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17 results for “silver standard”
PharmaCoNER Silver Standard: Participant predictions in PharmaCoNER shared task
<p><strong>Introduction</strong></p> <p>Predictions in the test and background set of PharmaCoNER participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per PharmaCoNER participant. Within each participant directory, there is one directory per run.</p> <p> </p> <p><strong>Corpus format description</strong></p> <p>For subtask 1 annotations are distributed in Brat format. See Brat webpage for more information <a href="https://brat.nlplab.org/standoff.html">https://brat.nlplab.org/standoff.html</a></p> <p>For subtask-2, codes are associated with each document are given in a TSV file with the following columns: </p> <pre>articleID code </pre> <p><br> <strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/pharmaconer/">Web</a></strong></li> <li><strong>Citation: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li> <li><a href="https://doi.org/10.5281/zenodo.4270157"><strong>Gold Standard corpus</strong></a></li> <li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li> <li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li> </ul> <p> </p> <p>All credit to PharmaCoNER participants. </p> <p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p> <p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p>
silverCID — a silver standard corpus for Chemical-induced Diseases relation extraction — Edit
<p>silverCID — a silver standard corpus for Chemical-induced Diseases relation extraction — Edit</p>
Files and code for English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19
<p><span lang="EN-GB">Automated information extraction with natural language processing (NLP) tools is required to gain systematic insights from the large number of COVID-19 publications, reports and social media posts, which far exceed human processing capabilities. </span></p> <p><span lang="EN-GB">Here we present an NLP toolbox comprising COVID-19-related dictionaries and annotated corpora in English as well as useful code and workflows for their update and use. The dictionaries contain terms referring to the COVID-19 disease, the SARS-CoV-2 virus, its variants and common mutations, respectively. They were used together with the EasyNER NLP tool to extract and annotate all 764 398 abstracts in the CORD-19 dataset, creating a very large silver standard corpus (named Lund-Annotated-CORD-19 corpus). This was complemented with a small gold standard corpus consisting of PubMed abstracts manually annotated for key entity classes such as disease, virus, symptom, protein/gene, cell type, chemical and species terms. </span></p> <p><span lang="EN-GB">The toolbox can support various text analysis tasks related to COVID-19 such as named entity recognition and co-mention analysis. A preliminary version of the toolbox, which was released early in the pandemic, was</span><span lang="EN-GB"> for example already used to create a COVID-19 knowledge graph and study the evolution and variation of COVID-19-related terminology. In addition, the toolbox can be applied in the development of other NLP tools, for example to train and evaluate large language models.</span></p> <p><span lang="EN-GB">When using the toolbox, please cite this record and the associated article.</span></p> <p> </p> <p> </p>
EvaNIL: silver standard dataset for large-scale NIL entity linking evaluation
<p>The EvaNIL dataset can be used to train or evaluate approaches developed for NIL entity linking. It was built from several Biomedical and Life Sciences corpora:</p> <ul> <li>PubMed DS</li> <li>CRAFT corpus</li> <li>MedMentions</li> </ul> <p>These corpora contain entities associated with knowledge base concepts. To build the EvaNIL dataset, we assumed that those knowledge base concepts did not exist in the respective knowledge bases, so each entity is associated instead with the direct ancestors of those original concepts.</p> <p>The EvaNIL dataset is divided into 6 partitions including annotations from several knowledge bases:</p> <ul> <li>"medic" (CTD-MEDIC)</li> <li>"ctd_anatomy" (CTD-Anatomy)</li> <li>"ctd_chemicals" (CTD-Chemicals)</li> <li>"chebi" (ChEBI)</li> <li>"go_bp" (GO-Biological Process)</li> <li>"hp" (HPO)</li> </ul> <p> </p>
DrugProt Silver Standard Knowledge Graph
<p><strong>DrugProt Silver Standard Knowledge Graph</strong></p><p> </p><p> </p><p><strong>Please cite if you use any DrugProt resource:</strong></p><p>Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, <i>Database</i>, Volume 2023, 2023, baad080</p><blockquote><p><i>@article{miranda2023overview, title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations}, author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, journal={Database}, volume={2023}, pages={baad080}, year={2023}, publisher={Oxford University Press UK} }</i></p></blockquote><p>Miranda, Antonio, et al. "Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations." <i>Proceedings of the seventh BioCreative challenge evaluation workshop</i>. 2021.</p><blockquote><p><i>@inproceedings{miranda2021overview, title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations}, author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop}, year={2021} }</i></p></blockquote><p><strong>Description</strong></p><p> </p><p> </p><p><strong>Files:</strong></p><ul><li>drugprot-silver-standard-kg.zip : JSON files with the relations predicted by the DrugProt systems and their precision</li><li>large_scale_network_abstracts.tsv : PubMed abstracts</li><li>large_scale_network_entities.tsv : CHEMICAL/drug and GENE/protein entities predicted by DrugProt NER Taggers</li><li>large_scale_network_pmids.txt : list of PMIDs</li></ul><p> </p><p><strong>Related resources:</strong></p><ul><li><a href="https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/">Web</a></li><li><a href="https://doi.org/10.5281/zenodo.4955410">DrugProt corpus</a></li><li><a href="https://github.com/tonifuc3m/drugprot-evaluation-library">Evaluation library</a></li><li><a href="https://codalab.lisn.upsaclay.fr/competitions/8293">Online evaluation (CodaLab)</a></li><li><a href="https://doi.org/10.5281/zenodo.4957137">Relation annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.4957576">Gene and protein annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.4957518">Chemicals and drugs annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.5042178">FAQ</a></li><li><a href="https://doi.org/10.5281/zenodo.5119878">DrugProt Large Scale Additional SubTrack</a></li><li><a href="https://doi.org/10.5281/zenodo.5656991">DrugProt Large Scale document collection protocol</a></li></ul>
Silver Standard of quantified positive, negative and neutral German noun phrases
<p>Repository: silver standard quantified (simple) noun phrases</p> <p>43,842 positive (+), negative (-) or neutral (0) NPs, </p> <p>e.g. "ein notorischer Verführer -5.625" is highly negative (-5.625)</p> <p>see References LREC for a description of the data</p> <p>Format: tsv</p> <p>References:</p> <p>@inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br> </p>
MEDDOPROF Silver Standard: Predictions for Occupations, Working Statuses and Activities in Clinical Case Reports
<p><strong>MEDDOPROF Silver Standard</strong></p> <p>This repository contains the <strong>MEDDOPROF Silver Standard</strong>, a collection of clinical case reports of multiple specialties that have been automatically annotated with occupations, working statuses and activities using named entity recognition models trained on the MEDDOPROF corpus. MEDDOPROF and this Silver Standard have been created by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis.</p> <p><strong>About the MEDDOPROF Corpus</strong></p> <p><a href="https://temu.bsc.es/meddoprof/">MEDDOPROF</a> is a corpus and shared task focused on the detection and normalization of occupations and related information such as working statuses and non-paid activities in clinical texts in Spanish. All mentions in the corpus have been normalized to SNOMED CT, with occupations also being normalized to the European Skills, Competences, and Occupations classification ESCO. MEDDOPLACE was released as part of the IberLEF 2021 workshop, which in turn was held within the SEPLN 2021 conference.</p> <p><strong>About this Silver Standard</strong></p> <p>The Silver Standard includes 13,357 documents and a total of 5,568,844 tokens. The documents belong to over 10 different medical specialties, including occupational health, oncology, radiology, infectology, dermatology, psichiatry and more.</p> <p>The corpus includes three different labels: PROFESION (occupation), SITUACION-LABORAL (working status) and ACTIVIDAD (activity/hobby). There is a total of 4,332 predictions (1,325 unique), with the most common label being PROFESION with 3,599 predictions (1,181 unique), followed by SITUACION-LABORAL with 682 (180 unique) and ACTIVIDAD with 51 (44 unique).</p> <p>The annotations are presented in .ann format, which is the format used by the annotation tool brat.</p> <p>For more information, please check the attached README file.</p> <p><strong>Citation</strong></p> <p>If you use this Silver Standard or any MEDDOPROF materials, please cite the task's overview paper:<br> Lima-López, Salvador, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias, & Martin Krallinger. “NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts.” Procesamiento del Lenguaje Natural [Online], 67 (2021): 243-256.</p> <p><strong>License</strong><br> This work is licensed under a Creative Commons Attribution 4.0 International License.</p> <p><strong>Contact</strong><br> If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br> - Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Acknowledgements</strong><br> MEDDOPROF was promoted by the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4ProfHealth - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)).</p> <p><strong>Related Links</strong></p> <p>- MEDDOPROF website: <a href="https://temu.bsc.es/meddoprof/">https://temu.bsc.es/meddoprof/</a></p> <p>- MEDDOPROF Gold Standard: <a href="https://doi.org/10.5281/zenodo.5070540">https://doi.org/10.5281/zenodo.5070540</a></p> <p>- MEDDOPROF Annotation Guidelines: <a href="https://doi.org/10.5281/zenodo.4694675">https://doi.org/10.5281/zenodo.4694675</a></p>
CodiEsp Silver Standard: Participant predictions in eHealth CLEF2020 - Spanish clinical cases coded in ICD10 (CIE10)
<p><strong>Introduction</strong></p> <p>Predictions in the background set of <a href="https://temu.bsc.es/codiesp/">eHealth CLEF 2020 Task 1</a> participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per CodiEsp subtask. Within each CodiEsp subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong><br> The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks CodiEsp-Diagnostic and CodiEsp-Procedure, the file files have the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X (explainability) contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://zenodo.org/record/3837305#.X7T9KVlKg5k">Gold Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to CodiEsp participants</p>
MESINESP: Post-workshop datasets. Silver Standard and annotator records
<p><em><strong>Please use the <a href="https://doi.org/10.5281/zenodo.4612274">MESINESP2 corpus (the second edition of the shared-task)</a> since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials).</strong></em></p> <p>The MESINESP (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) Challenge was held in May-June 2020, and as a result of a strong participation and the manual annotation of an evaluation dataset, two additional datasets are released now:</p> <p>1) "all_annotations_withIDsv3.tsv" contains a tab-separated file with all manual annotations (both validated and non-validated) of the evaluation dataset prepared for the competition. It contains the following fields:</p> <ul> <li>annotatorName: Human annotator id</li> <li>documentId: Document ID in the source database</li> <li> decsCode: A DeCS code added to it or validated</li> <li>timestamp: When it was added</li> <li>validated: if it was validated at that point by another annotator, or not yet</li> <li>SpanishTerm: The Spanish descriptor corresponding to the DeCS code</li> <li>mesinespId: The internal document id in the distributed evaluation file</li> <li>dataset: if part of the evaluation or the test sets</li> <li>source: which database it was taken from</li> </ul> <p><em><strong>Example:</strong></em></p> <p><strong>annotatorName documentId decsCode timestamp validated SpanishTerm mesinespId dataset source</strong><br> A7 biblio-1001069 6893 2020-01-17T11:27:07.000Z false caballos mesinesp-dev-671 dev LILACS<br> A7 biblio-1001069 4345 2020-01-17T11:27:12.000Z false perros mesinesp-dev-671 dev LILACS</p> <p> </p> <p>2) A "Silver Standard" created from the 24 system runs submitted by 6 participating teams. It contains each of the submitted DeCS code for each document in the test set, as well as other information that can help ascertain reliability and source for anyone that wants to use this dataset to enrich their training data. It contains more that 5.8 million datapoints, and is structured as follows</p> <ul> <li>SubmissionName: Alias of the team that submitted the run</li> <li>REALdocumentId: The real id of the document</li> <li>mesinespId: The mesinesp assigned id in the evaluation dataset</li> <li>docSource: The source database</li> <li>decsCode: the DeCS code assigned to it by the team's system</li> <li>SpanishTerm: The Spanish descriptor of the DeCS code</li> <li>MiF: The Micro-f1 scored by that system's run</li> <li>MiR: The Micro-Recall scored by that system's run</li> <li>MiP: The Micro-Precision scored by that system's run </li> <li>Acc: The Accuracy scored by that system's run</li> <li>consensus: The number of runs where that DeCS code was assigned to this document by the participating teams (max. is 24)</li> </ul> <p><strong><em>Example:</em></strong></p> <p><strong>SubmissionName REALdocumentId mesinespId docSource decsCode SpanishTerm MiF MiR MiP Acc consensus</strong><br> AN ibc-177565 mesinesp-evaluation-00001 IBECS 28567 riesgo 0.2054 0.1930 0.2196 0.1198 4<br> AN ibc-177565 mesinesp-evaluation-00001 IBECS 15335 trabajo 0.2054 0.1930 0.2196 0.1198 4<br> AN ibc-177565 mesinesp-evaluation-00001 IBECS 33182 conocimiento 0.2054 0.1930 0.2196 0.1198 7<br> </p> <p><strong>For citation and a detailed description of the Challenge, please cite:</strong><br> <em>Anastasios, Nentidis and Anastasia, Krithara and Konstantinos, Bougiatiotis and Martin, Krallinger and Carlos, Rodriguez-Penagos and Marta, Villegas and Georgios, Paliouras.</em> <strong>Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering</strong> (2020). Proceedings of the Eleventh International Conference of the CLEF Association (CLEF 2020). Thessaloniki, Greece, September 22--25</p> <p><strong>Citation</strong></p> <p>@inproceedings{durusan2019overview,<br> title={Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering},<br> author={Anastasios, Nentidis and Anastasia, Krithara and Konstantinos, Bougiatiotis and Martin, Krallinger and Carlos, Rodriguez-Penagos and Marta, Villegas and Georgios, Paliouras},<br> booktitle={Experimental IR Meets Multilinguality, Multimodality, and Interaction Proceedings of the Eleventh International Conference of the CLEF Association (CLEF 2020), Thessaloniki, Greece, September 22--25, 2020, Proceedings},<br> volume={12260},<br> year={2020},<br> organization={Springer}<br> }</p> <p> </p> <p>Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
Cantemist Silver Standard: Participant predictions in SEPLN IberLEF2020 - Spanish oncology clinical cases coded in ICD-O
<p><strong>Introduction</strong></p> <p>Predictions in the background set of Cantemist participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per Cantemist subtask. Within each Cantemist subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong></p> <p>The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks Cantemist-NER and Cantemist-Norm, the files are in Brat format.</p> <p>For the sub-track Cantemist-Coding files have the following fields:</p> <pre>articleID ICDO-code </pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/cantemist/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><a href="https://doi.org/10.5281/zenodo.3773228"><strong>Gold Standard corpus</strong></a></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to Cantemist participants. </p> <p> </p> <p>For more information, visit the track webpage: <a href="http://temu.bsc.es/cantemist/">http://temu.bsc.es/cantemist/</a> or email us at encargo-pln-life@bsc.es</p>
A Trial Comparing the Efficacy and Safety of Open Dressing With Petrolatum Jelly vs. Standard Gauze Dressing With Silver Sulfadiazine
ClinicalTrials.gov study NCT02109718. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Standard Versus Advanced Antimicrobial Dressing Containing Ionic Silver Following Colorectal Cancer Surgery
ClinicalTrials.gov study NCT00981110. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study Comparing the DOVER Silver Foley Catheter to a Standard of Care Catheter and a Marketed Silver Coated Foley Catheter
ClinicalTrials.gov study NCT00203515. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Silver-Coated vs Standard Catheter for UTI Prevention
ClinicalTrials.gov study NCT05408533. IPD Sharing: NO. Countries: 1. Publications: 1.
Topical Collagen-Silver Versus Standard Care Following Removal of Ingrown Nails
ClinicalTrials.gov study NCT00641433. IPD Sharing: Not stated. Countries: 1. Publications: 0.
GGPONC 2.3 (Minor Release with Silver Standard Annotations)
<h2>About this Release</h2> <p>Version 2.3 (minor release) contains more recent oncology guidelines (XML, plain text), metadata, and predictions using GGPONC 2.0 NER models and UMLS concepts identified with xMEN. </p> <p>In total, this version contains 34 guidelines with 2.3M tokens of text.</p> <p><strong>⚠️ If you are looking for the GGPONC 2.0 gold standard annotations, please refer to the major release: <a href="../records/12518458">https://zenodo.org/records/12518458</a></strong></p> <p> </p> <h3>Project Description</h3> <p>The <a href="https://www.leitlinienprogramm-onkologie.de/projekte/ggponc-english/">GGPONC project</a> aims to provide a freely distributable corpus of German medical text for NLP researchers. Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information (PHI), which distinguishes them from other kinds of medical text.</p> <p>The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely distributable corpus of German medical text at the moment.</p> <p>Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.</p>
Clinical entity annotations for clinical cases in French: gold and silver standard annotations
<p>Clinical entity annotations (gold and silver annotation files) produced for the CAS/DEFT corpus of clinical cases in French used in the 2020 DEFT challenge. These annotations are released with the paper "Privacy-preserving mimic models for clinical named entity recognition in French" and are available in the BRAT Rapid Annotation Tool (BRAT) standoff format. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.