{"url":"/dataset/bc8","name":"Bc8","full_name":"Bc8BioRED","description_markdown":"Bc8BioRED  is built upon BioRED 2022 with the addition of directionality annotations. The training and development sets from the original 2022 BioRED corpus were combined and reused as the training set, while the test set was used as the development set. Furthermore, the 400 test abstracts from the BioCreative VIII were utilized for evaluation. Bc8BioRED encompasses seven types of entities and eight types of relationships. Each relationship annotation in the Bc8BioRED corpus is categorized by novelty to indicate whether the relationship represents a significant finding or previously known background knowledge. Initially, the BioRED 2022 corpus comprised 600 abstracts for RE system development, with an additional 400 abstracts annotated to enhance coverage of emerging topics.  The dataset encompasses directionality annotations (subject/object roles) for each relation pair, resulting in 10,864 directionality annotations.","description_withheld":null,"homepage":"https://github.com/ncbi-nlp/BioREDirect","introduced_date":"2025-01-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/enhancing-biomedical-relation-extraction-with-1","title":"Enhancing Biomedical Relation Extraction with Directionality","first_author":"Po-Ting Lai","url":null},"license":null,"modalities":[],"tasks":[{"name":"Document-level Relation Extraction","url":"/task/document-level-relation-extraction","datasets_with_task":"/datasets/task/document-level-relation-extraction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Bc8"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/document-level-relation-extraction-on-bc8","task":"Document-level Relation Extraction","dataset_variant":"Bc8","rows":1,"metrics":["Evaluation Macro F1"],"first_row_in_archive_order":{"model":"BioRex+Directionality","paper":"/paper/enhancing-biomedical-relation-extraction-with-1","metrics":{"Evaluation Macro F1":"56.06"},"code_links":[{"title":"ncbi-nlp/bioredirect","url":"https://github.com/ncbi-nlp/bioredirect"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/enhancing-biomedical-relation-extraction-with-1","title":"Enhancing Biomedical Relation Extraction with Directionality","date":"2025-01-23","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"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."}