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.
19,342
datasets available to search
ShareScore release 0.9.0
Dataset results
19,342 results for “Sequences”
Alignments and ML trees of cassava brown streak virus and Ugandana cassava brown streak virus polyprotein nucleotide sequences
<p>Alignments of full and nearly full polyprotein-length nucleotide sequences from GenBank for the two ipomoviruses that cause cassava brown streak disease, in fasta format. Separate alignments for 67 cassava brown streak virus sequences and 81 Ugandan cassava brown streak virus sequences are provided, as well as a combined alignment of 148 sequences. Alignments were created with MUSCLE and then modified by eye in AliView.</p> <p>Also, two tree files (in nexus) format are supplied, resulting from a maximum likelihood analysis with IQTree on each of the two single-species datasets. Support for nodes with aLRT and 100 actual bootstrap replicates are provided (aLRT/bootstrap).</p>
A Simulated Heterozygous Diploid Genome for Third-gen Sequencing, Assembly, and Curation
<p>A simulated heterozygous diploid genome based on <em>Saccharomyces</em> <em>cerevisiae</em>, and <em>S. paradoxus</em> homologous chromosomes.</p> <p>Simulated PacBio subreads were generated from both parent haplomes and mixed together. A phased assembly was produced using FALCON assembler and FALCON Unzip (doi:10.1038/nmeth.4035). This dataset and assembly were then used to validate the Purge Haplotigs pipeline (https://bitbucket.org/mroachawri/purge_haplotigs). See workflow.sh for commands, comments and file descriptions.</p>
Data underpinning "Pulse sequence considerations for interleaved chemical exchange saturation transfer acquisition sequences."
<p>=================================================<br> Robert Casper Brand, PhD Candidate<br> Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.<br> =================================================</p> <p>This folder contains the images and datasets used to generate the figures of the paper named: "Pulse sequence considerations for interleaved chemical exchange saturation transfer acquisition sequences." </p> <p>Each figure of the paper, with its corresponding data, is contained in an opensource TikZ format file. The TikZ files include both information on the axis as well as the supporting data and can be opened with any generic text editor. For more information on TikZ, see:<br> https://www.sharelatex.com/learn/TikZ_package). </p> <p>Where datasets were too large to be run by standard TeX distributions, the data was attached in an alternative format, and a TikZ wrapper included.</p> <p>The figures can be generated through any of the opensource TeX distributions. For more information on LaTeX and TeX, please see: <br> https://www.latex-project.org/get/ and<br> https://www.sharelatex.com/learn/Pgfplots_package.</p> <p>A compilation example of all figures, which also lists any additional packages, is included in the "wrapper. Tex" file. The output of this process was added to this folder as well (wrapper.pdf).</p> <p>The included files were created using directly from Matlab using the matlab2tikz code:<br> https://www.mathworks.com/matlabcentral/fileexchange/22022-matlab2tikz-matlab2tikz</p>
Fly through a 2.3Mb Nanopore Sequence
<p>A spiral representation of the longest sequence found by <a href="https://doi.org/10.1101/312256">Alex Payne et al.</a>, which at the time of upload was the longest observed public single-pore nanopore sequence. This video shows about 30kb of the sequence at once, and zooms through the sequence at about 20kb per second in the animated GIF, or about 48kb per second in the AVI file.</p>
bin3C - simulated community and associated sequencing datasets
<p>We simulated a human gut microbiome comprising 63 genomes from the GTDB annotated with an isolation source of faeces. No two genomes are more than 96% similar in terms of ANI.</p> <p>A Generalized Pareto distribution was used to model an abundance profile, which was assigned in random order to the references. There is a 50:1 difference between the most and least abundant member.</p> <p>Illumina shotgun and Hi-C reads were simulated using MetaART and sim3C (https://github.com/cerebis/sim3C).</p> <p>A sweep was performed over depth of coverage, by serially subsampling initial high depth readsets. Shotgun depth was parameterised by the most abundant at 250x, while Hi-C was parameterised by the number of pairs (200 million pairs).</p> <p>Shotgun was subsampled once, at half depth (125x), while Hi-C was subsampled 4 times (12.5, 25, 50, 100, 200 million pairs).</p> <p>The random seed used throughout was 12345.</p> <p>These simulated readsets were then analyzed using bin3C to retrieve metagenome-assembled genomes (MAGs). The resulting genome bins were validated using CheckM to estimate completeness and contamination.</p> <p> </p> <p> </p> <p> </p>
Human Family With Sequence Similarity 83 Member B (FAM83B); A Target Enabling Package
<p>FAM83A-H are newly identified oncogenes characterised by a conserved DUF1669 domain. FAM83B can substitute for RAS to promote malignant transformation. Ablation of FAM83B or mutation of Lys230 inhibits malignant phenotypes, implicating FAM83B as potential therapeutic target. As part of this TEP, we solved the first crystal structures from the FAM83 family, including FAM83A and FAM83B. The structures of the DUF1669 domain reveal a phospholipase D-like fold lacking conservation of key catalytic residues. We deorphanise the FAM83 DUF1669 domain as a critical docking scaffold for binding of casein kinase 1 isoforms. Finally, using XChem fragment screening we report chemical fragments that bind to Lys230 in the central pocket of the DUF1669 and form starting points for potential drug development.</p>
Comparison of X-ray crystal structures of a tetradecamer sequence d(CCCGGGTACCCGGG)2 at 1.7 Å resolution.
<p>We presented a comparison of three different X-ray crystal structures of DNA tetradecamer sequence d(CCCGGGTACCCGGG)2 all at about 1.7 Å resolution. The sequence was designed as an attempt to form a DNA four-way junction with A-type helical arms. However, in the presence of zinc, magnesium, and in the absence of any metal ion, it does not take up the junction structure, but forms an A-type double helix. This allowed us to study possible conformational changes in the double helix due to the presence of metal ions. Upon addition of the zinc ion, there is a change in the space group from P41212 to P41. The overall conformation of the duplex remains the same. There are small changes in the interaction of the metal ions with the DNA. In the zinc-bound structure, there are two zinc ions that show direct interaction with the N7 atoms of terminal G13 bases at either end of the molecule. There are small changes in the interhelical contacts. The consequence of these differences is to break some of the symmetry and change the space group.</p>
Comparison of X-ray crystal structures of a tetradecamer sequence d(CCCGGGTACCCGGG)2 at 1.7 Å resolution.
<p>We presented a comparison of three different X-ray crystal structures of DNA tetradecamer sequence d(CCCGGGTACCCGGG)<sub>2 </sub>all at about 1.7 Å resolution. The sequence was designed as an attempt to form a DNA four-way junction with A-type helical arms. Crystals did not have any metal ion in the crystallization solution apart from the sodium in the buffer. However, the sequence does not take up the junction structure, but forms an A-type double helix in P4<sub>1</sub>2<sub>1</sub>2 space group.</p>
Comparison of X-ray crystal structures of a tetradecamer sequence d(CCCGGGTACCCGGG)2 at 1.7 Å resolution.
<p>We presented a comparison of three different X-ray crystal structures of DNA tetradecamer sequence d(CCCGGGTACCCGGG)<sub>2 </sub>all at about 1.7 Å resolution. The sequence was designed as an attempt to form a DNA four-way junction with A-type helical arms. However, in the presence of magnesium, it does not take up the junction structure, but forms an A-type double helix in P4<sub>1</sub>2<sub>1</sub>2 space group. This allowed us to study possible conformational changes in the double helix due to the presence of metal ions. Though the crystallization condition has magnesium chloride, it was not observed in the electron density map.</p>
Simulated Arabidopsis thaliana sequencing datasets for chloroplast assembler benchmarking
<p><strong>Changes</strong></p> <ul> <li>Fixed non-circular sampling from chloroplast and mitochondrion in version 1.1.0</li> <li>Fixed off-by-one error in reverse read in version 1.0.0</li> </ul> <p><strong>Purpose and Documentation</strong></p> <p>See: <a href="https://github.com/chloroExtractorTeam/benchmark">github.com/chloroExtractorTeam/benchmark</a></p> <p><strong>Original data</strong><br> The original <em>Arabidopsis thaliana </em>sequences were downloaded from TAIR: </p> <p>The Arabidopsis Information Resource (<a>TAIR</a>) on www.arabidopsis.org, Mar 22, 2019 available under the <a href="http://www.arabidopsis.org/doc/about/tair_terms_of_use/417">TAIR Terms of Use</a> </p> <p><em>Tanya Z. Berardini, Leonore Reiser, Donghui Li, Yarik Mezheritsky, Robert Muller, Emily Strait and Eva Huala. "The Arabidopsis Information Resource: Making and mining the "gold standard" annotated reference plant genome." genesis 2015 <a href="https://doi.org/10.1002/dvg.22877">doi:10.1002/dvg.22877</a></em></p> <p><strong>Programs used to generate this data</strong><br> - <a href="https://github.com/shenwei356/seqkit">seqkit</a> (v0.10.1): Shen W, Le S, Li Y, Hu F (2016) "SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation." PLOS ONE 11(10): e0163962. <a href="https://doi.org/10.1371/journal.pone.0163962">doi:10.1371/journal.pone.0163962</a></p> <p> </p>
Annotation of Phytozome V12 protein plant sequences using the ragp pipeline for hydroxyproline-rich glycoprotein mining
<p>Hydroxyproline aware annotation of hydroxyproline-rich glycoprotein (HRGP) sequences was performed on sequence data from 62 plant proteomes obtained from Phytozome database (<a href="https://phytozome.jgi.doe.gov/pz/portal.html">https://phytozome.jgi.doe.gov/pz/portal.html</a>, version 12) using the ragp R package (<a href="https://github.com/missuse/ragp">https://github.com/missuse/ragp</a>, version 0.3.0.0001). </p> <p>In each archive a single comma separated value table (.csv) is present along with a README.txt file describing the contents of the corresponding .csv file. The archives are:</p> <p>- phytozome_V12.tar.gz - sequences from 62 plant proteomes (phytozome V12) with a total of 2797062 protein sequences.</p> <p>- phytozome_V12_phobius.tar.gz -<strong> </strong> Signal peptide prediction using Phobius (<a href="http://phobius.sbc.su.se/">http://phobius.sbc.su.se/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_signalp.tar.gz -<strong> </strong> Signal peptide prediction using SignalP 4.1 (<a href="http://www.cbs.dtu.dk/services/SignalP-4.1/">http://www.cbs.dtu.dk/services/SignalP-4.1/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_targetp.tar.gz - Signal peptide prediction using TargetP 1.1 (<a href="http://www.cbs.dtu.dk/services/TargetP/">http://www.cbs.dtu.dk/services/TargetP/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_predict_hyp.tar.gz - Probability of proline hydroxylation for each proline from 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1).</p> <p>- phytozome_V12_maab.tar.gz - Motif and amino acid bias (MAAB) classification of hydroxyproline-rich glycoproteins performed on 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1). The number of predicted hydroxyprolines in each sequence is also indicated (based on predictions provided in phytozome_V12_predict_hyp.tar.gz).</p> <p>- phytozome_V12_scan_ag.tar.gz. - Hydroxyproline aware arabinogalactan motif scan performed on 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1). Hydroxyproline predictions are provided in phytozome_V12_predict_hyp.tar.gz. </p> <p>- phytozome_V12_scan_ag_hmmscan.tar.gz - Detection of domains in a subset of protein sequences which were found to contain arabinogalactan motifs (a subset of phytozome_V12_scan_ag.tar.gz).</p> <p>The list of the 62 plant species is provided in phytozome_V12.tar.gz README.txt.</p> <p>For questions contact mdragicevic@ibiss.bg.ac.rs.</p> <p> </p> <p> </p>
Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method
<p>This dataset contains a GitHub repository containing all the data, analysis, Nextflow workflows and Jupyter notebooks to replicate the manuscript titled "Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method".</p> <p>It also contains the Multiple Sequence Alignments (MSAs) generated and well as the main figures and tables from the manuscript.</p> <p>The repository is also available at GitHub (https://github.com/cbcrg/dpa-analysis) release `v1.2`.</p> <p>For details on how to use the regressive alignment algorithm, see the T-Coffee software suite (https://github.com/cbcrg/tcoffee).</p>
Dataset to the publication "A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking"
<p>This repository accompanies the paper:</p> <p>"A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking".</p> <p>Organized into 14 folders it provides the data allowing the replication of all analyses and the manuscript's figures. For a more convenient download files were zipped.</p> <p>Upon publication of results using this dataset, please cite the following paper:</p> <p>Uetz-von Allmen E, Rippl AV, Farhan H, Legler DF. 2018. A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking. J Leukoc Biol 104(2):375-389.</p>
Aligned Cox1 and Cob sequences from Oikopleura dioica and other tunicates.
<p>Supporting data for the manuscript « <em>Widespread use of the “ascidian” mitochondrial genetic code in tunicates</em> » containing a) assemblies of the cytochrome oxidase subunit 1 (Cox1) and cytochrome b (Cob) mitochondrial genes from ESTs downloaded from the Oikobase database and b) protein alignment of these sequences and other tunicate sequences to study which genetic code is used in tunicate mitochondria.</p>
Sample data for analysis of sequence variation in HIV
<p>These are downsampled interleaved paired fastq datasets from Jair et. 2019 (<a href="https://doi.org/10.1371/journal.pone.0214820">https://doi.org/10.1371/journal.pone.0214820</a>). The datasets were prepared by:</p> <ol> <li>Downloading original data from NCBI SRA (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA517147)</li> <li>Trimming contaminating Nextera adapters using trim-galore</li> <li>Mapping reads against nxb2 reference of HIV genome (K03455.1) with BWA MEM</li> <li>Restricting mapped reads to <em>pol</em> gene vicinity (K03455.1:2000-5100)</li> <li>Downsampling mapped data to ~10% of the original with Picard's DownsampleSam</li> <li>Converting BAM to Interleaved Fastq with Picard's SamToFastq</li> <li>Gzipping resultant interleaved paired fastq files</li> </ol>
Temperature and rainfall datasets for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting"
<p>This page includes spatiotemporal datasets used in the paper <a href="https://doi.org/10.1016/j.neucom.2020.09.060">STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting.</a></p> <p>ARIMA and deep learning models use datasets, as follow:</p> <ul> <li>ARIMA models</li> </ul> <p>baseline-chirps-1981-2019.nc (rainfall data)<br> baseline-ucar-1979-2015.nc (temperature data)</p> <ul> <li>Deep learning models:</li> </ul> <p><em>5-step ahead:</em></p> <p>dataset-chirps-1981-2019-seq5-ystep5.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep5.nc (temperature data)</p> <p><em>15-step ahead:</em></p> <p>dataset-chirps-1981-2019-seq5-ystep15.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep15.nc (temperature data)</p>
LevSeq epPCR data from ParPgb LQ from MinION sequencer
<p>This is the data published with the LevSeq preprint (https://doi.org/10.1101/2024.09.04.611255), these data provide a test case for users to confirm that they are able to run the pipelines and also the data to reproduce the results in the preprint. Each folder contains the raw fastq files from the MinION sequencer for a protoglobin variant for several plates, along with the required input file to run LevSeq.</p> <p>The data are basecalled reads, generated using the standard Oxford nanopore sequencing protocol. For experimental methods and details please see the paper.</p> <ol> <li><a href="../api/records/13694463/draft/files/20240421-YL-ParLQ-ep1.csv/content" target="_blank" rel="noopener noreferrer">20240421-YL-ParLQ-ep1.csv</a> is the reference file for one set of plates</li> <li><a href="../api/records/13694463/draft/files/20240502-YL-ParLQ-ep2.csv/content" target="_blank" rel="noopener noreferrer">20240502-YL-ParLQ-ep2.csv</a> is the reference file for the second set of plates</li> <li>20240421.zip is the raw data for 20240421-YL-ParLQ-ep1.csv </li> <li>20240502.zip 20240502-YL-ParLQ-ep2.csv</li> <li><a href="13694463" target="_blank" rel="noopener noreferrer">20240502-YL-ParLQ-ep2.fastq.zip</a> is the combined fastq files for the run from <a href="../api/records/13694463/draft/files/20240502-YL-ParLQ-ep2.csv/content" target="_blank" rel="noopener noreferrer">20240502-YL-ParLQ-ep2</a> for ease of use</li> <li><a href="../api/records/13694463/draft/files/20240422-YL-ParLQ-ep1.fastq.zip/content" target="_blank" rel="noopener noreferrer">20240422-YL-ParLQ-ep1.fastq.zip</a> is the combined fastq files for the run from <a href="../api/records/13694463/draft/files/20240421-YL-ParLQ-ep1.csv/content" target="_blank" rel="noopener noreferrer">20240421-YL-ParLQ-ep1</a> for ease of use</li> </ol>
Ferry et al. 2024 - Prey that is attractive but not repelled by predators suggests an asymmetric investment in the encounter-avoid-escape sequence. - R Code and Datasets
<p>R code for formating data and running PAMMs for all different combinations of predator-prey.</p> <p>Data of camera trap observation.</p> <p>Data of environmental variable associated to camera trap sites.</p>
Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars
<p>Supporting data for peer-reviewed publication entitled: 'Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars', published in A&A. For the purpose of open access, the authors have applied a CC BY licence to the author accepted manuscript version and made it publicly available: <a href="https://arxiv.org/abs/2410.12726">https://arxiv.org/abs/2410.12726</a></p> <p>Evolutionary models and stability window calculations courtesy of Jermyn et al. 2022 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac4e89">10.3847/1538-4357/ac4e89</a>) are publicly available via: <a href="https://github.com/adamjermyn/conv_trends">https://github.com/adamjermyn/conv_trends</a></p> <p>TESS full-frame image data are publicly available from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI): <a href="https://archive.stsci.edu/missions-and-data/tess">https://archive.stsci.edu/missions-and-data/tess</a></p> <p>TESS light curves (provided in this repository) were extracted using the publicly available tglc (Han & Brandt 2023; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/acaaa7">10.3847/1538-3881/acaaa7</a>) software package: <a href="https://github.com/TeHanHunter/TESS_Gaia_Light_Curve">https://github.com/TeHanHunter/TESS_Gaia_Light_Curve </a></p> <p>SLF variability parameters (provided in this repository; cf. Tables 1 and 2 of the paper) were obtained using GP regression with the publicly available celerite2 (Foreman-Mackey et al. 2017; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/aa9332">10.3847/1538-3881/aa9332</a>) software package: <a href="https://celerite2.readthedocs.io/en/latest/">https://celerite2.readthedocs.io/en/latest/</a> and confidence intervals were obtained using the publicly available pymc3 (Salvatier et al. 2016; <a href="https://doi.org/10.7717/peerj-cs.55">https://doi.org/10.7717/peerj-cs.55</a>) software package: <a href="https://github.com/pymc-devs/pymc">https://github.com/pymc-devs/pymc</a></p> <p>This research was supported in part by the National Science Foundation (NSF) under Grant Number NSF PHY-1748958; the Research Foundation Flanders (FWO) with grant agreement numbers 1286521N, 11F7120N, and V411621N; UK Research and Innovation (UKRI) in the form of a Frontier Research grant under the UK government's ERC Horizon Europe funding guarantee (SYMPHONY; grant number: EP/Y031059/1); a Royal Society University Research Fellowship (URF; grant number: URF\R1\231631); and the KU Leuven Research Council (grant number C16/18/005: PARADISE).</p>
gapseq reference sequence databases for Bacteria and Archaea
<p>The repository contains the protein sequences used by <a href="https://github.com/jotech/gapseq">gapseq</a> to predict the presence of metabolic reactions and to construct metabolic models.</p> <p>The workflow using gapseq to generate this set of reference protein sequences:</p> <p> </p> <p>```sh</p> <p># delete all "old" data<br>rm dat/seq/Bacteria/rev/*.fasta<br>rm dat/seq/Bacteria/unrev/*.fasta<br>rm dat/seq/Bacteria/rxn/*.fasta<br>rm dat/seq/Archaea/rev/*.fasta<br>rm dat/seq/Archaea/unrev/*.fasta<br>rm dat/seq/Archaea/rxn/*.fasta</p> <p># run gapseq find to re-download everything#<br># the genome is irrelevant as no blasting is performed ('-x')<br>gapseq find -p all -t Bacteria -n -x -U toy/ecoli.faa.gz > bac_update.log 2>&1<br>gapseq find -p all -t Archaea -n -x -U toy/ecoli.faa.gz > ar_update.log 2>&1</p> <p># create all sequence .tar.gz archives (rev/unrev/rxn)<br>cd dat/seq/Bacteria/rev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Bacteria/unrev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Bacteria/rxn/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/rev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/unrev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/rxn/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../</p> <p># create md5sum table for all tar.gz archives<br>cd dat/seq/<br>find -mindepth 2 -type f -name "*.tar.gz" -exec md5sum {} \; > md5sums.txt</p> <p># create taxon-specific final archive for Zenodo upload<br>tar -czvf Bacteria.tar.gz Bacteria/*/*.tar.gz<br>tar -czvf Archaea.tar.gz Archaea/*/*.tar.gz</p> <p># Upload Bacteria.tar.gz, Archaea.tar.gz, and md5sums.txt to Zenodo via the web-interface</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.