Machine learning-based evidence and attribution mapping of 100,000 climate impact studies - Data

Data for the paper Machine learning-based evidence and attribution mapping of 100,000 climate impact studies <strong>Document Metadata</strong> 0c_doc_info.csv contains basic document metadata for each document considered in our study <strong>Predictions</strong> In each predictions file, 1 refers to a document hand-labelled as belonging to a category, and 0 refers to a document hand-labelled as not belonging to a category. All values in between are predicted values, where for values greater than 0.5, a document is considered likely to belong to the given category. 1_document_relevance.csv contains the predicted relevance of a document to the study. 1_driver_predictions.csv contains the predicted climate driver of each document. 1_impact_predictions.csv contains the predicted impact type of each document <strong>Geographical data</strong> Place_df.csv contains a row for each geographical entity automatically extracted from each study Study_gridcell_2.5.csv contains a row matching each study with each grid cell covered by the study’s smallest mentioned geographical entity <strong>Merged data</strong> 2_study_da.csv contains a row for each study describing the aggregated detection and attribution characteristics of the grid cells the study refers to 2_merged_da_data.csv contains a row for each grid cell describing the attribution categories and the number of weighted grid cells for each climate driver.