{"url":"/dataset/ide","name":"IDE","full_name":"Identifying spatio-temporal drivers of extreme events","description_markdown":"This data set allows to systematically evaluate approaches for the task of identifying anomalies and extreme events in water cycle components by developing deep neural networks that detect anomalies and drivers of extremes in simulated data.","description_withheld":null,"homepage":"https://hakamshams.github.io/IDE/","introduced_date":"2024-10-31","introduced_date_note":null,"introduced_by":{"paper":"/paper/identifying-spatio-temporal-drivers-of","title":"Identifying Spatio-Temporal Drivers of Extreme Events","first_author":"Mohamad Hakam Shams Eddin","url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Anomaly Localization","url":"/task/anomaly-localization","datasets_with_task":"/datasets/task/anomaly-localization"}],"languages":[],"variants":["IDE"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}