Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11,174
datasets available to search
ShareScore release 0.7.1
Dataset results
11,174 results for “identifiers”
Identifying South African Marine Protected Areas at risk from marine heatwaves and cold spells
<p>This data reflects information on marine heatwaves (MHWs) and marine cold spells (MCSs) that occurred along the South African coast from January 1982 to April 2022, with special focus on Marine Protected Areas. Thermal metrics for MHW and MCS events were obtained using the HeatwaveR package (Schlegel and Smit, 2018) and the associated Marine Heatwave Tracker (Schlegel, 2020). </p> <p> </p> <p>THis data stems from Courtailac et al (in review) Indentifying South AFrican Marine Protected Areas at risk of marine heatwaves and cold-spells </p>
Dataset for Earth Sciences at Freie Universität Berlin: Open Access, Licenses and Persistent Identifiers Monitoring
<p>In <em>Version 4</em>, <strong>publishers </strong>and <strong>journals</strong> names has been extended.</p> <p>In <em>Version 3</em>, new entries have been added for both <strong>journal </strong>and <strong>non-journal article outputs</strong>, specifically including data from the year <strong>2023</strong>. Minor adjustments were also made to URLs and open access (OA) statuses.</p> <p><em>Note</em>: Data for journal and non-journal article outputs from the year 2023 were unavailable at the time of preparing the <strong>short paper</strong> presenting the results, findable under <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">10.5281/zenodo.14170751</a> [1]).</p> <p><br>Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.</p> <p>Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under <a href="https://quest-open-earthsciences.charite.de/">https://quest-open-earthsciences.charite.de/</a>.</p> <p>The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2023. These datasets consist of (i) <strong>"journal_articles_v3.csv"</strong> and (ii) <strong>"non_journal_articles_outputs_v3.csv"</strong>, the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).</p> <p>Data for the dashboard was obtained from the FU university bibliography (<a href="https://frub-berlin.primo.exlibrisgroup.com/">https://frub-berlin.primo.exlibrisgroup.com/</a>), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was <strong>enriched with manually researched information</strong>. Data enrichment was different for journal articles and for non-journal-article publications. For <strong><em>journal articles</em></strong>, <em>copyright/open licence</em> information was added, and <em>open access category</em> information was checked and added or corrected. For <strong><em>non-journal-article outputs</em></strong>, missing <em>PIDs</em> were added and <em>open access category</em> information was checked and added or corrected. </p> <p>The "<em>data_dictionary_earth_sciences_v3.csv"</em> table documents all variables of each data file containing here.</p> <p>Both for the dashboard, and in our following publications, we categorized <strong>"bronze"</strong> OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (<a href="https://quest-dashboard.charite.de/#tabStart">https://quest-dashboard.charite.de/#tabStart</a>) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under <a href="https://doi.org/10.5281/zenodo.1099821" target="_blank" rel="noopener">10.5281/zenodo.1099821</a>9 [2]</p> <p> </p> <p>[1] Duine, M., Iarkaeva, A., & Hübner, A. (2024, November 15). Initiating discipline-specific Open Science Monitoring with the Open Science Dashboard for Earth Sciences. 28th International Conference on Science, Technology and Innovation Indicators (STI2024), Berlin, Germany. <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14170751</a><br>[2] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. <a href="https://doi.org/10.5281/zenodo.10998219">https://doi.org/10.5281/zenodo.10998219</a></p>
Genome-wide association analyses identify novel Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility
<p>The Brugada syndrome GWAS summary statistics</p> <p>Brugada syndrome is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of <em>SCN5A</em>, encoding the cardiac sodium channel Na<sub>V</sub>1.5, susceptibility genes remain largely unknown. We performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with Brugada syndrome and 10,001 controls.</p> <p> </p>
Targeted Re-sequencing Identifies Candidate Fusiform Rust Resistance Genes in Loblolly Pine
<p>A fasta file containing the subset of the v2.01 Pita genome in addition to the novel NLR genes that were targeted by hybridization probes. </p> <p>A bed file describing the intervals targeted by the hybridization probes.</p> <p>Trinity assemblies of the 30 RNAseq libraries along with predictions by transdecoder of CDS and peptide sequences from those trinity assemblies. </p>
Relevance of "Building BioData.pt" indicators identified in ESFRI (E), OECD (O) and RI-PATHS (R) impact assessment frameworks
<p>The relevance of the indicators maintained by the "Building BioData.pt" project was assessed against the objectives of different organizations/initiatives: 1) Strategic objectives of BioData.pt; 2) Objectives of the Portuguese Roadmap for Research Infrastructures; 3) Objectives of ELIXIR; 4) Objectives of EOSC; 5) Sustainable Development Goals of the United Nations.</p>
RookID: an annotated dataset of vocalisations produced by individually-identified rooks housed together in an outdoors aviary in France
<p>A dataset of annotated recordings of a captive colony of rooks, recorded in Strasbourg, France in 2020 and 2021. Each rook was individually identifiable with leg rings. All recordings were taken in the morning a few hours after sunrise, when the birds were most vocally active. The colony was housed outdoors, so other noises are present, including both biotic (most notably various birds, human voices, and other animals) and abiotic (mostly car and train noises).</p> <p>Audio files (.wav): recorded at 48 kHz, 16-bit using 1 to 3 Song Meter 4 recorders (Wildlife Acoustics). Each recorder had two microphone with different gains to maximise dynamic range. The files were then manually synchronised and merged into multichannel (2 to 6) files.</p> <p>Label files (.tsv): Labels corresponding to each recording (each pair has the same name), noting the time stamps and individual emitter for each vocalisation. A single observer annotated all the recordings. Only rook vocalisations from the captive colony were annotated, not other bird vocalisations or the various noises in the data. The annotations consist of tables with 5 columns: </p> <ul> <li>Source: the individual producing the vocalisation. Note that only the bird's name is indicated. "Inc" and "Pls" are special cases: the first was for when identity could not be determined, the second when multiple individuals vocalised at once in such a manner that individuals could not be separated</li> <li>Start: starting time point for the vocalisation, in seconds (determined as the earliest point when the vocalisation was heard on any channel)</li> <li>End: ending time point for the vocalisation, in seconds (determined as the last point when the vocalisation was head on any channel)</li> <li>Event: gives information for the bird's activity at the time of the vocalisation, but largely in abbreviated form. One particular case is "sing", which correspond to vocalisations part of a song bout (which are defined as sequences of different vocalisations separated by less than approximately 10 seconds).</li> <li>Comment: other observations regarding the vocalisation. These are usually not standardised compared to the Event column. One special case is for "Pls": the Comment column then bears information regarding the identity of the individuals involved.</li> </ul> <p> </p> <p>This dataset was used in our article "Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks", to train neural networks to identify individual rooks. The dataset was therefore randomly split into train-validation-test datasets. For reproducibility, we provide the "splitting.csv" which contains the information pertaining to which files go in each dataset, and two scripts to do the split automatically.</p> <p>To do so: download and unpack the RookID folder somewhere on your computer, then download splitting.csv and either of the scripts to the same location. Both scripts will MOVE, not copy, the files to new folders corresponding to each dataset.</p> <ul> <li>with split_data.R: open the scrip in an RStudio environment, edit the out_path variable to the desired location, and run the script</li> <li>with split_data.py: run the following command line: python /path/to/split_data.py --out_path path/to/desired/location (note that the script will automatically create the necessary tree structure)</li> <li>Both scripts can be run without editing the out_path variables, in which case the new folders will be created at the same location</li> </ul> <p> </p> <p>For further information, see our code at <a href="https://gitlab.com/kimartin/rook-vocalisation-detection">https://gitlab.com/kimartin/rook-vocalisation-detection</a></p> <p>For any inquiries, please contact Killian Martin (<a href="mailto:killian.martin@ens-lyon.fr?subject=Inquiry%20about%20the%20RookID%20dataset">killian.martin@ens-lyon.fr</a>)</p>
Using snapshot measurements to identify high-emitting vehicles
<p>This repo includes codes and sample data for Qiu and Borken-kleefeld, ERL, 2022.</p> <p><strong>Material for reproducing figures in the paper</strong></p> <ul> <li>R script: plot.r</li> <li>Data for plot 2: <ul> <li><em>RS_Zurich_data.csv</em>: the sample RS data from Zurich.</li> <li><em>algorithm_eu5d_final_iteration.rds</em>: the estimated average emission factor for each city fleet (outputs from the iterative algorithm)</li> </ul> </li> <li>Data for plot 3:<em> </em> <ul> <li><em>Zurich_clean_identification.xlsx</em>: summary of the fraction of clean vehicles being identified by each potential RS threshold. </li> <li><em>Zurich_high_emitter_identification.xlsx</em>: summary of the fraction of high-emitters being identified by each potential RS threshold.</li> </ul> </li> <li>Data for plot 4: <ul> <li><em>validation_test_dataset.csv</em>: the original validation dataset that includes the underlying average emission factor and the simulated instantaneous emissions.</li> <li><em>validation_algorithm_results.rds</em>: algorithm outputs when applied to the validation dataset.</li> </ul> </li> </ul> <p><strong>The iterative algorithm and sample data that can be used for demonstration</strong></p> <ul> <li>Algorithm script: <em>iterative_algorithm.r</em></li> <li>Sample RS data: <em>RS_Zurich_data.csv</em></li> <li>Sample PEMS/Chassis test cycles: <em>sample_pems_chassis_cycles.csv</em></li> </ul>
Mapping the Atlantic Ocean i.e. the Gulf of Maine to identify suitable cultivation sites for kelp species
<p>Input source:</p> <ul> <li>Temperature data</li> <li>Depth data</li> <li>Wave data</li> <li>Nutrients data</li> <li>Current data</li> <li>Marine use data</li> </ul> <p><strong>All from other available sources outside the project</strong></p> <p> </p> <p>DATA SET GENERATED:</p> <ul> <li>Environmental data</li> <li>Training/validation data</li> <li>The socioeconomic datasets</li> </ul> <ul> <li>Map of suitable sites</li> <li>Model using GIS</li> </ul>
Identifying and profiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with Machaon - Pre-computed features for replication
<p>Machaon's computed features that were used in the structural comparisons with Spike protein.</p> <p>DATA_PDBS_vir_whole_1-3.zip files are parts of a single folder.</p> <p> </p>
Identifying Source Code File Experts
<p>This replication package contains the data which allows to reproduce and extend the results presented in the paper study.</p>
Raw data for the article "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping"
<p>Raw data used for the article "Gerber, Andreas, Markus Ulrich, Flurin X. Wäger, Marta Roca-Puigròs, João S.V. Gonçalves, and Patrick Wäger. 2021. "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping" <em>Sustainability</em> 13, no. 4: 1997. <a href="https://doi.org/10.3390/su13041997">https://doi.org/10.3390/su13041997</a>"</p> <p>The documents include the raw data (both as .csv and .xlsx files with the same content), as well as the publication (.pdf file). The data collection process and the data itself are described in the publication. The data is published as "supplementary material" on the publisher's homepage.</p>
ROR Identifiers That Have Disappeared
<p>During the period between October, 2019 and September, 2021 some organizations were given GRIDs (and RORs) that later disappeared from the GRID database and the ROR database. These identifiers currently exist in metadata, but they can not be resolved. This dataset was created by finding RORs that existed in some version(s) of the GRID database but were dropped in some subsequent version. The dataset includes ~900 RORs that existed in some versions of the GRID database, but not in other, more recent versions.</p> <p>The dataset has four columns separated by commas (CSV):</p> <ol> <li>found: the last GRID version to include the ROR</li> <li>notFound: the first GRID version without the ROR</li> <li>affiliation: the organization name associated with the GRID</li> <li>ror: the ROR</li> </ol> <p>The dates of the GRID versions are approximate.</p>
Summary statistics for "Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer's Disease"
<p>These are the burden test results (summary statistics) for the publication:</p> <p>"Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s Disease",</p> <p>Nature Genetics, 2022.</p> <p> </p> <p><em>Format: tab-separated-value.</em></p> <p><em>Fields:</em></p> <ul> <li><em>gene_stable_id: Ensembl gene id</em></li> <li><em>gene_name: standard gene name</em></li> <li><em>pvalue: burden test significance (likelihood ratio test, population structure correction based on 6 PCA components)</em></li> <li><em>cmac_all: sum of minor allele dosages across all contributing samples and variants</em></li> <li><em>group: variant group (LOF, LOF+REVEL>=75, LOF+REVEL>=50, LOF+REVEL>=25, see publication methods for further selection criteria).</em></li> <li><em>beta/se: beta/se of logistic ordinal regression (see publication methods). Positive = risk-increasing. Negative = risk-decreasing.</em></li> </ul> <p> </p>
GO-FISH: Geolocated Ocean-Fishery Identified Spawning Habitats
<p>This dataset represents geocoded spawning regions for 1,045 marine fish species described in the Fishbase (https://www.fishbase.se/) and Science and Conservation of Fish Aggregations (SCRFA, <a href="https://www.scrfa.org/database/">https://www.scrfa.org/database/</a>) datasets. These global databases have painstakingly aggregated the fieldwork of countless biologists and ecologists to summarize our knowledge of fish species. We further constrained geographic locations using AquaMaps (<a href="https://www.aquamaps.org/">https://www.aquamaps.org</a>) to produce 2,931 polygons or groups of polygons, which we call "spawning regions".</p> <p>Reproduction code for the dataset is available at <a href="https://github.com/openmodels/spawning-dataset">https://github.com/openmodels/spawning-dataset</a>, archived at <a href="../records/11098955">https://zenodo.org/records/11098955</a>.</p>
Plasmids Identified in Air Metagenomes
<p> Metagenomic data were selected in Web of Science (Clarivate) on October 2022 using keywords: txid655179[Organism:noexp] AND metagenome [Filter]; AIR Metagenome; Air microbiome; Troposphere; Aerosol; Atmosphere. Data were manually curated to remove sequencing originated from metabarcoding data (i.e., 16S). The assembled data supplied by MetaSUB consortium (Danko et al., 2021) when available was used for air metagenome in the built environments. </p> <div> <p>Plasmid contents were predicted using the assembled data. Metagenomes sequencing by Illumina (paired-illumina reads) were assembled by using megahit 1.2.9 with metalarge option (Li et al., 2015) after cleaning data with bbduk2 (qtrim=rl trimq=28 minlen=25 maq=20 ktrim=r k=25 mink=11 and a list of adaptators to remove) from bbtools suite (<a href="https://jgi.doe.gov/data-and-tools/software-tools/bbtools/" target="_blank" rel="noreferrer noopener">https://jgi.doe.gov/data-and-tools/software-tools/bbtools/</a>) </p> <div> <p><span><span>Plasmids were predicted for each assembling by using scripts describing in-depth in Hilpert et al. (Hilpert </span></span><span><span>et al.</span></span><span><span>, 2021; </span><span>Hennequin</span> </span><span><span>et al.</span></span><span><span>, 2022) and available in </span><span>github</span><span> website (</span></span><span><span><span>https://github.com/meb-team/PlasSuite/</span></span></span><span><span>). Briefly, contigs were analyzed using both reference-based and reference-free approaches.</span></span><span><span> The databases employed included those for chromosomes (archaea and bacteria) and plasmids from NCBI, as well as the MOB-suite tool (Robertson and Nash, 2018</span><span>) ,</span><span> SILVA (Quast </span></span><span><span>et al.</span></span><span><span>, 2013) and phylogenetic markers harbored by chromosomes (Wu </span></span><span><span>et al.</span></span><span><span>, 2013). Two reference-free methods were applied to contigs that were not affiliated with chromosomes (discarded) or plasmids (</span><span>retained</span><span> in the first step): </span><span>PlasFlow</span><span> (Krawczyk et al., 2018) and </span><span>PlasClass</span><span> (Pellow </span></span><span><span>et al.</span></span><span><span>, 2020). Viruses were removed by using </span><span>viralVerify</span><span> (</span></span><span><span><span>https://github.com/ablab/viralVerify</span></span></span><span><span>) (Antipov </span></span><span><span>et al.</span></span><span><span>, 2020) that provides in parallel provide plasmid/non-plasmid classification</span><span>. </span><span> </span><span>The database built for this purpose is available at this address </span></span><span><span><span>https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases</span></span></span> <span><span> Eukaryotes contaminants were removed by aligning the sequences against NT databases and human chromosomes (GRCh38) with minimap2 with -x asm5 </span><span>option (Li, 2018)</span><span>. Contigs mapping with an identity of 95% and a coverage of 80% were removed.</span></span><span> the final plasmidome set was clustered by mmseqs (Mirdita, Steinegger and Söding, 2019) with 80% of coverage and 90% of identity (--min-seq-id 0.90 -c 0.8 --cov-mode 1 --cluster-mode 2 --alignment-mode 3 --kmer-per-seq-scale 0.2). </span></p> </div> </div>
Variant dataset and code for "Population-level whole genome sequencing of Ascochyta rabiei identifies genomic loci associated with isolate aggressiveness"
<p>This dataset contains genetic variants (SNPs) of <em>Ascochyta rabiei</em> isolates and the R code used in their analysis to generate the results and figures described in the manuscript "<strong>Population-level whole genome sequencing of <em>Ascochyta rabiei</em> identifies genomic loci associated with isolate aggressiveness</strong>".</p> <div> <div> </div> </div>
Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty
<p>Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"</p>
Raw data of the study: Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a northern Andean city
<p>This datasheet contains raw data on bird count records made from 2016 and 2019. Data were taken in urban and adjacent non-urban areas of Medellín, Colombia. It was part of a collaborative sampling effort during environmental assessments and personal research, summarizing systematic information on 139 sampling points (124 within the city and 15 in adjacent non-urban areas). All points were sampled under the same protocol in order to facilited data for research; in all cases, sampling was in charge of ornithologist with at least 4 years of previous experience in bird surveys. This protocol consisted in sampling during 10 minutes, four times per point (i.e., repetitions), using a fixed radius of 25 m. </p> <p>Information on bird surveys (Count_Data within the corresponding datasheet tab) contains the ID of each site; whether corresponded to a urban or non-urban site; in what category of urban development the site was located, based on 1000, 500 and 200 m buffers (from the observer during bird counts: moderate, low or high); the taxonomic information of each species (order, family, scientific name); the number of recorded individuals; the repetition or number of the visit (1, 2, 3, or 4); the name of the project; the name of the observer, and the date of sampling. </p> <p>Information on categorization of bird species (Categorization within the corresponding datasheet tab) represents additional information on altitudinal ranges, trophic guilds, distribution, and others. In addition, information on frequency for each bird species is given, according to the location of each sampling site and the way it was grouped. This information was the base for categorizing bird species as urban avoider, utilizer, or dweller, under the calculations and decision rules that are also given within the corresponding cells of the datasheet.</p> <p>Any further information or questions about this data could be ask directly, writing to the e-mails: jgarizabal@unal.edu.co or njmacer@unal.edu.co.</p> <p> </p>
Mapping between zbMATH Open identifiers, DOIs, ORCIDs and arXiv identifiers
<p>The second version of the mapping between zbMATH Open identifiers for <a href="https://www.wikidata.org/w/index.php?title=Property:P1556&oldid=1755821772">authors</a> and <a href="https://www.wikidata.org/w/index.php?title=Property:P894&oldid=1766254659">documents</a> and <a href="https://www.wikidata.org/wiki/Property:P356">DOIs</a> and <a href="https://www.wikidata.org/wiki/Property:P496">ORCIDs</a> in CSV format.</p> <ul> <li>The file authors.csv contains the mapping between zbMATH Open author id and ORCIDs for 38 159 authors.</li> <li>The file documents.csv contains the mapping between zbMATH Open document id and DOI for 2 813 563 documents.</li> </ul> <p>Beginning from this version, we also provide the mapping between <a href="https://www.wikidata.org/w/index.php?title=Property:P894&oldid=1766254659">documents</a> and <a href="https://www.wikidata.org/w/index.php?title=Property:P818&oldid=2151126552">arXiv</a> in CSV format</p> <ul> <li>The file arxiv.csv contains the mapping between zbMATH Open document id and arXiv identifiers for 528 640 documents.</li> </ul> <p>See https://zbmath.org/about/ (section Full Text Links) for a live version of this dataset. That version is more current but less reproducible. Moreover, the dataset here is restricted to documents with a permanent zbMATH Open identifier in the form <code>Zbl d+.d+</code>.</p> <p> </p> <p> </p>
Identified Charcoal Hearths from "Slope Analysis of 'Digital Elevation Model for Blue Mountain Charcoal Research Project'"
<p>This is a GeoJSON file that lists all of the potential charcoal hearths along the Blue Mountain of eastern Pennsylvania. For a detailed description of how this data was produced, please see:</p> <p>Carter, Benjamin. (2018, May 29). Description of Methods for Identifying Charcoal Hearths along the Blue Mountain of Pennsylvania. (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1255101</p> <p>These hearths were identified using this data:</p> <p>Carter, Benjamin. (2018). Slope Analysis of "Digital Elevation Model for Blue Mountain Charcoal Research Project" (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252977</p> <p>The above is derived from:</p> <p>Carter, Benjamin P. (2018). Digital Elevation Model for Blue Mountain Charcoal Research Project (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252441</p> <p> </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.