533] is also significant. Looking at Wildlife Conservation, the level of the light orange colored sections depicting it shows an irregular fluctuating trend from {the one in 2015}[0.103, 0.321, 0.128, 0.445] to {the one in 2020}[0.723, 0.223, 0.748, 0.321]. The overall trend of Wildlife Conservation isn't clear if it's an improvement or a decrease, and the apparent lack of steady improvement can be seen as having the least positive change over the years.\nBased on the above thought, the answer is:\nWildlife Conservation. It does not show a consistent improvement as seen in the other three areas over the years." } ], "id_raw": 841 }, { "id": "SWPbom5lymFGASpR", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_57_1.png", "conversations": [ { "from": "human", "value": "\nIn which year did Green Energy see its biggest increase?" }, { "from": "gpt", "value": "To solve this, I need to observe the height of the orange section of the bars, representing Green Energy, across different years. Comparing the height of the {Green Energy section for 2015}[0.103, 0.445, 0.128, 0.522] and the subsequent {Green Energy section for 2016}[0.227, 0.434, 0.252, 0.522], I find that there's a slight increase. Continuously comparing the height differences between subsequent years, I find the biggest jump from the {Green Energy section for 2019}[0.599, 0.350, 0.624, 0.508] to the {Green Energy section for 2020}[0.723, 0.321, 0.748, 0.533]. So, the biggest increase in Green Energy came in the year 2020.\nBased on the above thought, the answer is:\n2020" } ], "id_raw": 841 }, { "id": "NxTiG2foIOEeIWgj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into a structured table with CSV format?" }, { "from": "gpt", "value": "The data from the bar chart can be compiled into a CSV table by extracting the values represented by each bar in every group. For example, in the year {\"2000\"}[0.140, 0.939, 0.175, 0.957], the horizontal coordinator ranges of the groups are {\"Wind Turbine\"}[0.103, 0.560, 0.130, 0.927], {\"Solar Cell\"}[0.130, 0.780, 0.157, 0.927], {\"Hydro Power\"}[0.157, 0.707, 0.184, 0.927], and {\"Thermal Power\"}[0.184, 0.634, 0.211, 0.927]. The percentage text on the left of each bar indicates the progress of each technology. From the text {\"50%\"}[0.101, 0.510, 0.132, 0.527], {\"20%\"}[0.264, 0.657, 0.295, 0.674], {\"30%\"}[0.426, 0.657, 0.457, 0.674], and {\"40%\"}[0.589, 0.363, 0.620, 0.381] we can tell that in 2000 the progress of Wind Turbine is 50%, the Solar Cell is 20%, the Hydro Power is 30%, and the Thermal Power is 40%. Repeat this process for following years.\nBased on the above thought, the answer is:\nYear, Wind Turbine, Solar Cell, Hydro Power, Thermal Power \n2000,50%,20%,30%,40%\n2004,40%,30%,30%,50%\n2008,30%,40%,30%,60%\n2012,20%,60%,20%,70%\n2016,10%,80%,20%,90%" } ], "id_raw": 842 }, { "id": "wkHz6R2SM3NrRyMJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which clean energy technology developed the most rapidly over the period 2000-2016?" }, { "from": "gpt", "value": "The chart's x-axis represents the years, which range from {\"2000\"}[0.140, 0.939, 0.175, 0.957] to {\"2016\"}[0.681, 0.939, 0.717, 0.957]. The bar heights and corresponding percentage values allow us to compare the development progress made by each clean energy technology over this period. It is clear to see that the \"Solar Cell\" technology has experienced the most substantial change, starting at 20%<{\"20%\"}[0.264, 0.657, 0.295, 0.674] in the year 2000 and soaring to 80%<{\"80%\"}[0.399, 0.583, 0.430, 0.601] by 2016. In comparison, other clean energy technologies, such as \"Wind Turbine\", \"Hydro Power\", and \"Thermal Power\", have either seen less significant increases or, in the case of \"Wind Turbine\", have actually decreased over the same time period. Therefore, \"Solar Cell\" technology has developed the most rapidly over the period 2000-2016.\nBased on the above thought, the answer is:\nSolar Cell technology." } ], "id_raw": 842 }, { "id": "5P0iYrh6mk6PiJhc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_57_2.png", "conversations": [ { "from": "human", "value": "\nDuring which year did the Solar Cell technology made the most progress?" }, { "from": "gpt", "value": "From the chart, we can observe year by year, the progress of the Solar Cell has increased. Its progress can be detected by the bar height and the number on its left. In 2000<{\"2000\"}[0.140, 0.939, 0.175, 0.957]>, the progress of Solar Cell was 20%,<{\"20%\"}[0.264, 0.657, 0.295, 0.674]>. Then each year, the Solar Cell increase its progress, until 2016<{\"2016\"}[0.681, 0.939, 0.717, 0.957]>, where its progress reached its peak with 80% <{\"80%\"}[0.399, 0.583, 0.430, 0.601]>. So in 2016, the progress of Solar Cell is the most significant.\nBased on the above thought, the answer is:\n2016" } ], "id_raw": 842 }, { "id": "i7eWgWQaMknbVz5V", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract the quarterly sales data for Apple, Google, Amazon and Microsoft from the bar chart into a structured CSV table format?" }, { "from": "gpt", "value": "By reading the {quarter labels}[0.138, 0.939, 0.158, 0.957]; [0.359, 0.939, 0.379, 0.957]; [0.580, 0.939, 0.600, 0.957]; [0.801, 0.939, 0.820, 0.957] on the x-axis and the legend entries {for the different companies}[0.909, 0.062, 0.949, 0.080]; [0.909, 0.088, 0.958, 0.106]; [0.909, 0.115, 0.965, 0.132]; [0.909, 0.141, 0.972, 0.158], I can see that the quartiles represent the x-axis and company represents the entries of the legend. The y-axis values are corresponding to sales data plotted by the bars. Given that, sales data can be extracted from the height of bars corresponding to each company for each quarter. For example, {Apple's Q1 sales bar}[0.104, 0.193, 0.148, 0.927] extends up to the tick mark for 50, representing a sales figure of 50 million for Apple in Q1. Extracting similarly for all companies across all quarters.\nBased on the above thought, the answer is:\nQuarter,Apple,Google,Amazon,Microsoft\nQ1,50,20,30,15\nQ2,48,25,26,20\nQ3,55,28,38,18\nQ4,45,27,35,25" } ], "id_raw": 843 }, { "id": "S7cc6bERN9XxTmaB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nBased on the sales trends observed in the bar chart, which company do you predict will have the highest sales growth in the next quarter? Explain your reasoning." }, { "from": "gpt", "value": "To predict the company with the highest sales growth, I need to analyze the trend over the last four quarters by company. Looking at {Apple's sales bars}[0.104, 0.193, 0.148, 0.927]; [0.325, 0.223, 0.369, 0.927]; [0.546, 0.120, 0.590, 0.927]; [0.766, 0.267, 0.811, 0.927], the trend seems relatively stable but overall a slight decrease. {Google's trend}[0.148, 0.634, 0.192, 0.927]; [0.369, 0.560, 0.413, 0.927]; [0.590, 0.516, 0.634, 0.927]; [0.811, 0.531, 0.855, 0.927] shows a steady increase, with each quarter posting higher sales than the previous one. For {Amazon's sales bars}[0.192, 0.487, 0.236, 0.927]; [0.413, 0.546, 0.457, 0.927]; [0.634, 0.370, 0.678, 0.927]; [0.855, 0.414, 0.899, 0.927], there is sharp rise in Q3 and Q4 indicating an increasing trend. {Microsoft's bars}[0.236, 0.707, 0.281, 0.927]; [0.457, 0.634, 0.501, 0.927]; [0.678, 0.663, 0.722, 0.927]; [0.899, 0.560, 0.943, 0.927] for the four quarters show a slow and steady increase, indicating a consistent growth in sales. But if we compared the absolute growth, the growth numbers from Google and Amazon are higher other two companies.\nBased on the above thought, the answer is:\nAmazon and Google are potential candidates, but based on recent acceleration, I predict Amazon will see the highest sales growth next quarter." } ], "id_raw": 843 }, { "id": "udGMG8BmbjebkckV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_57_3.png", "conversations": [ { "from": "human", "value": "\nWhich company had the highest average sales?" }, { "from": "gpt", "value": "To determine which company had the highest average sales across all four quarters, I can summarize the height of the bars for each company and then divide this sum by four. This can be visualized by lining up the height of the bars in the four quarters for each company. For example, {Apple's sales bars}[0.104, 0.193, 0.148, 0.927]; [0.325, 0.223, 0.369, 0.927]; [0.546, 0.120, 0.590, 0.927]; [0.766, 0.267, 0.811, 0.927] have an average height of close to 50, which is already higher than the highest bar of other companies, which means that Apple has the highest average sales.\nBased on the above thought, the answer is:\nApple." } ], "id_raw": 843 }, { "id": "1wOig5RbHCzUGsKZ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nPlease convert the chart data into a CSV table." }, { "from": "gpt", "value": "Judging from {the title}[0.349, 0.025, 0.721, 0.055] and the x-axis labels {Rural}[0.297, 0.919, 0.342, 0.942] and {Urban}[0.800, 0.919, 0.853, 0.942], the x-axis represents different types of areas. According to {the legend}[0.772, 0.074, 0.973, 0.220], the four color-coding bars each represent Life Expectancy, CO2 Emission, Water Usage, and Plastic Waste. The y-axis represents the values of these factors. I can get the values from each bar's height. For instance, the height of {the bar for \"Life Expectancy\" in Rural}[0.129, 0.603, 0.205, 0.903] is about 75 judging from its corresponding position on the y-axis.\nBased on the above thought, the answer is:\nArea,Life Expectancy,CO2 Emission,Water Usage,Plastic Waste\nRural,75,24,200,80\nUrban,68,50,150,115" } ], "id_raw": 845 }, { "id": "NsrCuXo0dSbfXAjg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat observation can we make about the differences in environmental sustainability factors between rural and urban areas using this chart?" }, { "from": "gpt", "value": "The comparison of sustainability factors such as CO2 Emission, Water Usage, and Plastic Waste between rural and urban areas requires analysis of the corresponding bars. According to {the legend}[0.772, 0.074, 0.973, 0.220], [{CO2 Emission}[0.828, 0.118, 0.947, 0.141], {Water Usage}[0.828, 0.153, 0.939, 0.176], {Plastic Waste}[0.828, 0.188, 0.943, 0.211]] are represented as yellow, red, and grey bars respectively. By comparing the heights of the bars for CO2 Emission [{rural}[0.205, 0.807, 0.281, 0.903], {urban}[0.712, 0.703, 0.788, 0.903]], Water Usage [{rural}[0.281, 0.102, 0.357, 0.903],{urban}[0.788, 0.302, 0.865, 0.903]] and Plastic Waste [{rural}[0.357, 0.583, 0.433, 0.903], {urban}[0.865, 0.443, 0.941, 0.903]] it's clear that these factors tend to increase in the Urban setting compared to the Rural one, except for Water Usage which is seen to decrease. Here, a higher value indicates more usage or emission which is a negative mark on sustainability.\nBased on the above thought, the answer is:\nThe CO2 Emission and Plastic Waste are significantly higher in urban areas compared to rural ones, while the Water Usage is lower in urban areas, which implies urban lifestyle may pose more substantial environmental challenges in terms of CO2 emissions and plastic waste. Despite lower water consumption, urban areas may still need to embrace more sustainable practices." } ], "id_raw": 845 }, { "id": "F9m9OqcgyAcyDcRV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in water usage between urban and rural areas?" }, { "from": "gpt", "value": "To derive the difference in water usage between urban and rural areas, I need to find the corresponding bars. According to {the legend}[0.772, 0.074, 0.973, 0.220], {Water usage}[0.828, 0.153, 0.939, 0.176] is depicted as red bars. The height of the {Water Usage bar for Rural}[0.281, 0.102, 0.357, 0.903] is at 200 while the height of {Water Usage bar for Urban}[0.788, 0.302, 0.865, 0.903] is at 150 on the y-axis. The difference can then be calculated by subtracting the Urban value from the Rural value.\nBased on the above thought, the answer is:\n50" } ], "id_raw": 845 }, { "id": "iAZAMzbMXNUCb0AB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, can you convert the data into a CSV format table?" }, { "from": "gpt", "value": "After observing the chart, I find that there are two groups of bars. The first group of bars denotes the Living Population and is color coded in {blue}[0.731, 0.083, 0.966, 0.106] according to the legend. The second group of bars signifies the Living Quality and is color coded in {orange}[0.731, 0.118, 0.851, 0.141] according to the legend. The chart has five categories shown on the x-axis {\"Ancient\"}[0.143, 0.919, 0.209, 0.942], {\"Medieval\"}[0.319, 0.919, 0.397, 0.942], {\"Industrial\"}[0.500, 0.919, 0.581, 0.942], {\"Modern\"}[0.690, 0.919, 0.755, 0.942], and {\"Future AI\"}[0.866, 0.919, 0.944, 0.942]. For each category, a pair of bars, the blue bar represents the living population, the orange bar indicates the living quality for that period, as denoted by their heights on the y-axis. Thus, the data can be extracted as:\nBased on the above thought, the answer is:\nScenario, Living Population, Living Quality\nAncient, 1200, 20\nMedieval, 950, 45\nIndustrial, 750, 60\nModern, 580, 80\nFuture AI, 250, 90" } ], "id_raw": 846 }, { "id": "Ht6WdDIlsGIlm6DK", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nBased on the data and the chart, how has the transition from ancient to future societies affected living conditions?" }, { "from": "gpt", "value": "Observing the {blue}[0.731, 0.083, 0.966, 0.106] and {orange}[0.731, 0.118, 0.851, 0.141] bars in the chart, it can be seen that as society transitions from ancient to future AI stages, the living population decreases, suggesting a high level of AI integration and automation. Contrasting to the decrease in population, the living quality increases, depicted by the increasing heights of {orange bars}[0.176, 0.891, 0.212, 0.903] to [0.905, 0.849, 0.941, 0.903], indicating improved living conditions due to advancements in technology and healthcare. Hence, the transition has brought about a decrease in population, but an improvement in living conditions.\nBased on the above thought, the answer is:\nThe societal transition from the ancients to the future has brought about a decrease in the living population but an increase in the quality of living as represented by the decrease in the height of the blue bars and increase in height of the orange bars. This suggests that though the population has decreased probably due to a higher level of AI integration and automation, the living conditions have improved drastically due to advancements in technology and healthcare." } ], "id_raw": 846 }, { "id": "JwaRQ0BhXddfEy5c", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_57_6.png", "conversations": [ { "from": "human", "value": "\nAt which societal stage does the population decrease by a larger margin?" }, { "from": "gpt", "value": "Looking at the height of the {blue bars}[\"Living Population (millions)\"] for each societal stage, the largest decrease in population seems to occur between {Industrial}[0.504, 0.453, 0.540, 0.903] and {Modern}[0.686, 0.555, 0.722, 0.903] society as the difference in their heights is the most significant compared to other stages.\nBased on the above thought, the answer is:\nBetween Industrial and Modern society." } ], "id_raw": 846 }, { "id": "LjmAiZBIN2azTSrE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured data table that represents the relevant details from the chart?" }, { "from": "gpt", "value": "Based on the subtitle, {\"Effect of Encouragement and Discouragement on Creativity and Design Innovation\"}[0.104, 0.025, 0.965, 0.055], and {xlabel}[0.462, 0.952, 0.608, 0.975] representing \"Design Elements\", I determined that the x-axis stands for two design elements: \"Encouragement\" and \"Discouragement\". The y-axis {ylabel}[0.019, 0.449, 0.036, 0.516], representing \"Effect\", ranges from 0 up to 100. Three aspects of design innovation: Imagination, Ongoing Discovery and Innovative Thinking, are assessed for both types of design elements based on their respective {legend}[0.739, 0.074, 0.973, 0.185]. For each aspect, there are two bars under two design elements, each holding value, corresponding to the y-axis specification. For example, the bar of Imagination under Encouragement{\"Imagination bar under 'encouragement'\"}[0.129, 0.307, 0.213, 0.903] represents the effect of 78. Using these details a CSV data table can be constructed.\nBased on the above thought, the answer is:\nDesign_Elements,Imagination,Ongoing_Discovery,Innovative_Thinking\nEncouragement,78,102,85\nDiscouragement,50,43,32" } ], "id_raw": 854 }, { "id": "Mm4pqb5GWUnLk7f1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis of the chart, which attitude - Encouragement or Discouragement - seems more impactful on the different aspects of design skill? Justify your answer." }, { "from": "gpt", "value": "To determine the impact of either Encouragement or Discouragement on different aspects of design skill, we need to analyze the lengths of the bars representing these aspects for both Encouragement and Discouragement. Starting with Imagination, the bar {\"Imagination bar under 'Encouragement'\"}[0.129, 0.307, 0.213, 0.903] representing Encouragement is longer than the bar {\"Imagination bar under 'Discouragement'\"}[0.689, 0.521, 0.773, 0.903] for Discouragement. The same trend is evident for the other aspects of Ongoing Discovery - {\"Ongoing Discovery bar under 'Encouragement'\"}[0.213, 0.123, 0.297, 0.903] and {\"Ongoing Discovery bar under 'Discouragement'\"}[0.773, 0.574, 0.857, 0.903], and Innovative Thinking - {\"Innovative Thinking bar under 'Encouragement'\"}[0.297, 0.253, 0.381, 0.903] and {\"Innovative Thinking bar under 'Discouragement'\"}[0.857, 0.658, 0.941, 0.903]. In all aspects, the bars for Encouragement are longer, indicating a stronger positive impact on these aspects as compared to Discouragement.\nBased on the above thought, the answer is:\nEncouragement is more impactful. As evident from the chart, for all three aspects - Imagination, Ongoing Discovery, and Innovative Thinking - the effect due to Encouragement is greater than the effect due to Discouragement." } ], "id_raw": 854 }, { "id": "PQtVKzEt7shf0T4Z", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_57_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which aspect of design skill shows maximum difference between the effects of encouragement and discouragement?" }, { "from": "gpt", "value": "To find the aspect of design that shows maximum difference between the effect of encouragement and discouragement, we would need to compare the length of bars under Encouragement with bars under Discouragement for each aspect. For example, the bar for Encouragement under Imagination{\"Imagination bar under 'Encouragement'\"}[0.129, 0.307, 0.213, 0.903], has the length corresponding to effect of 78 whereas, bar for Discouragement {\"Imagination bar under 'Discouragement'\"}[0.689, 0.521, 0.773, 0.903] has an effect of 50. The difference is 28. Continuing in similar way for rest of the aspects, it can be seen that \"Ongoing Discovery\", with bars at {\"Ongoing Discovery bar under 'Encouragement'\"}[0.213, 0.123, 0.297, 0.903] and {\"Ongoing Discovery bar under 'Discouragement'\"}[0.773, 0.574, 0.857, 0.903], shows the maximum difference of 59.\nBased on the above thought, the answer is:\nOngoing Discovery." } ], "id_raw": 854 }, { "id": "PaLGUywrJaSKnFM4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nCould you convert the bar chart data you observed into an organized structured CSV table?" }, { "from": "gpt", "value": "I see chart information like {Creativity Type}[0.034, 0.785, 0.050, 0.805], represented on the x-axis, with 18 different labels each corresponding to a bar. Based on {Innovation Design}[0.028, 0.564, 0.050, 0.584] marked on the y-axis, these bars show the percentage values related to each 'Creativity Type', which range between {0%}[0.034, 0.785, 0.050, 0.805] and {60%}[0.028, 0.124, 0.050, 0.144]. For instance, the bar for {Idea Generation}[0.054, 0.806, 0.118, 0.934] has {a length}[0.099, 0.401, 0.137, 0.793] roughly between 30% and 40%. Given that there isn't an exact annotation of the value, and we use raw data for confirmation instead. We find the 'Idea Generation' creativity type has a score of 35.5%. Likewise, we can interpret each bar's length to sift the corresponding percentages for all creativity types. We have the information we need to create a CSV table.\nBased on the above thought, the answer is:\nCreativity Type,Innovation Design\nIdea Generation,35.5\nConcept Development,25.9\nProduct Development,63.2\nStrategy Creation,50.3\nVisionary Thinking,40.7\nProblem Solving,45.5\nCollaborative Thinking,55.9\nPlatform Designing,47.2\nModel Building,28.6\nPrototyping,60.4\nExperimentation,39.7\nRisk-taking,35.9\nDivergent Thinking,54.8\nAesthetic Sensibility,62.3\nArtistic Expression,47.9\nStorytelling,30.6\nPattern Recognition,42.7\nSystem Thinking,29.9" } ], "id_raw": 855 }, { "id": "4gL0XgtOWMlbeRTV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, please explain which types of creativity are more effective in terms of innovation design?" }, { "from": "gpt", "value": "To answer his question, one must compare the lengths of all the bars within the chart. The longer the bar, the more effective that type of creativity is in terms of innovation design. It's apparent that {Product Development}[0.194, 0.096, 0.232, 0.793], {Collaborative Thinking}[0.385, 0.177, 0.423, 0.793] and {Aesthetic Sensibility}[0.718, 0.106, 0.756, 0.793] have longer bars compared to others which means their corresponding 'Innovation Design' percentages are higher indicating that these types are more effective in driving innovation design. On the other hand, {Concept Development}[0.146, 0.507, 0.185, 0.793], {Model Building}[0.480, 0.478, 0.518, 0.793] and {System Thinking}[0.909, 0.463, 0.947, 0.793] have shorter bars implying that these types are less effective in terms of innovation design.\nBased on the above thought, the answer is:\nProduct Development, Collaborative Thinking and Aesthetic Sensibility are the creativity types which are more effective in terms of innovation design. This is because these types have higher 'Innovation Design' percentages." } ], "id_raw": 855 }, { "id": "zS3MjgvJvenf0hSk", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_0.png", "conversations": [ { "from": "human", "value": "\nWhich creativity type has the lowest score of innovation design in the chart?" }, { "from": "gpt", "value": "To find the creativity type with the lowest innovation design score, I need to compare the lengths of all the bars on the chart. From left to right, {the first bar}[0.099, 0.401, 0.137, 0.793] corresponding to 'Idea Generation' has a quite long length, definitely not the shortest. {The second bar}[0.146, 0.507, 0.185, 0.793] corresponding to 'Concept Development' is shorter than the first one but it's not the shortest either. Then, as I continue on, some bars are much shorter than previous ones, such as {'System Thinking'}[0.862, 0.806, 0.928, 0.937]. The bar[0.909, 0.463, 0.947, 0.793] for 'System Thinking' is shorter than all the previous ones. After comparing all bars, it's clear 'System Thinking' has the shortest bar, making the lowest score.\nBased on the above thought, the answer is:\nSystem Thinking." } ], "id_raw": 855 }, { "id": "o8NbOVa8g2S2A9Xd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nPlease generate a CSV table based on the stacked bar chart you see." }, { "from": "gpt", "value": "The chart consists of various categories, or energy sources, which are outlined by the {x-axis labels}<{\"Fossil Fuels\"}[0.122, 0.761, 0.203, 0.924]; {\"Nuclear Power\"}[0.204, 0.761, 0.305, 0.963]; {\"Hydro\"}[0.322, 0.761, 0.371, 0.859]; {\"Renewables\"}[0.396, 0.761, 0.482, 0.933]; {\"Biomass\"}[0.499, 0.761, 0.563, 0.889]; {\"Coal\"}[0.603, 0.761, 0.643, 0.840]; {\"Natural gas\"}[0.673, 0.761, 0.757, 0.928]; {\"Oil\"}[0.792, 0.761, 0.821, 0.819]>. The y-axis, whose labels range from {0%}[0.041, 0.723, 0.068, 0.758] to {100%}[0.019, 0.122, 0.068, 0.157], represents the energy usage percentage of these sources. Each energy source bar is divided into segments representing three different years: Year1, Year2, Year3. These years are also signified in {the legend}[0.345, 0.063, 0.717, 0.126] with different colors for each year. For example, for {Fossil Fuels}[0.163, 0.737], Year1 is represented by {blue}[0.121, 0.523, 0.204, 0.737], Year2 by {purple}[0.121, 0.431, 0.204, 0.523], and Year3 by {orange}[0.121, 0.297, 0.204, 0.431]. I will need to calculate the height from each segment to the x-axis to determine the energy usage for each year and source.\nBased on the above thought, the answer is:\nSource,Year1,Year2,Year3\nFossil Fuels,35.5,15.4,22.3\nNuclear Power,25.4,30.2,22.6\nHydro,21.1,27.5,26.7\nRenewables,5.7,8.3,11.4\nBiomass,2.7,4.5,6.3\nCoal,50.6,32.3,18\nNatural gas,16.5,14.4,21.4\nOil,7.8,6.9,15.4" } ], "id_raw": 856 }, { "id": "vbkZ6YhFuPrjAwXF", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat general trend can you discern for energy sources from Year1 to Year3 according to the stacked bar chart?" }, { "from": "gpt", "value": "To discern the trend, I need to examine the bars of each energy source from Year1 to Year3. For example, the bars for {Fossil Fuels}[0.121, 0.297, 0.204, 0.737] and {Coal}[0.581, 0.130, 0.664, 0.737] decrease from Year1 to Year3, whilst those for {Renewables}[0.397, 0.584, 0.480, 0.737] and {Biomass}[0.489, 0.656, 0.572, 0.694] increase. {Nuclear Power}[0.213, 0.267, 0.296, 0.737] and {Hydro}[0.305, 0.284, 0.388, 0.445] show increments followed by reduction. {Natural Gas}[0.673, 0.422, 0.756, 0.737] and {Oil}[0.765, 0.556, 0.848, 0.649] show consistent increase from Year1 to Year3. The overall trend indicates a decrease in the use of fossil fuels and an increase in renewables.\nBased on the above thought, the answer is:\nThe usage of Fossil Fuels and Coal notably declines while the usage of Renewables and Biomass noticeably increases. The usage of Nuclear Power and Hydro generally strays a little but ends up in near-unchanged positions while Natural Gas and Oil show steady increase. This suggests a general shift towards more sustainable energy sources over the three years." } ], "id_raw": 856 }, { "id": "cXrKugBVR6jv2wRZ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage change in usage of coal from Year1 to Year3?" }, { "from": "gpt", "value": "To find the percentage change, I can calculate the difference in percentage usage of coal from Year1 to Year3. The Year1 usage can be determined from {the blue bar}[0.581, 0.433, 0.664, 0.737], which corresponds to 50.6%. The Year3 usage can be determined from {the orange bar}[0.581, 0.130, 0.664, 0.238], which corresponds to 18%. The percentage change equals the difference between the Year3 and Year1 usage divided by the Year1 usage, multiplied by 100%.\nBased on the above thought, the answer is:\n64.42%" } ], "id_raw": 856 }, { "id": "8A21BmMk7aGjWvqr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nPlease convert the stacked bar chart data into a CSV format table." }, { "from": "gpt", "value": "The chart does not have a visible title, but it clearly represents the distribution of different personality traits - Introversion, Extroversion and Ambiversion - across various social networking platforms. Based on the {x-axis}[{\"Facebook\"}[0.088, 0.812, 0.158, 0.953]; {\"Instagram\"}[0.166, 0.812, 0.241, 0.962]; {\"Twitter\"}[0.270, 0.812, 0.324, 0.920]; {\"LinkedIn\"}[0.343, 0.812, 0.407, 0.940]; {\"Snapchat\"}[0.419, 0.812, 0.489, 0.953]; {\"Reddit\"}[0.520, 0.812, 0.572, 0.916]; {\"WhatsApp\"}[0.580, 0.812, 0.655, 0.963]; {\"Zoom\"}[0.690, 0.812, 0.738, 0.908]; {\"TikTok\"}[0.770, 0.812, 0.820, 0.913]; {\"Skype\"}[0.854, 0.812, 0.903, 0.911]] we can see 10 different social media platforms that form the categories. The {y-axis}[{\"0%\"}[0.041, 0.774, 0.068, 0.809]; {\"20%\"}[0.030, 0.644, 0.068, 0.679]; {\"40%\"}[0.030, 0.513, 0.068, 0.548]; {\"60%\"}[0.030, 0.383, 0.068, 0.418]; {\"80%\"}[0.030, 0.252, 0.068, 0.287]; {\"100%\"}[0.019, 0.122, 0.068, 0.157]] ranges from \"0%\" to \"100%\", representing the percentage of each personality trait. The proportions of Introversion, Extroversion, and Ambiversion for each platform are represented by the respective rows of stacked bars. The position of the color split points correlates with the proportion relative to the y-axis. Therefore, I can determine the values for the CSV data by comparing the height of each colour split with the y-axis values. For example, for Facebook, {the green bar}[0.121, 0.557, 0.196, 0.788] represents Introversion and spans from 0% to 35.34%. {The gold bar}[0.121, 0.261, 0.196, 0.557] above it represents Extroversion and spans from 35.34% to 80.85%. {The blue bar}[0.121, 0.136, 0.196, 0.261] at the top represents Ambiversion and spans from 80.85% to 100%.\nBased on the above thought, the answer is:\nSocial Media,Introversion,Extroversion,Ambiversion\nFacebook,35.34,45.51,19.15\nInstagram,30.68,49.94,19.38\nTwitter,24.78,55.12,19.31\nLinkedIn,40.20,41.78,18.02\nSnapchat,22.55,57.99,19.46\nReddit,46.72,33.96,19.32\nWhatsApp,33.39,47.29,19.31\nZoom,42.23,38.64,19.13\nTikTok,20.97,59.68,19.35\nSkype,39.12,41.62,19.26" } ], "id_raw": 857 }, { "id": "QwhJ8MDbq0oxswUY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nBased on the distribution of personality traits on social media platforms, can you predict the environment of various platforms?" }, { "from": "gpt", "value": "The chart displays the relative prevalence of different personality traits across a range of social networking platforms. Each platform is represented by a stack of bars consisting of three segments: {Introversion}[0.121, 0.557, 0.196, 0.788] (represented by green), {Extroversion}[0.121, 0.261, 0.196, 0.557] (represented by gold), and {Ambiversion}[0.121, 0.136, 0.196, 0.261] (represented by blue). By analyzing the heights of these segments, we can make inferences about the personality profiles that likely dominate each platform. For example, {Reddit}[0.535, 0.483, 0.609, 0.788] appears to have a higher percentage of Introversion than any other platform. At the same time, {TikTok}[0.783, 0.651, 0.858, 0.788] has the highest percentage of Extroversion. Therefore, one can infer that Reddit tends to foster more reserved, introspective discussions, whereas TikTok likely caters to a more vibrant and extroverted crowd. Platforms like LinkedIn and Zoom have balanced distributions between Introverts and Extroverts and may be suitable for users of varying personalities. The majority of the platforms, irrespective of the prevailing personality trend, seem to maintain around ~19% of Ambiverts - a balance between Introversion and Extroversion.\nBased on the above thought, the answer is:\nYes, the Reddit platform may cater more to introverted personalities. In contrast, TikTok attracts a larger proportion of extroverts. LinkedIn and Zoom appear to be balanced platforms that cater to both introverted and extroverted users. Irrespective of the dominant personality trait, most platforms maintain a consistent distribution of ambiverts around ~19%." } ], "id_raw": 857 }, { "id": "r52OujOHrNrEbH3l", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_2.png", "conversations": [ { "from": "human", "value": "\nWhich social networking platform has the highest number of Ambiverts?" }, { "from": "gpt", "value": "To determine this, I need to compare the respective blue bar segments for all the platforms. I can check the top edge coordinates of each blue segment to estimate the value more accurately based on the Y-axis. Starting from the left, {Facebook}[0.121, 0.136, 0.196, 0.261] has the height about 19.15%; {Instagram}'s[0.204, 0.136, 0.278, 0.262] height is also similar at approximately 19.38%; {Twitter}[0.287, 0.136, 0.361, 0.262] -19.31%; {LinkedIn}[0.369, 0.136, 0.444, 0.253] - 18.02%; {Snapchat}[0.452, 0.136, 0.527, 0.263] - 19.46%; Reddit[0.535, 0.136, 0.609, 0.262] - 19.32%; WhatsApp[0.618, 0.136, 0.692, 0.262] - 19.31%; Zoom[0.700, 0.136, 0.775, 0.260] - 19.13%; {TikTok}[0.783, 0.136, 0.858, 0.262] - 19.35%; Skype[0.866, 0.136, 0.940, 0.261] - 19.26%. Hence, among all these social network platforms, Snapchat contains the highest proportion of Ambiverts.\nBased on the above thought, the answer is:\nSnapchat." } ], "id_raw": 857 }, { "id": "ay594TtV6r2opN9t", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nCan you generate a CSV table that captures the information presented in this bar graph?" }, { "from": "gpt", "value": "Looking at the {bars}[ {\"Param1\"}[0.121, 0.576, 0.196, 0.798]; {\"Param2\"}[0.204, 0.568, 0.278, 0.798]; {\"Param3\"}[0.287, 0.596, 0.361, 0.798]] in the chart, these represent different cultural paradigms across regions. The x-axis of the chart contains labels which represent the {regions}[0.127, 0.823, 0.190, 0.947], while the height of the bars represents the percentage for each paradigm. By considering these two piece of information, we can create a CSV table.\nBased on the above thought, the answer is:\nRegion,Param1,Param2,Param3\nRegion1,30.71,29.48,39.81\nRegion2,31.85,37.94,30.21\nRegion3,27.92,52.61,19.47\nRegion4,32.81,30.68,36.51\nRegion5,28.37,38.97,32.66\nRegion6,39.67,19.33,41.00\nRegion7,26.48,34.12,39.40\nRegion8,31.09,35.62,33.29\nRegion9,34.32,24.37,41.31\nRegion10,29.45,35.11,35.44" } ], "id_raw": 860 }, { "id": "phTuFpnvJ5jVGTLH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nWhat cultural paradigm seems to be most prevalent in Region3, and why?" }, { "from": "gpt", "value": "To answer this question, I would need to look at the {bars}[ {\"Param1\"}[0.121, 0.576, 0.196, 0.798]; {\"Param2\"}[0.204, 0.568, 0.278, 0.798]; {\"Param3\"}[0.287, 0.596, 0.361, 0.798]] corresponding to Region3. I can see that the tops of these bars mark the values for each parameter. The bar that reaches the highest point represents the most prevalent cultural paradigm. Upon observation, the second highest section which represents Param2, has the highest value among three parameters for Region3, indicating that Param2 is the most prevalent cultural paradigm in Region3.\nBased on the above thought, the answer is:\nParam2 is the most prevalent cultural paradigm in Region3, as its value is the highest among all three parameters in this region." } ], "id_raw": 860 }, { "id": "SJBmI6K6j3aOAZFJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_5.png", "conversations": [ { "from": "human", "value": "\nFor which region is the difference between Param1 and Param3 the greatest?" }, { "from": "gpt", "value": "From the chart, I can see that Param1 and Param3 are represented by the lowest and highest sections in the {bars}[ {\"Param1\"}[0.121, 0.576, 0.196, 0.798]; {\"Param3\"}[0.287, 0.596, 0.361, 0.798]]. I should directly compare these two parameters by region to find out which one has the largest difference. By looking at the bars corresponding to each of the 10 regions, I notice that the largest difference in height between Param1 and Param3 occurs in Region6, represented by the {Bar of Region6}[0.535, 0.511, 0.609, 0.798]}.\nBased on the above thought, the answer is:\nRegion6" } ], "id_raw": 860 }, { "id": "DhGc7RUUWSDZBROE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the bar chart into a CSV formatted table?" }, { "from": "gpt", "value": "Sure, by analyzing the X-axis, the seven categories in it are {\"Family_Size\"}[0.168, 0.634], {\"Number_of_rooms\"}[0.272, 0.634], {\"Home_Area\"}[0.375, 0.634], {\"Number_of_bathrooms\"}[0.479, 0.634], {\"Number_of_cars\"}[0.583, 0.634], {\"Public_Transport\"}[0.686, 0.634], {\"School_District\"}[0.790, 0.634] and {\"Proximity_to_work\"}[0.894, 0.634]. These categories have three options according to the {legend}[0.313, 0.078, 0.749, 0.141] represented by three different levels of opacity in the bars; {Option1}, {Option2} and {Option3}. Now, we calculate the values for each option's percentage for each category using the y-tick values range from {0%}[0.041, 0.620, 0.068, 0.655] to {100%}[0.019, 0.122, 0.068, 0.157], for example, {Family_Size, Option1}[0.121, 0.345, 0.215, 0.634] lies above {20%} and below {60%}, which can be interpreted as 58 considering the y-tick labels.\nBased on the above thought, the answer is:\nAttribute,Option1,Option2,Option3\nFamily_Size,58,45,40\nNumber_of_rooms,60,59,37\nHome_Area,72,55,44\nNumber_of_bathrooms,55,53,47\nNumber_of_cars,50,47,36\nPublic_Transport,45,44,34\nSchool_District,67,60,57\nProximity_to_work,70,49,40" } ], "id_raw": 862 }, { "id": "RlRLSekmBUqQlJlc", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nIn terms of public transportation, what opinion does the public prefer more? Please elaborate." }, { "from": "gpt", "value": "The preferences of community members regarding transportation are clearly depicted as three layers in the Public Transport category comprising of {Option1}[0.640, 0.410, 0.733, 0.634], {Option2}[0.640, 0.190, 0.733, 0.410] and {Option3}[0.640, 0.021, 0.733, 0.190]. Based on the chart, {Option1} has a length between {40%} and {80%}, {Option2} has a length between {20%} and {40%}, and {Option3} lies between {0%} and {20%}. Hence, the public members prefer {Option1} more than the other two options, {Option2} is the second most preferred, and the least preferred is {Option3}.\nBased on the above thought, the answer is:\nThe community members prefer Option1 the most when considering public transportation. This suggests that this option may be viewed as the most convenient, affordable or accessible among the given choices." } ], "id_raw": 862 }, { "id": "NQjs0qIE9fU4HHyI", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the average value of Option3 across all household living conditions?" }, { "from": "gpt", "value": "To find the average value of {Option3}, I need to add the values of all {Option3} under different categories then divide it by the number of categories. The values can be figured out according to the relative lengths of {Option3} bars against the y-axis, ranging between {0%}[0.019, 0.122, 0.068, 0.157] and {100%}[0.041, 0.620, 0.068, 0.655]. For instance, {Family_Size, Option3}[0.121, -0.079, 0.215, 0.121] is higher than {40%} and lower than {80%}, so it represents 40. Continue the same operation to get the values of {Option3} for Number_of_rooms, Home_Area, Number_of_bathrooms, Number_of_cars, Public_Transport, School_District and Proximity_to_work. After that, sum up all the values of {Option3} and divide by 8, which is the total number of categories.\nBased on the above thought, the answer is:\n47.625" } ], "id_raw": 862 }, { "id": "yNmwd9QcVnGpNWDA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the dual bar chart into a structured table?" }, { "from": "gpt", "value": "By observing the bar chart, I can see that it's a dual bar chart with two categories, AI Research and Automation Applications. These categories are indicated by two different patterns on the bars. Bars for AI Research have a light color pattern while bars for Automation Applications are filled in blue. Each year from 2010 to 2020 has two bars associated with it, one for each category. The height of the bars indicates the value of each category in that specific year. To generate the CSV table, I need to extract the values for AI Research and Automation Applications for each year. The AI Research bar for 2010 at coordinates [0.096, 0.648, 0.136, 0.901] has a value of 35, and the Automation Applications bar for the same year at coordinates [0.096, 0.504, 0.136, 0.648] has a value of 20. I follow a similar procedure for the remaining years.\nBased on the above thought, the answer is:\nYear,AI Research,Automation Applications\n2010,35,20\n2011,38,28\n2012,37,45\n2013,52,60\n2014,55,65\n2015,63,80\n2016,70,86\n2017,74,90\n2018,80,95\n2019,85,100\n2020,90,105" } ], "id_raw": 863 }, { "id": "azju062Snpb3pGRq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nCan you comment on the trends in AI Research and Automation Applications based on the bar chart?" }, { "from": "gpt", "value": "The values derived from the bar chart suggest a steady upward trend for both AI Research and Automation Applications from the year 2010 to 2020. The increase in AI Research has been somewhat gradual with a noticeable jump in 2013 as represented by the higher bar at coordinates [0.339, 0.525, 0.379, 0.901]. On the other hand, Automation Applications have surged quite dramatically, as evidenced by the rapidly escalating bars for this category. Exceptionally, the bar at {2020}[0.905, -0.507, 0.945, 0.251] indicates an outlier data point for Automation Applications as it exceeds the 100% mark with a value of 105.\nBased on the above thought, the answer is:\nBoth AI Research and Automation Applications have shown a consistent increase over the years from 2010 to 2020. AI Research has been growing gradually while Automation Applications have witnessed a dramatic surge. The year 2020 is particularly notable for Automation Applications as it surpasses the 100% mark, indicating a substantial growth in this domain." } ], "id_raw": 863 }, { "id": "P2F7Fr0FkHuFd4nk", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_8.png", "conversations": [ { "from": "human", "value": "\nWhich year saw the highest growth in Automation Applications?" }, { "from": "gpt", "value": "To determine the year with the highest growth in Automation Applications, I must find the year where the bar for Automation Applications is the tallest. The bar representing Automation Applications in 2020 with coordinates [0.905, -0.507, 0.945, 0.251] is higher than any other bar for Automation Applications in any year, extending well beyond the 100% mark with a value of 105.\nBased on the above thought, the answer is:\n2020" } ], "id_raw": 863 }, { "id": "z8lH4Gl4Ri3SaKn5", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nFrom your observation of the bar chart, could you generate a table in CSV format displaying the Quality, Availability, and Equity indices for each social welfare and public service category?" }, { "from": "gpt", "value": "Based on my analysis of the bar chart, the X-axis represents distinct categories of societal services-medical, environmental, transportational, etc which can be found at [{\"Public Safety\"}[0.123, 0.756, 0.196, 0.879], {\"Environment\"}[0.192, 0.756, 0.264, 0.877], {\"Transportation\"}[0.256, 0.756, 0.337, 0.891], {\"Health Services\"}[0.322, 0.756, 0.408, 0.900], {\"Social Services\"}[0.392, 0.756, 0.475, 0.895], {\"Education\"}[0.473, 0.756, 0.532, 0.854], {\"Public Library\"}[0.532, 0.756, 0.609, 0.884], {\"Local Council\"}[0.602, 0.756, 0.677, 0.881], {\"Welfare\"}[0.684, 0.756, 0.731, 0.835], {\"Parks\"}[0.758, 0.756, 0.794, 0.817], {\"Recreation Facilities\"}[0.791, 0.756, 0.899, 0.936], {\"Public Transport\"}[0.869, 0.756, 0.958, 0.903]]. The Y-axis on the other hand represents the percentage index, with markings at intervals of 20%, which can be seen at [{\"20%\"}[0.047, 0.609, 0.078, 0.632], {\"40%\"}[0.047, 0.486, 0.078, 0.510], {\"60%\"}[0.047, 0.364, 0.078, 0.388], {\"80%\"}[0.047, 0.242, 0.078, 0.265], {\"100%\"}[0.039, 0.120, 0.078, 0.143]]. Based on the {legend}[0.360, 0.049, 0.713, 0.091], white regions represent Quality, horizontal lines show Availability and diagonal lines convey Equity. I can discern the percentage values associated with each of these indices for a category by the {bars}[0.883, -0.794, 0.944, -0.317]. By undertaking this process systematically for each category, the data can be tabulated in a CSV format.\nBased on the above thought, the answer is:\nCategory,Quality,Availability,Equity\nPublic Safety,87,92,76\nEnvironment,91,95,83\nTransportation,94,98,90\nHealth Services,92,94,85\nSocial Services,88,92,78\nEducation,95,98,91\nPublic Library,91,93,84\nLocal Council,79,83,70\nWelfare,82,84,75\nParks,88,92,82\nRecreation Facilities,87,90,80\nPublic Transport,85,88,78" } ], "id_raw": 864 }, { "id": "xR58kW1KIqzBlzka", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which category of social welfare and public services needs significant improvements and why?" }, { "from": "gpt", "value": "The chart categories requiring significant improvements are those with the lowest overall index. The y-values determine this, which can be established from the top of each bar including the ones for (Public Safety)[0.190, 0.740], (Environment)[0.259, 0.740], (Transportation)[0.327, 0.740], (Health Services)[0.396, 0.740], (Social Services)[0.464, 0.740], (Education)[0.533, 0.740], (Public Library)[0.602, 0.740], (Local Council)[0.670, 0.740], (Welfare)[0.739, 0.740], (Parks)[0.807, 0.740], (Recreation Facilities)[0.876, 0.740] and (Public Transport)[0.944, 0.740]. Upon analysis, it's evident that 'Local Council' has the lowest overall index, indicating a need for enhancement. Also, the outside white area representing 'Quality'[0.608, 0.257, 0.670, 0.740] is significantly less for 'Local Council', indicating poor quality of output. Hence, 'Local Council' likely requires substantial improvement.\nBased on the above thought, the answer is:\nThe 'Local Council' category needs significant improvement because it has the lowest overall index. The quality of services in this category is particularly low compared to others, indicating that improvements in this area could significantly increase the overall index." } ], "id_raw": 864 }, { "id": "mFNwacXUIqgIszeS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_9.png", "conversations": [ { "from": "human", "value": "\nGiven the bar chart, which category has the highest total index value across Quality, Availability and Equity?" }, { "from": "gpt", "value": "To respond to this, I need to locate the top of each bar on the graph, which corresponds to the total index value of all three sub-categories. The exact location of the top of each bar can be found at (Public Safety)[0.190, 0.740], (Environment)[0.259, 0.740], (Transportation)[0.327, 0.740], (Health Services)[0.396, 0.740], (Social Services)[0.464, 0.740], (Education)[0.533, 0.740], (Public Library)[0.602, 0.740], (Local Council)[0.670, 0.740], (Welfare)[0.739, 0.740], (Parks)[0.807, 0.740], (Recreation Facilities)[0.876, 0.740] and (Public Transport)[0.944, 0.740]. Observing these y-values, I can conclude that the 'Education' category has the highest total index value.\nBased on the above thought, the answer is:\nEducation." } ], "id_raw": 864 }, { "id": "OUXadUtDovqmAiJY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart you are observing, can you tabulate the sales trend for each product category in percentage?" }, { "from": "gpt", "value": "The chart is titled {\"Sales Trend Percentage\"}[0.3, 0.9, 0.7, 0.91] on the top, indicating that the sales trends are divided into five parts based on the colors in the legend box {legend} [0.275, 0.032, 0.774, 0.109] i.e., 'Slow Decline', 'Inexorable Decline', 'Volatile Growth', 'Steady Increase', and 'Sharp Rise'. The x-axis corresponds to the product categories as indicated by the labels {\"Electronics\"}[0.151, 0.857, 0.215, 0.962], {\"Fashion\"}[0.330, 0.857, 0.377, 0.935], {\"Grocery\"}[0.500, 0.857, 0.549, 0.938] , {\"Automobiles\"}[0.660, 0.857, 0.731, 0.975], {\"Stationery\"}[0.836, 0.857, 0.897, 0.958]. To generate the table, I would extract the heights for the colored segments within each bar which corresponds to the exact percentage in each sales trend.\nBased on the above thought, the answer is:\nProduct Category, Slow Decline, Inexorable Decline, Volatile Growth, Steady Increase, Sharp Rise\nElectronics,92,80,9,88,96\nFashion,85,77,25,90,85\nGrocery,70,65,35,89,89\nAutomobiles,60,58,50,92,92\nStationery,55,53,48,95,95" } ], "id_raw": 865 }, { "id": "5oE0k9Ut3KSOEeCi", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart you are observing, which product category has shown the most fluctuation in sales trend?" }, { "from": "gpt", "value": "This requires a careful comparison of the sizes of different segments in each bar. For instance, looking at the {\"Electronics\"}[0.151, 0.184, 0.215, 0.357] category, we see a relatively consistent sales trend with little fluctuation: the size of the \"Slow Decline\" and \"Sharp Rise\" segments are both considerably larger than the \"Volatile Growth\", \"Inexorable Decline\", and \"Steady Increase\" segments. This pattern repeats in other categories as well but looking at the {\"Automobiles\"}[0.660, 0.208, 0.731, 0.373] category, we see that the sizes of the segments appear to be more evenly distributed, suggesting a more fluctuating pattern of sales.\nBased on the above thought, the answer is:\nAutomobiles. The relatively equal distribution of the different sales trends in the \"Automobiles\" category suggests a more fluctuating sales trend compared to other categories." } ], "id_raw": 865 }, { "id": "XVipn6dkEYQ3Q9KO", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_10.png", "conversations": [ { "from": "human", "value": "\nWhich product category has shown the highest percentage of steady increase?" }, { "from": "gpt", "value": "According to the legend and color of the bar {\"legend\"} [0.275, 0.032, 0.774, 0.109] , the \"Steady Increase\" trend is represented by the 4th segment from the bottom in all the product categories. Now, looking at the 4th segment of all bars for the following categories {\"Electronics\"}[0.151, 0.357, 0.215, 0.515], {\"Fashion\"}[0.330, 0.342, 0.377, 0.504], {\"Grocery\"}[0.500, 0.375, 0.549, 0.535], {\"Automobiles\"}[0.660, 0.373, 0.731, 0.538], {\"Stationery\"}[0.836, 0.389, 0.897, 0.560], it can be inferred that Stationery category has shown the highest \"Steady Increase\" segment.\nBased on the above thought, the answer is:\nStationery." } ], "id_raw": 865 }, { "id": "FNlWFclxrCIOgZBZ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nCould you provide a visual description of the frequency distributions of Tech Startups, Venture Capital, and Government Grants across different technology sectors in a structured table format?" }, { "from": "gpt", "value": "Examining the provided {stacked bar chart}[0.054, 0.127, 0.988, 0.827], each bar is divided into segments that represent the distribution of funding by {Tech Startups}[0.096, 0.766, 0.155, 0.827], {Venture Capital Coverage}[0.096, 0.458, 0.155, 0.766], and {Government Grants}[0.096, 0.127, 0.155, 0.458] across various sectors like {Green Tech}[0.099, 0.841, 0.153, 0.934], {Bio Tech}[0.170, 0.841, 0.212, 0.913], and so on. The segments are scaled proportionally to the percentage distributions and are distinguished by colors. By analyzing the heights of these segments and their relationship to the {y-axis tick labels}[0.027, 0.820, 0.045, 0.840], which represent percentages from 0% to 100%, we can convert the visual data into a table format.\nBased on the above thought, the answer is:\nSector,Tech Startups,Venture Capital,Government Grants\nGreen Tech,8.8,43.9,47.3\nBio Tech,32.6,51.3,16.1\nDigital Media,23.5,35.7,40.8\nFinTech,34.7,16.3,49.0\nFood Tech,20.0,61.6,18.4\nHealth Tech,15.6,31.1,53.3\ne-Commerce,34.1,34.8,31.1\nMaterials Science,45.9,27.0,27.1\nAI and Robotics,29.5,49.2,21.3\nSpace Tech,31.0,29.1,39.9\nAgri Tech,25.6,24.1,50.3\nOn-Demand,38.5,35.8,25.7\nAverage,30.0,35.6,34.4" } ], "id_raw": 866 }, { "id": "wbVqkCNXFxJRSbc7", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nWhich technology sector appears most reliant on Government Grants for funding?" }, { "from": "gpt", "value": "Upon observing the {stacked bar chart}[0.054, 0.127, 0.988, 0.827], I will look for the sector where the segment representing {Government Grants}[0.096, 0.127, 0.155, 0.458] is largest relative to the other sources of funding within that sector's bar. By examining the bar heights and comparing them, it is evident that the {Bio Tech sector}[0.162, 0.240, 0.221, 0.599] shows the smallest portion for {Government Grants}[0.162, 0.127, 0.221, 0.240] compared to {Tech Startups}[0.162, 0.599, 0.221, 0.827] and {Venture Capital Coverage}[0.162, 0.458, 0.221, 0.599], indicating that it relies less on Government Grants than the other sectors. Conversely, {Green Tech}[0.099, 0.841, 0.153, 0.934] shows a larger portion of its funding coming from {Government Grants}[0.096, 0.458, 0.155, 0.766] relative to the other sources, which suggests high reliance on this type of funding.\nBased on the above thought, the answer is:\nGreen Tech. It appears most reliant on Government Grants as a significant majority of its funding, 47.3%, comes from this source compared to tech startups and venture capital." } ], "id_raw": 866 }, { "id": "Fxt3ONkrwChPLz7F", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_11.png", "conversations": [ { "from": "human", "value": "\nHow much more Venture Capital Coverage funding does Bio Tech receive compared to Health Tech?" }, { "from": "gpt", "value": "I am looking at the portion of the bars that represent {Venture Capital Coverage funding}[0.162, 0.240, 0.221, 0.599] for the Bio Tech sector and {Health Tech}[0.425, 0.500, 0.484, 0.718]. By comparing the relative heights of these segments, respectively with the corresponding {y-axis percentage scale}[0.020, 0.679, 0.045, 0.699], I can determine the difference. {Bio Tech}[0.170, 0.841, 0.212, 0.913] receives 51.3% Venture Capital Coverage, and {Health Tech}[0.427, 0.841, 0.483, 0.937] receives 31.1% Venture Capital Coverage.\nBased on the above thought, the answer is:\n20.2%" } ], "id_raw": 866 }, { "id": "gwOhAjf1L73oiiZt", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nCould you translate the stack bar chart information into a CSV table structure that outlines the percentages of Recovery, Performance, and Decline for each industry?" }, { "from": "gpt", "value": "Observing the stack bar chart, I start from the {industry labels on the x-axis}[0.104, 0.820, 0.942, 0.900]. Each industry label corresponds to a stack bar composed of segments with different hatch patterns and edge colors, representing Recovery, Performance, and Decline. For instance, the Theatre's stack bar spans from {bottom of the stack bar}[0.106, 0.365, 0.151, 0.804] to {top of the stack bar}[0.106, 0.129, 0.151, 0.230]. By referencing the y-axis percentages, I can discern the values for Theatre as 65% for Recovery, 20% for Performance, and 15% for Decline. Repeating this process for all stack bars, I can construct the complete CSV table.\nBased on the above thought, the answer is:\nIndustry,Recovery,Performance,Decline\nTheatre,65,20,15\nArt Gallery,30,50,20\nMusic Production,45,35,20\nFilm Industry,80,10,10\nDance,57,30,13\nOpera,38,42,20\nDesign,64,19,17\nArchitecture,52,38,10\nFashion,72,18,10\nPhotography,30,60,10\nPoetry,40,30,30\nWriting,70,20,10\nMuseums,56,30,14\nFestivals,60,25,15\nComedy,15,50,35\nAnimation,75,15,10\nNovels,45,40,15" } ], "id_raw": 867 }, { "id": "edUNEyUIHKAvzObV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nBased on the trend analysis in the bar chart, which sector of Culture and Arts is most stable with a low decline percentage and yet shows significant overall growth? Explain your process of analysis." }, { "from": "gpt", "value": "Analyzing for a stable sector with low decline and significant growth entails looking for an industry with a low {Decline trend segment}[0.800, 0.129, 0.844, 0.365] and high values in {Recovery and Performance trend segments}[0.800, 0.365, 0.844, 0.804]; [0.800, 0.230, 0.844, 0.365]. I examine each industry's bar segments from left to right, noting the proportions. For instance, {Film Industry}[0.255, 0.264, 0.299, 0.804] has a high Recovery percentage and very low Decline, while {Art Gallery}[0.156, 0.264, 0.200, 0.804] displays strong Performance but with a higher Decline segment. By comparing the uppermost segments representing Decline and considering those with the least height from the y-axis 100% line (lowest Decline), against the total height of the bar (overall growth), I can determine which industry fits this criterion.\nBased on the above thought, the answer is:\nThe Film Industry stands out as the most stable sector in Culture and Arts, with the least percentage of Decline and strong overall growth, as indicated by a sizable Recovery segment and a minimal Decline segment in its bar on the chart." } ], "id_raw": 867 }, { "id": "qkPcOogGSCWi6JhW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_12.png", "conversations": [ { "from": "human", "value": "\nWhich industry has the lowest total percentage for Recovery and Performance combined?" }, { "from": "gpt", "value": "I must calculate the sum of Recovery and Performance percentages for each industry by examining the stack bars representing each industry. Each bar is divided into three segments, such as {Recovery segment of 'Theatre'}[0.106, 0.365, 0.151, 0.804] and {Performance segment of 'Theatre'}[0.106, 0.230, 0.151, 0.365]. By successively observing and adding these values for each industry, I can ascertain which industry has the lowest combined Recovery and Performance percentage. Matching these calculations against the y-axis labels confirms the percentage values.\nBased on the above thought, the answer is:\nComedy" } ], "id_raw": 867 }, { "id": "7WiNODOtG9HUHHce", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nBased on the stacked bar chart, could you represent the risks and measures across various categories in the form of a tabular CSV format?" }, { "from": "gpt", "value": "After closely observing the chart, I discern that the x-axes are composed of the various categories in the financial industry. This is evident from the labels placed at {x-axis tick labels}[{\"Debt Management\"}[0.068, 0.759, 0.170, 0.928], {\"Equity Management\"}[0.203, 0.759, 0.312, 0.940], {\"Credit Risk Control\"}[0.352, 0.759, 0.454, 0.928], {\"Reputation Risk Control\"}[0.470, 0.759, 0.596, 0.968], {\"Financial Reporting\"}[0.634, 0.759, 0.737, 0.932], {\"Operational Risk Control\"}[0.749, 0.759, 0.879, 0.975]]. The y-axis contains the ranges of the various measures for each category, ranging from {0%}[0.033, 0.733, 0.055, 0.757] to {200%}[0.015, 0.262, 0.055, 0.285].\nEach stacked bar in each category represents three different measures - \"Regulation Compliance\", \"Operational Oversight\" and \"Market Risk Control\", spotted from the {legend}[0.768, 0.025, 0.978, 0.137]. The value for these measures can be obtained by viewing the heights of the bars for each section.\nBased on the above thought, the answer is:\nCategory,Regulation Compliance,Operational Oversight,Market Risk Control\nDebt Management,80,60,55\nEquity Management,60,75,68\nCredit Risk Control,70,80,90\nReputation Risk Control,85,80,75\nFinancial Reporting,75,85,80\nOperational Risk Control,90,70,60" } ], "id_raw": 868 }, { "id": "KBdytLmjoULnPqus", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nFrom your observation of the chart, which measure seems to have been given the highest priority across all categories?" }, { "from": "gpt", "value": "This can be assessed by examining the thickness of the sections corresponding to each measure in the stacked bars for the various categories. For instance, \"Regulation Compliance\" may be noted by the color scheme dedicated to it in the {legend}[0.768, 0.025, 0.978, 0.137]. The respective annotations are \"Operational Oversight\" and \"Market Risk Control\". By assessing the thickness for each measure across the categories like {Debt Management}[0.106, 0.554, 0.234, 0.742], it appears that \"Regulation Compliance\" generally has a larger area in the bars than \"Operational Oversight\" or \"Market Risk Control\". A similar pattern is observed in other categories. Thus we can infer that \"Regulation Compliance\" has been given the highest priority across categories.\nBased on the above thought, the answer is:\nRegulation Compliance" } ], "id_raw": 868 }, { "id": "VbrWFXRzBKnoWlik", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_13.png", "conversations": [ { "from": "human", "value": "\nWhich category has the highest cumulative value across all measures?" }, { "from": "gpt", "value": "To find the category with the highest cumulative value, I need to measure the total height of the stacked bars in each category. For instance, the first stacked bar represents 'Debt Management', at a glance, it seems quite tall. The height of the bar representing \"Regulation Compliance\" {orange bar}[0.106, 0.554, 0.234, 0.742] is at the 80% mark; \"Operational Oversight\" {grey bar}[0.106, 0.412, 0.234, 0.554] is at 60% and \"Market Risk Control\" {white bar}[0.106, 0.283, 0.234, 0.412] is approximately at 55%. When these are summarized, the overall value for \"Debt Management\" is 195.\nThis process is repeated for all categories, including \"Equity Management\", \"Credit Risk Control\", \"Reputation Risk Control\", \"Financial Reporting\" and \"Operational Risk Control\". The category with the highest cumulative value is the one where the sum of the heights of all the partitions of the stack is the greatest.\nBased on the above thought, the answer is:\nCredit Risk Control" } ], "id_raw": 868 }, { "id": "PK67y8Lkb0WKYjXd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the bar chart data into a CSV data table?" }, { "from": "gpt", "value": "I observe that the chart is a stacked bar chart with the x-axis representing two categories: {Artificial Intelligence}[0.246, 0.686, 0.384, 0.963] and {Machine Learning}[0.686, 0.686, 0.807, 0.928], and the y-axis representing workload percentage from {0%}[0.041, 0.648, 0.068, 0.683] to {100%}[0.019, 0.122, 0.068, 0.157]. Each category contains three bars: {RegOpt}[0.121, 0.241, 0.509, 0.662] in blue, {BIOpt}[0.121, 0.188, 0.509, 0.241] in red, and {LoopOpt}[0.121, 0.136, 0.509, 0.188] in green, according to {the legend}[0.323, 0.074, 0.739, 0.137]. I can determine the weight of each bar by calculating the distance from the bottom of the bar to the y-axis' ticks. For example, for Artificial Intelligence, the height of {RegOpt bar}[0.121, 0.241, 0.509, 0.662] reaches 80% on the y-axis; the height of {BIOpt bar}[0.121, 0.188, 0.509, 0.241] adds another 10%, and the height of {LoopOpt bar}[0.121, 0.136, 0.509, 0.188] adds the rest 10%. Therefore, the weight of RegOpt, BIOpt, and LoopOpt for Artificial Intelligence is 80%, 10%, and 10% respectively.\nBased on the above thought, the answer is:\nCategory,RegOpt,BIOpt,LoopOpt\nArtificial Intelligence,80,10,10\nMachine Learning,20,60,20" } ], "id_raw": 869 }, { "id": "e6IpU1eHC3xec4wU", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nConsidering this chart, which optimization method should be the focus for future Machine Learning processes?" }, { "from": "gpt", "value": "To decide which optimization method to focus on, it's important to look at the heights of the bars for each method within the {Machine Learning}[0.686, 0.686, 0.807, 0.928] category - {RegOpt}[0.779, 0.728, 0.812, 0.967], {BIOpt}[0.812, 0.472, 0.846, 0.728], and {LoopOpt}[0.846, 0.399, 0.880, 0.472]. The largest segment is BIOpt, which reaches {80%}[0.030, 0.227, 0.068, 0.262] on the y-axis, significantly larger than the other two methods. Therefore, focusing on BIOpt would cover the majority of the workload for Machine Learning processes.\nBased on the above thought, the answer is:\nBIOpt, as it represents the majority (80%) of the workload in Machine Learning processes." } ], "id_raw": 869 }, { "id": "XK5KoAAX808RvbLp", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the workload difference between RegOpt and BIOpt for Machine Learning?" }, { "from": "gpt", "value": "I can find these percentages by comparing the bars for {RegOpt}[0.779, 0.728, 0.812, 0.967] and {BIOpt}[0.812, 0.472, 0.846, 0.728] for Machine Learning, according to {the legend}[0.323, 0.074, 0.739, 0.137]. The bar for RegOpt reaches {20%}[0.030, 0.543, 0.068, 0.578] on the y-axis, and the bar for BIOpt stretches beyond that point to the {80%}[0.030, 0.227, 0.068, 0.262] tick line. By subtracting the 20% represented by RegOpt from the 80% represented by BIOpt, I find that the workload of BIOpt is 60% more than that of RegOpt.\nBased on the above thought, the answer is:\n60%" } ], "id_raw": 869 }, { "id": "DOynnHpRdciIGmEN", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data of different areas of healthcare and their device accuracy into a CSV format table." }, { "from": "gpt", "value": "From the title placed in the chart, {\"Healthcare and Medical Technology\"}[0.322, 0.118, 0.689, 0.163] and the {x-axis' tick labels}[{\"Anesthesia\"}[0.158, 0.927, 0.254, 0.962]; {\"Radiology\"}[0.363, 0.927, 0.449, 0.962]; {\"Cardiology\"}[0.559, 0.927, 0.652, 0.962]; {\"Neurology\"}[0.761, 0.927, 0.849, 0.962]]; I can assert that the x-axis represents different areas in the healthcare and medical technology field. The y-axis tick labels represent the corresponding values, ranging from {0}[0.065, 0.889, 0.076, 0.924] to {100}[0.043, 0.160, 0.076, 0.195] percent. According to {the legend}[0.097, 0.046, 0.315, 0.161], it's possible for me to deduce that the {blue column bars}[0.126, 0.354, 0.179, 0.903; 0.326, 0.445, 0.379, 0.903; 0.526, 0.460, 0.579, 0.903; 0.725, 0.616, 0.778, 0.903] represent the accuracy of device1; the {dark blue column bars}[0.179, 0.401, 0.233, 0.903; 0.379, 0.501, 0.432, 0.903; 0.579, 0.513, 0.632, 0.903; 0.778, 0.666, 0.832, 0.903] represent the accuracy of device2. For each area, there are three bars representing the truthful and misleading recalls. The approximate values can be deduced from the top position of each bar, for example, in the Anesthesia area, the values for the two devices are 75.3 and 68.9 respectively, according to the {blue column bar}'s coordinates[0.126, 0.354, 0.179, 0.903; 0.179, 0.401, 0.233, 0.903] and their correspondent number {labeled on top}[0.134, 0.370, 0.172, 0.405; 0.240, 0.529, 0.278, 0.564].\nBased on the above thought, the answer is:\nArea,Device Accuracy1,Device Accuracy2,Misleading Information\nAnesthesia,75.3,68.9,30.2\nRadiology,62.9,55.1,35.2\nCardiology,60.8,53.5,38.6\nNeurology,39.4,32.6,28.2" } ], "id_raw": 870 }, { "id": "2P1mMoLHpFj6tXiw", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which healthcare area faces the most critical problem with misleading information from a device, and why do you think so?" }, { "from": "gpt", "value": "This question needs to comprehensively analyze {the red bar}[0.286, 0.174, 0.339, 0.614; 0.486, 0.174, 0.539, 0.687; 0.685, 0.174, 0.739, 0.737; 0.885, 0.174, 0.939, 0.585] across four different areas. Firstly, Anesthesia's misleading information, from the value labeled as 30.2 on the top of its red bar in coordinates[r][0.240, 0.529, 0.278, 0.564], the tallest misleading recall happened in Cardiology, considering it reaches up to 38.6 based on coordinates of its top{r}[0.639, 0.651, 0.678, 0.686]. Also, comparing the rest of the red bars for Radiology and Neurology, their values are 35.2 and 28.2 respectively, which are shown with the coordinates [0.440, 0.602, 0.478, 0.637; 0.839, 0.500, 0.877, 0.535], both lower than Cardiology. Therefore, Cardiology is the area that faces the most critical problem as per the given misleading information.\nBased on the above thought, the answer is:\nCardiology. Because the red column representing misleading information in the cardiology area reaches up to 38.6, which is the highest among all four healthcare areas according to the bar chart." } ], "id_raw": 870 }, { "id": "0cZY8yJ2TSNlh6Ke", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_0.png", "conversations": [ { "from": "human", "value": "\nWhich healthcare area experienced the largest drop in device accuracy from Device 1 to Device 2?" }, { "from": "gpt", "value": "To determine the healthcare area that experienced the largest drop in device accuracy from Device 1 to Device 2, I would need to compare the heights of the bars corresponding to Device 1 and Device 2 in each area. The bars corresponding to Device 1 and Device 2 for Anesthesia are {first set of bars}[0.126, 0.354, 0.179, 0.903; 0.179, 0.401, 0.233, 0.903], for Radiology are {second set of bars}[0.326, 0.445, 0.379, 0.903; 0.379, 0.501, 0.432, 0.903], for Cardiology are {third set of bars}[0.526, 0.460, 0.579, 0.903; 0.579, 0.513, 0.632, 0.903], and for Neurology are {fourth set of bars}[0.725, 0.616, 0.778, 0.903; 0.778, 0.666, 0.832, 0.903]. By comparing the heights of these bars, I see that the area of Cardiology shows the largest difference between the heights of bars corresponding to Device 1 and Device 2.\nBased on the above thought, the answer is:\nCardiology." } ], "id_raw": 870 }, { "id": "HwrMeOAsdbSEda0e", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nCan you abstract the data of 'AI Models Recall' and 'Advanced Automation Recall' in different models into a CSV table?" }, { "from": "gpt", "value": "Yes, I see a series of 8 category labels--one for each model--along the x-axis, ranging from {Model 1}[0.095, 0.942, 0.150, 0.970], {Model 2}[0.197, 0.942, 0.252, 0.970] to {Model 8}[0.810, 0.942, 0.865, 0.970]. Then there are two groups of bars according to different recalls for each model. The blue bars are labeled 'AI Models Recall', such as {Model 1}[0.102, 0.101, 0.143, 0.499], {Model 2}[0.204, 0.255, 0.245, 0.499] and so on; and the pink bars are labeled 'Advanced Automation Recall', such as {Model 1}[0.143, 0.499, 0.183, 0.701], {Model 2}[0.245, 0.499, 0.286, 0.839] and so on. I retrieve the corresponding values for each model and kind of recall according to the height of the bars and their value annotations at the top.\nBased on the above thought, the answer is:\nModel,AI Models Recall (%),Advanced Automation Recall (%)\nModel 1,56.3,23.9\nModel 2,34.5,40.2\nModel 3,27.8,38.8\nModel 4,18.4,12.4\nModel 5,33,45\nModel 6,37,24\nModel 7,43.5,33\nModel 8,30.1,22" } ], "id_raw": 871 }, { "id": "do2XibOdq7GF3ryk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nLooking at the data, what can you infer about the trend between AI Models and Advanced Automation?" }, { "from": "gpt", "value": "Observing all labeled bars of {AI Models}[0.102, 0.101, 0.143, 0.499]; [0.204, 0.255, 0.245, 0.499]; [0.306, 0.302, 0.347, 0.499]; [0.408, 0.369, 0.449, 0.499]; [0.510, 0.265, 0.551, 0.499]; [0.612, 0.237, 0.653, 0.499]; [0.715, 0.191, 0.755, 0.499]; [0.817, 0.286, 0.858, 0.499] and all labeled bars of {Advanced Automation}[0.143, 0.499, 0.183, 0.701]; [0.245, 0.499, 0.286, 0.839]; [0.347, 0.499, 0.388, 0.828]; [0.449, 0.499, 0.490, 0.604]; [0.551, 0.499, 0.592, 0.880]; [0.653, 0.499, 0.694, 0.702]; [0.755, 0.499, 0.796, 0.778]; [0.858, 0.499, 0.898, 0.685], I notice that these two types of recalls for each model seem not explicitly correlated as the AI Models Recall is not proportionally grow with Advanced Automation Recall. However, it is noticeable that Model 5 presents an outlier value for Advanced Automation.\nBased on the above thought, the answer is:\nThe trend between AI Models and Advanced Automation seems inconsistent, as shown by the bars for each model. There's not an obvious proportionality between the AI Models Recall and the Advanced Automation Recall. Interestingly, the Advanced Automation for 'Model 5' is significantly higher than other models, which displays as an outlier." } ], "id_raw": 871 }, { "id": "m4ujddqUoCZd67JW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_1.png", "conversations": [ { "from": "human", "value": "\nBy how much percent does AI Model recall exceed Advanced Automation recall in Model 1?" }, { "from": "gpt", "value": "To find out how much the AI Model recall exceeds the Advanced Automation recall in {Model 1}[0.095, 0.942, 0.150, 0.970], I need to read off the two groups of bars respectively. The {blue bar}[0.102, 0.101, 0.143, 0.499] with a value annotation of {56.3}[0.107, 0.100, 0.138, 0.128] represents the 'AI Models Recall' of 'Model 1'. The {pink bar}[0.143, 0.499, 0.183, 0.701] with a value annotation of {23.9}[0.148, 0.662, 0.178, 0.690] represents the 'Advanced Automation Recall' of 'Model 1'. Based on these data, I can calculate how much the former exceeds the latter.\nBased on the above thought, the answer is:\n32.4%" } ], "id_raw": 871 }, { "id": "gZPaU1NAm7PvSK8P", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chat you're observing, can you convert the data into a structured table in CSV format?" }, { "from": "gpt", "value": "The bar chart seems to demonstrate the impact of various media outlets on public opinion, rated by influence score along the y-axis{\"Influence Score\"}[0.019, 0.462, 0.036, 0.641], with four media outlets detailed on the x-axis{\"CNN\"}[0.171, 0.952, 0.209, 0.975]; {\"FOX News\"}[0.377, 0.952, 0.463, 0.975]; {\"BBC\"}[0.632, 0.952, 0.668, 0.975]; {\"Al Jazeera\"}[0.836, 0.952, 0.923, 0.975], and two colours of bars for each outlet {\"Positive Influence\"}[0.153, 0.035, 0.305, 0.058]; {\"Negative Influence\"}[0.153, 0.070, 0.316, 0.093]. The values can be inferred from the height of the bars, for instance, for CNN, the 'Positive Influence' bar {\"Positive Influence bar for CNN\"}[0.129, 0.238, 0.190, 0.647] indicates approximately 85.4 influence score, and similarly the 'Negative Influence' bar {\"Negative Influence bar for CNN\"}[0.190, 0.333, 0.251, 0.647] indicates about 65.5 influence score. Once the values for each media outlet are discerned, the information can be compiled into the CSV table.\nBased on the above thought, the answer is:\nMedia Outlet,Positive Influence,Negative Influence\nCNN,85.4,65.5\nFOX News,65.6,83.7\nBBC,90.2,54.5\nAl Jazeera,63.7,80.1" } ], "id_raw": 872 }, { "id": "FszoFPVp48zlFDcE", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which media outlet seems to be striking a better balance in terms of positive and negative influence in guiding public opinion, and can you explain your reasoning?" }, { "from": "gpt", "value": "To deduce the balance between positive and negative influence of each media outlet, we evaluate the similarity in heights of the positive and negative influence bars. After examining, the bars of BBC in both colors{\"Positive Influence bar for BBC\"}[0.588, 0.214, 0.650, 0.647] and {\"Negative Influence bar for BBC\"}[0.650, 0.386, 0.711, 0.647], have the least difference between their heights. This implies that BBC has the most balanced impact in guiding public opinion, as it is neither excessively positive, nor excessively negative.\nBased on the above thought, the answer is:\nBBC. This is because the balance between positive and negative influence appears to be relatively equitised for the BBC, as indicated by a minor difference in their heights. Hence, it indicates a balanced approach to guiding public opinion." } ], "id_raw": 872 }, { "id": "czJGCriYiwntwbNM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_2.png", "conversations": [ { "from": "human", "value": "\nWhich media outlet has the highest negative influence score according to the chart?" }, { "from": "gpt", "value": "To determine the media outlet with the highest negative influence, we look at the height of the negative influence graphical bars{\"Negative Influence\"}[0.153, 0.070, 0.316, 0.093]. We can see that the highest bar corresponds to FOX News{\"bar of FOX News\"}[0.420, 0.246, 0.481, 0.647]. Therefore, FOX News has the highest negative influence.\nBased on the above thought, the answer is:\nFOX News." } ], "id_raw": 872 }, { "id": "As1cVJ0vtdRb8qUa", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract and present the percentage values of Economic Development and Social Consciousness as a table from the chart you're analyzing?" }, { "from": "gpt", "value": "From {the x-axis labels}[0.113, 0.927, 0.873, 0.962] I see they represent the 11 data points over time from '0' through '10'. For 'Economic Development', I observe a series of {blue bars}[0.104, 0.443, 0.877, 0.903] with values labeled above each bar, e.g., {the first blue bar's value}[0.091, 0.415, 0.146, 0.450], and for 'Social Consciousness', there are {green bars}[0.134, 0.525, 0.907, 0.903] with their respective values. I use these values and create a structured table with each bar's corresponding data point and percentage.\nBased on the above thought, the answer is:\nData Point,Economic Development %,Social Consciousness %\n0,65.7,30.1\n1,70.1,35.2\n2,60.4,38.7\n3,69.3,34.0\n4,65.8,35.6\n5,67.9,39.2\n6,74.9,37.1\n7,85.1,43.2\n8,91.3,45.7\n9,95.8,49.9\n10,99.1,55.3" } ], "id_raw": 873 }, { "id": "ydFrqntyZXEyxr2r", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nOver the course of the data points, does the pace of change in 'Economic Development' consistently exceed that of 'Social Consciousness'?" }, { "from": "gpt", "value": "To assess the pace of change, I need to evaluate the trend of both Economic Development and Social Consciousness over the timeline. I observe each {set of bars}[0.104, 0.443, 0.877, 0.903] [0.134, 0.525, 0.907, 0.903], noting that Economic Development starts at 65.7 and ends at 99.1, while Social Consciousness starts at 30.1 and ends at 55.3. I examine the labels of each bar finding the rise in percentages. The sharp rise in Economic Development particularly at the {7th and 8th points}[0.624, 0.307, 0.654, 0.903] [0.699, 0.264, 0.728, 0.903] suggests there were times where Economic Development's pace exceeded the comparatively smooth but slower upward trend of Social Consciousness.\nBased on the above thought, the answer is:\nWhile both 'Economic Development' and 'Social Consciousness' show an overall upward trend, there are instances, particularly between the 7th and 8th data points, where 'Economic Development' demonstrates a much faster pace of change in comparison to 'Social Consciousness'. Hence, the pace of change in 'Economic Development' does occasionally exceed that of 'Social Consciousness'." } ], "id_raw": 873 }, { "id": "yNldWSmKdsNFckoc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage growth in Social Consciousness from data point '0' to data point '10'?" }, { "from": "gpt", "value": "To calculate the percentage growth, I look at the chart and locate the values for Social Consciousness at {data point '0'}[0.134, 0.525, 0.163, 0.903] and {data point '10'}[0.877, 0.209, 0.907, 0.903] which are 30.1% and 55.3% respectively. The percentage growth calculation involves subtracting the initial value from the final value and then dividing by the initial value. That is (55.3 - 30.1) / 30.1.\nBased on the above thought, the answer is:\n83.7%" } ], "id_raw": 873 }, { "id": "6c1z0xjXtW0t9JHf", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you have analyzed, please present the sales volumes of the different products in a CSV format table." }, { "from": "gpt", "value": "From {the title of the chart}[0.317, 0.118, 0.742, 0.163] and {the y-axis label 'Sales Volumes'}[0.019, 0.414, 0.036, 0.664] I can see that the y-axis represents the sales volumes. The x-axis labels indicate the names of different products, which include {LLaMA}[0.138, 0.927, 0.195, 0.962], {Vicuna}[0.379, 0.927, 0.438, 0.962], {Alpaca}[0.621, 0.927, 0.680, 0.962], and {WizardLM}[0.850, 0.927, 0.934, 0.962]. The sales volumes for these products can be found by checking the height of the bars and the label on top of them, such as {bar for LLaMA}[0.118, 0.407, 0.215, 0.903].\nBased on the above thought, the answer is:\nProduct,Sales Volume\nLLaMA,50\nVicuna,45\nAlpaca,54\nWizardLM,70" } ], "id_raw": 874 }, { "id": "OZ5bGerMdYYI8CqE", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you describe the sales trend among these products?" }, { "from": "gpt", "value": "The heights of the bars represent sales volumes for each product are sequentially displayed as [LLaMA][0.138, 0.927, 0.195, 0.962], [Vicuna][0.379, 0.927, 0.438, 0.962], [Alpaca][0.621, 0.927, 0.680, 0.962], and [WizardLM][0.850, 0.927, 0.934, 0.962]. The sales volumes are shown on top of each bar as 50, 45, 54, and 70 respectively. Thus we can observe a general upward trend in the sales volumes from LLaMA (50) to Alpaca (54) and WizardLM (70) with a slight decrease at Vicuna (45).\nBased on the above thought, the answer is:\nThe sales trend among the products is generally upward, with a slight dip for Vicuna. Thus, it can be observed that the total sales are increasing over time from LLaMA to WizardLM, with Vicuna being an exception." } ], "id_raw": 874 }, { "id": "YcdnBM6HfnPMb28k", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_4.png", "conversations": [ { "from": "human", "value": "\nWhich product has the largest sales volume?" }, { "from": "gpt", "value": "I can determine the product with the largest sales volume from the highest bar on the chart. If we look at the heights of the bars and the label flying on top of them, we can see that {WizardLM bar}[0.843, 0.209, 0.940, 0.903] with its label [0.881, 0.231, 0.903, 0.266] is the highest bar among the four products, thus it suggests that the sales volume is the largest.\nBased on the above thought, the answer is:\nWizardLM" } ], "id_raw": 874 }, { "id": "EbudF8Ace6ltx7ow", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you observed, could you kindly format the chart data into a CSV table?" }, { "from": "gpt", "value": "To dissect this chart, we should first identify the significant elements. One such critical element is the {subtitle}[0.386, 0.118, 0.649, 0.163], informs the chart shows an overview of Statistical Data. The x-axis, which doesn't explicitly carry any label, denotes 2 points in time: {Initial}[0.352, 0.927, 0.401, 0.962], and {End}[0.829, 0.927, 0.862, 0.962]. On the y-axis, the {values}[0.030, 0.525, 0.041, 0.560]; [0.019, 0.403, 0.041, 0.438]; [0.019, 0.282, 0.041, 0.317]; [0.019, 0.160, 0.041, 0.195], ranging from 0 to 60, denote the value of statistical fields. The {legend}[0.062, 0.046, 0.324, 0.161] describes the vivid relationship in our data comparing two Statistical Data fields: {Trend Analysis}[0.117, 0.060, 0.242, 0.095] and {Variance Measurement}[0.117, 0.112, 0.317, 0.147]. The initial and end values for these parameters are displayed on the chart via bars in different colors with their respective values. By analyzing these components, we can tabulate the data accordingly.\nBased on the above thought, the answer is:\nStatistical Field, Initial Values, End Values\nTrend Analysis, 45, 55\nVariance Measurement, 25, 50" } ], "id_raw": 875 }, { "id": "oCTDhPsVj4gbZNbT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data provided, can we draw any conclusions about the performance or trends of the two statistical fields over time?" }, { "from": "gpt", "value": "Looking at the {blue bars}[0.095, 0.265, 0.283, 0.539]; [0.564, 0.205, 0.752, 0.539] allocated to {Trend Analysis}[0.117, 0.060, 0.242, 0.095] and comparing their {annotated quantities}[0.178, 0.226, 0.200, 0.261]; [0.366, 0.347, 0.387, 0.382], it's evident this field saw a slight increase from 45 to 55 points. Thus it can be concluded the trend here is \"Abrupt Increase\". However, upon looking at {Variance Measurement}[0.117, 0.112, 0.317, 0.147] represented by {green bars}[0.283, 0.387, 0.470, 0.539]; [0.752, 0.235, 0.939, 0.539] and focusing on their {values}[0.647, 0.165, 0.669, 0.200]; [0.834, 0.195, 0.856, 0.230], it's clear that this field made a dramatic jump from 25 to 50 points. This characteristic indicates volatility or an \"Instable Increase\" in this statistical field data.\nBased on the above thought, the answer is:\nYes, we can conclude that the Trend Analysis saw an Abrupt Increase going from 45 to 55, while Variance Measurement went through an Instable Increase with its values rising dramatically from 25 to 50." } ], "id_raw": 875 }, { "id": "GcXinBlhfeHcfa4N", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_5.png", "conversations": [ { "from": "human", "value": "\nCan you tell me which Statistical Field experienced the greatest increase, and its precise numerical rise?" }, { "from": "gpt", "value": "The chart comparison provides us the insight to measure the exact increase each Statistical Field experienced. For the field {Trend Analysis}[0.117, 0.060, 0.242, 0.095] - the increase is derived from the final value {55}[0.366, 0.347, 0.387, 0.382] and the initial value {45}[0.178, 0.226, 0.200, 0.261], resulting in an increase of 10 points. Similarly, comparing the field {Variance Measurement}[0.117, 0.112, 0.317, 0.147], we see an increase from {25}[0.647, 0.165, 0.669, 0.200] to {50}[0.834, 0.195, 0.856, 0.230], resulting in a much larger increase of 25 points.\nBased on the above thought, the answer is:\nVariance Measurement experienced the greatest increase, precisely 25 points." } ], "id_raw": 875 }, { "id": "ZUafP0XMrFoegM1H", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart information into a structured data tabular format?" }, { "from": "gpt", "value": "The bar chart has a title {\"Trends in Media and Entertainment Industry\"}[0.272, 0.053, 0.728, 0.083] at the top. The x-axis labels are {\"Traditional\"}[0.151, 0.952, 0.242, 0.975], {\"Streaming\"}[0.354, 0.952, 0.444, 0.975], {\"Social\"}[0.575, 0.952, 0.627, 0.975], and {\"Interactive\"}[0.757, 0.952, 0.850, 0.975]. There are two y-axes. The left y-axis represents Media Growth (%) ranging from 0 to 15. The right y-axis represents Entertainment Decline (%) ranging from -10 to 0. For each type of media, there are two bars. The bars for Media Growth have the color of blue, labeled as {\"Media Growth\"}[0.142, 0.027, 0.262, 0.050]. The bars of Entertainment Decline have the color of pink, labeled as {\"Entertainment Decline\"}[0.711, 0.027, 0.907, 0.050]. The value of each bar can be determined by its height and the corresponding value annotation above or below it. For example, the height of the blue bar under \"Traditional\" Media [0.116, 0.637, 0.197, 0.935] corresponds to a value annotation of \"5.3\" [0.143, 0.675, 0.170, 0.698]. Therefore, Media Growth of \"Traditional\" Media equals to 5.3%.\nBased on the above thought, the answer is:\nMedia Type,Media Growth,Entertainment Decline\nTraditional,5.3,-3.2\nStreaming,11.4,-7.5\nSocial,9.2,-5.8\nInteractive,3.8,-2.7" } ], "id_raw": 876 }, { "id": "5e710IYMEJYDd9dn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nHow does Streaming Media compare with other types of media in terms of growth in the Media sector and the decline in the Entertainment sector?" }, { "from": "gpt", "value": "From the bar chart, I can see that Streaming Media has the highest growth in the Media sector, represented by {\"the blue bar\"}[0.318, 0.293, 0.399, 0.935] and {\"the value annotation '11.4'\"}[0.339, 0.331, 0.378, 0.355]. However, Streaming Media also has the largest decline in the Entertainment sector, represented by {\"the pink bar\"}[0.399, 0.090, 0.480, 0.724] and {\"the value annotation '-7.5'\"}[0.426, 0.664, 0.453, 0.687]. Compared to the other three types of media, Traditional, Social, and Interactive, Streaming Media stands out in both sectors, but in opposite ways.\nBased on the above thought, the answer is:\nStreaming Media has the highest growth in the Media sector, but also has the largest decline in the Entertainment sector, showing a large gap between the two sectors." } ], "id_raw": 876 }, { "id": "Kf4xfdyQBphH4ACU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_6.png", "conversations": [ { "from": "human", "value": "\nWhat type of media showed the largest growth in the media sector?" }, { "from": "gpt", "value": "From the height of the blue bars and the value annotations, I can deduce the growth rates for each media type. The coordinates of the value annotations are: {\"5.3\"}[0.143, 0.675, 0.170, 0.698], {\"11.4\"}[0.339, 0.331, 0.378, 0.355], {\"9.2\"}[0.547, 0.455, 0.574, 0.479], and {\"3.8\"}[0.749, 0.759, 0.777, 0.783]. The numbers correspond to the media types from left to right, that is, \"Traditional\", \"Streaming\", \"Social\", and \"Interactive\". The largest number is \"11.4\" associated with \"Streaming\" media.\nBased on the above thought, the answer is:\nStreaming." } ], "id_raw": 876 }, { "id": "HTqSK821HGC27sx8", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart observations including 'Effective Management' and 'Adaptable Leadership' scores for each organization into a structured table format?" }, { "from": "gpt", "value": "Starting from my interpretation of the x-axis, I note that it represents the different organizations including {SoftCorp}[0.174, 0.927, 0.249, 0.962], {FlexCorp}[0.406, 0.927, 0.482, 0.962], {DynamicInc}[0.626, 0.927, 0.728, 0.962], and {SteadFastLLC}[0.851, 0.927, 0.968, 0.962]. The y-axis using tick markers represents the score value, ranging from {0}[0.054, 0.889, 0.065, 0.924] to {80}[0.043, 0.104, 0.065, 0.139]. Focusing on the bars of different colors, the {blue bars}<{\"Effective Management\"}[0.142, 0.060, 0.338, 0.095]> represent the score for 'Effective Management' while the {green bars}<{\"Adaptable Leadership\"}[0.142, 0.112, 0.331, 0.147]> are indicative of the score for 'Adaptable Leadership'. By matching up the heights of the bars to the corresponding y-values, I can obtain the exact scores for each category. As an example, the first blue bar {SoftCorp Effective Management}[0.118, 0.209, 0.180, 0.903] aligns approximately with the 70 marker on the y-axis. This process is repeated to extract scores for all categories and compile the structured table.\nBased on the above thought, the answer is:\nOrganization,Effective Management,Adaptable Leadership\nSoftCorp,70.7,61.2\nFlexCorp,65.5,49.1\nDynamicInc,46.5,35.4\nSteadFastLLC,42.1,28.8" } ], "id_raw": 877 }, { "id": "xPwrMfAHhJgaEEgO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nFrom your interpretation of the bar chart, can you identify which area\u2014'Effective Management' or 'Adaptable Leadership'\u2014 organizations are generally performing better in?" }, { "from": "gpt", "value": "To determine which area organizations are generally performing better in, I need to look at the average heights of the {blue bars}<{\"Effective Management\"}[0.142, 0.060, 0.338, 0.095]> versus the {green bars}<{\"Adaptable Leadership\"}[0.142, 0.112, 0.331, 0.147]>. On average, it appears that the blue bars are higher than the green bars, suggesting higher scores for Effective Management. Thus, organizations are generally performing better in the area of Effective Management compared to Adaptable Leadership.\nBased on the above thought, the answer is:\nOrganizations are generally performing better in the area of 'Effective Management'." } ], "id_raw": 877 }, { "id": "5jC3dx92GZTqH2NV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_7.png", "conversations": [ { "from": "human", "value": "\nWhich organization has the highest absolute difference between their 'Effective Management' and 'Adaptable Leadership' scores?" }, { "from": "gpt", "value": "To find the organization with the largest absolute score difference between 'Effective Management' and 'Adaptable Leadership', the respective bar heights of {blue bars}<{\"Effective Management\"}[0.142, 0.060, 0.338, 0.095]> and {green bars}<{\"Adaptable Leadership\"}[0.142, 0.112, 0.331, 0.147]> for each organization need to be compared. The absolute differences are computed for each organization as follows: for {SoftCorp}[0.174, 0.927, 0.249, 0.962], the difference is calculated by noting the heights of the blue bar[0.118, 0.209, 0.180, 0.903] and the green bar[0.180, 0.302, 0.242, 0.903], and so forth for each organization. The organization with the largest such difference is identified as the one with the highest absolute difference.\nBased on the above thought, the answer is:\nSoftCorp" } ], "id_raw": 877 }, { "id": "Vucqgvf2olutJjWo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nCan you please create a table structure from the bar chart data in CSV format?" }, { "from": "gpt", "value": "The chart displays information on two separate trends represented by different colors, namely {Global trade}[0.113, 0.045, 0.200, 0.073] indicated by a blue color, and {Supply Chain Management}[0.113, 0.087, 0.301, 0.115] indicated by a green color. The X-axis enumerates the names of the continents, ranging from {Asia} [0.130, 0.942, 0.159, 0.970] to {Australia}[0.872, 0.942, 0.933, 0.970]. The Y-axis indicates the trade values from {0}[0.043, 0.570, 0.052, 0.598] to {60}[0.035, 0.159, 0.052, 0.187]. To create the CSV table, I need to derive the values of Global Trade and Supply Chain Management for each continent. We can refer to the heights of the bars and the annotations on each bar. For instance, the height of the {Global Trade bar for Asia}[0.104, 0.269, 0.144, 0.581] reaches approximately the 45.6 position on the Y-axis, which corresponds to the {annotation \"45.6\"}[0.109, 0.288, 0.139, 0.316] on the bar.\nBased on the above thought, the answer is:\nContinents,Global Trade,Supply Chain Management\nAsia,45.6,38.7\nNorth America,39.4,49.3\nEurope,34.7,42.5\nAfrica,27.6,35.1\nSouth America,29.5,33.8\nAustralia,15.2, -31.6" } ], "id_raw": 878 }, { "id": "LMIkaAmQ0v9Js5Dr", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, can you identify any significant trends or outlier in either Global Trade or Supply Chain Management?" }, { "from": "gpt", "value": "Observing the bar values on the chart, {Global Trade}[0.113, 0.045, 0.200, 0.073] seems to be higher in general than {Supply Chain Management}[0.113, 0.087, 0.301, 0.115] for the same continent. The general trend is that both Global Trade and Supply Chain Management show lower values for Australia than for the other continents. Additionally, a remarkable outlier is present in {Supply Chain Management for Australia}[0.903, 0.581, 0.943, 0.798], which displays a negative value label, {\"-31.8\"}[0.905, 0.735, 0.941, 0.763], while all others are positive.\nBased on the above thought, the answer is:\nYes, the general trend shows lower values in both Global Trade and Supply Chain Management for Australia in comparison to other continents. The significant outlier is the negative value in Supply Chain Management for Australia." } ], "id_raw": 878 }, { "id": "Wxryi0de1BiV1loM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_8.png", "conversations": [ { "from": "human", "value": "\nWhich continent has the highest combined value of Global Trade and Supply Chain Management?" }, { "from": "gpt", "value": "We need to add the trade values of Global Trade and Supply Chain Management for each continent and compare them. For example, {Asia}[0.130, 0.942, 0.159, 0.970] has a total trade value from {Global Trade}[0.104, 0.269, 0.144, 0.581] and {Supply Chain Management}[0.144, 0.316, 0.185, 0.581] of 45.6 + 38.7 = 84.3. By calculating and then comparing these total values for all continents, the continent with the highest combined trade value can be identified.\nBased on the above thought, the answer is:\nNorth America." } ], "id_raw": 878 }, { "id": "vEv1IEm2SOyqZCSt", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you saw, could you generate a data table in CSV format?" }, { "from": "gpt", "value": "Based on what I noticed, there are two bars for each segment of the x-axis, which are labelled as {Value1}[0.167, 0.927, 0.226, 0.962], {Value2}[0.471, 0.927, 0.529, 0.962], and {Value3}[0.774, 0.927, 0.833, 0.962]. These labels represent the financial periods. The heights of the bars for each period represent the financial performance of {Company A}[0.019, 0.398, 0.036, 0.598] and {Company B}[0.964, 0.398, 0.981, 0.599]. For CompanyA, the respective heights at Value1[0.116, 0.325, 0.197, 0.903], Value2[0.419, 0.498, 0.500, 0.903], and Value3[0.722, 0.614, 0.803, 0.903] are 50, 35, and 25. Similarly, for CompanyB, the corresponding heights at Value1[0.197, 0.209, 0.278, 0.903], Value2[0.500, 0.383, 0.581, 0.903], and Value3[0.803, 0.440, 0.884, 0.903] are 60, 45, and 40.\nBased on the above thought, the answer is:\nCompany,Value1,Value2,Value3\nCompanyA,50,35,25\nCompanyB,60,45,40" } ], "id_raw": 879 }, { "id": "YLgvNa2ClEgShA6J", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the financial growth trends for CompanyA and CompanyB?" }, { "from": "gpt", "value": "Looking at the bar heights for each financial period, I can see different trends for the two companies. {CompanyA's bars}[0.116, 0.325, 0.197, 0.903]; [0.419, 0.498, 0.500, 0.903]; [0.722, 0.614, 0.803, 0.903] show a decrease from Value1 to Value3, indicating a downward trend in financial performance. In contrast, {CompanyB's bars}[0.197, 0.209, 0.278, 0.903]; [0.500, 0.383, 0.581, 0.903]; [0.803, 0.440, 0.884, 0.903] also show a decrease from Value1 to Value3, but the overall values are higher than those of CompanyA, indicating a more positive financial performance despite the downward trend.\nBased on the above thought, the answer is:\nBoth companies show a downward trend in financial performance from Value1 to Value3. However, CompanyB has higher overall values for each financial period, indicating a more positive financial performance despite the downward trend." } ], "id_raw": 879 }, { "id": "vIgdVUth5uBQ0HoO", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_9.png", "conversations": [ { "from": "human", "value": "\nWhich company has the highest financial performance in the Value1 period?" }, { "from": "gpt", "value": "The bars representing the Value1 period for both companies are at {CompanyA Value1}[0.116, 0.325, 0.197, 0.903] and {CompanyB Value1}[0.197, 0.209, 0.278, 0.903]. By comparing the heights of these two bars, it's clear that CompanyB's bar is taller, indicating a higher financial performance in the Value1 period.\nBased on the above thought, the answer is:\nCompanyB" } ], "id_raw": 879 }, { "id": "YkvtzK8x20uVaGIV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nExtract data from your analysis of the bar chart and structure it in a table format." }, { "from": "gpt", "value": "The chart provides information on three different attributes; \"BlueChip\", \"Gold ETFs\", and \"Market Manipulation\" over a period of four weeks represented on the x-axis. The height of the bars represent the market performance in each week, which I can infer from the labels present on the y-axis. For instance, In \"Week1\" the height of the {blue bar}[0.115, 0.250, 0.168, 0.903] indicates a market performance of 48% for the \"BlueChip\" security. Using the same method, the data for the other weeks and securities can be obtained. This will form a table as follows.\nBased on the above thought, the answer is:\nSecurity,Week1,Week2,Week3,Week4\nBlueChip,48,45,51,47\nGold ETFs,38,31,40,29\nMarket Manipulation,45,48,37,52" } ], "id_raw": 880 }, { "id": "P8DOvCXRzmvsC1Se", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the market manipulation activities observed in the chart over the 4 weeks period?" }, { "from": "gpt", "value": "Yes, the performance of market manipulation activities is represented in the chart with pink bars. At a glance, we can observe substantial fluctuations in these activities over the weeks. Specifically, in {Week1}[0.221, 0.174, 0.274, 0.775] we see the market manipulation at 45%. In {Week2}[0.420, 0.174, 0.473, 0.815], there is an increase to 48% indicating heightened manipulation. In {Week3}[0.619, 0.174, 0.672, 0.668], we see a decline to 37% which could indicate improved regulation or less volatile market conditions. Finally, in {Week4}[0.817, 0.174, 0.870, 0.868] there is a significant increase to 52%, the highest in the period under review, which could indicate increased instability and risk in the market.\nBased on the above thought, the answer is:\nYes, the market manipulation activities fluctuated substantially over the four weeks. It was 45% in Week1, increased to 48% in Week2, decreased to 37% in Week3 and then significantly increased to 52% in Week4, indicating great volatility and increased risk." } ], "id_raw": 880 }, { "id": "I9vGtYtcrx180a6W", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_10.png", "conversations": [ { "from": "human", "value": "\nWhich week showed the highest market performance for the BlueChip security?" }, { "from": "gpt", "value": "The weekly performance data for the BlueChip security are represented by the set of bluish bars. Looking at the {bar for week1}[0.115, 0.250, 0.168, 0.903], with the label \"48\" at the top, it shows a performance of 48%, which we will use as a starting point for comparison. Following the same method, we see that the weekly performance for weeks 2, 3, and 4 are 45%, 51%, and 47% respectively. Therefore, we can see from bar heights that the highest performance for the BlueChip security was reached in week 3 as indicated by the {bar for Week3}[0.513, 0.209, 0.566, 0.903].\nBased on the above thought, the answer is:\nWeek 3." } ], "id_raw": 880 }, { "id": "uqr9m8C6JMfNBQbG", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nCould you help me convert the data related to international trade growth and globalization in the bar chart into a structured table?" }, { "from": "gpt", "value": "Looking at the chart title {\"International Trade and Globalization Trends\"}[0.271, 0.118, 0.729, 0.163] and the x-axis tick labels [{\"USA\"}[0.179, 0.927, 0.215, 0.962]; {\"China\"}[0.374, 0.927, 0.424, 0.962]; {\"UK\"}[0.589, 0.927, 0.613, 0.962]; {\"India\"}[0.782, 0.927, 0.825, 0.962]], it's clear that the x-axis represents different countries. The y-axis tick labels display corresponding values ranging from {0}[0.054, 0.525, 0.065, 0.560] to {60}[0.043, 0.160, 0.065, 0.195]. For each country, there are two bars representing International Trade and Globalization respectively according to {the legend}[0.086, 0.046, 0.313, 0.108] and [0.738, 0.046, 0.914, 0.108]. Therefore, to generate the CSV table, I need to extract the values for both categories in each country. I can check the height of the bars and the value annotations on top of the bars to find these values. For instance, {the bar for USA}[0.116, 0.257, 0.170, 0.539] in International Trade is annotated with 46.3 and {the bar for USA}[0.224, 0.539, 0.278, 0.758] in Globalization is annotated with 30.1.\nBased on the above thought, the answer is:\nCountry,International Trade,Globalization\nUSA,46.3,30.1\nChina,56.9,34\nUK,53.8,43.7\nIndia,19.4,20" } ], "id_raw": 881 }, { "id": "thxLNJPBCRaBqdi0", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nBased on your inspection of the chart, which country shows the most balanced growth between international trade and globalization? Explain your reasoning." }, { "from": "gpt", "value": "The question asks which country exhibits the most balanced growth between international trade and globalization. By assessing both sets of bars, I need to consider which country has similar values for both International Trade and Globalization. For {\"USA\"}[0.179, 0.927, 0.215, 0.962], the {International Trade bar}[0.116, 0.257, 0.170, 0.539] and {Globalization bar}[0.224, 0.539, 0.278, 0.758] show distinct heights; similarly, this is the case for {\"UK\"}[0.589, 0.927, 0.613, 0.962] bars {[0.520, 0.212, 0.574, 0.539],[0.628, 0.539, 0.682, 0.857]} and {\"India\"}[0.782, 0.927, 0.825, 0.962] bars {[0.723, 0.421, 0.776, 0.539],[0.830, 0.539, 0.884, 0.684]}. However, the {International Trade bar}[0.318, 0.193, 0.372, 0.539] and {Globalization bar}[0.426, 0.539, 0.480, 0.787] of {\"China\"}[0.374, 0.927, 0.424, 0.962] are both quite high and very close in value (56.9 for trade and 34 for globalization).\nBased on the above thought, the answer is:\nChina. Although its International Trade value is greater than its Globalization index, the two values are more balanced when compared to the other countries, which have a wider disparity between their International Trade and Globalization values." } ], "id_raw": 881 }, { "id": "olFd8wkodoOusVv2", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_11.png", "conversations": [ { "from": "human", "value": "\nWhich country has the highest globalization index according to the chart?" }, { "from": "gpt", "value": "To identify the country with the highest globalization index, I need to take a look at the bars on the right side of every pair in every country. These bars represent the globalization index. Starting from {\"USA's globalization bar\"}[0.224, 0.539, 0.278, 0.758] moving right to {\"China's globalization bar\"}[0.426, 0.539, 0.480, 0.787], {\"UK's globalization bar\"}[0.628, 0.539, 0.682, 0.857] and then ending at {\"India's globalization bar\"}[0.830, 0.539, 0.884, 0.684], the y coordinates inform me that the normalization index for these countries is 30.1, 34, 43.7, and 20 respectively. The UK's bar is the highest among the four.\nBased on the above thought, the answer is:\nUK" } ], "id_raw": 881 }, { "id": "EBeuytTUabUvEoru", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart about healthy habits into a structured CSV table?" }, { "from": "gpt", "value": "Looking at the chart with title {\"Healthy Habits: Healthy vs Freelance Lifestyles\"}[0.328, 0.025, 0.719, 0.055], I notice five categories of healthy habits on the x-axis: {Morning Run}[0.142, 0.811, 0.214, 0.931], {Healthy Eating}[0.322, 0.811, 0.404, 0.949], {Regular Checkups}[0.499, 0.811, 0.597, 0.975], {Limited Alcohol}[0.691, 0.811, 0.776, 0.954], and {Avoid Smoking}[0.877, 0.811, 0.960, 0.949]. Depending on the {legend}[0.798, 0.074, 0.978, 0.151] of the chart, the graph represents two different lifestyles: {Healthy Lifestyle}[0.843, 0.083, 0.959, 0.106] and {Freelance Lifestyle}[0.843, 0.118, 0.972, 0.141], each represented by different colored bars. I need to extract the values from the bars and their annotations to create a CSV table. For example, for the category \"Morning Run\", {the blue bar}[0.104, 0.209, 0.153, 0.795] and {the orange bar}[0.153, 0.499, 0.203, 0.795] represent a value of 53.1 and 26.8 respectively for \"Healthy Lifestyle\" and \"Freelance Lifestyle\".\nBased on the above thought, the answer is:\nHealthy Habits,Healthy Lifestyle,Freelance Lifestyle\nMorning Run,53.1,26.8\nHealthy Eating,63.2,37.9\nRegular Checkups,50.3,28.4\nLimited Alcohol,55.9,31.7\nAvoid Smoking,41.3,23.5" } ], "id_raw": 882 }, { "id": "VA84qTJJKYkERaLS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nCan you summarize the general trend shown in the chart regarding the habits and lifestyles?" }, { "from": "gpt", "value": "The bar chart outlines the comparison between a healthy lifestyle and a freelance lifestyle across five different healthy habits: {Morning Run}[0.142, 0.811, 0.214, 0.931], {Healthy Eating}[0.322, 0.811, 0.404, 0.949], {Regular Checkups}[0.499, 0.811, 0.597, 0.975], {Limited Alcohol}[0.691, 0.811, 0.776, 0.954], and {Avoid Smoking}[0.877, 0.811, 0.960, 0.949]. Looking at the bars for each habit, such as those for Morning Run {healthy lifestyle}[0.104, 0.209, 0.153, 0.795] and {freelance lifestyle}[0.153, 0.499, 0.203, 0.795], and continuing this trend for the rest of the habits, it's clear that across all habits, a higher percentage of people following a healthy lifestyle adhere to these habits than those follow a freelance lifestyle. This trend implies that people who lead a healthy lifestyle are more likely to engage in these healthy habits than those who live a freelance lifestyle.\nBased on the above thought, the answer is:\nAcross all healthy habits (Morning Run, Healthy Eating, Regular Checkups, Limited Alcohol, and Avoid Smoking), a higher proportion of people living a Healthy Lifestyle adhere to these habits than those living a Freelance Lifestyle." } ], "id_raw": 882 }, { "id": "MXw7PQaaTNXnmKJh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_12.png", "conversations": [ { "from": "human", "value": "\nWhich healthy habit has the largest percentage difference between the Healthy Lifestyle and the Freelance Lifestyle?" }, { "from": "gpt", "value": "To find the healthy habit with the largest percentage difference between the Healthy Lifestyle and the Freelance Lifestyle, I will need to calculate the difference for each habit. By observing the bar pairs for each habit, such as {Morning Run}[0.104, 0.209, 0.153, 0.795] and [0.153, 0.499, 0.203, 0.795], I can see that the difference is around 26.3%. By continuing this process for {Healthy Eating}[0.289, 0.097, 0.338, 0.795] and [0.338, 0.377, 0.388, 0.795]; {Regular Checkups}[0.474, 0.240, 0.523, 0.795] and [0.523, 0.481, 0.573, 0.795]; {Limited Alcohol}[0.659, 0.178, 0.709, 0.795] and [0.709, 0.445, 0.758, 0.795] ; and {Avoid Smoking}[0.844, 0.339, 0.894, 0.795] and [0.894, 0.536, 0.943, 0.795], it's clear that the largest difference is for Healthy Eating, which contains a difference of about 25.3%.\nBased on the above thought, the answer is:\nHealthy Eating" } ], "id_raw": 882 }, { "id": "NuD6zt1KRcHaf6aH", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you have seen, please convert the data into a CSV format table." }, { "from": "gpt", "value": "The chart is titled \"Career and Workplace Development Trends\"[0.304, 0.037, 0.755, 0.083] implying its representation of certain career paths and their associated trends in the workplace. The x-axis labels, observed at {\"Software Engineer\"}[0.182, 0.927, 0.341, 0.962] and {\"Data Analyst\"}[0.741, 0.927, 0.853, 0.962] specify two different career paths. The units for y-axis are in percentage (%) [0.019, 0.365, 0.036, 0.632] indicating measurement of job trends. There are two groups of bars specified in {the legend}[0.086, 0.111, 0.252, 0.226], representing \"Job Stability\"[0.093, 0.128, 0.128, 0.152] and \"Job Growth\"[0.093, 0.180, 0.128, 0.204]. For each career path, I could infer values from both trends by analysing the height of each bar and reading the annotations at the top of the bars. For instance, the bar at [0.118, 0.281, 0.261, 0.903] annotated with \"61.5\"[0.171, 0.238, 0.209, 0.273] specifies the Job Stability of Software Engineers.\nBased on the above thought, the answer is:\nCareer Path,Job Stability,Job Growth\nSoftware Engineer,61.5,75.3\nData Analyst,50.2,64.7" } ], "id_raw": 883 }, { "id": "zEowVhKI8Kp43Xix", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_13.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which profession has a higher Job Growth percentage?" }, { "from": "gpt", "value": "To answer this question, I need to compare the Job Growth values of Software Engineer and Data Analyst. The Job Growth values can be obtained from the height of the corresponding bars. Looking at the chart, the bar \"Job Growth for Software Engineer\" is located at [0.261, 0.141, 0.404, 0.903] while that for \"Job Growth for Data Analyst\" is at [0.797, 0.248, 0.940, 0.903]. The annotated values for each are \"75.3\"[0.314, 0.098, 0.352, 0.133] and \"64.7\"[0.849, 0.206, 0.888, 0.241] percent respectively after checking the annotations above the bars. Hence the Job Growth is higher for Software Engineer.\nBased on the above thought, the answer is:\nSoftware Engineer." } ], "id_raw": 883 }, { "id": "zjUd3HQSZQNi05Xv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nBased on the grouped bar chart, can you turn the represented data into a tabular CSV format?" }, { "from": "gpt", "value": "The title {\"Transportation and Logistics Industry Trends\"}[0.295, 0.080, 0.753, 0.125] indicates that the chart is about different types of freights over the years 2019, 2020 and 2021. There are 4 groups of bars identified by different colors according to {the legend}[0.076, 0.038, 0.248, 0.258], from which I know {\"Road Freight\"}[0.132, 0.052, 0.241, 0.087], {\"Air Freight\"}[0.132, 0.104, 0.221, 0.139], {\"Sea Freight\"}[0.132, 0.156, 0.230, 0.191] and {\"Rail Freight\"}[0.132, 0.209, 0.229, 0.244]. Each of these groups has 3 bars representing data for 3 years: 2019, 2020, and 2021 indicated by the {labels on the x-axis}[{\"2019\"}[0.206, 0.927, 0.250, 0.962]; {\"2020\"}[0.502, 0.927, 0.546, 0.962]; {\"2021\"}[0.799, 0.927, 0.843, 0.962]]. The numerical data for each bar can be obtained from the text annotations shown at the end of each bar, such as {\"78.3\"}[0.119, 0.138, 0.158, 0.173] for Road Freight in 2019. Hence, it is possible to compile this data into a CSV format.\nBased on the above thought, the answer is:\nTransport Type,2019,2020,2021\nRoad Freight,78.3,56.2,68.2\nAir Freight,45.6,30.3,41.6\nSea Freight, 58.5, 42.1, 50.9\nRail Freight, 36.7, 40.2, 42.8" } ], "id_raw": 884 }, { "id": "DV6SlelYdcbeDFnm", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nIn general, what is the trend seen in the logistics and transportation industry from 2019 to 2021, as depicted in the bar chart?" }, { "from": "gpt", "value": "The chart includes data for four different types of transportation: Road Freight, Air Freight, Sea Freight, and Rail Freight. Each type of transportation shows three bars for three different years: {\"2019\"}[0.206, 0.927, 0.250, 0.962], {\"2020\"}[0.502, 0.927, 0.546, 0.962], and {\"2021\"}[0.799, 0.927, 0.843, 0.962]. Comparing the bars' heights in the bar chart for each year across all transport types, I see a significant decline from 2019 to 2020, which can be observed from degree of lowering of the bars in 2020 compared to the bars in 2019 such as for {\"Road Freight\"}[0.109, 0.171, 0.168, 0.570] and [0.406, 0.283, 0.465, 0.570]. The industry then started to recover in 2021, as seen in the rising of the bars in 2021 compared to 2020, for example {\"Road Freight\"}[0.702, 0.222, 0.762, 0.570]. It can be inferred that the logistics and transportation industry probably faced a significant challenge such as the COVID-19 pandemic in 2020, and then started to recover in 2021.\nBased on the above thought, the answer is:\nThe general trend from 2019 to 2021 in the logistics and transportation industry shows a significant decline from 2019 to 2020, followed by a recovery in 2021." } ], "id_raw": 884 }, { "id": "aP08rQOjarqhIok5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_59_14.png", "conversations": [ { "from": "human", "value": "\nAmong all transportation types, which one experienced the least decline in 2020 from its 2019 numbers?" }, { "from": "gpt", "value": "To find which type of freight experienced the least decline in 2020 from 2019, I need to compare the differences between the heights of the bars of each group for 2019 and 2020. The bars for 2019 and 2020 for {\"Road Freight\"}[0.109, 0.171, 0.168, 0.570] and [0.406, 0.283, 0.465, 0.570], the bars for 2019 and 2020 for {\"Air Freight\"}[0.168, 0.337, 0.228, 0.570] and [0.465, 0.415, 0.524, 0.570], the bars for 2019 and 2020 for {\"Sea Freight\"}[0.228, 0.570, 0.287, 0.868] and [0.524, 0.570, 0.584, 0.785], and the bars for 2019 and 2020 for {\"Rail Freight\"}[0.287, 0.570, 0.346, 0.757] and [0.584, 0.570, 0.643, 0.775]. Among them, the difference between Rail Freight from 2019 to 2020, based on the bar heights [-36.7, -40.2] and the number annotations, is the smallest.\nBased on the above thought, the answer is:\nRail Freight." } ], "id_raw": 884 }, { "id": "76YMr0u9PZl5dcd1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data into a structured CSV table based on your observations?" }, { "from": "gpt", "value": "Based on the chart title {\"Trends in New Media and Digital Content Creation\"} It's observed that this bar chart represents trend scales for different types of new media and digital content creation under two scenarios: \"Rapid Recovery\" and \"Accelerating Decline\". Looking at the labels on the x-axis, we can see the four categories: {Podcast Subscribers}[0.116, 0.919, 0.255, 0.942], {Online Webinars}[0.355, 0.919, 0.469, 0.942], {Virtual Reality Content}[0.559, 0.919, 0.717, 0.942], and {Live Streaming}[0.812, 0.919, 0.916, 0.942]. Based on the y-axis range that varies from -5 to 5, we can determine the trend scale value for each category from the lengths of the bars. To create a structured CSV table, we look at each bar's height under both categories. For example, the height of the bar for {Podcast Subscribers in Rapid Recovery}[0.107, 0.196, 0.186, 0.483] is around 3, and in {Accelerating Decline}[0.186, 0.483, 0.265, 0.865] is around -4. Similar calculations are done for all bars in all categories to extract the values.\nBased on the above thought, the answer is:\nIndicator,Rapid Recovery,Accelerating Decline\nPodcast Subscribers,3,-4\nOnline Webinars,-2,0\nVirtual Reality Content,-1,4\nLive Streaming,2,-3" } ], "id_raw": 885 }, { "id": "BBRcXNjQk9lMMXtn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which new media or digital content creation trend is suffering the most decline?" }, { "from": "gpt", "value": "To locate the category suffering the most decline, it is necessary to observe each category's negative trend scale values. This can be done by evaluating the lengths of the negative bars representing \"Accelerating Decline\". For example, {Podcast Subscribers under Accelerating Decline}[0.186, 0.483, 0.265, 0.865] has a trend scale of -4. Similarly analysis is done for other categories. The category with the largest negative value is the one suffering the most decline. Upon making these evaluations, it's evident that both Podcast Subscribers and Live Streaming show the greatest decline, both having a trend scale of -4.\nBased on the above thought, the answer is:\nPodcast Subscribers and Live Streaming" } ], "id_raw": 885 }, { "id": "MGuCcU7Q5UTtQDnG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_0.png", "conversations": [ { "from": "human", "value": "\nIn which category is the difference between \"Rapid Recovery\" and \"Accelerating Decline\" highest?" }, { "from": "gpt", "value": "To answer this question accurately, I would need to evaluate the difference in trend scale values between \"Rapid Recovery\" and \"Accelerating Decline\" across all categories. This can be done by looking at the lengths of the corresponding bars for each category and calculating their differences. For instance, in {Podcast Subscribers}, we have {Rapid Recovery bar}[0.107, 0.196, 0.186, 0.483] at 3, and {Accelerating Decline bar}[0.186, 0.483, 0.265, 0.865] at -4. The absolute difference is 7. By conducting a similar exercise across all categories, it can be determined that Podcast Subscribers have the highest difference between Rapid Recovery and Accelerating Decline.\nBased on the above thought, the answer is:\nPodcast Subscribers" } ], "id_raw": 885 }, { "id": "kuMY1bLi2Gr38a05", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you visualized, can you convert the data into a table?" }, { "from": "gpt", "value": "Firstly, the x-axis {categories (Type 1 - Type 4)}[0.143, 0.903, 0.189, 0.931]; [0.379, 0.903, 0.424, 0.931]; [0.614, 0.903, 0.659, 0.931]; [0.849, 0.903, 0.895, 0.931] represent the different types of fuel. The y-axis values range from {-2}[0.023, 0.946, 0.043, 0.974] to {10}[0.026, -0.069, 0.043, -0.041], and the bars represent the scores awarded to each technology, with each technology being represented by a unique color according to {the legend}[0.060, 0.089, 0.238, 0.264]. To construct the table, I need to examine the height of each bar according to their y-coordinates, which eventually gives us the score for that technology in the respective fuel type category.\nBased on the above thought, the answer is:\nFuel Types,Hybrid Technology,Diesel Technology,Gasoline Technology\nType 1,0.5,0.8,1.4\nType 2,4,6,7\nType 3,7,6,5\nType 4,8,6,7" } ], "id_raw": 886 }, { "id": "L8hI9LZg0yFB9dol", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nBased on the trend shown by the graph, which technology has shown the most consistent performance across different fuel types?" }, { "from": "gpt", "value": "By tracking the height of each bar representing the different fuel types, I can determine that the technology that has been most consistent across various fuel types. Specifically, observing the height of the bars at {Hybrid technology}[0.096, 0.745, 0.143, 0.788]; [0.331, 0.449, 0.378, 0.788]; [0.566, 0.196, 0.613, 0.788]; [0.801, 0.111, 0.849, 0.788], {Diesel technology}[0.143, 0.720, 0.190, 0.788]; [0.378, 0.280, 0.425, 0.788]; [0.613, 0.280, 0.660, 0.788]; [0.849, 0.280, 0.896, 0.788], and {Gasoline technology}[0.190, 0.669, 0.237, 0.788]; [0.425, 0.196, 0.472, 0.788]; [0.660, 0.365, 0.707, 0.788]; [0.896, 0.449, 0.943, 0.788], I can see changes within each set. The Fuel Type ratings for Diesel Technology exhibit the least fluctuation around a generally steady value, making it the most consistent technology across different fuel types.\nBased on the above thought, the answer is:\nDiesel Technology" } ], "id_raw": 886 }, { "id": "xiNwoQOlytR6FhkN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which fuel type has the highest total score across all technologies?" }, { "from": "gpt", "value": "To find the fuel type with the highest total score, I combine all scores in each category. By comparing height of the bars of {Hybrid Technology}[0.096, 0.745, 0.143, 0.788]; [0.331, 0.449, 0.378, 0.788]; [0.566, 0.196, 0.613, 0.788]; [0.801, 0.111, 0.849, 0.788], {Diesel Tech}[0.143, 0.720, 0.190, 0.788]; [0.378, 0.280, 0.425, 0.788]; [0.613, 0.280, 0.660, 0.788]; [0.849, 0.280, 0.896, 0.788] and {Gasoline Tech}[0.190, 0.669, 0.237, 0.788]; [0.425, 0.196, 0.472, 0.788]; [0.660, 0.365, 0.707, 0.788]; [0.896, 0.449, 0.943, 0.788] in each category, we can see that Type 4 has the highest total score.\nBased on the above thought, the answer is:\nType 4" } ], "id_raw": 886 }, { "id": "6Of1PP38TtSWDsHP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a CSV format table?" }, { "from": "gpt", "value": "Observing the x-axis, it contains 4 categories which are {Artificial Intelligence}[0.086, 0.903, 0.228, 0.931], {Machine Learning}[0.341, 0.903, 0.464, 0.931], {Cloud-based Learning}[0.573, 0.903, 0.724, 0.931] and {VR/MR Technology}[0.830, 0.903, 0.958, 0.931]. And the y-axis range is from {0}[0.035, 0.872, 0.057, 0.900] to {4.5}[0.035, 0.006, 0.057, 0.034]. From the {legend}[0.073, 0.089, 0.287, 0.181], two color bars are used to represent two trends which are {EdTech Adoption}[0.118, 0.100, 0.233, 0.128] and {Online Learning Growth}[0.118, 0.142, 0.282, 0.170]. Therefore, to generate the CSV table, I need to extract the value of each bar using both colors under each category. For example, the height of {EdTech Adoption bar}[0.108, 0.595, 0.157, 0.883] under the Artificial Intelligence category is around 1.5 while the height of {Online Learning Growth bar}[0.157, 0.498, 0.206, 0.883] under the same category is about 2.\nBased on the above thought, the answer is:\nFeatures,EdTech Adoption,Online Learning Growth\nArtificial Intelligence,1.5,2\nMachine Learning,2,2.5\nCloud-based Learning,2.5,3\nVR/MR Technology,3,4" } ], "id_raw": 887 }, { "id": "bbDWxa8m6nGZl9uD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nIn the perspective of the future development of online education, which technology do you think will receive the most attention? And please explain why." }, { "from": "gpt", "value": "This question requires a comprehensive analysis of the height of bars which represent the {Online Learning Growth}[0.118, 0.142, 0.282, 0.170] in each category. {VR/MR Technology}[0.830, 0.903, 0.958, 0.931] has {the highest bar}[0.894, 0.113, 0.943, 0.883] in Online Learning Growth, indicating it will expectedly receive the most attention in the future. Such growth can be attributed to the immersive and interactive experiences it brings to online education. This technology can transform the way we learn and teach, making knowledge transfer more efficient and effective.\nBased on the above thought, the answer is:\nVR/MR Technology. From the chart, it's evident that the adoption of VR/MR Technology in online learning is gaining remarkable growth compared to other technologies which signifies its importance and attention in the upcoming future." } ], "id_raw": 887 }, { "id": "4uNE3FVHtzek0Rpz", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_2.png", "conversations": [ { "from": "human", "value": "\nWhich feature has the smallest difference between EdTech Adoption and Online Learning Growth?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height difference between the two bars which represent {EdTech Adoption} and {Online Learning Growth} for each feature. For example, for the feature of {Artificial Intelligence}[0.086, 0.903, 0.228, 0.931], the heights of the bars {EdTech Adoption}[0.108, 0.595, 0.157, 0.883] and {Online Learning Growth}[0.157, 0.498, 0.206, 0.883] are 1.5 and 2 respectively, giving a difference of 0.5. Similarly, for {Machine Learning}[0.341, 0.903, 0.464, 0.931], the difference between the heights of the two bars {EdTech Adoption}[0.354, 0.498, 0.403, 0.883] and {Online Learning Growth}[0.403, 0.402, 0.452, 0.883] is 0.5 as well. After doing this for all the four features, it can be seen that the feature with the smallest difference between the value of EdTech Adoption and Online Learning Growth is {Artificial Intelligence}[0.086, 0.903, 0.228, 0.931] and {Machine Learning}[0.341, 0.903, 0.464, 0.931].\nBased on the above thought, the answer is:\nArtificial Intelligence, Machine Learning" } ], "id_raw": 887 }, { "id": "TVixQFwGN73SKFXb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nPlease generate a CSV format table from the bar chart you are observing." }, { "from": "gpt", "value": "To generate the CSV table, I need to get the value of sales for each product in each quarter firstly. The chart shows 4 quarters from {Q1}[0.172, 0.903, 0.191, 0.931] to {Q4}[0.864, 0.903, 0.884, 0.931] on the x-axis while shows a products' sales represented by bars with different colors according to {the legend}[0.881, 0.089, 0.978, 0.264]. There are four products: {iPhone}[0.926, 0.100, 0.972, 0.128], {iPad}[0.926, 0.142, 0.955, 0.170], {Mac}[0.926, 0.183, 0.954, 0.211], and {Watch}[0.926, 0.225, 0.969, 0.253]. For iPhone's sales in Q1, the value is represented by the height of {the blue bar}[0.112, 0.421, 0.147, 0.883], which can be calculated as 120,000 units based on the y-axis. Similarly, the rest of the sales can be obtained by referencing the heights of the bars corresponding to each product in each quarter.\nBased on the above thought, the answer is:\nProduct,Q1,Q2,Q3,Q4\niPhone,120,130,150,200\niPad,100,90,85,80\nMac,50,70,75,100\nWatch,30,50,70,100" } ], "id_raw": 888 }, { "id": "dUBjFmOqocLLRb3D", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nWhich product's sales is the most stable throughout the four quarters?" }, { "from": "gpt", "value": "To determine the product with the most stable sales, we need to compare the fluctuation of product sales from Q1 to Q4 for each product. The fluctuation is represented by the changes in the bar heights for each product. For example, for {iPhone}[0.926, 0.100, 0.972, 0.128], its sales from Q1 to Q4 have been increasing, which indicates strong growth, but also more fluctuations. {iPad}[0.926, 0.142, 0.955, 0.170] sales have been declining, which indicates that iPad has fluctuated sales, too. {Mac}[0.926, 0.183, 0.954, 0.211] sales also show constant growth, meaning less stability. Lastly, {Watch}[0.926, 0.225, 0.969, 0.253] sales shows an even and steady increase, which indicates the most stability.\nBased on the above thought, the answer is:\nWatch. Despite the fact that its sales are the lowest among the four products, its quartal sales performance shows the steadiest growth throughout the year, indicating the most stability." } ], "id_raw": 888 }, { "id": "FV4q4zpI23GfxGj5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_3.png", "conversations": [ { "from": "human", "value": "\nWhich product has the greatest increase in sales from Q1 to Q4?" }, { "from": "gpt", "value": "In order to determine which product had the greatest sales increase from Q1 to Q4, I need to compare the initial and final quarter sales of each product. This can be done by looking at the height of each bar corresponding to each product in the Q1 and Q4 quarters. For iPhone, {the bar for Q1 sales}[0.112, 0.421, 0.147, 0.883] is approximately 120 and {the bar for Q4 sales}[0.805, 0.113, 0.840, 0.883] is 200, indicating an increase of 80; for the other products, I can similarly get the sales increase. After comparing the increase of each product, it can be seen that the iPhone has the greatest sales increase.\nBased on the above thought, the answer is:\niPhone." } ], "id_raw": 888 }, { "id": "6RpIF5JYM8psqvYw", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nPlease decipher the information in the chart and arrange it in a CSV format table." }, { "from": "gpt", "value": "Looking at the bar chart title {\"Human Health and Disease Management\"}[0.357, 0.030, 0.699, 0.066], it is a statistical analysis relating to disease management in different management areas which are located on the x-axis {\"Management Areas\"}[0.460, 0.942, 0.595, 0.970]. Specifically , this list includes {\"Minor Illness\"}[0.118, 0.903, 0.204, 0.931], {\"Chronic Disease\"}[0.350, 0.903, 0.461, 0.931], {\"Accident & Injury\"}[0.591, 0.903, 0.710, 0.931] and {\"Mental Health\"}[0.846, 0.903, 0.943, 0.931]. The y-axis provides efficiency scores {\"Efficiency Score\"}[0.015, 0.369, 0.029, 0.590]. According to the legend {\"Efficient Care Continuum\", \"Personalized Care\"}[0.416, 0.089, 0.640, 0.181], the chart analyzes 2 indicators for health and disease management: Efficient care continuum and Personalized care. Their values can be deciphered from the height of the bars corresponding to each indicator and each management area. For example, for Minor Illness management, the score of Efficient care continuum is obtained from the height of the first bar which reaches about 70 {[0.112, 0.344, 0.161, 0.883]}, and that of Personalized care is obtained from 100, as deduced from the second bar's height {[0.161, 0.113, 0.210, 0.883]}. Similarly, for the other aspects, the scores of each indicator can be obtained. Following this manner, I can construct the table format needed.\nBased on the above thought, the answer is:\nManagement Areas,Efficient care continuum,Personalized care\nMinor Illness Management, 70,100\nChronic Disease Management, 90,75\nAccident & Injury Prevention, 85,80\nMental Health Therapy, 60,95" } ], "id_raw": 889 }, { "id": "Qr8gD0dzPPq4ctTn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nWhat conclusions can be drawn about the efficiency of disease management based on the information in the chart?" }, { "from": "gpt", "value": "The chart provides lots of useful insights regarding the efficiency of disease management. For the management performance of {\"Efficient Care Continuum\"}[0.460, 0.100, 0.634, 0.128] and {\"Personalized Care\"}[0.460, 0.142, 0.585, 0.170], one can conclude that the efficiency of the latter is generally higher in all four aspects: {\"Minor Illness\"}[0.118, 0.903, 0.204, 0.931], {\"Chronic Disease\"}[0.350, 0.903, 0.461, 0.931], {\"Accident & Injury\"}[0.591, 0.903, 0.710, 0.931]and {\"Mental Health\"}[0.846, 0.903, 0.943, 0.931]. According to the legend, the corresponding bars {[0.161, 0.113, 0.210, 0.883], [0.406, 0.306, 0.454, 0.883], [0.650, 0.267, 0.699, 0.883], [0.895, 0.152, 0.943, 0.883]} of 'Personalized Care' are evidently higher than that of 'Efficient Care Continuum' {[0.112, 0.344, 0.161, 0.883], [0.357, 0.190, 0.406, 0.883], [0.601, 0.229, 0.650, 0.883], [0.846, 0.421, 0.895, 0.883]}. Therefore, it seems that personalized treatment may be a good development direction and worth further research and investment.\nBased on the above thought, the answer is:\nPersonalized care may provide more efficient disease management in the areas of Minor Illness, Chronic Disease, Accident & Injury, and Mental Health, as it performs better than Efficient Care Continuum in these aspects. This suggests that personalized treatment could be a promising way forward in improving disease management efficiency." } ], "id_raw": 889 }, { "id": "S24vkc4tH35bJxLg", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_4.png", "conversations": [ { "from": "human", "value": "\nFor which management area is the improvement in efficiency score from 'Efficient care continuum' to 'Personalized care' the highest?" }, { "from": "gpt", "value": "To compute this, I will compare the bar heights in each of the four management areas from 'Efficient care continuum' to 'Personalized care'. The pairs of bars to be compared are as follows: {\"Minor Illness Management\"}[0.112, 0.344, 0.161, 0.883, 0.161, 0.113, 0.210, 0.883], {\"Chronic Disease Management\"}[0.357, 0.190, 0.406, 0.883, 0.406, 0.306, 0.454, 0.883], {\"Accident & Injury Prevention\"}[0.601, 0.229, 0.650, 0.883, 0.650, 0.267, 0.699, 0.883], {\"Mental Health Therapy\"}[0.846, 0.421, 0.895, 0.883, 0.895, 0.152, 0.943, 0.883]. The difference of score in each management area can be calculated by subtracting the value of 'Efficient care continuum' from the value of 'Personalized care'. For instance, in the case of 'Minor Illness', the improvement in score would be 100 - 70 = 30. From my calculations the most substantial improvement in efficiency score is seen in 'Mental Health Therapy' with an increase of 35 units.\nBased on the above thought, the answer is:\nMental Health Therapy" } ], "id_raw": 889 }, { "id": "IukzXXsOUulUt50V", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you are observing, please convert the data into a CSV format table." }, { "from": "gpt", "value": "The title of the bar chart is {\"Culture and Arts Industry Development\"}[0.360, 0.030, 0.687, 0.066], and there are four major categories along the x-axis, which represent different sectors in the Culture and Arts Industry: {\"Visual Arts\"}[0.116, 0.903, 0.190, 0.931], {\"Performing Arts\"}[0.346, 0.903, 0.454, 0.931], {\"Sculpture\"}[0.614, 0.903, 0.680, 0.931], and {\"Writing\"}[0.869, 0.903, 0.919, 0.931]. The y-axis represents the development score, ranging from {0}[0.043, 0.872, 0.052, 0.900] to {80}[0.035, 0.051, 0.052, 0.079]. According to {the legend}[0.853, 0.089, 0.978, 0.181], the {blue bars}<{\"Developed\"}[0.898, 0.100, 0.972, 0.128]> represent scores of the developed sectors while the {orange bars}<{\"Emerging\"}[0.898, 0.142, 0.964, 0.170]> represent the emerging sectors. The score for each sector is calculated from the height of the bars. From this, I can construct a CSV format data representation.\nBased on the above thought, the answer is:\nCulture Aspects, Developed, Emerging\nVisual Arts, 75, 55\nPerforming Arts, 68, 46\nSculpture, 67, 35\nWriting, 49, 68" } ], "id_raw": 890 }, { "id": "CJWKUli2APe4I207", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the development in the Culture and Arts Industry based on the data presented in the bar chart?" }, { "from": "gpt", "value": "This question focuses on the comprehensive understanding of the bar chart titled as {\"Culture and Arts Industry Development\"}[0.360, 0.030, 0.687, 0.066] which displays the development scores in the sectors of visual arts, performing arts, sculpture, and writing. The analysis can be done by comparing the {blue bars}<{\"Developed\"}[0.898, 0.100, 0.972, 0.128]> and the {orange bars}<{\"Emerging\"}[0.898, 0.142, 0.964, 0.170]>. We can see that in most cases except {'Writing'}[0.869, 0.903, 0.919, 0.931], the Emerging sector shows lower scores as compared to Developed sectors. This could imply that these areas may be less prevalent or less developed in the Emerging sectors. However, the Emerging score is significantly higher in the {Writing} sector [0.894, 0.185, 0.943, 0.883], suggesting it has exceeded the Development score [0.844, 0.380, 0.894, 0.883], indicating a tremendous growth and attention in this particular field.\nBased on the above thought, the answer is:\nVisual arts, performing arts, and sculpture are better developed in the Developed sectors compared to the Emerging sectors, suggesting these sectors are relatively mature and have received more attention and investment. However, in the writing sector, the Emerging sector scores higher than Developed sectors, indicating a rapid development and potentially a shift in focus towards writing, possibly due to the rise of digital media making it more accessible and popular." } ], "id_raw": 890 }, { "id": "hFZWOZzDZGzkkBlj", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_5.png", "conversations": [ { "from": "human", "value": "\nWhich Culture Aspect has the most significant development in the Emerging sectors as compared to Developed sectors?" }, { "from": "gpt", "value": "We are looking for the category in which the height of the orange bar, representing the \"#Emerging\" sector, is higher than the blue bar, representing the \"#Developed\" sector. By observing the bars in each category, we find the pairs of bars in the category {\"Visual Arts\"}[<{\"Developed\"}[0.104, 0.113, 0.153, 0.883]; {\"Emerging\"}[0.153, 0.319, 0.203, 0.883]>], the category {\"Performing Arts\"}[<{\"Developed\"}[0.351, 0.185, 0.400, 0.883]; {\"Emerging\"}[0.400, 0.411, 0.449, 0.883]>], the category {\"Sculpture\"}[<{\"Developed\"}[0.597, 0.195, 0.647, 0.883]; {\"Emerging\"}[0.647, 0.524, 0.696, 0.883]>] and the category {\"Writing\"}[<{\"Developed\"}[0.844, 0.380, 0.894, 0.883]; {\"Emerging\"}[0.894, 0.185, 0.943, 0.883]>]. It can be observed that in the \"Writing\" category, the {\"Emerging\"}[0.894, 0.185, 0.943, 0.883] bar is visibly higher than the {\"Developed\"}[0.844, 0.380, 0.894, 0.883] bar, hence showing the most significant development in the Emerging sectors as compared to Developed sectors.\nBased on the above thought, the answer is:\nWriting" } ], "id_raw": 890 }, { "id": "1E6WsendRqi7k3MQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you summarize the performance scores data into a structured table?" }, { "from": "gpt", "value": "The chart's title {\"Social Responsibility and Sustainable Business\"}[0.326, 0.030, 0.713, 0.066] gives us the idea that the performance scores of CSR and Sustainable development strategies in Default and HAG are shown. The tick labels of the x-axis {\"2019\", \"2020\", \"2021\", \"2022\", \"2023\"}[{\"2019\"}[0.148, 0.903, 0.184, 0.931]; {\"2020\"}[0.325, 0.903, 0.360, 0.931]; {\"2021\"}[0.502, 0.903, 0.537, 0.931]; {\"2022\"}[0.678, 0.903, 0.713, 0.931]; {\"2023\"}[0.855, 0.903, 0.890, 0.931]] represent the years. The y-axis tick labels ranging from {0}[0.035, 0.872, 0.043, 0.900] to {7}[0.035, -0.026, 0.043, 0.002] represent the performance scores. According to {the sublegend}[0.060, 0.089, 0.247, 0.264], each color stands for a different sub-category.\nEach year has a cluster of four bars, such as {2019}[0.096, 0.242, 0.237, 0.883]. The heights of the bars represent values, and the positions of the bars match up with the y-axis tick labels. Let's take 2019 performance scores as an example: the heights of the bars {\"CSR-Default\"}[0.096, 0.242, 0.131, 0.883], {\"CSR-HAG\"}[0.131, 0.370, 0.166, 0.883], {\"Sustainable-Default\"}[0.166, 0.370, 0.201, 0.883] and {\"Sustainable-HAG\"}[0.201, 0.498, 0.237, 0.883] are corresponding to scores 5, 4, 4, 3 respectively. Similarly, we can decode the data from 2020 to 2023.\nBased on the above thought, the answer is:\nYear,CSR-Default,CSR-HAG,Sustainable-Default,Sustainable-HAG\n2019,5,4,4,3\n2020,4,5,3.5,4\n2021,3,5.5,3,5\n2022,2,5.5,2.5,5\n2023,2,6,2.5,5.5" } ], "id_raw": 891 }, { "id": "RrSvRv5IxiIk0kpf", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which strategy shows constant growth in performance scores through the years?" }, { "from": "gpt", "value": "To determine which strategy shows constant growth, I should examine the trend of the bars' heights for each strategy from 2019 to 2023. Let's scrutinize the bars representing each sub-category, such as {CSR-HAG}[{\"2019\"}[0.131, 0.370, 0.166, 0.883]; {\"2020\"}[0.307, 0.242, 0.343, 0.883]; {\"2021\"}[0.484, 0.177, 0.519, 0.883]; {\"2022\"}[0.660, 0.177, 0.696, 0.883]; {\"2023\"}[0.837, 0.113, 0.872, 0.883]]. Similarly, we can analyze CSR-Default, Sustainable-Default, and Sustainable-HAG. By looking at the bar heights and comparing them across years, it can be noted that the 'CSR-HAG' strategy shows a constant upward trend across the years.\nBased on the above thought, the answer is:\nThe 'CSR-HAG' strategy shows constant growth in performance scores from 2019 to 2023." } ], "id_raw": 891 }, { "id": "txi3ZlZpIRUDum12", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_6.png", "conversations": [ { "from": "human", "value": "\nWhich year did the 'CSR-HAG' score the lowest?" }, { "from": "gpt", "value": "I can find the answer by comparing the heights of the bars representing 'CSR-HAG' scores across different years. Let's look at the second bar in each year: they are {\"2019\"}[0.131, 0.370, 0.166, 0.883], {\"2020\"}[0.307, 0.242, 0.343, 0.883], and so forth. The heights of the bars correspond to the y-axis tick labels {\"0\"}[0.035, 0.872, 0.043, 0.900] to {\"7\"}[0.035, -0.026, 0.043, 0.002], allowing me to read the score for each year. By this comparison, 'CSR-HAG' gets the lowest score in {2019}[0.131, 0.370, 0.166, 0.883].\nBased on the above thought, the answer is:\n2019" } ], "id_raw": 891 }, { "id": "Gn3bu8WdAhX3Ixv1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nPlease convert the raw data of the product sales trend for the first half of the year according to the bar chart." }, { "from": "gpt", "value": "From the title at the top of the chart {Product Sales Trend} [0.455, 0.030, 0.618, 0.066] and {the x-axis labels} [Cosmeceuticals [0.169, 0.903, 0.277, 0.931], Natural Products [0.374, 0.903, 0.490, 0.931], Superfoods [0.602, 0.903, 0.680, 0.931], Battery [0.824, 0.903, 0.876, 0.931]], it is evident that the x-axis represents different product categories. The y-axis labels show the sales values that start from 0 and go up to 60000. The colored bars represent sales in different months. For example, {the blue bar for Cosmeceuticals} [0.129, 0.340, 0.160, 0.883] reaches approximately the 36000 position on the y-axis, indicating that the sales for Cosmeceuticals in January were around $36000. Using the heights of the bars, I can derive the sales for each product and month, enabling the creation of a CSV table.\nBased on the above thought, the answer is:\nProduct,Jan,Feb,Mar,Apr,May,Jun\nCosmeceuticals,36000,33800,36750,37250,38000,40000\nNatural Products,42250,44000,45800,47500,49250,51000\nSuperfoods,48500,45500,43000,40000,37500,35500\nBattery,47500,49250,45500,45500,47500,49250" } ], "id_raw": 892 }, { "id": "BvBoeNUqeL6qxYHX", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhich product category has shown a predictable growth over the six months considering the sales trend?" }, { "from": "gpt", "value": "To answer the question, I need to compare the height of the bars representing sales for each product over the months Jan to Jun. {Cosmeceuticals bars} [{'Jan'}[0.129, 0.340, 0.160, 0.883], {'Feb'}[0.160, 0.373, 0.192, 0.883], {'Mar'}[0.192, 0.328, 0.223, 0.883], {'Apr'}[0.223, 0.321, 0.254, 0.883], {'May'}[0.254, 0.310, 0.286, 0.883], {'Jun'}[0.286, 0.279, 0.317, 0.883]] show a slight but consistent growth. {Natural Products bars} fluctuate slightly but show a strong and stable rise, indicating a positive and predictable growth. The bars for {Superfoods} and {Battery} do not depict a predictable growth as their height decreases or fluctuates inconsistently. Therefore, based on the bar chart, Natural Products have shown a predictable and positive growth over the first six months of the year.\nBased on the above thought, the answer is:\nNatural Products." } ], "id_raw": 892 }, { "id": "U27yVDVaekv7cLA5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_7.png", "conversations": [ { "from": "human", "value": "\nWhich product shows the highest sale volume in June?" }, { "from": "gpt", "value": "To locate the product with the highest June sale, I hunt for the rectangle bar that reaches the highest on the y-axis in each group, all of them are represented by a brown color. Starting from the left, {the Cosmeceuticals bar} [0.286, 0.279, 0.317, 0.883] hits around the 40000 mark, {the Natural Products bar} [0.495, 0.113, 0.526, 0.883] touches approximately the 51000 mark, {the Superfoods bar} [0.704, 0.347, 0.735, 0.883] touches around the 35500 mark, and finally, {the Battery bar} [0.913, 0.140, 0.944, 0.883] hits near the 49250 mark. Therefore, 'Natural Products' achieves the highest sale in June.\nBased on the above thought, the answer is:\nNatural Products." } ], "id_raw": 892 }, { "id": "4nTXj2bFy1aYMEMx", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the data presented in the bar chart into a structured CSV table?" }, { "from": "gpt", "value": "The chart primarily represents four categories of media types on the x-axis {\"Media Type 1\"}[0.145, 0.903, 0.237, 0.931], {\"Media Type 2\"}[0.370, 0.903, 0.461, 0.931], {\"Media Type 3\"}[0.594, 0.903, 0.686, 0.931], and {\"Media Type 4\"}[0.819, 0.903, 0.910, 0.931] and their corresponding influence score values on the y-axis, ranging from {0}[0.052, 0.872, 0.061, 0.900] to {250}[0.035, -0.003, 0.061, 0.025]. The interpretation of each bar is given by {the legend}[0.841, 0.089, 0.978, 0.181] which states that the entries {\"Local News\"}[0.885, 0.100, 0.963, 0.128] and {\"Social Media\"}[0.885, 0.142, 0.972, 0.170] have different colors. Therefore, I can determine the influence scores for each media type in both categories using the height of the bars. For example, the height of {\"the first bar in Local News\"}[0.112, 0.463, 0.191, 0.883] corresponds to an influence score of about 120, which matches the start of the y-range text {\"0.00 -> 120.00\"} on the bar itself.\nBased on the above thought, the answer is:\nMedia Type,Local News,Social Media\nMedia Type 1,120,220\nMedia Type 2,150,130\nMedia Type 3,80,100\nMedia Type 4,130,190" } ], "id_raw": 893 }, { "id": "qW082OVZXTTA78AY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which type of news, Local or Social media, do you think has a more significant influence on the audience overall? Explain your answer." }, { "from": "gpt", "value": "This question requires me to compare the overall influence of Local News and Social Media. By examining {the legend entries}[0.841, 0.089, 0.978, 0.181], the colors indicate the categories. For comparison, I shall compare the bars from {\"Local News\"}[0.885, 0.100, 0.963, 0.128] and {\"Social Media\"}[0.885, 0.142, 0.972, 0.170] in each media type. In detail, for {\"Media Type 1\"}[0.145, 0.903, 0.237, 0.931], the bar for Social Media seems much taller than that for Local News. Similarly, in {\"Media Type 2\"}[0.370, 0.903, 0.461, 0.931], although the bar for Local News is taller, the difference is not as substantial. In {\"Media Type 3\"}[0.594, 0.903, 0.686, 0.931] and {\"Media Type 4\"}[0.819, 0.903, 0.910, 0.931], the bars for Social Media are again taller. Summarizing these observations, Social Media appear to have a more significant influence on audiences overall.\nBased on the above thought, the answer is:\nSocial Media. Because in three of out four media types, Social Media has a higher influence score than Local News. It suggests that Social Media tends to have a more significant influence on the audience overall." } ], "id_raw": 893 }, { "id": "GnCtI3lJrJSneOQ9", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_8.png", "conversations": [ { "from": "human", "value": "\nWhich media type has the highest difference in influence score between Local News and Social Media?" }, { "from": "gpt", "value": "In order to determine which media type has the highest difference in influence score, I will need to calculate the absolute difference between the heights of the corresponding bars in the \"Local News\" and \"Social Media\" categories. Looking at all four sets of bars, it appears that {the first set}[{Local News}[0.112, 0.463, 0.191, 0.883], {Social Media}[0.191, 0.113, 0.269, 0.883]] (representing \"Media Type 1\") has the largest difference. This would be consistent with the observation that {the bar for \"Local News\"}[0.112, 0.463, 0.191, 0.883] is around the middle of the y-axis, while {the bar for \"Social Media\"}[0.191, 0.113, 0.269, 0.883] is almost reaching the top, indicating a significant difference in the influence score.\nBased on the above thought, the answer is:\nMedia Type 1" } ], "id_raw": 893 }, { "id": "iFbFz9qG7RpQwpHh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the total development scores for each technology from the bar chart into a structured table?" }, { "from": "gpt", "value": "First, observing the chart, I identify the years on {the x-axis labels}[0.099, 0.939, 0.124, 0.957]; [0.180, 0.939, 0.205, 0.957]; [0.261, 0.939, 0.286, 0.957]; [0.342, 0.939, 0.367, 0.957]; [0.423, 0.939, 0.448, 0.957]; [0.504, 0.939, 0.529, 0.957]; [0.585, 0.939, 0.610, 0.957]; [0.666, 0.939, 0.691, 0.957]; [0.747, 0.939, 0.772, 0.957]; [0.828, 0.939, 0.854, 0.957]; [0.910, 0.939, 0.935, 0.957], which range from 2010 to 2020. The bars corresponding to each technology are marked as {Artificial Intelligence bars}[0.087, 0.669, 0.099, 0.814]; [0.168, 0.596, 0.180, 0.814]; [0.249, 0.451, 0.261, 0.814]; ... for Artificial Intelligence, {Quantum Computing bars}[0.099, 0.814, 0.111, 0.814]; [0.180, 0.669, 0.193, 0.814]; [0.261, 0.596, 0.274, 0.814]; ... for Quantum Computing, {5G bars}[0.111, 0.814, 0.124, 0.887]; [0.193, 0.814, 0.205, 0.887]; [0.274, 0.814, 0.286, 0.851]; ... for 5G, and {Data Science bars}[0.124, 0.742, 0.136, 0.814]; [0.205, 0.705, 0.217, 0.814]; [0.286, 0.669, 0.298, 0.814]; ... for Data Science. By assessing the height of each bar and referencing the {y-axis labels}[0.023, 0.953, 0.037, 0.971]; [0.031, 0.807, 0.037, 0.825]; [0.031, 0.662, 0.037, 0.679]; ..., I can calculate the scores for each year and summarize them into a table.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Quantum Computing,5G,Data Science,Total\n2010,2,0,-1,1,2\n2011,3,2,-1,1.5,5.5\n2012,5,3,-0.5,2,9.5\n2013,5,4,0,2.5,11.5\n2014,4,6,0.5,3,13.5\n2015,3,7,0.5,2.5,13\n2016,5,8,1,3,17\n2017,6,8,2,3.5,19.5\n2018,4.5,6,2,3,15.5\n2019,5,9,3,3.5,20.5\n2020,7,10,2.5,4,23.5" } ], "id_raw": 894 }, { "id": "9nl8dIE7wLeFagHM", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nWhich sector showed the most stable positive development over the 2010 to 2020 period?" }, { "from": "gpt", "value": "To determine stability and positivity of development, I must analyze the growth pattern of each sector over the years. Observing the {Artificial Intelligence bars}[0.087, 0.669, 0.099, 0.814]; ... [0.898, 0.305, 0.910, 0.814] where there are several ups and downs, it indicates erratic growth. For {Quantum Computing}[0.099, 0.814, 0.111, 0.814]; ... [0.910, 0.087, 0.922, 0.814], the growth seems moderate but consistent. {5G}[0.111, 0.814, 0.124, 0.887]; ... [0.922, 0.632, 0.934, 0.814] started from negative but shows stable growth throughout. {Data Science}[0.124, 0.742, 0.136, 0.814]; ... [0.934, 0.523, 0.946, 0.814] shows a consistent positive slope without negative years. Evaluating the bars related to each sector, with special attention given to the absence of negative bars, steady height increase, and absence of sharp jumps or dips, I can interpret the overall trend stability and positivity.\nBased on the above thought, the answer is:\nData Science. This sector not only displayed a consistent positive development score through all the years without ever dipping below zero but also showed an upward trend, which indicates stable positive growth." } ], "id_raw": 894 }, { "id": "7bHvAukN57IZYk7K", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat was the average score of Data Science across all years?" }, { "from": "gpt", "value": "By analyzing the chart, I can obtain the value for Data Science in each year by referencing the heights of the {Data Science bars}[0.124, 0.742, 0.136, 0.814]; [0.205, 0.705, 0.217, 0.814]; [0.286, 0.669, 0.298, 0.814]; ... [0.934, 0.523, 0.946, 0.814]. To calculate the average, I sum up the scores provided for each year from the chart and divide by the number of years which is 11, from 2010 to 2020.\nBased on the above thought, the answer is:\n2.77" } ], "id_raw": 894 }, { "id": "Tv7ovqLO3m519eDT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nCan you help me translate the bar chart information you observed into a CSV table?" }, { "from": "gpt", "value": "Based on the information I observed from the bar chart, we have both {Sustained Growth and Rapid Expansion}[0.060, 0.089, 0.234, 0.181] trends for four different educational technologies. These technologies are represented on the x-axis as {Online Classes}[0.095, 0.903, 0.196, 0.931], {MOOCs}[0.369, 0.903, 0.420, 0.931], {Webinars}[0.611, 0.903, 0.676, 0.931], and {Virtual Reality}[0.844, 0.903, 0.942, 0.931]. The y-axis, labelled 'Growth Units'[0.015, 0.389, 0.029, 0.569], represents the growth in terms of units. The green and orange bars represent the units of growth for Sustained Growth and Rapid Expansion trends, respectively. For example, looking at Online Classes, the green bar for Sustained Growth has a height at approximately {2 units}[0.096, 0.702, 0.145, 0.883] and the orange bar for Rapid Expansion is at {1 unit}[0.145, 0.793, 0.195, 0.883]. Using this process, I can collect the data from all the bars.\nBased on the above thought, the answer is:\nPlatforms,Sustained Growth,Rapid Expansion\nOnline Classes,2.0,1.0\nMOOCs,3.0,3.5\nWebinars,3.5,6.0\nVirtual Reality,4.0,8.5" } ], "id_raw": 895 }, { "id": "ChPF2pxSF0ms1FGA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the chart, which growth trend would you say is more beneficial for the advancement of educational technologies?" }, { "from": "gpt", "value": "This question calls for an evaluation of the merits of the {Sustained Growth}[0.105, 0.100, 0.228, 0.128] and {Rapid Expansion}[0.105, 0.142, 0.220, 0.170] trends. Looking at the individual bars for each trend per technology, I see that in Online Classes, the Sustained Growth value is {higher}[0.096, 0.702, 0.145, 0.883] than the Rapid Expansion value{1.0}[0.145, 0.793, 0.195, 0.883]. However, in MOOCs, both growth trends are {the same}[0.345, 0.612, 0.394, 0.883; 0.394, 0.566, 0.444, 0.883]. In both Webinars and Virtual Reality, the Rapid Expansion trend values are {greater}[0.644, 0.340, 0.693, 0.883; 0.893, 0.113, 0.943, 0.883] than their Sustained Growth counterparts. It seems that while the Sustained Growth trend offers stable and reliable growth, the Rapid Expansion trend has the capacity for considerable leaps.\nBased on the above thought, the answer is:\nRapid Expansion. While Sustained Growth offers guaranteed steady progress, Rapid Expansion provides the potential for significant advancements that can outpace the steady pace of Sustained Growth in technologies like Webinars and Virtual Reality." } ], "id_raw": 895 }, { "id": "iaDjirhlyVzno7w7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_10.png", "conversations": [ { "from": "human", "value": "\nIn which educational technology did the Rapid Expansion trend exhibit the most growth?" }, { "from": "gpt", "value": "To determine where the Rapid Expansion trend exhibit the most growth, I need to compare the height of the orange bars across all four educational technologies. {The bar for Online Classes}[0.145, 0.793, 0.195, 0.883] is approximately at 1 unit on the y-axis. {The MOOCs bar}[0.394, 0.566, 0.444, 0.883] is approximately at 3.5 units on the y-axis. {The Webinars bar}[0.644, 0.340, 0.693, 0.883] is approximately at 6 units, and finally, {the bar for Virtual Reality}[0.893, 0.113, 0.943, 0.883] is the highest, approximately at 8.5 units. This suggests that the Rapid Expansion trend exhibited the most growth in Virtual Reality.\nBased on the above thought, the answer is:\nVirtual Reality." } ], "id_raw": 895 }, { "id": "zTOjgaGJfFJDtBIL", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, could you turn the observations into a CSV table?" }, { "from": "gpt", "value": "From the x-axis tick labels, it can be seen that each area corresponds to two bars, one representing Traditional Media and the other Digital Media. The {x-axis labels}[0.172, 0.903, 0.200, 0.931]; [0.358, 0.903, 0.466, 0.931]; [0.579, 0.903, 0.697, 0.931]; [0.811, 0.903, 0.917, 0.931]shows the different areas namely SEO, Content Editing, Video Production, and Graphic Design. According to {the legend}[0.072, 0.089, 0.241, 0.181], {the red bars} \"Traditional Media\"[0.077, 0.102, 0.105, 0.122] represent Traditional Media, and {the blue bars}\"Digital Media\"[0.077, 0.144, 0.105, 0.164] represent Digital Media. For instance, the height of {the red bar for SEO}[0.107, 0.380, 0.186, 0.524] is at the 0.8 position, so the score for Traditional Media in the SEO category is 0.8. Similarly, I can extract the values for other categories and for Digital Media.\nBased on the above thought, the answer is:\nType,SEO,Content_Editing,Video_Production,Graphic_Design\nTraditional_Media,0.8,1.1,-1.4,1.5\nDigital_Media,1.2,1.6,-1.8,2.3" } ], "id_raw": 896 }, { "id": "6vZQZih7jgDRb53l", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, what would you say about the position of traditional media and digital media in different dimensions of new media and digital content creation?" }, { "from": "gpt", "value": "According to the chart, we can see that both Traditional Media and Digital Media have positive scores in SEO, Content Editing, and Graphic Design represented by {bars}[0.107, 0.380, 0.186, 0.524]; [0.333, 0.327, 0.412, 0.524]; [0.785, 0.255, 0.864, 0.524]; [0.186, 0.309, 0.265, 0.524]; [0.412, 0.237, 0.491, 0.524]; [0.864, 0.111, 0.943, 0.524]; and negative scores in Video Production, shown by {bars}[0.559, 0.524, 0.638, 0.775]; [0.638, 0.524, 0.717, 0.847]. The general pattern, therefore, indicates a more positive reception or success in the former three fields and less in Video Production for both types of media. However, the digital media, with the unique color of {blue bars}\"Digital Media\"[0.077, 0.144, 0.105, 0.164], consistently outperforms traditional media in all areas.\nBased on the above thought, the answer is:\nBoth Traditional Media and Digital Media perform well in SEO, Content Editing, and Graphic Design, albeit with Digital Media performing slightly better. However, both media types underperform in Video Production, with the decline being especially sharp for Digital Media. This suggests that while Digital Media is more successful in most areas, both types of media might need to address some challenges in Video Production." } ], "id_raw": 896 }, { "id": "p33JroDRt8AZ5l40", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_11.png", "conversations": [ { "from": "human", "value": "\nWhich media type has more score in SEO?" }, { "from": "gpt", "value": "To answer this question, I need to compare the heights of the Traditional and Digital Media bars in the SEO category. From the coordinate data, we can see that {the red bar for SEO}[0.107, 0.380, 0.186, 0.524] representing Traditional Media reaches a height of about 0.8, while {the blue bar for SEO}[0.186, 0.309, 0.265, 0.524] representing Digital Media reaches a height of about 1.2. So, Digital Media has more score in the SEO category.\nBased on the above thought, the answer is:\nDigital Media" } ], "id_raw": 896 }, { "id": "8K91qQrLt0BpB0tG", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart you have observed, could you please convert the data into a CSV format table?" }, { "from": "gpt", "value": "sure, given the title at the top of the chart {\"Research Quantity in Different Academic Fields\"}[0.328, 0.030, 0.719, 0.066] and the x-axis labels {\"Humanities\"}[0.153, 0.883], {\"Natural_Sciences\"}[0.400, 0.883], {\"Formal_Sciences\"}[0.647, 0.883], {\"Social_Sciences\"}[0.894, 0.883], I infer that these are the academic fields being compared. The y-axis represents research quantities from 0 to 35. We have two sets of bars according to {the legend}[0.802, 0.089, 0.978, 0.181], {the 'Bachelors_Degree' bars}<{\"BLUE\"}[0.808, 0.102, 0.835, 0.122]> and {the 'PhD' bars}<{\"ORANGE\"}[0.808, 0.144, 0.835, 0.164]>, for each academic field. I will approximate the values by looking at the height of the bars. For instance, for the academic field 'Humanities', the {Bachelors_Degree bar}[0.104, 0.370, 0.153, 0.883] is at approximately 20 units on the y-axis, while the {PhD bar}[0.153, 0.498, 0.203, 0.883] is at approximately 15 units. Therefore, I can generate a CSV table converting this chart into tabular data.\nBased on the above thought, the answer is:\nAcademic_fields, Bachelors_Degree, PhD\nHumanities, 20, 15\nNatural_Sciences, 25, 22\nFormal_Sciences, 30, 28\nSocial_Sciences, 27, 23" } ], "id_raw": 897 }, { "id": "MOVkB0vqRjinZTks", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat insights can be gained relating to research quantity in different academic fields and educational levels, from the chart?" }, { "from": "gpt", "value": "The chart provides a good comparison of research quantity across different academic fields such as {the \"Humanities\"}<[0.114, 0.903, 0.193, 0.931]>, {the \"Natural_Sciences\"}<[0.340, 0.903, 0.460, 0.931]>, the {the \"Formal_Sciences\"}[0.589, 0.903, 0.704, 0.931], and the {the \"Social_Sciences\"}[0.839, 0.903, 0.948, 0.931] for Bachelor's degree holders and PhDs. By analyzing the bar heights, we can see bachelors have conducted more research in all fields when compared to individuals with PhDs. However, the pattern of research quantity across different fields is similar for both education levels, with an overall increase from Humanities to Formal Sciences, and then decreasing in the Social Sciences field. The notable exception is a significant drop in Humanities for PhDs when compared to Bachelor's degree holders, possibly indicating a shift towards more empirical sciences.\nBased on the above thought, the answer is:\nThe pattern of research quantity is similar for both education levels showing humanities as an exception. PhD holders focus more on empirical sciences and bachelor's degree holders conduct more research." } ], "id_raw": 897 }, { "id": "205orYBgbVfbMGrn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_12.png", "conversations": [ { "from": "human", "value": "\nWhich academic field has the smallest difference in research quantity between Bachelors_Degree and PhD?" }, { "from": "gpt", "value": "In order to find out which academic field has the smallest difference between the numbers of research of Bachelors and PhDs, I need to compare the bar heights of Bachelors and PhDs for each academic field respectively. By referencing the y-axis where the bar tops align, the differences in 'Humanities' is [20-15=5], in 'Natural_Sciences' is [25-22=3], in 'Formal_Sciences' is [30-28=2], and in 'Social_Sciences' is [27-23=4]. Out of these differences, 2 is the smallest, which corresponds to the 'Formal_Sciences' academic field.\nBased on the above thought, the answer is:\nFormal Sciences" } ], "id_raw": 897 }, { "id": "TBYeyd3Hx35p858b", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nCould you create a CSV table from the grouped bar chart showing the sales data for each laptop model per month?" }, { "from": "gpt", "value": "Observing the sales data presented in the grouped bar chart, I am processing the visual information. Starting with {Apple Macbook}[0.093, 0.564, 0.108, 0.917], I notice bars corresponding to each month with the top end indicating sales figures. By examining their height relative to {the y-axis tick labels}[0.050, 0.917, 0.044, 0.929], it's feasible to extract their corresponding values. Parallelly, I process the monthly sales data for {Samsung Chromebook}[0.108, 0.670, 0.122, 0.917], {Lenovo Thinkpad}[0.122, 0.682, 0.137, 0.917], and {Dell XPS}[0.137, 0.329, 0.151, 0.917]. Each group of bars is analyzed in relation to the {x-axis tick labels}[0.103, 0.931, 0.141, 0.951] to determine the month they represent.\nBased on the above thought, the answer is:\nMonth,Apple_Macbook,Samsung_Chromebook,Lenovo_Thinkpad,Dell_XPS\nJanuary,150,105,100,250\nFebruary,140,110,120,220\nMarch,130,115,110,215\nApril,125,120,115,300\nMay,120,150,110,350\nJune,110,145,105,330\nJuly,100,140,120,320\nAugust,90,130,140,320\nSeptember,80,125,150,310\nOctober,75,120,160,305\nNovember,70,110,170,300\nDecember,60,100,180,295" } ], "id_raw": 899 }, { "id": "FmhKzpzCkCsJKlV4", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the data, which laptop model shows a significant increase in sales at any point during the year?" }, { "from": "gpt", "value": "To identify a significant sales increase, I examine the height of each bar for every laptop brand throughout the monthly groupings. I observe the {Dell XPS bars}[0.137, 0.329, 0.151, 0.917], particularly in April and May. For these months, Dell XPS bars show a substantial increase in height compared to the previous months. This indicates a notable rise in sales figures. In April, the bar starts around y0.329, contrasting sharply with March's bar, which starts around y0.412. A similarly significant increase is visible for May, as the top of the bar is even higher, starting around y0.094.\nBased on the above thought, the answer is:\nThe Dell XPS model shows a significant increase in sales, particularly in the months of April and May, as indicated by the taller bars in the chart for these months when compared to others." } ], "id_raw": 899 }, { "id": "9b0OxQWhCyodQEYV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the total sales figure for Lenovo Thinkpad over the 12-month period?" }, { "from": "gpt", "value": "In order to calculate the total sales figure for {Lenovo Thinkpad}[0.122, 0.682, 0.137, 0.917], I will sum the sales numbers represented by the heights of each individual bar corresponding to the months January through December. These sales figures are indicated by the space between the bottom and top of each bar as compared to the {sales indicators on the y-axis}[0.025, 0.087, 0.044, 0.107]. For instance, for January, the bar extends from approximately y0.682 to the baseline at y0.917, which corresponds to the sales figure of 100. I will perform a similar analysis for each subsequent month.\nBased on the above thought, the answer is:\n1420" } ], "id_raw": 899 }, { "id": "hYsF66w8yqECQxIq", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into a structured table including all indexes and their corresponding performance for each year?" }, { "from": "gpt", "value": "After analyzing {the bar chart}[0.050, 0.047, 0.988, 0.874] representing the performance of various market indexes, I can identify four different sets of bars for each year, each set corresponding to an index. The labels on the x-axis {years}[0.105, 0.881, 0.933, 0.898] range from 2007 to 2019. The y-axis has performance values which range from {\u22123}[0.029, 0.868, 0.046, 0.885] to {5}[0.039, 0.040, 0.046, 0.057]. Each index is represented by a unique color, as described in {the legend}[0.256, 0.959, 0.781, 0.990]. The bars corresponding to the SPX-Default index, for example, range from coordinates [0.093, 0.430, 0.106, 0.564] to [0.892, 0.140, 0.905, 0.564]. By noting the heights of the bars and their positions in relation to the y-axis, I can extract the individual performance values for each index in each year.\nBased on the above thought, the answer is:\nYear,SPX-Default,DOW-Default,NASDAQ-Default,CRSP-Default\n2007,1.3,1.0,1.1,1.2\n2008,-1.2,-1.4,-1.3,-1.5\n2009,-2.0,-1.9,-2.1,-2.2\n2010,0.8,0.6,0.9,1.0\n2011,1.2,1.3,1.4,1.1\n2012,1.9,1.7,2.0,1.8\n2013,2.4,2.6,2.5,2.3\n2014,2.8,3.0,2.9,2.7\n2015,3.2,3.1,3.3,3.0\n2016,3.6,3.5,3.7,3.4\n2017,4.5,4.3,4.4,4.1\n2018,3.8,3.6,3.7,3.4\n2019,4.1,4.2,4.3,4.0" } ], "id_raw": 930 }, { "id": "uLmVyu9qhPy4foGa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nWhich index had the most negative impact during the financial crisis of 2008 as depicted in the chart?" }, { "from": "gpt", "value": "To determine the index most negatively impacted by the financial crisis in 2008, I need to observe the bar chart's representation of index performance for that specific year. I locate {the set of bars for 2008}[0.159, 0.564, 0.186, 0.709] to [0.199, 0.564, 0.212, 0.719] and identify the one with the lowest endpoint relative to the y-axis, which indicates the most significant decrease. The {CRSP-Default label}[0.700, 0.965, 0.777, 0.983] and its corresponding bar [0.199, 0.564, 0.212, 0.719] show the lowest point, indicating the largest negative performance value.\nBased on the above thought, the answer is:\nThe CRSP-Default index had the most negative impact during the financial crisis of 2008, as it is represented by the bar with the lowest endpoint for that year." } ], "id_raw": 930 }, { "id": "l38BR3buR5pBqWke", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the average performance of the SPX-Default index over the years presented in the chart?" }, { "from": "gpt", "value": "To calculate the average performance of the SPX-Default index, I need to identify all the representing bars for SPX-Default across the years on the chart. Examining {the SPX-Default bars}[0.093, 0.430, 0.106, 0.564] to [0.892, 0.140, 0.905, 0.564], I note the values for each year. Adding all the values from years 2007 to 2019 and then dividing by the total number of years (13) will give me the average. The values are 1.3, -1.2, -2.0, 0.8, 1.2, 1.9, 2.4, 2.8, 3.2, 3.6, 4.5, 3.8, and 4.1.\nBased on the above thought, the answer is:\n2.1" } ], "id_raw": 930 }, { "id": "WH8ul456frbBKzHS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart you observed, please convert the data into a CSV format table." }, { "from": "gpt", "value": "Based on the figure, the x-axis is represented by technology areas provided by the labels - {Quantum}[0.128, 0.939, 0.182, 0.957], {Robotics}[0.370, 0.939, 0.419, 0.957],{Genomics}[0.605, 0.939, 0.662, 0.957], and {CyberSecurity}[0.832, 0.939, 0.914, 0.957]. The y-axis represents the percentage increases, the scale for which ranges from {0}[0.029, 0.926, 0.036, 0.944] to {7}[0.029, -0.028, 0.036, -0.011]. Each technology area has three bars colored in accordance with {the legend}[0.830, 0.055, 0.982, 0.139]: {TechArea}[0.867, 0.062, 0.920, 0.080],{ResearchGrowth}[0.867, 0.088, 0.962, 0.106] and {InnovationIncrease}[0.867, 0.115, 0.977, 0.132]. By noting the height of each bar, the corresponding values can be discerned. For example, for {Quantum ResearchGrowth}[0.131, 0.524, 0.179, 0.933] the bar reaches approximately to the 3 mark on the y-axis, thus its value is 3.\nBased on the above thought, the answer is:\nTechArea,ResearchGrowth, InnovationIncrease\nQuantum,1.5,3\nRobotics,3,4\nGenomics,2.5,5\nCyberSecurity,4,4.5" } ], "id_raw": 931 }, { "id": "LXRVkIYONASyLUEe", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nJudging from the chart, which technology area should receive more investment and why?" }, { "from": "gpt", "value": "To answer this question, I need to comprehensively compare the 'ResearchGrowth'-{second color}[0.867, 0.088, 0.962, 0.106] and 'InnovationIncrease'-{third color}[0.867, 0.115, 0.977, 0.132] in each technology area. Generally, areas with high 'ResearchGrowth' are ripe for innovation, and areas where 'InnovationIncrease' has far outpaced 'ResearchGrowth' are likely to have a lot of untapped potential. The largest difference between 'ResearchGrowth' and 'InnovationIncrease' occurs in the {CyberSecurity}[0.832, 0.939, 0.914, 0.957] area where {ResearchGrowth}[0.849, 0.115, 0.897, 0.933] is strikingly lower than {InnovationIncrease}[0.897, 0.797, 0.944, 0.933]. Therefore, it might be beneficial to increase investment in 'CyberSecurity' research to maximize innovation potential.\nBased on the above thought, the answer is:\nCyberSecurity. This is because its 'InnovationIncrease' significantly surpasses the 'ResearchGrowth', indicating there could be substantial untapped potential in this area." } ], "id_raw": 931 }, { "id": "OE27qVPMVtcef6T1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_1.png", "conversations": [ { "from": "human", "value": "\nWhich technology area has the highest growth in innovation?" }, { "from": "gpt", "value": "To answer the question, I have to find the bar representing the highest 'Innovation Increase'. The bars for 'Innovation Increase' are identified by {the third color}[0.867, 0.115, 0.977, 0.132] in the legend. As the y-axis represents the percentage increase, I scanned each technology area by comparing the height of the corresponding 'Innovation Increase' bars, namely: {Quantum}[0.179, 0.729, 0.227, 0.933], {Robotics}[0.418, 0.660, 0.466, 0.933], {Genomics}[0.657, 0.592, 0.705, 0.933], and {CyberSecurity}[0.897, 0.797, 0.944, 0.933]. After analysis, {Genomics}[0.657, 0.592, 0.705, 0.933] has the highest bar and therefore the highest growth in innovation.\nBased on the above thought, the answer is:\nGenomics." } ], "id_raw": 931 }, { "id": "fsdTailTo4UTDVaE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information represented in the bar chart into a CSV table?" }, { "from": "gpt", "value": "The chart displays data on the investment or interest level for different areas of healthcare and medical technology between the years 2016 and 2020. According to the x-axis labels that I observed {{\"2016\"}[0.160, 0.880, 0.195, 0.900]; {\"2017\"}[0.334, 0.880, 0.369, 0.900]; {\"2018\"}[0.508, 0.880, 0.543, 0.900]; {\"2019\"}[0.682, 0.880, 0.717, 0.900]; {\"2020\"}[0.856, 0.880, 0.891, 0.900]}, these represent the years under consideration. The y-axis labels indicate the level of investment or interest, ranging from 0 (observed at {{\"0\"}[0.052, 0.865, 0.061, 0.885]}) to 100 (observed at {{\"100\"}[0.035, 0.045, 0.061, 0.065]}). The chart's subtitle {\"Healthcare and Medical Technology\"}[0.378, 0.021, 0.672, 0.047] indicates the fields of interest. The bars represented in four different colors correspond to specific healthcare and technology categories. The corresponding legend at the bottom, {\"Telemedicine\"}[0.288, 0.958, 0.378, 0.978]; {\"Health Apps\"}[0.445, 0.958, 0.529, 0.978]; {\"EHR\"}[0.596, 0.958, 0.625, 0.978]; and {\"AI in Healthcare\"}[0.691, 0.958, 0.802, 0.978] shows how the bars map to specific healthcare categories. To create the CSV table, I need to extract values for each category for each year from the chart. I will use the height of the bars to determine these values.\nBased on the above thought, the answer is:\nYear,Telemedicine,HealthApps,EHR,AIinHealthcare\n2016,30,35,20,15\n2017,40,47,25,22\n2018,55,38,30,35\n2019,50,60,40,30\n2020,75,65,45,40" } ], "id_raw": 932 }, { "id": "mHNVgQFgIwGncQbQ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nConsidering the trend of each technology field, which technology would you say is currently drawing more investment or interest?" }, { "from": "gpt", "value": "To answer this question, I need to look at the bars representing each technology in 2020, as they would provide the most recent data on investment or interest levels. The relevant coordinates for each technology in 2020 are {\"2020 Telemedicine\"}[0.804, 0.258, 0.839, 0.873], {\"2020 Health Apps\"}[0.839, 0.340, 0.874, 0.873], {\"2020 EHR\"}[0.874, 0.504, 0.908, 0.873], and {\"2020 AI in Healthcare\"}[0.908, 0.545, 0.943, 0.873]. By considering the height of these bars, I can observe that the \"Telemedicine\" bar for 2020 reaches the highest point on the y-axis, indicating that it currently attracts the most investment or interest.\nBased on the above thought, the answer is:\nTelemedicine, as it has the highest bar for the year 2020, meaning it is currently attracting the most investment or interest." } ], "id_raw": 932 }, { "id": "1UkvWv35Mc9TSnzU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_2.png", "conversations": [ { "from": "human", "value": "\nWhich technology had the highest increase in investment or interest from 2016 to 2020?" }, { "from": "gpt", "value": "By comparing the height of each technology's bars in 2016 and 2020, I can determine which technology saw the greatest increase in investment or interest. The bars for 2016 can be found at these coordinates {\"2016 Telemedicine\"}[0.108, 0.627, 0.142, 0.873], {\"2016 Health Apps\"}[0.142, 0.586, 0.177, 0.873], {\"2016 EHR\"}[0.177, 0.709, 0.212, 0.873], and {\"2016 AI in Healthcare\"}[0.212, 0.750, 0.247, 0.873]. The equivalent coordinates for 2020 are {\"2020 Telemedicine\"}[0.804, 0.258, 0.839, 0.873], {\"2020 Health Apps\"}[0.839, 0.340, 0.874, 0.873], {\"2020 EHR\"}[0.874, 0.504, 0.908, 0.873], and {\"2020 AI in Healthcare\"}[0.908, 0.545, 0.943, 0.873]. By comparing the bar heights from both years, it can be observed that the bar for \"Telemedicine\" has the highest increase in height.\nBased on the above thought, the answer is:\nTelemedicine." } ], "id_raw": 932 }, { "id": "RBIwyauJZu0x1n9E", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nPlease convert the bar chart's data into a table in CSV format." }, { "from": "gpt", "value": "Firstly, the chart provides indicators for years at the bottom represented on the x-axis. These are {2025}[0.177, 0.880, 0.212, 0.900], {2030}[0.395, 0.880, 0.430, 0.900], {2035}[0.613, 0.880, 0.648, 0.900], and {2040}[0.830, 0.880, 0.865, 0.900]. These years will be the columns of our CSV table along with the sectors. We have 4 sectors labeled at {legend}[0.125, 0.950, 0.917, 0.986]. These are Automobile Production, Oil Production, Renewable Energy Production, and AI Adoption. The y-axis indicates the projected value. I can identify the value for each sector in each year by observing the height of the corresponding bars in the graph. For example, the bar representing Automobile Production in 2025[0.107, 0.217, 0.151, 0.873] extends to approximately 120 on the y-axis.\nBased on the above thought, the answer is:\nSector,Years,2025,2030,2035,2040\nAutomobile_Production,120,115,110,100\nOil_Production,85,80,75,60\nRenewable_Energy_Production,50,70,90,120\nAI_Adoption,20,40,60,100" } ], "id_raw": 933 }, { "id": "RxlMrpbonimsehKn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nAnalyze the trend and predict which sector will show the most growth in the future." }, { "from": "gpt", "value": "Analyzing the trend, we can compare the growth in each sector across the years. The bars representing each sector are as follows: {Automobile Production}[0.107, 0.217, 0.151, 0.873],[0.325, 0.245, 0.369, 0.873], [0.543, 0.272, 0.586, 0.873], [0.761, 0.327, 0.804, 0.873], {Oil Production}[0.151, 0.409, 0.194, 0.873],[0.369, 0.436, 0.412, 0.873], [0.586, 0.463, 0.630, 0.873], [0.804, 0.545, 0.848, 0.873], {Renewable Energy Production}[0.194, 0.600, 0.238, 0.873],[0.412, 0.490, 0.456, 0.873], [0.630, 0.381, 0.674, 0.873], [0.848, 0.217, 0.891, 0.873], and {AI Adoption}[0.238, 0.764, 0.281, 0.873],[0.456, 0.654, 0.499, 0.873], [0.674, 0.545, 0.717, 0.873], [0.891, 0.327, 0.935, 0.873]. From these bars, it is clear that the height of bars representing AI adoption increases dramatically from 2025 to 2040, symbolizing significant growth. Therefore, the most growth is predicted in the AI Adoption sector.\nBased on the above thought, the answer is:\nThe AI Adoption sector is projected to show the most growth in the future." } ], "id_raw": 933 }, { "id": "LT4ZmoA9fHE0eyFv", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_3.png", "conversations": [ { "from": "human", "value": "\nWhich sector is projected to have the highest value in 2040?" }, { "from": "gpt", "value": "To find out which sector is projected to have the highest value in 2040, I need to compare the height of the bars for the year 2040. The bars for 2040 are {Automobile Production}[0.761, 0.327, 0.804, 0.873], {Oil Production}[0.804, 0.545, 0.848, 0.873], {Renewable Energy Production}[0.848, 0.217, 0.891, 0.873], and {AI Adoption}[0.891, 0.327, 0.935, 0.873]. Comparing these bars, the {AI Adoption}[0.891, 0.327, 0.935, 0.873] bar is the highest, reaching to value 100 on the y-axis.\nBased on the above thought, the answer is:\nAI Adoption" } ], "id_raw": 933 }, { "id": "qP72x17ToWM92CDg", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nGiven the bar chart, could you create a CSV format table from it?" }, { "from": "gpt", "value": "The x-axis represents different companies which include {Goldman Sachs}[0.111, 0.818, 0.169, 0.942], {J.P. Morgan}[0.270, 0.818, 0.312, 0.909], {Morgan Stanley}[0.413, 0.818, 0.471, 0.942], {Bank Of America}[0.562, 0.818, 0.623, 0.951], {Wells Fargo}[0.721, 0.818, 0.765, 0.912], and {Citigroup}[0.876, 0.818, 0.912, 0.896]. The y-axis represents scores for each of them that ranging from {0}[0.023, 0.796, 0.038, 0.816] to {3.5}[0.023, -0.011, 0.038, 0.009]. Each company has two bars, for 'Economic Affairs' and 'Risk Control' according to {legend}[0.875, 0.063, 0.985, 0.129]. {The blue bars}[0.904, 0.071, 0.982, 0.091] represent scores in 'Economic Affairs' and {the green bars}[0.904, 0.101, 0.960, 0.121] represent scores in 'Risk Control'. By analysing the height of the bars and corresponding to the y-axis, we are able to estimate the scores for 'Economic Affairs' and 'Risk Control' for each company.\nBased on the above thought, the answer is:\nCompany,Economic Affairs,Risk Control\nGoldman Sachs,1.7,2.3\nJ.P. Morgan,2.5,3.1\nMorgan Stanley,1.5,2.2\nBank Of America,2.1,1.9\nWells Fargo,1.9,1.5\nCitigroup,1.4,2.0" } ], "id_raw": 934 }, { "id": "6mOMFtf3jZzJ2KL1", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, which company is the most balanced when it comes to managing Economic Affairs and Risk Control?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height difference between the {blue bar}[0.904, 0.071, 0.982, 0.091] and the {green bar}[0.904, 0.101, 0.960, 0.121] for each company as it represents the difference between the scores in Economic Affairs and Risk Control. The company with the smallest height difference will demonstrate the most balance between the two sectors. From the comparison, it can be seen that {Bank Of America}[0.540, 0.320, 0.593, 0.804] and [0.593, 0.366, 0.645, 0.804] has the smallest height difference among all companies, indicating a balance between managing Economic Affairs and Risk Control.\nBased on the above thought, the answer is:\nBank Of America. As it shows the smallest difference in scores between Economic Affairs and Risk Control according to the bar chart." } ], "id_raw": 934 }, { "id": "O9j0hjplno79wPpH", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_4.png", "conversations": [ { "from": "human", "value": "\nWhich company has the highest score in Risk Control?" }, { "from": "gpt", "value": "To find the company with the highest Risk Control score, I have to compare the height of the {green bars}[0.904, 0.101, 0.960, 0.121] for each company. Starting from Goldman Sachs, the height of its green bar is [0.140, 0.274, 0.193, 0.804] which corresponds to a score of 2.3. Repeating this process for all companies, I can see that the green bar for {J.P. Morgan}[0.291, 0.089, 0.344, 0.804] rises above the 3 score line on the y-axis, which is the highest among all companies.\nBased on the above thought, the answer is:\nJ.P. Morgan." } ], "id_raw": 934 }, { "id": "bKAZP3jKp8mgbnBi", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nCould you convert the bar chart data into a CSV format?" }, { "from": "gpt", "value": "Definitely. I have the title {\"Entrepreneurship and Innovation Ecosystem\"}[0.337, 0.021, 0.705, 0.047] at the top of the chart and I can see that we have two sets of data representing \"Community Engagement\" and \"R&D Investment\". The X-axis represents the years {2018}[0.131, 0.880, 0.166, 0.900], {2019}[0.379, 0.880, 0.415, 0.900], {2020}[0.628, 0.880, 0.663, 0.900], {2021}[0.876, 0.880, 0.911, 0.900]. The Y-axis represents scores starting from {0}[0.043, 0.865, 0.052, 0.885] going up to {45}[0.035, 0.045, 0.052, 0.065], linked to each respective year. According to the legend with {Community Engagement}[0.325, 0.960, 0.352, 0.974] and {R&D Investment}[0.565, 0.960, 0.592, 0.974] bars, I can see the performance of each category over the years. The scores for each year can be estimated from the height of the bars. For instance, in the year 2018, the score for Community Engagement is roughly 5, as per the height of the bar at [0.099, 0.782, 0.149, 0.873], and for R&D Investment is approximately 30 observing the bar height at [0.149, 0.327, 0.198, 0.873]. I can go on and do the same for the remaining years.\nBased on the above thought, the answer is:\nYear,Community Engagement,R&D Investment\n2018,5,30\n2019,12,35\n2020,25,37\n2021,40,40" } ], "id_raw": 935 }, { "id": "0J0jZ2LCx3khj83V", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nBased on the data, what conclusion can you draw about the Entrepreneurship and Innovation Ecosystem?" }, { "from": "gpt", "value": "Looking at the {bar chart}[0.057, 0.053, 0.985, 0.873], the chart shows data over a period of time for {Community Engagement}[0.325, 0.960, 0.352, 0.974] and {R&D Investment}[0.565, 0.960, 0.592, 0.974]. Observing the bars' heights, it's clear that both had seen increments over the years, with the bar heights representing Community Engagement starting at [0.099, 0.782, 0.149, 0.873] in 2018 and reaching [0.844, 0.144, 0.893, 0.873] in 2021, and the bar heights for R&D Investment starting at [0.149, 0.327, 0.198, 0.873] in 2018 and reaching [0.893, 0.144, 0.943, 0.873] in 2021. Community Engagement experienced a more significant increase, going from 5 to 40, while R&D investments increased at a slower rate, going from 30 to 40.\nBased on the above thought, the answer is:\nWe can conclude that both Community Engagement and R&D Investment in the Entrepreneurship and Innovation Ecosystem have increased over the period from 2018 to 2021. The most notable improvement has been seen in the area of Community Engagement, signalling an active involvement of the community in entrepreneurship activities. R&D Investments also increased, but at a steadier pace, indicating a consistent dedication of resources towards research and development in this field." } ], "id_raw": 935 }, { "id": "johcmT2b2VAeS10Y", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_5.png", "conversations": [ { "from": "human", "value": "\nWhich category has the largest increment over the data timeline?" }, { "from": "gpt", "value": "To answer this question, I'll look at the change in bar heights from 2018 to 2021 for both categories. For {Community Engagement}[0.325, 0.960, 0.352, 0.974], the bar for 2018 is here [0.099, 0.782, 0.149, 0.873] and for 2021 is here [0.844, 0.144, 0.893, 0.873]. Looking at these two bars, it appears that there was a significant increase. Now, let's look at {R&D Investment}[0.565, 0.960, 0.592, 0.974]. The bar for 2018 is here [0.149, 0.327, 0.198, 0.873] and for 2021 at these coordinates [0.893, 0.144, 0.943, 0.873]. So, the increment in the R&D Investment category appears to be smaller compared to the Community Engagement category.\nBased on the above thought, the answer is:\nCommunity Engagement." } ], "id_raw": 935 }, { "id": "B7I3LFzGCCaPJGy9", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nBased on the provided bar chart, can you convert the data into a CSV format?" }, { "from": "gpt", "value": "From the provided JSON data, it is indicated that the x-axis represents a set of years with tick labels spanning {\"2010\"}[0.140, 0.880, 0.175, 0.900], {\"2015\"}[0.285, 0.880, 0.320, 0.900], {\"2020\"}[0.431, 0.880, 0.466, 0.900], {\"2025\"}[0.576, 0.880, 0.611, 0.900], {\"2030\"}[0.722, 0.880, 0.757, 0.900] and {\"2035\"}[0.867, 0.880, 0.902, 0.900]. These years correspond to different bars each presenting data pertaining to \"Planning Strategy\", \"Pollution Control\", \"Sustainable Energy\", and \"Wildlife Conservation\" as provided in {legend}[0.136, 0.950, 0.906, 0.986]. The y-axis represents the progress score in these key areas, with values ranging from 0 to 60. Following the color coding and bars length, we can generate a CSV table through extracting the upper edge of each bar and mapping it onto the y-axis.\nBased on the above thought, the answer is:\nYear,Planning_Strategy,Pollution_Control,Sustainable_Energy,Wildlife_Conservation\n2010,23,17,30,25\n2015,25,19,33,20\n2020,29,25,40,15\n2025,35,35,45,11\n2030,45,38,56,14\n2035,43,38,55,20" } ], "id_raw": 936 }, { "id": "36Bbd8VgqgRoDTOJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you deduce from the progress trend of \"Wildlife Conservation\"?" }, { "from": "gpt", "value": "Observing the bars representing \"Wildlife Conservation\" {brown bar}[0.186, 0.531, 0.216, 0.873]; [0.332, 0.600, 0.361, 0.873]; [0.477, 0.668, 0.506, 0.873]; [0.623, 0.723, 0.652, 0.873]; [0.768, 0.682, 0.797, 0.873]; and [0.914, 0.600, 0.943, 0.873] for the years 2010, 2015, 2020, 2025, 2030 and 2035 respectively, it's evident that efforts in this area initially showed a decline till 2025, but post-2025 there was a considerable increase in progress scores. This might suggest a delayed response in recognizing the critical importance of wildlife conservation which was eventually rectified post 2025, leading to enhanced efforts in this key area of sustainable development.\nBased on the above thought, the answer is:\nThe progress trend of \"Wildlife Conservation\" indicates a delayed response in addressing the critical need of wildlife conservation. While there was a decline in effort till 2025, there was a substantial increase post-2025, highlighting an enhanced recognition of the importance of wildlife conservation in sustainable development." } ], "id_raw": 936 }, { "id": "DWHcqYQCVwndNELw", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which mitigation strategy shows the highest progress score in 2030?" }, { "from": "gpt", "value": "To answer this, I need to look into the bar heights in 2030. Judging by the bar lengths, for {\"2030\"}[0.722, 0.880, 0.757, 0.900], the bars representing \"Planning Strategy\" {green bar}[0.681, 0.258, 0.710, 0.873], \"Pollution Control\" {yellow bar}[0.710, 0.354, 0.739, 0.873], and \"Sustainable Energy\" {blue bar}[0.739, 0.108, 0.768, 0.873] are quite close to each other, however, the \"Sustainable Energy\" bar is the longest among them.\nBased on the above thought, the answer is:\nSustainable Energy." } ], "id_raw": 936 }, { "id": "d1G6f2eQ1VfXlxOr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data in CSV format presented in this Grouped Bar Plot titled \"Health Status by Age Group\"?" }, { "from": "gpt", "value": "{The chart title \"Health Status by Age Group\"}[0.425, 0.019, 0.618, 0.041] suggests that the data deals with the health status of different age groups. The {Age groups}[{\"20-30\"}[0.136, 0.881, 0.169, 0.898]; {\"30-40\"}[0.320, 0.881, 0.353, 0.898]; {\"40-50\"}[0.505, 0.881, 0.538, 0.898]; {\"50-60\"}[0.689, 0.881, 0.722, 0.898]; {\"60 and above\"}[0.850, 0.881, 0.930, 0.898]] used on the x-axis suggest the different categories for our table rows. The categories along the Y-axis are percentages ranging from {0}[0.043, 0.868, 0.051, 0.885] to {100}[0.029, 0.040, 0.051, 0.057] and will be the values in our table. The legends provide the columns we should consider in our table: {\"Healthy Lifestyle\"}[0.258, 0.965, 0.356, 0.983], {\"Sedentary Lifestyle\"}[0.411, 0.965, 0.523, 0.983], {\"Chronic Disease\"}[0.579, 0.965, 0.672, 0.983], and {\"Severe Disease\"}[0.727, 0.965, 0.816, 0.983]. Each bar represents the value for corresponding age group and category. For instance, looking at the \"20-30\" category and the \"Healthy Lifestyle\" category, {the green bar}[0.097, 0.212, 0.125, 0.874] represents the percentage of people within this intersection; judging by its height and the corresponding Y-axis, we can see this value is around 80.\nBased on the above thought, the answer is:\nAge Group,Healthy Lifestyle,Sedentary Lifestyle,Chronic Disease,Severe Disease\n20-30,80,20,10,5\n30-40,70,25,20,10\n40-50,60,30,30,15\n50-60,55,40,35,20\n60 and above,50,45,40,25" } ], "id_raw": 937 }, { "id": "uk3rOLkVY4rEMtVx", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nLooking at the data represented in the chart, which age group seems to face the biggest health challenges?" }, { "from": "gpt", "value": "To determine which age group faces the biggest health challenges, I must analyze the data for each age group. The height of bars in each group corresponds to the percentage of people in that category. In the context of health challenges, Sedentary Lifestyle, Chronic Disease, and Severe Disease categories suggest undesired health paradoxes. Thus, the age group with high percentages in these three categories faces the biggest health challenges. Let\u2019s assess that using the chart. For the age group {\"60 and above\"}[0.850, 0.881, 0.930, 0.898], I notice that {the orange colored bar}[0.862, 0.502, 0.890, 0.874] representing Sedentary Lifestyle marks around 45%, {the red colored bar}[0.890, 0.543, 0.917, 0.874] for Chronic Disease around 40% and {the pink colored bar}[0.917, 0.667, 0.945, 0.874] for Severe Disease around 25%. These percentages are high compared to the other groups; hence people over 60 face the biggest health challenges.\nBased on the above thought, the answer is:\nAge Group 60 and above." } ], "id_raw": 937 }, { "id": "wqMpeFUCt4oAeyop", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference between healthy lifestyle and sedentary lifestyle for the age group 50-60?" }, { "from": "gpt", "value": "We need to compare the two coloured bars for the age group, \"50-60\". The height of the {green bar}[0.650, 0.419, 0.678, 0.874] represents the percentage of the healthy lifestyle for this group, which appears to be 55% according to the Y-axis. Similarly, the height of the {orange bar}[0.678, 0.543, 0.705, 0.874] represents the percentage of the sedentary lifestyle, which appears to be around 40%. The difference between these two values is what the question is asking for.\nBased on the above thought, the answer is:\n15%" } ], "id_raw": 937 }, { "id": "RokeGCFzpB1fSZOF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nCould you process the available data to generate a CSV format table that displays the variation in socia-cultural values from 1980 to 2020?" }, { "from": "gpt", "value": "According to the chart's {title}[0.391, 0.021, 0.667, 0.047], this chart demonstrates the change in certain socio-cultural values from 1980 to 2020. The x-axis denotes the years as illustrated by {the x-axis labels}[\"1980\"[0.293, 0.880, 0.329, 0.900]; \"2020\"[0.730, 0.880, 0.765, 0.900]], and the y-axis represents the scores ranging from -3.5 to 0 as depicted by {the y-axis labels}[\"\u22123.5\"[0.035, 0.865, 0.068, 0.885]; \"\u22123.0\"[0.035, 0.748, 0.068, 0.768]; \"\u22122.5\"[0.035, 0.631, 0.068, 0.651]; \"\u22122.0\"[0.035, 0.514, 0.068, 0.534]; \"\u22121.5\"[0.035, 0.397, 0.068, 0.417]; \"\u22121.0\"[0.035, 0.280, 0.068, 0.300]; \"\u22120.5\"[0.035, 0.163, 0.068, 0.183]; \"0.0\"[0.046, 0.045, 0.068, 0.065]].The significant socio-cultural values: Globalization, Environmental Awareness, and Information Age are represented by individual colored bars as indicated by {the legend}[0.246, 0.950, 0.812, 0.986]. The length of these bars corresponds to the scores of these socio-cultural values. For instance, {the blue bar for \"Globalization\"}[0.114, 0.053, 0.245, 0.405] in 1980 extends to nearly -1.5 on the y-axis, thus indicating the score as -1.5 as per the raw data.\nBased on the above thought, the answer is:\nYear,Globalization,Environmental Awareness,Information Age\n1980,-1.5,-3,-0.5\n2020,-1,-2.8,-0.7" } ], "id_raw": 938 }, { "id": "IuXi2QGxhx4abIyI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nReferring to the chart, which socio-cultural value has become more influential in 2020 compared to 1980, and why do you infer it this way?" }, { "from": "gpt", "value": "From the analysis of the dimensions embodied in {the chart}[0.073, 0.053, 0.985, 0.873], it can be glimpsed that the value of all three socio-cultural constants has increased from 1980 to 2020, depicted through a shorter length of the bars in 2020 relative to 1980. However, most notably, {Globalization}[\"1980\"[0.114, 0.053, 0.245, 0.405]; \"2020\"[0.551, 0.053, 0.682, 0.288]] has undergone the most extraordinary change from -1.5 to -1. This means that despite retaining a negative score, Globalization has increased in influence drastically. Since a more negative score on the y-axis suggests a stronger degree of influence of that value, Globalization having less negative scores means it has become more influential in 2020 compared to 1980.\nBased on the above thought, the answer is:\nGlobalization has become more influential in 2020 compared to 1980 because it reflects the most significant increase in score, indicating a rising level of influence." } ], "id_raw": 938 }, { "id": "FVQ29YM7AjaY9ROL", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_8.png", "conversations": [ { "from": "human", "value": "\nIn the context of the chart, which socio-cultural value has witnessed the most significant change from 1980 to 2020?" }, { "from": "gpt", "value": "To resolve this, it is crucial to observe and analyze the difference in the lengths of the bars, standing for each value from 1980 to 2020. The change can be computed as the difference in scores of {Globalization}[\"1980\"[0.114, 0.053, 0.245, 0.405]; \"2020\"[0.551, 0.053, 0.682, 0.288]], {Environmental Awareness}[\"1980\"[0.245, 0.053, 0.376, 0.756]; \"2020\"[0.682, 0.053, 0.813, 0.709]], and {Information Age}[\"1980\"[0.376, 0.053, 0.507, 0.170]; \"2020\"[0.813, 0.053, 0.944, 0.217]]. Given that the scores for Globalization changes from -1.5 to -1, the difference is 0.5; for Environmental Awareness, it transitions from -3 to -2.8, so the difference is 0.2; finally, for Information Age, transforming from -0.5 to -0.7 gives a difference of 0.2 as well. Hence, {Globalization} has experienced the most profound change.\nBased on the above thought, the answer is:\nGlobalization." } ], "id_raw": 938 }, { "id": "7VXHGwmRlqXy9VZG", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nPlease convert the bar chart data about the development of human civilization through different eras into CSV format." }, { "from": "gpt", "value": "To generate a CSV table from the chart, I first identify the categories and variables. The x-axis describes the {different eras}[{\"Bronze Age\"}[0.130, 0.931, 0.210, 0.951]; {\"Iron Age\"}[0.321, 0.931, 0.379, 0.951]; {\"Middle Ages\"}[0.488, 0.931, 0.572, 0.951]; {\"Industrial Revolution\"}[0.638, 0.931, 0.781, 0.951]; {\"Contemporary Era\"}[0.826, 0.931, 0.954, 0.951]], which are 5 in total. The y-axis describes the scores or counts of four indicators, namely {Life Expectancy}[0.087, 0.073, 0.115, 0.087], {Human Population}[0.087, 0.103, 0.115, 0.117], {Technology Index}[0.087, 0.133, 0.115, 0.147], and {Culture Index}[0.087, 0.163, 0.115, 0.177], according to {the legend}[0.081, 0.063, 0.259, 0.189]. Each indicator is represented by stacked bars of different colors. To obtain the values, I have to analyze the height of each bar. For example, the height of {the Life Expectancy bar in the Bronze Age}[0.116, 0.880, 0.143, 0.884] represents its score/count, which is 35.\nBased on the above thought, the answer is:\nEra,Life_Expectancy,Human_Population,Technology_Index,Culture_Index\nBronze Age,35,7.2,-2.7,-1.9\nIron Age,50,15,-2.3,-1.1\nMiddle Ages,33,100,-1.5,-0.5\nIndustrial Revolution,50,700,-0.8,0.1\nContemporary Era,70,7600,-0.2,1.5" } ], "id_raw": 939 }, { "id": "AYPXxiS816Ek5B8u", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, how has human civilization developed over the different eras?" }, { "from": "gpt", "value": "Looking at this bar chart, it appears that human civilization has developed significantly over time. The {Life Expectancy}[0.087, 0.073, 0.115, 0.087] started at around 35 in the Bronze Age, dipped to 33 in the Middle Ages, before climbing to 70 by the Contemporary Era. {Human Population}[0.087, 0.103, 0.115, 0.117] has grown massively, from just 7.2 million in the Bronze Age to a staggering 7600 million in the Contemporary Era. On the other hand, the {Technology Index}[0.087, 0.133, 0.115, 0.147] initially had negative values, implying a lack of technological advances but has progressively moved towards positivity, reflecting the growth in technologies. Similarly, the {Culture Index}[0.087, 0.163, 0.115, 0.177] also moved from negative to positive values, suggesting cultural development. The negative values in these indices during the Bronze Age indicate their rudimentary beginnings. The increase in positive values over time reflects the path of human development.\nBased on the above thought, the answer is:\nHuman civilization has seen significant development over different eras. Life Expectancy has generally increased despite a temporary regression during the Middle Ages, reflecting improvements in healthcare and living conditions. The Human Population has witnessed explosive growth, indicating human's ability to adapt and survive in varying circumstances. Furthermore, Technology and Culture Index show a clear upward trend from their negative beginnings, reflecting the advancements and progress in technology and cultural aspects of human civilization." } ], "id_raw": 939 }, { "id": "bKp4gPfEXuVptQXF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_9.png", "conversations": [ { "from": "human", "value": "\nIn which Era is the Life Expectancy highest?" }, { "from": "gpt", "value": "To answer this question, I need to compare the height of the Life Expectancy bars across different eras. These are displayed as green bars. Looking at these bars, {the bar in the Contemporary Era}[0.836, 0.877, 0.863, 0.884] is the highest compared to all other eras. It reaches closer to the value of 70 on the vertical axis.\nBased on the above thought, the answer is:\nContemporary Era." } ], "id_raw": 939 }, { "id": "JEkhLPdT5tP6GfWj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data shown in the bar chart into a CSV format table?" }, { "from": "gpt", "value": "The data in the bar chart represents trends in the education and training industry over a period of 5 years, from 2015 to 2019. The categories represented include {Online Education}[0.227, 0.965, 0.326, 0.983], {Traditional Education}[0.381, 0.965, 0.503, 0.983], {Education Technology}[0.559, 0.965, 0.685, 0.983], and {Graduate Employment}[0.740, 0.965, 0.872, 0.983] as given in the legend. For each category, there are five bars each representing a year from 2015 to 2019. The values for these categories can be extracted based on the height of the bars on the y-axis, which denotes the 'Number of Individuals (in thousands)'. For instance, in the category of Online Education for the year 2015, the bar[0.120, 0.047, 0.147, 0.476] reaches approximately to -7000 on the y-axis. By following the same method, the values for each of the categories throughout the years can be extracted.\nBased on the above thought, the answer is:\nYear,Online Education,Traditional Education,Education Technology,Graduate Employment\n2015,-7000,-13000,-500,-2500\n2016,-7300,-12800,-600,-2300\n2017,-7600,-12500,-750,-2400\n2018,-7900,-12000,-850,-2350\n2019,-8200,-11500,-950,-2000" } ], "id_raw": 940 }, { "id": "JlePtMADbo0MP9jc", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nBased on the trends you're seeing in the chart, which category would you say is having the most negative trend over the years and why?" }, { "from": "gpt", "value": "This question requires me to analyze trend changes over the years in each category. Looking at the bar heights of {Online Education}[0.227, 0.965, 0.326, 0.983], {Traditional Education}[0.381, 0.965, 0.503, 0.983], {Education Technology}[0.559, 0.965, 0.685, 0.983], and {Graduate Employment}[0.740, 0.965, 0.872, 0.983], it's clear that all categories are showing a downward trend over the years. However, Traditional Education category shows the least decline as the bars are getting taller each year from 2015 to 2019. In contrast, the {Education Technology}[0.559, 0.965, 0.685, 0.983] seems to have the most negative trend. The decrease in its enrollment was not only continuous but also the slope of descent is the steepest, particularly the bars from 2017 to 2019[0.533, 0.047, 0.560, 0.093; 0.713, 0.047, 0.740, 0.099; 0.892, 0.047, 0.919, 0.105] show the most significant drops compared to previous years.\nBased on the above thought, the answer is:\nThe Education Technology category is showing the most negative trend since it consistently exhibits a more significant drop each year compared to the other categories." } ], "id_raw": 940 }, { "id": "Pew7NFCVnL3T3qbb", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_10.png", "conversations": [ { "from": "human", "value": "\nIn which year was the number of individuals in Online Education the lowest?" }, { "from": "gpt", "value": "To answer this question, I need to look at the height of the bars representing {Online Education}[0.227, 0.965, 0.326, 0.983] across the different years given on the x-axis. According to the bars, the number of individuals in Online Education decreased consecutively each year. Therefore, the lowest value is represented by the bar in {2019}[0.838, 0.047, 0.865, 0.549] which reaches approximately -8200 on the y-axis which is the lowest point.\nBased on the above thought, the answer is:\n2019." } ], "id_raw": 940 }, { "id": "NB8KuQgEK7NCwoWy", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the bar chart data into a table format based on your observations?" }, { "from": "gpt", "value": "Looking at the chart, it's evident from the labels and coordinating elements that it's about the tech industry growth over the years from 2017 to 2020 for Apple and Google in the sectors of Hardware and Software. The year labels are placed on the x-axis [0.178, 0.803, 0.873, 0.823] and the score labels are placed on the y-axis [0.043, 0.788, 0.061, 0.065]. Based on the legend [0.357, 0.916, 0.572, 0.960], the blue bars represent Google Hardware, the light blue bars represent Google Software, the orange bars represent Apple Hardware and the light orange bars represent Apple Software. By noticing the height of the bars, we can determine the scores in each sector for every specific year.\nBased on the above thought, the answer is:\nYear,Google_Hardware,Google_Software,Apple_Hardware,Apple_Software\n2017,30,50,60,70\n2018,40,55,70,75\n2019,50,60,80,80\n2020,60,65,85,85" } ], "id_raw": 941 }, { "id": "pCNbAnHErcBtZWpW", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nAnalyze and compare the overall growth and progress of Google and Apple over the four years presented according to the data reflected in the bar chart." }, { "from": "gpt", "value": "The bar chart highlights the growth of Google and Apple over four years, separated by hardware and software sectors. According to the bars, Google's hardware sector [0.108, 0.573, 0.152, 0.796] has had a linear growth starting from a score of 30 in 2017 to 60 in 2020. Its software sector [0.152, 0.425, 0.196, 0.796] has also shown a steady growth from 50 in 2017 to 65 in 2020. Together, Google sectors have shown a steady upward trend over the years. As for Apple, their hardware sector [0.196, 0.350, 0.240, 0.796] has grown from a score of 60 in 2017 to 85 in 2020, and their software sector [0.240, 0.276, 0.283, 0.796] has improved from 70 to 85 in the same timeframe. Overall, Apple sectors have shown a greater growth compared to Google. However, both companies have indeed shown significant progress.\nBased on the above thought, the answer is:\nBoth companies have seen positive growth throughout the four years. Though Google has shown linear growth in both their sectors, Apple has shown greater progress in the same timeframe. Apple's growth rate in both sectors is higher than Google's." } ], "id_raw": 941 }, { "id": "FNWAadRDNLljE0Yn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_11.png", "conversations": [ { "from": "human", "value": "\nWhich year showed the highest combined score for Google's hardware and software sectors?" }, { "from": "gpt", "value": "To answer this question, I'll have to calculate the total scores for Google's Hardware and Software sectors each year. By examining the heights of the bars representing Google's sectors, I see that bar heights for Google in 2020 [0.767, 0.350, 0.811, 0.796] and [0.811, 0.313, 0.855, 0.796] are the maximum, implying that their total score is the highest in 2020.\nBased on the above thought, the answer is:\n2020." } ], "id_raw": 941 }, { "id": "dTrx3WA53t2NKuDE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the chart data by month for the category 'Delivery' in a structured table?" }, { "from": "gpt", "value": "I will analyze the bar data for the category labeled 'Delivery', recognizing its corresponding color from {the legend}[0.652, 0.960, 0.680, 0.974]. As I move along the x-axis, each group of bars corresponds to a month starting with January at [0.142, 0.764, 0.156, 0.873] and ending with December at [0.929, 0.436, 0.943, 0.873]. Each bar within these coordinates provides the count for 'Delivery' as labeled with text coordinates above the bars. Extracting these values based upon their locations on the y-axis allows me to construct a CSV formatted table representing 'Delivery' data by month.\nBased on the above thought, the answer is:\nMonth,Delivery\nJanuary,4\nFebruary,7\nMarch,11\nApril,10\nMay,8\nJune,13\nJuly,8\nAugust,12\nSeptember,14\nOctober,15\nNovember,12\nDecember,16" } ], "id_raw": 942 }, { "id": "ippHZvxOXsM8XNs2", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nWhat are your observations about the trend in the number of orders from January to December?" }, { "from": "gpt", "value": "Observing the 'Order' bars, represented by the color in {the legend}[0.546, 0.960, 0.574, 0.974], starting with January at [0.128, 0.627, 0.142, 0.873], there is a progressive increase towards March, visible at [0.271, 0.190, 0.285, 0.873], followed by a drop in April at [0.342, 0.327, 0.357, 0.873], and a fluctuating pattern thereafter until reaching the peak in December at [0.914, 0.108, 0.929, 0.873]. Overall, despite fluctuations, there's an observable upward trend in the number of orders throughout the year.\nBased on the above thought, the answer is:\nThe trend in the number of orders shows an initial increase reaching a high in March followed by a drop in April. Post-April, the trend is irregular, with numbers fluctuating before peaking in December. While the monthly values exhibit some inconsistency, the overall direction is upward, indicating an increase in order activity throughout the year." } ], "id_raw": 942 }, { "id": "j3gIwN9LNytykU6j", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_12.png", "conversations": [ { "from": "human", "value": "\nHow many total complaints are there across all months?" }, { "from": "gpt", "value": "To calculate the total number of complaints, I must add the value at the top of each bar pertaining to complaints, identified by the color specified in {the legend}[0.408, 0.960, 0.436, 0.974]. Starting from January with the bar at [0.114, 0.791, 0.128, 0.873] through December at [0.900, 0.490, 0.914, 0.873], I sum the numerical values displayed atop each bar.\nBased on the above thought, the answer is:\n109" } ], "id_raw": 942 }, { "id": "NBQS7dD76yntBmwp", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the group bar chart into a CSV table?" }, { "from": "gpt", "value": "I can see four product categories: {Mobile}[0.328, 0.950, 0.374, 0.970], {Electronics}[0.440, 0.950, 0.516, 0.970], {Furniture}[0.583, 0.950, 0.645, 0.970], and {Supplies}[0.712, 0.950, 0.770, 0.970] from the chart legend. For every category, there are four data points, which correspond to four quarters, which I can find on the {x-axis tick labels}[{\"Q1\"}[0.193, 0.824, 0.213, 0.844]; {\"Q2\"}[0.411, 0.824, 0.431, 0.844]; {\"Q3\"}[0.629, 0.824, 0.648, 0.844]; {\"Q4\"}[0.847, 0.824, 0.866, 0.844]]. The way to locate these data points is based on the different colors of the group bars represented by the rectangles of different colors. Heights of rectangles represent profits, such as the height of {the first bar of the Mobile category}[0.116, 0.531, 0.159, 1.104] stands for $3000 profit in thousands of dollars on the {Q1 point}[0.203, 0.817]. From these observations, I can convert the chart into a CSV format table.\nBased on the above thought, the answer is:\nCategory, Q1, Q2, Q3, Q4\nMobile, 3000, 3500, 3200, 4000\nElectronics, 4800, 4300, 5000, 5300\nFurniture, 2500, 2900, 2600, 2800\nSupplies, 1900, 2100, 2200, 1800" } ], "id_raw": 943 }, { "id": "oT60fdwtCOvgAH7c", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nWhich product category has shown a consistent rise in profits throughout the quarters?" }, { "from": "gpt", "value": "From the bar chart, we can see that the profit changes of each category are represented by the change of bar heights from the first to the fourth quarter. So, we need to compare the heights of each category's bars. We can see that the height of the {Electronics category}'s bar increases from {Q1}[0.334, 0.435, 0.377, 1.104] to {Q2}[0.377, 0.283, 0.421, 1.104], {Q3}[0.595, 0.149, 0.639, 1.104] and then to {Q4}[0.813, 0.092, 0.856, 1.104]. Thus, it appears that the \"Electronics\" category has shown a consistent rise in profits throughout the quarters.\nBased on the above thought, the answer is:\nElectronics" } ], "id_raw": 943 }, { "id": "hY5gdussfXrMqbVV", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum profit earned by Mobile in any quarter?" }, { "from": "gpt", "value": "In order to answer this question, I need to look at the height of each rectangle bar that represents the Mobile category. The y-coordinate of the {Mobile category}[0.116, 0.531, 0.159, 1.104] in Q1 is 0.531, corresponding to 3000 on the Y-axis tick labels. Then I analyze the other rectangles' profit values in [\"Q2, Q3, Q4\"], and find that the profit of the {Mobile category in Q4}[0.769, 0.340, 0.813, 1.104] has the y-coordinate at the highest position, which is 0.340, equal to 4000 in thousands of dollars.\nBased on the above thought, the answer is:\n4000" } ], "id_raw": 943 }, { "id": "jeK7MiQG1UlhjF9R", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart you see, can you convert the task numbers and completion times of the three types of Big Data Integration into a CSV table format?" }, { "from": "gpt", "value": "Given the title {\"Big Data Integration Overview\"}[0.395, 0.021, 0.647, 0.047] at the top of the chart and the x-axis which represents different types of data integration, {\"Full Integration\"}[0.117, 0.931, 0.222, 0.951], {\"Partial Integration\"}[0.459, 0.931, 0.584, 0.951], and {\"Manual Integration\"}[0.807, 0.931, 0.938, 0.951]. The y-axis represents the corresponding values, which span from {0}[0.043, 0.916, 0.052, 0.936] to {70}[0.035, 0.045, 0.052, 0.065]. For each data integration type, there are two bars reflecting specific values given in the legend which includes {\"Number of Tasks\"}[0.801, 0.071, 0.916, 0.091] and {\"Time to Complete (days)\"}[0.801, 0.101, 0.973, 0.121]. The CSV table can be generated by gathering the values for each category by observing the height of the bars e.g., the value of {\"Number of Tasks for Full Integration\"}[0.099, 0.489, 0.170, 0.924] can be seen to be around the 35 mark of the y-axis while its {\"Time to Complete\"}[0.170, 0.737, 0.240, 0.924] is around the 15 mark.\nBased on the above thought, the answer is:\nIntegration Type,Number of Tasks,Time to Complete (days)\nFull Integration,35,15\nPartial Integration,55,30\nManual Integration,20,60" } ], "id_raw": 944 }, { "id": "Ts20AngLFYcWUstA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nDrawing from the chart, can you suggest the most efficient method of Big Data Integration and provide a brief justification?" }, { "from": "gpt", "value": "Efficiency in terms of Big Data Integration can be measured as achieving the most number of tasks in the shortest time. By looking at the bar chart, you can clearly see that {\"Number of Tasks for Full Integration\"}[0.099, 0.489, 0.170, 0.924] is superior, at 35 tasks, and the {\"Time to Complete for Full Integration\"}[0.170, 0.737, 0.240, 0.924] only takes 15 days, which shows a balance between a substantial number of tasks and a shorter completion time. In contrast, the task count and completion time for both {\"Partial Integration\"}[0.459, 0.931, 0.584, 0.951] and {\"Manual Integration\"}[0.807, 0.931, 0.938, 0.951] are less optimal, either needing longer completion time or achieving fewer tasks within the stated timeframe.\nBased on the above thought, the answer is:\nFull Integration method is the most efficient. It completes a substantial number of tasks (35) in the shortest time (15 days) compared to the other methods." } ], "id_raw": 944 }, { "id": "asFk93D6B68dI8ex", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_63_14.png", "conversations": [ { "from": "human", "value": "\nAmong the three methods, which one completes tasks at the quickest average pace? In other words, calculate the days per task for each method and find the least value." }, { "from": "gpt", "value": "To determine the quickest average task pace, I need to calculate the days per task, which is given by the value of the {\"Time to Complete (days)\"}[0.801, 0.101, 0.973, 0.121] divided by the {\"Number of Tasks\"}[0.801, 0.071, 0.916, 0.091] for each integration type. For instance, {'Full Integration'}[0.117, 0.931, 0.222, 0.951] has a bar height of {15 days}[0.170, 0.737, 0.240, 0.924] for task completion time and {35 tasks}[0.099, 0.489, 0.170, 0.924] for number of tasks, and so the calculation would be 15/35. Applying the same method for Partial Integration {[0.459, 0.931, 0.584, 0.951]} and Manual Integration {[0.807, 0.931, 0.938, 0.951]}. I can then compare the days per task for each integration type and find the least value.\nBased on the above thought, the answer is:\nFull Integration." } ], "id_raw": 944 }, { "id": "h2XOjhSMxlm1ZwgQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, can you convert the sales data of each product into a CSV format table?" }, { "from": "gpt", "value": "Observing the bar chart, I notice that the x-axis represents the months of the year, with months {January}[0.164, 0.879, 0.230, 0.914] through {May}[0.855, 0.879, 0.891, 0.914] labeled. The y-axis, ranging from {0.0}[0.065, 0.841, 0.076, 0.876] to {900}[0.043, 0.025, 0.076, 0.060], signifies the sales data. There are five different product groups color-coded on the chart according to {the legend}[0.036, 0.037, 0.048, 0.083], such as Apple, Banana, Cherry, Durian, and Eggplant. The sales data for each product from January to May can be determined by analyzing the height of each bar. For example, for {Apple in January}[0.129, 0.492, 0.264, 0.854], its bar height corresponds to 400 according to the y-axis.\nBased on the above thought, the answer is:\nProduct,January,February,March,April,May\nApple,400,450,500,600,700\nBanana,800,750,700,750,800\nCherry,700,650,600,550,500\nDurian,300,350,400,450,500\nEggplant,500,550,600,550,500" } ], "id_raw": 945 }, { "id": "e8nEWOlWUtAXQVCm", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nWhich product(s) demonstrated a consistent increase in sales over the five-month period based on the chart?" }, { "from": "gpt", "value": "To analyze this, I need to observe the trend of sales for each product. If the bars corresponding to each month gradually increase in height, it means the sales for that product have persistently risen. I can see that {Apple}[{'January'}[0.129, 0.492, 0.264, 0.854]; {'February'}[0.298, 0.447, 0.433, 0.854]; {'March'}[0.467, 0.401, 0.602, 0.854]; {'April'}[0.636, 0.311, 0.772, 0.854]; {'May'}[0.805, 0.220, 0.941, 0.854]] and {Durian}[{'January'}[0.129, 0.583, 0.264, 0.854]; {'February'}[0.298, 0.537, 0.433, 0.854]; {'March'}[0.467, 0.492, 0.602, 0.854]; {'April'}[0.636, 0.447, 0.772, 0.854]; {'May'}[0.805, 0.401, 0.941, 0.854]] have rising bars for sales from January to May, implying an unbroken sales growth.\nBased on the above thought, the answer is:\nApple and Durian" } ], "id_raw": 945 }, { "id": "8QeofuJlLlXOmSyc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_0.png", "conversations": [ { "from": "human", "value": "\nWhich product had the highest sales in March considering the bar chart?" }, { "from": "gpt", "value": "To find the product with the highest sales in March, I need to observe the heights of the {bars for March}[0.467, 0.401, 0.602, 0.854] for each product. Comparing the heights for Cherry, Durian, Banana, Apple, and Eggplant, I can see that the tallest bar belongs to the Banana which has the sales amount reaching up to 700.\nBased on the above thought, the answer is:\nBanana" } ], "id_raw": 945 }, { "id": "YuWcosKQJ1nTjDeu", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data represented in the bar chart into a CSV format?" }, { "from": "gpt", "value": "After observing the chart's title {\"Innovation and Intellectual Property in Tech Sectors\"}[0.424, 0.021, 0.710, 0.047], one can decipher that there are different types of technologies displayed on the y-axis listed as {'ytick labels'}[{'Robotic Technology'}[0.048, 0.823, 0.138, 0.843], {'AI Technology'}[0.074, 0.708, 0.138, 0.728], {'BlockChain'}[0.086, 0.593, 0.138, 0.613], {'Wireless Communication'}[0.023, 0.477, 0.138, 0.497], {'Quantum Computing'}[0.041, 0.362, 0.138, 0.382], {'BioTech'}[0.102, 0.246, 0.138, 0.266], {'Cybersecurity'}[0.073, 0.131, 0.138, 0.151]]. Each technology has a corresponding horizontal bar illustrating the number of patents they hold. For instance, the bar for {'Robotic Technology'}[0.144, 0.785, 0.579, 0.877] shows it holds around 2700 patents. Following the same method, one can construct the CSV data by pairing each technology sector with its number of patents.\nBased on the above thought, the answer is:\nTechnology,Number of Patents\nRobotic Technology,2700\nAI Technology,3200\nBlockChain,2200\nWireless Communication,5000\nQuantum Computing,1300\nBioTech,2900\nCybersecurity,4600" } ], "id_raw": 946 }, { "id": "If8OmaWHIkVtxC8F", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the chart, which sector do you think prioritizes innovation and intellectual property?" }, { "from": "gpt", "value": "Looking at the chart, {'Wireless Communication'}[0.144, 0.439, 0.950, 0.531] has the longest bar, which means it has the highest number of patents. The number of patents in a sector indicates the amount of innovation and intellectual property in that area. Having many patents signifies that this sector has spent a significant amount of time and resources on research and development activities, leading to the creation of new technologies or processes. Therefore, it can be inferred that the Wireless Communication sector prioritizes innovation and intellectual property the most among the listed sectors.\nBased on the above thought, the answer is:\nWireless Communication" } ], "id_raw": 946 }, { "id": "DVJQws8kyCtdTxty", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_1.png", "conversations": [ { "from": "human", "value": "\nCan you find out which sector has the most patents based on the plot?" }, { "from": "gpt", "value": "To find out which sector has the most patents, one must look at the lengths of the bars corresponding to each technology sector. By observing each bar, one can see that the bar for {'Wireless Communication'}[0.144, 0.439, 0.950, 0.531] is the longest amongst all representing around 5000 patents, concluded based on its coordinates and the scale given on x-axis{'xticks labels'}[{'5000'}[0.938, 0.931, 0.961, 0.951]]. Thus, the Wireless Communication sector has the most patents.\nBased on the above thought, the answer is:\nWireless Communication" } ], "id_raw": 946 }, { "id": "MtTHmhZ7OZwklGI6", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nAs per the bar chart, could you convert the given data into a table format?" }, { "from": "gpt", "value": "The chart title is {\"Internal Business Process Analysis\"}[0.381, 0.030, 0.666, 0.066] indicating that the data is related to various internal processes. These are represented on the {x-axis}[0.497, 0.942, 0.550, 0.970], which specifically are 'Product Development', 'Marketing', 'Sales', 'Management', 'Customer Service', 'Manufacturing' and 'Financial'. The y-axis shows the 'Effectiveness' ranging from {0}[0.043, 0.872, 0.052, 0.900] to {100}[0.026, 0.035, 0.052, 0.063]. The values for each process can be inferred from the height of the bars, for instance, {the bar for 'Product Development'}[0.104, 0.172, 0.203, 0.883] approximately aligns with 85 on the y-axis, hence, the effectiveness for 'Product Development' is 85. By extrapolating this method to remaining bars, we can convert the chart data into a CSV format table.\nBased on the above thought, the answer is:\nProcess,Effectiveness\nProduct Development,85\nMarketing,90\nSales,78\nManagement,88\nCustomer Service,92\nManufacturing,80\nFinancial,89" } ], "id_raw": 947 }, { "id": "KstrQDWLdU68d8RA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you suggest which process requires immediate attention in terms of effectiveness and why?" }, { "from": "gpt", "value": "The chart contains a set of bars{bars_of_group_0}, each representing a different process. Effectiveness is calculated on a scale from 0 to 100, where 100 signifies the highest effectiveness. {The 'Sales' bar}[0.351, 0.230, 0.449, 0.883] is the shortest among all, indicating that it has the lowest effectiveness of 78. Similarly, 'Product Development' and 'Manufacturing' have lower effectiveness ratings, closer to 'Sales'. Comparing these ratings with the higher ones (viz. 'Customer Service' and 'Marketing'), it seems clear that these low-scoring areas, and especially 'Sales', might require immediate attention in order to improve the overall effectiveness of business processes.\nBased on the above thought, the answer is:\nThe 'Sales' process requires immediate attention because it has the lowest effectiveness rating of 78, which is significantly lower compared to other processes like 'Customer Service' and 'Marketing'." } ], "id_raw": 947 }, { "id": "4iXZ0XMIecSt16Wl", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_2.png", "conversations": [ { "from": "human", "value": "\nFrom the given chart, which process has the least effectiveness rating?" }, { "from": "gpt", "value": "To answer this question I need to evaluate the bars on the chart which reflect the effectiveness of each process. Looking at the bar chart, {the bar for 'Sales'}[0.351, 0.230, 0.449, 0.883] seems to have the smallest height, indicating that 'Sales' has the smallest value of all processes. Comparing this with other bars, it can be confirmed that 'Sales' indeed has the lowest effectiveness rating.\nBased on the above thought, the answer is:\nSales" } ], "id_raw": 947 }, { "id": "V0qBEOi4RjULhAY1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the time series data you see on this bar chart into a CSV format table?" }, { "from": "gpt", "value": "From the title {\"Time Series Data Comparison Over Years\"}[0.348, 0.030, 0.690, 0.066] at the top and the {x-axis tick labels}[{\"Slow expansion\"}[0.105, 0.903, 0.213, 0.931]; {\"Unstable behavior\"}[0.275, 0.903, 0.402, 0.931]; {\"Slight increase\"}[0.468, 0.903, 0.570, 0.931]; {\"Inconsistent decline\"}[0.630, 0.903, 0.769, 0.931]; {\"Downward trend\"}[0.822, 0.903, 0.937, 0.931]], it is clear that the x-axis represents different trends. The y-axis tick labels represent values, ranging from {0}[0.035, 0.872, 0.043, 0.900] to {9}[0.035, 0.037, 0.043, 0.065]. For each trend, there are two bars that represent different years as indicated by {the legend}[0.893, 0.089, 0.978, 0.181] - {the light blue bars}<{\"2000\"}[0.937, 0.100, 0.972, 0.128]> represent the year 2000, and {the dark blue bars}<{\"2020\"}[0.937, 0.142, 0.972, 0.170]> represent the year 2020. The height of the bars and the labels at the y-axis visually indicate the corresponding values for each trend and year.\nBased on the above thought, the answer is:\nTrends,2000,2020\nSlow expansion,4.9,8.3\nUnstable behavior,6.1,6.8\nSlight increase,3.2,7.5\nInconsistent decline,7.1,4.6\nDownward trend,8.3,6.1" } ], "id_raw": 948 }, { "id": "x3YuJODYFmx8jBGv", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nBased on the observed chart, which trend are we seeing an improvement?" }, { "from": "gpt", "value": "To answer the question, we should identify which trends show an increase over the twenty-year period. That would be inferred from comparing {the bars representing the year 2000}<{\"2000\"}[0.937, 0.100, 0.972, 0.128]> and {the bars representing the year 2020}<{\"2020\"}[0.937, 0.142, 0.972, 0.170]>. By Observation, we could say \"Slow expansion\": {the bar of year 2000}[0.096, 0.429, 0.159, 0.883] appears shorter than {the bar of year 2020}[0.159, 0.113, 0.222, 0.883]; \"Slight increase\": {the bar of year 2000}[0.456, 0.586, 0.519, 0.883] appears shorter than {the bar of year 2020}[0.519, 0.187, 0.582, 0.883] indicating an upward pattern over the twenty years. Thus, these trends can be interpreted as improvements.\nBased on the above thought, the answer is:\nFrom the chart, we can infer that the trends \"Slow expansion\" and \"Slight increase\" have shown significant improvement over the last twenty years." } ], "id_raw": 948 }, { "id": "Vc9ESiGHcjx7lcz5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_3.png", "conversations": [ { "from": "human", "value": "\nBetween the year 2000 and 2020, which trend shows the highest increase?" }, { "from": "gpt", "value": "To answer this, I need to compare the increase in each trend by subtracting the value of the year 2000, represented by {the light blue bars}<{\"2000\"}[0.937, 0.100, 0.972, 0.128]>, from the value of the year 2020, represented by {the dark blue bars}<{\"2020\"}[0.937, 0.142, 0.972, 0.170]>. The trend that shows the highest increase is that with the maximum difference between the heights of the two bars. For each trend, I locate the two bars and estimate their heights by comparing with the y-axis labels. For example, for \"Slow expansion\": {the bar of year 2000}[0.096, 0.429, 0.159, 0.883] with an estimated height of 4.9 and {the bar of year 2020}[0.159, 0.113, 0.222, 0.883] with an estimated height of 8.3. The difference is 8.3 - 4.9 = 3.4. I apply the same process for the other trends and find out that \"Slight increase\" trend has the highest increase value of 4.3.\nBased on the above thought, the answer is:\nSlight increase" } ], "id_raw": 948 }, { "id": "t5I5bKDwOEOmVGtL", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you observed, please organize the data into a structured table in CSV format." }, { "from": "gpt", "value": "From {the title, \"Market Research of BMW Vehicle Series\"}[0.366, 0.025, 0.694, 0.055], it's clear that this chart depicts data related to BMW's vehicle series market shares. The x-axis, where {the labels are the names of \"BMW Series\"}[0.482, 0.952, 0.578, 0.975], act as the primary categories, and the percentages on the y-axis represent the corresponding values, which ranges from {0.0}[0.043, 0.894, 0.065, 0.917] to {20.0}[0.035, 0.051, 0.065, 0.074]. The height of each bar represents the market share of the corresponding BMW series. For example, the bar for the {1 Series}[0.116, 0.397, 0.214, 0.903] reaches up to around the 12-spot on the y-axis, implying that the 1 series has a market share of 12%. This process can be repeated for each BMW series.\nBased on the above thought, the answer is:\nBMW Series,Market Share(%)\n1 Series,12.0\n3 Series,16.0\n5 Series,19.0\n7 Series,9.0\nx Series,15.0\nZ Series,14.0\ni Series,15.0" } ], "id_raw": 949 }, { "id": "AXIiZ9J1qVOOYmkg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nWhat insight can you draw from this chart about BMW's market strategy?" }, { "from": "gpt", "value": "This bar chart provides market share data for different BMW vehicle series. From {title}[0.366, 0.025, 0.694, 0.055], it seems that the strategy is distributed across multiple series rather than focusing on one model. This is evident from the fact that all models except {7 Series}[0.481, 0.524, 0.579, 0.903] have market shares close to 15%. The {7 Series}[0.481, 0.524, 0.579, 0.903] has lower market shares indicating it may be a luxury or niche offering. The {5 Series}[0.360, 0.102, 0.457, 0.903] has the highest share, suggesting that it may be the brand\u2019s popular or flagship model. Overall, the diversity in the market share of different models indicates a broad target market.\nBased on the above thought, the answer is:\nBMW's market strategy is diversified rather than focused. While the 5 Series appears to be the primary and popular model, the company also maintains a variety of other series to cater to different market segments. The 7 Series, with its somewhat lower market share, could be a niche or luxury offering." } ], "id_raw": 949 }, { "id": "pirJyv0c88g3YS9P", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_4.png", "conversations": [ { "from": "human", "value": "\nWhich BMW Series has the highest market share according to the Chart?" }, { "from": "gpt", "value": "To answer the question, I need to identify the tallest bar in the chart. By examining each of the bars, I can see that the {5 Series}[0.360, 0.102, 0.457, 0.903] bar is reaching highest on the y-axis. Therefore, the BMW 5 Series has the highest market share.\nBased on the above thought, the answer is:\n5 Series." } ], "id_raw": 949 }, { "id": "IWZuQj43CB2DCeG4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, could you extract the displayed data and present it in a table format?" }, { "from": "gpt", "value": "The chart is showing the conditions of different family members based on a bar chart. The family members {\"Dad\"}[0.157, 0.879, 0.193, 0.914], {\"Mom\"}[0.296, 0.879, 0.338, 0.914], {\"Brother\"}[0.426, 0.879, 0.491, 0.914], {\"Sister\"}[0.575, 0.879, 0.625, 0.914], {\"Pet\"}[0.728, 0.879, 0.755, 0.914] and {\"Carer\"}[0.860, 0.879, 0.907, 0.914] are identified on the x-axis, and their associated conditions are represented on the y-axis. The conditions values can be extracted from the height of each bar, which correlate with the percentages on the y-axis. For example, the bar corresponding to {\"Dad\"}[0.118, 0.210, 0.232, 0.854] reaches up to approximately 80 on the y-axis. Therefore, the conditions of \"Dad\" is 80.\nBased on the above thought, the answer is:\nfamily_members, conditions\nDad, 80\nMom, 82\nBrother, 90\nSister, 85\nPet, 90\nCarer, 70" } ], "id_raw": 950 }, { "id": "F8YtMmcNyDMDdDrL", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nDoes this chart indicate any need for an intervention? State your reasons." }, { "from": "gpt", "value": "Yes, there might be a need for intervention. The observation is made based on the following comparisons. The bar chart shows living conditions of all the family members including a Pet and a Carer. Whilst all family members and the pet have a decent living condition score above 80%, the {Carer}[0.827, 0.291, 0.940, 0.854] on the far right is at 70%, which is quite low compared to others. As the Carer might be taking care of all family members including the pet, their relatively lower living condition might have an effect on the services they provide, hence intervention might be necessary.\nBased on the above thought, the answer is:\nYes, an intervention might be needed to improve the living conditions of the Carer, as their conditions are notably lower compared to all other family members and the pet. A low live condition of the Carer might negatively impact the care being provided to the family members." } ], "id_raw": 950 }, { "id": "8FIEwoYlPElVIj9u", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_5.png", "conversations": [ { "from": "human", "value": "\nWhich family member has the lowest living conditions percentage according to the chart?" }, { "from": "gpt", "value": "By comparing the heights of the bars corresponding to each family member, I can find out who has the lowest living conditions percentage. Starting from the left, the height of the bar representing {Dad}[0.118, 0.210, 0.232, 0.854] is 80, for {Mom}[0.260, 0.194, 0.373, 0.854] it's 82, {Brother}[0.402, 0.130, 0.515, 0.854] it's 90, {Sister}[0.543, 0.170, 0.657, 0.854] it's 85, {Pet}[0.685, 0.130, 0.798, 0.854] it's 90 and finally, the height of the bar representing {Carer}[0.827, 0.291, 0.940, 0.854] is 70, which is lower than all the rest, indicating that the Carer has the lowest living conditions.\nBased on the above thought, the answer is:\nCarer." } ], "id_raw": 950 }, { "id": "o3Jys53ViZGW480H", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data presented in the bar chart into a spreadsheet format?" }, { "from": "gpt", "value": "Yes, I can. Based on the chart, I can observe that the x-axis represent various segments within the Education and Training industry, and is labeled 'Sector' as seen in the xlabel [{\"Sector\"}[0.501, 0.942, 0.546, 0.970]]. These sectors include 'Software Development', 'Instructional Design', 'E-Learning', 'Corporate Training', 'Language Learning', and 'Career Counseling', as read from the xticks category. The y-axis correspondingly represents a hypothetical measure of 'Industry Bloom'[0.015, 0.374, 0.029, 0.584] and varies between 0 to 35 as read from the yticks category. Each bar in the chart depicts a specific sector, and the height of the bar indicates the level of Industry Bloom. The data for each sector is extracted by comparing the coordinates of each bar with the y-axis ticks. For instance, the bar corresponding to 'Software Development'[0.104, 0.402, 0.220, 0.883] extends up to 20 on the y-axis, indicating an Industry Bloom level of 20 for this sector. Following similar methodology for other sectors, the data can be tabulated into a CSV format.\nBased on the above thought, the answer is:\nSector,Industry Bloom\nSoftware Development,20\nInstructional Design,25\nE-Learning,32\nCorporate Training,21\nLanguage Learning,19\nCareer Counseling,12" } ], "id_raw": 952 }, { "id": "97Q9Zw9fqfq1tdpa", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis, what conclusion can you draw about the Industry Bloom trends in the Education and Training industry?" }, { "from": "gpt", "value": "The chart indicates various trends in the Education and Training industry. Judging from the bar heights, it appears that 'E-Learning'[0.393, 0.113, 0.509, 0.883] is the sector experiencing the most growth, or \"industry bloom,\" with a measurement of 32. On the other hand, 'Career Counseling'[0.827, 0.595, 0.943, 0.883] presents less growth with a low value of 12. 'Software Development'[0.104, 0.402, 0.220, 0.883] and 'Corporate Training'[0.538, 0.378, 0.654, 0.883] have similar and modest levels of bloom around 20, while 'Instructional Design'[0.249, 0.282, 0.364, 0.883] shows a higher bloom of 25. 'Language Learning'[0.683, 0.426, 0.798, 0.883] has a level slightly lower at 19. Therefore, it seems like growth or industry bloom is largely concentrated in 'E-Learning', suggesting a possible trend towards online learning solutions, while areas like 'Career Counseling' may be experiencing less growth or focus in the Education and Training industry.\nBased on the above thought, the answer is:\nThe 'E-Learning' sector is experiencing the highest growth in the Education and Training industry, suggesting a trend towards online learning solutions. In contrast, 'Career Counseling' has the lowest growth indicating less focus on this area in the industry." } ], "id_raw": 952 }, { "id": "LvH5nZzroDz59Rn3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which sector has the highest industry bloom?" }, { "from": "gpt", "value": "To determine the sector with the highest industry bloom, I need to compare the heights of different bars. Each bar corresponds to a different sector. By inspecting the coordinates of each bar and comparing their heights (which represent the Industry Bloom level), it is clear that the bar for 'E-Learning'[0.393, 0.113, 0.509, 0.883] is the highest. It extends up to 32 on the y-axis, while other bars such as 'Software Development'[0.104, 0.402, 0.220, 0.883] or 'Instructional Design'[0.249, 0.282, 0.364, 0.883] do not reach this height. Therefore, 'E-Learning' is the sector with the highest industry bloom.\nBased on the above thought, the answer is:\nE-Learning." } ], "id_raw": 952 }, { "id": "7zjKius7fZeeZPIa", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nPlease transform the data demonstrated on the bar chart into a structured CSV format table." }, { "from": "gpt", "value": "Taking note of the chart title {\"Content Views on Different Social Media Platforms\"}[0.290, 0.037, 0.812, 0.083] at the top, and the labels of the {X-axis}[{\"Facebook\"}[0.184, 0.879, 0.266, 0.914], {\"Instagram\"}[0.344, 0.879, 0.433, 0.914], {\"Twitter\"}[0.522, 0.879, 0.580, 0.914], {\"YouTube\"}[0.679, 0.879, 0.749, 0.914], {\"LinkedIn\"}[0.841, 0.879, 0.913, 0.914]], the vertical distribution of numbers on the {Y-axis} indicates the views in hundreds of thousands. For each platform, there is a single bar denoting its associated content views. These bars' heights and their respective {labels}[{\"Facebook\"}[0.160, 0.492, 0.291, 0.854], {\"Instagram\"}[0.323, 0.347, 0.453, 0.854], {\"Twitter\"}[0.486, 0.564, 0.616, 0.854], {\"YouTube\"}[0.649, 0.130, 0.779, 0.854], {\"LinkedIn\"}[0.812, 0.637, 0.942, 0.854]] can be used to determine their corresponding values in the CSV table. For instance, the bar associated with Facebook extends to a {label of \"250000\"}[0.098, 0.696, 0.109, 0.731], signifying its value for the metric \"Views\". This process will be followed for all the platforms to generate the CSV table.\nBased on the above thought, the answer is:\nPlatform,Views\nFacebook,250000\nInstagram,350000\nTwitter,200000\nYouTube,500000\nLinkedIn,150000" } ], "id_raw": 953 }, { "id": "92NKHBxBIRxT4px9", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nBased on the graphical representation, which platform would you categorize as the most influential in dispersing content based on views? Provide a detailed explanation." }, { "from": "gpt", "value": "To identify the most influential platform, I need to find the highest bar on the chart as it signifies the highest number of content views. Starting from the left, I see the bars for {Facebook}[0.160, 0.492, 0.291, 0.854], {Instagram}[0.323, 0.347, 0.453, 0.854], {Twitter}[0.486, 0.564, 0.616, 0.854], {YouTube}[0.649, 0.130, 0.779, 0.854], and {LinkedIn}[0.812, 0.637, 0.942, 0.854]. Among all these, the {YouTube bar}[0.649, 0.130, 0.779, 0.854] stands out as the tallest, indicating the maximum content views. Therefore, based on the number of content views depicted in the bar chart, YouTube seems to be the most influential platform for content dispersion.\nBased on the above thought, the answer is:\nYouTube, as it has the highest number of content views among all the platforms present in the graph, making it the most influential in dispersing content." } ], "id_raw": 953 }, { "id": "ZzWhhMzlltjpMqSb", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_8.png", "conversations": [ { "from": "human", "value": "\nWhich platform drives the least number of content views according to the bar chart?" }, { "from": "gpt", "value": "To find the platform with the least views, I would compare the heights of all the bars in the chart visually. The bar with the smallest height will indicate the platform with the least views. Starting from the left, we observe the bars for {Facebook}[0.160, 0.492, 0.291, 0.854], {Instagram}[0.323, 0.347, 0.453, 0.854], {Twitter}[0.486, 0.564, 0.616, 0.854], {YouTube}[0.649, 0.130, 0.779, 0.854], and {LinkedIn}[0.812, 0.637, 0.942, 0.854]. Visually, the bar for LinkedIn is the shortest among all bars representing the least views.\nBased on the above thought, the answer is:\nLinkedIn" } ], "id_raw": 953 }, { "id": "Vc8hheP4muNWhKMT", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nCould you create a table from the chart's data in CSV format?" }, { "from": "gpt", "value": "To create a CSV table of the data from the bar chart, we'll first have to understand what the x and y-axis represent. Looking at {the x-axis label}[0.489, 0.942, 0.558, 0.970], it represents different business processes. The y-axis, according to {the y-axis label}[0.015, 0.295, 0.029, 0.663], represents the time consumed by each process. With these roles identified, we can now analyze the height of each bar to determine the time consumption for each process. For example, the {bar for \"Order Processing\"}[0.104, 0.428, 0.244, 0.883] reaches up to more than half of the y-axis, indicating a high time consumption. By checking the y-axis scales, we can determine that the time consumed here is approximately \"15.2 hours\". By proceeding this way for all the bars, we can extract the data for our CSV table.\nBased on the above thought, the answer is:\nProcesses,Time Consumption\nOrder Processing,15.2\nInventory Management,10.8\nDelivery,25.7\nCustomer Service,19.5\nQuality Assurance,9.9" } ], "id_raw": 954 }, { "id": "wrUHAdHQc7bBtVCg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which process needs improvement in terms of reducing time consumption? And why?" }, { "from": "gpt", "value": "To answer this question, I need to consider which process has the highest time consumed. In this chart, that is represented by the length of the bars. The bar for {Delivery}[0.454, 0.113, 0.593, 0.883] has the highest reach on the y-axis, indicating that it takes the longest time. As such, it would be the most obvious candidate for time consumption reduction improvements. Furthermore, it outliers the rest of the bars significantly, which also suggests a potential issue in this process.\nBased on the above thought, the answer is:\nThe Delivery process needs improvement in terms of reducing time consumption. It takes far more time than any other process, indicating a high level of inefficiency. Reducing the time it takes to deliver could significantly streamline the company's operations." } ], "id_raw": 954 }, { "id": "pJZMnsWWmAoi9Wpz", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_9.png", "conversations": [ { "from": "human", "value": "\nWhich process has the longest time consumption?" }, { "from": "gpt", "value": "To answer the question, I need to look at the bar chart and determine which of the five processes has the longest corresponding bar. Observing the bars, I see that {the bar for \"Delivery\"}[0.454, 0.113, 0.593, 0.883] is significantly longer than the others, indicating that it represents the process with the highest time consumption.\nBased on the above thought, the answer is:\nDelivery." } ], "id_raw": 954 }, { "id": "bVmFmbHNPnBAQZs1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the given bar chart data on global environmental changes from 1980 to 2015 into a table format?" }, { "from": "gpt", "value": "In order to create a table from this chart, I can take the data directly from the x-axis {'Year'}[0.522, 0.942, 0.551, 0.970] and y-axis {'CO2 Emission and Deforestation'}[0.015, 0.004, 0.029, 0.955]. The bars represent the emissions and deforestation percentage each year from the x-axis tick labels [\"1980\", \"1985\", \"1990\", \"1995\", \"2000\", \"2005\", \"2010\", \"2015\"]['coordinates'], and their respective height/location represents the value on the y-axis. By referencing the y-axis and the position of each bar, I can determine the respective amounts for CO2 emissions in million metric tons and deforestation in percentage of forest area. We have two sets of bars: one for CO2 emissions<{'bars_of_group_0'}[{'bar_coordinates': '[0.171, 0.268, 0.213, 0.883]'}...{'bar_coordinates': '[0.902, 0.113, 0.944, 0.883]'}]> and one for deforestation<{'bars_of_group_1'}[{'bar_coordinates': '[0.129, 0.883, 0.171, 0.883]'}...{'bar_coordinates': '[0.861, 0.883, 0.902, 0.883]'}]>. The location of each bar vertically represents the percentage for that particular year.\nBased on the above thought, the answer is:\nYear,CO2 Emission (Milion Metric Tons),Deforestation (% of Forest Area)\n1980,20698,1.3\n1985,21497,1.6\n1990,22147,1.8\n1995,22734,2.0\n2000,23155,2.2\n2005,23961,2.5\n2010,24836,2.9\n2015,25904,3.4" } ], "id_raw": 955 }, { "id": "01udu5qx9avRN3Ip", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what are the environmental implications for the trend of CO2 emissions and deforestation percentage from 1980 to 2015?" }, { "from": "gpt", "value": "The chart delineates two primary trends: CO2 emissions and deforestation percentage. The bar graph representing {CO2 emissions}[0.171, 0.268, 0.213, 0.883]...[0.902, 0.113, 0.944, 0.883], indicated in the legend, shows a clear upward trend from 1980 to 2015. This tells us that the release of CO2 into the atmosphere has been increasing consistently over these years. The second bar graph representing {deforestation percentage}[0.129, 0.883, 0.171, 0.883]...[0.861, 0.883, 0.902, 0.883] shows an almost flat trend. This could suggest that while total forest area may be decreasing, it's not doing so at an exceptionally accelerated rate. It is essential to note that the rising CO2 emission trend is a significant factor contributing to global warming and climate change.\nBased on the above thought, the answer is:\nThere is a continuous increase in CO2 emissions from 1980 to 2015, contributing to global warming and climate change. Simultaneously, the deforestation percentage has not seen a drastically steep increase during the same period. This could indicate some level of conservation efforts, but the fact that it is not decreasing is still a cause for concern for biodiversity and natural carbon sinks." } ], "id_raw": 955 }, { "id": "Kij9Z2LP1wahvIaF", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the year with the highest CO2 emission according to the data from the bar chart?" }, { "from": "gpt", "value": "Based on each year's bar for CO2 emission<{'bars_of_group_0'}[{'bar_coordinates': '[0.171, 0.268, 0.213, 0.883]'}...{'bar_coordinates': '[0.902, 0.113, 0.944, 0.883]'}]>, I can tell that the bar with the highest value indicates the year with the highest CO2 emission. By comparing all these bars' heights, I can see that the bar representing the year 2015<{'bar_coordinates': '[0.902, 0.113, 0.944, 0.883]'}> has the largest height among all. It is located higher on the y-axis '{CO2 Emissions / Deforestation}'[0.015, 0.004, 0.029, 0.955] compared to other years.\nBased on the above thought, the answer is:\n2015" } ], "id_raw": 955 }, { "id": "vq4KGYEzrYkNArRv", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data you observed in the bar chart into a structured CSV table?" }, { "from": "gpt", "value": "From my analysis, I observe that this is a bar chart with the title {\"Greenhouse Gas Emissions by Country\"}[0.396, 0.025, 0.665, 0.055]. Each bar on the chart represents a different country as depicted by the x-axis labels or tick marks {\"USA\"}[0.160, 0.919, 0.184, 0.942], {\"China\"}[0.299, 0.919, 0.332, 0.942], {\"India\"}[0.445, 0.919, 0.473, 0.942], {\"Russia\"}[0.584, 0.919, 0.621, 0.942], {\"Japan\"}[0.729, 0.919, 0.762, 0.942], and {\"Germany\"}[0.862, 0.919, 0.915, 0.942]. On the y-axis are values labeled from {0}[0.058, 0.894, 0.065, 0.917] to {14,000}[0.029, -0.059, 0.065, -0.036], indicating the greenhouse gas emissions in million tons for each country. Each bar's height {\"USA\"}[0.115, 0.471, 0.230, 0.903], {\"China\"}[0.258, 0.102, 0.373, 0.903], {\"India\"}[0.402, 0.736, 0.516, 0.903], {\"Russia\"}[0.545, 0.731, 0.659, 0.903], {\"Japan\"}[0.688, 0.816, 0.803, 0.903], {\"Germany\"}[0.831, 0.850, 0.946, 0.903], indicates the emissions amounts. For converting the bar chart to a CSV table, each bar's country with corresponding greenhouse gas emission will be transformed into a row in the table.\nBased on the above thought, the answer is:\nCountry,Greenhouse Gas Emissions (million tons)\nUSA,6341\nChina,11767\nIndia,2453\nRussia,2532\nJapan,1272\nGermany,776" } ], "id_raw": 956 }, { "id": "ZUhyg9tb9RyA9vEA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which country is most responsible for global warming from the perspective of greenhouse gas emissions, and why?" }, { "from": "gpt", "value": "To answer this, I look at the height of each bar representing the amount of greenhouse gas emissions by each country. The bar representing {China}[0.258, 0.102, 0.373, 0.903] is significantly higher compared to others, meaning that China emits the most greenhouse gases. The larger the amount of greenhouse gases emitted by a country, the greater its contribution to global warming is, assuming all other factors are equal. Hence, China holds the most responsibility for global warming in terms of greenhouse gas emissions.\nBased on the above thought, the answer is:\nChina. Because it emits the most greenhouse gases among the countries shown in the chart." } ], "id_raw": 956 }, { "id": "TPgOBS0dRIryV8V4", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_11.png", "conversations": [ { "from": "human", "value": "\nHow much more greenhouse gas does China emit compared to the USA in terms of million tons?" }, { "from": "gpt", "value": "To derive the difference in greenhouse gas emissions between China and the USA, I must compare the relative heights of the bars representing each. The rectangle {USA}[0.115, 0.471, 0.230, 0.903] implies the USA emits about 6341 million tons of greenhouse gases. Meanwhile, the rectangle {China}[0.258, 0.102, 0.373, 0.903] shows that China emits about 11767 million tons. The difference in emissions is determined by subtracting the emission of the USA from China's emission.\nBased on the above thought, the answer is:\n5426 million tons" } ], "id_raw": 956 }, { "id": "DGPfxBu1sUUHXPVQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart's data, can you format the information into a CSV table?" }, { "from": "gpt", "value": "Yes, I can see that the bar chart's title is \"Efficiency Improvement Potential Assessment\", positioned at [0.291, 0.037, 0.767, 0.083]. The chart is organized into {five bars}[0.118, 0.415, 0.255, 0.854]; [0.290, 0.525, 0.427, 0.854]; [0.461, 0.239, 0.598, 0.854]; [0.632, 0.635, 0.769, 0.854]; [0.803, 0.130, 0.940, 0.854], each representing a unique process in the company's operations. In specific, they are {Manufacturing}[0.124, 0.879, 0.250, 0.914], {Distribution}[0.307, 0.879, 0.409, 0.914], {Logistics}[0.492, 0.879, 0.567, 0.914], {Management}[0.643, 0.879, 0.758, 0.914], and {Sales}[0.849, 0.879, 0.895, 0.914]. The height of each bar indicates the efficiency improvement potential for each process. For example, the height of the Manufacturing bar indicates a potential efficiency improvement of 20%, while the Sales bar indicates a potential increase of 33%.\nBased on the above thought, the answer is:\nProcesses,Efficiency_Improvement_Potential(%)\nManufacturing,20\nDistribution,15\nLogistics,28\nManagement,10\nSales,33" } ], "id_raw": 957 }, { "id": "oPxjfzsmNFNuHivV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nConsidering the organization's strategic planning, which process do you think should be given priority for improvement and why?" }, { "from": "gpt", "value": "According to the bar chart, the organization's performance enhancement should focus on the process with the highest efficiency improvement potential. By examining the {Sales bar}[0.803, 0.130, 0.940, 0.854] particularly stands out due to its significantly higher potential for efficiency improvement compared to other processes such as {Manufacturing}[0.118, 0.415, 0.255, 0.854], {Distribution}[0.290, 0.525, 0.427, 0.854], {Logistics}[0.461, 0.239, 0.598, 0.854], and {Management}[0.632, 0.635, 0.769, 0.854]. This suggests that investing resources in improving the Sales process would yield the highest return in terms of improving overall operational efficiency.\nBased on the above thought, the answer is:\nThe Sales process should be prioritized for improvement since it has the highest potential for efficiency enhancement." } ], "id_raw": 957 }, { "id": "vClPrOyzfb12oEwJ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which process has the highest efficiency improvement potential?" }, { "from": "gpt", "value": "The bar chart shows five different processes on the x-axis, with the corresponding efficiency improvement potential (%) indicated by the heights of the bars on the y-axis. By comparing the heights of all the bars like {Manufacturing}[0.118, 0.415, 0.255, 0.854], {Distribution}[0.290, 0.525, 0.427, 0.854], {Logistics}[0.461, 0.239, 0.598, 0.854], {Management}[0.632, 0.635, 0.769, 0.854], and {Sales}[0.803, 0.130, 0.940, 0.854], I can see that the highest bar corresponds to the Sales process. This suggests that Sales has the highest efficiency improvement potential.\nBased on the above thought, the answer is:\nSales" } ], "id_raw": 957 }, { "id": "KIsTrPQAxhzTBcd1", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the ecological environment sustainability assessment shown on the bar chart into a structured table with CSV format?" }, { "from": "gpt", "value": "Looking at the graph with the title {\"Ecological Environment Sustainability Assessment\"}[0.268, 0.037, 0.790, 0.083], I can see that the X axis, marked as {the 'Country' label}[0.495, 0.927, 0.564, 0.963], and the Y axis, marked as {the 'Environment Score' label}[0.019, 0.309, 0.036, 0.639], have the countries and their environment points respectively. I can read the corresponding values from the green bars and generate the CSV based on these information. For instance, the USA is represented by {a green bar}[0.118, 0.180, 0.232, 0.854] with an approximate height aligning with the y-axis 80, so it's environment point is around 78.6.\nBased on the above thought, the answer is:\nCountry,Environment_Points\nUSA,78.6\nGermany,84.5\nChina,62.3\nIndia,60.8\nBrazil,76.2\nAustralia,82.1" } ], "id_raw": 958 }, { "id": "Cq4DL9u0Ni2I8L9w", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nIs any of the countries treated as an outlier in terms of the environmental sustainability score? Provide reasons for your answer." }, { "from": "gpt", "value": "An outlier is a data element that significantly differs from other observations. In the context of this bar chart, if a country's environmental sustainability score is significantly lower or higher than the scores of most other countries, it would be considered an outlier. Looking at the distinct bars such as {USA}[0.118, 0.180, 0.232, 0.854], {Germany}[0.260, 0.130, 0.373, 0.854], {Brazil}[0.685, 0.201, 0.798, 0.854] and {Australia}[0.827, 0.150, 0.940, 0.854], their environment points don't deviate much as they are all above approximately 75 with a narrow range of less than 10 points. However, comparing {China}[0.402, 0.320, 0.515, 0.854], which has a score of 62.3, to these four countries and to the entire data set, the score of China deviates significantly from the others. Thus, we might consider China as an outlier in terms of the environment sustainability score.\nBased on the above thought, the answer is:\nYes, China is an outlier. This is because its environmental sustainability score of 62.3 is significantly lower than the scores of the other countries, which are all above approximately 75, causing a large deviation." } ], "id_raw": 958 }, { "id": "R3jHLI7h5lUi5M2d", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_13.png", "conversations": [ { "from": "human", "value": "\nWhat was the environment points difference between the country with the highest and lowest scores?" }, { "from": "gpt", "value": "To find the difference between highest and lowest scores, I should identify the bars with highest and lowest heights. {The Germany bar}[0.260, 0.130, 0.373, 0.854] extends to the highest position on the Y-axis at 84.5, indicating Germany has the highest score. On the other hand, {the India bar}[0.543, 0.333, 0.657, 0.854] extends to the lowest position on the Y-axis at around 60.8, thus representing the lowest score. The point difference between those bar heights shows the required answer.\nBased on the above thought, the answer is:\n23.7" } ], "id_raw": 958 }, { "id": "BfNNRunXj4BiLDh4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nCould you transform the information on the bar chart into a CSV format?" }, { "from": "gpt", "value": "Sure, I can accomplish that. This chart coveres different types of investments. From {the subtitle}[0.372, 0.025, 0.675, 0.055] and {the x-axis tick labels}[{\"Bonds\"}[0.152, 0.919, 0.195, 0.942]; {\"Stocks\"}[0.326, 0.919, 0.371, 0.942]; {\"Real Estates\"}[0.481, 0.919, 0.566, 0.942]; {\"Commodities\"}[0.653, 0.919, 0.744, 0.942]; {\"Cryptocurrencies\"}[0.814, 0.919, 0.932, 0.942]], I can infer that the x-axis represents different investment vehicles while {the y-axis}[0.015, 0.390, 0.029, 0.575] represents the return rate in percentage. I can infer the return rate by the height of each bar. For instance, {the bar representing the Bonds}[0.104, 0.717, 0.244, 0.903] about 3.5%, which is confirmed by the y-axis label nearby. Taking similar steps for the rest of the bars produces a CSV table.\nBased on the above thought, the answer is:\nInvestment Vehicles, Return Rate (%)\nBonds,3.5\nStocks,8.3\nReal Estates,6.2\nCommodities,2.7\nCryptocurrencies,15.1" } ], "id_raw": 959 }, { "id": "noSD9mhb95T2SQwT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nIf an investor wants steady returns and avoids high fluctuations, which investment vehicle would you recommend based on the chart data?" }, { "from": "gpt", "value": "If stability is a priority, the investor has to look for investments that have moderate but relatively consistent returns. According to the chart, {the bonds bar}[0.104, 0.717, 0.244, 0.903] depicts a stable growth of 3.5%. While it doesn't offer the highest return, it does promise less volatility than something like Cryptocurrencies, which, based on {the bar for Cryptocurrencies}[0.803, 0.102, 0.943, 0.903], can yield a high return of 15.1% but is also highly unpredictable. Thus, considering the stable growth and lower risks, I would recommend bonds as an investment vehicle for the investor.\nBased on the above thought, the answer is:\nBonds." } ], "id_raw": 959 }, { "id": "TD5EsQlhB2tEB2dG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_64_14.png", "conversations": [ { "from": "human", "value": "\nWhat investment vehicle presents the highest return as illustrated by the bar chart?" }, { "from": "gpt", "value": "In order to determine which investment vehicle has the highest return, I need to compare the height of each bar, which indicates the return rate. Observing the graph, it's clear that {the Cryptocurrencies bar}[0.803, 0.102, 0.943, 0.903] is the tallest bar among the five, rising to about 15.1% according to the scale on y-axis. Therefore, it has the highest return rate.\nBased on the above thought, the answer is:\nCryptocurrencies." } ], "id_raw": 959 }, { "id": "jj2gjrxNsJ5bwMHS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nCan you generate a CSV table displaying the annual return rates for Stock, Bond, and Real Estate Investments from 2020 to 2030?" }, { "from": "gpt", "value": "I have analyzed the chart and identified three subplots, each representing a different investment type with their respective return rates over years. By referring to {the coordinates for Stock Investment bars}[0.098, 0.211, 0.160, 0.297] through [0.882, 0.032, 0.945, 0.297] and their corresponding years from the x-axis labels, I can extract the return rates for Stock Investment. A similar approach is taken for {Bond Investment bars}[0.098, 0.568, 0.160, 0.612] through [0.882, 0.550, 0.945, 0.612] and {Real Estate Investment bars}[0.098, 0.827, 0.160, 0.927] through [0.882, 0.749, 0.945, 0.927] to gather the corresponding return rates for those investments as well.\nBased on the above thought, the answer is:\nYear,Stock Investment,Bond Investment,Real Estate Investment\n2020,0.12,0.05,0.18\n2021,0.15,0.06,0.19\n2022,0.16,0.06,0.20\n2023,0.19,0.07,0.21\n2024,0.23,0.08,0.25\n2025,0.27,0.09,0.23\n2026,0.30,0.10,0.25\n2027,0.32,0.11,0.27\n2028,0.33,0.09,0.28\n2029,0.35,0.08,0.30\n2030,0.37,0.07,0.32" } ], "id_raw": 960 }, { "id": "jTVKkTAvLT9oHCNV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nCan you comment on the trend observed in Bond Investment returns after 2026?" }, { "from": "gpt", "value": "To analyze the bond investment returns after 2026, I have to observe the series of bars representing these years within the Bond Investment subplot. For the years 2027 [0.647, 0.515, 0.710, 0.612], 2028 [0.725, 0.533, 0.788, 0.612], 2029 [0.804, 0.542, 0.867, 0.612], and 2030 [0.882, 0.550, 0.945, 0.612], it's clear that the bars' tops are descending, albeit not steadily. While the return rate for 2027 is at its peak for the discussed range, there is a slight drop in 2028 and 2029 before rising again in 2030. This fluctuation suggests an erratic decrease with some recovery rather than a steady downturn.\nBased on the above thought, the answer is:\nThe Bond Investment returns after 2026 exhibit a declining trend with fluctuations, including a recovery in 2030. Despite this recovery, the returns do not reach the peak observed in 2027 within the discussed timeframe." } ], "id_raw": 960 }, { "id": "nbhT1XDOmJNZr62U", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_0.png", "conversations": [ { "from": "human", "value": "\nWhat was the return rate for Real Estate Investment in the year 2027?" }, { "from": "gpt", "value": "To find the return rate for Real Estate Investment in the year 2027, I will locate the bar corresponding to 2027 on the Real Estate Investment subplot. The bar is identified within {the coordinates}[0.647, 0.777, 0.710, 0.927] and by checking the y-axis tick labels from earlier years, we can extrapolate the return rate referring to the y-ticks [0.055, 0.699] for 0.2 and [0.055, 0.642] for 0.3. The bar's top aligns with the 0.27 return rate based on its position relative to the y-axis ticks.\nBased on the above thought, the answer is:\n0.27" } ], "id_raw": 960 }, { "id": "HwhTrtfcE8W0fRfN", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nCan you structure the Duration of Reading data from the bar chart into a CSV format?" }, { "from": "gpt", "value": "Observing the bottom bar chart, {the ylabel}[0.015, 0.023, 0.029, 0.255] indicates that this chart represents 'Duration of Reading'. The social networks are labeled on the x-axis as {xticks}[0.150, 0.808] to [0.910, 0.808], including Facebook, Twitter, and others. The corresponding bars for each social network such as {Facebook's bar}[0.116, 0.036, 0.184, 0.254], {Twitter's bar}[0.201, 0.109, 0.268, 0.254], and so on, represent the reading duration, which can be converted to exact values using the y-range labels like {0.00}[0.075, 0.254] and {0.90}[0.075, 0.012]. By comparing the heights of the bars against these y-range labels, the duration of reading can be deducted for each social network. The data will then be formatted into the requested CSV structure.\nBased on the above thought, the answer is:\nSocial Networks,Duration of Reading\nFacebook,0.9\nTwitter,0.6\nInstagram,0.85\nSnapchat,0.8\nLinkedIn,0.7\nReddit,0.65\nWhatsApp,0.9\nWeChat,0.75\nTikTok,0.5\nPinterest,0.55" } ], "id_raw": 962 }, { "id": "1tSmR9wLUhOVjDUH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nWhich social network from the chart seems to balance online and offline interactions most equally?" }, { "from": "gpt", "value": "Comparing the balance between online and offline interactions requires analyzing the middle and bottom sub-charts. I will observe the height of {bars indicating \"Online Interaction\"}[0.116, 0.326, 0.184, 0.531] and {bars indicating \"Offline Interaction\"}[0.116, 0.648, 0.184, 0.808] for all social platforms. For instance, if {Facebook\u2019s bar for online}[0.116, 0.326, 0.184, 0.531] is close to {Facebook's bar for offline}[0.116, 0.648, 0.184, 0.808], it indicates a balance. I should repeat this comparison for each social network and identify which one has the closest values between the two interactions. This will require a visual estimate based on the bar heights in relation to the y-axis ticks.\nBased on the above thought, the answer is:\nWhatsApp. Balance is inferred from the similar heights of {WhatsApp's online bar}[0.623, 0.313, 0.690, 0.531] and {offline bar}[0.623, 0.625, 0.690, 0.808], indicating both interaction types are near equal values around 0.8." } ], "id_raw": 962 }, { "id": "62aV6iFipOL801ju", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the average value of Offline Interaction among the social networks illustrated on the bar chart?" }, { "from": "gpt", "value": "To calculate the average value of Offline Interaction, I will identify the third sub-chart by referencing {the ylabel for \"Offline Interaction\"}[0.024, 0.589, 0.038, 0.798]. Each bar, such as {Facebook's bar}[0.116, 0.648, 0.184, 0.808] and {Twitter's bar}[0.201, 0.716, 0.268, 0.808], represents a social network's Offline Interaction value. Using the ytick measurements {0.0}[0.075, 0.808] to {1.0}[0.075, 0.579], I will calculate the values by approximating the top of each bar's position relative to the labeled y-values. The values will then be summed and divided by the number of social networks, which is 10, to find the average value of Offline Interaction.\nBased on the above thought, the answer is:\n0.65" } ], "id_raw": 962 }, { "id": "RqMbSId90bXrO7vQ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nCreate a table to segment and showcase the change in appreciation rates for each of the three cuisines over the 10-year period as observed on the chart." }, { "from": "gpt", "value": "Analyzing the bar charts, we observe that the first subplot {Italian cuisine appreciation}[0.075, 0.025, 0.985, 0.292] shows bars descending in height from left to right, indicating a decline in appreciation rates from the first to the tenth year. The x-axis represents the years ranked 1 through 10, and the y-axis has ratings ranging from 0 to 1. Each bar's upper edge aligns with its corresponding value on the y-axis. The same process can be applied to the subplots for {French}[0.075, 0.331, 0.985, 0.597] and {Mexican cuisine}[0.075, 0.636, 0.985, 0.903] appreciation, with each bar on these charts also showing a slight decline from left to right. By reading off the coordinates for each bar, we can construct a structured table reflecting the appreciation rates over a 10-year period for each cuisine.\nBased on the above thought, the answer is:\nYear,Italian,French,Mexican\n1,0.95,0.85,0.75\n2,0.93,0.82,0.68\n3,0.91,0.79,0.61\n4,0.88,0.76,0.54\n5,0.85,0.73,0.47\n6,0.82,0.70,0.40\n7,0.79,0.67,0.33\n8,0.76,0.64,0.26\n9,0.73,0.61,0.19\n10,0.70,0.58,0.12" } ], "id_raw": 963 }, { "id": "LP7rfPwBooPwbJLr", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nHow does the average appreciation rate of Mexican cuisine over the 10-year period compare with the overall decreasing trend observed in the other two cuisines?" }, { "from": "gpt", "value": "To perform this comparison, I need to first determine the trend for each of the cuisines. Observing the {Italian cuisine plot}[0.075, 0.025, 0.985, 0.292], I note the bars decrease in height steadily, reflecting a declining trend. Similarly, in the {French cuisine plot}[0.075, 0.331, 0.985, 0.597], the bars also show a decreasing trend yet not as pronounced as the Italian. In the {Mexican cuisine plot}[0.075, 0.636, 0.985, 0.903], I see a sharp decline with more significant decreases each year, demonstrated by the bars' height from left to right. By comparing the steepness of the decline in the bar heights across all three plots, I am able to infer the comparative trends.\nBased on the above thought, the answer is:\nMexican cuisine shows a more persistent and remarkable decline in appreciation when compared with the Italian and French cuisines. While all three cuisines exhibit a negative trend over the 10-year period, the decline in Mexican cuisine is steeper, as seen in the diminished bar heights for successive years." } ], "id_raw": 963 }, { "id": "cr2ewuvJuG8LACQU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_3.png", "conversations": [ { "from": "human", "value": "\nAcross all three cuisines, which cuisine had the highest average appreciation rate over the 10-year period?" }, { "from": "gpt", "value": "To find the highest average appreciation rate, I will analyze the area of the bars representing each cuisine's appreciation over the 10 years. I will add the {heights of the bars for Italian cuisine}[0.116, 0.038, 0.184, 0.292]; [0.201, 0.043, 0.268, 0.292]; ... [0.876, 0.104, 0.944, 0.292] from their respective y-axis values and calculate the average. The same methodology will be applied to the {French cuisine}[0.116, 0.343, 0.184, 0.597]; ... [0.876, 0.424, 0.944, 0.597] and {Mexican cuisine bars}[0.116, 0.721, 0.184, 0.903]; ... [0.876, 0.874, 0.944, 0.903]. After comparing the results, the cuisine with the highest summed value divided by 10 will present the highest average appreciation rate.\nBased on the above thought, the answer is:\nItalian cuisine." } ], "id_raw": 963 }, { "id": "IGkaFKKxylVhMFIP", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nHow would you structure the forecast trend data of the three movie genres from 2020 to 2029 into a CSV table format?" }, { "from": "gpt", "value": "To convert the forecast trend data into a CSV table, I've analyzed the bar charts for each genre. The first sub-chart represents Genre_1 with {blue bars}[0.108, 0.195, 0.176, 0.289] starting from 2020 at 0.6 and increasing progressively to 1.6 in 2029. The second sub-chart for Genre_2 is indicated by {red bars}[0.108, 0.344, 0.176, 0.527] beginning at 0.8, rising to a peak, and then declining to -0.25. The third sub-chart for Genre_3 with {purple bars}[0.108, 0.779, 0.176, 0.815] shows variations beginning at 0.4 and rising to 1.4 by the end of the period. I used the y-axis values and compared the relative heights of each bar corresponding to each year on the x-axis to determine the popularity figures.\nBased on the above thought, the answer is:\nYear,Genre_1,Genre_2,Genre_3\n2020,0.6,0.8,0.4\n2021,0.8,0.6,0.9\n2022,1.2,0.4,0.85\n2023,1.15,0.37,0.88\n2024,1.25,0.25,1.0\n2025,1.35,0.15,1.05\n2026,1.45,0.05,1.15\n2027,1.4,-0.05,1.25\n2028,1.5,-0.15,1.3\n2029,1.6,-0.25,1.4" } ], "id_raw": 964 }, { "id": "CZ6GKc4SjphWOI73", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nWhich movie genre shows the most consistent growth over the period?" }, { "from": "gpt", "value": "To assess consistency in growth, I must review the trends represented by the bars in each sub-chart. Genre_1 has {blue bars}[0.108, 0.195, 0.176, 0.289] that show incremental growth each year from 2020 through 2029 without any decreases. Meanwhile, Genre_2 and Genre_3, represented by {red bars}[0.108, 0.344, 0.176, 0.527] and {purple bars}[0.108, 0.779, 0.176, 0.815] respectively, exhibit fluctuations and not steady growth. After comparing the height of the bars year over year, it is clear which genre provides consistent growth.\nBased on the above thought, the answer is:\nGenre_1 shows the most consistent growth over the period, with steady increases from 2020 to 2029 without any years of decline." } ], "id_raw": 964 }, { "id": "c4vEKICfx0RtMQ5f", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_4.png", "conversations": [ { "from": "human", "value": "\nFor Genre_3, what was the maximum popularity value shown, and in which year did it occur?" }, { "from": "gpt", "value": "I will locate the tallest {purple bar}[0.875, 0.691, 0.943, 0.815] in the Genre_3 sub-chart to determine the maximum popularity value. By observing the heights of each bar and referencing the y-axis, which has increments denoting popularity values, the tallest bar indicates the highest popularity. The year labels beneath each bar show that this peak occurs in the final year of the data set.\nBased on the above thought, the answer is:\n1.4 in 2029." } ], "id_raw": 964 }, { "id": "cp3hJLJd9B9N0JGb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the profits over the years into a structured table format?" }, { "from": "gpt", "value": "After examining the bar chart for profits from {subplot_2}[0.097, 0.642, 0.985, 0.903] with the years labeled on the x-axis from {x-ticks}[0.170, 0.903] to {r}[0.912, 0.903] and profit values on the y-axis from \"0\" at {y-tick}[0.078, 0.894, 0.087, 0.917] to \"120000\" at {y-tick}[0.035, 0.633, 0.087, 0.657], I see colored bars representing annual profit. Each bar, like {the bar for 2011}[0.137, 0.859, 0.203, 0.903], corresponds to a profit value, labeled as \"0.00 -> 20000.00\" in the dataset. Repeating this process for each year allows for the creation of a table.\nBased on the above thought, the answer is:\nYear,Profit\n2011,20000\n2012,19500\n2013,22200\n2014,25000\n2015,27500\n2016,28000\n2017,29000\n2018,31500\n2019,34000\n2020,36000" } ], "id_raw": 965 }, { "id": "na1R0Z8pYL5YkKxl", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nWhich year experienced the highest growth in profits when compared to the previous year?" }, { "from": "gpt", "value": "To identify the year with the highest profit growth, I need to compare the difference in heights of adjacent bars in the profit chart {subplot_2}[0.097, 0.642, 0.985, 0.903]. For example, the difference between {2011}[0.137, 0.859, 0.203, 0.903] and {2012}[0.220, 0.861, 0.286, 0.903] and so on through to {2019}[0.796, 0.829, 0.862, 0.903] and {2020}[0.879, 0.825, 0.945, 0.903]. By analyzing the positive changes in profit bars' heights and comparing these changes year over year, the largest growth can be determined.\nBased on the above thought, the answer is:\nBetween the years 2019 and 2020, as the profit bars for {2019}[0.796, 0.829, 0.862, 0.903] and {2020}[0.879, 0.825, 0.945, 0.903] show the highest difference in height, indicating the largest growth in profit." } ], "id_raw": 965 }, { "id": "DCfXBpAtsdU5zrdi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_5.png", "conversations": [ { "from": "human", "value": "\nBy how much did the revenue increase from 2011 to 2020?" }, { "from": "gpt", "value": "To calculate the total increase in revenue, I look at the first bar {the bar for 2011}[0.137, 0.110, 0.203, 0.285], which has a value of \"0.00 -> 75000.00\" as per the dataset, and compare it against {the bar for 2020}[0.879, 0.037, 0.945, 0.285] with a value of \"0.00 -> 106000.00\". Subtracting the 2011 revenue from the 2020 revenue yields the total increase.\nBased on the above thought, the answer is:\n31000" } ], "id_raw": 965 }, { "id": "oq9VlbH0IG9J77SH", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nCould you organize the information depicted in the bar chart into a structured table, using a CSV format?" }, { "from": "gpt", "value": "The chart is a three-panel bar chart, each panel representing a different measure, which is shown by the y-label of each subplot: {Efficiency}[0.024, 0.102, 0.038, 0.214], {Reliability}[0.024, 0.407, 0.038, 0.521] and {Diversity}[0.024, 0.718, 0.038, 0.821]. Furthermore, each panel depicts information pertaining to two different areas of business, as can be seen from the x-axis of the Diversity subplot: {Global Trade}[0.257, 0.919, 0.344, 0.942] and {Supply Chain Management}[0.666, 0.919, 0.854, 0.942]. Appropriate values for each combination of Business Area and Measure can be found by looking at the height of the bar. For example, the bar for Global Trade in the Efficiency subplot [0.116, 0.041, 0.484, 0.292] stretches up to approximately 0.87 on the y-axis [0.043, 0.282, 0.065, 0.306]. Thus, the efficiency of Global Trade is 0.87.\nBased on the above thought, the answer is:\nBusiness Area, Measure1 (Efficiency), Measure2 (Reliability), Measure3 (Diversity)\nGlobal Trade, 0.87, 0.43, 0.67\nSupply Chain Management, 0.88, 0.65, 0.79" } ], "id_raw": 966 }, { "id": "OOQie5sFKya2N8MT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which area of business would you consider more stable across all measures: Efficiency, Reliability, and Diversity?" }, { "from": "gpt", "value": "This question requires assessing the variability in the ratings across diverse measures rather than just the net total. Looking at the Efficiency subplot, we can see that Global Trade and Supply Chain Management have similar ratings, {Global Trade}[0.116, 0.041, 0.484, 0.292] is at 0.87 and {Supply Chain Management}[0.576, 0.038, 0.944, 0.292] is at 0.88. However, in the Reliability subplot, {Supply Chain Management}[0.576, 0.343, 0.944, 0.597] has a noticeably higher rating at 0.65 than {Global Trade}[0.116, 0.429, 0.484, 0.597] which is at 0.43. Lastly, in the Diversity subplot, the gap in rating increases further; {Supply Chain Management}[0.576, 0.649, 0.944, 0.903] is at 0.79, while {Global Trade}[0.116, 0.688, 0.484, 0.903] falls at 0.67. Hence, Global Trade shows less variation across measures.\nBased on the above thought, the answer is:\nGlobal Trade is more stable across all measures." } ], "id_raw": 966 }, { "id": "JF559eCwr6ouvRL9", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_6.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which area of business has better overall ratings in all three measures: Efficiency, Reliability, and Diversity?" }, { "from": "gpt", "value": "To answer the question, I need to analyze the subplots for each of the measures and compare the heights of the bars for Global Trade and Supply Chain Management. For Efficiency, the {Global Trade bar}[0.116, 0.041, 0.484, 0.292] reaches approximately 0.87, while the {Supply Chain Management bar}[0.576, 0.038, 0.944, 0.292] reaches approximately 0.88. For Reliability, the {Global Trade bar}[0.116, 0.429, 0.484, 0.597] reaches approximately 0.43, while the {Supply Chain Management bar}[0.576, 0.343, 0.944, 0.597] reaches approximately 0.65. And for Diversity, the {Global Trade bar}[0.116, 0.688, 0.484, 0.903] reaches approximately 0.67, while the {Supply Chain Management bar}[0.576, 0.649, 0.944, 0.903] reaches approximately 0.79. Adding together all the measures for each area of business, Global Trade totals roughly 1.97, and Supply Chain Management totals roughly 2.32.\nBased on the above thought, the answer is:\nSupply Chain Management." } ], "id_raw": 966 }, { "id": "03gKZUH0Lgj4FuuM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nPlease convert the chart data showing charitable donations per year into a table format." }, { "from": "gpt", "value": "By examining the chart with {the y-axis label \"Charitable donations\"}[0.015, 0.034, 0.029, 0.276], I see a list of bars with their heights corresponding to the value in dollars for each year from {2010}[0.121, 0.065, 0.188, 0.285] to {2019}[0.877, 0.037, 0.944, 0.285]. Each bar is plotted on the x-axis labeled with the specified year represented by the horizontal position of the bar and the dollar amount which is proportional to the bar's height. These heights determine the charitable donation values for their respective years. I will extract these values to create the table.\nBased on the above thought, the answer is:\nYear,Charitable Donations\n2010,8000\n2011,8050\n2012,8100\n2013,8120\n2014,8200\n2015,8300\n2016,8400\n2017,8500\n2018,8500\n2019,9000" } ], "id_raw": 967 }, { "id": "Cm9vIH7rOGKuNfzt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nHas there been consistent growth in social services from 2010 to 2019?" }, { "from": "gpt", "value": "I will analyze the sizes of the bars in the subplot dedicated to social services, indicated by {the ylabel}[0.015, 0.379, 0.029, 0.549]. Noting each year's bar from {2010}[0.121, 0.362, 0.188, 0.594] to {2019}[0.877, 0.352, 0.944, 0.594], some bars are taller while others are shorter compared to the previous year, depicting both periods of increase and decrease in the value. For instance, the bar representing 2016 [0.625, 0.348, 0.692, 0.594] is shorter than the one representing 2015 [0.541, 0.346, 0.608, 0.594], indicating a drop in social services spending. The fluctuating bar heights over the years show that there has not been consistent growth in social services spending between 2010 and 2019.\nBased on the above thought, the answer is:\nNo, the social services spending has seen both increases and decreases throughout the years, indicating inconsistent growth." } ], "id_raw": 967 }, { "id": "PEPRtieOpgnYrbyY", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the relative growth in volunteer hours from 2010 to 2015?" }, { "from": "gpt", "value": "By the {y-axis label \"Volunteer hours\"}[0.015, 0.681, 0.029, 0.864] and the bars representing 2010 and 2015 located at [0.121, 0.903, 0.188, 0.903] and [0.541, 0.903, 0.608, 0.903] respectively, I can see that volunteer hours grew from 2 to 2.6. To calculate the relative growth, I find the difference between the two values and divide by the original value from 2010 (2.6 hours - 2 hours) / 2 hours.\nBased on the above thought, the answer is:\n30%" } ], "id_raw": 967 }, { "id": "E3H1mGvise9eMkfj", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data from the efficiency bar chart into a structured CSV format table?" }, { "from": "gpt", "value": "Observing the bar chart for efficiency, {the bars of group_0}<[0.116, 0.158, 0.184, 0.292]> display individual data points for the AI algorithms. Each bar's height corresponds to its efficiency value. For instance, {the first bar}<[0.116, 0.158, 0.184, 0.292]> relates to the Neural Network with an efficiency range of 0.00 to 0.50. The bar coordinates and their respective efficiency are noted for all ten AI algorithms displayed on the x-axis, marked by their labeled positions from 1 to 10.\nBased on the above thought, the answer is:\nAlgorithm, Efficiency\nNeural Network, 0.5\nSupport Vector Machine, 0.7\nDecision Tree, 0.75\nNaive Bayes, 0.82\nDeep Learning, 0.8\nRandom Forest, 0.88\nGradient Boosting, 0.92\nLogistic Regression, 0.74\nK-Nearest Neighbors, 0.79\nXGBoost, 0.95" } ], "id_raw": 968 }, { "id": "4Nztyxl9bGxQ2T6L", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nWhich AI algorithm appears to be most consistently reliable across efficiency, performance, and reliability, as per the charts?" }, { "from": "gpt", "value": "To answer this, I must evaluate the overall consistency of each AI algorithm across the three measures shown in the sub-charts. Examining the bars corresponding to each AI algorithm, {XGBoost}<[0.876, 0.038, 0.944, 0.292]> shows the highest values in all three sub-charts with {efficiency}<[0.876, 0.038, 0.944, 0.292]>, {performance}<[0.876, 0.343, 0.944, 0.597]>, and {reliability}<[0.876, 0.765, 0.944, 0.903]>. Each sub-chart indicates that XGBoost has the maximum valued bars, suggesting high and consistent ratings across all three dimensions.\nBased on the above thought, the answer is:\nXGBoost appears to be the most consistently reliable AI algorithm as it demonstrates top performance in efficiency, performance, and reliability on all counts, evident from its visually tallest bars in each respective sub-chart." } ], "id_raw": 968 }, { "id": "ycdMvVobjO4ehzZT", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the average efficiency of the AI algorithms as indicated by the bar chart?" }, { "from": "gpt", "value": "To determine the average efficiency, I analyze the individual efficiency values shown by the heights of the bars in the first sub-chart. The efficiencies are {0.5}<[0.116, 0.158, 0.184, 0.292]>, {0.7}<[0.201, 0.104, 0.268, 0.292]>, {0.75}<[0.285, 0.091, 0.353, 0.292]> and so on till the tenth algorithm, which are duly noted at {0.95}<[0.876, 0.038, 0.944, 0.292]>. Summing these up and dividing by the count (10), we get the average efficiency.\nBased on the above thought, the answer is:\n0.78" } ], "id_raw": 968 }, { "id": "SrZMSs8a1HVgnlce", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nBased on the bar charts you've observed, can you convert the data into a structured CSV format?" }, { "from": "gpt", "value": "To convert the visual data into a tabular format, I need to extract the measurements for each stage of Big Data Integration from each of the three bar graphs. First, let's look at the top subplot at location {'subplot_0'}[0.075, 0.025, 0.985, 0.292]. Here, the y-axis {'ylabel'}[0.024, 0.105, 0.038, 0.211] tells us that it represents the Accuracy of the measurements. And if we observe carefully, each bar in the subplot represents the accuracy of a corresponding stage. For instance, {\"Size of data\"}[{'bar_coordinates'}[0.116, 0.151, 0.230, 0.292]] shows the accuracy of the \"Size of data\" stage is 0.5, judging from its height and the y-axis values. We will repeat this process for the remaining 5 different stages in this subplot. Next, we will look at the middle subplot {'subplot_1'}[0.075, 0.331, 0.985, 0.597]. Following a similar process, we can extract Recall of the measurements for each stage. For instance, the bar for \"Size of data\" {[{'bar_coordinates'}[0.116, 0.525, 0.230, 0.597]]} shows a recall value of 0.2. Let's repeat the same for the remaining stages. Finally, we'll look at the bottom subplot {'subplot_2'}[0.075, 0.636, 0.985, 0.903], and following the same process, we can extract the Precision of the measurements for each stage. For example, \"Size of data\" {[{'bar_coordinates'}[0.116, 0.709, 0.230, 0.903]]} displays a precision value of 0.8. The same will be done for the other stages.\nBased on the above thought, the answer is:\nStages, Accuracy, Recall, Precision\nSize of data, 0.5, 0.2, 0.8\nData processing, 0.67, 0.45, 0.54\nData storage, 0.73, 0.53, 0.61\nData retrieval, 0.6, 0.4, 0.9\nData analysis, 0.9, 0.7, 0.75\nData integration, 0.8, 0.63, 0.85" } ], "id_raw": 969 }, { "id": "c236ZoyGpLJofU5l", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nCan you suggest a stage in Big Data Integration which might need some improvement based on the chart?" }, { "from": "gpt", "value": "To suggest a stage that needs improvement, we need to take all three subplots into account and look for a stage that has lower values in at least two metrics. From the top subplot \"Accuracy\" {'subplot_0'}[0.075, 0.025, 0.985, 0.292], {\"Data processing\"}[{'bar_coordinates'}[0.259, 0.103, 0.373, 0.292]] has a quite low bar, representing a low Accuracy. In the middle subplot \"Recall\" {'subplot_1'}[0.075, 0.331, 0.985, 0.597], Data processing {[{'bar_coordinates'}[0.259, 0.434, 0.373, 0.597]]} again has a relatively low score. In the case of the bottom subplot \"Precision\" {'subplot_2'}[0.075, 0.636, 0.985, 0.903], it also shows an average level of Precision. Since the \"Data processing\" stage has lower values in two metrics and an average score in the third one, it could be suggested for improvement.\nBased on the above thought, the answer is:\nThe stage in Big Data Integration that might need some improvement is Data processing, as it has lower values in both Accuracy and Recall and an average Precision score." } ], "id_raw": 969 }, { "id": "QS78Mj8VJuTf4nDD", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_9.png", "conversations": [ { "from": "human", "value": "\nWhich stage in Big Data Integration achieved the highest Precision according to the chart?" }, { "from": "gpt", "value": "To find out which stage achieved the highest precision, we need to look at the bottom subplot {'subplot_2'}[0.075, 0.636, 0.985, 0.903], which represents Precision values. By comparing heights of all bars, we can notice that the highest one belongs to \"Data retrieval\". This conclusion can be made by noticing that the bar at location {'Data retrieval'}[{'bar_coordinates'}[0.544, 0.685, 0.658, 0.903]] reaches closest to the maximum value of Precision, which is 1 based on the y-axis labels.\nBased on the above thought, the answer is:\nData retrieval." } ], "id_raw": 969 }, { "id": "DxJRhAryUsX4LA4c", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the chart data into a structured table for the efficiency percentages across all three quarters for HR?" }, { "from": "gpt", "value": "Observing the bar chart, I note that {the y-axis label \"Efficiency\"}[0.024, 0.015, 0.038, 0.295] and {x-axis labels}[{label '1'}[0.146,0.919,0.154,0.942] to {label '10'}[0.901, 0.919, 0.919, 0.942]] represent the quarters and processes respectively. The third process, which is HR, has bars in three different sections of the chart. The efficiency for HR in the first quarter is represented by the bar located at {HR first quarter}[0.285, 0.051, 0.353, 0.285] with a y-range representing 0.85 efficiency, in the second quarter located at {HR second quarter}[0.285, 0.392, 0.353, 0.594] with a y-range representing 0.74 efficiency, and in the third quarter located at {HR third quarter}[0.285, 0.701, 0.353, 0.903] indicating an efficiency of 0.91.\nBased on the above thought, the answer is:\nProcess,First Quarter,Second Quarter,Third Quarter\nHR,0.85,0.74,0.91" } ], "id_raw": 970 }, { "id": "AVC1xJLTKx8LnHcn", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nWhich quarter demonstrates the most consistent performance across all processes, and why do you come to this conclusion?" }, { "from": "gpt", "value": "I need to compare the variation in bar heights within each quarter's section to evaluate consistency. The first quarter's bars are at [0.075, 0.025, 0.985, 0.285], and they show varied heights, indicating fluctuating efficiencies across different processes. The second quarter's bars at [0.075, 0.334, 0.985, 0.594] show less variation, implying more consistency, but still have notable differences like for HR at {HR second quarter}[0.285, 0.392, 0.353, 0.594]. The third quarter's bars at [0.075, 0.642, 0.985, 0.903] display similar heights, particularly among Finance at {Finance third quarter}[0.623, 0.675, 0.690, 0.903] and Marketing at {Marketing third quarter}[0.538, 0.708, 0.606, 0.903], illustrating the most consistent performance.\nBased on the above thought, the answer is:\nThe third quarter shows the most consistent performance because the bar heights are similar across different processes, indicating that efficiency levels are more even." } ], "id_raw": 970 }, { "id": "boSGaawhql0pUIez", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in efficiency for the Finance process from the first to the third quarter?" }, { "from": "gpt", "value": "I must locate the Finance process in the first and third quarters and calculate the increase. The Finance bar for the first quarter is at {Finance first quarter}[0.623, 0.043, 0.690, 0.285] with an efficiency of 0.88, and for the third quarter at {Finance third quarter}[0.623, 0.675, 0.690, 0.903] with an efficiency of 0.95. The calculation for percentage increase is then (0.95 - 0.88) / 0.88 * 100%.\nBased on the above thought, the answer is:\n7.95%" } ], "id_raw": 970 }, { "id": "O0M7BpblCWeyc9Q0", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nCan you provide me with a CSV table from the presented bar chart data?" }, { "from": "gpt", "value": "From the chart, I need to convert the bars into numerical data. Let's start with the {Local Government funding}[0.066, 0.031, 0.985, 0.448] bar chart. Starting from the left, each bar from the first bar at {bar 1}[0.108, 0.076, 0.176, 0.448] to the last bar at {bar 10}[0.875, 0.051, 0.943, 0.448], each represents a sequence of fund allocation with the increase gradually from 0.9 to 0.96. Then, moving on to the {Community Development}[0.066, 0.487, 0.985, 0.903] bar chart, the same procedure can be applied. The first bar at {bar 1}[0.108, 0.562, 0.176, 0.903] to the last bar {bar 10}[0.875, 0.540, 0.943, 0.903], depict a trend of increase from 0.9 to 0.96. The only exception is at sequences 5 and 6, where Community Development funding remains constant.\nBased on the above thought, the answer is:\nLocal Government,Community Development\n1,0.9\n2,0.85\n3,0.82\n4,0.79\n5,0.78\n6,0.78\n7,0.82\n8,0.86\n9,0.91\n10,0.96" } ], "id_raw": 971 }, { "id": "Sn2Yxk62fMzfoW4q", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_11.png", "conversations": [ { "from": "human", "value": "\nAt what sequence does the funding for community development start to increase again after reaching the lowest point?" }, { "from": "gpt", "value": "The bar chart for {Community Development}[0.066, 0.487, 0.985, 0.903] shows a decreasing trend at the beginning. Looking at the height of the bars from the first bar at {bar 1}[0.108, 0.562, 0.176, 0.903] to the fifth bar at {bar 5}[0.449, 0.612, 0.517, 0.903], the height of the bars decrease which indicates the funding decreases from 0.9 to 0.78. The sixth bar at {bar 6}[0.534, 0.608, 0.602, 0.903] maintains at the same height, indicating that the funding remains at 0.78. It is the seventh bar at {bar 7}[0.619, 0.596, 0.688, 0.903] where the bar starts to increase in height, indicating the increase of funding from 0.78 to 0.82. Therefore, the funding for community development starts to increase again at sequence 7.\nBased on the above thought, the answer is:\n7" } ], "id_raw": 971 }, { "id": "qrV5sBxA8LE7DRLf", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nCould you generate a structured CSV format table from the bar chart data for the GDP growth rate over the years 2000 to 2009?" }, { "from": "gpt", "value": "Analyzing the first subplot which is dedicated to the GDP growth rate, and examining the bars representing each year from left to right; for instance, {the first bar}[0.116, 0.183, 0.184, 0.292] corresponds to the year 2000 with a growth rate ranging from {0.00}[0.043, 0.282, 0.065, 0.306] to {0.30}[0.116, 0.183]. By analyzing each bar's top and bottom coordinates relative to the y-axis labels, I can determine the GDP growth rate for each year. This process is repeated for each subsequent bar in the chart.\nBased on the above thought, the answer is:\nYear,GDP Growth Rate\n2000,0.3\n2001,0.4\n2002,0.5\n2003,0.6\n2004,0.7\n2005,0.6\n2006,0.65\n2007,0.7\n2008,0.6\n2009,0.45" } ], "id_raw": 972 }, { "id": "iLWWUwDbFedewMHZ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the overall trend of inflation rate based on the bar chart data?" }, { "from": "gpt", "value": "Focusing on the second subplot, which depicts the inflation rates, I begin by looking at the first year, {2000}[0.133, 0.919, 0.168, 0.942], with its corresponding bar reaching just below {0.5}[0.043, 0.475, 0.065, 0.499]. Surveying the bars chronologically, I notice fluctuations in the inflation rate, as some years, like {2003}[0.386, 0.919, 0.421, 0.942], show higher rates approaching {0.9}[0.043, 0.362, 0.065, 0.386]. However, after {2004}[0.470, 0.919, 0.505, 0.942], there's a visible downturn with a decrease in bar height, eventually leading to a gradual decline until it reaches a low in {2009}[0.892, 0.919, 0.927, 0.942], where the bar height is just marginally higher than the starting point in {2000}[0.116, 0.456, 0.184, 0.597]. This pattern indicates an erratic but overall downward trend in inflation after 2004, followed by some stabilization at rates just below {0.5}[0.043, 0.475, 0.065, 0.499].\nBased on the above thought, the answer is:\nInflation rate showed an erratic behavior with peaks around 2003 and 2004; however, after 2004, the trend was a downward trajectory, ending the decade at a lower rate almost similar to that of the year 2000." } ], "id_raw": 972 }, { "id": "g6TcF7lx9JK0fkfY", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_12.png", "conversations": [ { "from": "human", "value": "\nWhat was the unemployment rate in 2008?" }, { "from": "gpt", "value": "To determine the unemployment rate in 2008, I will assess the third subplot dedicated to unemployment rates. Locating the specific bar representing the year {2008}[0.808, 0.919, 0.843, 0.942], I then examine the top of the bar at [0.792, 0.721] and compare it to the y-axis labels, finding that the top aligns with the label {0.75}[0.043, 0.700, 0.065, 0.723], which indicates an unemployment rate of 0.75.\nBased on the above thought, the answer is:\n0.75" } ], "id_raw": 972 }, { "id": "MvvK1fRhiT8plwWt", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nTurn the data on \"Internet Users\" from the bar chart into a structured table." }, { "from": "gpt", "value": "Analyzing the bar chart specifically for {Internet Users}[0.066, 0.025, 0.985, 0.292] by examining the {bars}[0.108, 0.228, 0.176, 0.292]; [0.193, 0.196, 0.261, 0.292]; ..., I observe a consistent ascending trend. Starting with the year 2010, a {bar}[0.108, 0.228, 0.176, 0.292] presents a value range that starts from 0 indicated by the coordinate base at [0.108, 0.292] and rises to approximately 0.3 based on the top of the bar at [0.108, 0.228]. Proceeding year by year, the top of each bar indicates the higher range of Internet Users for the corresponding year. I shall convert the heights of these bars into a structured table.\nBased on the above thought, the answer is:\nYear,Internet Users\n2010,0.3\n2011,0.45\n2012,0.5\n2013,0.55\n2014,0.7\n2015,0.85\n2016,0.9\n2017,0.95\n2018,1.0\n2019,1.2" } ], "id_raw": 973 }, { "id": "L1IB9NmE02fNUekq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nIs there any indicator among \"Internet Users,\" \"Mobility Rate,\" or \"Political Protests\" that has demonstrated a consistent decline or increase over the period shown?" }, { "from": "gpt", "value": "To answer this, I will review the trends observed in the bars for {Internet Users}[0.066, 0.025, 0.985, 0.292], {Mobility Rate}[0.066, 0.331, 0.985, 0.597] and {Political Protests}[0.066, 0.636, 0.985, 0.903]. For a consistent increase, the bars' tops should move upwards progressively, and for a consistent decrease, they should move down year after year. By examining the heights of bars, such as [0.108, 0.228, 0.176, 0.292] for 2010 and progressing to [0.875, 0.038, 0.943, 0.292] for 2019, I can determine the trend for \"Internet Users.\" The other indicators' bars are analyzed similarly.\nBased on the above thought, the answer is:\n\"Internet Users\" has shown a consistent increase over the period, with the top of the bars rising higher each year from {2010}[0.108, 0.228, 0.176, 0.292] to {2019}[0.875, 0.038, 0.943, 0.292]. Contrarily, \"Political Protests\" demonstrated an overall decline, especially noticeable with low peaks in {2018}[0.790, 0.882, 0.858, 0.903] and {2019}[0.875, 0.872, 0.943, 0.903]. \"Mobility Rate\" fluctuated and does not show a consistent trend." } ], "id_raw": 973 }, { "id": "4Z7KRBMdNPVX1EXk", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_13.png", "conversations": [ { "from": "human", "value": "\nHow many times did the value of \"Political Protests\" drop below 0.20 between 2010 to 2019?" }, { "from": "gpt", "value": "To find the number of times the \"Political Protests\" dropped below 0.20, I will analyze each {bar}[0.108, 0.813, 0.176, 0.903]; [0.193, 0.825, 0.261, 0.903]; ... on the bar chart dedicated to {Political Protests}[0.066, 0.636, 0.985, 0.903]. A lower bar top compared to {0.2 tick mark on the y-axis}[0.035, 0.853, 0.057, 0.876] will indicate the value is less than 0.20. Each respective year-wise bar is analyzed and compared against this benchmark to count the occurrences.\nBased on the above thought, the answer is:\nThree times." } ], "id_raw": 973 }, { "id": "SrOWlSpm58wv8GRs", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nCan you transform the efficiency values of the three products for series 1 to 5 into a structured table format?" }, { "from": "gpt", "value": "Upon analysis of the bar chart, particularly the three subplots, I can see that for each product series there are corresponding bars for products 1 to 3 representing their respective efficiency values. The first subplot corresponds to the efficiency of {Product 1}[0.075, 0.025, 0.985, 0.292] with {bars}[0.116, 0.108, 0.184, 0.292]; [0.201, 0.103, 0.268, 0.292]; [0.285, 0.091, 0.353, 0.292]; [0.370, 0.088, 0.437, 0.292]; [0.454, 0.080, 0.522, 0.292]. The values of these bars can be derived from their height in relation to the {y-ticks}[0.043, 0.000, 0.065, 0.024]. The second and third subplots follow the same structure for {Product 2}[0.075, 0.331, 0.985, 0.597] and {Product 3}[0.075, 0.636, 0.985, 0.903], respectively. For instance, the first bar of Product 1 located at [0.116, 0.108, 0.184, 0.292] corresponds to an efficiency value of 0.65, as indicated by the bar's top reaching just above the 0.6 mark on the y-axis.\nBased on the above thought, the answer is:\nProduct Series,Product 1,Product 2,Product 3\n1,0.65,0.73,0.89\n2,0.67,0.75,0.85\n3,0.71,0.67,0.93\n4,0.72,0.64,0.91\n5,0.75,0.62,0.96" } ], "id_raw": 974 }, { "id": "Yg2CiUfYTkm7DHPi", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nWhich product shows the steadiest increase in efficiency over the series?" }, { "from": "gpt", "value": "To identify which product shows the most consistent rise in efficiency, it is essential to examine the slope of the bars representing efficiency across all series for each product. Starting with {Product 1}[0.075, 0.025, 0.985, 0.292], the bars gradually increase in height from the first series located at [0.116, 0.108, 0.184, 0.292] to the tenth series located at [0.876, 0.038, 0.944, 0.292], indicating a steady increase. Next, evaluating {Product 2}[0.075, 0.331, 0.985, 0.597], I notice a slight decrease in efficiency from the bar located at [0.538, 0.404, 0.606, 0.597] to the one at [0.623, 0.413, 0.690, 0.597], which implies a less stable progression. Lastly, {Product 3}[0.075, 0.636, 0.985, 0.903] also shows larger fluctuations, particularly a significant peak at the bar located at [0.538, 0.665, 0.606, 0.903]. This analysis allows me to conclude that Product 1 has the most gradual and steady increase in efficiency.\nBased on the above thought, the answer is:\nProduct 1 shows the steadiest increase in efficiency. This is evident by the progressive and consistent rise of the bars in its respective subplot, indicating minimal fluctuations in its growth throughout the series." } ], "id_raw": 974 }, { "id": "VOb1N7zvD5xYuDw2", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_65_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum efficiency recorded for Product 3 across all series?" }, { "from": "gpt", "value": "To calculate the maximum efficiency value for {Product 3}[0.075, 0.636, 0.985, 0.903], I scan all the bars corresponding to Product 3 in series 1 through 10. The vertical height of each bar linked to Product 3 on the y-axis tells us its efficiency value. On careful observation, I notice that the bar for series 6, located at [0.538, 0.665, 0.606, 0.903], reaches the highest point just at the level of the y-axis' maximum value, which corresponds to an efficiency of 1.0.\nBased on the above thought, the answer is:\n1.0" } ], "id_raw": 974 }, { "id": "v51zkKl4K5EREC75", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the information in the visual data into a structured table in CSV format?" }, { "from": "gpt", "value": "From the {study of the legend}[0.078, 0.074, 0.273, 0.325], I can see that each color represents one technological industry. Combining this information with the data represented by individual bars in the chart, we can figure out the development level each industry reached in each particular year. For example, the {blue rectangle}[0.112, 0.535, 0.133, 0.935] represents the 'Artificial Intelligence (AI)' industry in the year 2015, and according to the y-axis, its development level was about 55. Each industry has four bars, each located in a different x-axis category, which corresponds to the years 2015, 2020, 2025, and 2030 respectively. By examining the height of each bar and cross-verifying with the y-axis, I can determine the development level of each industry in each year.\nBased on the above thought, the answer is:\nYear,AI,Cloud Computing,Quantum Computing,Mobile Technology,IoT,AR,VR\n2015,55,60,15,45,30,25,10\n2020,80,75,55,70,85,30,15\n2025,110,90,70,75,100,40,20\n2030,90,100,85,80,105,50,30" } ], "id_raw": 975 }, { "id": "8Gi1ZsRcSmnBv7Gg", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nIn your opinion, which technological industry will be the most developed by the year 2030 and why do you say this?" }, { "from": "gpt", "value": "The question asks me to identify the most developed technological industry by 2030, which can be determined by the height of the bars in the 2030 category. In this case, examining the {height of bars}[0.799, 0.281, 0.820, 0.935];[0.820, 0.208, 0.840, 0.935];[0.840, 0.317, 0.861, 0.935];[0.861, 0.353, 0.882, 0.935];[0.882, 0.171, 0.902, 0.935];[0.902, 0.572, 0.923, 0.935];[0.923, 0.717, 0.943, 0.935] in the x-axis 2030 category, I found that the IoT sector bar was the tallest, indicating the IoT will be the most developed sector by 2030.\nBased on the above thought, the answer is:\nThe Internet of Things (IoT) sector will be the most developed by 2030 according to the chart. This is indicated by the height of its corresponding bar in the 2030 category, which is visibly higher than all other sectors' bars." } ], "id_raw": 975 }, { "id": "NWZaIjWIwp9f19Tv", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which technological sector has the highest developmental growth from 2015 to 2030?" }, { "from": "gpt", "value": "In order to find out the technological sector that has the highest developmental growth from 2015 to 2030, I need to calculate the difference between the development levels in 2030 and 2015 for each sector. For example, for AI, I need to compare the heights of the bars {\"in 2015\"}[0.112, 0.535, 0.133, 0.935] and {\"in 2030\"}[0.799, 0.281, 0.820, 0.935]. The same method can be applied to Cloud Computing with bars {\"in 2015\"}[0.133, 0.499, 0.153, 0.935] and {\"in 2030\"}[0.820, 0.208, 0.840, 0.935]. After the differences are calculated for all sectors, the largest one is chosen. Through my observations, I find that the IoT sector represented by bars {\"in 2015\"}[0.195, 0.717, 0.215, 0.935] and {\"in 2030\"}[0.882, 0.171, 0.902, 0.935] has the most significant growth from about 30 to 105, which amounts to a 75 point increase.\nBased on the above thought, the answer is:\nInternet of Things (IoT)." } ], "id_raw": 975 }, { "id": "amKe2TQyQZhVPzV9", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you generate a CSV table to detail the market share percentage of each game from 2016 to 2022?" }, { "from": "gpt", "value": "From the chart title at the top, {Game Development in the Entertainment Industry}[0.314, 0.025, 0.733, 0.055], it is clear that the chart is about popular games from 2016 to 2022. For each game, there are four bars with different colors. The bars are grouped by the game names located on the x-axis, with labels such as {\"WoW\"}[0.163, 0.952, 0.198, 0.975] and {\"LoL\"}[0.315, 0.952, 0.338, 0.975]. According to the colored lyrics located at {the legend}[0.893, 0.074, 0.978, 0.220], the colors of the bars indicate different years. Each bar's height corresponds to its game's market share for that year. \nTo generate the CSV table, I will extract the height of the bars for each game in each year. For instance, with {the bar for WoW in 2016}[0.104, 0.312, 0.126, 0.935], I estimate the market share to be approximately 60%. I do the same for each game in each year.\nBased on the above thought, the answer is:\nGame,2016,2018,2020,2022\nWoW,60,50,40,35\nLoL,55,65,75,80\nDOTA,50,55,60,65\nPUBG,5,15,25,45\nFortnite,10,15,40,40\nOverwatch,45,45,45,40" } ], "id_raw": 976 }, { "id": "90bN2YBOeYor0ekd", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_1.png", "conversations": [ { "from": "human", "value": "\nBy 2022, what is the growth rate of the \"PUBG\" market share from 2016?" }, { "from": "gpt", "value": "To find the growth rate, I first need to find the market shares of PUBG in both 2016 and 2022. Looking at {the 2016 bar for PUBG}[0.542, 0.883, 0.564, 0.935], it stands at around 5%, and in 2022 {the 2022 bar for PUBG}[0.629, 0.468, 0.651, 0.935] stands at around 45%. Using the formula (End Value - Start Value)/ Start Value* 100, I can calculate the growth rate.\nBased on the above thought, the answer is:\n800%" } ], "id_raw": 976 }, { "id": "AFyCW9giq6QDv341", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nBased on your inspection of the bar chart, can you convert the depicted data into a table form?" }, { "from": "gpt", "value": "First, I note that the x-axis labels represent the different stages of the business value chain which include: {R&D}[0.149, 0.942, 0.193, 0.970]; {Raw Materials}[0.222, 0.942, 0.362, 0.970]; {Product Design}[0.338, 0.942, 0.488, 0.970]; {Manufacturing}[0.461, 0.942, 0.606, 0.970]; {Distribution}[0.596, 0.942, 0.712, 0.970]; {Sales}[0.749, 0.942, 0.801, 0.970] and {After Sales}[0.842, 0.942, 0.949, 0.970]. Each stage features two corresponding bars: one for {Value Creation}[0.162, 0.100, 0.307, 0.128], represented by the blue color, and another for {Value Erosion}[0.162, 0.142, 0.295, 0.170], represented by the red color. I can determine the specific value at each stage by examining the height of the corresponding bars. For example, for the stage R&D, the blue bar measures approximately 38 units, as indicated by the height of {the bar}[0.129, 0.462, 0.171, 0.923] and the red bar measures approximately 12 units, as indicated by the height of {the bar}[0.171, 0.777, 0.213, 0.923]. Therefore, I can convert this information into table form.\nBased on the above thought, the answer is:\nValue Chain Element,Value Creation,Value Erosion\nR&D,38,12\nRaw Materials,42,10\nProduct Design,45,10\nManufacturing,50,8\nDistribution,55,6\nSales,60,5\nAfter Sales,65,3" } ], "id_raw": 977 }, { "id": "9ekC0CbAFwJWAusk", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, can you deduce at which stage of the value chain the business is most successful at minimizing Value Erosion?" }, { "from": "gpt", "value": "To answer this question, I need to identify the stage where Value Erosion is the lowest. The Value Erosion bars are represented by {red}[0.106, 0.144, 0.146, 0.164] on the chart. By tracking the red bar heights across various stages such as {R&D}[0.171, 0.777, 0.213, 0.923]; {Raw Materials}[0.292, 0.801, 0.334, 0.923]; {Product Design}[0.413, 0.801, 0.455, 0.923]; {Manufacturing}[0.534, 0.826, 0.576, 0.923]; {Distribution}[0.654, 0.850, 0.697, 0.923]; {Sales}[0.775, 0.862, 0.817, 0.923]; {After Sales}[0.896, 0.886, 0.938, 0.923], I can determine that the business is most successful at minimizing Value Erosion in the After Sales Services stage, as this stage has the smallest Value Erosion bar.\nBased on the above thought, the answer is:\nAfter Sales Services." } ], "id_raw": 977 }, { "id": "0ojSX4MCBqH2ZKWS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_2.png", "conversations": [ { "from": "human", "value": "\nIn which value chain stage is the difference between Value Creation and Value Erosion the smallest?" }, { "from": "gpt", "value": "To derive the answer, I need to locate where the value difference between Value Creation and Value Erosion is at its smallest. To do this, I need to compare the heights of two corresponding bars at each stage. After comparing the bars from various stages such as {R&D}[0.129, 0.462, 0.171, 0.923], [0.171, 0.777, 0.213, 0.923] ; {Raw Materials}[0.250, 0.414, 0.292, 0.923], [0.292, 0.801, 0.334, 0.923]; {Product Design}[0.370, 0.377, 0.413, 0.923], [0.413, 0.801, 0.455, 0.923]; {Manufacturing}[0.491, 0.317, 0.534, 0.923], [0.534, 0.826, 0.576, 0.923]; {Distribution}[0.612, 0.256, 0.654, 0.923], [0.654, 0.850, 0.697, 0.923]; {Sales}[0.733, 0.196, 0.775, 0.923], [0.775, 0.862, 0.817, 0.923]; {After Sales}[0.854, 0.135, 0.896, 0.923], [0.896, 0.886, 0.938, 0.923]. From this comparison, I can see the smallest difference (of 2 units) is at the {Sales} stage.\nBased on the above thought, the answer is:\nSales." } ], "id_raw": 977 }, { "id": "iUqAXcJqsKtnNxRl", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart's data representing various AI model's performance over four different sequences into a CSV format?" }, { "from": "gpt", "value": "From the dataset's title {\"AI Models Performance Over Sequences\"}[0.296, 0.030, 0.770, 0.066], I can infer that the x-axis represents different AI models and the y-axis represents their corresponding performance scores. Some of the AI models noted are {\"Perceptron\"}[0.129, 0.942, 0.236, 0.970], {\"SVM\"}[0.301, 0.942, 0.344, 0.970], {\"KNN\"}[0.442, 0.942, 0.484, 0.970], {\"NaiveBayes\"}[0.545, 0.942, 0.661, 0.970], {\"DecisionTree\"}[0.680, 0.942, 0.806, 0.970], and {\"GradientBoosting\"}[0.797, 0.942, 0.970, 0.970]. By measuring the heights of the bars themselves, I can estimate the performance scores for each model. For instance, the height of {\"Perceptron\"} bar sequence 1 [0.140, 0.287, 0.161, 0.923] is approximately at the 75 position on the y-axis, so the value of Seq-1 for the Perceptron model is 75.\nAnd we can follow the same pattern for the other bars as well.\nBased on the above thought, the answer is:\nAI_Models,Seq-1,Seq-2,Seq-3,Seq-4\nPerceptron,75,80,85,70\nSVM,65,70,90,95\nKNN,60,70,80,55\nNaiveBayes,70,60,90,85\nDecisionTree,75,85,95,70\nRandomForest,80,95,90,80\nGradientBoosting,90,95,85,75" } ], "id_raw": 978 }, { "id": "nwTpjG9ahS9n8c63", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which AI model has seen a decrease in performance from Seq-1 to Seq-4?" }, { "from": "gpt", "value": "I can answer this question by observing the heights of the bars representing Seq-1 and Seq-4 for each AI model. For instance, in the case of {\"Perceptron\"} [0.140, 0.287, 0.161, 0.923] [0.203, 0.329, 0.224, 0.923], the performance appears to decrease from 75 in Seq-1 to 70 in Seq-4. Similarly, for {\"KNN\"} [0.421, 0.414, 0.442, 0.923] [0.484, 0.456, 0.505, 0.923], the performance goes from 60 in Seq-1 to 55 in Seq-4. Thus, both these models show a decrease in performance between Seq-1 and Seq-4.\nBased on the above thought, the answer is:\nPerceptron and KNN" } ], "id_raw": 978 }, { "id": "zCMJBBNop4aTtw4q", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_3.png", "conversations": [ { "from": "human", "value": "\nWhich AI model performs best in Seq-2 based on the bar chart?" }, { "from": "gpt", "value": "In order to find the best-performing AI model in Seq-2, I need to compare the heights of the second bar in each group. Looking from left to right, I examine the bars representing each AI model in sequence 2. For instance, the second bar for the {\"Perceptron\"} model [0.161, 0.244, 0.182, 0.923] represents score 80, and the second bar for the {\"SVM\"} model [0.301, 0.329, 0.323, 0.923] represents score 70. Continuing in this manner, I can find that the {\"GradientBoosting\"} [0.862, 0.117, 0.884, 0.923] model has the tallest second bar, which corresponds to a score of 95. This is the highest among the AI models investigated.\nBased on the above thought, the answer is:\nGradientBoosting" } ], "id_raw": 978 }, { "id": "HOPmV8bpEdvLDOt4", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nPlease structure the data from the bar chart into a CSV file." }, { "from": "gpt", "value": "Based on the data in the chart, each year from 2000 to 2020 has data for startups founded, patents filed, investments received, and funds raised which are marked with distinct colors according to {the legend}[0.123, 0.089, 0.413, 0.264]. To form a CSV table, I'll need to denote each data group for every year. For example, in 2000, the bar that corresponds to startups founded reaches up to approximately 55 on the y-axis. Then, the bar that stands for patents filed extends up to around 120. The bar for investments received measures about 150, and the bar for funds raised is nearly non-visible, around 5 million USD. Similar steps will be followed for subsequent years.\nBased on the above thought, the answer is:\nYear,Startups Founded,Patents Filed,Investments Received,Funds Raised\n2000,55,120,150,5000000\n2005,150,200,300,12000000\n2010,450,560,650,40000000\n2015,600,700,850,55000000\n2020,650,780,900,60000000" } ], "id_raw": 979 }, { "id": "FugqN2KChZCP80zD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nWhat do you infer about investments and entrepreneurial trends based on the bar chart trends?" }, { "from": "gpt", "value": "Based on the bar chart, there is a significant increase in all four parameters - Startups Founded, Patents Filed, Investments Received, and Funds Raised - over the 20-year period. The chart suggests that the entrepreneurial sector was becoming more active and attracting more investment. For instance, looking at the bars for {startups founded}[{\"2000\"}[0.153, 0.876, 0.178, 0.923]; {\"2005\"}[0.324, 0.795, 0.349, 0.923]; {\"2010\"}[0.495, 0.541, 0.520, 0.923]; {\"2015\"}[0.666, 0.414, 0.691, 0.923]; {\"2020\"}[0.837, 0.371, 0.862, 0.923]], these bars show that the number of startups founded increased rapidly from 2000 to 2015 with a slight decrease in 2020. This indicates the booming startup culture and entrepreneurial spirit during this period. Additionally, {the investments received}[{\"2000\"}[0.204, 0.795, 0.230, 0.923]; {\"2005\"}[0.375, 0.668, 0.401, 0.923]; {\"2010\"}[0.546, 0.371, 0.572, 0.923]; {\"2015\"}[0.717, 0.202, 0.743, 0.923]; {\"2020\"}[0.888, 0.159, 0.914, 0.923]] and {the funds raised}[{\"2000\"}[0.230, 0.918, 0.255, 0.923]; {\"2005\"}[0.401, 0.912, 0.426, 0.923]; {\"2010\"}[0.572, 0.889, 0.597, 0.923]; {\"2015\"}[0.743, 0.876, 0.768, 0.923]; {\"2020\"}[0.914, 0.872, 0.939, 0.923]] also increased over time, signaling growing investor confidence and mobilization of funds into the sector despite the varying number of startups in the latter years.\nBased on the above thought, the answer is:\nThe bar chart suggests a positive trend in entrepreneurial spirit and investments over the period from 2000 to 2020. The increase in startups founded, patents filed, investments received, and funds raised suggests that the startup culture boomed during this period, attracting more investments. The slight decline in the number of startups in 2020 didn't deter the growth of investments, suggesting that the industry continued to have a positive outlook and investor confidence remained high." } ], "id_raw": 979 }, { "id": "ok2dwUGL3W4HA3F6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_4.png", "conversations": [ { "from": "human", "value": "\nIn what year were the number of startups founded the highest?" }, { "from": "gpt", "value": "To determine the year with the highest number of startups, I'll need to compare the heights of the {bars representing startups}[{\"2000\"}[0.153, 0.876, 0.178, 0.923]; {\"2005\"}[0.324, 0.795, 0.349, 0.923]; {\"2010\"}[0.495, 0.541, 0.520, 0.923]; {\"2015\"}[0.666, 0.414, 0.691, 0.923]; {\"2020\"}[0.837, 0.371, 0.862, 0.923]]. Looking at the height of these bars, I see that the highest bar is for the year 2015. Therefore, the number of startups founded was the highest in that year.\nBased on the above thought, the answer is:\n2015" } ], "id_raw": 979 }, { "id": "4rPMIRnbsnbUXGGk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nCan you read the bar chart and convert the data into a CSV format data table?" }, { "from": "gpt", "value": "From the title at the top of the chart {Cuisine and Gastronomy Culture Analysis}[0.356, 0.021, 0.700, 0.047] and {the x-axis tick labels}[{\"Mediterranean\"}[0.116, 0.959, 0.217, 0.979]; {\"Asian\"}[0.328, 0.959, 0.366, 0.979]; {\"Anglo Saxon\"}[0.485, 0.959, 0.571, 0.979]; {\"Latin America\"}[0.660, 0.959, 0.757, 0.979]; {\"African\"}[0.865, 0.959, 0.914, 0.979]], I can see that the x-axis represents different Cuisines. The y-axis tick labels represent the corresponding values, ranging from {0}[0.052, 0.937, 0.061, 0.957] to {100}[0.035, 0.045, 0.061, 0.065]. For each Cuisine, there are three bars which represent Rating, Popularity, and Diversity respectively. According to {the legend}[0.858, 0.063, 0.978, 0.159], {the red bars}[{\"Rating\"}[0.903, 0.071, 0.948, 0.091]] represent Rating, the {blue bars}[{\"Popularity\"}[0.903, 0.101, 0.972, 0.121]] denote Popularity, and the {green bars}[{\"Diversity\"}[0.903, 0.131, 0.964, 0.151]] denote Diversity. I need to extract all values of each bar, reading from the left. For example, the height of the {red bar for \"Mediterranean\"}[0.112, 0.187, 0.148, 0.945] is approximately at the 85 position, so I infer that the Rating for the Mediterranean is 85.\nBased on the above thought, the answer is:\nCuisine,Rating,Popularity,Diversity\nMediterranean,85,90,75\nAsian,80,85,88\nAnglo Saxon,60,65,70\nLatin America,75,70,85\nAfrican,55,52,58" } ], "id_raw": 980 }, { "id": "EkkJhbXX5NnOUGPB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nHow would you rank these cuisines based on their popularity according to this chart?" }, { "from": "gpt", "value": "The Popularity of each cuisine is represented by the {blue bars}[{\"handle\": {\"color\": \"#b2d8f7\"}}] in each group. To rank the cuisines, I need to compare the heights of these blue bars. The higher the bar, the more popular the cuisine. For example, the height of {the blue bar for Mediterranean}[0.148, 0.142, 0.184, 0.945] corresponds to a score of 90, which is the highest among all the cuisines, so Mediterranean is the most popular one. Similarly, I can find that Asian is the second most popular cuisine with a score of 85 from {the blue bar for Asian}[0.329, 0.187, 0.365, 0.945], and so on.\nBased on the above thought, the answer is:\nMediterranean, Asian, Latin America, Anglo Saxon, African." } ], "id_raw": 980 }, { "id": "qCnt2wes3nGxmVTX", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_5.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine has the highest sum of rating, popularity and diversity scores?" }, { "from": "gpt", "value": "First, I need to measure the score of each cuisine by adding the score positions of the three bars in each group (Rating, Popularity and Diversity). For example, {the group representing Mediterranean cuisine}[0.112, 0.142, 0.221, 0.945] consists of a {red bar}[0.112, 0.187, 0.148, 0.945], a {blue bar}[0.148, 0.142, 0.184, 0.945] and a {green bar}[0.184, 0.276, 0.221, 0.945], with the positions of their tops corresponding to 85, 90 and 75 respectively, so the total score of the Mediterranean is 85 + 90 + 75 = 250. I repeat this calculation for each of the other cuisines {Asian}[0.293, 0.187, 0.401, 0.945], {Anglo Saxon}[0.474, 0.365, 0.582, 0.945], {Latin America}[0.654, 0.187, 0.763, 0.945] and {African}[0.835, 0.428, 0.943, 0.945] to find out the cuisine with the highest total score.\nBased on the above thought, the answer is:\nMediterranean." } ], "id_raw": 980 }, { "id": "jSJXpSg5u6kcAJip", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV table showing the performance of the Information Security sector from 2011 to 2020 as depicted in the bar chart?" }, { "from": "gpt", "value": "Observing the bar chart, which displays the trends in technology sectors over several years, I focus on the bars representing the Information Security sector. These bars are assigned a green color, with relevant coordinates provided for each bar. Based on the chart's structure, the years 2011 to 2020 are indicated on the x-axis, with coordinates provided for each year label. The y-axis denotes the performance metric, with each tick labeled accordingly. To create the CSV table, I will extract the information relating to the Information Security sector by translating the bar height in relation to the y-axis tick labels, which indicate performance values. The coordinates represent the bottom and top of each green bar for years 2011 to 2020 as follows: {bar 2011}[0.130, 0.722, 0.143, 0.945], {bar 2012}[0.216, 0.700, 0.229, 0.945], through to {bar 2020}[0.904, 0.499, 0.917, 0.945].\nBased on the above thought, the answer is:\nYear,Information Security Performance\n2011,20\n2012,22\n2013,24\n2014,26\n2015,28\n2016,30\n2017,32\n2018,34\n2019,36\n2020,40" } ], "id_raw": 982 }, { "id": "mAxAy59NygZyNgjH", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nConsidering the chart data, which sector has shown the most inconsistent growth pattern between the years 2011 and 2020?" }, { "from": "gpt", "value": "To determine the sector with the most inconsistent growth pattern, I will analyze the year-over-year changes in the height of bars for each sector. By examining the {Computer Manufacturing}[0.104, 0.610, 0.117, 0.945], {Software Development}[0.117, 0.443, 0.130, 0.945], {Information Security}[0.130, 0.722, 0.143, 0.945], {Data Analysis}[0.143, 0.555, 0.156, 0.945], and {AI Research}[0.156, 0.666, 0.168, 0.945], I observe the intervals between the bars within each sector. Notable fluctuations such as the surge in {AI Research}[0.672, 0.298, 0.685, 0.945] in 2017 indicate inconsistency. This sector\u2019s performance jumps sharply compared to the gradual increase in the prior years, suggesting it has the most inconsistent growth pattern.\nBased on the above thought, the answer is:\nAI Research. The AI Research sector displays a sudden significant increase in performance in 2017, making it the most inconsistent compared to the other sectors, which exhibit more gradual trends." } ], "id_raw": 982 }, { "id": "fwGq1fTN9jxSB5vm", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_7.png", "conversations": [ { "from": "human", "value": "\nWhat was the average annual growth rate for the AI Research sector between 2011 to 2020?" }, { "from": "gpt", "value": "To calculate the average annual growth rate for the AI Research sector, I first locate the bars corresponding to this sector, denoted by a purple color. I then note the performance value at the top of the bars for each year from 2011 to 2020, given by the y-range attribute in the JSON: {2011}[0.156, 0.666, 0.168, 0.945] with a value of 25, through to {2020}[0.930, 0.165, 0.943, 0.945] with a value of 70. With these values, I compute the average annual growth rate using the formula [(final value / initial value) ^ (1 / number of years) - 1], where 'final value' is 70, 'initial value' is 25, and 'number of years' is 9.\nBased on the above thought, the answer is:\n11.07%" } ], "id_raw": 982 }, { "id": "9FORkjvAH4zNxzRy", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nGiven the bar chart data, can you convert this into a CSV table format?" }, { "from": "gpt", "value": "Observing the {chart title \"Future Energy and Resource Allocation\"}[0.310, 0.030, 0.769, 0.066], I can discern that the data talks about resource allocation percentages across distinctive energy sectors. The {x-axis}[0.540, 0.981, 0.540, 0.981] represents the various energy sectors: {Solar}[0.148, 0.942, 0.198, 0.970], {Wind}[0.331, 0.942, 0.381, 0.970], {Geothermal}[0.481, 0.942, 0.598, 0.970], {Hydro}[0.694, 0.942, 0.753, 0.970], null and {Nuclear}[0.868, 0.942, 0.945, 0.970]. \nThe Y Axis provides a range from {0%}[0.075, 0.912, 0.087, 0.940], up to {100%}[0.006, 0.064, 0.033, 0.092], and each corresponding vertical bar height gives an indication of each energy sector's resource allocation, which can be compared with the y-axis ticks to get its value. For instance, {the bar representative of Solar's allocation}[0.141, 0.626, 0.205, 0.923] clearly reaches upwards to about 35% on the y-axis. This data can be transcribed into a CSV format based on these observations.\nBased on the above thought, the answer is:\nEnergies, Resource Allocation\nSolar, 35\nWind, 25\nGeothermal, 40\nHydro, 20\nNuclear, 60" } ], "id_raw": 983 }, { "id": "7qc42s9gS27iDMPy", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, what can you infer about the future allocation of resources?" }, { "from": "gpt", "value": "The chart titled {Future Energy and Resource Allocation}[0.310, 0.030, 0.769, 0.066] depicts a distinct trend in resource allocation for different energy sectors. Examining the bars for each sector [0.141, 0.626, 0.205, 0.923]; [0.324, 0.711, 0.388, 0.923]; [0.508, 0.583, 0.572, 0.923]; [0.691, 0.753, 0.755, 0.923]; [0.874, 0.414, 0.939, 0.923], I can infer that the graphic indicates a higher future priority for nuclear energy, given its highest allocation at 60%, followed by Geothermal having a reasonable focus with a 40% allocation. Conversely, the Hydro sector, with the lowest allocation of 20%, seems to be of lesser priority in future strategies based on these numbers.\nBased on the above thought, the answer is:\nIn the future, the majority of resources are expected to be allocated to the Nuclear sector, with a significant consideration for the Geothermal sector. However, allocation for the Hydro sector seems to be a lower priority based on current trends." } ], "id_raw": 983 }, { "id": "d4uTPWkOZxJ7nhM3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_8.png", "conversations": [ { "from": "human", "value": "\nWhat energy sector consumes the second largest resource allocation?" }, { "from": "gpt", "value": "By examining the {heights of the bars}, [{Solar bar}[0.141, 0.626, 0.205, 0.923]; {Wind bar}[0.324, 0.711, 0.388, 0.923]; {Geothermal bar}[0.508, 0.583, 0.572, 0.923]; {Hydro bar}[0.691, 0.753, 0.755, 0.923]; {Nuclear bar}[0.874, 0.414, 0.939, 0.923]] for each sector, I can identify that the {Nuclear's bar}[0.874, 0.414, 0.939, 0.923] is the tallest reaching the y-axis value of 60 representing the greatest resource allocation. The next tallest bar right after it is {the Geothermal's}[0.508, 0.583, 0.572, 0.923], indicating that it has the second-largest resource allocation.\nBased on the above thought, the answer is:\nGeothermal" } ], "id_raw": 983 }, { "id": "9XG50K4taiY9ggJV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the bar chart, can you organize the utilization data for the educational resources in each series into a CSV format table?" }, { "from": "gpt", "value": "According to the information provided by the x-axis, it is clear that we have several categories for the nature of the educational resources, as found in {\"Tutorials\"}[0.145, 0.959, 0.203, 0.979], {\"Videos\"}[0.328, 0.959, 0.374, 0.979], {\"Exercises\"}[0.495, 0.959, 0.560, 0.979], {\"RealCases\"}[0.669, 0.959, 0.740, 0.979], and {\"SampleCodes\"}[0.834, 0.959, 0.929, 0.979]. The y-axis gives us numerical values to assign for the utilization rate of each resource in the two series, as indicated in the ranges from {0}[0.052, 0.937, 0.061, 0.957] to {100}[0.035, 0.045, 0.061, 0.065]. According to {the legend}[0.078, 0.063, 0.309, 0.129], {the light green bars}<{\"Fluctuating Decline Series\"}[0.122, 0.071, 0.303, 0.091]> express the declining utilization trend, while {the light blue bars}<{\"Continuous Rise Series\"}[0.122, 0.101, 0.281, 0.121]> express the continuous increase in utilization. From these details, the utilization rates for both trends can be extracted per each educational resource, referring to the height of the bars. For instance, the height of {the light green bar for \"Tutorials\"}[0.112, 0.383, 0.174, 0.945] is about 63 units, and the height of {the light blue bar for \"Tutorials\"}[0.174, 0.633, 0.236, 0.945] is about 35 units in comparison, to correspond with the data for the two series. All the values must follow the same extraction method to complete the table accordingly.\nBased on the above thought, the answer is:\nResources,FluctuatingDeclineSeries,ContinuousRiseSeries\nTutorials,63,35\nVideos,58,45\nExercises,50,55\nRealCases,46,65\nSampleCodes,40,75" } ], "id_raw": 984 }, { "id": "CppMF3NucbyU1r2K", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nReflecting on the bar chart, what insights emerge about the trends in educational resource utilization?" }, { "from": "gpt", "value": "The bar chart depicts two distinct trends of resource utilization: one fluctuating decline as represented by {series 1 bars}<{\"Fluctuating Decline Series\"}[0.122, 0.071, 0.303, 0.091]> and one continuous rise represented by {series 2 bars}<{\"Continuous Rise Series\"}[0.122, 0.101, 0.281, 0.121]>. Looking at each resource, the \"Fluctuating Decline Series\" starts with a high utilization for \"Tutorials\"[0.112, 0.383, 0.174, 0.945] at 63%, then continuously drops reaching 40% at {sample codes}[0.820, 0.588, 0.882, 0.945] with minor ups and downs in-between. On the other hand, the \"Continuous Rise Series\" starts low for \"Tutorials\"[0.174, 0.633, 0.236, 0.945] and sees a steady rise, reaching 75% at {sample codes}[0.882, 0.276, 0.943, 0.945]. Correlations seem to appear between the complexities of resources (as we move from tutorials to sample codes) and trends in learning behaviours. Learners may prefer hands-on and practical resource types such as RealCases and SampleCodes. The rising trend in such resources seems encouraging, as students are relying more on these resources over time.\nBased on the above thought, the answer is:\nOver time, learners seem to prefer using hands-on, practical resources and reduce their reliance on basic instructional content. The decline in utilization of traditional learning resources (such as tutorials and videos) and the increase in utilization of more practical learning resources (such as RealCases and SampleCodes) suggest a shift towards more application-oriented learning styles. This is a positive development, as it shows learners are willing to engage with more complex educational content over time." } ], "id_raw": 984 }, { "id": "cEu8poTDC8V15Z8i", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_9.png", "conversations": [ { "from": "human", "value": "\nAs per the bar chart, which type of educational resource had the largest increase in utilization from the Fluctuating Decline Series to the Continuous Rise Series?" }, { "from": "gpt", "value": "To identify the educational resource with the greatest increase in utilization set between the Fluctuating Decline Series and the Continuous Rise Series, we need to compare the differences in the bar heights across all available resources. Starting with {\"Tutorials\"}[0.112, 0.383, 0.174, 0.945] and [0.174, 0.633, 0.236, 0.945] the difference is 28 units. The comparison must proceed with {\"Videos\"}, {\"Exercises\"}, {\"RealCases\"}, and {\"SampleCodes\"} to check for possible larger differences in the chart. Particularly while reviewing the category of {RealCases}[0.669, 0.959, 0.740, 0.979], {the associated bars}[{FluctuatingDeclineSeries}[0.643, 0.535, 0.705, 0.945]; {ContinuousRiseSeries}[0.705, 0.365, 0.767, 0.945]] show larger values of differences, reaching to about about 19. This would be considered the largest increase amongst the computed differences through the chart.\nBased on the above thought, the answer is:\nRealCases." } ], "id_raw": 984 }, { "id": "1WtenYMDfFG3XccA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nCan you give us a table capturing the information from the bar chart showing different investment strategies?" }, { "from": "gpt", "value": "From the setup, we see that the x-axis represents different investment strategies, which features {six models}[0.131, 0.942, 0.226, 0.970],[0.254, 0.942, 0.387, 0.970],[0.405, 0.942, 0.520, 0.970],[0.538, 0.942, 0.671, 0.970],[0.658, 0.942, 0.835, 0.970],[0.820, 0.942, 0.957, 0.970]. The y-axis represents the level of return rate and Risk level, which ranges from {0}[0.062, 0.912, 0.075, 0.940] to {40}[0.050, 0.064, 0.075, 0.092]. From the {legend}[0.098, 0.089, 0.287, 0.181], we know that there are two types of bars: The green ones represent the 'Return Rate' and the blue ones represent the 'Risk Level'. The height of the bars indicates the value according to the y-axis. Based on this, I can generate a CSV table using the height of each bar. For instance, the height of the {'Return Rate' bar under 'Blue Chip'}[0.129, 0.711, 0.179, 0.923] is around 10, and the height of the {'Risk Level' bar under 'Blue Chip'}[0.179, 0.774, 0.228, 0.923] is around 7. So the 'Return Rate' and 'Risk Level' for 'Blue Chip' are 10 and 7 respectively.\nBased on the above thought, the answer is:\nModels,Return Rate,Risk Level\nBlue Chip,10,7\nGrowth Stock,20,25\nValue Stock,15,12\nIncome Stock,12,4\nSpeculative Stock,18,30\nCyclical Stock,8,20" } ], "id_raw": 985 }, { "id": "cxCJjkAkaN5yW6xD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nWhich stock would an investor seeking high returns but wanting to avoid extreme risk likely choose to invest in, and why is this the case?" }, { "from": "gpt", "value": "To answer this, I'm looking for stocks that have a high return rate and a relatively low-risk level. So I will compare all the stocks, starting with the 'Blue Chip'{bar1}[0.129, 0.711, 0.179, 0.923].\n{bar2}[0.179, 0.774, 0.228, 0.923] where we get a Return Rate of 10 and Risk Level of 7, so this strategy does not provide a high return. Continuing with the other stocks till I get to 'Income Stock'{bar1}[0.555, 0.668, 0.604, 0.923],{bar2}[0.604, 0.838, 0.654, 0.923], we see it has a return of 12 with a very low-risk level of 4. It provides a balance between return and risk. Therefore, considering this balance, an investor seeking high returns but wanting to avoid extreme risk would choose this stock.\nBased on the above thought, the answer is:\nThe investor would likely choose to invest in 'Income Stock' because it provides a balance between a high return rate and a lower risk level when compared to other stocks." } ], "id_raw": 985 }, { "id": "BbqDMpSPCAXrkXW7", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_10.png", "conversations": [ { "from": "human", "value": "\nWhich stock provides the highest return relative to its associated risk?" }, { "from": "gpt", "value": "To find which stock provides the highest return relative to its associated risk, I'll need to compute the ratio between the return rate and risk level for each stock. Using the bar heights as a visual representation, we can determine this: for instance, for 'Blue Chip', the {'Return Rate'}[0.129, 0.711, 0.179, 0.923] is 10, and the {'Risk Level'}[0.179, 0.774, 0.228, 0.923] is 7, giving a ratio of 10/7. Similar calculations can be done for the 'Growth Stock', 'Value Stock', 'Income Stock', 'Speculative Stock', and 'Cyclical Stock'. Comparing these ratios will provide the answer.\nBased on the above thought, the answer is:\nIncome Stock." } ], "id_raw": 985 }, { "id": "FHcVxXm30S0Ky4Tx", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nBased on your observations, please convert the funds for different charity activities into a CSV format table." }, { "from": "gpt", "value": "Looking at the {subtitle of the chart}[0.371, 0.021, 0.685, 0.047], I understand that the chart focuses on the funding analysis for different charity activities. The x-axis contains the names of different activities, represented by {the x-axis tick labels}[{\"Canister_Collection\"}[0.103, 0.958, 0.236, 0.979]; {\"Church_Opening\"}[0.256, 0.958, 0.370, 0.979]; {\"Social_Service\"}[0.406, 0.958, 0.506, 0.979]; {\"Coastal_Cleanup\"}[0.541, 0.958, 0.658, 0.979]; {\"Book_Sale\"}[0.707, 0.958, 0.779, 0.979]; {\"Food_Drive\"}[0.847, 0.958, 0.925, 0.979]]. The y-axis represents the funding amount, based on the range provided by {the labels of y-axis tick}[{\"0\"}[0.052, 0.937, 0.061, 0.957]; {\"100\"}[0.035, 0.045, 0.061, 0.065]]. Each activity has four colored bars corresponding to different funding sources, represented by {the legend}[0.078, 0.063, 0.251, 0.189]. To generate the CSV table, I need to extract the values each bar stands for. For example, {the bar for \"Canister_Collection\" from \"Local Giving\"}[0.112, 0.499, 0.141, 0.944] has a height corresponding to approximately 50 according to the y-axis range. Hence, the value of \"Local Giving\" for the \"Canister_Collection\" activity is 50.\nBased on the above thought, the answer is:\nActivities,Local_Giving,Foreign_Aid,Public_Funding,Private_Donations\nCanister_Collection,50,65,35,70\nChurch_Opening,40,55,60,50\nSocial_Service,85,90,75,80\nCoastal_Cleanup,10,15,20,30\nBook_Sale,55,60,45,40\nFood_Drive,35,40,25,32" } ], "id_raw": 986 }, { "id": "sXEJuwNW3hAQJG6N", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which funding source is considered more reliable for the charity activities, and why?" }, { "from": "gpt", "value": "From the height of the bars representing each funding source across different activities, one can evaluate their reliability. Specifically, {Local Giving}[0.122, 0.071, 0.207, 0.091] has funded around 50 to 85 funds except for Coastal Cleanup which might be the sporadic funding requirements. {Foreign Aid}[0.122, 0.101, 0.200, 0.121] has funded around 60 to 90 all the time and {Public Funding}[0.122, 0.131, 0.223, 0.151] changed drastically from 35 to 75. Lastly, {Private Donations}[0.122, 0.161, 0.245, 0.181] performed more stable in most categories, but with a lower amount, around 30 to 70.\nBased on the above thought, the answer is:\nConsidering the stability and amount of funds, Foreign Aid seems to be the most reliable funding source because it consistently provides a relatively high amount of resources." } ], "id_raw": 986 }, { "id": "PFem9vhmFeTAfCrc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_11.png", "conversations": [ { "from": "human", "value": "\nWhich activity receives the least amount of total funds?" }, { "from": "gpt", "value": "To identify which activity receives the least total funds, I need to sum up the funds from different sources for each activity. Looking from left to right, the first activity is {Canister_Collection}[0.103, 0.958, 0.236, 0.979] with bars located at [0.112, 0.499, 0.141, 0.944], [0.141, 0.365, 0.170, 0.944], [0.170, 0.633, 0.198, 0.944] and [0.198, 0.321, 0.227, 0.944], the heights which correspond to the funds are about 50, 65, 35 and 70 respectively and the sum is 220. Continuing for other activities, it turns out that the {Coastal_Cleanup}[0.541, 0.958, 0.658, 0.979], with bars located at [0.542, 0.855, 0.571, 0.944], [0.571, 0.811, 0.599, 0.944] , [0.599, 0.766, 0.628, 0.944] and [0.628, 0.677, 0.657, 0.944], has the smallest total amount of around 75.\nBased on the above thought, the answer is:\nCoastal Cleanup." } ], "id_raw": 986 }, { "id": "gj6XYexHitSF5EDd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the career and workplace development trends into csv format using raw data for each category between 2019 and 2020?" }, { "from": "gpt", "value": "To transform the chart data into CSV format, I need to take into account the categories represented on the X-axis {\"Professional Training\"}[0.148, 0.774, 0.162, 0.979], {\"Skill Acquisition\"}[0.296, 0.774, 0.310, 0.930], {\"Networking\"}[0.443, 0.774, 0.457, 0.888], {\"Entrepreneurship\"}[0.590, 0.774, 0.604, 0.946], {\"Mentoring\"}[0.737, 0.774, 0.751, 0.875], {\"Career Counseling\"}[0.885, 0.774, 0.899, 0.957]. Then I'll look at the two sets of bars, each representing the data for a particular year. According to {the legend}[0.069, 0.063, 0.154, 0.129], {the light blue bars}<{\"2019\"}[0.113, 0.071, 0.149, 0.091]> correspond to the year 2019 and {the dark red bars}<{\"2020\"}[0.113, 0.101, 0.148, 0.121]> correspond to 2020. The height of each bar represents the value for each category in each year, and can be determined using the Y-axis {\"y_range\"} values ranging from {0}[0.043, 0.753, 0.052, 0.773] to {90}[0.035, -0.024, 0.052, -0.004]. The CSV table will comprise columns for each category and rows for each year.\nBased on the above thought, the answer is:\nYear, Professional Training, Skill Acquisition, Networking, Entrepreneurship, Mentoring, Career Counseling\n2019, 60, 35, 48, 53, 67, 72\n2020, 64, 32, 50, 58, 70, 78" } ], "id_raw": 987 }, { "id": "nxAPSGcEQvukev5O", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nConsidering the visible trends from 2019 to 2020, which aspect of professional development appears to be becoming less popular?" }, { "from": "gpt", "value": "I need to analyze and compare the heights of the pairs of bars representing {2019}<[\"2019\"][0.113, 0.071, 0.149, 0.091]> and {2020}<[\"2020\"][0.113, 0.101, 0.148, 0.121]> for the categories {\"Professional Training\"}([0.104, 0.242, 0.155, 0.761], [0.155, 0.208, 0.207, 0.761]), {\"Skill Acquisition\"}([0.251, 0.458, 0.303, 0.761], [0.303, 0.484, 0.354, 0.761]), {\"Networking\"}([0.398, 0.346, 0.450, 0.761], [0.450, 0.329, 0.501, 0.761]), {\"Entrepreneurship\"}([0.546, 0.303, 0.597, 0.761], [0.597, 0.260, 0.649, 0.761]), {\"Mentoring\"}([0.693, 0.182, 0.744, 0.761], [0.744, 0.156, 0.796, 0.761]), and {\"Career Counseling\"}([0.840, 0.139, 0.892, 0.761], [0.892, 0.087, 0.943, 0.761]). The only aspect of professional development which saw a decrease in its bar height from 2019 to 2020 was 'Skill Acquisition'.\nBased on the above thought, the answer is:\nSkill Acquisition. This category has shown a decrease in the presented activity level between 2019 and 2020." } ], "id_raw": 987 }, { "id": "pVhuzdic7D16S5XO", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_12.png", "conversations": [ { "from": "human", "value": "\nIn 2019, in which category was the highest level of career and workplace development activity observed?" }, { "from": "gpt", "value": "To answer this question, one needs to compare the heights of the {light blue bars}<{\"2019\"}[0.113, 0.071, 0.149, 0.091]> representing categories {\"Professional Training\"}[0.104, 0.242, 0.155, 0.761], {\"Skill Acquisition\"}[0.251, 0.458, 0.303, 0.761], {\"Networking\"}[0.398, 0.346, 0.450, 0.761], {\"Entrepreneurship\"}[0.546, 0.303, 0.597, 0.761], {\"Mentoring\"}[0.693, 0.182, 0.744, 0.761], and {\"Career Counseling\"}[0.840, 0.139, 0.892, 0.761] in 2019. The bar with the greatest height indicates the highest level of activity. This can be verified by comparing the heights of the bars which are judged in accordance to the Y-axis {\"y_range\"} values. After the comparison, we find that the one corresponds to 'Career Counseling' is the tallest.\nBased on the above thought, the answer is:\nCareer Counseling." } ], "id_raw": 987 }, { "id": "Tqll871AoVnDDJ8Q", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nFrom the observed bar chart, could you please generate a CSV format table for each type of technology and the activities in technological innovation and intellectual property?" }, { "from": "gpt", "value": "Looking at the chart title {\"Tech Innovation & Intellectual Property\"}[0.304, 0.030, 0.763, 0.066], it can be observed that the x-axis represents the types of technology which are {VR}[0.169, 0.942, 0.197, 0.970], {AR}[0.314, 0.942, 0.341, 0.970], {Blockchain}[0.418, 0.942, 0.526, 0.970], {AI}[0.607, 0.942, 0.626, 0.970], {Robotics}[0.719, 0.942, 0.803, 0.970], {IoT}[0.891, 0.942, 0.921, 0.970] and y-axis represents the count ranges from {0}[0.062, 0.912, 0.075, 0.940] to {100}[0.037, 0.014, 0.075, 0.042]. The legend shows four categories: {Innovation}[0.162, 0.100, 0.267, 0.128], {Patents Infrienged}[0.162, 0.142, 0.343, 0.170], {Failed Patents}[0.162, 0.183, 0.300, 0.211] and {Abandoned Patents}[0.162, 0.225, 0.355, 0.253]. I can generate the CSV table by taking these into account and matching the bar heights to the count on the y-axis.\nBased on the above thought, the answer is:\nTechType,Innovation,PatentsInfrienged,FailedPatents,AbandonedPatents\nVR,15,35,5,10\nAR,22,45,8,12\nBlockchain,25,60,10,14\nAI,30,70,12,16\nRobotics,35,80,15,20\nIoT,40,90,18,25" } ], "id_raw": 988 }, { "id": "eykq4lB1XO1S6ND5", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nConsidering the overall trends in the chart, where should a technology company invest for most intellectual property rights and highest innovation chance?" }, { "from": "gpt", "value": "To answer this, I have to look at multiple things including highest Innovation score and lowest counts of patents that have been infringed, failed and abandoned. Looking at the chart, it can be seen that most innovations are happening in {IoT}[0.851, 0.564, 0.873, 0.923]. Technology companies should also avoid areas where many patents have been infringed and this is highest in {IoT}[0.873, 0.115, 0.895, 0.923], but also quite high in {Robotics}[0.729, 0.205, 0.750, 0.923]. Thus, considering between the lowest Failed Patents and Abandoned Patents, {VR}[0.172, 0.878, 0.194, 0.923; 0.194, 0.833, 0.216, 0.923] and {AR}[0.317, 0.851, 0.338, 0.923; 0.338, 0.815, 0.360, 0.923] have the lowest counts. Therefore, the safest bet might be VR or AR. It is observed that IoT has a substantial amount of innovation, though it also comes with a high risk of patent infringement, failed patents and abandoned patents.\nBased on the above thought, the answer is:\nA company looking for highest intellectual property rights and innovation prospects should consider investing in VR or AR." } ], "id_raw": 988 }, { "id": "DN9zD1WrW6Lm0Ck1", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_13.png", "conversations": [ { "from": "human", "value": "\nWhich technological category has the highest accumulated score across all activities of technological innovation and intellectual properties?" }, { "from": "gpt", "value": "To determine the technology category with the highest accumulated score, I have to assign the height of each bar to its accurate technology, and add up all the counts under each technology. For instance, in the VR category, {the innovation bar height}[0.129, 0.788, 0.151, 0.923] interprets to 15, {the patents infringed bar height}[0.151, 0.609, 0.172, 0.923] interprets to 35, {the failed patents bar height}[0.172, 0.878, 0.194, 0.923] interprets to 5, and {the abandoned patents bar height}[0.194, 0.833, 0.216, 0.923] interprets to 10. Then moving across and performing the same analysis for AR, Blockchain, AI, Robotics and IoT. Then totaling up the cumulative counts recorded for each technology.\nBased on the above thought, the answer is:\nIoT." } ], "id_raw": 988 }, { "id": "rzRl17pphBCYhF7w", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nCould you please construct a CSV table from the data you've observed in the bar chart?" }, { "from": "gpt", "value": "Looking at the bar chart titled {Big Data Integration Overview}[0.363, 0.025, 0.678, 0.055], I can see that each bar represents a specific technology. The x-axis labels are the names of each technology and include {Cloudera}[0.131, 0.952, 0.209, 0.975], {Hortonworks}[0.255, 0.952, 0.365, 0.975], {MapR}[0.426, 0.952, 0.475, 0.975], {AWS-Glue}[0.548, 0.952, 0.633, 0.975], {Azure Data Factory}[0.648, 0.952, 0.814, 0.975], and {Google Cloud Dataprep}[0.769, 0.952, 0.973, 0.975]. Notice that for each technology, there are three bars of different colors, representing different types of data: unstructured data, semi-structured data, and structured data according to {the legend}[0.097, 0.074, 0.348, 0.185]. The y-axis indicates the efficiency of handling each type of data, ranging from {0}[0.065, 0.926, 0.076, 0.950] to {100}[0.043, 0.132, 0.076, 0.156]. Therefore, I would need to extract the height of each colored bar under each technology in order to build the table.\nBased on the above thought, the answer is:\nTechnologies,Unstructured Data,Semi-Structured Data,Structured Data\nCloudera,60,80,45\nHortonworks,40,60,50\nMapR,55,75,60\nAWS-Glue,80,95,72\nAzure Data Factory,75,88,65\nGoogle Cloud Dataprep,85,100,77" } ], "id_raw": 989 }, { "id": "HpzmRX9DxRHgIch6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which technology appears to be the most efficient across all types of data and why?" }, { "from": "gpt", "value": "In order to determine the most efficient technology, I must compare the overall performance of the six technologies across all types of data. This means analyzing the three bars under each technology. The bars include unstructured data, semi-structured data, and structured data, according to the chart's {legend}[0.097, 0.074, 0.348, 0.185]. By comparing the height of these bars, which represent efficiency, I can assess each technology's performance. From left to right, the first group of bars correlate with {Cloudera}[0.131, 0.952, 0.209, 0.975], the second group with {Hortonworks}[0.255, 0.952, 0.365, 0.975], and so on. The last group of bars, which correlate with {Google Cloud Dataprep}[0.769, 0.952, 0.973, 0.975], seem to have the highest average height, indicating the highest overall efficiency across all types of data, thereby making it the most efficient.\nBased on the above thought, the answer is:\nGoogle Cloud Dataprep. This technology has the highest average efficiency across all types of data as visualized on the bar chart." } ], "id_raw": 989 }, { "id": "ML6tc9drICvY6qRC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_66_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest efficiency a technology has achieved in handling semi-structured data according to the chart?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the orange bars which represent the {Semi-Structured Data}[0.153, 0.118, 0.341, 0.141] according to the legend. Each bar is aligned with a specified technology on the x-axis. By examining the maximum height reached by the orange bars, I can reach a conclusion. The highest orange bar corresponds to the technology {Google Cloud Dataprep}[0.857, 0.142, 0.885, 0.935], which reaches almost the top of the y-axis, indicating an efficiency level of 100%.\nBased on the above thought, the answer is:\n100%" } ], "id_raw": 989 }, { "id": "9ofQw4JX6vuR6lJV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nCould you generate a CSV format table based on the bar chart?" }, { "from": "gpt", "value": "The bar chart presents the changing values of five different digital currencies across three periods. The currencies include {Bitcoin}[0.129, 0.039, 0.177, 0.059], {Ethereum}[0.129, 0.069, 0.197, 0.089], {Ripple}[0.129, 0.099, 0.173, 0.119], {Stellar}[0.129, 0.129, 0.174, 0.149], and {Tether}[0.129, 0.159, 0.172, 0.179]. The change in the value of the currencies over time can be obtained by the relative heights of the bars, noting that the y-axis ticks are in the form of \u201cChange in Value\u201d. Let\u2019s consider Bitcoin: for the first period, the corresponding bar is located at {[0.166, 0.674, 0.191, 0.674]}, which means there's no change from its initial value. For the second period, the bar is significantly higher, spanning from 0 to 0.2 in value as indicated by the bar's coordinates {[0.416, 0.521, 0.441, 0.674]}. For the third period, the change in value is smaller, being 0.05, and for the fourth period, it's 0.03, as deduced from the coordinates of the respective bars. The same process can be done for the other currencies.\nBased on the above thought, the answer is:\nPeriod,Bitcoin,Ethereum,Ripple,Stellar,Tether\n0,0.0,0.0,0.0,0.0,0.0\n1,0.2,0.6,-0.1,0.02,-0.1\n2,0.05,0.7,-0.2,0.03,0.1\n3,0.03,0.8,-0.3,0.04,-0.05" } ], "id_raw": 992 }, { "id": "D9YZ8efiYU19a6iV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nBased on your analysis, which currency would you recommend an investor who wants a steady growth investment?" }, { "from": "gpt", "value": "From the analysis of the bar chart, each currency shows a different growth trend over time. Let's analyze them one by one. Starting with {Bitcoin}, the bars of Bitcoin, as per their coordinates, show inconsistent changes over time. The second currency we consider is {Ethereum}, which exhibits positive growth consistently over all periods. {Ripple}, on the other hand, shows a steady decline in its values over time. {Stellar} exhibits a very small but steady increase throughout the periods, indicating minimal growth. Lastly, {Tether} demonstrates inconsistent changes, showcasing the fluctuating nature of its value. Based on all this, if an investor wants steady growth, Ethereum seems to be a good choice as it has shown consistent positive growth over the periods.\nBased on the above thought, the answer is:\nEthereum. Ethereum has consistently demonstrated positive growth over the periods, which makes it a sound choice for investors seeking steady growth." } ], "id_raw": 992 }, { "id": "jMmqyzFPhQHQaLwG", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which cryptocurrency has shown the greatest positive change in value?" }, { "from": "gpt", "value": "To find the cryptocurrency with the highest positive change, I need to compare the heights of the highest bars for each cryptocurrency. Starting from Bitcoin, the highest bar {[0.416, 0.521, 0.441, 0.674]} corresponds to a change of 0.2. Ethereum's highest bar reaches {[0.943, 0.063, 0.968, 0.674]}, indicating a change of 0.8. Ripple's bar doesn't show a positive change since its maximum location is at the baseline. Stellar's maximum bar {[0.993, 0.643, 1.018, 0.674]} shows a change of 0.04. For Tether, the maximum change, 0.1, is indicated by the bar {[0.767, 0.598, 0.793, 0.674]}. Comparing these, Ethereum has the highest bar, thus the greatest positive change.\nBased on the above thought, the answer is:\nEthereum" } ], "id_raw": 992 }, { "id": "YzKdUFc3lp04aQsM", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data displayed in the bar chart into a CSV formatted table?" }, { "from": "gpt", "value": "From the chart's legend, {the legend}[0.086, 0.037, 0.237, 0.218], I recognize that there are five groups, each representing a social media platform: Facebook, Twitter, Instagram, LinkedIn, and Snapchat. {The x-axis}{\"score_1\"}[0.044, 0.952, 0.111, 0.975]; {\"score_2\"}[0.335, 0.952, 0.401, 0.975]; {\"score_3\"}[0.625, 0.952, 0.691, 0.975]; {\"score_4\"}[0.915, 0.952, 0.981, 0.975] contains the labels for four different scores. The chart uses different colored bars to represent user engagement scores for the platforms, and the height of each bar signifies the corresponding numerical score. Looking at the bars for each group across different scores, I can perceive the corresponding user engagement scores for each social media platform at each score interval.\nBased on the above thought, the answer is:\nscores,Facebook,Twitter,Instagram,LinkedIn,Snapchat\nscore_1,5,3,8,2,7\nscore_2,15,10,20,15,14\nscore_3,20,25,35,28,38\nscore_4,75,83,68,70,78" } ], "id_raw": 993 }, { "id": "EJlAdlCZEMOrNarD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, what is the trend of user engagement on these social media platforms over time?" }, { "from": "gpt", "value": "This question requires analyzing the height increment of each group's bars from {score_1}[\"score_1\" label][0.044, 0.952, 0.111, 0.975], {score_2}[\"score_2\" label][0.335, 0.952, 0.401, 0.975], {score_3}[\"score_3\" label][0.625, 0.952, 0.691, 0.975] to {score_4}[\"score_4\" label][0.915, 0.952, 0.981, 0.975]. Based on the respective heights of the bars, I can infer that there is a general increase in user engagement scores across all platforms over time. All the bars of the final score, which I assume to be the latest score, {score_4}[\"score_4\" label][0.915, 0.952, 0.981, 0.975], are higher than the previous ones, which indicates an overall increase in user engagement over time.\nBased on the above thought, the answer is:\nOver time, there is a general increasing trend of user engagement on all these social media platforms tested." } ], "id_raw": 993 }, { "id": "JGUGV4LRLw5irO2E", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_3.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform had the highest user engagement score at the second score interval?" }, { "from": "gpt", "value": "To answer this question, I need to examine the height of the bars corresponding to the second score interval for each social media platform. {score_2}[\"score_2\" label][0.335, 0.952, 0.401, 0.975]. Looking at the bars under the label 'score_2', I observe that the highest bar is for Instagram {Instagram bar for score_2}[0.208, 0.727, 0.237, 0.935]. Therefore, Instagram had the highest user engagement score at the second score interval.\nBased on the above thought, the answer is:\nInstagram." } ], "id_raw": 993 }, { "id": "PDmKftCqP52tM0Yw", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract the energy consumption values from the bar chart and create a CSV table?" }, { "from": "gpt", "value": "Looking at the chart, I can see five different energy resources: {'Coal'}[0.174, 0.055, 0.218, 0.083], {'Natural Gas'}[0.174, 0.097, 0.292, 0.125], {'Renewable'}[0.174, 0.139, 0.283, 0.167], {'Nuclear'}[0.174, 0.181, 0.251, 0.209], and {'Oil'}[0.174, 0.223, 0.201, 0.251]. For each resource, there are four bars at {'2020'}[0.076, 0.942, 0.126, 0.970], {'2030'}[0.360, 0.942, 0.410, 0.970], {'2040'}[0.644, 0.942, 0.694, 0.970], and {'2050'}[0.929, 0.942, 0.979, 0.970]. The height of each bar on the 'Y' axis represents the consumption of the respective resources. For example, for Coal in 2020, the bar height extends up to 100 mark shown at [0.075,0.912,0.087,0.940] on y-axis. Following a similar approach, I can extract the consumption values for all resources for all years.\nBased on the above thought, the answer is:\nResource,2020,2030,2040,2050\nCoal,100,90,75,50\nNatural Gas,120,140,130,150\nRenewable,60,90,150,200\nNuclear,80,85,90,95\nOil,90,85,75,65" } ], "id_raw": 994 }, { "id": "xWeq8mg8eDWLxkYS", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what can you infer about the future of energy resource consumption?" }, { "from": "gpt", "value": "By comparing the height of the bars, it is apparent that the usage of renewable energy {Renewable}[0.174, 0.139, 0.283, 0.167] is expected to increase significantly over the years. The increase is highlighted by the bars at [0.229, 0.540, 0.257, 0.923], [0.513, 0.285, 0.541, 0.923], and [0.797, 0.073, 0.826, 0.923]. At the same time, the consumption of traditional energy resources like Coal <{Coal}[0.174, 0.055, 0.218, 0.083]> and Oil <{Oil}[0.174, 0.223, 0.201, 0.251]> are projected to decline, as indicated by the shrinking bars, for example, [0.456, 0.604, 0.485, 0.923] and [0.854, 0.646, 0.883, 0.923]. This trend suggests a positive shift towards clean and renewable energy in the future.\nBased on the above thought, the answer is:\nThe energy consumption in the future is shifting significantly towards renewable resources, while the consumption of traditional energy resources like coal and oil is on the decline. This indicates a positive movement towards sustainability and environmental consciousness." } ], "id_raw": 994 }, { "id": "0bhOIns3RK86lNDU", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_4.png", "conversations": [ { "from": "human", "value": "\nIn which year is the total energy consumption projected to be the highest among the five resources?" }, { "from": "gpt", "value": "To answer this question, I need to compare the total bar heights of the five energy resources in each of the four years. For year 2020 the total is calculated by summing heights of bars at [-0.112, 0.498, -0.084, 0.923], [-0.084, 0.413, -0.055, 0.923], [-0.055, 0.668, -0.027, 0.923], [-0.027, 0.583, 0.001, 0.923], [0.001, 0.540, 0.030, 0.923]. Following same approach for other years it is clear that the highest total energy consumption is expected in year 2050 with the total height of bars at series of coordinates - [0.740, 0.710, 0.769, 0.923],[0.769, 0.285, 0.797, 0.923],[0.797, 0.073, 0.826, 0.923],[0.826, 0.519, 0.854, 0.923],[0.854, 0.646, 0.883, 0.923].\nBased on the above thought, the answer is:\n2050" } ], "id_raw": 994 }, { "id": "Q4jfX0mwUPs18ALE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV format table based on the data shown in the chart?" }, { "from": "gpt", "value": "Sure, starting from the {legend}[0.411, 0.028, 0.636, 0.164], I know that this chart represents five different global trends from 2020-2040, including 'Inflation rates', 'Poverty rates', 'Population increase', 'Carbon emission', and 'Renewable energy usage'. Each type of data is displayed as a separate bar graph with time stamps {2020, 2025, 2030, 2040}[0.114, 0.964, 0.149, 0.981]; [0.329, 0.964, 0.364, 0.981]; [0.544, 0.964, 0.579, 0.981]; [0.758, 0.964, 0.793, 0.981]. I will organize this data into a CSV table with the timestamps as column headers and each global trend as a row.\nBased on the above thought, the answer is:\ntime,Inflation rates,Poverty rates,Population increase,Carbon emission,Renewable energy usage\n2020,2.3,14.5,1.1,33.2,17.5\n2025,2.1,13.7,0.8,32.1,19.8\n2030,3.1,11.2,0.5,29.3,27.1\n2040,2.7,13.8,0.4,27.4,33.6" } ], "id_raw": 995 }, { "id": "Anpn0wxF9pxzrwsB", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nBased on the data represented in this bar chart, do you believe efforts to decrease carbon emissions and increase renewable energy usage are working? Explain your reasoning." }, { "from": "gpt", "value": "To answer this question, I need to focus on the bars related to 'Carbon emission' and 'Renewable energy usage'. For 'Carbon emission'[0.099, 0.074, 0.132, 0.952]; [0.314, 0.104, 0.346, 0.952]; [0.529, 0.178, 0.561, 0.952]; [0.743, 0.228, 0.776, 0.952], the heights of the bars decrease consistently from 2020 to 2040, indicating the amount of carbon emissions has been steadily declining during this period. On the contrary, 'Renewable energy usage'[0.132, 0.489, 0.164, 0.952]; [0.346, 0.428, 0.379, 0.952]; [0.561, 0.236, 0.593, 0.952]; [0.776, 0.064, 0.808, 0.952] demonstrates a consistent rise, particularly between 2025 and 2030 there is a significant increase. This shows that the amount of renewable energy usage has continuously been on the rise. Combining these findings, it seems that efforts to reduce carbon emissions and increase renewable energy usage are indeed working.\nBased on the above thought, the answer is:\nYes, the efforts are working. The data shows a significant decrease in carbon emissions and a notable increase in renewable energy usage over time." } ], "id_raw": 995 }, { "id": "QEK2pIcsDREG28z3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_5.png", "conversations": [ { "from": "human", "value": "\nAt what year has the carbon emissions been the lowest according to the data in the chart?" }, { "from": "gpt", "value": "I need to examine the {bars representing Carbon Emission}[0.099, 0.074, 0.132, 0.952]; [0.314, 0.104, 0.346, 0.952]; [0.529, 0.178, 0.561, 0.952]; [0.743, 0.228, 0.776, 0.952]. According to the graph, the last bar which corresponds to the year 2040 has the lowest value of all bars and thus represents the lowest carbon emission level.\nBased on the above thought, the answer is:\n2040" } ], "id_raw": 995 }, { "id": "BNNcYztAaexp6oHA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nPlease convert the progress of the 'Digital Diagnostics' and 'Telemedicine' sectors as shown in your observed chart into a CSV format table." }, { "from": "gpt", "value": "This is a bar chart tracing the progress of two key healthcare technology sectors across 12 years. The horizontal axis represents the years from 2008 to 2019 as indicated by {the x-axis tick labels}[{\"2008\"}[0.137, 0.959, 0.172, 0.979]; {\"2009\"}[0.207, 0.959, 0.242, 0.979]; so on till {\"2019\"}[0.912, 0.959, 0.947, 0.979]]. The vertical axis represents the scores from 0 to 120, as denoted by {the y-axis tick labels}[{\"0\"}[0.052, 0.937, 0.061, 0.957]; {\"20\"}[0.043, 0.761, 0.061, 0.781]; so on till {\"120\"}[0.035, -0.118, 0.061, -0.098]]. There are two categories of bars, distinguished by color, as indicated by {the legend}[0.078, 0.031, 0.258, 0.097]. The blue bars {Digital Diagnostics}{\"Digital Diagnostics\"}[0.122, 0.039, 0.252, 0.059]represent one sector and the orange bars {Telemedicine} represent the other sector{\"Telemedicine\"}[0.122, 0.069, 0.213, 0.089]. To create the table, I need to identify the score for each sector in a given year, as indicated by the respective bar's height. For instance, in 2008, the blue bar{\"Digital Diagnostics\"}[0.112, 0.804, 0.140, 0.945] corresponds to a score of 16 whereas the orange bar{\"Telemedicine\"}[0.140, 0.681, 0.169, 0.945] corresponds to a score of 30. I will repeat this exercise for every year to capture all data.\nBased on the above thought, the answer is:\nYear,Digital Diagnostics,Telemedicine\n2008,16,30\n2009,28,64\n2010,30,70\n2011,35,80\n2012,40,85\n2013,43,88\n2014,45,90\n2015,40,92\n2016,38,95\n2017,30,97\n2018,25,99\n2019,21,100" } ], "id_raw": 996 }, { "id": "hCvIBodCkYsdCssb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how do the trends depicted by the 'Telemedicine' and 'Digital Diagnostics' differ over the years 2008 to 2019?" }, { "from": "gpt", "value": "The chart depicts the progression of two healthcare technology sectors, namely {'Digital Diagnostics'}[0.122, 0.039, 0.252, 0.059] and {'Telemedicine'}[0.122, 0.069, 0.213, 0.089] over a period of 12 years, from {'2008'}[0.137, 0.959, 0.172, 0.979] to {'2019'}[0.912, 0.959, 0.947, 0.979]. The bars corresponding to these sectors represent their respective scores in a particular year. Looking at the height of these bars over the years gives us an insight into their trends. The Digital Diagnostics sector, represented by the sequence of blue bars, exhibits a decreasing trend over the years, with its highest value in {'2014'}[0.560, 0.959, 0.595, 0.979] and the smallest towards {'2019'}[0.912, 0.959, 0.947, 0.979]. On the other hand, the Telemedicine sector, symbolized by the orange bars, shows a steady increase in values from {'2008'}[0.137, 0.959, 0.172, 0.979] to {'2019'}[0.912, 0.959, 0.947, 0.979], indicating an upward trend in its progress over the years.\nBased on the above thought, the answer is:\nThe 'Digital Diagnostics' sector shows a broadly decreasing trend over the years with its scores reducing from 45 in 2014 to 21 in 2019. On the other hand, the 'Telemedicine' sector has shown a consistent upward trend with scores increasing incrementally from 30 in 2008 to 100 in 2019. Hence, the growth and prominence of 'Telemedicine' in the healthcare sector is increasing while 'Digital Diagnostics' seems to be declining towards the end of the observed period." } ], "id_raw": 996 }, { "id": "6r2nIXE1BJ4L9SEa", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_6.png", "conversations": [ { "from": "human", "value": "\nIn which year did 'Telemedicine' score the highest value?" }, { "from": "gpt", "value": "To find the year in which Telemedicine recorded its highest score, I need to identify the tallest orange bar on the chart. Looking through the chart, I find that the tallest orange bar is for the year 2019{\"Telemedicine's bar for 2019\"}[0.915, 0.065, 0.943, 0.945]. When I look at its height in relation to the y-axis values{\"yticks\"}[0.043, 0.585, 0.061, 0.605], it is evident that the bar touches the y-axis at 100. Therefore, 2019 is the year when Telemedicine scored the highest value of 100.\nBased on the above thought, the answer is:\n2019" } ], "id_raw": 996 }, { "id": "3kF7Fnr9zsaGMCxs", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the visualized bar chart, can you convert the depicted data into a structured CSV format table?" }, { "from": "gpt", "value": "Given the information in the chart, I can see that the x-axis refers to different score intervals represented by {xticklabels}[{0}[0.170, 0.952, 0.181, 0.975];{20}[0.430, 0.952, 0.451, 0.975];{40}[0.694, 0.952, 0.716, 0.975];{60}[0.959, 0.952, 0.981, 0.975]]. The y-axis, labeled as {\"Scores\"}[0.019, 0.442, 0.036, 0.519], indicates the corresponding values spanning from {0}[0.043, 0.926, 0.071, 0.950] to {1}[0.043, 0.059, 0.071, 0.083]. Meanwhile, {the legend}[0.440, 0.037, 0.613, 0.218] provides the categories illustrated by different colors in the bar chart. The height of each bar can be used to extract the scores for different categories at each score interval. For instance, the colored bar representative of {Running}[0.447, 0.048, 0.482, 0.064] has a height approximately at the 0.7 position at score interval {60}[0.959, 0.952, 0.981, 0.975], allowing me to confirm the value of Running at a score of 60 to be 0.7. Processing the values based on the above reasoning, we can construct a CSV table.\nBased on the above thought, the answer is:\nScore Type,Running,Yoga,Swimming,Weightlifting,Boxing\n0,0,0.6,0.7,0.4,0.6\n20,0.2,0.9,0.5,0.3,0.2\n40,0.4,0.8,0.6,0.6,0.7\n60,0.7,1,0.7,0.8,0.9" } ], "id_raw": 997 }, { "id": "iunTUeyzKReOWMxV", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, which fitness workout seemed to perform the best across all score intervals and why?" }, { "from": "gpt", "value": "To determine which fitness workout performed the best, I should concentrate on the one that consistently generated the highest scores across all score intervals. By comparing the bar heights in each score interval, such as at {Score 0}[0.170, 0.952, 0.181, 0.975], {Score 20}[0.430, 0.952, 0.451, 0.975], {Score 40}[0.694, 0.952, 0.716, 0.975], and {Score 60}[0.959, 0.952, 0.981, 0.975], and reviewing {the legend}[0.440, 0.037, 0.613, 0.218], I notice that the {Yoga}[0.447, 0.083, 0.482, 0.099] category consistently has some of the tallest bars across the intervals. This suggests that Yoga has achieved the highest scores among the workouts overall.\nBased on the above thought, the answer is:\nYoga. Because it consistently scores among the highest across all intervals." } ], "id_raw": 997 }, { "id": "qxta7KjbUAxOLn4A", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which workout showed the highest increase in score between the intervals of Score Type 20 and 40?" }, { "from": "gpt", "value": "To find out which workout had the biggest score increase from Score Type 20 to 40, I have to compare the score difference for each workout from {20}[0.430, 0.952, 0.451, 0.975] to {40}[0.694, 0.952, 0.716, 0.975] bars. The score of {Running}[0.447, 0.048, 0.482, 0.064] from 20 interval bar[{2nd}[0.142, 0.762, 0.182, 0.935]] to 40 intervals[{3rd}[0.407, 0.589, 0.447, 0.935]] is grown by about .2. Similarly, other workouts like {Yoga}[0.447, 0.083, 0.482, 0.099], {Swimming}[0.447, 0.118, 0.482, 0.134], {Weightlifting}[0.447, 0.153, 0.482, 0.169], {Boxing}[0.447, 0.188, 0.482, 0.204] have individually different score increases between score intervals 20 and 40 by examining their respective bar heights at those intervals.\nBased on the above thought, the answer is:\nYoga" } ], "id_raw": 997 }, { "id": "H4HvMz0yykkoXkEr", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the performance evaluation of the Biotech companies mentioned in the chart at the respective time intervals? Could you provide the readings in a structured CSV format?" }, { "from": "gpt", "value": "The chart displays the normalized performance evaluation of five pharmaceutical companies. They are indicated with distinctive colors and are labeled in {the legend}[0.105, 0.044, 0.359, 0.262]. The companies are Pfizer (denoted by blue), BioNTech (denoted by light blue), Moderna (denoted by beige), Novavax (denoted by deep blue), and Johnson & Johnson (denoted by navy blue).\nThis bar chart displays the performance scores of these companies at specific intervals of time which are denoted on the x-axis labeled as '0', '20', '40', and '60'. For each time period, the y-axis is used to denote the normalized performance score, labeled from '0.0'[0.050, 0.912, 0.081, 0.940] up to '1.2'[0.049, -0.016, 0.081, 0.012].\nI can see each bar for each company, and the height of the bars, and compute the score. For instance, in bar set labeled '20', Pfizer's score [0.284, 0.536, 0.309, 0.923] is '0.5', BioNTech's score [0.309, 0.382, 0.335, 0.923] is '0.7', Moderna's score [0.335, 0.459, 0.360, 0.923] is '0.6', Novavax's score [0.360, 0.768, 0.385, 0.923] is '0.2', and Johnson & Johnson's score [0.385, 0.613, 0.410, 0.923] is '0.4'.\nSimilarly, I can extract scores for other time intervals and map them to the relevant companies. Let's transform these numerical scores into a CSV format.\nBased on the above thought, the answer is:\nTime, Pfizer, BioNTech, Moderna, Novavax, Johnson & Johnson\n0,0,0,0,0,0\n20,0.5,0.7,0.6,0.2,0.4\n40,0.3,0.5,0.4,0.15,0.25\n60,0.9,1.1,1,0.45,0.75" } ], "id_raw": 998 }, { "id": "a1vUhknLFIl5a081", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nBased on the performance evaluation, which pharmaceutical firm has consistently underperformed compared to others?" }, { "from": "gpt", "value": "This requires a comprehensive analysis of the entire bar chart. A company that has underperformed consistently would have shorter bars (lower scores) compared to the others at each time interval.\nInspecting the chart, it becomes evident that the company denoted by the deep-blue color, Novavax as per the {legend}[0.105, 0.044, 0.359, 0.262], consistently has bars that are shorter than those of the other companies. The bars for Novavax at time intervals '0', '20', '40', and '60' are {bar_1}[0.107, 0.923, 0.133, 0.923], {bar_2}[0.360, 0.768, 0.385, 0.923], {bar_3}[0.612, 0.807, 0.638, 0.923], and {bar_4}[0.865, 0.575, 0.890, 0.923] respectively. Comparing these bars to the corresponding bars of the other companies at each time interval, it's clear that Novavax generally has the lowest performance scores.\nBased on the above thought, the answer is:\nBased on the chart, Novavax is the pharmaceutical firm that has underperformed consistently compared to the other firms." } ], "id_raw": 998 }, { "id": "HkrYb9vLV5CMw6Mr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_8.png", "conversations": [ { "from": "human", "value": "\nWhich company has shown the greatest increase in performance score from time '0' to time '60'?" }, { "from": "gpt", "value": "To answer this question, I need to identify the score at '0' and at '60' for all companies. In the bars labeled '0' and '60', the respective score for each company can be calculated. Looking at the time '0', all companies {Pfizer, BioNTech, Moderna, Novavax, and Johnson & Johnson} bars [0.032, 0.923, 0.057, 0.923], [0.057, 0.923, 0.082, 0.923], [0.082, 0.923, 0.107, 0.923], [0.107, 0.923, 0.133, 0.923], [0.133, 0.923, 0.158, 0.923] started with a score of '0'. At time '60', {Pfizer, BioNTech, Moderna, Novavax, and Johnson & Johnson} bars [0.789, 0.227, 0.814, 0.923], [0.814, 0.073, 0.840, 0.923], [0.840, 0.150, 0.865, 0.923], [0.865, 0.575, 0.890, 0.923], [0.890, 0.343, 0.915, 0.923] showed scores of '0.9', '1.1', '1', '0.45', and '0.75' respectively. Subtracting the initial value from the final, we can see that BioNTech has shown the highest increase of '1.1'.\nBased on the above thought, the answer is:\nBioNTech" } ], "id_raw": 998 }, { "id": "I3de3QvPQEctdCEI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you translate the observed scores of 'Community Development' and 'Urban Planning' over time into a structured data table?" }, { "from": "gpt", "value": "The bar chart shows values for two categories \u2013 'Community Development' and 'Urban Planning' \u2013 over four periods of time ('0', '20', '40', '60'). These are displayed on {the x-axis}[0.261, 0.942, 0.957, 0.970]. The y-axis [0.050, 0.912, 0.062, -0.101] represents the scores, which range from 0 to 5. I can interpret the data from the chart into a structured table by examining {the height of the bars}['[0.149, 0.125, 0.228, 0.923]', '[0.375, 0.333, 0.454, 0.923]', '[0.601, 0.610, 0.680, 0.923]', '[0.826, 0.853, 0.905, 0.923]'] and ['[0.228, 0.073, 0.307, 0.923]', '[0.454, 0.246, 0.533, 0.923]', '[0.680, 0.489, 0.759, 0.923]', '[0.905, 0.714, 0.984, 0.923]'] for each period, which according to {the legend}[0.643, 0.044, 0.969, 0.136] represent the scores for 'Community Development' and 'Urban Planning' respectively.\nBased on the above thought, the answer is:\nPeriod,Community Development,Urban Planning\n0,4.6,4.9\n20,3.4,3.9\n40,1.8,2.5\n60,0.4,1.2" } ], "id_raw": 999 }, { "id": "uYV5kW00TBRngMHb", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which trend - 'Community Development' or 'Urban Planning' - declines more drastically over the periods observed?" }, { "from": "gpt", "value": "The question requires an evaluation of the degree of decline in the scores for the two trends 'Community Development' and 'Urban Planning', represented by {dark blue}['[0.149, 0.125, 0.228, 0.923]', '[0.375, 0.333, 0.454, 0.923]', '[0.601, 0.610, 0.680, 0.923]', '[0.826, 0.853, 0.905, 0.923]'] and {light blue}['[0.228, 0.073, 0.307, 0.923]', '[0.454, 0.246, 0.533, 0.923]', '[0.680, 0.489, 0.759, 0.923]', '[0.905, 0.714, 0.984, 0.923]'] bars respectively. Observing the heights of the bars for both trends over the four periods, it's evident that while both trends decrease over time, the 'Community Development' trend goes from a high of 4.6 at the period of 0 to a mere 0.4 by the period of 60 \u2013 a sharp decrease. In contrast, the 'Urban Planning' trend decreases at a slower rate, moving from 4.9 to 1.2 over the same time frame.\nBased on the above thought, the answer is:\n'Community Development'. This trend demonstrates a more drastic decrease in scores than 'Urban Planning' over the observed periods." } ], "id_raw": 999 }, { "id": "4jsijNDTBAtNJzeW", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_9.png", "conversations": [ { "from": "human", "value": "\nAt which period was the score difference between 'Community Development' and 'Urban Planning' the smallest?" }, { "from": "gpt", "value": "To find the time period with the smallest score difference, I need to examine the difference in bar heights at each period. For the time period of 0, the score difference between 'Community Development' and 'Urban Planning' is found by comparing the heights of {the first pair of bars}[0.149, 0.125, 0.228, 0.923], [0.228, 0.073, 0.307, 0.923]. These procedure has to be repeated for the other periods as well, with comparison of bar heights at {20}[0.375, 0.333, 0.454, 0.923], [0.454, 0.246, 0.533, 0.923], at {40}[0.601, 0.610, 0.680, 0.923], [0.680, 0.489, 0.759, 0.923] and at {60}[0.826, 0.853, 0.905, 0.923], [0.905, 0.714, 0.984, 0.923]. After checking the differences in the four pairs of bars, the smallest difference is seen at the period of 60.\nBased on the above thought, the answer is:\n60" } ], "id_raw": 999 }, { "id": "5B9DEqzeFiGHBXro", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract all the scores for each social media platform and time period and present this information in a properly structured table?" }, { "from": "gpt", "value": "From the structure of bar chart, it seems that it represents the performance of different social media platforms in different time periods. I see the title labeled as {'Scores'} given on the y-axis[0.021, 0.430, 0.041, 0.522], which indicates the metric of the scores represented. There are 5 groups of bars, each related to a specific social media platform, indicated by the {Social Media Legend} located at [0.105, 0.044, 0.277, 0.262]. From the x-axis, I can confirm the three different time periods { '20', '40', '60' }. Using the chart's legend to identify the social media platforms and the height of the bars to determine the scores, I can extract the data for the respective time period. The values were determined by the height of the bars, such as the {bar of facebook for '20'}[0.167, 0.734, 0.196, 0.923], for example, This bar stretches from 0.734 to 0.923 on the y-axis, which corresponds to a score of 0.2 according to the y-axis labels.\nBased on the above thought, the answer is:\nPlatforms,20,40,60\nFacebook,0.2,0.5,0.7\nInstagram,0.4,0.3,0.7\nTwitter,0.6,0.8,0.5\nSnapchat,0.1,0.2,0.4\nWhatsApp,0.5,0.7,0.9" } ], "id_raw": 1000 }, { "id": "q5ONmCJ4zixKrZVU", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which social media platform seems to be consistently growing in popularity across all stages?" }, { "from": "gpt", "value": "To answer this question, I need to examine the trends in the bar heights for each social media platform across all time periods. For example, {Facebook}, indicated by the {white bars}[{\"bar_coordinates\": \"[-0.123, 0.923, -0.094, 0.923]\"}, {\"bar_coordinates\": \"[0.167, 0.734, 0.196, 0.923]\"}, {\"bar_coordinates\": \"[0.458, 0.450, 0.487, 0.923]\"}, {\"bar_coordinates\": \"[0.748, 0.261, 0.777, 0.923]\"}], appears to be growing at each consecutive measurement point \u2014 with its score rising from 0.2 to 0.5 to 0.7 across the time periods of '20', '40', and '60' respectively. A similar analysis can be performed for Instagram, Twitter, Snapchat, and WhatsApp. By doing so, we can see that Facebook and WhatsApp both show consistent growth, but the growth rate of Facebook (from 0.2 to 0.7, a 0.5 increase) is higher than WhatsApp (from 0.5 to 0.9, a 0.4 increase).\nBased on the above thought, the answer is:\nFacebook, as its score continuously increases throughout the periods and it has the highest growth rate." } ], "id_raw": 1000 }, { "id": "qHUHrkD2l379cSaq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_10.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform had the most growth from the first to the last time period?" }, { "from": "gpt", "value": "The growth in scores from the first to last time period can be determined by subtracting the score at {\"20\"}[0.167, 0.734, 0.196, 0.923] from the score at {\"60\"}[0.748, 0.261, 0.777, 0.923]. For each platform, I can calculate this difference using the respective bar heights. If we analyze {WhatsApp} for instance, the height of the bar in the first time period[0.283, 0.450, 0.313, 0.923] corresponds to a score of 0.5, and the height of the bar in the last time period[0.864, 0.073, 0.893, 0.923] is a score of 0.9. The difference would be 0.9 - 0.5 = 0.4. Doing similar calculations for Facebook, Instagram, Twitter, and Snapchat, it's clear that the largest growth was experienced by WhatsApp.\nBased on the above thought, the answer is:\nWhatsApp." } ], "id_raw": 1000 }, { "id": "pYDBKQM4fbv4tCBS", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nConvert the bar chart data of production efficiency you observed for Samsung S20 and iPhone 12 into a Table in CSV format." }, { "from": "gpt", "value": "At the x axis, I observe four different labels at {'Continuous decrease'}[0.021, 0.942, 0.230, 0.970],{'Sudden increase'}[0.287, 0.942, 0.452, 0.970],{'Sustained growth'}[0.526, 0.942, 0.700, 0.970] and {'Outliers'}[0.818, 0.942, 0.895, 0.970], which certifies varied categories. The y-axis, on the other hand, shows score ranges from {'0.0'}[0.080, 0.912, 0.112, 0.940] to {'1.4'}[0.080, -0.080, 0.112, -0.052]. To generate the CSV table, the value of each level for both products need to be determined. This can be worked out by correlating the height of each bar within the coordinates of each category. For instance, the height of {'Samsung S20 Continuous decrease'}[0.065, 0.781, 0.089, 0.923] is about 0.2 while that of {'iPhone 12 Continuous decrease'}[0.089, 0.604, 0.113, 0.923] is about 0.45. This can be followed suit for \"Sudden increase\", \"Sustained growth\" and \"Outliers\".\nBased on the above thought, the answer is:\nProduct,Continuous decrease,Sudden increase,Sustained growth,Outliers\nSamsung S20,0.2,0.6,0.75,1.2\niPhone 12,0.45,0.1,0.6,0.8" } ], "id_raw": 1001 }, { "id": "L8NcfJPwR8sS9jVA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nHow would you interpret the overall differences in the production efficiency patterns between the Samsung S20 and the iPhone 12 based on the chart?" }, { "from": "gpt", "value": "To interpret the overall differences in the production efficiency patterns between Samsung S20 and iPhone 12, I would consider both the height and position of their respective bars across all four trends. Starting with 'Continuous decrease', the bar for iPhone 12 at [0.089, 0.604, 0.113, 0.923] is taller than that of Samsung S20 at [0.065, 0.781, 0.089, 0.923], suggesting the Samsung S20 has a lower score here and therefore performs better. However, in the 'Sudden increase' trend, the Samsung S20 bar is found at [0.308, 0.498, 0.333, 0.923] which is lower than the iPhone 12 bar at [0.333, 0.852, 0.357, 0.923], revealing that Samsung S20 performs worse here. For the 'Sustained growth' trend, both products appear to perform similarly with almost the same height bars. In the final 'Outliers' category, the Samsung S20 is found at [0.796, 0.073, 0.820, 0.923] and is higher than the iPhone 12 at [0.820, 0.356, 0.845, 0.923], indicating that Samsung S20 has a higher score for outliers, implying a worse performance.\nBased on the above thought, the answer is:\nThe Samsung S20 performs better in the 'Continuous decrease' trend, and worse in the 'Sudden increase' and 'Outliers' trends. However, in the 'Sustained growth' trend, both the Samsung S20 and iPhone 12 perform at a comparable level. This shows that while both phones have varied efficiencies in different trends, they seem to perform similarly under sustained growth scenarios." } ], "id_raw": 1001 }, { "id": "MLJYqCNMgVkDtquh", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_11.png", "conversations": [ { "from": "human", "value": "\nWhat type of efficiency trend does the Samsung S20 perform notably worse than the iPhone 12?" }, { "from": "gpt", "value": "To find the efficiency trend where Samsung S20 performs notably worse than iPhone 12, I must compare the heights of the paired bars under each trend. For instance, in the 'Continuous decrease' category, the Samsung S20 bar at [0.065, 0.781, 0.089, 0.923] is notably lower than the iPhone 12 bar at [0.089, 0.604, 0.113, 0.923]. Similar comparisons can also be made for the bars under the other trends such as 'Sudden increase', 'Sustained growth', and 'Outliers'. Upon comparing these, it is noticeable that the bar heights for Samsung S20 under the 'Sudden increase' trend located at [0.308, 0.498, 0.333, 0.923] shows that the Samsung S20 performs notably worse than the iPhone 12; which has its bar at [0.333, 0.852, 0.357, 0.923].\nBased on the above thought, the answer is:\nSudden increase" } ], "id_raw": 1001 }, { "id": "k9yzqqMrYOSDRbrz", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the parameters of the two regions from the bar chart into a CSV table?" }, { "from": "gpt", "value": "From the bar chart, I see that the parameters include 'Income Equality', 'Healthcare Quality', 'Climate Policy', 'Education Budget', and 'R&D Investment'. These parameters are represented by the x-axis labels. I will use the bar names on the x-axis {\"Income Equality\"}[0.044, 0.833, 0.133, 0.960], {\"Healthcare Quality\"}[0.207, 0.833, 0.309, 0.979], {\"Climate Policy\"}[0.407, 0.833, 0.486, 0.946], {\"Education Budget\"}[0.565, 0.833, 0.663, 0.972], {\"R&D Investment\"}[0.749, 0.833, 0.839, 0.962] as the parameters. The two sets of bars under each parameter correspond to two regions or eras, labeled as {\"Region 1\"}[0.912, 0.039, 0.972, 0.059] and {\"Region 2\"}[0.912, 0.069, 0.972, 0.089] in the legend. The y coordinate of the top of each bar indicates the value of the parameter for the corresponding region. For example, {the top of the bar for Income Equality in Region 1}[0.071, 0.650, 0.133, 0.819] is about at 0.2, so the value is 0.2. I'll repeat this for all parameters and regions to construct the table.\nBased on the above thought, the answer is:\nParameter,Region 1,Region 2\nIncome Equality,0.2,0.7\nHealthcare Quality,0.8,0.9\nClimate Policy,0.3,0.7\nEducation Budget,0.45,0.8\nR&D Investment,0.1,0.4" } ], "id_raw": 1002 }, { "id": "CR0ffs0uJ4ivQ3O5", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nCould you infer from the chart data about which region or era has a better socioeconomic and policy making status? Kindly give reasons for your conclusion." }, { "from": "gpt", "value": "To infer about the socioeconomic and policy making status, let's compare the parameters for each region. Here, {\"Income Equality\"}[0.044, 0.833, 0.133, 0.960], {\"Healthcare Quality\"}[0.207, 0.833, 0.309, 0.979], {\"Climate Policy\"}[0.407, 0.833, 0.486, 0.946], {\"Education Budget\"}[0.565, 0.833, 0.663, 0.972], and {\"R&D Investment\"}[0.749, 0.833, 0.839, 0.962] are the parameters to be considered. In the case of each parameter, the height of the bars can be directly compared. The heights of the bars for 'Region 2' are either equal to or higher than their corresponding 'Region 1' bars. This suggests that in terms of all parameters considered, Region 2 has the same or better conditions than Region 1.\nBased on the above thought, the answer is:\nRegion 2 seems to have a better socioeconomic and policy making status because it shows either the same or a higher value in all parameters considered compared to Region 1." } ], "id_raw": 1002 }, { "id": "I5iwDoyfOhACJvMu", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_12.png", "conversations": [ { "from": "human", "value": "\nHow much increase in Climate Policy value is observed from Region 1 to Region 2?" }, { "from": "gpt", "value": "To find the increase in 'Climate Policy', I'll examine the bars corresponding to 'Climate Policy'. The bars for 'Climate Policy' in Region 1 and Region 2 are {\"[0.424, 0.566, 0.486, 0.819]\"}[0.424, 0.566, 0.486, 0.819] and {\"[0.486, 0.228, 0.548, 0.819]\"}[0.486, 0.228, 0.548, 0.819] respectively. Based on the y coordinates of the bar tops, the 'Climate Policy' value for Region 1 is approximately 0.3 and for Region 2, it is approximately 0.7. Subtracting the Region 1 value from the Region 2 value will give the increase.\nBased on the above thought, the answer is:\n0.4" } ], "id_raw": 1002 }, { "id": "x2OnkOEw99l65hok", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nTaking into account the bar chart you've observed, can you convert the raw data into a CSV formatted table?" }, { "from": "gpt", "value": "From the chart's x-axis labels {\"2012\"}[0.061, 0.952, 0.105, 0.975], {\"2016\"}[0.316, 0.952, 0.360, 0.975], {\"2020\"}[0.571, 0.952, 0.614, 0.975], {\"2024\"}[0.826, 0.952, 0.869, 0.975], it appears to be displaying data over time \u2013 spanning from 2012 to 2024. The y-axis {\"Population (In Millions)\"}[0.019, 0.349, 0.036, 0.611] indicates the volume of population across these years for different regions. Our key comparators are two regions: {Asia}[0.905, 0.046, 0.941, 0.069] and {Europe}[0.905, 0.081, 0.966, 0.104] which are distinguished by different colored bars. The bar heights approximate the population in millions, so for instance, for 2012 the {Asia bar}[-0.006, 0.321, 0.083, 0.935] represents 850,000 and the {Europe bar}[0.083, 0.068, 0.172, 0.935] denotes 1,200,000 individuals.\nBased on the above thought, the answer is:\nRegion,2012,2016,2020,2024\nAsia,850000,950000,1050000,1150000\nEurope,1200000,1150000,1120000,1080000" } ], "id_raw": 1003 }, { "id": "QMQ4H17eZIg37I8R", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart's data, what can you infer about the population migration and mobility patterns in the two regions?" }, { "from": "gpt", "value": "The bar chart visually represents population migration and mobility trends for two regions: {Asia}[0.905, 0.046, 0.941, 0.069] and {Europe}[0.905, 0.081, 0.966, 0.104]. The {Asia bars}[-0.006, 0.321, 0.083, 0.935]; [0.249, 0.249, 0.338, 0.935]; [0.503, 0.177, 0.593, 0.935]; [0.758, 0.104, 0.847, 0.935] consistently become taller over the years, denoting an increasing trend in population migration and mobility. On the contrary, the {Europe bars}[0.083, 0.068, 0.172, 0.935]; [0.338, 0.104, 0.427, 0.935]; [0.593, 0.126, 0.682, 0.935]; [0.847, 0.155, 0.937, 0.935] show a downwards trend, suggesting a decreasing trend in population migration and mobility over the same period. These trends suggest increasing population mobility in Asia and potentially factors like job opportunities or economic growth could be driving this trend. Conversely, the downwards trend in Europe could hint towards declining job opportunities, economic slowdown, or possibly tighter immigration controls.\nBased on the above thought, the answer is:\nThe population migration and mobility pattern in Asia appears to be increasing, possibly indicating growing economic opportunities or appealing living conditions in the region. In contrast, European mobility trends are declining, potentially due to fewer available opportunities, stricter immigration policies, or diminishing appeal." } ], "id_raw": 1003 }, { "id": "1OP94uPLyWpJYAR3", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_13.png", "conversations": [ { "from": "human", "value": "\nOver the period in the chart, which region has the largest population increase?" }, { "from": "gpt", "value": "To pinpoint the region with the largest population increase, firstly, we need to look at the {Asia bars}[-0.006, 0.321, 0.083, 0.935]; [0.249, 0.249, 0.338, 0.935]; [0.503, 0.177, 0.593, 0.935]; [0.758, 0.104, 0.847, 0.935] and {Europe bars}[0.083, 0.068, 0.172, 0.935]; [0.338, 0.104, 0.427, 0.935]; [0.593, 0.126, 0.682, 0.935]; [0.847, 0.155, 0.937, 0.935] for the years 2012 to 2024, respectively. The first Asia bar shows 850,000 while the last indicates 1,150,000 \u2013 an increase of 300,000. For Europe, the first bar represents 1,200,000 while the last portrays 1,080,000, indicating a decrease of 120,000 over the period. So, Asia experiences the most significant increase in population during these years.\nBased on the above thought, the answer is:\nAsia." } ], "id_raw": 1003 }, { "id": "f0bUgI2lOqzJIn91", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart into a table format for easier reference?" }, { "from": "gpt", "value": "Looking at the bar chart, it's clear we have four risk categories on the {x-axis}[0.095, 0.923, 0.960, 0.923]: {High Risk}[0.328, 0.942, 0.420, 0.970], {Medium Risk}[0.590, 0.942, 0.716, 0.970], {Low Risk}[0.889, 0.942, 0.976, 0.970] as well as labels for the Initial Risk and Residual Risk having separate bars in each Risk category as determined from {the legend}[0.748, 0.044, 0.950, 0.136]. The vertical, or {y-axis}, represent the proportions from {0.0}[0.050, 0.912, 0.081, 0.940] to {0.7}[0.050, -0.080, 0.081, -0.052]. The height of each bar corresponds to the proportion in its respective risk category. Using this information, I will be able to convert this into a table format.\nBased on the above thought, the answer is:\nRisk_Level,High_Risk,Medium_Risk,Low_Risk,Very_Low_Risk\nInitial_Risk,0.6,0.2,0.1,0\nResidual_Risk,0.2,0.5,0.3,0" } ], "id_raw": 1004 }, { "id": "eGLBmTmXsS1koO5U", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how effective would you say the risk mitigation strategies are?" }, { "from": "gpt", "value": "The effectiveness of the risk mitigation strategies can be judged on the basis of how the distribution of risk changes from the Initial Risk stage to the Residual Risk stage. Considering the highest risk category, {High Risk}[0.164, 0.073, 0.248, 0.923] for the initial stage and [0.248, 0.639, 0.332, 0.923] for the residual stage, we can see that it has decreased significantly. The bar for {Medium Risk}[0.444, 0.639, 0.528, 0.923] in the initial stage is also notably taller than the one for the residual stage [0.528, 0.214, 0.611, 0.923]. Most importantly, {low risk}[0.723, 0.781, 0.807, 0.923] has increased in the residual stage [0.807, 0.498, 0.891, 0.923]. This shows that risks have been effectively managed and reduced by the strategies implemented.\nBased on the above thought, the answer is:\nThe risk mitigation strategies are very effective, as indicated by the significant reduction in high and medium risk categories and the increase in lower risk categories from the Initial Risk to the Residual Risk stages." } ], "id_raw": 1004 }, { "id": "lpqdHcKyZ65wnxjN", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_67_14.png", "conversations": [ { "from": "human", "value": "\nAt which risk level did the largest reduction in proportion occur from the Initial Risk stage to the Residual Risk stage?" }, { "from": "gpt", "value": "I'm comparing the initial and residual risk for each risk category {High Risk}[0.164, 0.073, 0.248, 0.923] and [0.248, 0.639, 0.332, 0.923], {Medium Risk}[0.444, 0.639, 0.528, 0.923] and [0.528, 0.214, 0.611, 0.923], and {Low Risk}[0.723, 0.781, 0.807, 0.923] and [0.807, 0.498, 0.891, 0.923]. After examining the lengths of these bars, it is apparent that the greatest reduction occurs under the High Risk category where we moved from a proportion of 0.6 to 0.2 between the Initial Risk and Residual Risk stages.\nBased on the above thought, the answer is:\nHigh Risk." } ], "id_raw": 1004 }, { "id": "BljCP0iinekNKSOF", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nBased on the observed bar chart, turn the data for \"Emerging Markets and Consumer Behavior\" into a structured CSV table." }, { "from": "gpt", "value": "The title of the chart {Emerging Markets and Consumer Behavior}[0.394, 0.019, 0.750, 0.041] and the labels on the y-axis referring to different market sectors, such as {Electronics}[0.074, 0.844, 0.149, 0.861], indicate different Market names. The x-axis displays the Growth (%) from range of {0%}[0.155, 0.939, 0.164, 0.957] to {30%}[0.858, 0.939, 0.876, 0.957], denoting the corresponding growth rates for respective sectors. The length of each bar, such as the bar for {Electronics}[0.159, 0.814, 0.733, 0.887], reflects growth percentage for that market sector. To construct a CSV file, the market sectors will be extracted with their corresponding growth rates. This is done by analysing the length of each bar along the chart's x-axis.\nBased on the above thought, the answer is:\nMarket,Growth (%)\nElectronics,24.3\nTextiles,18.5\nPharmaceuticals,11.7\nFootwears,14.9\nBeverages,17.4\nSoftware,29.2\nAutomobile,15.6\nCosmetics,13.8\nToys,12.3" } ], "id_raw": 1005 }, { "id": "r8wUBamyyTqXzWmi", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart's representation, which sectors are doing well and which are not? And why?" }, { "from": "gpt", "value": "The chart {Emerging Markets and Consumer Behavior}[0.394, 0.019, 0.750, 0.041] provides details about the percentage growth of various market sectors. The length of the bars represent the growth rate, with longer bars indicating higher growth. For instance, {Software}[0.159, 0.360, 0.848, 0.432] and {Electronics}[0.159, 0.814, 0.733, 0.887] sectors are doing well because they have long bars representing high growth rates of 29.2% and 24.3% respectively. On the other hand, sectors such as {Pharmaceuticals}[0.159, 0.632, 0.435, 0.705], {Cosmetics}[0.159, 0.178, 0.485, 0.250] and {Toys}[0.159, 0.087, 0.449, 0.159] are not doing as well as they have short bars representing less growth percentages of 11.7%, 13.8% and 12.3% respectively.\nBased on the above thought, the answer is:\nThe sectors doing well are Software and Electronics, as they have high growth percentages of 29.2% and 24.3% respectively. This is reflected by their long bars. On the contrary, Pharmaceuticals, Cosmetics and Toys sectors are not doing well with less growth percentages of 11.7%, 13.8% and 12.3% respectively, reflected by their short bars." } ], "id_raw": 1005 }, { "id": "ffoQgnYW6mK1g2G6", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_0.png", "conversations": [ { "from": "human", "value": "\nIn which market sector has the highest growth been observed?" }, { "from": "gpt", "value": "To find the market sector with the highest growth, I need to compare the lengths of all bars. The longest bar represents the highest growth. From the bars representing {Electronics}[0.159, 0.814, 0.733, 0.887], {Textiles}[0.159, 0.723, 0.596, 0.796] up to {Toys}[0.159, 0.087, 0.449, 0.159], the longest bar is that of {Software}[0.159, 0.360, 0.848, 0.432] market sector. Hence, it can deduced that Software sector has the highest growth.\nBased on the above thought, the answer is:\nSoftware." } ], "id_raw": 1005 }, { "id": "JnD0Remn93ESKWnL", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information displayed on the bar chart into a structured CSV format?" }, { "from": "gpt", "value": "From the chart, I see three categories depicted by their respective horizontal bars. The categories are {\"Rural\"}[0.082, 0.761, 0.127, 0.779], {\"Suburban\"}[0.043, 0.466, 0.127, 0.484], and {\"Urban\"}[0.074, 0.171, 0.127, 0.189]. Each bar represents the development index value for its respective category. The x-axis labels range from {0}[0.134, 0.939, 0.145, 0.957] to {10}[0.959, 0.939, 0.981, 0.957] which indicates the range of development index. By observing the bar for each category, and comparing its ending point to the development index labels on the x-axis, I can find the development index value for each category. The bar for the \"Rural\" category[0.139, 0.650, 0.264, 0.886] end just above the 1 mark on the x-axis. The \"Suburban\" bar[0.139, 0.355, 0.455, 0.591] ends near the 4 mark, and the \"Urban\" bar[0.139, 0.060, 0.738, 0.296] ends just before the 7 mark. On this basis, I am able to structure the data presented in the chart into CSV format.\nBased on the above thought, the answer is:\nAreas,Development_Index\nRural,1.5\nSuburban,3.8\nUrban,7.2" } ], "id_raw": 1006 }, { "id": "7S6LrYQo0CN8pSrm", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, how can you describe the general trend of development index among these areas?" }, { "from": "gpt", "value": "Looking at the lengths of the bars representing {\"Rural\"}[0.082, 0.761, 0.127, 0.779], {\"Suburban\"}[0.043, 0.466, 0.127, 0.484], and {\"Urban\"}[0.074, 0.171, 0.127, 0.189] areas, I notice that the lengths increase from \"Rural\" to \"Urban\". The \"Rural\" bar[0.139, 0.650, 0.264, 0.886] extends to the 1.5 mark on the x-axis[0.222, 0.927], which indicates a lower development index value. The \"Suburban\" bar[0.139, 0.355, 0.455, 0.591] extends to the 3.8 mark on the x-axis[0.472, 0.927], showing a higher development index value than \"Rural\". And finally, the \"Urban\" bar[0.139, 0.060, 0.738, 0.296] extends the furthest to the 7.2 mark on the x-axis[0.721, 0.927], indicating the highest development index value among the three. This suggests a general trend of increasing development index from \"Rural\" to \"Urban\" areas.\nBased on the above thought, the answer is:\nThe general trend of development index increases from Rural to Urban areas." } ], "id_raw": 1006 }, { "id": "IqexGsHwggFVZ6qM", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_1.png", "conversations": [ { "from": "human", "value": "\nWhich category has the highest development index based on the bar chart?" }, { "from": "gpt", "value": "To find the category with the highest development index, I need to compare the bars for each category. {\"Rural\"}[0.082, 0.761, 0.127, 0.779] ends at the 1.5 mark[0.222, 0.927], {\"Suburban\"}[0.043, 0.466, 0.127, 0.484] ends at the 3.8 mark[0.472, 0.927], and {\"Urban\"}[0.074, 0.171, 0.127, 0.189] ends at the 7.2 mark[0.721, 0.927]. The \"Urban\" bar extends the farthest, indicating that it has the highest development index value.\nBased on the above thought, the answer is:\nUrban." } ], "id_raw": 1006 }, { "id": "XNStHGN36g1DB9bd", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nCan you analyze the chart and create a table listing each industry and its corresponding development trend?" }, { "from": "gpt", "value": "From the {chart title}[0.383, 0.019, 0.835, 0.041], I can see that this chart is about Technological Industry Development Trends. The {y-axis}[0.019, 0.451, 0.036, 0.523] represents different industries, and the {x-axis}[0.524, 0.964, 0.694, 0.981] represents the corresponding development trend. For each industry, there is a horizontal bar showing its development trend. I can calculate the trend value by comparing the length of this bar with the x-axis range. For instance, the {bar for \"AI\"}[0.237, 0.822, 0.670, 0.887] corresponds approximately to the x-position 3.2. Therefore, the development trend for AI is approximately 3.2.\nBased on the above thought, the answer is:\nIndustry,Development Trend\nAI,3.2\nIoT,2.9\nAutomation,4.5\nCloud Computing,4.8\nBlockchain,1.9\nAR/VR,1.7\nCybersecurity,3.5\nData Science,5.0\n5G Technology,3.0\nQuantum Computing,0.8" } ], "id_raw": 1007 }, { "id": "0QhW70btRkP3T90O", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be deduced about the development trend of Quantum Computing as compared to AI, based on the chart?" }, { "from": "gpt", "value": "From the chart, {the bar for \"Quantum Computing\"}[0.237, 0.087, 0.345, 0.152] and {the bar for \"AI\"}[0.237, 0.822, 0.670, 0.887] can be used to compare the development trends. The length of the bar for Quantum Computing is noticeably shorter than the one for AI, indicating that Quantum Computing has a lower development trend. This suggests that, while both are emerging technology industries, AI currently shows more development and advancement than Quantum Computing.\nBased on the above thought, the answer is:\nThe development trend of Quantum Computing is lower compared to AI. This suggests that AI has seen significant advancements and developments compared to Quantum Computing, which is still an emerging field." } ], "id_raw": 1007 }, { "id": "k61F9Ew5QJnRV7sn", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_2.png", "conversations": [ { "from": "human", "value": "\nWhich industry has the highest development trend in the chart?" }, { "from": "gpt", "value": "To find the industry with the highest development trend, I need to compare the lengths of the bars. The longest bar represents the highest development trend. Observing the chart, I can see that {the bar for \"Data Science\"}[0.237, 0.250, 0.914, 0.315] is the longest, extending to the 5.0 position on the x-axis, and therefore has the highest development trend.\nBased on the above thought, the answer is:\nData Science." } ], "id_raw": 1007 }, { "id": "GJ1QQpe1TvjwyVvV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nPlease convert the bar chart data into a tabular form." }, { "from": "gpt", "value": "The bar chart provided lists various categories on {the y-axis}[0.019, 0.411, 0.036, 0.517] and their frequency on the {x-axis}[0.325, 0.919, 0.336, 0.942]. The categories include {Hotel Bookings}[0.331, 0.769, 0.651, 0.863], {Restaurant Reservations}[0.331, 0.652, 0.731, 0.746], {Tour Package Purchases}[0.331, 0.534, 0.544, 0.628], {Airport Transfers}[0.331, 0.417, 0.810, 0.511], {Spa Appointments}[0.331, 0.300, 0.491, 0.394], {Entertainment Ticket Purchases}[0.331, 0.182, 0.864, 0.276], and {Gym Memberships}[0.331, 0.065, 0.384, 0.159]. We evaluate the frequency of each category by the length of its respective bar on the x-axis. For instance, the bar for Entertainment Ticket Purchases ends near the {20 on the x-axis}[0.853, 0.919, 0.875, 0.942].\nBased on the above thought, the answer is:\nCategory,Frequency\nHotel Bookings,12\nRestaurant Reservations,15\nTour Package Purchases,8\nAirport Transfers,18\nSpa Appointments,6\nEntertainment Ticket Purchases,20\nGym Memberships,2" } ], "id_raw": 1008 }, { "id": "AtBqliosUDlItpKt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nIn the context of planning business strategies, what insights does this chart provide for the Tourism and Hospitality industry?" }, { "from": "gpt", "value": "The bar chart illustrates the frequency of different categories in the Tourism and Hospitality industry. From the longer bars for {Restaurant Reservations}[0.331, 0.652, 0.731, 0.746], {Airport Transfers}[0.331, 0.417, 0.810, 0.511] and {Entertainment Ticket Purchases}[0.331, 0.182, 0.864, 0.276], it can be inferred that these services are highly demanded, hence, businesses may seek to strengthen such sectors. With the shortest bar representing {Gym memberships}[0.331, 0.065, 0.384, 0.159] near the {2 mark}[0.325, 0.919, 0.336, 0.942] on the x-axis, it is suggested that this aspect of the industry is less popular among consumers, prompting a need for businesses to either innovate their offerings in this sector or divert resources to more profitable sectors.\nBased on the above thought, the answer is:\nEssential insights include high demand for Restaurant Reservations, Airport Transfers, and Entertainment Ticket Purchases, suggesting these areas as potential growth sectors. The less popular Gym Memberships could indicate a need for business innovation or resource reallocation." } ], "id_raw": 1008 }, { "id": "kOZ6ukGAy25qBvBy", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_3.png", "conversations": [ { "from": "human", "value": "\nWhich category has the highest frequency in the chart?" }, { "from": "gpt", "value": "I compare each bar length on the x-axis and observe the {Entertainment Ticket Purchases bar}[0.331, 0.182, 0.864, 0.276] extends the furthest on the x-axis, reaching almost up to the {20 mark}[0.853, 0.919, 0.875, 0.942]. Thus, Entertainment Ticket Purchases have the highest frequency.\nBased on the above thought, the answer is:\nEntertainment Ticket Purchases." } ], "id_raw": 1008 }, { "id": "2KBm7BNQhDojXs9p", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nCould you organize the data from the bar chart into a CSV format?" }, { "from": "gpt", "value": "The graph is a horizontal bar chart with 'Machine Learning Areas' on the y-axis and 'Frequency (%)' on the x-axis. The chart title {\"Popularity of Machine Learning Areas\"}[0.424, 0.019, 0.810, 0.041] suggests that the x-axis values indicate the popularity of each Machine Learning Area. According to the y-axis labels, these areas include 'Supervised Learning', 'Unsupervised Learning', 'Reinforcement Learning', 'Deep Learning', 'Neural Networks', 'Prediction Modeling', 'Classification', 'Regression', 'Clustering', and 'Anomaly Detection'. Correspondingly, the x-axis labels range from {0}[0.258, 0.939, 0.269, 0.957] to {25}[0.959, 0.939, 0.981, 0.957]. I can determine the frequency for each Machine Learning Areas by analyzing the length of the bars. For example, the bar for {Supervised Learning}[0.263, 0.822, 0.687, 0.887] extends to approximately the 15 position on the x-axis, so the frequency of 'Supervised Learning' is about 15%.\nBased on the above thought, the answer is:\nMachine Learning Areas,Frequency (%)\nSupervised Learning,15\nUnsupervised Learning,10\nReinforcement Learning,5\nDeep Learning,20\nNeural Networks,22\nPrediction Modeling,13\nClassification,18\nRegression,16\nClustering,11\nAnomaly Detection,9" } ], "id_raw": 1009 }, { "id": "r5BOXYXw0Cx6jBp6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nJudging from the chart, can you provide a brief overview of the popularity trends in the different Machine Learning areas?" }, { "from": "gpt", "value": "Looking at the bar chart titled {\"Popularity of Machine Learning Areas\"}[0.424, 0.019, 0.810, 0.041], it can be analyzed that areas such as 'Supervised Learning', 'Deep Learning', 'Neural Networks', 'Classification', and 'Regression' have higher frequencies, which are represented by longer bars such as {[0.263, 0.822, 0.687, 0.887] \"Supervised Learning\"}[0.263, 0.822, 0.687, 0.887], {[0.263, 0.577, 0.829, 0.642] \"Deep Learning\"}[0.263, 0.577, 0.829, 0.642], and {[0.263, 0.495, 0.885, 0.560] \"Neural Networks\"}[0.263, 0.495, 0.885, 0.560]. This indicates that these areas are more commonly used in AI applications. On the contrary, areas like 'Reinforcement Learning' and 'Anomaly Detection', represented by smaller bars like {[0.263, 0.658, 0.405, 0.724] \"Reinforcement Learning\"}[0.263, 0.658, 0.405, 0.724] and {[0.263, 0.087, 0.518, 0.152] \"Anomaly Detection\"}[0.263, 0.087, 0.518, 0.152], are less frequently employed, which likely speaks to the complexity or specific use cases of these techniques.\nBased on the above thought, the answer is:\nAccording to the chart, the most popular areas in Machine Learning are 'Neural Networks', 'Deep Learning', 'Classification', 'Supervised Learning', and 'Regression' due to their relatively higher frequency of usage in AI applications. At the same time, 'Reinforcement Learning' and 'Anomaly Detection' appear less popular, likely reflecting the complexity or specialized nature of these techniques." } ], "id_raw": 1009 }, { "id": "BqVYCgZT6bcFVIAo", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_4.png", "conversations": [ { "from": "human", "value": "\nWhat Machine Learning Area is the most popular according to the data in the chart?" }, { "from": "gpt", "value": "To find the most popular Machine Learning Area, I need to identify the bar with the longest length. Noting the lengths of each bar, the bar corresponding to {Neural Networks}[0.263, 0.495, 0.885, 0.560] appears to extend the farthest on the x-axis, with a frequency close to the 22 mark on the x-axis. Thus the 'Neural Networks' area appears to have the highest popularity in terms of frequency.\nBased on the above thought, the answer is:\nNeural Networks" } ], "id_raw": 1009 }, { "id": "1KJcAh20t3wtQ5tY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured CSV table according to the bar chart you noticed?" }, { "from": "gpt", "value": "Noticing the chart title as {\"New Energy and Clean Technology Growth Rate Survey\"}[0.347, 0.085, 0.689, 0.115] and x-axis, I found that the x-axis represents different technologies. The y-axis tick labels represent the corresponding growth rates, ranging from {0}[0.108, 0.881, 0.116, 0.904] to {8}[0.099, 0.107, 0.116, 0.130]. The bars on the chart represent the growth rates of these technologies. Each technology's growth rate is read from the right end of each bar. For instance, the growth rate of {Fusion Energy}[0.068, 0.841, 0.241, 0.861] is 7.7%, read from {the length of the bar}[0.247, 0.060, 0.912, 0.127].\nBased on the above thought, the answer is:\nTechnology,Growth Rate (%)\nSolar Energy,2.1\nWind Energy,2.7\nBiomass,3.0\nHydro Power,3.5\nCarbon Capture,3.8\nEnergy Storage,4.7\nSmart Grid,5.0\nGeothermal Energy,5.8\nWave and Tidal Power,6.0\nFusion Energy,7.7" } ], "id_raw": 1010 }, { "id": "Sf4Xk3d3Ub5NYhV6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nFrom the standpoint of this chart, which areas in the field of New Energy and Clean Technology Development are the most promising?" }, { "from": "gpt", "value": "According to the length of the bar in the chart, it can be inferred that the longer the bar, the higher the growth rate, which means the more promising the technology is. Specifically, from the comparison of {Fusion Energy bar}[0.247, 0.060, 0.912, 0.127], {Wave and Tidal Power bar}[0.247, 0.144, 0.765, 0.212], and {Geothermal Energy bar}[0.247, 0.229, 0.748, 0.296], these are the three longest bars in the figure. It can be concluded that Fusion Energy, Wave and Tidal Power, and Geothermal Energy are the three most promising technology areas.\nBased on the above thought, the answer is:\nFusion Energy, Wave and Tidal Power, and Geothermal Energy." } ], "id_raw": 1010 }, { "id": "vqW16sJQZ45V6D8H", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_5.png", "conversations": [ { "from": "human", "value": "\nWhich technology has the highest growth rate in the New Energy and Clean Technology sectors according to the chart?" }, { "from": "gpt", "value": "To find the technology with the highest growth rate, I need to identify the longest bar in the chart. By reading the chart from top to bottom, {the bar representing Fusion Energy}[0.247, 0.060, 0.912, 0.127] is the longest, extending all the way to the position marking 7.7% on the x-axis. Therefore, Fusion Energy has the highest growth rate.\nBased on the above thought, the answer is:\nFusion Energy" } ], "id_raw": 1010 }, { "id": "EsOLdwTK19Z5nEGV", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information from the bar chart into a CSV format?" }, { "from": "gpt", "value": "If we consider the way this horizontal bar chart is set up, the 'Categories' or 'Companies' are the Y-Axis labels, which include {\"Apple\"}[0.074, 0.839, 0.124, 0.857], {\"Samsung\"}[0.043, 0.737, 0.124, 0.754], {\"Huawei\"}[0.059, 0.634, 0.124, 0.651], {\"Xiaomi\"}[0.064, 0.531, 0.124, 0.549], {\"OPPO\"}[0.076, 0.429, 0.124, 0.446], {\"Vivo\"}[0.086, 0.326, 0.124, 0.344], {\"Motorola\"}[0.047, 0.223, 0.124, 0.241], and {\"Google\"}[0.062, 0.121, 0.124, 0.138]. Meanwhile, the X-Axis labels include {0}[0.130, 0.939, 0.141, 0.957], {5}[0.236, 0.939, 0.247, 0.957], {10}[0.336, 0.939, 0.358, 0.957], {15}[0.442, 0.939, 0.464, 0.957], {20}[0.548, 0.939, 0.569, 0.957], {25}[0.653, 0.939, 0.675, 0.957], {30}[0.759, 0.939, 0.781, 0.957], {35}[0.865, 0.939, 0.887, 0.957] which represent the market shares in percentage of these companies. If I consider these coordinates in selecting the Company names and the corresponding Market Shares and put them in the correct format, I can create the CSV based on this bar chart.\nBased on the above thought, the answer is:\nCompany,Market Share\nApple,34\nSamsung,29\nHuawei,20\nXiaomi,9\nOPPO,4\nVivo,3.1\nMotorola,0.65\nGoogle,0.25" } ], "id_raw": 1011 }, { "id": "tnpu3elvdzWv8pFE", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nBased on the displayed chart, what remarks can you make about the smartphone market?" }, { "from": "gpt", "value": "Observing the chart, it's clear that the market share in the smartphone industry is dominated by the major players {\"Apple\"}[0.136, 0.805, 0.854, 0.887], {\"Samsung\"}[0.136, 0.702, 0.749, 0.785], and {\"Huawei\"}[0.136, 0.600, 0.559, 0.682]. Apple occupies the leading position, with a market share of approximately {\"34%\"}[0.136, 0.805, 0.854, 0.887]. On the other hand, companies such as {\"Motorola\"}[0.136, 0.189, 0.150, 0.271] and {\"Google\"}[0.136, 0.087, 0.141, 0.169] maintain a relatively small portion of the market share, under 1%. The smartphone market competition is fierce among the top three players, while other brands hold significantly less market proportion.\nBased on the above thought, the answer is:\nThe smartphone market is primarily dominated by Apple, Samsung, and Huawei. Apple holds the leading position with the highest market share. Other brands like Motorola and Google hold a significantly smaller portion of the market, indicating a wide disparity among the smartphone manufacturers in the global market." } ], "id_raw": 1011 }, { "id": "22PrvlHMwkaFoPlC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which company has the highest market share?" }, { "from": "gpt", "value": "To determine the company with the highest market share, I'll look for the longest bar in the bar chart. By observing the bar chart, it is clear that the longest bar corresponds to {\"Apple\"}[0.136, 0.805, 0.854, 0.887]. Therefore, it would be safe to conclude that Apple has the highest market share.\nBased on the above thought, the answer is:\nApple." } ], "id_raw": 1011 }, { "id": "uzKod4HUI29JblJY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nGiven the chart, please summarise the information in a CSV table format." }, { "from": "gpt", "value": "The horizontal bars in the plot represent the funding distribution for multiple categories of social welfare and charity activities. These categories can be read from the labels on the {y-axis}[{\"Educational Aid\"}[0.100, 0.839, 0.236, 0.857]; {\"Food Distribution\"}[0.087, 0.737, 0.236, 0.754]; {\"Healthcare Assistance\"}[0.043, 0.634, 0.236, 0.651]; {\"Housing Aid\"}[0.132, 0.531, 0.236, 0.549]; {\"Disaster Relief\"}[0.110, 0.429, 0.236, 0.446]; {\"Research & Advocacy\"}[0.048, 0.326, 0.236, 0.344]; {\"Animal Welfare\"}[0.104, 0.223, 0.236, 0.241]; {\"Environmental Issues\"}[0.051, 0.121, 0.236, 0.138]]. The distribution values for each category are deduced from the {x-axis}[{\"0\"}[0.242, 0.939, 0.253, 0.957]; {\"5\"}[0.347, 0.939, 0.358, 0.957]; {\"10\"}[0.446, 0.939, 0.468, 0.957]; {\"15\"}[0.551, 0.939, 0.573, 0.957]; {\"20\"}[0.656, 0.939, 0.678, 0.957]; {\"25\"}[0.761, 0.939, 0.783, 0.957]; {\"30\"}[0.866, 0.939, 0.887, 0.957]] using the end points of the bars in each category [{\"bar\"}[0.248, 0.805, 0.585, 0.887]; {\"bar\"}[0.248, 0.702, 0.680, 0.785]; {\"bar\"}[0.248, 0.600, 0.893, 0.682]; {\"bar\"}[0.248, 0.497, 0.514, 0.579]; {\"bar\"}[0.248, 0.395, 0.455, 0.477]; {\"bar\"}[0.248, 0.292, 0.422, 0.374]; {\"bar\"}[0.248, 0.189, 0.260, 0.271]; {\"bar\"}[0.248, 0.087, 0.269, 0.169]]. Therefore, to structure a CSV table, I can record the name of each category along with its corresponding value read from the x-axis.\nBased on the above thought, the answer is:\nCategory,Percentage\nEducational Aid,16.1\nFood Distribution,20.6\nHealthcare Assistance,30.8\nHousing Aid,12.7\nDisaster Relief,9.9\nResearch & Advocacy,8.3\nAnimal Welfare,0.6\nEnvironmental Issues,1.0" } ], "id_raw": 1012 }, { "id": "aue9NlX3qo3jG5ci", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nWhat inferences about spending priorities related to social welfare activities can be drawn from the chart?" }, { "from": "gpt", "value": "Upon observing the chart, the vertical length of each horizontal {bar}[0.248, 0.805, 0.585, 0.887, 'Educational Aid'.. 'Environmental Issues'][0.248, 0.087, 0.269, 0.169] in comparison to the others, indicates a spending priority. 'Healthcare Assistance', which has the longest bar, indicates that it receives the highest amount of funding. This suggests that within the sectors of social welfare, the highest priority is given to healthcare. Conversely, 'Animal Welfare' and 'Environmental Issues' have the shortest bars, indicating they receive least funding, showing that these areas are lower priority. Overall, the chart indicates that social welfare spending tends to prioritize human-related aid and services, while environmental causes receive less priority.\nBased on the above thought, the answer is:\nThe chart suggests that in the context of social welfare, the spending priorities are dominated by human-related aid such as Healthcare and Food distribution, while less emphasis is placed on issues like Animal Welfare and Environmental Concerns." } ], "id_raw": 1012 }, { "id": "LjfHpfejvxmHCoL5", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what category received the lowest funding?" }, { "from": "gpt", "value": "To find which category received the lowest funding, I have to examine the {horizontal bars}[{\"bar\"}[0.248, 0.805, 0.585, 0.887]; {\"bar\"}[0.248, 0.702, 0.680, 0.785]; {\"bar\"}[0.248, 0.600, 0.893, 0.682]; {\"bar\"}[0.248, 0.497, 0.514, 0.579]; {\"bar\"}[0.248, 0.395, 0.455, 0.477]; {\"bar\"}[0.248, 0.292, 0.422, 0.374]; {\"bar\"}[0.248, 0.189, 0.260, 0.271]; {\"bar\"}[0.248, 0.087, 0.269, 0.169]] for each category and locate the one that extends least towards the right. After considering each of the bars, I can observe that the horizontal bar for 'Environmental Issues'{[0.248, 0.087, 0.269, 0.169]} extends to approximately 1% on the x-axis, being the shortest bar. Therefore, the 'Environmental Issues' category has the least funding.\nBased on the above thought, the answer is:\nEnvironmental Issues" } ], "id_raw": 1012 }, { "id": "5TRi26iJWCCvkQ2J", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nCould you transform the bar chart data into a CSV format for each type of internet usage with its respective frequency?" }, { "from": "gpt", "value": "To transform the data into a CSV format, I can use the y-axis labels as the types of internet usage and the lengths of their respective bars as the frequency. The y-axis labels can be found at {\"Mobile Internet Users\"}[0.058, 0.851, 0.243, 0.868], {\"Desktop Internet Users\"}[0.043, 0.781, 0.243, 0.799], {\"E-commerce Growth\"}[0.066, 0.711, 0.243, 0.729], and so on, up to {\"Cryptocurrency\"} [0.109, 0.081, 0.243, 0.099]. The lengths of the respective bars range from {Mobile Internet Users'} [0.255, 0.830, 0.554, 0.886] indicating a frequency of 3.5, to {Cryptocurrency}[0.255, 0.060, 0.614, 0.116] indicating a frequency of 4.2. I'll match each type of internet usage with its corresponding frequency to create the CSV data.\nBased on the above thought, the answer is:\nType of Internet Usage,Frequency\nMobile Internet Users,3.5\nDesktop Internet Users,2.6\nE-commerce Growth,7.3\nEmail Usage,5.0\nSocial Media Usage,6.6\nVideo Streaming,7.9\nOnline Gaming,4.3\nVideo Conferencing,7.2\nOnline Education,6.1\nVirtual Reality,2.8\nInternet of Things,4.7\nCryptocurrency,4.2" } ], "id_raw": 1013 }, { "id": "f3eky6Ql9OTTbgZY", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nFrom examining the chart, which areas of internet usage do you think are trending upward based on their high frequency?" }, { "from": "gpt", "value": "From the overall observation of the chart, it's evident that the types of internet usage that have bars extending towards the higher numbers on the x-axis [8 tick mark - 0.933, 0.939, 0.944, 0.957] are trending upward due to their high frequency. Specifically, these are {Video Streaming}[0.255, 0.480, 0.930, 0.536], {Video Conferencing}[0.255, 0.340, 0.870, 0.396], and {E-commerce Growth}[0.255, 0.690, 0.879, 0.746], with all their bars reaching a frequency of over 7. This suggests that these types of internet usage are experiencing rapid growth or increased usage.\nBased on the above thought, the answer is:\nVideo Streaming, Video Conferencing, and E-commerce Growth are the areas of internet usage that appear to be trending upward based on their high frequency." } ], "id_raw": 1013 }, { "id": "C2UmpzsjZhqp8PMT", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_8.png", "conversations": [ { "from": "human", "value": "\nJudging by the bar lengths in the chart, which type of internet usage has the highest frequency?" }, { "from": "gpt", "value": "To find the type of internet usage with the highest frequency, I need to look for the longest bar within the chart. I can see that {the bar corresponding to Video Streaming}[0.255, 0.480, 0.930, 0.536] is the longest, reaching towards the {8 tick mark} on the x-axis [0.933, 0.939, 0.944, 0.957], thereby indicating the highest frequency among all the categories.\nBased on the above thought, the answer is:\nVideo Streaming." } ], "id_raw": 1013 }, { "id": "LGKOTa59by0PrcQR", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, can you convert the visual representation into a table which details the item categories and their respective values?" }, { "from": "gpt", "value": "The chart titled {\"Statistical Data Overview\"}[0.495, 0.015, 0.705, 0.033] depicts the instances of different categories of items denoted by bars. Each bar corresponds to the number of instances of an item category. The item categories are represented on the Y-axis with the names {\"High-quality Automobile\"}[0.035, 0.858, 0.205, 0.872], {\"Mid-Quality Automobile\"}[0.042, 0.764, 0.205, 0.778], and so on. The instance count is reflected in the length of the bars on the X-axis ranging from {0}[0.211, 0.951, 0.219, 0.965] to roughly {15}[0.886, 0.951, 0.903, 0.965]. For example, the bar for \"High-Quality Automobile\" extends to 15.2, signifying 15.2 instances of High-Quality Automobiles represented by the bar at [0.215, 0.826, 0.903, 0.901]. Using this information, I can then construct a table.\nBased on the above thought, the answer is:\nItems,Instances\nHigh-Quality Automobile,15.20\nMid-Quality Automobile,12.40\nLow-Quality Automobile,10.50\nHigh-Quality Electronics,14.60\nMid-Quality Electronics,11.20\nLow-Quality Electronics,9.80\nHigh-Quality Clothing,14.40\nMid-Quality Clothing,12.20\nLow-Quality Clothing,10.30" } ], "id_raw": 1014 }, { "id": "wfrVIUKab0lnxQvz", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nFrom a quality perspective, can you interpret the distribution of all the items?" }, { "from": "gpt", "value": "Looking at the chart, we notice that the bars for each major category (Automobile, Electronics, and Clothing) are grouped into three quality types: High, Mid, and Low. Each major category has a bar representing each of the quality types such as {\"High-Quality Automobile\"}[0.215, 0.826, 0.903, 0.901], {\"Mid-Quality Automobile\"}[0.215, 0.732, 0.777, 0.807] and so on. Generally, the High-Quality items have the longest bars, Mid-Quality items have medium-length bars, and Low-Quality items have the shortest bars. This indicates that High-Quality versions of items are more prevalent, followed by Mid-quality and then Low-Quality items. However, an exception to this trend is seen in the clothing category where the {\"Mid-Quality Clothing\"}[0.215, 0.172, 0.768, 0.247] bar is longer than the {\"Mid-Quality Electronics\"}[0.215, 0.452, 0.722, 0.527] bar.\nBased on the above thought, the answer is:\nFrom a quality perspective, it looks like High-Quality items generally have the most instances, followed by Mid-quality and then Low-Quality items. However, there is an exception in the Mid-Quality category where Clothing has more instances than Electronics." } ], "id_raw": 1014 }, { "id": "G7pc4BU71EBoE0vO", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_9.png", "conversations": [ { "from": "human", "value": "\nWhich category has the least number of instances according to the chart?" }, { "from": "gpt", "value": "I can determine the category with the least instances by comparing the length of each bar. The shortest bar represents the category with the least instances. By looking at all the bars such as {\"High-Quality Automobile\"}[0.215, 0.826, 0.903, 0.901], {\"Mid-Quality Automobile\"}[0.215, 0.732, 0.777, 0.807] and so forth, I notice that the shortest bar is {\"Low-Quality Electronics\"}[0.215, 0.359, 0.659, 0.433], which signifies that the \"Low-Quality Electronics\" category has the least instances.\nBased on the above thought, the answer is:\nLow-Quality Electronics" } ], "id_raw": 1014 }, { "id": "QBBjrs08ExtAtoPE", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nCan you please generate a table from the chart data in CSV format?" }, { "from": "gpt", "value": "Looking at the chart, I can see that the y-axis has listed various industries {\"Biotechnology\"}[0.097, 0.855, 0.221, 0.873], {\"Cryptocurrency\"}[0.087, 0.795, 0.221, 0.813] etc. and the x-axis presents the trend score {\"0\"}[0.228, 0.939, 0.239, 0.957],{\"1\"}[0.307, 0.939, 0.318, 0.957] and so on till 9. Each industry {e.g., \"Biotechnology\"}[0.233, 0.838, 0.643, 0.886] has a horizontal bar representing a specific trend score. Value can be determined from the x-axis corresponding to the bar length. To convert the chart to a CSV format, the industry names would form the first column and the corresponding trend scores would form the second column.\nBased on the above thought, the answer is:\nIndustry,Trend\nBiotechnology,5.2\nCryptocurrency,7.7\nArtificial Intelligence,6.9\nVirtual Reality, 3.6\nFintech, 7.3\nData Science,8.2\nBlockchain, 4.9\nRobotics,5.7\nCloud Computing, 6.1\nInternet of Things,4.4\nBig Data,8.5\nCybersecurity,6.1\nNanotechnology,2.5\nAugmented Reality,4.0" } ], "id_raw": 1015 }, { "id": "LA7fbjR2ki3UwbtW", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nBased on the provided trends, which industry shows the most significant growth?" }, { "from": "gpt", "value": "This can be determined by analysing the lengths of different horizontal bars representing different industries. The bar with the maximum length {\"Data Science\"}[0.233, 0.539, 0.879, 0.587] represents the industry with equal trend value on x-axis and consequently the most significant growth.\nBased on the above thought, the answer is:\nData Science shows the most significant growth." } ], "id_raw": 1015 }, { "id": "Nte43WYl1174JfHb", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_10.png", "conversations": [ { "from": "human", "value": "\nWhat's the mean trend value across all industries?" }, { "from": "gpt", "value": "To determine the mean, I need to take into account all the industries represented by the bars of the chart such as {Biotechnology}[0.233, 0.838, 0.643, 0.886], {Cryptocurrency}[0.233, 0.778, 0.840, 0.826], and append corresponding x-axis values to obtain trend values. Adding all those individual trend values and then divide by the total number of industries represented in the chart.\nBased on the above thought, the answer is:\n5.6" } ], "id_raw": 1015 }, { "id": "oTI9sUB9gqVbWdVb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a CSV format table for me?" }, { "from": "gpt", "value": "I see that the chart has vertical labelled bars to represent {'Political Trends'}[0.019, 0.408, 0.036, 0.537] such as Increasing Partisanship, Political Disaffection, Electoral Volatility etc. The length of the bars corresponds to the Frequency % labelled on {'x-axis'}[0.556, 0.964, 0.681, 0.981]. To generate a CSV table, I would extract the length of the bars which corresponds to the Frequency of the which specific Political Trend. For example, the bar for {Increasing Partisanship}[0.256, 0.825, 0.691, 0.886] is projected to approximately the 30 mark on the x-axis, so the frequency for \"Increasing Partisanship\" is 30.\nBased on the above thought, the answer is:\nPolitical_Trend,Frequency\nIncreasing Partisanship,30\nPolitical Disaffection, 24\nElectoral Volatility,14\nPolitical Polarization,37\nSocial Movements,26\nDigital Politics,41\nPolitical Scandal,32\nIdentity Politics,28\nPopulism,33\nAlternative Media,35\nPolitical Satire,19" } ], "id_raw": 1016 }, { "id": "6icZiBVaulwDmqdJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nWhich aspect of Political Trend requires more attention as a social issue based on its frequency in the society?" }, { "from": "gpt", "value": "To answer this question, I need to look for the trend that is most frequently occurring, as it is likely to require the most attention based on these occurrences. Observing the bars, I see that the longest bar is {Digital Politics}[0.256, 0.442, 0.851, 0.504] touching the position of 40. Hence, 'Digital Politics' is the most recorded trend indicating that it needs the most attention among these political trends.\nBased on the above thought, the answer is:\n\"Digital Politics\" requires more attention as a social issue, due to its highest recorded frequency." } ], "id_raw": 1016 }, { "id": "PZqaDh7cSNvJIRCY", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum frequency noticed in the chart, and under which Political Trend is it occurring?" }, { "from": "gpt", "value": "To answer this question, I need to look for the longest bar in the chart, which represents the highest frequency. I detect that the bar named {Digital Politics}[0.256, 0.442, 0.851, 0.504] is longer than all the others, reaching almost to the position of 40 on the x-axis. So, 'Digital Politics' is associated with the highest frequency.\nBased on the above thought, the answer is:\n\"Digital Politics\" has the maximum frequency of 41." } ], "id_raw": 1016 }, { "id": "rPc6INwqGhjdpFSo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart depicting the frequency of various technology innovations into a CSV-formatted table for me?" }, { "from": "gpt", "value": "Sure, the chart title {\"Education and Training Technology Innovations\"}[0.427, 0.015, 0.817, 0.033] gives me an insight into what to expect on the chart. From {the x-axis}[0.572, 0.971, 0.672, 0.985], we have the frequency of different technology innovations expressed in percentages. The {y-axis}[0.015, 0.451, 0.029, 0.528] is labeled with different technology innovations like \"Augmented Reality\", \"Virtual Reality\", \"Artificial Intelligence\", etc. The length of the bars which are examples {\"Augmented Reality\"}[0.264, 0.856, 0.312, 0.901], {\"Virtual Reality\"}[0.264, 0.801, 0.351, 0.845], represents the frequency of each technology innovation. Let me generate the CSV table from the chart.\nBased on the above thought, the answer is:\nTechnology,Frequency (%)\nAugmented Reality,0.6\nVirtual Reality,1.1\nArtificial Intelligence,1.3\nGame-Based Learning,1.7\nFlipped Classroom,2.0\nBring Your Own Device,2.1\nWearable Technology,2.7\nMobile Browsers,3.5\nLearning Management Systems,3.6\nCloud-Based Systems,4.1\nMassive Open Online Courses,4.8\nStudent Engagement Systems,5.8\nDigital Badges,8.0\nBig Data,6.1\nLearning Analytics,8.2" } ], "id_raw": 1017 }, { "id": "wNpqONUygzYP7srK", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nGiven the data on the chart, which technology innovation would you recommend to a beginner in the EdTech industry considering the integration frequency?" }, { "from": "gpt", "value": "The frequency on the chart indicates the current acceptance and usage in the industry. Therefore, a high frequency suggests that the technology has been widely embraced and likely has a strong community and substantial resources for support. Upon close examination, the bar with the biggest length represents the technology with the highest frequency {\"Digital Badges\"}[0.264, 0.190, 0.726, 0.234]. This makes it a safer bet for someone starting in the EdTech industry.\nBased on the above thought, the answer is:\nDigital Badges would be a good start since it has the highest integration frequency indicating a wider acceptance in the industry." } ], "id_raw": 1017 }, { "id": "YPTIjO3PkfY7Enaf", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the least commonly incorporated technology innovation in education and training according to the bar chart?" }, { "from": "gpt", "value": "By looking at the bar lengths, I can find the least commonly used technology. Relatively shorter bars represent less frequent usage. So, I need to mainly look for the shortest bar. In this chart, the shortest bar is {\"Augmented Reality\"}[0.264, 0.856, 0.312, 0.901].\nBased on the above thought, the answer is:\nAugmented Reality." } ], "id_raw": 1017 }, { "id": "sxoC8qgoVPxfxGBY", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the bar chart, can you provide the data representing different types of networks and relationships in the form of a CSV formatted table?" }, { "from": "gpt", "value": "The title {\"Frequency of Relationship Types\"}[0.229, 0.019, 0.981, 0.927] indicates this chart classifies different relationships and their frequencies. As per the markers {\"Social Network\"} [0.087, 0.822, 0.217, 0.840], {\"Personal Interaction\"} [0.043, 0.680, 0.217, 0.697], {\"Family Ties\"} [0.121, 0.537, 0.217, 0.555], {\"Work Relations\"} [0.087, 0.395, 0.217, 0.412], {\"Online Friendship\"} [0.066, 0.253, 0.217, 0.270], {\"Communities\"} [0.102, 0.110, 0.217, 0.128] on the y-axis, we can infer the respective type of relationship. The width of the individual bars such as {\"Social Network\"} [0.229, 0.772, 0.921, 0.886], {\"Personal Interaction\"} [0.229, 0.630, 0.740, 0.743], {\"Family Ties\"} [0.229, 0.487, 0.628, 0.601], {\"Work Relations\"} [0.229, 0.345, 0.537, 0.459], {\"Online Friendship\"} [0.229, 0.202, 0.402, 0.316], {\"Communities\"} [0.229, 0.060, 0.304, 0.174] highlights the frequency percentage for that relationship type. By using the markers on the X-axis, the width of the bars can be calculated to obtain numerical values according to each relationship category. For instance, the marker \"9\" [0.900, 0.939, 0.912, 0.957] corresponds to the end of bar \"Social Network\", indicating a value of 9.2. The CSV file can then be produced by compiling these values against respective categories.\nBased on the above thought, the answer is:\nRelationship,Frequency\nSocial Network,9.2\nPersonal Interaction,6.8\nFamily Ties,5.3\nWork Relations,4.1\nOnline Friendship,2.3\nCommunities,1" } ], "id_raw": 1018 }, { "id": "kXJ3EvtAmKJAyWoJ", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which type of relationships do people engage in most frequent? And please explain why." }, { "from": "gpt", "value": "From the observation of the bar chart,{\"Social Network\"}[0.087, 0.822, 0.217, 0.840] has the longest bar{\"Social Network Bar\"}[0.229, 0.772, 0.921, 0.886]. So, it is safe to say that Social Networks are the most frequent relationships that people engage in. This trend could potentially be due to the increase in accessibility and convenience of social networking platforms. These platforms allow the possibility to connect with a diverse group of people irrespective of geographic location.\nBased on the above thought, the answer is:\nSocial Network. Because with the widespread use of social media, people increasingly use social networks to communicate, keep up with friends, share their lives and connect around shared interests." } ], "id_raw": 1018 }, { "id": "7JFrkqMiG2TyXDbe", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_13.png", "conversations": [ { "from": "human", "value": "\nWhich relationship type has the least frequency according to the chart?" }, { "from": "gpt", "value": "To identify the relationship type with the least frequency, I can analyse the width of each bar in the chart. From the visual observation, {\"Communities\"}[0.102, 0.110, 0.217, 0.128] has the shortest bar {\"Communities Bar\"}[0.229, 0.060, 0.304, 0.174] representing the least frequency. Examining its length in relation to the x-axis labels indicates that it represents approximately 1 percent.\nBased on the above thought, the answer is:\nCommunities." } ], "id_raw": 1018 }, { "id": "qWbBnLwl0I72BkGb", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart you visualized, could you please generate the data in a structured CSV table?" }, { "from": "gpt", "value": "The visualization displays a horizontal bar chart of various categories in the knowledge economy and innovation industry. {The chart's title}[0.329, 0.015, 0.843, 0.033] indicates that the x-axis represents the frequency (%) of each category, while the y-axis lists the categories. The {blue bars}[0.187, 0.833, 0.857, 0.146] extending from the y-axis labels to the right represent the percentage of each category. By examining the length of each bar and correspondingly aligning it with the x-axis labels, the percentage frequency of each category can be determined. For instance, the {bar of \"Biotechnology\"}[0.187, 0.833, 0.785, 0.901] extends to roughly 7.5 on the x-axis, indicating that its frequency is 7.5%.\nBased on the above thought, the answer is:\nCategory,Frequency(%)\nBiotechnology,7.5\nSemiconductors,5.1\nInternet,4.2\nSoftware,6.7\nIT service,3.2\nDigital media,5.8\nNetwork security,2.0\nArtificial Intelligence,8.4\nTelecommunications,4.6\nE-commerce,7.3" } ], "id_raw": 1019 }, { "id": "q5lB63ifChaYiepO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nConsidering the variations in percentage frequencies, what industry knowledge would you suggest a new graduate to focus on?" }, { "from": "gpt", "value": "A good measure of potential industry growth and hence better job prospects could be the current frequency of each category. This could suggest the industry's current demand and possible future trend. Looking at the data, the {Artificial Intelligence bar}[0.187, 0.246, 0.857, 0.313] is the longest, suggesting that it currently represents the largest proportion, which could be interpreted as a high demand. Additionally, the data provided also documents that Artificial Intelligence is showcasing abrupt growth. Therefore, it can be recommended for new graduates to focus on the field of Artificial Intelligence.\nBased on the above thought, the answer is:\nArtificial Intelligence due to its high frequency, suggesting a high demand and substantial growth." } ], "id_raw": 1019 }, { "id": "g09SSgYFSruKfvwv", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_68_14.png", "conversations": [ { "from": "human", "value": "\nWhich industry has the least frequent occurrence according to the chart?" }, { "from": "gpt", "value": "To determine the industry with the least frequent occurrence, I need to find the shortest {blue bar}[0.187, 0.833, 0.857, 0.146]. By scanning the bars vertically from the top to the bottom, I find that the {Network security}[0.187, 0.330, 0.346, 0.397] industry has the shortest bar on the chart, thus making it the least frequent industry.\nBased on the above thought, the answer is:\nNetwork security." } ], "id_raw": 1019 }, { "id": "pNkkbMNdRmPimmef", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the bar chart data into a CSV format table?" }, { "from": "gpt", "value": "The chart contains information on seven categories, which are clearly labeled on the y-axis. These include {\"reinforcement_learning\"}[0.025, 0.107, 0.295, 0.131], {\"neural_networks\"}[0.103, 0.231, 0.295, 0.254], {\"regression\"}[0.174, 0.354, 0.295, 0.377], {\"unsupervised_learning\"}[0.033, 0.477, 0.295, 0.501], {\"convolutions\"}[0.148, 0.601, 0.295, 0.624], {\"SVM\"}[0.244, 0.724, 0.295, 0.748] and {\"RNN\"}[0.244, 0.848, 0.295, 0.871]. The x-axis features percentage values for two types of data sources: synthetic data and human-authored data. These are represented by different colored bars. For instance, in the 'reinforcement_learning' category, synthetic data has a value of {'-5%'}[0.520, 0.105, 0.557, 0.128], and human-authored data has a value of {'18%'}[0.778, 0.475, 0.829, 0.498]. Following this pattern for every category, I can generate the CSV table for this data.\nBased on the above thought, the answer is:\nCategory,Synthetic data, Human-authored data\nreinforcement_learning,-5,18\nneural_networks,-25,33\nregression,-15,20\nunsupervised_learning,-10,22\nconvolutions,-18,11\nSVM,-12,30\nRNN,-20,15" } ], "id_raw": 1020 }, { "id": "VHJ10e7ebb3oIQvz", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart indicate about the difference between synthetic and human-authored data in terms of AI and Machine Learning categories?" }, { "from": "gpt", "value": "The chart allows for a comparative analysis between synthetic data and human-authored data across several AI and Machine Learning categories. Comparing the two types of data in terms of percentages, one can observe certain patterns. For instance, synthetic data show a fall in percentages from 'reinforcement_learning' to 'RNN', with a sharp drop in 'neural_networks'. On the other hand, human-authored data display an irregular growth trend, with the 'neural_networks' and 'SVM' categories showing the greatest occurrences. Particularly noticeable is the significant disparity between synthetic <{\"-25%\"}[0.745, 0.105, 0.796, 0.128] and human-authored data <{\"33%\"}[0.868, 0.228, 0.919, 0.251] in the 'neural_networks' category. Meanwhile, the least occurrence in synthetic data is also observed in 'neural_networks', whereas the least occurrence in human-authored data is in the 'convolutions' category.\nBased on the above thought, the answer is:\nThe chart suggests a discrepancy in the prevalence of different AI and Machine Learning categories between synthetic data and human-authored data, with some categories being more prevalent in synthetic data and others in human-authored data. Especially, there is a significant divergence in the 'neural_networks' category." } ], "id_raw": 1020 }, { "id": "twfVbVC2I90jEvJS", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum percentage difference between synthetic data and human-authored data across all categories?" }, { "from": "gpt", "value": "To find the maximum percentage difference, I need to compare the percentages of synthetic data and human-authored data in each category by subtracting the synthetic percentage from the human-authored percentage. For instance, for the category 'reinforcement_learning', synthetic data is <{\"-5%\"}[0.520, 0.105, 0.557, 0.128] and human-authored data is <{\"18%\"}[0.778, 0.475, 0.829, 0.498], so the difference is 23. By calculating this difference for each category, I can conclude that the 'neural_networks' category, with synthetic data being <{\"-25%\"}[0.745, 0.105, 0.796, 0.128] and human-authored data being <{\"33%\"}[0.868, 0.228, 0.919, 0.251], shows the maximum percentage difference of 58.\nBased on the above thought, the answer is:\n58%" } ], "id_raw": 1020 }, { "id": "XyKylIPBBBYggySh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nBased on the horizontal bar chart, could you please extract and structure the visual data in the form of a CSV table?" }, { "from": "gpt", "value": "Certainly, starting with the chart title at the top {\"Social Trends vs Political Trends\"}[0.346, 0.083, 0.679, 0.113], one can understand that there are two groups that have been represented on a horizontal bar chart. The {y-axis} represents the categories: {\"Dramatic surge\"}[0.074, 0.125, 0.253, 0.148], {\"Subdued expansion\"}[0.025, 0.295, 0.253, 0.318], {\"Rapid recovery\"}[0.078, 0.465, 0.253, 0.488], {\"Slow increase\"}[0.094, 0.635, 0.253, 0.658] and {\"Sluggish growth\"}[0.067, 0.805, 0.253, 0.828]. The x-axis represents the values corresponding to the categories. The {purple rectangles}[0.348, 0.066, 0.544, 0.202], [0.504, 0.236, 0.544, 0.372], [0.387, 0.406, 0.544, 0.542], [0.465, 0.576, 0.544, 0.712], [0.426, 0.746, 0.544, 0.882] to the left of the y-axis and the {pink rectangles}[0.544, 0.066, 0.818, 0.202], [0.544, 0.236, 0.661, 0.372], [0.544, 0.406, 0.897, 0.542], [0.544, 0.576, 0.700, 0.712], [0.544, 0.746, 0.622, 0.882] to the right of the y-axis represent the values of the two trends. Using this information, I can construct a CSV table showing the values of Social Trends and Political Trends for each category.\nBased on the above thought, the answer is:\nCategory,Political Trends,Social Trends\nDramatic surge,-35,35\nSubdued expansion,-5,15\nRapid recovery,-20,45\nSlow increase,-10,20\nSluggish growth,-15,10" } ], "id_raw": 1021 }, { "id": "Nkw6CzdQMUzcFIK8", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nObserve the chart and tell us which segment, social or political, exhibited a better trend across all categories?" }, { "from": "gpt", "value": "The chart is split into two segments: {Social Trends}[0.544, 0.066, 0.818, 0.202], [0.544, 0.236, 0.661, 0.372], [0.544, 0.406, 0.897, 0.542], [0.544, 0.576, 0.700, 0.712], [0.544, 0.746, 0.622, 0.882] and {Political Trends}[0.348, 0.066, 0.544, 0.202], [0.504, 0.236, 0.544, 0.372], [0.387, 0.406, 0.544, 0.542], [0.465, 0.576, 0.544, 0.712], [0.426, 0.746, 0.544, 0.882]. Clearly, the bar lengths for all categories in the Social Trends are positive, projecting an overall positive trend. However, the bars under Political Trends are all negative, indicating a general contraction across categories. So, comparing the two segments of the chart, Social Trends exhibit a better, more positive trend across all categories.\nBased on the above thought, the answer is:\nSocial Trends, because they show growth and expansion in all categories, contrary to Political Trends which show contraction in each category." } ], "id_raw": 1021 }, { "id": "9sEPO0aneoUfEnSq", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_1.png", "conversations": [ { "from": "human", "value": "\nWhich category under Political Trends has shown the least contraction?" }, { "from": "gpt", "value": "Observing the {negative horizontal bars}[0.348, 0.066, 0.544, 0.202], [0.504, 0.236, 0.544, 0.372], [0.387, 0.406, 0.544, 0.542], [0.465, 0.576, 0.544, 0.712], [0.426, 0.746, 0.544, 0.882] on the chart aligned with the {labels of the categories}[0.074, 0.125, 0.253, 0.148], [0.025, 0.295, 0.253, 0.318], [0.078, 0.465, 0.253, 0.488], [0.094, 0.635, 0.253, 0.658], [0.067, 0.805, 0.253, 0.828] on the y-axis, I deduce that each bar represents the level of regression in each category of Political Trends. Although all categories show a decrease, the category that shows the least contraction is represented by the shortest negative bar. Visually, the {Subdued expansion bar}[0.504, 0.236, 0.544, 0.372] is the shortest among all bars.\nBased on the above thought, the answer is:\nSubdued expansion" } ], "id_raw": 1021 }, { "id": "4iiaP0AHm04WinGB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the bar chart into a CSV format table?" }, { "from": "gpt", "value": "From the provided data and the bounding boxes around the y-axis tick labels, like {\"Dramatic surge\"}[0.095, 0.107, 0.274, 0.131] and {\"Rapid decline\"}[0.117, 0.231, 0.274, 0.254], it seems the y-axis lists different investment trends. The x-axis seems to depict the Return Rate in Private Equity and Public Equity Investments based on the label color of the bars, indicated by {The legend}, with Private Equity Investments showing as {purple bars}<{\"bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"}> and Public Equity Investments showing as {pink bars}<{\"bar_coordinates\": \"[0.589, 0.067, 0.705, 0.166]\"}>. Each trend has corresponding bars that depict their respective return rates for each investment type. For example, the left bar under \"Dramatic surge\" trend {\"bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"} which is -8%, and the one on the right {\"bar_coordinates\": \"[0.589, 0.067, 0.705, 0.166]\"} is 16%.\nBased on the above thought, the answer is:\nInvestment Type,Private Equity Investment,Public Equity Investment\nDramatic surge,-8,16\nRapid decline,-31,28\nPronounced decrease,-24,11\nConsistent expansion,-7,5\nUpward trajectory,-10,15\nRapid recovery,-10,16\nPersistent growth,-10,9" } ], "id_raw": 1022 }, { "id": "5AecpLNM01YWXeyK", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which investment trend receives a better return rate on Public Equity Investments as compared to Private Equity Investments?" }, { "from": "gpt", "value": "In order to determine which investment trend has a better return rate for Public Equity Investments compared to the Private ones, I need to compare the lengths of the two bars associated with each investment trend. For instance, if in an investment trend the pink bar {\"bar_coordinates\": \"[0.589, 0.067, 0.705, 0.166]\"}, which denotes Public Equity Investments, extends further to the right from the center compared to the purple bar {\"bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"}, which denotes Private Equity Investments, then the Public Equity Investment outperforms the Private Equity Investment. For example, in the \"Dramatic surge\" trend, the Public Equity Investments rate {\"Public Equity bar_coordinates\": \"[0.589, 0.067, 0.705, 0.166]\"}, which is +16%, is greater than the Private Equity Investments rate {\"Private Equity bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"}, which is -8%. All the bar lengths for each trend need to be compared in a similar fashion.\nBased on the above thought, the answer is:\nAll investment trends have a better return rate on Public Equity Investments as compared to Private Equity Investments. This is due to each investment trend having a longer pink bar (Public Equity Investments) extending to the right from the center in comparison to the purple bar (Private Equity Investments)." } ], "id_raw": 1022 }, { "id": "F7OG28FcVX574uTT", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest positive return rate for Private Equity investments according to the chart?" }, { "from": "gpt", "value": "To find the highest positive return rate for Private Equity investments, I need to identify the longest bar extending to the right from the center. Since all the bars associated with Private Equity investments are negative as indicated by their left extension from the center like in the bar {\"bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"}, it means that there is no positive return rate for Private Equity investments in all the investment trends.\nBased on the above thought, the answer is:\nThere is no positive return rate for Private Equity investments." } ], "id_raw": 1022 }, { "id": "jQHniqx7BXeWnRvI", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data you analyzed; please convert the data into a structured table in CSV format." }, { "from": "gpt", "value": "The chart is a {bar chart}\n\n[0.157, 0.025, 0.985, 0.948] with two data groups namely {Policy Data}[0.216, 0.067, 0.420, 0.906] and {Effectiveness Evaluation Data}[0.420, 0.067, 0.927, 0.906] spanning 7 different categories on the y-axis: 'Public Health', 'Economic Policy', 'Infrastructure', 'Education', 'Defense', 'Energy', and 'Governments Ethics'.\nThe values of Policy data range from -35 to -11 and Effectiveness Evaluation Data ranges from 30 to 87 with every bar pair representing a category. For example, for Public Health, the Policy data{bar}[0.216, 0.067, 0.420, 0.166]\nshows -35 and Effectiveness Evaluation Data{bar}[0.420, 0.067, 0.927, 0.166] shows 87. It's the same for every other category, where the values together form the data for that category.\nBased on the above thought, the answer is:\nCategories, Policy Data, Effectiveness Evaluation Data\nPublic Health, -35, 87\nEconomic Policy, -22, 30\nInfrastructure, -11, 67\nEducation, -19, 75\nDefense, -14, 55\nEnergy, -17, 45\nGovernment Ethics, -21, 80" } ], "id_raw": 1023 }, { "id": "9naAo1rYiwY1jkxT", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which policy area is having a strong impact despite a negative policy data trend, and why do you think so?" }, { "from": "gpt", "value": "This requires analyzing both the policy data group, {Policy Data}[0.216, 0.067, 0.420, 0.906], and the effectiveness evaluation data group, {Effectiveness Evaluation Data}[0.420, 0.067, 0.927, 0.906]. Specifically, we need to find categories where policy data is declining (negative), but the effectiveness evaluation data is high. After analyzing each category, I found that the category of 'Public Health', {Public Health bars}[{Policy data bar}[0.216, 0.067, 0.420, 0.166]; {Effectiveness evaluation data bar}[0.420, 0.067, 0.927, 0.166]], fits this condition the best. Despite a significant decline of -35% in policy data, it has a high effectiveness evaluation of 87%, which suggests that the impact of policy actions in Public Health is powerful.\nBased on the above thought, the answer is:\nPublic Health, because even though there's a significant decline in policy data, the effectiveness evaluation is high, indicating the substantial impact of these policies." } ], "id_raw": 1023 }, { "id": "JMPOlDSHju2qOnYi", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_3.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, in which category is the policy data above -20 percent?" }, { "from": "gpt", "value": "I need to analyze the bars corresponding to the policy data group {Policy Data}[0.216, 0.067, 0.420, 0.906]. The policy data for the seven categories are represented by these bars, and their lengths indicate the values. By checking each bar one by one, it indicates that the policy data for category 'Infrastructure', {Infrastructure bar}[0.355, 0.314, 0.420, 0.413], is above -20 percent as its length notably smaller than other bars of the group.\nBased on the above thought, the answer is:\nInfrastructure." } ], "id_raw": 1023 }, { "id": "hlWXxS8ToJLKlVvo", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nCan you help to generate a structured table that captures the data shown on the chart?" }, { "from": "gpt", "value": "Yes, I can do that. The applications of AI are listed along {the y-axis}[{'label': 'Speech Recognition', 'label_coordinates': '[0.060, 0.107, 0.289, 0.131]', 'tick_coordinates': '[0.305, 0.116]'}, {'label': 'Autonomous Driving', 'label_coordinates': '[0.052, 0.231, 0.289, 0.254]', 'tick_coordinates': '[0.305, 0.240]'}, {'label': 'Medical Diagnosis', 'label_coordinates': '[0.080, 0.354, 0.289, 0.377]', 'tick_coordinates': '[0.305, 0.363]'}, {'label': 'Supply Chain', 'label_coordinates': '[0.136, 0.477, 0.289, 0.501]', 'tick_coordinates': '[0.305, 0.487]'}, {'label': 'Advertisement', 'label_coordinates': '[0.119, 0.601, 0.289, 0.624]', 'tick_coordinates': '[0.305, 0.610]'}, {'label': 'Smart Homes', 'label_coordinates': '[0.131, 0.724, 0.289, 0.748]', 'tick_coordinates': '[0.305, 0.733]'}, {'label': 'Customer Service Bots', 'label_coordinates': '[0.025, 0.848, 0.289, 0.871]', 'tick_coordinates': '[0.305, 0.857]'}]. The bar chart is divided into two groups - one representing synthetic data and the other representing real use cases. Synthetic data is represented by {bars with color '#d4a3da'}[{'bar_coordinates': '[0.338, 0.067, 0.612, 0.166]', 'text_coordinates': '[0.287, 0.105, 0.338, 0.128]', 'text_params': {'s': '82%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '-82.00 -> 0.00'}, {...}, {'bar_coordinates': '[0.392, 0.808, 0.612, 0.906]', 'text_coordinates': '[0.317, 0.475, 0.368, 0.498]', 'text_params': {'s': '73%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '-66.00 -> 0.00'}]. Real use cases data is represented by {bars with color '#75147c'}[{'bar_coordinates': '[0.612, 0.067, 0.930, 0.166]', 'text_coordinates': '[0.883, 0.475, 0.934, 0.498]', 'text_params': {'s': '81%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '0.00 -> 95.00'}, {...}, {'bar_coordinates': '[0.612, 0.808, 0.870, 0.906]', 'text_coordinates': '[0.870, 0.845, 0.921, 0.869]', 'text_params': {'s': '77%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '0.00 -> 77.00'}]. The length of the bars and the percentages with the bars represent the scores. Therefore, by recognizing and extracting values from the lengths of these bars in both categories, I can construct a table that accurately captures the data displayed on the chart.\nBased on the above thought, the answer is:\nAI Applications,Synthetic Data Scores,Real Use Case Scores\nSpeech Recognition,82,95\nAutonomous Driving,63,78\nMedical Diagnosis,56,67\nSupply Chain,73,81\nAdvertisement,72,85\nSmart Homes,57,69\nCustomer Service Bots,66,77" } ], "id_raw": 1025 }, { "id": "mFm6H1oJUSFp5i0L", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nComment on the practicality and credibility of synthetic data based on the chart." }, { "from": "gpt", "value": "As displayed in the chart, the synthetic data scores {synthetic data scores}[{'bar_coordinates': '[0.338, 0.067, 0.612, 0.166]', 'text_coordinates': '[0.287, 0.105, 0.338, 0.128]', 'text_params': {'s': '82%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '-82.00 -> 0.00'}, {...}, {'bar_coordinates': '[0.392, 0.808, 0.612, 0.906]', 'text_coordinates': '[0.317, 0.475, 0.368, 0.498]', 'text_params': {'s': '73%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '-66.00 -> 0.00'}] are consistently lower than the real use case scores [{real use case scores}[{'bar_coordinates': '[0.612, 0.067, 0.930, 0.166]', 'text_coordinates': '[0.883, 0.475, 0.934, 0.498]', 'text_params': {'s': '95%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '0.00 -> 95.00'}, {...}, {'bar_coordinates': '[0.612, 0.808, 0.870, 0.906]', 'text_coordinates': '[0.870, 0.845, 0.921, 0.869]', 'text_params': {'s': '77%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '0.00 -> 77.00'}]. This gap may suggest that synthetic data, while indeed useful for various AI applications, may not capture all the intricate characteristics and subtle complexities inherent in real use case data. Whether it's for Speech Recognition or Customer Service Bots, there exists a clear discrepancy between the scores associated with the synthetic data and their real use case counterparts. This may potentially undermine the use of synthetic data for these applications. However, it's worth noting the relatively close scores for Supply Chain application, suggesting that in some cases, synthetic data may be a reasonably reliable representation of the real-world data.\nBased on the above thought, the answer is:\nSynthetic data, while useful, often fall short in capturing the intricacies and overall essence of real use case data across numerous AI applications. However, in some special cases, like Supply chain management, synthetic data can closely mirror the real-world data, implying its potential in such domains." } ], "id_raw": 1025 }, { "id": "cZx73hW8kC2AiLPp", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_5.png", "conversations": [ { "from": "human", "value": "\nWhich AI application has the smallest difference between the real use case scores and the synthetic data scores?" }, { "from": "gpt", "value": "I will compare the score difference between the synthetic data and the real use cases for each AI application. The calculation will be done by subtracting {synthetic data scores}[{'bar_coordinates': '[0.338, 0.067, 0.612, 0.166]', 'text_coordinates': '[0.287, 0.105, 0.338, 0.128]', 'text_params': {'s': '82%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '-82.00 -> 0.00'}, {...}, {'bar_coordinates': '[0.392, 0.808, 0.612, 0.906]', 'text_coordinates': '[0.317, 0.475, 0.368, 0.498]', 'text_params': {'s': '73%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '-66.00 -> 0.00'}] from {real use case scores}[{'bar_coordinates': '[0.612, 0.067, 0.930, 0.166]', 'text_coordinates': '[0.883, 0.475, 0.934, 0.498]', 'text_params': {'s': '95%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '0.00 -> 95.00'}, {...}, {'bar_coordinates': '[0.612, 0.808, 0.870, 0.906]', 'text_coordinates': '[0.870, 0.845, 0.921, 0.869]', 'text_params': {'s': '77%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '0.00 -> 77.00'}] one by one, and then I'll find the AI application with the smallest difference.\nBased on the above thought, the answer is:\nSupply Chain" } ], "id_raw": 1025 }, { "id": "YOUnhxiFQf1zHcXy", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table that would illustrate the change in participation for sports activities represented in the bar chart?" }, { "from": "gpt", "value": "Based on the bar chart, the categories are listed from the {top}[{\"Basketball\"}[0.051, 0.872, 0.172, 0.895]] to the {bottom}[{\"Running\"}[0.077, 0.097, 0.172, 0.121]], these are sports activities. We have two trends: {the first one, Traditional, shown in pink}<{\"Traditional\"}[0.347, 0.068, 0.556, 0.145]> and {the second one, Gym, displayed in purple}<{\"Gym\"}[0.556, 0.068, 0.806, 0.145]>. Each sports activity has two bars representing the change in participation for traditional and gym-based settings respectively, for example, for {Basketball}[0.430, 0.842, 0.556, 0.920] and [0.556, 0.842, 0.744, 0.920]. The percentages shown above each bar like {\"45%\"}[0.744, 0.869, 0.795, 0.893] and {\"30%\"}[0.379, 0.869, 0.430, 0.893] show the changes. Based on all these observations, I can generate the CSV table.\nBased on the above thought, the answer is:\nSport,Traditional,Gym\nRunning,-50,60\nSwimming,-20,30\nCycling,-35,50\nYoga,-15,20\nWeightlifting,-60,75\nCrossfit,-65,88\nBoxing,-78,79\nGolf,-12,14\nBasketball,-30,45" } ], "id_raw": 1026 }, { "id": "LN6ugoIMoHYdJgYr", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, do gym-based activities seem to be on the rise compared to traditional ones over time? Please explain why you think so." }, { "from": "gpt", "value": "To answer this question, I will observe the comparison between {Traditional}[0.347, 0.068, 0.556, 0.145] and {Gym}[0.556, 0.068, 0.806, 0.145] for each sport. Specifically, I observe that for each sport from {Running}[{\"Traditional\"}[0.347, 0.068, 0.556, 0.145]; {\"Gym\"}[0.556, 0.068, 0.806, 0.145]] to {Basketball}[{\"Traditional\"}[0.430, 0.842, 0.556, 0.920]; {\"Gym\"}[0.556, 0.842, 0.744, 0.920]], the {Traditional bars} are to the left of the center line, indicating a decrease in participation, and the {Gym bars} are to the right of the center line, indicating an increase in participation. This trend can be observed for all sports activities, with the Gym bars always showing an increase in participation.\nBased on the above thought, the answer is:\nYes. The bar chart clearly shows a common trend that gym-based activities are on the rise for all sports activities from Running to Basketball, whereas traditional ones are witnessing a decline." } ], "id_raw": 1026 }, { "id": "48NYaMYgi9sltXkE", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_6.png", "conversations": [ { "from": "human", "value": "\nWhat sport has shown the maximum change in gym-based settings compared to traditional settings over the period?" }, { "from": "gpt", "value": "To answer this question, I will examine the differences between the lengths of Traditional and Gym bars for each sport. Taking {Crossfit} as an example, the two bars representing Traditional and Gym changes are {bar 1}[0.284, 0.552, 0.556, 0.629] and {bar 2}[0.556, 0.552, 0.923, 0.629] respectively, with a difference between them. By comparing the differences of all sports, it is clear that {Crossfit} has the most substantial difference between Traditional and Gym settings.\nBased on the above thought, the answer is:\nCrossfit" } ], "id_raw": 1026 }, { "id": "vVLu0TVSAETDkbfN", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data represented in the bar chart into a table in CSV format for me?" }, { "from": "gpt", "value": "Sure. The chart indicates the performance of 'SynRobo-Investment' and 'Human-Investment' in different categories. The categories include 'Risk Management', 'Financial Planning', 'Wealth Management', 'Asset Allocation', 'Investment Strategy', 'Cash Flow', and 'Retirement Planning'. The data from 'SynRobo-Investment' is represented by pastel-colored bars {pastel-colored bars}[0.446, 0.067, 0.583, 0.166; 0.473, 0.190, 0.583, 0.289; 0.373, 0.314, 0.583, 0.413; 0.409, 0.437, 0.583, 0.536; 0.437, 0.561, 0.583, 0.659; 0.400, 0.684, 0.583, 0.783; 0.418, 0.808, 0.583, 0.906] and 'Human-Investment' data is represented by bold-colored bars {bold-colored bars}[0.583, 0.067, 0.802, 0.166; 0.583, 0.190, 0.774, 0.289; 0.583, 0.314, 0.875, 0.413; 0.583, 0.437, 0.838, 0.536; 0.583, 0.561, 0.783, 0.659; 0.583, 0.684, 0.884, 0.783; 0.583, 0.808, 0.856, 0.906]. I will determine the values by examining the width of the bars and the text labels on them. I will now start transcribing this data into a CSV format.\nBased on the above thought, the answer is:\nASX Stock, SynRobo-Investment, Human-Investment\nRisk Management, -15, 24\nFinancial Planning, -12, 21\nWealth Management, -23, 32\nAsset Allocation, -19, 28\nInvestment Strategy, -16, 22\nCash Flow, -20, 33\nRetirement Planning, -18, 30" } ], "id_raw": 1027 }, { "id": "al3doeT0mdWh2XTN", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which type of investment is considered safer regarding 'Financial Planning'? Please explain why you think so." }, { "from": "gpt", "value": "The pastel-colored bar {pastel-colored bar}[0.473, 0.190, 0.583, 0.289] represents 'SynRobo-Investment' performance on 'Financial Planning', and the bold-colored bar {bold-colored bar}[0.583, 0.190, 0.774, 0.289] represents 'Human-Investment' on the same category. The 'Human-Investment' bar is located on the right side of the 0 point, indicating a positive result, while the 'SynRobo-Investment' bar is on the left, indicating loss. Considering that safety in financial planning refers to lower risk and positive returns, it seems 'Human-Investment' is the safer option for 'Financial Planning' based on this chart.\nBased on the above thought, the answer is:\nHuman-Investment. Because it presents a positive result in the 'Financial Planning' category while 'SynRobo-Investment' shows a loss in the same category." } ], "id_raw": 1027 }, { "id": "RbU6UYFCCGHWfC4n", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_7.png", "conversations": [ { "from": "human", "value": "\nUnder which category is the 'SynRobo-Investment' showing the highest loss?" }, { "from": "gpt", "value": "To determine where 'SynRobo-Investment' suffered the most loss, I need to find out the widest bar of the pastel-colored bars representing 'SynRobo-Investment'. By looking at their coordinates {pastel-colored bars}[0.446, 0.067, 0.583, 0.166; 0.473, 0.190, 0.583, 0.289; 0.373, 0.314, 0.583, 0.413; 0.409, 0.437, 0.583, 0.536; 0.437, 0.561, 0.583, 0.659; 0.400, 0.684, 0.583, 0.783; 0.418, 0.808, 0.583, 0.906], I can see that the bar associated with 'Wealth Management'[0.373, 0.314, 0.583, 0.413] is the widest one, indicating the highest loss for 'SynRobo-Investment'.\nBased on the above thought, the answer is:\nWealth Management" } ], "id_raw": 1027 }, { "id": "Rgc088NHhu7NSfP9", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nPlease organize the chart data into a structured table using a csv format." }, { "from": "gpt", "value": "Firstly, the {y-axis tick labels}[0.096, 0.102, 0.292, 0.125]; [0.106, 0.210, 0.292, 0.233]; [0.101, 0.319, 0.292, 0.342]; [0.134, 0.427, 0.292, 0.450]; [0.094, 0.536, 0.292, 0.559]; [0.054, 0.644, 0.292, 0.668]; [0.056, 0.753, 0.292, 0.776]; [0.074, 0.861, 0.292, 0.885] tell us that the categories are 'Domestic Robots', 'Climate Change', 'Self-driving Cars', 'Smart Homes', 'Cryptocurrencies', 'Artificial Intelligence', 'Genetic Engineering' and 'VR - Virtual Reality' respectively. Meanwhile, there are two sets of bars for each category, one representing {synthetic data}[0.252, 0.067, 0.475, 0.154]; [0.252, 0.176, 0.753, 0.263]; [0.252, 0.284, 0.641, 0.371]; [0.252, 0.393, 0.419, 0.480]; [0.252, 0.501, 0.586, 0.588]; [0.252, 0.610, 0.697, 0.697]; [0.252, 0.718, 0.808, 0.805]; [0.252, 0.827, 0.419, 0.914] and the other representing {human-authored data}[0.252, 0.067, 0.586, 0.154]; [0.252, 0.176, 0.530, 0.263]; [0.252, 0.284, 0.808, 0.371]; [0.252, 0.393, 0.697, 0.480]; [0.252, 0.501, 0.475, 0.588]; [0.252, 0.610, 0.864, 0.697]; [0.252, 0.718, 0.363, 0.805]; [0.252, 0.827, 0.641, 0.914]. The values of the bars can be fetched from the text labels attached to the bars. For example, {the label of the first bar of the first row}[0.423, 0.099, 0.475, 0.122] tells us that the synthetic data value for 'Domestic Robots' is '20%'. Similar steps should be taken for each category and each bar.\nBased on the above thought, the answer is:\ncategories,synthetic_data,human_authored_data\nDomestic Robots,20%,30%\nClimate Change,45%,25%\nSelf-driving Cars,35%,50%\nSmart Homes,15%,40%\nCryptocurrencies,30%,20%\nArtificial Intelligence,40%,55%\nGenetic Engineering,50%,10%\nVR - Virtual Reality,15%,35%" } ], "id_raw": 1028 }, { "id": "r5iVINN73sA5FwU6", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nAre there significant differences between the synthetic model and human predictions for the impact of future societal and living scenarios? Please provide your analysis." }, { "from": "gpt", "value": "To answer this question, I need to compare the {synthetic data}[0.252, 0.067, 0.475, 0.154]; [0.252, 0.176, 0.753, 0.263]; [0.252, 0.284, 0.641, 0.371]; [0.252, 0.393, 0.419, 0.480]; [0.252, 0.501, 0.586, 0.588]; [0.252, 0.610, 0.697, 0.697]; [0.252, 0.718, 0.808, 0.805]; [0.252, 0.827, 0.419, 0.914] with the {human-authored data}[0.252, 0.067, 0.586, 0.154]; [0.252, 0.176, 0.530, 0.263]; [0.252, 0.284, 0.808, 0.371]; [0.252, 0.393, 0.697, 0.480]; [0.252, 0.501, 0.475, 0.588]; [0.252, 0.610, 0.864, 0.697]; [0.252, 0.718, 0.363, 0.805]; [0.252, 0.827, 0.641, 0.914]. There are significant differences observed in several categories. For example, for 'Genetic Engineering' the synthetic model shows a far greater impact (50%) compared to the human prediction (10%). For 'Artificial Intelligence', the human prediction (55%) is significantly higher than the synthetic prediction (40%). In some categories such as 'Climate Change', both the synthetic model (45%) and humans (25%) predict a notable impact but the synthetic estimate is considerably higher. Hence, there are some differences between the predictions of the synthetic model and humans.\nBased on the above thought, the answer is:\nYes, there are significant differences. For some categories such as 'Genetic Engineering' and 'Climate Change', the synthetic model predicts a notably higher impact compared to humans. In contrast, for 'Artificial Intelligence', humans foresee a larger impact than what the synthetic model suggests." } ], "id_raw": 1028 }, { "id": "ygrRSRJO3ifRpmPy", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which future societal and living scenario is predicted to have the greatest impact from the synthetic model?" }, { "from": "gpt", "value": "The scenario with the greatest impact will be the one with the highest value for the {synthetic data}[0.252, 0.067, 0.475, 0.154]; [0.252, 0.176, 0.753, 0.263]; [0.252, 0.284, 0.641, 0.371]; [0.252, 0.393, 0.419, 0.480]; [0.252, 0.501, 0.586, 0.588]; [0.252, 0.610, 0.697, 0.697]; [0.252, 0.718, 0.808, 0.805]; [0.252, 0.827, 0.419, 0.914]. By comparing the bar lengths and their corresponding labels, I observe that the value for {Genetic Engineering}[0.252, 0.718, 0.808, 0.805] is 50%, which appears to be the largest among all scenarios.\nBased on the above thought, the answer is:\nGenetic Engineering." } ], "id_raw": 1028 }, { "id": "4TIBDWB4RVLcwZhA", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the data represented by the bar chart into a structured CSV format?" }, { "from": "gpt", "value": "Sure. The chart title is not explicitly mentioned, but considering the context of this chart, it is concerned with the declining trend in percentage of teams following various leadership styles in a corporate organization. The y-axis lists the eight leadership styles, for example {Transformational Leadership}[0.019, 0.099, 0.266, 0.116]. For each leadership style, there is a colored bar on the left-hand side of the y-axis. The length of each bar represents the respective decline in percentage for that leadership style, as also mentioned in labels above each bar for example {one such label saying '18%'}[0.575, 0.097, 0.614, 0.114]. Taking these into account, we can structure the CSV data as follows:\nBased on the above thought, the answer is:\nLeadership Style,Decline of Business Teams (%)\nTransformational Leadership,18\nAuthentic Leadership,24\nParticipative Leadership,21\nLaissez-faire Leadership,12\nTransactional Leadership,9\nCharismatic Leadership,16\nService Leadership,22\nCoaching Leadership,20" } ], "id_raw": 1029 }, { "id": "4ttkwwoAQuroCg2V", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you infer a trend about the state of leadership styles in the organization from the perspective of team adoption?" }, { "from": "gpt", "value": "Certainly, the chart is primarily showing us a downward or negative trend in the adoption of various leadership styles. Each bar, this one for {Transactional Leadership}[0.758, 0.502, 0.901, 0.590] for instance, extending to the left from the y-axis, indicates a decline in the number of teams demonstrating that leadership style. This trend is reinforced by the percentages illustrated above each bar like this one indicating {9%}[0.730, 0.537, 0.758, 0.554] in the case of Transactional Leadership. This visually suggests that fewer and fewer teams in the organization are showing different leadership styles over time.\nBased on the above thought, the answer is:\nThe trend of leadership styles in the corporate organization is on a decline. This is shown by the negative percentages for all leadership styles, indicating less number of teams are demonstrating these leadership styles over the course of time." } ], "id_raw": 1029 }, { "id": "NnLieVUUhSghBobr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_9.png", "conversations": [ { "from": "human", "value": "\nWhich leadership style shows the highest percentage decline in business teams adopting it?" }, { "from": "gpt", "value": "To answer this, I need to see which bar extends the furthest to the left on the chart. Each case is represented by a color-coded bar, denoting the percentage decline for that leadership style. By comparing these, I can see that the bar for {Authentic Leadership}[0.518, 0.172, 0.901, 0.260] is the longest. This implies a higher percentage decline in teams adopting Authentic Leadership, which is also confirmed by the percentage label of {24%}[0.480, 0.207, 0.518, 0.224].\nBased on the above thought, the answer is:\nAuthentic Leadership" } ], "id_raw": 1029 }, { "id": "DOriQD32bYauoxuk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nCould you convert all the recent trend information across different sectors of the Transportation and Logistics industry from the bar chart into a CSV format table?" }, { "from": "gpt", "value": "From the given chart, it is evident that the y labels represent the various sectors of the industry {\"Air Freight Volume\"}[0.122, 0.839, 0.281, 0.857], {\"Ocean Freight Volume\"}[0.090, 0.737, 0.281, 0.754], {\"Last Mile Delivery\"}[0.126, 0.634, 0.281, 0.651], {\"Wholesale and Distribution\"}[0.047, 0.531, 0.281, 0.549], {\"Retail and ECommerce\"}[0.083, 0.429, 0.281, 0.446], {\"Manufacturing and Production\"}[0.019, 0.326, 0.281, 0.344], {\"3PL/4PL Services\"}[0.135, 0.223, 0.281, 0.241] and {\"Transportation Management\"}[0.035, 0.121, 0.281, 0.138]. The x-axis represents the activity trend in percentage between Benchmark Trends denoted by the {red bars}['Benchmark Trends'][0.518, 0.087, 0.669, 0.887] and the recently Surveyed Trends shown by the {green bars}['Surveyed Trends'][0.669, 0.087, 0.919, 0.887]. The values can be extracted by locating the edge of each bar along the x-axis, values range from -30 to 30. Each sector has two bars corresponding to the two trends. So, I should collect these details for constructing the CSV table.\nBased on the above thought, the answer is:\nSectors,Benchmark Trends (%),Surveyed Trends (%)\nAir Freight Volume,-12,6\nOcean Freight Volume,-25,15\nLast Mile Delivery,-18,6\nWholesale and Distribution,7,-9\nRetail and ECommerce,10,-7\nManufacturing and Production,-20,14\n3PL/4PL Services,-13,9\nTransportation Management,-15,20" } ], "id_raw": 1030 }, { "id": "DbIMUwlAZg0WaqfI", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which sector do you think is most negatively impacted? Why?" }, { "from": "gpt", "value": "The most negatively impacted sector would be the one where the Surveyed Trends portray a large negative deviation from the Benchmark Trends. I can see that {\"Ocean Freight Volume\"}[0.090, 0.737, 0.281, 0.754] shows a large negative value for {Surveyed Trends}[0.669, 0.702, 0.856, 0.785] and a negative {Benchmark Trends}[0.356, 0.702, 0.669, 0.785] value as well, indicating a decrease in activity. And the difference between the Benchmark Trends and the Surveyed Trends for this sector is also the greatest, meaning that this sector not only performed badly, its performance also deteriorated more than any other sector compared to the benchmark.\nBased on the above thought, the answer is:\nThe Ocean Freight Volume sector is the most negatively impacted because it has the most substantial negative deviation in the Surveyed Trends compared to the Benchmark Trends." } ], "id_raw": 1030 }, { "id": "rv5yPssFeHEQtweD", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which sector shows the most positive deviation in Surveyed Trends compared to the Benchmark Trends?" }, { "from": "gpt", "value": "To answer this question, I need to look for the sector in which the Surveyed Trend is greater than the Benchmark Trend by the most significant margin. Upon scanning the chart, I can see that the {bar for Transportation Management}[0.669, 0.087, 0.919, 0.169] in Surveyed Trends have the highest value on the positive side compared to its corresponding {bar in Benchmark Trends}[0.481, 0.087, 0.669, 0.169], indicating that it has the most positive deviation in Surveyed Trends compared to Benchmark Trends.\nBased on the above thought, the answer is:\nTransportation Management" } ], "id_raw": 1030 }, { "id": "3zY4ITKjZDqTwtc3", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data in the bar chart into a CSV format table?" }, { "from": "gpt", "value": "The y-axis labels represent different types of media which include {Newspapers}[0.029, 0.107, 0.171, 0.131], {TV}[0.141, 0.231, 0.171, 0.254], {Social Media}[0.025, 0.354, 0.171, 0.377], {Blogs}[0.107, 0.477, 0.171, 0.501], {Podcasts}[0.069, 0.601, 0.171, 0.624], {Forums}[0.086, 0.724, 0.171, 0.748] and {Memes}[0.088, 0.848, 0.171, 0.871]. To create the table, I need to interpret the bar locations on the x-axis. The x-axis ranges from -25 to 40 which signifies the percentage of influence. For instance, the {Newspapers} category for synthetic data is represented by a bar from 0 to -15[0.370, 0.067, 0.514, 0.166] and human-related data is represented by a bar from 0 to 25[0.514, 0.067, 0.754, 0.166]. Following this method for every category, I can create a CSV table from the data.\nBased on the above thought, the answer is:\nMedia Type,Synthetic Data,Human Data\nNewspapers,-15,25\nTV,-24,38\nSocial Media,-21,16\nBlogs,-10,5\nPodcasts,-5,12\nForums,-9,20\nMemes,-6,8" } ], "id_raw": 1031 }, { "id": "jKxY0oJRGUaEsKm4", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart tell us about the impact of synthetic data and human data on different media types?" }, { "from": "gpt", "value": "Interpreting the bar chart, it is clear that the impact of synthetic and human data varies across different media types. The color-coded bars clearly distinguish between the two types of data for each media, with one color for {synthetic data}0.836, 0.141, 0.888, 0.164] and another for {human data}0.836, 0.176, 0.888, 0.199]. Each media type shows a unique relationship between synthetic and human data. For example, {TV}[0.141, 0.231, 0.171, 0.254]shows a negative influence from synthetic data and a high positive influence from human data, indicating a shift in trust towards human-created content in this sector. On the other hand, {Podcasts}[0.069, 0.601, 0.171, 0.624] show minimal impact from synthetic data and a moderate increase in impact from human data. This could be due to the nature of Podcasts being highly personal and requiring human touch. Looking at all the media types, it is clear that human data has more influence than synthetic data in all areas, suggesting a wider trust in human-generated content.\nBased on the above thought, the answer is:\nEach media type has a unique response to synthetic versus human influence. Generally, human-authored data has more impact on all categories, reflecting the wider trust in human-generated content. Notably, TV experiences the most considerable shift from synthetic to human influence, possibly due to increased trust in human-authored content. By contrast, Podcasts display minimal impact from synthetic influence and a modest rise in human influence, revealing a preference for the personal touch in this medium." } ], "id_raw": 1031 }, { "id": "3Xo34cIV63VYN231", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_11.png", "conversations": [ { "from": "human", "value": "\nWhich media type has the highest difference between synthetic data and human data?" }, { "from": "gpt", "value": "To find out the media type with the highest difference between synthetic data and human data, I need to compare the bar lengths in each such pair. By visual inspection of the bars, I notice that {TV for synthetic data}[0.283, 0.190, 0.514, 0.289] and {TV for human data}[0.514, 0.190, 0.879, 0.289] have the longest combined length, implying the highest difference in values. This pair presents a difference of 38 (human data) - (-24) (synthetic data) = 62.\nBased on the above thought, the answer is:\nTV" } ], "id_raw": 1031 }, { "id": "ATCMvntdCvo0qNgh", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nCan you please extract the information about outsourced and internal handling processes in different departments of a business and represent it in structured table format (CSV)?" }, { "from": "gpt", "value": "According to the bar chart, the y-axis represents specific departments of a business, from {\"Customer inquiries\"}[0.041, 0.104, 0.205, 0.122], {\"Product development\"}[0.019, 0.230, 0.205, 0.247] to {\"After-sales service\"}[0.046, 0.855, 0.205, 0.873]. The bars extending towards the left denote the outsourced processes, and towards the right are internal handling processes. Blue bars represent outsourced processes, while the light blue bars represent internal handling processes according to {the legend section}[0.7199cf: Internal Handling, 0.98abc5: Outsourced]. Now, to create the CSV format, I examine each department. For example, for \"Customer inquiries\", the outsourced bar's coordinates are [0.462, 0.061, 0.556, 0.161] and the internal handling bar's coordinates are [0.556, 0.061, 0.707, 0.161], indicating a performance of -25% for outsourcing and 40% for internal handling respectively.\nBased on the above thought, the answer is:\nDepartment,Outsourced,Internal_Handling\nCustomer inquiries,-25,40\nProduct development,-55,70\nPurchasing,-20,30\nManufacturing,-80,100\nQuality control,-50,65\nSales,-75,90\nAfter-sales service,-35,50" } ], "id_raw": 1032 }, { "id": "fueCUPZyLIPp2Kv9", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, in which process do you think the company should definitely consider internal handling instead of outsourcing? Explain your reasoning." }, { "from": "gpt", "value": "For this analysis, we examine where the difference between {internal handling}[#7199cf] and {outsourced}[#98abc5] is most significant. For {manufacturing}[0.079, 0.480, 0.205, 0.497], the extremities of the positive bar is at {100%}[0.556, 0.437, 0.933, 0.537] and of the negative bar is at {-80%}[0.255, 0.437, 0.556, 0.537]. This indicates a striking contrast between the effectiveness of internal handling versus outsourcing, with internal handling far outperforming outsourcing. This massive difference suggests that the company would substantially benefit from internal handling for the manufacturing process.\nBased on the above thought, the answer is:\nManufacturing. As per the chart, the efficiency of internal handling in manufacturing is 100% as compared to -80% for outsourced. This significant gap suggests that the company is significantly more efficient when handling manufacturing processes internally." } ], "id_raw": 1032 }, { "id": "LU2Y8YmueFgcuw4I", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_12.png", "conversations": [ { "from": "human", "value": "\nFor which business process does outsourcing exhibit the least effectiveness as per the chart?" }, { "from": "gpt", "value": "To find which business process has the least effectiveness in terms of outsourcing, I need to inspect the size of each {bar representing outsourced process}[#98abc5] relative to the x-axis coordinate 0. Examining them, I can observe the longest bar representing outsourcing fall under the label {\"Manufacturing\"}[0.079, 0.480, 0.205, 0.497]. It extends from 0 to approximately {-80}[0.255, 0.437, 0.556, 0.537] on the negative x-axis, which makes it the least effective process when outsourced.\nBased on the above thought, the answer is:\nManufacturing" } ], "id_raw": 1032 }, { "id": "gWxIL6DayICww8gg", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nCan you parse the chart data and convert it into a structured table format?" }, { "from": "gpt", "value": "The chart compares the efforts invested in synthetic and human-driven projects throughout various stages of an Innovation Project Evaluation and Management cycle. The major categories are {'Project Initialization'}[0.019, 0.104, 0.190, 0.122], {'Ideation'}[0.119, 0.229, 0.190, 0.246], {'Design'}[0.130, 0.354, 0.190, 0.371], {'Development'}[0.073, 0.478, 0.190, 0.496], {'Testing'}[0.129, 0.603, 0.190, 0.621], {'Deployment'}[0.084, 0.728, 0.190, 0.745], and {'Review and Control'}[0.022, 0.852, 0.190, 0.870]. For each stage, there are two bars indicating synthetic and human efforts. According to {the legend}, {the teal bars}\"synthetic data\"[0.393, 0.061, 0.488, 0.161] represent efforts by synthetic projects, and {the purple bars}\"human-authored data\"[0.488, 0.061, 0.536, 0.161] represent efforts by human-driven projects. The values corresponding to the bar lengths indicate the percentage of effort. Using this information, a structured table can be created.\nBased on the above thought, the answer is:\nProject Stage,Synthetic Effort (%),Human Effort (%)\nProject Initialization,20,-10\nIdeation,45,-30\nDesign,10,-50\nDevelopment,25,-70\nTesting,40,-90\nDeployment,50,-80\nReview and Control,35,-60" } ], "id_raw": 1033 }, { "id": "jcNWCj0j92DoOkex", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, what can you infer about the allocation of effort between synthetic and human-authored projects across all stages of the Innovation Project Evaluation and Management cycle?" }, { "from": "gpt", "value": "Based on the chart, it seems that the allocation of effort between synthetic and human-authored efforts varies across different stages of the Innovation Project Evaluation and Management cycle. In general, human-authored projects tend to give greater effort during the 'Design', 'Development', 'Testing', and 'Deployment' stages, represented by the bars [0.488, 0.311, 0.726, 0.410], [0.488, 0.435, 0.822, 0.535], [0.488, 0.560, 0.917, 0.660], and [0.488, 0.685, 0.869, 0.784]. On the other hand, synthetic projects seem to prioritize the 'Ideation', 'Deployment', and 'Review and Control' stages, with bars [0.274, 0.186, 0.488, 0.286], [0.250, 0.685, 0.488, 0.784], and [0.321, 0.809, 0.488, 0.909]. This could suggest that synthetic and human-authored projects focus on different aspects within the Innovation Project Evaluation and Management cycle.\nBased on the above thought, the answer is:\nBased on the chart, it appears that synthetic projects focus more on the 'Ideation', 'Deployment', and 'Review and Control' stages, while human-authored projects prioritize the 'Design', 'Development', 'Testing', and 'Deployment' stages of an Innovation Project Evaluation and Management cycle." } ], "id_raw": 1033 }, { "id": "6ZFhXtQhWtKCWvGC", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in effort percentages between human and synthetic during the 'Development' stage?" }, { "from": "gpt", "value": "To answer this question, I can examine the 'Development' category in the chart, which includes {'synthetic bar'}[0.369, 0.435, 0.488, 0.535] and {'human bar'}[0.488, 0.435, 0.822, 0.535]. The lengths of these bars represent the percentages of effort made by synthetic and human projects, respectively. The synthetic project invested 25% effort, and the human project invested 70% (as indicated by the negative sign, it actually means to the left of the y-axis). The difference can be calculated by subtracting 25 from 70.\nBased on the above thought, the answer is:\n45%" } ], "id_raw": 1033 }, { "id": "g4SadXzfM0QEFVzu", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nCan you help convert the bar chart data into a CSV format?" }, { "from": "gpt", "value": "Sure. From the y-axis labels on the left side {y-ticks labels}[{\"Infrastructural Development\"}[0.015, 0.103, 0.212, 0.117]; {\"Educational Facilities\"}[0.067, 0.229, 0.212, 0.243]; {\"Healthcare Facilities\"}[0.072, 0.355, 0.212, 0.369]; {\"Agriculture\"}[0.136, 0.481, 0.212, 0.495]; {\"Technological Advancement\"}[0.018, 0.607, 0.212, 0.621]; {\"Employment Opportunities\"}[0.025, 0.733, 0.212, 0.747]; {\"Transportation\"}[0.112, 0.859, 0.212, 0.873]], these are the sectors of development. There are two groups of colored bars, each representing a different setting. The group on the left-side of the x-axis is purple with {label \"Rural Growth\"}[0.308, 0.058, 0.653, 0.159] and on the right-side is blue with {label \"Urban Growth\"}[0.653, 0.058, 0.905, 0.159]. For every sector labeled on the y-axis, there is a pair of bars from both groups. The x-value of each bar represents the growth percentage in its respective setting. I will extract the growth percentage for both settings for all sectors and format the extracted data into a CSV format.\nBased on the above thought, the answer is:\nSector,Rural Growth, Urban Growth\nInfrastructural Development,-52,38\nEducational Facilities,-25,15\nHealthcare Facilities,-35,10\nAgriculture,-40,25\nTechnological Advancement,-55,30\nEmployment Opportunities,-30,40\nTransportation,-45,35" } ], "id_raw": 1034 }, { "id": "bcLXU3cs5xc6cSsm", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nJudging from the chart, in which sector has the most balanced development between the rural and urban areas?" }, { "from": "gpt", "value": "To find the sector with the most balanced development, I need to compare the length of the purple and blue bars of each sector because their lengths correspond to the growth in the rural and urban areas, respectively. The sector where the lengths of the two bars are closest will have the most balanced development. For example, for the {Infrastructural Development sector}[{\"Rural Growth\"}[0.308, 0.058, 0.653, 0.159]; {\"Urban Growth\"}[0.653, 0.058, 0.905, 0.159]], the difference between the two bars is large, so the development is not balanced. The comparison process continues for all sectors until I find the sector with the most balanced development.\nBased on the above thought, the answer is:\nHealthcare Facilities. Because in this sector, the growth percentages of the rural area and the urban area are the closest to each other." } ], "id_raw": 1034 }, { "id": "s92VuIeC9ZAP3V25", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_69_14.png", "conversations": [ { "from": "human", "value": "\nIn which factor, the growth difference between rural area and urban area is the smallest?" }, { "from": "gpt", "value": "To compare the differences between rural and urban developments, I'll find the absolute value differences between the corresponding bars in the two groups for each sector. Starting with Infrastructural Development at the bottom, the purple bar at {coordinates}[0.308, 0.058, 0.653, 0.159] spans roughly from -52 to 0 on the x-axis, while the corresponding blue bar at {coordinates}[0.653, 0.058, 0.905, 0.159] spans from 0 to 38. The difference is 90. Similarly, I'll compare the differences for all other factors. The sector with the smallest difference will be the one I'm looking for.\nBased on the above thought, the answer is:\nHealthcare Facilities" } ], "id_raw": 1034 }, { "id": "c7GgibmGwFcugQkw", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data showing improvements in symptom scores across medical specialties into a CSV format table?" }, { "from": "gpt", "value": "Upon inspection of the bar chart, the y-axis is labeled with medical specialties such as {Cardiology}[0.031, 0.426, 0.105, 0.440], {Neurology}[0.034, 0.309, 0.105, 0.323], {Dermatology}[0.015, 0.192, 0.105, 0.206], and {Urology}[0.052, 0.075, 0.105, 0.089]. Each specialty has bars representing different constraint levels, each with a unique color as specified in {the legend}[0.117, 0.516, 0.309, 0.604]. The four bars within each group correspond to improvements in scores for 3, 5, 7, and 10 constraint words respectively. The individual scores are inscribed above each colored bar, allowing for precise data extraction.\nBased on the above thought, the answer is:\nSpecialty,3 Constraint Words,5 Constraint Words,7 Constraint Words,10 Constraint Words\nCardiology,-0.9,-0.5,-2.1,-0.7\nNeurology,-0.2,0.1,-1.7,-0.3\nDermatology,-2.1,-1.4,-1.9,-1.2\nUrology,-0.7,-2.0,-2.2,-1.8" } ], "id_raw": 1051 }, { "id": "bjbMokx7XXed9vXA", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the effect of increasing constraint words on symptom improvement in Dermatology from the bar chart?" }, { "from": "gpt", "value": "To understand the effect of increasing constraint words on symptom improvement in the Dermatology field, I need to evaluate the lengths and positions of the bars related to {Dermatology}[0.015, 0.192, 0.105, 0.206]. Observing the decrease or increase in value inscribed at the top of each bar for 3, 5, 7, and 10 constraint words, which are -2.1, -1.4, -1.9, and -1.2 respectively. A further insight is gained by reading the positions of these inscriptions along the x-axis, which reflect the actual value changes.\nBased on the above thought, the answer is:\nThe improvement in symptom scores in Dermatology tends to fluctuate as the number of constraint words increases. While there is a general negative trend, showing improvement, the rate of improvement is inconsistent and even reverses between 5 and 7 constraint words, before improving again at 10 constraint words." } ], "id_raw": 1051 }, { "id": "EhO6hpqfKyJqVMHO", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_1.png", "conversations": [ { "from": "human", "value": "\nHow many medical specialties showed improvement across all four constraint levels?" }, { "from": "gpt", "value": "To find out how many specialties showed improvement across all four constraint levels, I need to examine the values above each bar for the specialties listed on the y-axis. The improvements in symptom scores are indicated by negative values, as positive values would indicate worsening conditions. For each medical specialty such as {Cardiology}[0.031, 0.426, 0.105, 0.440], {Neurology}[0.034, 0.309, 0.105, 0.323], {Dermatology}[0.015, 0.192, 0.105, 0.206], and {Urology}[0.052, 0.075, 0.105, 0.089], I observe the bar labels to check if all values are negative.\nBased on the above thought, the answer is:\nFour" } ], "id_raw": 1051 }, { "id": "KxyrRB5HNN6Bzatk", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data in the visualization into a CSV table, using the coordinates to determine the category and values for each bar?" }, { "from": "gpt", "value": "The bar chart compares {Graphene Research}[0.015, 0.347, 0.151, 0.364] and {AI Technology}[0.056, 0.155, 0.151, 0.172] in four terms, which are {3-Year Average}[0.208, 0.527, 0.311, 0.544], {5-Year Average}[0.208, 0.553, 0.311, 0.570], {7-Year Average}[0.208, 0.579, 0.311, 0.597] and {10-Year Average}[0.208, 0.605, 0.320, 0.623] respectively. The value for each data point is represented by the x-coordinate of each bar since the x-axis starts at 0 and extends outwards negatively. By using the located start of each bar as well as the x-axis ticks, I can calculate the value for each data point and then compile all given information into a CSV table.\nBased on the above thought, the answer is:\nItem,3-Year Average,5-Year Average,7-Year Average,10-Year Average\nGraphene Research,-1.4,-0.9,-2.3,-2.7\nAI Technology,-0.9,-1.3,-2.0,-2.4" } ], "id_raw": 1054 }, { "id": "7OV3aJ5GDJGPObpu", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nHow does the trend of R&D budget change over different time periods for AI Technology?" }, { "from": "gpt", "value": "This requires examination of the four bars representing AI Technology. Observing from the {3-Year Average}[0.764, 0.674, 0.974, 0.722] to the {10-Year Average}[0.413, 0.530, 0.974, 0.578] bars in order, the bars lengthen as the time period increases. This suggests that the R&D budget for AI Technology is decreasing at a quicker rate over larger time frames. Therefore, the trend of R&D budget change over different time periods for AI Technology is that the longer the average period, the greater the decrease in budget.\nBased on the above thought, the answer is:\nThe longer the average period, the greater the decrease in the R&D budget for AI Technology." } ], "id_raw": 1054 }, { "id": "urwbRz8cNiXCSexc", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_4.png", "conversations": [ { "from": "human", "value": "\nWhich research field had the biggest decrease in R&D budget over the 10-year average?" }, { "from": "gpt", "value": "To determine which research field had a bigger budget decrease in the 10-year average, I can compare the length of their respective bars. {Graphene Research}[0.343, 0.722, 0.974, 0.770] has a bar reaching to about the -2.7 point on the x-axis, while the bar for {AI Technology}[0.413, 0.530, 0.974, 0.578] reaches to about the -2.4 point on the x-axis. Hence, the budget decrease for Graphene Research is larger than AI Technology over a 10-year average.\nBased on the above thought, the answer is:\nGraphene Research" } ], "id_raw": 1054 }, { "id": "ayvb5xObPT0D4AkK", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nCould you provide the data from the bar plots in csv format?" }, { "from": "gpt", "value": "Sure, I can convert the raw data into a CSV format by structuring it into rows and columns, with each row representing a location (Urban or Rural) and each column representing a trend. In this case, the first column would contain the regions, and the following columns would contain the scores for the trends of Gradual Recovery, Sporadic Growth, Marginal Decrease, and Gradual Downturn respectively. The score for each trend and each location can be obtained from {the length of the corresponding bars}[{Gradual Recovery Urban bar}[0.523, 0.871, 0.690, 0.914]; {Gradual Recovery Rural bar}[0.523, 0.658, 0.653, 0.701]; {Sporadic Growth Urban bar}[0.523, 0.829, 0.782, 0.871]; {Sporadic Growth Rural bar}[0.523, 0.616, 0.763, 0.658]; {Marginal Decrease Urban bar}[0.450, 0.786, 0.523, 0.829]; {Marginal Decrease Rural bar}[0.394, 0.573, 0.523, 0.616]; {Gradual Downturn Urban bar}[0.154, 0.743, 0.523, 0.786]; {Gradual Downturn Rural bar}[0.191, 0.530, 0.523, 0.573]]. By looking at the x-axis ticks, I can see that they range from -2.0 to 2.0, which represents the score. I can estimate the score of each bar by comparing its length to this scale. For instance, the Urban bar for Gradual Recovery extends from 0.0 up to approximately 0.9 on the axis, indicating a score of 0.9. A similar process can be followed for every bar to extract the scores for all trends and regions, and these scores can then be structured into the CSV format.\nBased on the above thought, the answer is:\nRegion,Gradual Recovery,Sporadic Growth,Marginal Decrease,Gradual Downturn\nUrban,0.9,1.4,-0.4,-2\nRural,0.7,1.3,-0.7,-1.8" } ], "id_raw": 1055 }, { "id": "Q1yWUKOR1vfR05tq", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nBased on the bar plots, in which trend is the development difference between Urban and Rural regions the least noticeable?" }, { "from": "gpt", "value": "To find the trend with the smallest development difference between Urban and Rural regions, I need to compare the lengths of the Urban and Rural bars for each trend. First, looking to {the subplot of Gradual Recovery}[{\"Urban\"}[0.523, 0.871, 0.690, 0.914], {\"Rural\"}[0.523, 0.658, 0.653, 0.701]], I can see that the length difference is about 0.2. Similarly, considering {the subplot of Sporadic Growth}[{\"Urban\"}[0.523, 0.829, 0.782, 0.871], {\"Rural\"}[0.523, 0.616, 0.763, 0.658]], the difference is roughly 0.1. Checking {the Marginal Decrease subplot}[{\"Urban\"}[0.450, 0.786, 0.523, 0.829], {\"Rural\"}[0.394, 0.573, 0.523, 0.616]], the difference is approximately 0.3, and finally, for {the Gradual Downturn subplot}[{\"Urban\"}[0.154, 0.743, 0.523, 0.786], {\"Rural\"}[0.191, 0.530, 0.523, 0.573]], the difference is around 0.2. Through these comparisons, it is evident that the Sporadic Growth trend has the smallest development difference between the Urban and Rural regions.\nBased on the above thought, the answer is:\nSporadic Growth. Because the difference in development scores between Urban and Rural regions is the least in the Sporadic Growth trend." } ], "id_raw": 1055 }, { "id": "we5h0FtiiTwxuQyo", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_5.png", "conversations": [ { "from": "human", "value": "\nWhat score did the Rural region get in the Sporadic Growth trend according to the chart?" }, { "from": "gpt", "value": "For the Sporadic Growth trend in the Rural region, I need to observe the corresponding bar, which is {the green bar under Rural on subplot_1}[0.523, 0.616, 0.763, 0.658]. By looking at the x-axis marks from {0.0}[0.523, 0.939, 0.534, 0.957] to {1.5}[0.789, 0.939, 0.811, 0.957], I can see that the length of the bar shows that the score is about 1.3, which happens to be where the endpoint of the Rural Sporadic Growth green bar exists.\nBased on the above thought, the answer is:\n1.3" } ], "id_raw": 1055 }, { "id": "auCi8JeFfAh8G60h", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nCan you generate a CSV table that captures the emerging technology trends according to the bar chart?" }, { "from": "gpt", "value": "Based on the data visually represented in the chart, there are five different technologies specified on {the y-axis}[{\"AI\"}[0.122, 0.462, 0.136, 0.479], {\"Blockchain\"}[0.060, 0.356, 0.136, 0.374], {\"Cloud Computing\"}[0.015, 0.251, 0.136, 0.268], {\"5G\"}[0.116, 0.145, 0.136, 0.163], {\"IoT\"}[0.115, 0.040, 0.136, 0.057]]. Each technology has two bars representing two different years, {2020}[{\"Trend_2020\"}[0.892, 0.527, 0.972, 0.544]] and {2021}[{\"Trend_2021\"}[0.892, 0.553, 0.973, 0.571]], as specified in {the legend}[0.848, 0.520, 0.978, 0.578].\nI can measure the lengths of each bar to determine their corresponding values. For instance, for AI, {the 2020 bar}[0.563, 0.885, 0.647, 0.914] extends from the centerline to about 0.8 on the {x-axis}[0.563], and {the 2021 bar}[0.563, 0.857, 0.932, 0.885] extends even further to about 3.5. By similarly measuring the lengths of the bars for the remaining technologies, I can generate the CSV table.\nBased on the above thought, the answer is:\nTechnology,Trend_2020,Trend_2021\nAI,0.8,3.5\nBlockchain,-2.5,1.4\nCloud Computing,2.8,3.7\n5G,0.8,3.3\nIoT,-3.0,0.8" } ], "id_raw": 1056 }, { "id": "LFOY6WzTWjbWZNeC", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nWhich emerging technology, based on the bar chart, witnessed the most significant improvement from 2020 to 2021 and why do you come to this conclusion?" }, { "from": "gpt", "value": "To figure out which technology witnessed the most substantial improvement from {2020}[{\"Trend_2020\"}[0.892, 0.527, 0.972, 0.544]] to {2021}[{\"Trend_2021\"}[0.892, 0.553, 0.973, 0.571]], I need to look at the changes in bar lengths for each technology from 2020 to 2021. Specifically, I can compare the difference in lengths of the bars representing 2020 and 2021 for each technology. This will help me determine which technology had the biggest jump.\nFor instance, {the AI bar}[0.563, 0.885, 0.647, 0.914] seemed to have one of the longest lengths, corresponding to a value of 0.8 in 2020, and increased to a length symbolizing a value of 3.5 in 2021. The change from 0.8 to 3.5 is significant, amounting to a difference of 2.7. Now, I can compare this difference to those of the other technologies to determine which technology saw the greatest improvement. Based on the analysis, I can conclude that the {IoT bar}[[\"Trend_2020 bar\"}[0.246, 0.559, 0.563, 0.587], {\"Trend_2021 bar\"}[0.563, 0.530, 0.647, 0.559]] has the biggest difference, from -3 in 2020 to 0.8 in 2021, with a difference of 3.8, which is the highest among all technologies.\nBased on the above thought, the answer is:\nIoT. Since it has the most substantial improvement in trend scores, from -3 in 2020 to 0.8 in 2021, a net positive change of 3.8." } ], "id_raw": 1056 }, { "id": "hunAwfceq1b3VWcr", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_6.png", "conversations": [ { "from": "human", "value": "\nWhich technology had the greatest trend score in 2021?" }, { "from": "gpt", "value": "In order to determine the technology with the greatest trend score in 2021, I must compare the lengths of {the 2021 bars}[{\"AI\"}[0.563, 0.857, 0.932, 0.885], {\"Blockchain\"}[0.563, 0.775, 0.710, 0.804], {\"Cloud Computing\"}[0.563, 0.694, 0.953, 0.722], {\"5G\"}[0.563, 0.612, 0.911, 0.641], {\"IoT\"}[0.563, 0.530, 0.647, 0.559]]. Upon examination, it's clear that {the Cloud Computing bar}[0.563, 0.694, 0.953, 0.722] extends to the furthest point on the {x-axis}[0.932], indicating that Cloud Computing had the greatest trend score in 2021.\nBased on the above thought, the answer is:\nCloud Computing" } ], "id_raw": 1056 }, { "id": "9tshltoHr10RdMKx", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nCould you provide a structured table capturing the data in the bar chart on the trends in urban and rural development?" }, { "from": "gpt", "value": "From the title in the subplot {Rural and Urban Development Comparison}[0.392, 0.019, 0.749, 0.041], I can identify that key categories are Urban Development and Rural Development from the subplot's legend. Each of these categories has four different trends: {Steep Decline}[0.066, 0.423, 0.163, 0.440], {Insignificant Increase}[0.015, 0.310, 0.163, 0.327], {Sudden Decrease}[0.041, 0.197, 0.163, 0.214], and {Persistent Decline}[0.038, 0.084, 0.163, 0.101]. The x-axis labels from {\u22123.5}[0.252, 0.463, 0.285, 0.480] to {0.0}[0.963, 0.463, 0.985, 0.480] represent score values for each trend. For Urban Development the score values for various trends can be inferred by the length of bars and their endpoints' positions. For instance, {the bar}[0.510, 0.421, 0.974, 0.438] for Steep Decline indicates a score of -2.3, while {the bar}[0.631, 0.308, 0.974, 0.325] for Insignificant Increase indicates a score of -1.7. Similar steps are followed for assessing values under Rural Development.\nBased on the above thought, the answer is:\nCategory,Trend,Score\nUrban Development,Steep Decline,-2.3\nUrban Development,Insignificant Increase,-1.7\nUrban Development,Sudden Decrease,-3.0\nUrban Development,Persistent Decline,-2.5\nRural Development,Insignificant Increase,-2.5\nRural Development,Steep Decline,-2.0\nRural Development,Sudden Decrease,-1.8\nRural Development,Persistent Decline,-2.3" } ], "id_raw": 1057 }, { "id": "E79nCQyi7kPaZrsO", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, do the trends of urban or rural development appear to be more progressive or improving over time?" }, { "from": "gpt", "value": "The bar lengths extending from 0 towards -3.5 indicate the intensity of each trend. If the bar is closer to the 0 mark, the trend is less steep. The terms 'steep decline', 'insignificant increase', 'sudden decrease', and 'persistent decline' indicate negative trends. The bars for {Urban Development}[0.510, 0.421, 0.974, 0.438]; [0.631, 0.308, 0.974, 0.325]; [0.369, 0.195, 0.974, 0.212]; [0.470, 0.082, 0.974, 0.099] and {Rural Development}[0.571, 0.404, 0.974, 0.421]; [0.470, 0.291, 0.974, 0.308]; [0.611, 0.178, 0.974, 0.195]; [0.510, 0.065, 0.974, 0.082] point to the left (negative x direction), indicating a decline or decrease. By comparing the bar lengths, I can see that the bars associated with Rural Development appear closer to 0, indicating lower intensity of negativity as opposed to Urban Development.\nBased on the above thought, the answer is:\nRural Development appears to be more progressive or improving over time as compared to Urban Development, as its bar lengths indicate lower levels of negative impacts." } ], "id_raw": 1057 }, { "id": "EAcCOAASp37ODaeQ", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_7.png", "conversations": [ { "from": "human", "value": "\nWhich development trend has the least negative impact or highest score for Urban development?" }, { "from": "gpt", "value": "By studying the bar lengths for various trends under Urban Development from the sub-legend, we can observe that the least negative or highest score is when the bar is closer to the 0 mark on the x-axis. We can see that {the bar}[0.631, 0.308, 0.974, 0.325] for \"Insignificant Increase\" stops at the most rightward point towards zero among all trends.\nBased on the above thought, the answer is:\nInsignificant Increase" } ], "id_raw": 1057 }, { "id": "d3bS88xmglM4amSJ", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nPlease transform the bar chart data on societal change into a CSV format table." }, { "from": "gpt", "value": "The bar chart shows the impacts of several trends on two categories of societal change. The x-axis represents the effect from {\u22123.5}[0.261, 0.939, 0.294, 0.957] to {0.0}[0.963, 0.939, 0.985, 0.957] and the y-axis lists the categories: {Social Cultural Change}[0.015, 0.818, 0.173, 0.836] and {Value Change}[0.077, 0.598, 0.173, 0.616]. The lengths and labels on the bars in each category represent the impact of four different growth trends ({Erratic Growth}[0.229, 0.513, 0.330, 0.530], {Marginal Growth}[0.229, 0.539, 0.344, 0.556], {Moderate Decline}[0.229, 0.565, 0.352, 0.583], and {Sporadic Growth}[0.229, 0.591, 0.345, 0.609]). To create the CSV table, I'll take each combination of category and trend, then use the label on the bar itself to find the numerical impact value. For instance, the bar for {Erratic Growth in Social Cultural Change}[0.337, 0.869, 0.974, 0.913] has a label \"{-3.2}\"[0.324, 0.883, 0.351, 0.900], which is the impact level.\nBased on the above thought, the answer is:\nCategory,Erratic Growth,Marginal Growth,Moderate Decline,Sporadic Growth\nSocial Cultural Change,-3.2,-1.4,-0.8,-2.7\nValue Change,-2.9,-2.1,-1.6,-2.3" } ], "id_raw": 1058 }, { "id": "LnK41vrqeeKqpPsp", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nHow does the impact of erratic growth compare to moderate decline on social cultural change and value change according to the chart?" }, { "from": "gpt", "value": "This question requires comparison of impact values of erratic growth and moderate decline on both categories. For the 'Social Cultural Change' category, {erratic growth}[0.324, 0.883, 0.351, 0.900] has the impact of -3.2 and {moderate decline}[0.383, 0.662, 0.410, 0.680] gives an impact of -0.8. Comparatively, erratic growth leads to a more adverse effect than moderate decline. The same must be done for the 'Value Change' category. For {erratic growth}[0.682, 0.839, 0.709, 0.856] the impact is -2.9, whereas {moderate decline}[0.543, 0.618, 0.570, 0.636] leads to -1.6, again indicating a worse effect of erratic growth.\nBased on the above thought, the answer is:\nThe impact of erratic growth on both 'Social Cultural Change' and 'Value Change' is more adverse than moderate decline. Specifically, for 'Social Cultural Change', erratic growth scores -3.2 while moderate decline scores -0.8. On 'Value Change', erratic growth scores -2.9, and moderate decline generates a score of -1.6. Therefore, erratic growth has a more significant negative impact in both categories compared to the moderate decline." } ], "id_raw": 1058 }, { "id": "pPkIIz03ewouiVSE", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_8.png", "conversations": [ { "from": "human", "value": "\nWhich factor has the most marginal growth on 'Value Change'?" }, { "from": "gpt", "value": "To solve this task, I need to find the bar representing marginal growth in the 'Value Change' category and extract the value from this bar. The suitable bar is located at coordinates [0.556, 0.605, 0.974, 0.649] and the label of the bar {-2.1}[0.423, 0.750, 0.450, 0.768] gives away the value of marginal growth on 'Value Change'. The value is negative which represents the negative impact but since the scale ranges from -3.5 to -0.5, -2.1 represents a moderate level of impact.\nBased on the above thought, the answer is:\n-2.1" } ], "id_raw": 1058 }, { "id": "Reo8SL0gsn71L47Q", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, transform the data detailing the Critical and Recovered Cases for Various Diseases." }, { "from": "gpt", "value": "Observing {the title}[0.144, 0.019, 0.560, 0.041] of the bar chart, I can infer that the chart displays the measurements of critical and recovered cases of various diseases. Each disease is represented as a separate bar on the y-axis, with the corresponding bar length on the x-axis representing the respective score of each disease. Looking at {the labels on x-axis for the lower chart}[\"\u22123.5\", \"\u22123.0\", \"\u22122.5\", \"\u22122.0\", \"\u22121.5\", \"\u22121.0\", \"\u22120.5\", \"0.0\"], the x-axis ranges from -3.5 to 0.0, representing the score of recovered cases. Similarly, the bars on the upper chart represent critical cases, with the same range on the x-axis. The diseases represented on the y-axis include Heart Stroke, Influenza, COPD, and Diabetes {y-axis labels}[0.015, 0.409, 0.102, 0.427], [0.039, 0.300, 0.102, 0.317], [0.063, 0.190, 0.102, 0.207], [0.041, 0.557, 0.102, 0.575]. By observing the length of each bar and the corresponding label, I can convert these values into a CSV format table.\nBased on the above thought, the answer is:\nType,Critical Cases,Recovered Cases\nHeart Stroke,-1.8,-0.5\nInfluenza,2.0,-2.3\nCOPD,-3.0,-1.2\nDiabetes,1.5,-1.5" } ], "id_raw": 1060 }, { "id": "f098FT8HgP0JscA9", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, can you infer which disease is experiencing the worst situation and why?" }, { "from": "gpt", "value": "To infer from the chart, we need to analyze both {the Critical Cases bar chart}[0.107, 0.523, 0.597, 0.933] and {the Recovered Cases bar chart}[0.107, 0.047, 0.597, 0.457]. For a disease to be in the worst situation, it would need to have a high number of critical cases and a low number of recovered cases. From the upper chart, {the COPD bar}[0.230, 0.175, 0.597, 0.219] is the longest, which implies that it has the highest critical cases. Meanwhile, looking at the lower chart, {the COPD bar}[0.450, 0.652, 0.597, 0.695] is not the shortest but has a significantly low score, which means the number of recovered cases is low. Therefore, among the four diseases, COPD can be considered experiencing the worst situation.\nBased on the above thought, the answer is:\nCOPD. As it has the highest number of critical cases and relatively low number of recovered cases." } ], "id_raw": 1060 }, { "id": "VSM2elRjdkeLZTIa", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the lowest score for the Recovered Cases in the four diseases?" }, { "from": "gpt", "value": "To know the lowest score for the recovered diseases, I need to look at the second chart where recovered cases are plotted. Thus, considering {the bars}[0.536, 0.871, 0.597, 0.915], [0.316, 0.761, 0.597, 0.805], [0.450, 0.652, 0.597, 0.695], [0.414, 0.542, 0.597, 0.586] of each disease from the second chart, the score for each recovered case can be calculated by the position of the end of the bars on the x-axis, comparing the lengths. The lowest score is represented by the shortest bar, which corresponds to the disease Influenza {shortest bar}[0.316, 0.761, 0.597, 0.805].\nBased on the above thought, the answer is:\n-2.3 for Influenza." } ], "id_raw": 1060 }, { "id": "6vGgywqFRjj3LkBB", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nCan you extract the values from this bar chart and represent them in a CSV format?" }, { "from": "gpt", "value": "The bar chart features two subplots, one labeled {Ratings}[0.498, 0.019, 0.561, 0.041] and the other labeled {Interest}[0.497, 0.509, 0.562, 0.532]. Each subplot has two bars representing the cuisines Japanese and Italian. For the first subplot, {the Rating bar for Japanese}[0.083, 0.265, 0.932, 0.425] extends to roughly the 9.5 position on the x-axis and {the Rating bar for Italian}[0.083, 0.065, 0.815, 0.225] extends to approximately 8.2. Similarly, in the Interest subplot, {the Interest bar for Japanese}[0.083, 0.755, 0.637, 0.915] extends to about 6.2 on the x-axis and {the Interest bar for Italian}[0.083, 0.555, 0.521, 0.715] extends to approximately 4.9. Hence, the CSV representation will be as shown below.\nBased on the above thought, the answer is:\nCuisine,Rating,Interest\nJapanese,9.5,6.2\nItalian,8.2,4.9" } ], "id_raw": 1063 }, { "id": "4saQ58vifPq1mZqD", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nBased on the values presented in the chart, which cuisine would you suggest to someone more interested in high-rated and popular food?" }, { "from": "gpt", "value": "To answer this question, I need to interpret the chart's information on the popularity and ratings of both cuisines. The popularity can be gleaned from the Interest values represented by {the Interest bar for Japanese}[0.083, 0.755, 0.637, 0.915] which roughly equals 6.2 and {the Interest bar for Italian}[0.083, 0.555, 0.521, 0.715] which is approximately 4.9. The Rating values can be understood from {the Rating bar for Japanese}[0.083, 0.265, 0.932, 0.425] whose value is 9.5 and {the Rating bar for Italian}[0.083, 0.065, 0.815, 0.225] which is roughly 8.2. Combining, these data reveals that Japanese cuisine has a higher Rating and Interest value compared to Italian cuisine, signifying that it is more popular and higher-rated.\nBased on the above thought, the answer is:\nJapanese, because it has higher rating and interest, indicating it is better-rated and of more interest to people." } ], "id_raw": 1063 }, { "id": "p3b37S91MEUb36EB", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_13.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine has a higher overall score when the scores for both Rating and Interest are combined?" }, { "from": "gpt", "value": "The score for each cuisine is the sum of its rating and interest values. For Japanese cuisine, this includes {the Rating bar for Japanese}[0.083, 0.265, 0.932, 0.425] which is around 9.5 and {the Interest bar for Japanese}[0.083, 0.755, 0.637, 0.915] which extends to approximately 6.2. For Italian cuisine, the values to calculate are from {the Rating bar for Italian}[0.083, 0.065, 0.815, 0.225] which is roughly 8.2 and {the Interest bar for Italian}[0.083, 0.555, 0.521, 0.715] which extends to approximately 4.9. By adding these together, I can conclude which cuisine has the highest combined score.\nBased on the above thought, the answer is:\nJapanese" } ], "id_raw": 1063 }, { "id": "v7yBLhNsWtPHmpjz", "Chart_type": "bar", "QA_type": "Chart2table", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data of both rows in the bar chart regarding local government and community development into a CSV format table?" }, { "from": "gpt", "value": "From the ylabel at the left side of the chart {\"Governance Structure\"}[0.054, 0.417, 0.206, 0.435], {\"Population Engagement\"}[0.040, 0.348, 0.206, 0.366], {\"Decentralization Measures\"}[0.022, 0.279, 0.206, 0.297], {\"Public Service Provision\"}[0.043, 0.210, 0.206, 0.228], {\"Local Government Stronger\"}[0.015, 0.141, 0.206, 0.159], {\"Community Insights Better\"}[0.020, 0.072, 0.206, 0.090], I can see that the y-axis represent different categories. The x-axis labels represent the corresponding scores, ranging from -3.5 to 0. The lengths of the bars represent the scores for each category. For the first row, {the orange bars}<{\"Row1 bars\"}[{'bar1'}[0.974, 0.410, 1.241, 0.438];{'bar2'}[0.974, 0.341, 1.413, 0.369];{'bar3'}[0.974, 0.272, 1.069, 0.300];{'bar4'}[0.974, 0.203, 1.203, 0.231];{'bar5'}[0.898, 0.134, 0.974, 0.162];{'bar6'}[0.764, 0.065, 0.974, 0.093]]> indicate the scores for category in the first row. The scores for the category in the second row are given by the lengths of {the green bars}<{\"Row2 bars\"}[{'bar1'}[0.593, 0.887, 0.812, 0.901];{'bar2'}[0.593, 0.818, 0.879, 0.832];{'bar3'}[0.593, 0.749, 0.745, 0.763];{'bar4'}[0.593, 0.680, 0.679, 0.694];{'bar5'}[0.497, 0.611, 0.593, 0.625];{'bar6'}[0.469, 0.542, 0.593, 0.556]]> of the second plot. By mapping the right ends of the bars to the x-axis values, I can retrieve the scores for each category.\nBased on the above thought, the answer is:\nCategory,Row1,Row2\nGovernance Structure,1.4,2.3\nPopulation Engagement,2.3,3.0\nDecentralization Measures,0.5,1.6\nPublic Service Provision,1.2,0.9\nLocal Government Stronger,-0.4,-1.0\nCommunity Insights Better,-1.1,-1.3" } ], "id_raw": 1064 }, { "id": "C7fzwEhL2YSNCCCt", "Chart_type": "bar", "QA_type": "Open QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nBased on your observation of the chart, do you believe that the local government initiatives in Row 2 are more beneficial to the community development in comparison with the initiatives in Row 1?" }, { "from": "gpt", "value": "This question requires a comprehensive analysis of the comparisons of {each pair of orange and green bars}<{\"Row1 and Row2 bars\"}[{'pair1'}[0.974, 0.410, 1.241, 0.438, 0.593, 0.887, 0.812, 0.901];{'pair2'}[0.974, 0.341, 1.413, 0.369, 0.593, 0.818, 0.879, 0.832];{'pair3'}[0.974, 0.272, 1.069, 0.300, 0.593, 0.749, 0.745, 0.763];{'pair4'}[0.974, 0.203, 1.203, 0.231, 0.593, 0.680, 0.679, 0.694];{'pair5'}[0.898, 0.134, 0.974, 0.162, 0.497, 0.611, 0.593, 0.625];{'pair6'}[0.764, 0.065, 0.974, 0.093, 0.469, 0.542, 0.593, 0.556]]>. Notably, the green bar of 'Decentralization Measures' {the third bar of Row2}[0.593, 0.749, 0.745, 0.763] extends far beyond the corresponding orange bar of Row1 {the third bar of Row1}[0.974, 0.272, 1.069, 0.300]. 'Population Engagement' {the second bar of Row2}[0.593, 0.818, 0.879, 0.832] and 'Public Service Provision' {the fourth bar of Row2}[0.593, 0.680, 0.679, 0.694] also shows greater values for Row2 as compared to Row1. While 'Local Government Stronger' and 'Community Insights Better' have seen some declines, as shown by their shorter bars in Row2 {the fifth and sixth bars of Row2}[0.497, 0.611, 0.593, 0.625, 0.469, 0.542, 0.593, 0.556] as compared to the corresponding bars in Row1 {the fifth and sixth bars of Row1}[0.898, 0.134, 0.974, 0.162, 0.764, 0.065, 0.974, 0.093], these declines seem to be not as drastic as the improvements.\nBased on the above thought, the answer is:\nYes. Although there are some declines in the metrics 'Local Government Stronger' and 'Community Insights Better', the overall initiatives in Row 2 seem to show more noticeable improvements, especially in the 'Decentralization Measures', 'Population Engagement', and 'Public Service Provision' categories, which could be significant for community development." } ], "id_raw": 1064 }, { "id": "08c53UA90vxlx25q", "Chart_type": "bar", "QA_type": "Numerical QA", "image": "png_bar/bar_71_14.png", "conversations": [ { "from": "human", "value": "\nWhich metric has seen the most improvement from Row 1 to Row 2?" }, { "from": "gpt", "value": "To figure out the metric that has seen the most improvement from Row1 to Row2, I need to compare the scores of each metric for both rows. {The orange bars}<{\"Row1 bars\"}[{'bar1'}[0.974, 0.410, 1.241, 0.438];{'bar2'}[0.974, 0.341, 1.413, 0.369];{'bar3'}[0.974, 0.272, 1.069, 0.300];{'bar4'}[0.974, 0.203, 1.203, 0.231];{'bar5'}[0.898, 0.134, 0.974, 0.162];{'bar6'}[0.764, 0.065, 0.974, 0.093]]> represent the scores of metrics for Row1 while {the green bars}<{\"Row2 bars\"}[{'bar1'}[0.593, 0.887, 0.812, 0.901];{'bar2'}[0.593, 0.818, 0.879, 0.832];{'bar3'}[0.593, 0.749, 0.745, 0.763];{'bar4'}[0.593, 0.680, 0.679, 0.694];{'bar5'}[0.497, 0.611, 0.593, 0.625];{'bar6'}[0.469, 0.542, 0.593, 0.556]]> represent the scores of metrics for Row2. By subtracting the right end of the orange bar from the right end of the green bar, I get improvement (or decline) in score from Row1 to Row2 for each metric. After comparing all metrics, I find that \"Decentralization Measures\" has seen the most improvement as this metric's bar in Row2 {the third green bar}[0.593, 0.749, 0.745, 0.763] extends far beyond that in Row1 {the third orange bar}[0.974, 0.272, 1.069, 0.300].\nBased on the above thought, the answer is:\nDecentralization Measures." } ], "id_raw": 1064 } ]