claim: Though CRU neglected to provide an exact list of temperature stations, it could not have hid or tampered with data.
evidences.evidence_id: Climatic Research Unit email controversy:162
evidences.article: Climatic Research Unit email controversy
evidences.evidence: The MPs had seen no evidence to support claims that Jones had tampered with data or interfered with the peer-review process.
evidences.votes: NOT_ENOUGH_INFO NOT_ENOUGH_INFO NOT_ENOUGH_INFO
evidences.evidence_id: Climatic Research Unit email controversy:255
evidences.article: Climatic Research Unit email controversy
evidences.evidence: The Climatic Research Unit developed its gridded CRUTEM data set of land air temperature anomalies from instrumental temperature records held by National Meteorological Organisations around the world, often under formal or informal confidentiality agreements that restricted use of this raw data to academic purposes, and prevented it from being passed onto third parties.
evidences.votes: SUPPORTS REFUTES NOT_ENOUGH_INFO
evidences.evidence_id: Climatic Research Unit email controversy:263
evidences.article: Climatic Research Unit email controversy
evidences.evidence: On 27 July 2011 CRU announced that the raw instrumental data not already in the public domain had been released and was available for download, with the exception of Poland which was outside the area covered by the FOIA request.
evidences.votes: REFUTES SUPPORTS NOT_ENOUGH_INFO
evidences.evidence_id: Data mining:13
evidences.article: Data mining
evidences.evidence: The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data; in contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.
evidences.votes: NOT_ENOUGH_INFO NOT_ENOUGH_INFO NOT_ENOUGH_INFO
evidences.evidence_id: Data visualization:141
evidences.article: Data visualization
evidences.evidence: DPA has two main objectives: To use data to provide knowledge in the most efficient manner possible (minimize noise, complexity, and unnecessary data or detail given each audience's needs and roles) To use data to provide knowledge in the most effective manner possible (provide relevant, timely and complete data to each audience member in a clear and understandable manner that conveys important meaning, is actionable and can affect understanding, behavior and decisions) With the above objectives in mind, the actual work of data presentation architecture consists of: Creating effective delivery mechanisms for each audience member depending on their role, tasks, locations and access to technology Defining important meaning (relevant knowledge) that is needed by each audience member in each context Determining the required periodicity of data updates (the currency of the data) Determining the right timing for data presentation (when and how often the user needs to see the data) Finding the right data (subject area, historical reach, breadth, level of detail, etc.)
evidences.votes: NOT_ENOUGH_INFO NOT_ENOUGH_INFO NOT_ENOUGH_INFO