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99 results for “Annotated corpus”
List of TEI rolename annotations in the ISicily EpiDoc corpus
<p>This CSV file details every instance of a 'roleName' tag in the I.Sicily (sicily.classics.ox.ac.uk) EpiDoc TEI files, reporting the ID number of the file in which it appears, and the value of the @type and @subtype attributes in each case - as such it serves as an index of roleName attestations in the I.Sicily dataset (also recoverable directly from the EpiDoc files). The file will be updated in future.</p>
Murreviikko: an Annotated and Normalized Corpus of Dialectal Finnish Tweets
<p>Murreviikko (literally 'Dialect week') is a campaign founded in the University of Eastern Finland to promote the use of Finnish dialects in social media. It started in 2020 and takes place mid-October.</p> <p>The original data was collected from Twitter with the search word murreviikko ('dialect week') and hashtag #murreviikko separately for 2020, 2021 and 2022. The current dataset combines all the original collections.</p> <p>The tweets are dialectologically annotated on two levels: following the East-West division of Finnish dialects, and following a seven-way division of Finnish dialects (South-West, Häme, Southern Ostrobothnia, Central and Northern Ostrobothnia, Far North, Savo, and South-East), appended with the Helsinki slang. There is also a class for dialectal tweets, which are not discernible (NA) because of contrasting or scarce dialectal features.</p> <p>The original tweets are normalized to a phonetic standard, but word order is not altered, or grammar rules of standard Finnish followed otherwise. This means that for instance standard Finnish possessive suffixes (minun kirja-ni 'my book-my') are not added if they are not present in the original tweet (minun kirja). Likewise, dialect words are not corrected to the standard alternative, even if such words would exist (pruukata > pruukata instead of standard tavata).</p> <p>Following the rules of the Twitter API, this repository only includes the tweet id's, dialect annotations and normalizations. The original tweets are available for scientific use by request, as granted by the European Union’s Digital Single Market directive (2019/790).</p>
Corpus of Occitan Written Traditional Folktales Annotated with Part-Of-Speech (OWT-Tag)
<p>This resource contains 5 extracts of texts in Occitan which were manually annotated with lemmas and parts-of-speech, following the Grace standard. It was produced during the ExpressioNarration project, funded by a Marie Curie Individual Fellowship, in order to evaluate the performance of an Occitan Part-Of-Speech tagger, Talismane, to the specifities of the corpus of the project called Oral Occitan (OcOr), also available on https://zenodo.org/record/1451753#.W78FJWOYSpo.<br> Each extract contains around 1500 words. They are extracted from 'Contes et proverbes populaires recueillis en armagnac et Contes populaires recueillis en agenais' de J.-F. Bladé, 'Coundes biarnés, couéilhuts aüs parsàas miéytadès dou péys dé Biarn' de J.-V. Lalanne, 'Contes populaires du Languedoc' de L. Lambert and 'Contes populaires recueillis dans la Grande-Lande' de F. Arnaudin.<br> The annotation process is described in the following article available on https://www.openscience.fr/IMG/pdf/iste_modocv1n1_2.pdf.</p>
MiRoR11 - P2 - Annotated corpus for semantic similarity of clinical trial outcomes
<p>Outcome similarity corpus</p> <p>This dataset contains annotations of semantic similarity for pairs of primary and reported outcomes.<br> Tab-separated format is used. The files contain the following columns:<br> filename, sentence pair ID, sentence pair text, primary outcome, primary outcome start position, primary outcome end position, reported outcome, reported outcome start position, reported outcome end position, label</p> <p>The folder out_relations_split contains the dataset splits for 10-fold cross-validation.</p>
MiRoR11 - P2 - Annotated corpus for the relation between reported outcomes and their significance levels
<p>Corpus of relations between outcomes and significance levels</p> <p>This dataset contains annotations of the relations between reported outcomes and their significance levels.<br> Tab-separated format is used. The file contains the following comumns:<br> filename, sentence text, outcome, primary outcome start position, primary outcome end position, reported outcome, reported outcome start position, reported outcome end position, label</p> <p>The folder out_sig_rel contains the dataset splits for 10-fold cross-validation.</p>
French Entity-Linking dataset between annotated tweets collected during major crises in France and French Wikipedia corpus
<p>Most of the available datasets are not particularly adapted to our target application: geolocate natural disasters from social networks. First, social media posts are largely underrepresented in these datasets, and the only Twitter dataset lacks Entity-Linking annotations. Second, none of the datasets focuses on a crisis or natural disaster event.</p> <p>To mitigate these issues, we extracted a collection of French tweets written during earthquakes and major floods that have occurred in France in recent years. We set up Label-Studio in order to annotate these tweets. A total of 4617 tweets were annotated, including 1678 tweets posted during earthquakes and 2939 during floods. For each annotated tweet, mentions were annotated using the set of labels described earlier in the paper as well as, when possible, the target Wikipedia title.</p> <p>Named “RéSoCIO” in reference to the research project in which it was carried out, the dataset resulting from this work contains a total of 12 828 annotated mentions and 1 513 distinct Wikipedia entities. 85% of mentions were associated with a Wikipedia page and 94 % if we ignore the RISKNAT and DAMAGES labels, which are often difficult to map to an existing entity.</p> <table> <tbody> <tr> <td><strong>Labels</strong></td> <td><strong>#Mentions</strong></td> <td><strong>#Linked</strong></td> <td><strong>#Entities</strong></td> </tr> <tr> <td>PERSON</td> <td>315</td> <td>263</td> <td>136</td> </tr> <tr> <td>ORG</td> <td>863</td> <td>790</td> <td>281</td> </tr> <tr> <td>GEOLOC</td> <td>4375</td> <td>4234</td> <td>701</td> </tr> <tr> <td>TRANSPORT</td> <td>250</td> <td>203</td> <td>101</td> </tr> <tr> <td>EVENT</td> <td>35</td> <td>21</td> <td>16</td> </tr> <tr> <td>FACILITY</td> <td>129</td> <td>94</td> <td>49</td> </tr> <tr> <td>RISKNAT</td> <td>5502</td> <td>4994</td> <td>128</td> </tr> <tr> <td>DAMAGES</td> <td>1136</td> <td>121</td> <td>56</td> </tr> <tr> <td>OTHER</td> <td>223</td> <td>200</td> <td>46</td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>12828</strong></td> <td><strong>1322</strong></td> <td><strong>1513</strong></td> </tr> </tbody> </table> <p>Overview of the mentions annotated in the Twitter dataset. #Mentions shows the total number of mentions per label, #Linked the number of mentions linked to an entity and #Entities the number of distinct entities per label present in the dataset.</p> <table> <tbody> <tr> <td><strong>Labels</strong></td> <td><strong>#Mentions</strong></td> <td><strong>#Linked</strong></td> <td><strong>#Entitie</strong>s</td> </tr> <tr> <td>PERSON</td> <td>1100102</td> <td>1098406</td> <td>557697</td> </tr> <tr> <td>ORG</td> <td>750925</td> <td>749504</td> <td>130394</td> </tr> <tr> <td>GEOLOC</td> <td>2729702</td> <td>2728296</td> <td>215924</td> </tr> <tr> <td>TRANSPORT</td> <td>161539</td> <td>160487</td> <td>53405</td> </tr> <tr> <td>EVENT</td> <td>798433</td> <td>798251</td> <td>86471</td> </tr> <tr> <td>FACILITY</td> <td>258835</td> <td>258513</td> <td>109867</td> </tr> <tr> <td>RISKNAT</td> <td>5502</td> <td>4994</td> <td>127</td> </tr> <tr> <td>DAMAGES</td> <td>1136</td> <td>121</td> <td>56</td> </tr> <tr> <td>OTHER</td> <td>4340621</td> <td>4339658</td> <td>682458</td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>10146795</strong></td> <td><strong>10138230</strong></td> <td><strong>1836399</strong></td> </tr> </tbody> </table> <p>Overview of the mentions annotated in the full dataset. #Mentions shows the total number of mentions per label, #Linked the number of mentions linked to an entity and #Entities the number of distinct entities per label present in the dataset.</p>
OcWikiAnnot: Annotated Wikipedia Corpus of Occitan
<p>OcWikiAnnot is a corpus of Wikipedia content in Occitan that is tokenized, PoS-tagged and lemmatized. The corpus contains 100 000 sentences for a total of 2 037 723 tokens. It is based on the Wikipedia corpus in Occitan that is part of the <a href="https://corpora.uni-leipzig.de/en?corpusId=oci_wikipedia_2021">Leipzig Corpora Collection</a>.</p> <p> </p> <p> </p>
Manually Annotated Instances of Ich ('I') from the German KoLas Corpus
<p>Dataset used in Andresen/Knorr (2020). The dataset comprises 360 instances of <em>ich</em> ('I') taken from the German learner corpus KoLaS (Andresen/Knorr 2017, see <a href="http://hdl.handle.net/11022/0000-0001-B732-8">http://hdl.handle.net/11022/0000-0001-B732-8</a> for full corpus access) and manually annotated with categories taken from Steinhoff (2007).</p> <p>Column descriptions:</p> <ul> <li>document: name of the document by which it can be found in the KoLaS corpus</li> <li>code_annotator1 - code_annotator4: Annotations by four annotators. Possible values: Verfasser-<em>Ich</em> (author <em>I</em>), Forscher-<em>Ich</em> (researcher <em>I</em>), Erzähler-<em>Ich</em> (narrator <em>I</em>)</li> <li>max_agreement_freq: Highest number of anntators that agreed on one label</li> <li>max_agreement_label: Label on which the highest number of annotators agreed</li> <li>context_before: 150 characters of context before the match</li> <li>match: the match itself (either <em>ich</em> or <em>Ich</em>)</li> <li>context_after: 150 characters of context after the match</li> </ul> <p><strong>References</strong></p> <p>Andresen M, Knorr D. KoLaS – Ein Lernendenkorpus in der Schreibberatungsausbildung einsetzen. <em>Zeitschrift Schreiben</em>. Published online July 5, 2017:10-17.</p> <p>Andresen M, Knorr D. Exploring the Use of the Pronoun I in German Academic Texts with Machine Learning. In: Burghardt M, Müller-Birn C, eds. <em>Methoden und Anwendungen der Computational Humanities</em>. Lecture Notes in Informatics (LNI). Gesellschaft für Informatik; 2020.</p> <p>Steinhoff T. Zum ich-Gebrauch in Wissenschaftstexten. <em>Zeitschrift für germanistische Linguistik</em>. 2007;35(1-2):1–26.</p>
An Annotated Corpus of Tonal Piano Music from the Long 19th Century
<p>This corpus has been created within the <a href="https://github.com/DCMLab/dcml_corpora">DCML corpus initiative</a> and employs the <a href="https://github.com/DCMLab/standards">DCML harmony annotation standard</a>.</p> <p><strong>Version 1</strong> has been released for submitting it as part of the data report <code>Hentschel, J., Rammos, Y., Neuwirth, M., Rohrmeier, M. (forthcoming). An Annotated Corpus of Tonal Piano Music from the Long 19th Century</code> that accompanies nine corpora grouped under the DOI <a href="https://doi.org/10.5281/zenodo.7483349">10.5281/zenodo.7483349</a>.</p> <p><strong>Version 1.1</strong> comes with a complete set of metadata and score headers. Among more accurate composition dates, the metadata now include URIs that identify the compositions in terms of the <a href="https://viaf.org/">Virtual International Authority File (VIAF)</a>, <a href="https://www.wikidata.org/">Wikidata</a>, <a href="https://imslp.org/">IMSLP</a> and <a href="https://musicbrainz.org/">MusicBrainz</a>. The data has been re-extracted from the scores using <a href="https://pypi.org/project/ms3/">ms3 1.1.1</a>.</p> <p>The publication covers the following corpora (the DOI links always point at the latest version respectively):</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.7473560">Ludwig van Beethoven - Piano Sonatas</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473566">Frédéric Chopin - Mazurkas</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473568">Claude Debussy - Suite Bergamasque</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473576">Antonín Dvořák - Silhouettes</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473580">Franz Liszt - Années de Pèlerinage</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473528">Nikolai Medtner - Tales</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473582">Robert Schumann - Kinderszenen</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473586">Pyotr Tchaikovsky - The Seasons</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473578">Edvard Grieg - Lyric Pieces</a></li> </ul> <p> </p>
The Tsez Annotated Corpus Project
<p>Cite the source of the dataset as:</p> <blockquote> <p>Abdulaev, A.K. & I. K. Abdullaev. 2010. Cezyas folklor/Dido (Tsez) folklore/Didojskij (cezskij) fol´klor. Leipzig–Makhachkala: "Lotos".</p> </blockquote>
The grammatically annotated corpus of the pericopes of the Old Lithuanian Postil of Jonas Bretkūnas
<p>This grammatically annoted corpus aims at facilitating linguistic research on Old Lithuanian based on the Postil of Jonas Bretkūnas from the year 1591. In addition to the two subcorpora "pericopes" and "homilies" of the version 1.0, the current version 2.0<br>includes also "passion harmony", "pericope (prophet)" and "prayer". In this new version, the tokens occurring in biblical citations can be specifically searched for. The search results also show the corresponding passage in the BrP pericopes.<br>For full description see the BrP_2.0._documentation.md included in the files.</p> <p>The pericopes were annotated using SIL Toolbox and converted to be used in the search-tool ANNIS using the conversion tool PEPPER.</p> <p>Three formats are provided in this release: 1. the Toolbox files, 2. the transitional Excel files and 3. a zipped folder to be imported into ANNIS.</p> <p>Created in the project B02, <em>Emergence and change of registers: The case of Lithuanian and Latvian</em> of the CRC 1412 "Register" (funded by the Deutsche Forschungsgemeinschaft: DFG, German Research Foundation: 416591334).</p> <p> </p> <p> </p>
PPORTAL_ner: An Annotated Corpus of Portuguese Literary Entities
<h2><a href="https://marianaossilva.github.io/pportal_ner/" target="_blank" rel="noopener">PPORTAL_ner</a></h2> <h3>An Annotated Dataset of Portuguese Literary Entities</h3> <p>The corpus is tailored to Brazilian and Portuguese literary texts, containing annotations for five entity categories, including PER, LOC, GPE, ORG, and DATE. Within a diverse collection of 25 literary works, it offers a total of 125,059 tokens and 5,266 annotated entities. This dataset contributes to the development of potentially more accurate and context-aware NER models, as well as to encourage further exploration within Portuguese literature.<br><br></p> <h2>Corpus Statistics</h2> <p>Our corpus is sourced from <a href="https://doi.org/10.5281/zenodo.5178063">PPORTAL</a>, an extensive repository of metadata containing over 80,000 public domain literary works in the Portuguese language, predominantly derived from Brazil and Portugal. <a href="https://doi.org/10.5281/zenodo.5178063">PPORTAL</a> aggregates data from three digital libraries: <a href="https://www.dominiopublico.gov.br/">Domínio Público</a>, <a href="https://projectoadamastor.org/">Projecto Adamastor</a>, and <a href="https://www.literaturabrasileira.ufsc.br/">Biblioteca Digital de Literatura dos Países Lusófonos (BLPL)</a>.</p> <p>To simplify referencing, this new dataset is called PPORTAL_ner. PPORTAL_ner selection process contains a diverse range of 25 individual literary works, spanning different authors and literary styles. All of these texts were published prior to 1953, adhering to the current criteria for public domain status in Brazil, with the majority falling within the timeframe spanning from 1554 to 1938.</p>
AWOFRO : Annotated tweet corpus of mixed Wolof-French for detecting obnoxious messages
<p>These data are tweets of mixed Wolof-French codes annotated by three(3) annotators. <br>They were extracted during the period from 1 January 2021 to 31 May 2023.</p> <p>Content description :</p> <p><strong>Corpora.rar</strong> : The dataset contains 3510 annotated tweets</p>
Kobalt: Extension Corpus and Annotation Guidelines for Verb Classification and Dependency Adjustments
<p>Kobalt (Zinsmeister et al. 2012) is a task-based corpus of essays written by learners and native speakers of German. This repository contains data that was not included in the original corpus and new layers of annotation to the original and the extended corpus, specifically morphological and syntactic classification of verbs and corrections and changes to dependency parses. Please refer to the annotation guidelines included in this repository for further information.<br> </p>
Metadata and annotation data for the XSample corpus on German academic language
<p>The XSample corpus war created in the project <em>XSample</em> (https://www.izus.uni-stuttgart.de/fokus/fdm-projekte/xsample/) at Universität Stuttgart in 2021 by Melanie Andresen and Axel Pichler. It contains 135 German academic journal articles, 45 each from the disciplines linguistics, literary studies and philosophy. The texts themselves cannot be made public for copyright reasons. However, metadata and some annotation data are published here.</p> <p><strong>xsample-metadata.csv</strong><br> This file contains metadata on the texts in the corpus, like journal, title, authors, text length, and the URL to the original paper. It also contains two analytical metrics, 'past-ratio' and 'temp-expr-ratio', that are based on the annotations in the other two files. The variable 'past-ratio' expresses the proportion of verbs in past tense relative to all finite verbs in the text. The variable 'temp-expr-ratio' gives the number of temporal expressions per 1000 token.</p> <p><strong>xsample-heidel.csv</strong><br> This file contains all temporal expressions found and classified by the annotation tool <em>HeidelTime</em> (https://github.com/HeidelTime/heideltime, V. 2.2.1, Strötgen & Gertz 2013 ). The variable 'position' expresses the position of the first character of the temporal expression in the text in characters.</p> <p><strong>xsample-sticker2.csv</strong><br> This file contains all finite verbs found and classified by the annotation tool <em>sticker2</em> (https://github.com/stickeritis/sticker2). The variable 'position' expresses the position of the first character of the finite verb in the text in characters.</p> <p><strong>References</strong><br> Strötgen, Jannik & Michael Gertz. 2013. Multilingual and cross-domain temporal tagging. <em>Language Resources and Evaluation</em>. Springer 47(2). 269–298. <a href="https://doi.org/10.1007/s10579-012-9179-y">https://doi.org/10.1007/s10579-012-9179-y</a>.</p> <p> </p>
CafeteriaSA corpus: Scientific abstracts annotated across different food semantic resources
<p>In the last decades, a great amount of work has been done in predictive modeling of issues related to human and environmental health. Resolution of issues related to healthcare is made possible by the existence of several biomedical vocabularies and standards, which play a crucial role in understanding health information, together with a large amount of health-related data. However, despite the large number of available resources and work done in the health and environmental domains, there is a lack of semantic resources that can be utilized in the food and nutrition domain, as well as their interconnections. For this purpose, in an European Food Safety Authority-funded project CAFETERIA, we have developed the first annotated corpus of 500 scientific abstracts that consists of 6,407 annotated food entities with regard to Hansard taxonomy, 4,299 for FoodOn, and 3,623 for SNOMED-CT. The CafeteriaSA corpus will enable further development of natural language processing methods for food information extraction from textual data that will allow extracting of food information from scientific textual data.</p>
CafeteriaFCD corpus: Food consumption data annotated with regard to different food semantic resources
<p>The FoodBase curated version which contains 1,000 manually evaluated recipes, annotated with the appropriate semantic tags from the Hansard Taxonomy, FoodON and SNOMED-CT.</p>
Hypertension - Florida Annotated Corpus for Translational Science (FACTS), Vital Sign Ontology Annotations
<p>Florida Annotated Corpus for Translational Science (FACTS), which currently consists of 20 case reports about hypertension annotated with Vital Sign Ontology (VSO) classes (version 2012-04-25). </p>
Gold standard corpus, ontologies, and Entity-Quality ontology annotations for evolutionary phenotypes
<p>This data set includes a gold-standard corpus of evolutionary phenotype descriptions (in the form of character state descriptions pulled from a variety of phylogenetic systematics studies), and their corresponding expert-curated annotations with ontology terms in the form of Entity-Quality (EQ) statements. EQ annotatons allow machine-reasoning (through the semantics encoded in the requisite ontologies from which the ontology terms are drawn), and machine-reasoning in turn enables computing metrics for quantifying the semantic similarity between different phenotype descriptions as represented by their EQ annotations.</p> <p>Also included are the ontologies, and the human expert-generated and Semantic Charaparser (i.e., machine) generated EQ annotations used to assess Semantic Charaparser performance relative to inter-curator variation and to the effect of having access to external knowledge. The ontologies include those used as input, the "augmented" ontologies created by human curators in each experiment round, and the merged ontology used to maximize Semantic Charaparser's performance.</p> <p>The production of the gold standard corpus, annotation experiments, and evaluation of the results are described in detail in the following manuscript:</p> <blockquote> <p>Dahdul et al (2018) Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems. BioRxiv https://doi.org/10.1101/322156. Submitted to Database.</p> </blockquote> <p>The analysis code for evaluating the gold standard corpus (and the input data and ontologies for that) are available separately from the following:</p> <blockquote> <p>Manda et al (2018) Code and data for analysis of evolutionary phenotype ontology annotations and gold standard corpus. Zenodo. https://doi.org/10.5281/zenodo.1218010</p> </blockquote> <p>In comparison to the previous version (v1.0.0), this record includes a file of MD5 checksums of the Gold Standard data files. The data files themselves are unchanged.</p>
MiRoR11 - P2 - Annotated corpus for primary and reported outcomes extraction
<p>Annotated corpus for outcome extraction</p> <p>This folder contains 2 subfolders:<br> 1. Primary_outcomes - a corpus annotated for primary outcomes<br> The folder contains the following files:<br> po_sent_marked_p1_1000.txt - sentences 1 - 1000 of the annotated corpus, ConstruKT format; coordinated outcomes annotated as single entity<br> po_sent_marked_p2_1000.txt - sentences 1001 - 2000 of the annotated corpus, ConstruKT format; coordinated outcomes annotated as single entity</p> <p>po_sent_marked_col_p1.txt - sentences 1 - 1000 of the annotated corpus, tabulated format; coordinated outcomes annotated as single entity<br> po_sent_marked_col_p2.txt - sentences 1001 - 2000 of the annotated corpus, tabulated format; coordinated outcomes annotated as single entity</p> <p>po_sent_marked_col_p1_coord.txt - sentences 1 - 1000 of the annotated corpus, tabulated format; coordinated outcomes annotated as separate entities<br> po_sent_marked_col_p2_coord.txt - sentences 1001 - 2000 of the annotated corpus, tabulated format; coordinated outcomes annotated as separate entities</p> <p>Subfolders:<br> po - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets); coordinated outcomes annotated as separate entities<br> po_coord - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets); coordinated outcomes annotated as separate entities</p> <p>2. Reported_outcomes - a corpus annotated for reported outcomes<br> The corpus contains sentences from Results and Conclusions sections of the articles for which primary outcomes were annotated. The first part of reported outcomes corpus contains sentences from articles for the first half of the primary outcomes corpus (sentences 1 - 1000). The second part of reported outcomes corpus contains sentences from articles for the second half of the primary outcomes corpus (sentences 1001 - 2000).</p> <p>The folder contains the following files:<br> res_sent_marked_p1.txt - first part of the annotated corpus, ConstruKT format<br> res_sent_marked_p2.txt - second part of the annotated corpus, ConstruKT format</p> <p>res_sent_marked_p1_col.txt - first part of the annotated corpus, tabulated format<br> res_sent_marked_p2_col.txt - first part of the annotated corpus, tabulated format</p> <p>Subfolders:<br> rep - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets)</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.