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1,655 results for “subset”
Wikidata Subsets of 4 Gene Wiki Classes (wdsub)
<p>Chemical compound (Q11173), disease (Q12136), gene (Q7187), and protein (Q8054) are some of the main classes containing the Gene Wiki WikiProjects. In this repository, we put the corresponding subsets of each class. The subsets contain all instances of the four main classes (no sub-classes). All subsets are extracted from the Wikidata JSON dump of 3 January 2022 (<a href="https://t.co/vdmJc8V1v2">https://t.co/vdmJc8V1v2</a>) using wdsub (https://github.com/weso/wdsub)</p>
Subset of global model sea level data for "Challenges, Advances and Opportunities in Regional Sea Level Projections: the Role of Ocean-shelf Dynamics"
<p>Monthly sea surface height above the geoid data in NW European seas from six global simulations using the NEMO ocean model (https://www.nemo-ocean.eu/) for 1990 to 2009</p> <p><strong>ORCA0083_DFS_NWS_ssh_1990_2009, ORCA025_DFS_NWS_ssh_1990_2009, ORCA1_DFS_NWS_ssh_1990_2009,</strong> are the N006 simulation set created by Andrew Coward and the NOC Marine Systems Modelling team as used by:</p> <p>Baker et al 2022 Biological Carbon Pump Sequestration Efficiency in the North Atlantic: A Leaky or a Long-Term Sink? Global Biogeochemical Cycles <a href="https://doi.org/10.1029/2021GB007286">https://doi.org/10.1029/2021GB007286</a>,</p> <p>Wilson, C. <em>et al.</em> 2021 Significant variability of structure and predictability of Arctic Ocean surface pathways affects basinwide connectivity. <em>Commun. Earth Environ.</em> <strong>2</strong>, 164. <a href="https://doi.org/10.1038/s43247-021-00237-0">https://doi.org/10.1038/s43247-021-00237-0</a> (2021).</p> <p>These simulations are forced by the Drakkar Forcing Set 5.2 (DFS) and initialised at 1958, with a nominal 1/12, 1/4 and 1 degree resolution. See references for further model details.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009, ORCA025_JRA_tides_NWS_ssh_1990_2009, ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009, </strong>are new simulations produced by Chris Wilson, James Harle and the Shelf Enabled NEMO team. All are forced by the JRA reanalysis, initialised in 1976.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009</strong> is a reference run based on GO9, an evolution of the Joint Marine Modelling Programme configuration described by Storkey et al 2018 UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geoscientific Model Development https://gmd.copernicus.org/articles/11/3187/2018/</p> <p><strong>ORCA025_JRA_tides_NWS_ssh_1990_2009</strong> adds explicit tides to this.</p> <p><strong>ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009</strong> adds tides, Generic Length Scale Mixing and Multi-envelope vertical coordinates</p> <p>Details of these simulations can be found here:</p> <p>https://github.com/NOC-MSM/SE-NEMO </p>
WarpTools and MCore processing of a 5 tilt series subset of EMPIAR-10491
<p>This dataset contains an example of cryo-electron tomography (cryo-ET) data processed using command line tools available in <a href="https://anaconda.org/warpem/warp">Warp v2.0.0dev9.</a></p> <p>Processing a 5 tilt series subset led to a 2.92 Å reconstruction of apoferritin.</p> <p>The subset processed includes</p> <ul> <li>TS_1</li> <li>TS_11</li> <li>TS_17</li> <li>TS_23</li> <li>TS_32</li> </ul> <p>External packages used for processing include <a href="https://github.com/czimaginginstitute/AreTomo2"><em>AreTomo2</em></a> for tilt series alignment and <a href="https://github.com/3dem/relion/tree/ver5.0"><em>RELION 5</em></a> for initial 3D refinement.</p> <p>The raw data is not included but can be downloaded from <a href="https://www.ebi.ac.uk/empiar/">EMPIAR</a> using the following code snippet on UNIX systems</p> <pre><code>wget --show-progress -N -q -nd -P ./ ftp://ftp.ebi.ac.uk/empiar/world_availability/10491/data/gain_ref.mrc; for i in 1 11 17 23 32; do echo "======================================================" echo "================= Downloading TS_${i} ================" wget --show-progress ---timestamping --quiet --no-directories --directory-prefix ./mdoc ftp://ftp.ebi.ac.uk/empiar/world_availability/10491/data/tiltseries/mdoc/TS_${i}.mrc.mdoc; wget --show-progress ---timestamping --quiet --no-directories --directory-prefix ./frames ftp://ftp.ebi.ac.uk/empiar/world_availability/10491/data/tiltseries/data/*-${i}_*.tif; done </code></pre> <p>Processing is detailed in the `processing.sh` shell script. This script is not designed to be run end to end and instead should be run one command at a time.</p>
Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries
<p>Processed data files for manuscript: "Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries" <a href="https://doi.org/10.1093/nar/gky1204">https://doi.org/10.1093/nar/gky1204</a> . Scripts for generating figures are found here: https://github.com/rnabioco/scrna-subsets</p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
Earth Microbiome Project and Global Soil Mycobiome Data Subsets
<p>These are subsets of the publicly available Earth Microbiome Project (<a href="https://earthmicrobiome.org/">https://earthmicrobiome.org/</a>, <a href="../records/890000">https://zenodo.org/records/890000</a>) [1] and Global Soil Mycobiome project datasets (<a href="https://doi.org/10.15156/BIO/2263453"><span>https://doi.org/10.15156/BIO/2263453</span></a>) [2,3], provided for benchmarking and testing purposes. These are re-released with attribution under a CC-BY-4.0 license, following the license of the original source creators. If you use this resource, please cite the original references as given below:</p> <p>1. Thompson, L. R., <em>et al.</em> (2017). A communal catalogue reveals Earth’s multiscale microbial diversity. <em>Nature</em>, 551:457-463. <a href="http://doi.org/10.1038/nature24621" target="_blank" rel="noopener noreferrer">doi:10.1038/nature24621</a>.</p> <p>2. Tedersoo, L., <em>et al.</em> The Global Soil Mycobiome consortium dataset for boosting fungal diversity research. <em>Fungal Diversity</em> <strong>111</strong>, 573–588 (2021). https://doi.org/10.1007/s13225-021-00493-7</p> <p>3. Tedersoo, L. (2021): The Global Soil Mycobiome consortium dataset for boosting fungal diversity research v2. University of Tartu. 10.15156/BIO/2263453</p>
Aging is associated with functional and molecular changes in distinct hematopoietic stem cell subsets
<p>Contains the input data used in the analysis of the paper: Aging is associated with functional and molecular changes in distinct hematopoietic stem cell subsets (Su, Hauenstein, Somuncular et al., Nature Communications, 2024).</p>
Subset of stochastically generated interacting molecules for CH GAP interatomic potential
<p>This is a subset of the dataset used to train general-purpose CH GAP interatomic potential [1].</p> <p>This subset contains the interacting molecules generated stochastically in a following manner. The subset is generated using active learning and uncertainty-based configuration selection. We started with randomly chosen pairs of CH-containing molecules from the QM9 database up to 7 carbon atoms. The probability of selecting the molecules is set based on the energy and size of the molecule. The probability is lower as the energy above the convex hull is higher. A bigger size of the molecule also lowers the probability to favor the inclusion of small structures. Then, we estimate the uncertainties for the new structures based on how far away from the existing interacting molecules in the training set they are (in configuration space), and identify those with the largest expected errors. This way, we generated about 3k structures. </p>
Two ChEMBL-34 subsets (lead-like and drug-like molecules)
<p>Two subsets of molecules from ChEMBL-34[1].</p> <p>Those molecular datasets might be useful to people training molecular generators.</p> <p>After decompression, you will get:<br>chembl34_stable_ES_OA_LL.smi: 585,272 molecules.<br>chembl34_stable_ES_OA_DL.smi: 756,420 molecules.</p> <p>stable=non-reactive molecules (filtered-out reactive functional groups from [5]).<br><a href="https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_stable.py">https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_stable.py</a></p> <p>ES=Easy Synthesis (SAscore <= 3.0) [2].<br><a href="https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_SA.py">https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_SA.py</a></p> <p>OA=Orally Available (according to a classifier trained on the dataset from [6]).</p> <p>LL=Lead-Like (almost the definition from [3]).<br><a href="https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_lead.py">https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_lead.py</a></p> <p>DL=Drug-Like (definition from [4]).<br><a href="https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_drug.py">https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_drug.py</a></p> <div> <h1>Bibliography</h1> <a href="https://github.com/UnixJunkie/chembl34_subsets#bibliography"></a></div> <ol> <li> <p>Zdrazil, B., Felix, E., Hunter, F., Manners, E. J., Blackshaw, J., Corbett, S., ... & Leach, A. R. (2024). The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic acids research, 52(D1), D1180-D1192. <a href="https://doi.org/10.1093/nar/gkad1004" rel="nofollow">https://doi.org/10.1093/nar/gkad1004</a></p> </li> <li> <p>Ertl, P., & Schuffenhauer, A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of cheminformatics, 1, 1-11. <a href="https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-1-8" rel="nofollow">https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-1-8</a></p> </li> <li> <p>Hann, M. M., & Oprea, T. I. (2004). Pursuing the leadlikeness concept in pharmaceutical research. Current opinion in chemical biology, 8(3), 255-263. <a href="https://doi.org/10.1016/j.cbpa.2004.04.003" rel="nofollow">https://doi.org/10.1016/j.cbpa.2004.04.003</a></p> </li> <li> <p>Tran-Nguyen, V. K., Jacquemard, C., & Rognan, D. (2020). LIT-PCBA: an unbiased data set for machine learning and virtual screening. Journal of chemical information and modeling, 60(9), 4263-4273. <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00155" rel="nofollow">https://pubs.acs.org/doi/10.1021/acs.jcim.0c00155</a></p> </li> <li> <p>Lisurek, M., Rupp, B., Wichard, J., Neuenschwander, M., von Kries, J. P., Frank, R., ... & Kühne, R. (2010) Design of chemical libraries with potentially bioactive molecules applying a maximum common substructure concept. Molecular diversity, 14, 401-408. <a href="https://link.springer.com/article/10.1007/s11030-009-9187-z" rel="nofollow">https://link.springer.com/article/10.1007/s11030-009-9187-z</a></p> </li> <li> <p>Falcon-Cano, G., Molina, C., & Cabrera-Perez, M. A. (2020). ADME prediction with KNIME: development and validation of a publicly available workflow for the prediction of human oral bioavailability. Journal of chemical information and modeling, 60(6), 2660-2667. <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00019" rel="nofollow">https://pubs.acs.org/doi/10.1021/acs.jcim.0c00019</a></p> </li> </ol>
Wikidata Subsets of 6 Wikiproject (Gene Wiki, Taxonomy, Astronomy, Music, Law, Ships)
<p>This dataset contains the N-Triples files of 6 Wikidata subsets corresponding to 6 different Wikidata WikiProject extracted from October 2016 and June 2021 dumps. For each project, there are .nt.gz files containing RDF data. The projects are:</p> <ul> <li>Gene Wiki: There is a GeneWiki_2016.zip (extracted from the 2016 dump) and a GeneWiki_2021.zip (extracted from the 2021 dump), Each of which contains 25 .nt.gz files extracted by the WDumper tool.</li> <li>Astronomy: Astronomy_2016.nt.gz (extracted from the 2016 dump) and Astronomy_2021.nt.gz (extracted from the 2021 dump).</li> <li>Taxonomy: There is a Taxonomy_2016.zip (extracted from the 2016 dump) and a Taxonomy_2021.zip (extracted from the 2021 dump), Each of which contains two .nt.gz files.</li> <li>Law: Law_2016.nt.gz (extracted from the 2016 dump) and Law_2021.nt.gz (extracted from the 2021 dump).</li> <li>Music: Music_2016.nt.gz (extracted from the 2016 dump) and Music_2021.nt.gz (extracted from the 2021 dump).</li> <li>Ships: Ships_2016.nt.gz (extracted from the 2016 dump) and Ships_2021.nt.gz (extracted from the 2021 dump).</li> </ul> <p> </p> <ul> </ul>
Clinical outcome prediction in COVID-19 patients by lymphocyte subsets analysis and monocytes' iTNF-α expression
<p>Raw Box & Whiskers plot related to a manuscript submitted to "Biology" journal - MDPI - https://www.mdpi.com/journal/biology - https://doi.org/10.3390/biology10080735</p> <p><strong>Manuscript Title</strong>: Clinical outcome prediction in COVID-19 patients by lymphocyte subsets analysis and monocytes’ iTNF-α expression</p> <p><strong>Authors: G</strong>abriele Madonna <sup>1†</sup>, Silvia Sale <sup>2†</sup>, Mariaelena Capone <sup>1</sup>, Chiara De Falco <sup>2</sup>, Valentina Santocchio <sup>2</sup>, Tiziana Di Matola <sup>2</sup>, Giuseppe Fiorentino <sup>3</sup>, Caterina Pirozzi <sup>2</sup>, Anna D’Antonio <sup>2</sup>, Rocco Sabatino <sup>2</sup>, Lidia Atripaldi<sup>4</sup>, Umberto Atripaldi <sup>4</sup>, Marcello Raffone <sup>5</sup>, Marcello Curvietto<sup> 1</sup>, Antonio Maria Grimaldi <sup>1</sup>, Vito Vanella <sup>1</sup>, Lucia Festino <sup>1</sup>, Luigi Scarpato <sup>1</sup>, Marco Palla <sup>1</sup>, Michela Spatarella <sup>6</sup>, Francesco Perna <sup>7</sup>, Pellegrino Cerino <sup>8</sup>, Gerardo Botti <sup>9</sup>, Roberto Parrella <sup>10</sup>, Vincenzo Montesarchio <sup>11</sup>, Paolo Antonio Ascierto <sup>1††* </sup>and Luigi Atripaldi <sup>2††</sup></p> <p><strong>Affiliations:</strong></p> <p>1 Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy</p> <p>2 UOC Biochimica Clinica, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>3 UOC Fisiopatologia e Riabilitazione respiratoria, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>4 University of Campania "Luigi Vanvitelli", Naples, Italy</p> <p>5 UOC Microbiologia e Virologia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>6 UOSD di Farmacia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>7 Università degli Studi di Napoli "Federico II", Naples, Italy.</p> <p>8 Istituto Zooprofilattico Sperimentale del Mezzogiorno, Portici (Na), Italy.</p> <p>9 Scientific Direction, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy</p> <p>10 UOC Malattie Infettive ad Indirizzo Respiratorio, AORN Ospedali dei Colli - Monaldi – Cotugno -CTO, Napoli, Italy</p> <p>11 UOC Oncologia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>†These authors have contributed equally to this work and share first authorship</p> <p>††These authors have contributed equally to this work and share senior authorship</p> <p>*Correspondence:</p> <p><strong>Abstract of submitted Manuscript:</strong> T</p> <p>In December 2019 a novel coronavirus, “SARS-CoV-2”, was recognized as the cause of Coronavirus disease-2019 (COVID-19-disease). Several studies have explored the changes and the role of inflammatory cells and cytokines in the immunopathogenesis of disease, but until today the results have been controversial. Based on these premises, we carried out a retrospective assessment of monocytes’ intracellular TNF-α expression (iTNF-α) and on frequencies of lymphocyte sub-populations in twenty-five patients with moderate/severe COVID-19-disease. We found lymphopenia in all COVID-19 infected subjects compared with healthy subjects. On initial observation, in patients with favorable outcome we detected high absolute eosinophils count and high CD4+/CD8+ T lymphocytes ratio, while in the exitus group we observed high neutrophils and CD8+ T lymphocytes counts. During infection, in patients with favorable outcome we observed a rise in lymphocyte count, in monocytes and in Treg lymphocytes counts, in CD4+ and in CD8+ T lymphocytes count but a reduction in CD4+/CD8+ T lymphocytes ratio. Instead, in the exitus group we observed a reduction in Treg lymphocytes counts and a decrease in iTNF-α expression. Our preliminary findings point to a modulation of the different cellular mediators of immune system, which probably have a key role in the outcome of the COVID-19-disease.</p> <p><strong>Funding:</strong> This work was supported by Grants from “Regione Campania” through “POR FESR CAMPANIA 2014-2020”, CUP H64I20000300002.</p> <p><strong>Cite</strong>: Madonna, G.; Sale, S.; Capone, M.; De Falco, C.; Santocchio, V.; Di Matola, T.; Fiorentino, G.; Pirozzi, C.; D’Antonio, A.; Sabatino, R.; Atripaldi, L.; Atripaldi, U.; Raffone, M.; Curvietto, M.; Grimaldi, A.M.; Vanella, V.; Festino, L.; Scarpato, L.; Palla, M.; Spatarella, M.; Perna, F.; Cerino, P.; Botti, G.; Parrella, R.; Montesarchio, V.; Ascierto, P.A.; Atripaldi, L. Clinical Outcome Prediction in COVID-19 Patients by Lymphocyte Subsets Analysis and Monocytes’ iTNF-α Expression. <em>Biology</em> <strong>2021</strong>, <em>10</em>, 735. doi: <a href="https://doi.org/10.3390/biology10080735">10.3390/biology10080735</a></p> <p> </p>
Rapid adaptive evolution to drought in a subset of plant traits in a large-scale climate change experiment
<p>Rapid evolution of traits and of plasticity may enable adaptation to climate change, yet solid experimental evidence under natural conditions is scarce. Here, we imposed rainfall manipulations (+30%, control, -30%) for ten years on entire natural plant communities in two Eastern Mediterranean sites. Additional sites along a natural rainfall gradient and selection analyses in a greenhouse assessed whether potential responses were adaptive. In both sites, our annual target species <i>Biscutella didyma</i> consistently evolved earlier phenology and higher reproductive allocation under drought. Multiple arguments suggest that this response was adaptive: it aligned with theory, corresponding trait shifts along the natural rainfall gradient, and selection analyses under differential watering in the greenhouse. However, another seven candidate traits did not evolve, and there was little support for evolution of plasticity. Our results provide compelling evidence for rapid adaptive evolution under climate change. Yet, several non-evolving traits may indicate potential constraints to full adaptation.</p>
Subset of #MeToo-tweets in English, German, Spanish from 2019 and 2021
<p>The zip contains two tables with tweets on #MeToo in English, Spanish and German from the months 2019-07, 2019-08, 2021-07 and 2021-08. Tweets were mostly annotated with evaluative categories (positive, negative, neutral, ambiguous) and whether they represent a concrete or a meta discourse.</p>
Initial wikidata subsets on genes and diseases
<p>These two data sets are initial subsets generated at the Elixir Biohackathon 2022 from the Wikidata dump from Jan 3rd, 2022 using the WDSub application. The wikidata dump is in JSON, the generated subsets in Turtle</p>
unarXive: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network (open subset)
<h2><strong>Description</strong></h2><p>unarXive is a scholarly data set containing publications' structured full-text, annotated in-text citations, linked non-text content (mathematical notation, figure/table captions) and a citation network.</p><p>The data is generated from all LaTeX sources on <a href="https://arxiv.org/">arXiv</a> and therefore of higher quality than data generated from PDF files.</p><p>Typical uses are</p><ul><li>Training of ML models (citation recommendation, summarization, LLMs)</li><li>Citation context analysis</li><li>Bibliographic analyses</li></ul><h2><strong>Access</strong></h2><p>┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓<br>┃ <a href="https://github.com/IllDepence/unarXive/raw/master/doc/unarXive_data_sample.tar.gz"><strong>D O W N L O A D S A M P L E</strong></a> ┃<br>┗━━━━━━━━━━━━━━━━━━━━━━━━━━┛</p><p>Regarding the full data set, please note the following:</p><blockquote><p><strong>Note</strong>: this Zenodo record is the "open subset" of unarXive, which contains all permissively licensed papers from arXiv.org. You can find the <a href="https://doi.org/10.5281/zenodo.7752754">full version here</a>.</p></blockquote><p>The code used for generating the data set is <a href="https://github.com/IllDepence/unarXive">publicly available</a>.</p>
Subset of TCGA & GTEx
<p>Subset of TCGA and GTEx for teaching purposes.</p>
Generated Wikidata Subset for geneWiki based on dump: GeneWiki_wikidata-20220630
<p>Source file: GeneWiki_wikidata-20220630-all.ttl</p>
Generated Wikidata Subset for geneWiki based on dump: wikidata-20150601-all
<p>Source file: wikidata-20150601-all.ttl</p>
Generated Wikidata Subset for geneWiki based on dump: GeneWiki_wikidata-20210531-all
<p>Source file: GeneWiki_wikidata-20210531-all.ttl.gz</p>
Generated Wikidata Subset for geneWiki based on dump: GeneWiki_wikidata-20180115-all
<p>Source file: GeneWiki_wikidata-20180115-all.ttl.gz</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.