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17 results for “Biomedical Literature”
RRID Gold Set of annotations for software tools in the biomedical literature
<p>This data is a subset of a larger human curated, machine assisted gold standard data set of RRID citations within the text of the scientific literature. The full set is accessible via Hypothes.is at https://hypothes.is/users/scibot?q=group%3A__world__ and via individual RRID records such as RRID:SCR_016250 https://scicrunch.org/resolver/SCR_016250/mentions?q=&i=rrid:scr_016250 </p><p>The data is based on authors who added RRIDs into their manuscripts. The data was then extracted by SciBot (RRID:SCR_016250), into Hypothes.is (RRID:SCR_000430) and then manually checked by a curator to determine if the author and the database agreed. The list of annotators is available in Hypothes.is user group: SciBotCurationGroup.</p><p>There are 78,140 rows and each row contains an annotation (annotation id, URI), linked to a paper (paper identifiers: PMID, DOI, PMC) and linked to the RRID (scr_id, exact, text_quote_selector). </p><p>A second spreadsheet contains a list of 8,322 software tools from the SciCrunch Registry (available here https://scicrunch.org/resources/data/source/nlx_144509-1/search ), enhanced by additions by thousands of individual authors, and curated over 10 years (Ozyurt et al., 2016). </p><p>The third spreadsheet contains a data dictionary and links to related ontologies, and tagging sets. </p><p> </p><p> </p>
Preprints in biology as a fraction of the biomedical literature
<p>These data and chart present an approximate calculation of the proportion of preprints in biology when compared to publications in PubMed, based on monthly figures and incorporating monthly preprint submissions (or counts) across a selection of servers relevant to biology. </p> <p>Version 1.0 of these data represents data from January 2007 until May 31, 2019 for preprint servers: arXiv q-bio, Nature Precedings, F1000Research*, PeerJ Preprints*, bioRxiv**, Winnower*, <a href="http://preprints.org">preprints.org</a>, Wellcome Open Research*. </p> <p>* Counts may not be specific to biology preprints only; ** Counts may include all versions posted that month, so may be an overestimate for version 1 submissions.</p> <p>From January 2019, data has been gathered manually by the authors, as per the methods described in the .csv here, and is included here in 'Preprints_per_month_direct_2019-01to05.csv'. Until December 2018, monthly preprint submissions data are based on those contributed by Jordan Anaya (ORCID: <a href="https://orcid.org/0000-0002-6166-4113">https://orcid.org/0000-0002-6166-4113</a>) for PrePubMed, source: <a href="https://raw.githubusercontent.com/OmnesRes/prepub/master/analyses/preprint_data.txt">https://raw.githubusercontent.com/OmnesRes/prepub/master/analyses/preprint_data.txt</a>; Github repository: <a href="https://github.com/OmnesRes/prepub">https://github.com/OmnesRes/prepub</a>; website: <a href="http://www.prepubmed.org/">http://www.prepubmed.org</a>). Data are not included here, they are provided from the source linked above under MIT license associated with the website code: <a href="https://github.com/OmnesRes/prepub/blob/master/LICENSE">https://github.com/OmnesRes/prepub/blob/master/LICENSE</a>.</p> <p>A live version of these data and the chart are available from this GSheet: <a href="https://docs.google.com/spreadsheets/d/1bkGEcfQcL0LpIanVqNHci1ZFY6oVNGz7IQbEugzkqkU/edit?usp=sharing">https://docs.google.com/spreadsheets/d/1bkGEcfQcL0LpIanVqNHci1ZFY6oVNGz7IQbEugzkqkU/edit?usp=sharing</a>. Between version updates here, please refer to this sheet for updated counts and method updates e.g. to include more servers and ensure only version 1 submissions are counted.</p> <p>For more information, please contact naomi.penfold@asapbio.org.</p> <p>When presenting these data and/or chart, please attribute to ASAPbio (https://asapbio.org, twitter: @ASAPbio_).</p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences in Biomedical Literature and Its Integration with PubChemRDF
<p>This Zenodo record contains the co-occurrence RDF data generated in the work described in the paper “<strong>A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF</strong>” by Li et al., published in the Journal of Cheminformatics (<a href="https://doi.org/10.1186/s13321-025-01017-0" target="_blank" rel="noopener">https://doi.org/10.1186/s13321-025-01017-0</a>). It also contains the SPARQL query examples, the RDF schema in SHACL and ShEx, and the validation scripts.</p> <p>All content in this Zenodo record is for archival purposes. The latest version of the co-occurrence RDF data and other PubChemRDF data can be accessed via the PubChem FTP site (<a href="https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/" target="_blank" rel="noopener">https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/</a>). The up-to-date RDF schema in various formats is available on the PubChemRDF Schema page (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema</a>). A set of SPARQL query examples can be found on the PubChemRDF use case pages (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases</a>).</p>
CZ Software Mentions: A large dataset of software mentions in the biomedical literature
<p>We describe the CZ Software Mentions dataset, a new dataset of software mentions in biomedical papers. Plain-text software mentions are extracted with a trained SciBERT model from several sources: the NIH PubMed Central collection and from papers provided by various publishers to the Chan Zuckerberg Initiative. The dataset provides sources, context and metadata, and, for a number of mentions, the disambiguated software entities and links. We extract 1.12 million unique string software mentions from 2.4 million papers in the NIH PMC-OA Commercial subset, 481k unique mentions from the NIH PMC-OA Non-Commercial subset (both gathered in October 2021) and 934k unique mentions from 4 million papers in the Publishers' collection. There is variation in how software is mentioned in papers and extracted by the NER algorithm. We propose a clustering-based disambiguation algorithm to map plain-text software mentions into distinct software entities and apply it on the NIH PubMed Central Commercial collection. Through this methodology, we disambiguate 1.12 million unique strings extracted by the NER model into ~97000 unique software entities, covering 78% of all links. We link 185 000 of the mentions to a repository, covering about 55% of all software-paper links. We make all data and code publicly available as a new resource to help assess the impact of software (in particular scientific open source projects) on science.</p>
Multicontextual Phenotype Models: Biomedical Database and Literature Phenotype and Social Media Phenotype
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CZ Software Mentions: A large dataset of software mentions in the biomedical literature
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CZ Software Mentions: A large dataset of software mentions in the biomedical literature - Expanded 2024
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Enhanced Kinase Dictionaries associated with KinDER: A Biocuration Tool for Extracting Kinase Knowledge from Biomedical Literature
<p>This zip file contains Kinase dictionaries used for annotating documents with KinDER described in the following paper: </p> <p>Dopp, Daniel, Adam Morrone, and Indika Kahanda. "KinDER: A Biocuration Tool for Extracting Kinase Knowledge from Biomedical Literature." <em>Proceedings of the BioCreative VI Workshop</em>. 2017.</p>
Two document-concept representations of the biomedical literature
<p>These two datasets represent the biomedical literature (Medline abstracts and PubMedCentral articles) in the "document-concept matrix" format produced by <a href="https://github.com/erwanm/tdc-tools">TDC Tools</a>. These datasets can be used in downstream IR applications such as Literature-Based Discovery.</p> <p>Each of the two datasets corresponds to a specific data extraction method, see details <a href="https://erwanm.github.io/tdc-tools/input-data-format/">here</a> and in the paper linked below.</p> <ul> <li>Paper: <em>pending </em></li> <li>Code: <a href="https://github.com/erwanm/tdc-tools">https://github.com/erwanm/tdc-tools</a> <ul> <li>Documentation:<a href="https://erwanm.github.io/tdc-tools/">https://erwanm.github.io/tdc-tools/</a></li> </ul> </li> </ul> <p><strong>Important:</strong> the raw data from which this data is derived was downloaded from <a href="https://www.nlm.nih.gov/medline/medline_overview.html">Medline</a>, <a href="https://www.ncbi.nlm.nih.gov/pmc/">PubMedCentral</a> and <a href="https://www.ncbi.nlm.nih.gov/research/pubtator/">PubTatorCentral</a>, provided <a href="https://www.nlm.nih.gov/databases/download/terms_and_conditions.html">courtesy of the U.S. National Library of Medicine (NLM)</a>. The data was extracted in January 2021 and do not reflect the most current/accurate data available from NLM. See the github repository above in order to generate similar datasets from up to date data.</p>
Table Recognition Benchmark on Biomedical Literature on Neurological Disorders
<p>The dataset contains 1650 tables from 1164 PMC OA articles in the context of neurological disorders. The tables are structured in the International Conference on Document Analysis and Recognition (ICDAR) format.</p> <p>The additional csv file contains a labeling into 3 different complexity classes in the format:</p> <p>class document_id table_id</p> <p>with the classes being:</p> <p>0 = simple<br> 1 = complicated<br> 2 = complex</p> <p>You may use the scripts from <a href="https://github.com/TimAdams84/pmc-downloader">this repository</a> to bulk download PDF sources from PMC.</p> <p>A script for evaluating results against the groundtruth data can be found <a href="https://github.com/mnamysl/benchmarking_table_recogn">here</a>.</p>
Biomedical preprints per month, by source and as a fraction of total literature
<p>This is a snapshot of a <a href="https://docs.google.com/spreadsheets/d/1c1WzYIoLhzY09AkwyhSVDtan3oYV8qrHa3WgPHAeVVM/edit?usp=sharing">Google sheet</a> containing counts of biomedical preprints by source and as a fraction of the total biomedical literature <strong>through 2020-06.</strong></p> <p>Note: this does not yet include >6,000 preprints on relevant OSF platforms.</p> <p><strong>Data sources</strong></p> <p>arXiv q-bio, PeerJ Preprints, and bioRxiv counts through 2018-12 were sourced from Jordan Anaya's <a href="https://github.com/OmnesRes/prepub/tree/master/analyses">PrePubMed</a>. Following that, arXiv q-bio counts were sourced from <a href="https://arxiv.org/year/q-bio/19">arXiv statistics</a>, and PeerJ Preprints and bioRxiv counts were taken from searches of <a href="https://europepmc.org/">EuropePMC</a>.</p> <p>All counts from F1000 & Open Research platforms, preprints.org, ChemRxiv, and medRxiv were taken from searches of <a href="https://europepmc.org/">EuropePMC</a>.</p> <p>Counts for Research Square are derived from Crossref through 2020-03, thereafter from EuropePMC. Counts for the Lancet and Sneak Peek were taken from web searches.</p> <p>Total biomedical literature is from PubMed.</p>
Large-scale semantic indexing of Spanish biomedical literature using contrastive transfer learning
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Data for "RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature"
<div> <p><strong>RegulaTome corpus</strong>: this <a href="../api/records/10808330/files/RegulaTome-corpus.tar.gz/content" target="_blank" rel="noopener">file</a> contains the RegulaTome corpus in <a href="https://brat.nlplab.org/">BRAT</a> format. The directory <strong>"splits" </strong>has the corpus split based on the train/dev/test used for the training of the relation extraction system</p> <p><strong>RegulaTome annodoc</strong>: The annotation guidelines along with the annotation configuration files for BRAT are provided in <a href="../api/records/10808330/files/annodoc+config.tar.gz/content" target="_blank" rel="noopener">annodoc+config.tar.gz</a>. The online version of the annotation documentation can be found here: <a href="https://katnastou.github.io/s1000-corpus-annotation-guidelines/">https://katnastou.github.io/regulatome-annodoc/ </a></p> <p>The tagger software can be found here: <a href="https://github.com/larsjuhljensen/tagger">https://github.com/larsjuhljensen/tagger</a>. The command used to run tagger before large-scale execution of the RE system is:</p> <p><code>gzip -cd `ls -1 pmc/*.en.merged.filtered.tsv.gz` `ls -1r pubmed/*.tsv.gz` | cat dictionary/excluded_documents.txt - | tagger/tagcorpus --threads=16 --autodetect --types=dictionary/curated_types.tsv --entities=dictionary/all_entities.tsv --names=dictionary/all_names_textmining.tsv --groups=dictionary/all_groups.tsv --stopwords=dictionary/all_global.tsv --local-stopwords=dictionary/all_local.tsv --type-pairs=dictionary/all_type_pairs.tsv --out-matches=all_matches.tsv</code></p> <p><strong>Input documents </strong>for large-scale execution, which is done on entire <a href="https://a3s.fi/March-2024-PubMed/PubMed_20230314.tar.gz" target="_blank" rel="noopener">PubMed</a> (as of March 2024) and <a href="https://a3s.fi/Jan-2024-documents/PMC_Nov_23.tar.gz" target="_blank" rel="noopener">PMC Open Access</a> (as of November 2023) articles in BioC format. The files are converted to a <a href="https://a3s.fi/March-2024-PubMed/all_documents.tsv" target="_blank" rel="noopener">tab-delimited format </a>to be compatible with the RE system input (see below).</p> <p><strong>Input dictionary files</strong>: all the files necessary to execute the command above are available in <a href="../api/records/10808330/files/tagger_dictionary_files.tar.gz/content" target="_blank" rel="noopener">tagger_dictionary_files.tar.gz </a></p> <p><strong>Tagger output</strong>: we filter the results of the tagger run down to gene/protein hits, and documents with more than 1 hit (since we are doing relation extraction) before feeding it to our RE system. The filtered output is available in <a href="../api/records/10808330/files/tagger_matches_ggp_only_gt_1_hit.tsv.gz/content" target="_blank" rel="noopener">tagger_matches_ggp_only_gt_1_hit.tsv.gz</a></p> <p><strong>Relation extraction system input</strong>: <a href="../api/records/10808330/files/combined_input_for_re.tar.gz/content" target="_blank" rel="noopener">combined_input_for_re.tar.gz</a>: these are the directories with all the .ann and .txt files used as input for the large-scale execution of the relation extraction pipeline. The files are generated from the tagger tsv output (see above, <a href="../api/records/10808330/files/tagger_matches_ggp_only_gt_1_hit.tsv.gz/content" target="_blank" rel="noopener">tagger_matches_ggp_only_gt_1_hit.tsv.gz</a>) using the <a href="https://github.com/spyysalo/string-db-tools/blob/main/tagger2standoff.py">tagger2standoff.py</a> script from the <a href="https://github.com/spyysalo/string-db-tools/">string-db-tools</a> repository.</p> <p><strong>Relation extraction models</strong>. The Transformer-based model used for large-scale relation extraction and prediction on the test set is at <a href="../api/records/10808330/files/relation_extraction_multi-label-best_model.tar.gz/content" target="_blank" rel="noopener">relation_extraction_multi-label-best_model.tar.gz</a></p> <p>The pre-trained RoBERTa model on PubMed and PMC and MIMIC-III with a BPE Vocab learned from PubMed (RoBERTa-large-PM-M3-Voc), which is used by our system is available <a href="https://github.com/facebookresearch/bio-lm/blob/main/README.md">here</a>.</p> <p><strong>Relation extraction system output</strong>: the tab-delimited outputs of the relation extraction system are found at <a href="https://a3s.fi/regulatome-ls/large_scale_relation_extraction_results.tar.gz" target="_blank" rel="noopener">large_scale_relation_extraction_results.tar.gz </a><strong>!!!ATTENTION this file is approximately 1TB in size, so make sure you have enough space to download it on your machine!!!</strong></p> <p>The relation extraction system output files have 86 columns: PMID, Entity BRAT ID1, Entity BRAT ID2, and scores per class produced by the relation extraction model. Each file has a header to denote which score is in which column.</p> </div>
Reproducible results for: pmparser and PMDB: resources for large-scale, open studies of the biomedical literature
<p>The zip file contains all code and results. See the included readme.</p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences Derived from Biomedical Literature in the PubChemRDF
<p>This is the dataset used for the publication of "Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences Derived from Biomedical Literature in the PubChemRDF". </p>
Inference and validation of predictive gene networks from biomedical literature and gene expression data
GEO Series GSE53091. Homo sapiens. 125 samples. Type: Expression profiling by array.
Dataset: Relationship extraction for knowledge graph creation from biomedical literature (Gene-Disease relationships)
<p>This is the dataset used for classifying Gene-Disease relationship types from sentences. The dataset consists of 3 files:</p> <ul> <li>manually_annotated_set.xlsx - set of 2000 manualy annotated sentences with entities</li> <li>Unbalanced_dataset.xlsx - set of 12000 sentences, out of which 2000 are from the first set, manually annotated, and the rest have been added using rule based method by adding sentences where extraction had confidence 1.</li> <li>Balanced_dataset_SUB_PRED.xlsx - balanced dataset generated by taking 2000 manually annotated sentences, but then adding sentences from the rule-based method with confidence 1 in such a way that each relationship class had at least 1400 sentences (for biomarkers, we could obtain 1243 sentences with confidence 1 from a processed portion of the data we had at the time of building the dataset).</li> </ul> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.