{"url":"/dataset/opend5","name":"OpenD5","full_name":null,"description_markdown":"**OpenD5** is a a meta-dataset which aggregates 675 open-ended problems ranging across business, social sciences, humanities, machine learning, and health, and uses a set of unified evaluation metrics: validity, relevance, novelty, and significance. It is designed for the new task, D5, that automatically discovers differences between two large corpora in a goal-driven way.\r\n\r\nSource: Goal Driven Discovery of Distributional Differences via Language Descriptions\r\n\r\nImage Source: [https://github.com/ruiqi-zhong/d5](https://github.com/ruiqi-zhong/d5)","description_withheld":null,"homepage":"https://github.com/ruiqi-zhong/D5","introduced_date":"2023-02-28","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["OpenD5"],"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":"2026-09-25T09:33:49+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."}