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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: cc-by-4.0 |
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bigbio_license_shortname: APACHE_2p0 |
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multilinguality: monolingual |
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pretty_name: FlaMBe |
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homepage: https://github.com/ylaboratory/flambe |
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bigbio_pubmed: false |
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bigbio_public: true |
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bigbio_tasks: |
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- NAMED_ENTITY_RECOGNITION |
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- NAMED_ENTITY_DISAMBIGUATION |
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--- |
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# Dataset Card for Flambe |
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## Dataset Description |
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- **Homepage:** https://github.com/ylaboratory/flambe |
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- **Pubmed:** False |
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- **Public:** True |
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- **Tasks:** NER,NED |
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FlaMBe is a dataset aimed at procedural knowledge extraction from biomedical texts, particularly focusing on single cell research methodologies described in academic papers. It includes annotations from 55 full-text articles and 1,195 abstracts, covering nearly 710,000 tokens, and is distinguished by its comprehensive named entity recognition (NER) and disambiguation (NED) for tissue/cell types, software tools, and computational methods. This dataset, to our knowledge, is the largest of its kind for tissue/cell types, links entities to identifiers in relevant knowledge bases and annotates nearly 400 workflow relations between tool-context pairs. |
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## Citation Information |
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``` |
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@inproceedings{, |
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author = {Dannenfelser, Ruth and Zhong, Jeffrey and Zhang, Ran and Yao, Vicky}, |
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title = {Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts}, |
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publisher = {Advances in Neural Information Processing Systems}, |
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volume = {36}, |
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year = {2024}, |
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url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/23e3d86c9a19d0caf2ec997e73dfcfbd-Paper-Datasets_and_Benchmarks.pdf}, |
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} |
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``` |
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