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BioNLP Corpus - NIH
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<h3>AI Datasets</h3>
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<div class="issue labs-docsums labs-content-box ">
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<div class='toolname'>
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<a href='https://ftp.ncbi.nlm.nih.gov/pub/lu/ComputedAuthors/'>PubMed Computed Authors (2024)</a>
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<div class='usa-width-three-fourths tooldesc' id='tool_7'>
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<b>Description</b>
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<br>The PubMed Computed Authors dataset consists of disambiguated author names from PubMed, freely available via API queries and FTP downloads. Using advanced AI algorithms, the PubMed Computed Authors disambiguated more than 21 million individual authors across nearly 36 million PubMed articles with high accuracy. With regular weekly update, the PubMed Computed Authors continuously provide the most recent disambiguated authors for all PubMed articles.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMChem/'>NLM-Chem corpus</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_6'>
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<b>Description</b>
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<br>The NLM-Chem corpus is a manually annotated full-text resource on chemicals in the biomedical literature. The corpus contains 150 full-text journal articles selected both to be rich in chemical mentions and for articles where human annotation was expected to be most valuable. The corpus was doubly annotated by ten expert NLM indexers, with high inter-annotator agreement, and contains ~5000 unique chemical name annotations mapped to ~2000 MeSH identifiers.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://ftp.ncbi.nlm.nih.gov/pub/lu/PubMedPhrase/PubMed_Phrases.tar.gz'>PubMed Phrases</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_5'>
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<b>Description</b>
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<br>The dataset contains a collection of 705,915 PubMed Phrases (Kim et al., 2018) that are beneficial for information retrieval and human comprehension.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='ftp://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/Peng2016CID/CID.PubTator.txt.zip'>CTD-Pfizer dataset</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_4'>
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<b>Description</b>
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<br>The weakly-labeled corpus used in (<a href="/pubmed/28316651" target="_blank">Peng et al., 2016</a>) consists of 18,410 abstracts and 33,224 CID relations. The raw data was extracted from curated data in the <a href="/pubmed/24288140" target="_blank">CTD-Pfizer collaboration</a> with document-level annotations of drug-disease and drug-phenotype interactions. We applied <a href="/bionlp/Tools/tmchem" target="_blank">tmChem</a> and <a href="/bionlp/Tools/dnorm" target="_blank">DNorm</a> to recognize and normalize chemical and disease mentions, respectively. To maximize recall, we also applied a dictionary look-up method with a controlled vocabulary (MeSH). Finally, we filtered those without CID relations in the title/abstracts as some asserted relations are only in the full text.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/tmVar/tmVarCorpus.zip'>tmVar corpus</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_3'>
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<b>Description</b>
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<br>tmVar Corpus contains 500 PubMed articles manually annotated with mutation mentions of various kinds.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://ftp.ncbi.nlm.nih.gov/pub/lu/BC5CDR/'>BioCreative V CDR corpus</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_2'>
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<b>Description</b>
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<br>BC5CDR corpus consists of 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='/bionlp/Data/disease'>NCBI disease corpus</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_1'>
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<b>Description</b>
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<br>NCBI disease corpus is a collection of 793 PubMed abstracts fully annotated at both mention and concept levels.
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</div>
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