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.
2,286
datasets available to search
ShareScore release 0.9.0
Dataset results
2,286 results for “Microbiome”
Full-length and split homologs of human proteins in the gut microbiome
<p>These files were generated as part of the manuscript "Human xenobiotic metabolism proteins have full-length and split homologs in the gut microbiome" (submitted).</p> <p>The .tar file contains .ipc files that are tables of full-length (full_humcover3.ipc) and split homologs (part_humcover3.ipc) of human proteins in the gut microbiome, organized by alignment coverage threshold. For example, the directory `HumanUPR_0.67_src_20000_70` contains results obtained at a 67% alignment coverage threshold for the bacterial protein, and 70% for the human protein. Note that our pipeline collapses full-length alignments to the same UHGP-90 protein family into a single entry per species, with the number of genomes reported in the column nGenomes. Split homologs are not collapsed because genomic context is used to define them, and this context may differ across individual genomes.</p> <p>These files are in Arrow <a href="https://arrow.apache.org/docs/python/ipc.html#ipc">IPC</a> format, which provides compression and fast I/O for large tables. We recommend reading them using <a href="https://pola.rs/">pola.rs</a> or the <a href="https://arrow.apache.org/docs/r/">R Arrow</a> package. In particular, because the full-length homolog table is large, you may wish to work with it without loading it into memory, which can be accomplished using <a href="https://docs.pola.rs/api/python/dev/reference/api/polars.scan_ipc.html">scan_ipc</a> in pola.rs or <a href="https://arrow.apache.org/docs/r/reference/open_dataset.html">open_dataset</a> in R Arrow.</p> <p>We also provide gzipped .csv format datasets of full-length (pgkb_FH_drugs.csv.gz) and split (pgkb_SH_drugs.csv.gz) homologs, at the default 67% alignment coverage threshold for bacterial and 70% for human proteins, organized by their <a href="https://www.pharmgkb.org/">PharmGKB</a> annotations. For each drug annotated in PharmGKB as being metabolized by a human protein with full-length or split homologs, we provide the human protein(s) responsible, its xenobiotic enzyme class, the bacterial protein homolog(s), length and percent identity of the alignment, and either the specific genome (g, split homologs only) or the number of genomes (nGenomes, full homologs only). Xenobiotic enzyme classes are defined as in Figure 4 of the manuscript, with the additional classes "nucl" (nucleobase-containing metabolic proteins not annotated to any other class), "redox" (oxidoreductases not annotated to any other class), and "other" (all remaining proteins).</p>
Microbiome homeostasis on rice leaves is regulated by a precursor molecule of lignin biosynthesis
<p>A GWAS pipeline for identification of the loci associated with >3000 bacterial species (Selected from over 6000 bacterial species of rice Phyllosphere).</p><p> </p>
Inter-Chemical Correlation results for the study: HHEARx2018-2120 (The impact of tobacco smoke exposure and environmental exposures on the pulmonary microbiome and outcomes of critically ill children)
Title: The impact of tobacco smoke exposure and environmental exposures on the pulmonary microbiome and outcomes of critically ill children <br>Species: Homo sapiens <br>Number of samples: 1090 <br>Number of named analytes: 12 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=42 <br>
Human Microbiome Compendium dataset
<p>The Human Microbiome Compendium is an ongoing project to build a large collection of human microbiome sequencing data processed with a uniform pipeline. Currently, the compendium contains 16S rRNA amplicon sequencing data for human gut microbiome samples retrieved from the Sequence Read Archive. Our website at <strong><a href="https://microbiomap.org">microbiomap.org</a></strong> has more information about the project and links to related resources.</p> <p>This data is freely available under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International license</a> (<strong>CC BY 4.0</strong>). If you use it in your work, please cite our publication:</p> <p>Abdill, Richard J., Samantha P. Graham, Vincent Rubinetti, et al. “Integration of 168,000 Samples Reveals Global Patterns of the Human Gut Microbiome.” Cell 188, no. 4 (2025): 1100–18. <a href="https://doi.org/10.1016/j.cell.2024.12.017" target="_blank" rel="noopener">https://doi.org/10.1016/j.cell.2024.12.017</a></p> <p>If you are using this dataset in combination with your own results, it's important to note that the taxonomic classifications may differ between releases, as documented in CHANGELOG.md. The most recent release (1.1.1) includes assignments made using <strong><a href="https://www.arb-silva.de/news/view/2024/07/11/silva-release-1382/">SILVA 138.2</a></strong> (SSU Ref NR 99) and <strong>Greengenes2</strong> (2022.10 backbone).</p>
Dietary fibers boost gut microbiome-produced B vitamin pool and alter host immune landscape
<p>This dataset contains fcs files of lymphocytes from the colonic lamina propria, lungs, and spleens of specific-pathogen-free (SPF), gnotobiotic (14-member synthetic microbiota, 14SM) or germ-free (GF) mice fed five distinct rodent diets (Standard chow 1, SC1; Standard chow 2, SC2; Fiber-supplemented diet, FS; Inulin-supplemented diet, IN; or Fiber-free diet, FF), analysed by mass cytometry. Three million cells per organ per animal were transferred into 15 mL conical tubes. For live/dead staining, cells were incubated with 5 μM cisplatin for 5 minutes. Cells were washed, and cell surface staining mix was added containing pre-conjugated antibodies for 30 minutes at room temperature. Samples were washed twice with FACS buffer, then fixed using the FoxP3 Fix/Perm kit (eBiosciences) for 45 minutes at 4°C, followed by permeabilization wash. Samples were then incubated with the intracellular staining mix for 30 minutes at room temperature. Cells were washed with FACS buffer twice, and pellets were resuspended in Cell-ID™ Intercalator-Ir (Fluidigm) in MaxPar fixation solution (Fluidigm, catalogue no. 201192B) and refrigerated overnight, or for up to five days. Prior to acquisition, samples were washed twice with 1X PBS, and then washed twice with deionized water. Cell pellets were further resuspended in deionized water at 0.5 × 10^6 cells/mL and topped up with 10% calibration beads (EQ Four Element Calibration Beads, Fluidigm). All samples were acquired on the Helios Mass Cytometer (Fluidigm). Effector immune populations and activated T cells in the gut accumulate in a microbiota-dependent manner. Shifts in the microbiome according to dietary fiber source and content result in altered concentrations of B vitamins available to the host, which is tied to distinct alterations in innate and adaptive immune populations. </p>
Graphic Illustration of Kelly Speer's Keynote Talk: Hosts, parasites, and microbiomes: A system for studying natural complexity in a changing world
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded and synthesized the Keynote Talk by Kelly Speer at the Digital Data 2024 Conference in Lawrence, Kansas in May of 2024. We include this resource, with permission, because of its relevance to our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Experimental Factors Influence Diversity Metrics of the Gut Microbiome in Laboratory Mice
<p>Abstract<br> Introduction</p> <p>Gut microbiome studies often overlook experimental factors that could influence gut microbiome diversity and could impact findings. Large-scale studies investigating these experimental factors are lacking. Thus, we aimed to determine which experimental factors influence the gut microbiome diversity in pre-clinical animal model studies.</p> <p><br> Methods</p> <p>We extracted DNA and sequenced the V4 region of the 16S rRNA gene of a total of 538 samples from various sections of the gastrointestinal tract of 303 young and aged male and female C57BL/6J mice of three different genotypes on five diets from three animal house facilities. As a proof-of-concept in a disease model, some mice were treated with sham or angiotensin II, a commonly studied agent used as a hypertension model. Some samples were sequenced twice as a matched-comparison group.</p> <p>Results</p> <p>Using over 17 million sequencing reads, we found that experimental factors such as animal house facility, genotype, diet, age, sex, sampling site, and technical factor (i.e., sequencing batch) affected both α- and β-diversity (weighted and unweighted UniFrac), and were associated with compositional changes in the microbiome at varying magnitude, with diet and sampling site having the largest effect. After adjustment by these factors, treatment with angiotensin II had no impact on α-diversity and was only significant in unweighted UniFrac (presence/absence of bacteria) analyses.</p> <p><br> Conclusion</p> <p>Our data identified several key experimental and technical factors that affect the gut microbiome in laboratory mice. Our findings support that not accounting or adjusting for these factors may lead to false-positive discoveries and non-biologically relevant findings in the gut microbiome field.</p>
Animal Gut Microbiome (AGM) Data from 91 Published Studies for 224 Animal Species
Diversity and heterogeneity often are conflated but are fundamentally different. An aphorism proposed by Shavit and Ellison (2021; J. Phil. 118: 525–548) for distinguishing them is that “a zoo is diverse whereas an ecosystem is heterogeneous.” That is, a zookeeper measuring diversity simply enumerates the different types of animals; interactions are not expected to occur between animals separated by fences or other barriers. In contrast, measures of heterogeneity ought to include both interspecific interactions and relationships between species and their heterogeneous habitats. Here, we use cross-scale, dual scaling-law analyses of heterogeneity and diversity of animal gut microbiomes (AGMs) to address three objectives: (i) estimate the spatial heterogeneity and diversity of animal-gut microbiomes; (ii) analyze influences of phylogeny and diets on scaling of diversity and heterogeneity; (iii) explore mechanistic differences between diversity and heterogeneity in AGMs. From 4903 AGM samples collected from 318 animal species covering all six classes of vertebrates and four major classes of invertebrates, we estimated that ≈640,000 operational taxonomic units (OTUs or “species”) make up the pool of microbial species that could inhabit animal guts, among which ≈8000 are relatively common and ≈800 are dominant. The gut of any single animal, however, includes only 0.01–0.5% of the total species pool. We extended Ma’s diversity-area relationship for scaling diversity and extend Taylor’s Power Law and Luna et al.’s (2020; Diversity 12: 86) interaction diversity for scaling heterogeneity. At the community scale, phylogeny significantly influenced heterogeneity, but diets did not. Phylogeny and diets had limited influence on diversity at both community and landscape scales. Although two common measures of diversity—beta diversity and unevenness—commonly are synonymized with heterogeneity, our data lead us to conclude that diversity and heterogeneity measure two very different
Helminths, polyparasitism, and the gut microbiome in the Philippines
<p>Uploaded here are the raw FASTA files associated with this publication. Labelled P1-10, and 11-220.</p> <p><a href="https://zenodo.org/api/files/e3103a63-45d0-4b24-9786-d4f3a0f6534b/OTU%20tables%2C%20annotations%20and%20code%20book.zip">OTU tables, annotations and code book.zip</a> Annotations and OTU files for International Journal of Parasitology paper "Helminths, Polyparasitism, and the Gut Microbiome in the Philippines". Data set from human helminth infections from Palapag, the Philippines. Code book contains information on headings for the annotation file. OTUs generated by AGRF. Originally uploaded here; (<a href="http://dx.doi.org/10.17632/59j46prhvf.1">https://doi.org/10.17632/59j46prhvf.1</a>) </p> <p> </p> <p> </p>
Psoriasis is associated with elevated gut IL-1α and intestinal microbiome alterations
<p>Background: Psoriasis is a chronic inflammatory condition that predominantly affects the skin and is associated with extracutaneous disorders, such as inflammatory bowel disease and arthritis. Changes in gut immunology and microbiota are important drivers of proinflammatory disorders and could play a role in the pathogenesis of psoriasis. Therefore, we explored whether psoriasis in a Central Asian cohort is associated with alterations in select immunological markers and/or microbiota of the gut. Methods: We undertook a case-control study of stool samples collected from outpatients, aged 30-45 years, of a dermatology clinic in Kazakhstan presenting with plaque, guttate or palmoplantar psoriasis (n=20), and age-sex matched subjects without psoriasis (n=20). Stool supernatant was subjected to multiplex ELISA to assess the concentration of 47 cytokines and immunoglobulins and to 16S rRNA gene sequencing to characterize microbial diversity in both psoriasis participants and controls. Results: The psoriasis group tended to have higher concentrations of most analytes in stool (29/47=61.7%) and gut IL-1α was significantly elevated (4.19-fold, p=0.007) compared to controls. Levels of gut IL-1α in the psoriasis participants remained significantly unaltered up to three months after the first sampling (p=0.430). Psoriasis was associated with alterations in gut Firmicutes, including elevated Faecalibacterium and decreased Oscillibacter and Roseburia abundance, but no association was observed between gut microbial diversity or Firmicutes/Bacteroidetes ratios and disease status.<br> Conclusions: Psoriasis may be associated with gut inflammation and dysbiosis. Studies are warranted to explore the use of gut microbiome-focused therapies in the management of psoriasis in this under-studied population.</p>
Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis (supporting data)
<div> <div>This archive contains the supporting data and code for <a href="https://doi.org/10.1093/ismeco/ycaf089" target="_blank" rel="noopener">Meilander et al., 2024: <em>Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis</em></a>.</div> <div> </div> <div><strong>Clicking the links below will open the corresponding files using QIIME 2 View (<a href="https://view.qiime2.org" target="_blank" rel="noopener">https://view.qiime2.org</a>). </strong></div> <div> </div> <div> <div> <div>Summaries of master data files:</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/asv-table.qzv/content" target="_blank" rel="noopener">Summary of master feature table (<code>asv-table.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/sample-metadata.qzv/content">Tabulated view of sample metadata (<code>sample-metadata.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/asv-seqs-ms10.qzv?download=1" target="_blank" rel="noopener">Summary of ASV sequences observed in at least 10 samples: (<code>asv-seqs-ms10.qzv</code>)</a></div> <div> </div> <div>PCoA plots:</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/braycurtis.qzv/content" target="_blank" rel="noopener">Bray-Curtis Emperor plot (<code>braycurtis.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/jaccard.qzv/content" target="_blank" rel="noopener">Jaccard Emperor plot (<code>jaccard.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/unweighted_unifrac.qzv/content" target="_blank" rel="noopener">Unweighted UniFrac Emperor plot (<code>unweighted_unifrac.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/weighted_unifrac.qzv/content" target="_blank" rel="noopener">Weighted UniFrac Emperor plot (<code>weighted_unifrac.qzv</code>)</a></div> <div> </div> <div>Taxonomy barplots:</div> <div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/taxa-bar-plots-bucket2-gtdb-r214.1-weighted-stool-taxonomy.qzv?download=1">Taxonomy bar plot for Bucket 2 only (<code>taxa-bar-plots-bucket2-gtdb-r214.1-weighted-stool-taxonomy.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/taxa-bar-plots-bucket3-gtdb-r214.1-weighted-stool-taxonomy.qzv?download=1" target="_blank" rel="noopener">Taxonomy bar plot for Bucket 3 only (<code>taxa-bar-plots-bucket3-gtdb-r214.1-weighted-stool-taxonomy.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/taxa-bar-plots-gtdb-r214.1-weighted-stool-taxonomy.qzv/content" target="_blank" rel="noopener">Taxonomy bar plot for all samples (<code>taxa-bar-plots-gtdb-r214.1-weighted-stool-taxonomy.qzv</code></a>)</div> </div> <div> </div> <div>q2-fmt "raincloud plots":</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/hec-raincloud.qzv/content">Raincloud plot (<code>hec-raincloud.qzv</code>)</a></div> <div> </div> </div> </div> <div> </div> <div>The linked <code>.qzv</code> files are also contained in the <code>gut-to-soil-qiime2.zip</code> zip file, along with all relevant data artifacts (<code>.qza</code> files).</div> <div>The <code>.qzv</code> files are also maintained outside of the <code>.zip</code> file to facilitate their viewing with QIIME 2 View.</div> </div> <div> </div> <div> <div>Code for generating figures 1 and 2 (and corresponding supplemental figures):</div> <div><code>gut-to-soil-manuscript-figures-main.zip</code> (also see: <a href="https://github.com/caporaso-lab/gut-to-soil-manuscript-figures" target="_blank" rel="noopener">https://github.com/caporaso-lab/gut-to-soil-manuscript-figures</a>)</div> <div> </div> <div>Code for generating ridgeline plots (Figure S6):</div> <div><code>gut-to-soil-ridgeline-plots-main.zip</code> (also see: <a href="https://github.com/caporaso-lab/gut-to-soil-ridgeline-plots" target="_blank" rel="noopener">https://github.com/caporaso-lab/gut-to-soil-ridgeline-plots</a>)</div> </div> <div> <div> </div> </div>
Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment.
<p>The R code scripts and Supplementary data files from the paper: "Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment," accepted in Communications Biology. All R code and data necessary to reproduce the published results are available. </p>
Asaia spp. accelerate development of the yellow fever mosquito, Aedes aegypti, via interactions with the vertically transmitted larval microbiome
<p><strong><span>Background:</span></strong><em> Aedes aegypti</em> mosquitoes are the primary vectors of yellow fever, dengue, chikungunya and Zika virus. Control programs primarily rely on insecticide application, which encounter challenges related to efficacy and resistance evolution. Alternative strategies, such as the sterile insect technique, highly depend on efficient mass-rearing of healthy insects prior to mass release. Based on effects seen in other mosquito species, we tested the hypothesis that acetic acid bacteria <span>of the </span><em>Asaia</em> <span>genus are</span> mutualist<span>s</span> for developing <em>Ae. aegypti</em> larvae. We tested for beneficial interactions across three <em>Asaia </em>species and whether <em>Asaia</em> inoculation benefited both axenic and conventionally reared larvae. To better understand the underlying mechanisms, we characterized the larval microbiome<span> </span>using culture-based methods and 16S rRNA gene amplicon sequencing.</p> <p><strong>Results:</strong><span> <span>Even</span></span> though <em>Asaia </em>bacteria were transient members of the gut community in conventionally reared insects<span>, t</span>wo <em>Asaia </em>species accelerated larval development relative to controls.<span> Despite their transient nature, </span>the two mutualist <em>Asaia</em> species had lasting impacts on the larval microbiome, mostly by altering the relative abundance of the most dominant bacteria genera <em>Klebsiella</em> and <em>Pseudomonas</em> and other minor components<span>.</span> Axenic larvae that were inoculated with <em>Asaia </em>were dominated by this group, but always exhibited slower development than conventionally reared insects.</p> <p><strong>Conclusions:</strong> These results reveal <em>Asaia</em> as a poor mutualist for <em>Ae. aegypti</em>, with its<em> </em>positive effect on the host mediated by interactions with other bacteria. A practical application of <em>Asaia </em>for improving mass-rearing efficiency results from the acceleration of development time to pupation by a day.</p>
Supplementary data: Winter cover cropping: Effect on soybean and synergistic implications on soil microbiome
<p>Supplementary data: (i) Agronomic and quality data of soybean (2 varieties) grown in 2 years (2020 & 2021) in two management systems (organic & low-input) with different cover crops; (ii) Soil microbiome analysis of the soybean field trials.</p>
Mink fecal microbiomes are influenced by sex, temperature and time post-defecation
<p>Mink metadata, QIIME2 artifacts and demultiplexed EMP-paired end sequences from Argonne National laboratory, R code for statistical analyses and figure generation, and QIIME2 pipeline for Lafferty et al. 2021.</p>
Data from: Public human microbiome data are dominated by highly developed countries
<p>Supplementary tables and datasets associated with the <em>PLOS Biology </em>publication "Public human microbiome data are dominated by highly developed countries." See README.txt for a description of the files and the data fields they contain.</p> <p>Update, 8 Apr 2022: The "figures.md" file described in the readme was inadvertently left out of the initial upload. It has been added here.</p>
Altered infective proficiency of the gut microbiome following COVID-19
<p><strong>The effects of SARS-CoV-2 infections comprise of many heterogeneous symptoms including several involving the human gastrointestinal tract. We assess the effects of COVID-19 on the host microbiome</strong></p>
Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome
<p>Demultiplexed fastq files used for sequencing analysis, processed taxonomy read data for all samples and controls, and relevant environmental metadata as described in "Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome".</p>
EMERGE Sample Metadata Sheet for Samples with Microbiomes
<p>The EMERGE Sample Metadata Sheet is a data compilation published by the <a href="https://emerge-bii.github.io/">EMERGE Biology Integration Institute</a>, providing basic (meta)data (including field sample info and biogeochemistry data) for samples with linked metagenomes (metaGs). It was generated by parsing and cleaning up the "Depth-Info" and "Biogeochemistry" query output from the EMERGE Database (EMERGE-DB; <a href="https://emerge-db.asc.ohio-state.edu/">https://emerge-db.asc.ohio-state.edu/</a>). Additional information, including column descriptions, is provided in the accompanying PDF (see list of included files below).</p> <p>FILES:</p> <ul> <li><strong>coring_geochem_sequenced_samples_1.1.0.csv </strong>- The Sample Metadata Sheet.</li> <li><strong>variable_defs_availability_sequenced_samples_public_1.1.0.pdf</strong> - A README / FAQ file with additional information and variable descriptions for the Sample Metadata Sheet.</li> <li><strong>variables_coring_geochem_sequenced_samples_public_1.1.0.csv</strong> - CSV version of the variable descriptions table from the README / FAQ.</li> </ul> <p>FUNDING:<br> This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p>
The effect of dietary bioactive on gut microbiome diversity (DIME) – a pilot study
<p>The DIME study consists of a randomised 2x2 cross-over human intervention where healthy participants (n = 20) are subjected to a diet high in bioactive-rich food for two weeks and a diet low in bioactive-rich food. There is a four-week washout between the two interventions. </p> <p>The continuous glucose monitoring was achieved using the Abbott freesylte libre flash glucose device. The baseline of the participants were determined 7 days before the start of the intervention, followed by the first arm and second arm. The period between the two arms (washout) was not recorded.</p> <p>We also included sleep data which consists of the amount of time spent in bed and during that time the amount of time spent in light, deep and rem in all 20 participants during the course of the dietary intervention, both the high and low bioactive diet. that was captured using Fitbit wearables during both stages of the dietary intervention,</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.