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.
595
datasets available to search
ShareScore release 0.9.0
Dataset results
595 results for “Repertoire”
Multifaceted quality assessment of gene repertoire annotation with OMArk
<p>Dataset associated to the OMArk paper.</p><p>Contain eight archives:</p><p>Supplementary_Tables</p><p>The Supplementary Table files referred to in the paper</p><p>OMAmerDB:</p><p>The OMAmer database constructed using the whole dataset of the OMA database (November 2022 Release) and used in the paper. An OMAmer database is necessary to run OMArk.</p><p>Simulation:<br>Proteomes with artificially introduced errors, contaminants or depleted completeness, used to assess OMArk's performance. The archive contains the generated proteomes (Simulated_Data) and their OMArk quality assessments (omark). They also contains the OMAmer results (OMAmerResults) that were used to run OMArk and BUSCO completeness assessments (BUSCO).</p><p>*Note that for storage efficiency, only the non-redundant part of the data (added errors, added contamination, random fraction of proteomes) are stored there. The full modified proteome can be regenerated from these data and the source proteomes.</p><p>Reference Proteomes:</p><p>The UniProt Reference Proteomes (Proteomes) (2021_04) and their proteome quality assesment results according to OMArk. The archive contains the source proteome FASTA (Source folder), OMAmer results for these proteomes (omamer folder) , OMArk results (omark folder), and BUSCO completeness assesments (BUSCO folder). It also contains a subfolder that contains part of the Contamination detection experiment (Contamination folder).</p><p>Ensembl_Metazoa_AssemblyChange.<br><br>Contains Ensembl Metazoa proteomes with version change between version 52 and 54 as well as their quality assesment resuls for both version. The archive contains the source proteomes FASTA (Source folder), a Splice file that group together all proteins coded by the same gene (Splice folder), omamer results for the proteomes (omamer folder) and the omark results (omark folder)</p><p>MissingGenesBLAST<br><br>Contains sequences of HOGs considered as missing in the Human proteome, that was used to look for sequences in the human genome.</p><p>Ensembl_NCBI_Results</p><p>Contains OMArk and BUSCO results for Ensembl and NCBI proteomes. These results were then used to evaluate OMArk biais due to source of proteomes in the OMA database.</p><p>Notebooks<br>Jupyter Notebooks that were used to perform the analysis described in the paper<br><br> </p>
PRJNA638224 - BCR repertoire sequencing from COVID-19 patients
<p><strong>Description</strong></p> <p>These are the processed BCR repertoire sequence data that accompany the following manuscript: “Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures”. The manuscript preprint is available at doi: <a href="https://doi.org/10.1101/2020.05.20.106294">https://doi.org/10.1101/2020.05.20.106294</a>. The raw sequence data are available on SRA under the BioProject PRJNA638224</p> <p> </p> <p><strong>Sequence processing</strong></p> <p>The Immcantation framework (docker container v3.0.0) was used for sequence processing. Briefly, paired-end reads were joined based on a minimum overlap of 20 nt, and a max error of 0.2, and reads with a mean phred score below 20 were removed. Primer regions, including UMIs and sample barcodes, were then identified within each read, and trimmed. Together, the sample barcode, UMI, and constant region primer were used to assign molecular groupings for each read. Within each grouping, usearch, was used to subdivide the grouping, with a cutoff of 80% nucleotide identity, to account for randomly overlapping UMIs. Each of the resulting groupings is assumed to represent reads arising from a single RNA. Reads within each grouping were then aligned, and a consensus sequence determined. For each processed sequence, IgBlast was used to determine V, D and J gene segments, and locations of the CDRs and FWRs. Isotype was determined based on comparison to germline constant region sequences. Sequences annotated as unproductive by IgBlast were removed.</p> <p> </p> <p><strong>Sequence data column description</strong></p> <ul> <li><strong>sample_id </strong>Unique identifier for each sequencing library</li> <li><strong>sequence_id </strong>Unique identifier for a sequence within a sample_id</li> <li><strong>sequence_alignment </strong>IMGT gapped nucleotide sequence</li> <li><strong>germline_alignment </strong>IMGT gapped germline sequence</li> <li><strong>v_call </strong>IGHV gene segment(s) and allele</li> <li><strong>d_call </strong>IGHD gene segment(s) and allele</li> <li><strong>j_call </strong>IGHJ gene segment(s) and allele</li> <li><strong>c_call </strong>Isotype subclass</li> <li><strong>junction </strong>Junction nucleotide sequence</li> <li><strong>junction_aa </strong>Junction amino acid sequence</li> <li><strong>duplicate_count </strong>UMI count for the given unique sequence</li> <li><strong>consensus_count </strong>Raw read count for the given unique sequence</li> </ul> <p> </p> <p><strong>Sequence metadata column description</strong></p> <ul> <li><strong>sample_id </strong>Unique identifier for each sequencing library</li> <li><strong>bioproject_accession </strong>NCBI BioProject accession number</li> <li><strong>biosample_accession </strong>NCBI BioSample accession number</li> <li><strong>sra_accession </strong>NCBI SRA accession number</li> <li><strong>sex </strong>Sex of patient</li> <li><strong>age </strong>Age of patient at time of sampling</li> <li><strong>ethnicity </strong>Ethnicity of patient</li> <li><strong>health_state </strong>One of worsening, stable, or improving</li> </ul>
Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection
<p>Processed TCRbeta and TCRalpha repertoires after mild COVID-19 (Version 2.0: day 85 timepoints added) infection, see preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3">https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3</a></p> <p>and GitHub repository: <a href="https://github.com/pogorely/Minervina_COVID">https://github.com/pogorely/Minervina_COVID</a></p> <p>Two donors (M and W), two biological replicates of PBMC (F1 and F2), CD4+, CD8+, and Memory subpopulations for each post-infection time points (day 15, 30, 37, 45, 85 post-infection), and pre-infection PBMC repertoires sampled in 2019 and 2018. </p>
Datasets for "The Venturia inaequalis effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins "
<p>Datasets for preprint entitled "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi"</p> <p><strong>1) ViAnnotation.gff3</strong><br> Gene annotation of <em>Venturia inaequalis</em> MNH120 (<a href="https://genome.jgi.doe.gov/Venin1/Venin1.home.html">https://genome.jgi.doe.gov/Venin1/Venin1.home.html</a>) generated as part of the study "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi". </p> <p>Gene reannotation was performed to include genes that would have been missed in the previous annotation by Deng et al. (2017), especially those genes encoding putative effector proteins, which are difficult to predict. For this purpose, we used a three-step approach. In the first step, coding sequences (CDSs) from <em>V. inaequalis</em> isolate 05/172, which were predicted as part of a previous study by Passey et al. (2018) (<a href="https://journals.asm.org/doi/full/10.1128/MRA.01062-18">https://journals.asm.org/doi/full/10.1128/MRA.01062-18</a>), were downloaded from the National Center for Biotechnology Information (<a href="https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/">https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/</a>) and mapped to the MNH120 genome using GMAP v2021-02-22. In the second step, RNA-seq reads from one biological replicate representing each <em>in planta</em> time point of <em>Malus domestica</em> infection by <em>V. inaequalis </em>(12 hour post-inoculation [hpi], 24 hpi, 2 days post-inoculation [dpi], 3 dpi, 5 dpi, 7 dpi), as well as one time point representing growth of the fungus in culture, were mapped to the MNH120 genome using HISAT2 v2.2.1. Then, a genome-guided <em>de novo</em> transcriptome assembly was performed using Trinity v2.12.0 and likely CDSs were identified using Transdecoder v5.5.0 (<a href="https://github.com/TransDecoder/TransDecoder">https://github.com/TransDecoder/TransDecoder</a>) in conjunction with a minimum open frame (ORF) length of 50 amino acids. Finally, in the third step, all annotations were visualized in Geneious v9.05, together with the previous annotation from Deng et al. (2017), and a manual curation was performed to create a consensus prediction. Note: this reannotation was generated with the aim of identifying as many genes as possible, and as a result, it contains many spurious genes. </p> <p><strong>2) Protein_sequences_ViAnnotation.fasta</strong></p> <p><strong>3) ECs_Families_AlphaFold.zip</strong></p> <p>This dataset is made up of predicted protein tertiary structures representing the main member of each up-regulated <em>V. inaequalis</em> effector candidate family. Structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). In cases where the effector candidate had less than 30 proteins with amino acid sequence similarity in the NCBI database, a custom multiple sequence alignment (MSA) was generated and used as input for AlphaFold2. Here, mature protein sequences were used.</p> <p><strong>4) singletons_AlphaFold_OpenSourceCASP14.zip</strong></p> <p>This dataset set is made up of predicted protein tertiary structures representing up-regulated<em> V. inaequalis</em> singleton effector candidates. Structures were predicted using AlphaFold (<a href="https://github.com/deepmind/alphafold">https://github.com/deepmind/alphafold</a>) open source code v2.0.1 and v2.1.0, with pre-set casp14, max_template_date: 2020-05-14. Mature protein sequences were used as input. </p> <p><strong>5) ECs_Avrs_phytopathogens_AlphaFold.zip</strong></p> <p>Predicted tertiary structures of avirulence (Avr) proteins or candidate Avr proteins from other fungal pathogens included in the "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi" study. These structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). Mature protein sequences were used as input. </p> <p>If you have any questions about the datasets, please contact us.<br> Mercedes Rocafort: <a href="mailto:m.rocafort.ferrer@massey.ac.nz">m.rocafort.ferrer@massey.ac.nz</a><br> Carl Mesarich: <a href="mailto:c.mesarich@massey.ac.nz">c.mesarich@massey.ac.nz</a></p>
Development of ferret immune repertoire reference resources and single-cell-based high- throughput profiling assays
<p>We performed long read transcriptome sequencing of ferret splenocyte and lymph node samples full-length, non-chimeric circular consensus sequencing (CCS) reads to obtain over 120,000 high-quality immunoglobin (Ig) and T cell receptor (TCR) transcripts.</p>
The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins
<p>Supplementary Dataset for the work entitled "The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins".</p> <p>This dataset includes files for the T3SS2 reconstructed phylogenetic tree (Newick tree and fasta file), hierarchical clustering data analysis file from MORPHEUS, Table S1 with genome accession numbers, and all the data of the absence/presence of T3SS2-related components, Table S2 with the prediction of novel effector proteins.</p>
Figure 7 in The vocal repertoire of Myrmeciza loricata (Lichtenstein, 1823) (Aves: Thamnophilidae)
Figure 7. Sonograms of other notes (call III) of Myrmeciza loricata. (A) Notes "D + E". (B) Note "F". (C) Note "I" emitted with call II. (D) Note "G". (E) Note "H". (F) Note "J" emitted with call II.
Figure 4 in The vocal repertoire of Myrmeciza loricata (Lichtenstein, 1823) (Aves: Thamnophilidae)
Figure 4. Sonograms of the call I (alarm) of Myrmeciza loricata. (A) Rattle phrases sequence. (B) Zoom showing the series of vertical tick notes.
Immune repertoire profiling reveals that clonally expanded B and T cells infiltrating diseased human kidneys can also be tracked in the blood
<p>Recent advances in high-throughput sequencing allow for the competitive analysis of the human B and T cell immune repertoire. In this study we compared Immunoglobulin and T cell receptor repertoires of lymphocytes found in kidney and blood samples of 10 patients with various renal diseases based on next-generation sequencing data.</p>
Dynamics of individual T cell repertoires: from cord blood to centenarians
<p>The dataset contains processed T-cell receptor repertoire sequencing data from 79 individuals of different sex and age, originally published in [1] and [2]. Note that [1] describes only a subset of samples, while [2] describes the full cohort.</p> <p>The libraries were prepared using a 5'RACE protocol and sequenced on HiSEQ. The libraries incorporate unique molecular identifier (UMI) tags that were mainly used for counting cDNA molecules. Preprocessing was performed using the MIGEC software [3] as follows: all UMI tags represented by a single sequencing read were discarded, the remaining UMI tags were used to assemble cDNA consensus sequences. Note that this procedure eliminates most of cross-sample contamination (batch effect) as described in [2]. VDJ partitioning and CDR3 extraction was performed using MiTCR software [4], sequencing error correction was performed using ETE option in MiTCR. All datasets are converted into VDJtools [5] format, see http://vdjtools-doc.readthedocs.io/en/latest/input.html#vdjtools-format.</p> <p><strong>Sample description:</strong></p> <ul> <li>The A* in sample identifier is the batch ID.</li> <li>Age and sex data is provided in the metadata.txt file.</li> <li>Samples having age "0" are umbilical cord blood samples.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>The T-cell repertoire aging study was a project ran in the Genomics of Adaptive Immunity Lab (Prof. Dmitry Chudakov)</li> <li>The samples were acquired, prepared and sequenced by Dr. Olga Britanova</li> <li>The data was analyzed and uploaded by Dr. Mikhail Shugay</li> </ul> <p><strong>Citations:</strong></p> <ul> <li>[1] OV Britanova, EV Putintseva, M Shugay, EM Merzlyak, MA Turchaninova, et al. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. The Journal of Immunology 2014; 192 (6), 2689-2698</li> <li>[2] OV Britanova, M Shugay, EM Merzlyak, DB Staroverov, EV Putintseva, et al. Dynamics of individual T cell repertoires: from cord blood to centenarians. The Journal of Immunology 2016; 196 (12), 5005-5013</li> <li>[3] M Shugay, OV Britanova, EM Merzlyak, MA Turchaninova, IZ Mamedov, et al. Towards error-free profiling of immune repertoires. Nature methods 2014; 11 (6), 653-655</li> <li>[4] DA Bolotin, M Shugay, IZ Mamedov, EV Putintseva, MA Turchaninova, et al. MiTCR: software for T-cell receptor sequencing data analysis. Nature methods 2013; 10 (9), 813-814</li> <li>[5] M Shugay, DV Bagaev, MA Turchaninova, DA Bolotin, OV Britanova, et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS computational biology 2015; 11 (11), e1004503</li> </ul>
Fig. 1. Residentmalependulinetitsreacttoplaybacksongandadummypendulinetit aroundtheirnest. Behaviouralresponsesincludedattacking, i.e in Acoustic Signalling In Eurasian Penduline Tits Remiz Pendulinus: Repertoire Size Signals Male Nest Defence
Fig. 1. Residentmalependulinetitsreacttoplaybacksongandadummypendulinetit aroundtheirnest. Behaviouralresponsesincludedattacking, i.e. peckingatthedummy, as
Fig. 2 in Acoustic Signalling In Eurasian Penduline Tits Remiz Pendulinus: Repertoire Size Signals Male Nest Defence
Fig. 2. SonogramsofsometypicalsyllabletypesofEurasianpendulinetits. Songbouts mayconsistofvarioussyllables (topandbottomsonograms) ormayincludemonotone
Fig. 3 in Acoustic Signalling In Eurasian Penduline Tits Remiz Pendulinus: Repertoire Size Signals Male Nest Defence
Fig. 3. Approachdistance (a) and % behaviouralresponses (b) towardsanintruderinre- lationtotheresidentmale'sownrepertoiresize. Behaviouralresponsesincludedcalling, singing, tailquiveringandattacking. Opencirclesindicateresponsesofchallengedresi- dentsonsmallrepertoireplayback, whereasfilledcirclesindicatethesamemales' respons- esonlargerepertoireplayback. Notethatpointsshownontheupperhalfregionof (a) represent males that were mostly present very close to their nest (15 m from the stimulus,
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: discovery cohort meta data and parsed TCR repertoire data
<p>Meta data corresponding the the discovery cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include: </p> <p>(1) a file mapping the SNP data subject IDs to the TCR repertoire data subject IDs (gwas_id_mapping.tsv)<br> (2) a file including the PCAir PCs, self-reported ancestry, and genomic ancestry for each subject (all_pc_air.txt)<br> (3) a file including the PCAir variance explained by each PC (all_pc_air_variance.txt)<br> (3) a file including the SNP ID, chromosome, hg19 position, allele, rsid, and quality control metrics for each SNP in the SNP array (emerson_snp_rs_data.tsv)<br> (4) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (5) a file including predicted TRBD2 allele genotypes for each subject (emerson_trbd2_alleles.tsv)<br> (6) Parsed TCRB repertoire data. These raw data were first published in Emerson et. al, <em>Nature Genetics </em>2017. (emerson_parsed_tcrb.tgz)</p> <p><strong>Corresponding discovery cohort raw TCR repertoire data is available here: </strong>https: //doi.org/10.21417/B7001Z (ImmuneACCESS database)<br> <strong>Corresponding discovery cohort SNP data is available here:</strong> https: //www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001918.v1.p1 (The database of Genotypes and Phenotypes, accession number: phs001918)<br> <br> <strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
Gewandhaus repertoire research dataset 1781–1895
<p>This dataset contains the listed repertoire (played works) of the Gewandhausorchestra in Leipzig from 1781–1895. Every case is represented by a work and its performance date and each work is enriched by a number of categories like "language of vocal work", "nationality of composer" and "genre".</p> <p>The research data is the main product of the dissertation "Repertoire and canon", <<a href="https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-810514">https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-810514</a>></p>
Fig. 2 in Song Repertoire And Origins Of Crimean Population Of Chiffchaff, Phylloscopus Collybita (Sylviidae)
Fig. 2. Songs of Crimean (A) and Caucasian (B) Chiffchaffs with identical specific Crimean song elements (marked).
Integrated data-driven reannotation of the Kluyveromyces marxianus genome reveals an expanded protein coding repertoire
<p>Supplementary data for Fenton et al. 2022. </p> <table> <tbody> <tr> <td>Supplementary Table</td> <td>ID</td> <td>Table Description</td> </tr> <tr> <td>supplementary table 1</td> <td>S1</td> <td>Transcript Start Site (TSS) metrics</td> </tr> <tr> <td>supplementary table 2</td> <td>S2</td> <td>Polyadenylation Site (PAS) metrics</td> </tr> <tr> <td>supplementary table 3</td> <td>S3</td> <td>NTE candidates </td> </tr> <tr> <td>supplementary table 4</td> <td>S4</td> <td>MTS candidates</td> </tr> <tr> <td>supplementary table 5</td> <td>S5</td> <td>iORFs candidates</td> </tr> <tr> <td>supplementary table 6</td> <td>S6</td> <td>uORFs candidates</td> </tr> <tr> <td>supplementary table 7</td> <td>S7</td> <td>ouORFs candidates</td> </tr> <tr> <td>supplementary table 8</td> <td>S8</td> <td>aORFs candidates</td> </tr> <tr> <td>supplementary table 9</td> <td>S9</td> <td>tRNA copy numbers</td> </tr> <tr> <td>supplementary table 10</td> <td>S10</td> <td>novel gene periodicity scores</td> </tr> <tr> <td>supplementary table 11</td> <td>S11</td> <td>description of novel genes</td> </tr> <tr> <td>supplementary table 12</td> <td>S12</td> <td>comparison of published genomes</td> </tr> <tr> <td>supplementary table 13</td> <td>S13</td> <td>table corrections</td> </tr> <tr> <td>supplementary table 14</td> <td>S14</td> <td>start codon corrections</td> </tr> <tr> <td>supplementary table 15</td> <td>S15</td> <td>Genes with splicing (at least one intron)</td> </tr> </tbody> </table>
Data from: Plant ammonium sensitivity is associated with the external pH adaptation, repertoire of nitrogen transporters, and nitrogen requirement
<p>Modern crops exhibit diverse sensitivities to ammonium as the primary nitrogen source, influenced by environmental factors such as external pH and nutrient availability. Despite its significance, there is currently no systematic classification of plant species based on their ammonium sensitivity. This study conducts a meta-analysis of 50 plant species and presents a new classification method based on the comparison of fresh biomass obtained under ammonium and nitrate nutrition. The classification uses the natural logarithm of biomass ratio as the size effect indicator of ammonium sensitivity. This numerical parameter is associated with critical factors for nitrogen demand and form preference, such as Ellenberg indicators and the repertoire of nitrogen transporters for ammonium and nitrate uptake. Finally, a comparative analysis of the developmental and metabolic responses, including hormonal balance, is conducted in two species with divergent ammonium sensitivity values in the classification. Results indicate that nitrate has a key counteracting role of ammonium toxicity in species with a higher abundance of genes encoding NRT2-type proteins and fewer of the AMT2-type proteins. Additionally, the study confirms the reliability of the phytohormone balance and methylglyoxal content as indicators for anticipating ammonium toxicity.</p>
Figure 4 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus
Figure 4. Seasonality in breeding records of White-rumped Monjita Xolmis velatus in Brazil based on citizen science data, the literature and this study.
Figure 3 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus
Figure 3. Food delivered to nestlings of White-rumped Monjita Xolmis velatus, Rio Claro, São Paulo, Brazil. A: larva, B: Oligochaeta, C: Erythemis vesiculosa, D: Zammara tympanum, E: Lepidoptera (moth), F: Blattodea, G: Myriapoda, H: Kentropyx aff. paulensis (Luiz Carlos Ramassotti)
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.