{
 "id": "bigbio/neurotrial_ner",
 "owner": "bigbio",
 "name": "neurotrial_ner",
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 "last_modified": "2024-12-06T09:46:22.000Z",
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 "citation": "@inproceedings{doneva-etal-2024-neurotrialner,\n    title = \"{N}euro{T}rial{NER}: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries\",\n    author = \"Doneva, Simona Emilova  and\n      Ellendorff, Tilia  and\n      Sick, Beate  and\n      Goldman, Jean-Philippe  and\n      Cannon, Amelia Elaine  and\n      Schneider, Gerold  and\n      Ineichen, Benjamin Victor\",\n    editor = \"Al-Onaizan, Yaser  and\n      Bansal, Mohit  and\n      Chen, Yun-Nung\",\n    booktitle = \"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2024\",\n    address = \"Miami, Florida, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.emnlp-main.1050\",\n    pages = \"18868--18890\",\n    abstract = \"Extracting and aggregating information from clinical trial registries could provide invaluable insights into the drug development landscape and advance the treatment of neurologic diseases. However, achieving this at scale is hampered by the volume of available data and the lack of an annotated corpus to assist in the development of automation tools. Thus, we introduce NeuroTrialNER, a new and fully open corpus for named entity recognition (NER). It comprises 1093 clinical trial summaries sourced from ClinicalTrials.gov, annotated for neurological diseases, therapeutic interventions, and control treatments. We describe our data collection process and the corpus in det",
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 "reason": "The declared licence (cc0-1.0) is classified as permissive.",
 "revision": "ac195f0072a2bb64706dfab2998edb1ffaa43467",
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