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.
1,940
datasets available to search
ShareScore release 0.9.0
Dataset results
1,940 results for “data sample”
Knowledge-Driven Perceptual Organization Reshapes Information Sampling Via Eye Movements – data
<p>Data from the article <strong>Knowledge-Driven Perceptual Organization Reshapes Information Sampling Via Eye Movements</strong> published in the Journal of Experimental Psychology: Human Perception and Performance by Marek A. Pedziwiatr, Elisabeth von dem Hagen, and Christoph Teufel</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@mail.com<br> January 2023</p>
Natrolite - Sample 2 NanED, Data ESR1 & ESR2
<p>The following submission contains the data collection frames for the sample Natrolite under the NanED round-robin project. Precession Electron Diffraction (PED) as well as Continuous Rotation Electron Diffraction were carried out on two different single crystal. The datasets were processed with PETS2 and XDS software and refined both kinematically and dynamically using Jana. The table below summarizes the data collection parameters for the data sets.</p> <p><strong>Continuous Rotation:</strong></p> <table> <tbody> <tr> <td><strong>General Information</strong></td> <td> </td> </tr> <tr> <td>Project</td> <td>NanED (www.naned.eu)</td> </tr> <tr> <td>ESR Project</td> <td>ESR1 & ESR2</td> </tr> <tr> <td><strong>Instrumental</strong></td> <td> </td> </tr> <tr> <td>Instrument</td> <td>Zeiss Libra 120</td> </tr> <tr> <td>Radiation source</td> <td>LaB6</td> </tr> <tr> <td>Accelerating voltage</td> <td>120 kV</td> </tr> <tr> <td>Wavelength</td> <td>0.0335 Å</td> </tr> <tr> <td>Probe Type</td> <td>Nanodiffraction</td> </tr> <tr> <td>Beam Diameter</td> <td>600 nm</td> </tr> <tr> <td>Beam Convergence</td> <td>Parallel beam</td> </tr> <tr> <td>Detector</td> <td>Timepix Single Electron Detector</td> </tr> <tr> <td>Number of pixels in the image</td> <td>512 x 512</td> </tr> <tr> <td>Physical pixel size</td> <td>55 µm * 55 µm</td> </tr> <tr> <td>Effective camera length</td> <td>144 mm</td> </tr> <tr> <td>Calibration constant</td> <td>0.006027 Å<sup>-1</sup>/pixel</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Sample description</strong></td> <td> </td> </tr> <tr> <td>Name</td> <td>Natrolite</td> </tr> <tr> <td>Chemical composition</td> <td>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>10</sub>·2H<sub>2</sub>O</td> </tr> <tr> <td>Sample source</td> <td>Natural sample from Marianska Skala, Usti nad Labem, Czechia</td> </tr> <tr> <td>Sample preparation</td> <td>Crushed with a mortar and diluted using isopropanol before depositing a drop on a Cu grid.</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Experimental</strong></td> <td> </td> </tr> <tr> <td>Data type</td> <td>3D Electron Diffraction (3D-ED)</td> </tr> <tr> <td>Data collection method</td> <td>Continuous Rotation Electron Diffraction (CRED)</td> </tr> <tr> <td>Rotation Speed</td> <td>2.00 º/s</td> </tr> <tr> <td>Temperature</td> <td>293 K</td> </tr> <tr> <td>Number of crystals contributing to the dataset</td> <td>1</td> </tr> <tr> <td>Number of experimental frames</td> <td>293</td> </tr> <tr> <td>Tilt range, tilt step</td> <td>-60º to +55, 0.4º</td> </tr> <tr> <td>Exposure time per frame</td> <td>200 ms</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Software</strong></td> <td> </td> </tr> <tr> <td>Software used for data collection</td> <td>WinTEM (Zeiss), Digistar (NanoMEGAS) and Sophy (Software for Physics)</td> </tr> <tr> <td>Software used for processing</td> <td>XDS</td> </tr> <tr> <td>Software used for solution</td> <td>Shelxt</td> </tr> <tr> <td>Software used for refinement</td> <td>Jana</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Authorship and bibliography</strong></td> <td> </td> </tr> <tr> <td>Author(s) of the data</td> <td>Moussa Diame FAYE (ESR1) & Vincentia Emerson Agbemeh (ESR2)</td> </tr> <tr> <td>Related data</td> <td> </td> </tr> <tr> <td>Publication(s)</td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> </tbody> </table> <p> </p> <p><strong>Precession:</strong></p> <table> <tbody> <tr> <td><strong>General Information</strong></td> <td> </td> </tr> <tr> <td>Project</td> <td>NanED (www.naned.eu)</td> </tr> <tr> <td>ESR Project</td> <td>ESR1 & ESR2</td> </tr> <tr> <td><strong>Instrumental</strong></td> <td> </td> </tr> <tr> <td>Instrument</td> <td>Zeiss Libra 120</td> </tr> <tr> <td>Radiation source</td> <td>LaB6</td> </tr> <tr> <td>Accelerating voltage</td> <td>120 kV</td> </tr> <tr> <td>Wavelength</td> <td>0.0335 Å</td> </tr> <tr> <td>Probe Type</td> <td>Nanodiffraction</td> </tr> <tr> <td>Beam Diameter</td> <td>150 nm</td> </tr> <tr> <td>Beam Convergence</td> <td>Parallel beam</td> </tr> <tr> <td>Detector</td> <td>Timepix Single Electron Detector</td> </tr> <tr> <td>Number of pixels in the image</td> <td>512 x 512</td> </tr> <tr> <td>Physical pixel size</td> <td>55 µm * 55 µm</td> </tr> <tr> <td>Effective camera length</td> <td>144 mm</td> </tr> <tr> <td>Calibration constant</td> <td>0.006027 Å<sup>-1</sup>/pixel</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Sample description</strong></td> <td> </td> </tr> <tr> <td>Name</td> <td>Natrolite</td> </tr> <tr> <td>Chemical composition</td> <td>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>10</sub>·2H<sub>2</sub>O</td> </tr> <tr> <td>Sample source</td> <td>Natural sample from Marianska Skala, Usti nad Labem, Czechia</td> </tr> <tr> <td>Sample preparation</td> <td>Crushed with a mortar and diluted using isopropanol before depositing a drop on a Cu grid.</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Experimental</strong></td> <td> </td> </tr> <tr> <td>Data type</td> <td>3D Electron Diffraction (3D-ED)</td> </tr> <tr> <td>Data collection method</td> <td>Precession</td> </tr> <tr> <td>Precession Semiangle</td> <td>1º </td> </tr> <tr> <td>Temperature</td> <td>293 K</td> </tr> <tr> <td>Number of crystals contributing to the dataset</td> <td>1</td> </tr> <tr> <td>Number of experimental frames</td> <td>116</td> </tr> <tr> <td>Tilt range, tilt step</td> <td>-59º to +55, 1º</td> </tr> <tr> <td>Exposure time per frame</td> <td>1000 ms</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Software</strong></td> <td> </td> </tr> <tr> <td>Software used for data collection</td> <td>WinTEM (Zeiss), Digistar (NanoMEGAS) and Sophy (Software for Physics)</td> </tr> <tr> <td>Software used for processing</td> <td>PETS2</td> </tr> <tr> <td>Software used for solution</td> <td>SIR2019</td> </tr> <tr> <td>Software used for refinement</td> <td>Jana</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Authorship and bibliography</strong></td> <td> </td> </tr> <tr> <td>Author(s) of the data</td> <td>Moussa Diame FAYE (ESR1) & Vincentia Emerson Agbemeh (ESR2)</td> </tr> <tr> <td>Related data</td> <td> </td> </tr> <tr> <td>Publication(s)</td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> </tbody> </table>
A global data set of realized treelines sampled from Google Earth aerial images
<div> <span>We </span><span>sampled</span><span> Google Earth aerial images</span><span> to get a representative and globally distributed dataset of treeline locations</span><span>. </span><span>Google Earth images</span><span> are available to everyone, but may not be automatically downloaded and processed according to Google's license terms. Since we only wanted to detect tree individuals, we evaluated the aerial images manually by hand.</span> </div> <div> </div> <div> <span>Doing so, we scaled Google Earth's GUI interface to a buffer size of approximately 6000 m from a perspective of 100 m (+/- 20 m) above Earth's surface. Within this buffer zone, we took coordinates and elevation of the highest </span><span>realized </span><span>treeline locations. In some remote areas of Russia and Canada, individual trees were not identifiable due to insufficient image resolution. If this was the case, no treeline was sampled, unless we detected another visible treeline within the 6,000 m buffer and took this next highest treeline</span><span>. We did not ap</span><span>p</span><span>ly an automated image processing approach. </span><span>We calculated mass elevation effect as the distance to the nearest mountain chain limits. Continentality was assessed by the distance to the nearest coastline. Isolation was calculated by the nearest distance of a mountain chain to another mountain chain within a comparable elevational band. </span> </div>
qPCR data for "An innovative passive sampling approach for the detection of cyanobacterial gene targets in freshwater sources"
<p>qPCR data to accompany the manuscript "An innovative passive sampling approach for the detection of cyanobacterial gene targets in freshwater sources"</p>
SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs
<p>SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs.</p>
Data for "Randomness in Local Optima Network Sampling" (GECCO Companion Proceedings, 2023)
<p>Data for "Randomness in Local Optima Network Sampling" (GECCO Companion Proceedings, 2023)</p>
data for Multi-tissue H3K27ac profiling of GTEx samples links epigenomic variation to disease
<p>processed data for "Multi-tissue H3K27ac profiling of GTEx samples links epigenomic variation to disease"</p>
Immune single-cell RNA-seq data from PyMT-M tumor and its peripheral blood samples
<p><em>Immune single-cell RNA-seq data collection and preprocessing</em></p> <p>Blood and tumor samples were harvested from PyMT-M tumor-bearing mice. Blood samples (n=3) are collected retro-orbitally using caliper tubes and processed with red blood cell lysis buffer (Tonbo Biosciences) before library preparation. A tumor sample are dissociated with Tumor Dissociation Kit following the manufacturer’s instructions (Miltenyi Biotec). After isolation and filtering through a 70µm filter, CD45+DAPI- cells were sorted using FACSAria cell sorter (BD Biosciences) at the Cytometry and Cell Sorting Core. The single-cell libraries were prepared using Chromium Controller (10X Genomics) at the Single Cell Genomics Core and sequenced using NovaSeq 6000 at the Genomics and RNA Profiling Core of Baylor College of Medicine. The FASTQ files were processed using Cell Ranger pipelines (10X Genomics) to generate feature-barcode matrices.</p> <p><em>Integrating immune single-cell RNA-seq data from the blood and tumor of PyMT-M mouse</em></p> <p>We followed the Seurat tutorial on single-cell RNA integration from <a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>. Specifically, both datasets were library-size normalized and log-scaled. Then, variable genes from both datasets were extracted, and overlapped variable genes were used as anchors to integrate the two datasets to generate a combined immune single-cell RNA-seq dataset.</p> <p> </p> <p><em>Immune cell type annotation using SingleR</em></p> <p>After having the integrated immune single-cell RNA-sequencing data, we performed the standard pipeline for clustering, including scaling the expression data, performing dimension reduction using PCA and UMAP, and finding clusters by a shared nearest neighbor (SNN) modularity optimization (<a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>). Then, we used the R package <em>SingleR </em>to assign cell-type labels to each identified cluster using the <em>ImmGen</em> reference data from the Immunological Genome Project.</p> <p> </p>
Swan 3.0 sample data
<p>Data for use in running the <a href="https://freese.gitbook.io/swan/">Swan</a> tutorials. Included are the following tgz'd files:</p> <ul> <li> <p>all_pass_list.csv: TALON pass list of transcript / gene IDs from HFFc6 / HepG2 dataset</p> </li> <li> <p>temp_anndata.h5ad: AnnData used to pass to PyDESeq2</p> </li> <li> <p>all_talon_observedOnly.gtf: Transcripts from HFFc6 / HepG2 dataset</p> </li> <li> <p>metadata.tsv: Metadata for the HFFc6 / HepG2 dataset</p> </li> <li> <p>swan.p: Pickled SwanGraph from the HFFc6 / HepG2 dataset</p> </li> <li> <p>swan_anndata.h5ad: AnnData version of the HFFc6 / HepG2 data</p> </li> <li> <p>swan_modelad.p: Pickled SwanGraph subset from the ENCODE4 long-read RNA-seq dataset with just the samples from MODEL-AD</p> </li> <li> <p>talon.db: TALON db from HFFc6 / HepG2 dataset</p> </li> <li> <p>all_talon_abundance_filtered.tsv: Counts matrix from HFFc6 / HepG2 dataset</p> </li> <li> <p>gencode.v29.annotation.gtf: Human reference transcriptome annotation</p> </li> </ul>
Microsatellite and morphological data for Carpobrotus species sampled from 40 different populations
<p>Microsatellite data for six loci and 698 individuals of Carpobrotus species sampled from 40 different population</p> <p>Morphological data for individuals of Carpobrotus species from 40 different populations</p>
Data from: Morphological measurements and mercury levels of Saltmarsh Sparrows sampled across their breeding range
<p><span>Malaria parasites (genus <em>Plasmodium</em>) are important agents of infectious disease in birds and multiple factors including warming temperatures and environmental contamination may act to increase their geographic and host ranges. </span>Here, we examined the role of geographical variation and environmental mercury exposure in malaria parasite infection dynamics in an imperiled songbird species with high mercury exposition, the Saltmarsh Sparrow (<em>Ammospiza caudacutus</em>). Using PCR methods, we <span>screened 280 Saltmarsh Sparrows from across their breeding range for malaria parasite infection. </span>We detected malaria parasites in 17% of sampled birds and a total of six <em>Plasmodium</em> lineages. <span>Prevalence of infection and diversity of parasite lineages varied across the breeding range of the Saltmarsh Sparrow and increased at more northern latitudes. Although mercury is a known immunosuppressant and has been documented to alter an individual's susceptibility to pathogens, we did not find a significant difference in blood mercury levels between infected and not infected birds, perhaps due to sampling methods and/or small sample sizes. As a specialist of coastal wetlands, the Saltmarsh Sparrow is an excellent indicator species for ecological health, and the patterns of malaria parasite infection with host distribution and mercury suggest that birds at northern latitudes are at greater risk of disease and should be priorities for conservation, habitat, and pathogen monitoring. </span></p>
10x chromium Sample data
<p>Processed and filtered data 10X Chromium CRISPR-Cas9 data</p> <p>Contains annotated data with only one KO on the Rab1a gene and a control KO. The annotated data were passed through the Crispr QC workflow.</p>
The Dangers of Using Cq to Quantify Nucleic Acid in Biological Samples: A Lesson From COVID-19 - supplementary data
<p>Dataset in relation to </p> <p>Evans, Daniel, Cowen, Simon, Kammel, Martin, O'Sullivan, Denise M, Stewart, Graham, Grunert, Hans-Peter, Moran-Gilad, Jacob, Verwilt, Jasper, In, Jiwon, Vandesompele, Jo, Harris, Kathryn, Hong, Ki Ho, Storey, Nathaniel, Hingley-Wilson, Suzie, Dühring, Ulf, Bae, Young-Kyung, Foy, Carole A, Braybrook, Julian, Zeichhardt, Heinz, & Huggett, Jim F. (2021). The Dangers of Using Cq to Quantify Nucleic Acid in Biological Samples: A Lesson From COVID-19. Clinical Chemistry, 68(1), 153–162. https://doi.org/10.1093/clinchem/hvab219 </p>
Data and code for: Order-of-Magnitude SNR Improvement for High-Field EPR Spectrometers via 3D-Printed Quasioptical Sample Holders
<p>*.py and data files for publication titled "Order-of-Magnitude SNR Improvement for High-Field EPR Spectrometers via 3D-Printed Quasioptical Sample Holders"</p>
Supplementary material 2 from: Hubancheva A, Bozicevic V, Morinière J, Goerlitz HR (2023) DNA metabarcoding data from faecal samples of the lesser (Myotis blythii) and the greater (Myotis myotis) mouse-eared bats from Bulgaria. Metabarcoding and Metagenomics 7: e106844. https://doi.org/10.3897/mbmg.7.106844
Taxonomic relationships and relative abundance of prey and parasite species in faecal samples from M. myotis and M. blythii from Bulgaria
Data from: Across space and time: a review of sampling and analytical biases in fossil data across macroecological scales
<p>Quantitative studies of fossil data have proven critical to a number of major macroevolutionary and macroecological discoveries, such as the 'Big 5' mass extinctions of the Phanerozoic. The development and easy accessibility of major meta-data sources such as the Paleobiology Database and Geobiodiversity Database have also spurred the widespread application of these data to testing ecological hypotheses at finer spatiotemporal and phylogenetic scales. However, issues of preservational/taphonomic biases, sampling/collecting biases, taxonomic issues, and analytical choice can impact the degree of interpretative resolution possible, and even obscure biological 'signal' from error/bias-introduced 'noise'. The degree to which these factors can impact analytical interpretations is not well-documented in comparison to the scale of use of these data sources. Here, we review the many forms of systematic error that can creep into a paleoecological study, from the stage of data collection to the interpretation of analytical results, and provide two case studies based upon re-analysis of previously-published datasets to illustrate the varying impacts of such biases. The first case study focuses on the Cambrian Burgess Shale, and the second on the Belly River Group, with both representing highly-sampled, taphonomically characterized, and spatiotemporally-constrained datasets developed through multiple years of sustained field collecting. In the former, we illustrate the impacts of collecting bias through quantitative comparisons of collected vs. discarded specimens over multiple field seasons, illustrating the impact of this data loss on ecological reconstructions and analysis. In the latter case study, we review the impact of preservational biases, the approaches to their quantification and mitigation, where these approaches have led to misinterpretations in the past, and the differences in ecological resolution that result from occurrence vs abundance approaches in macroecological analysis. Lastly, we synthesize these case studies with our review of past approaches to propose a series of recommendations for future paleoecological and macroecological studies, emphasizing the continued importance of high-quality primary data and ongoing need for a first-principles approach to address existing issues of missing data.</p>
BL Newspapers sample plain-text data
<p>A dataset of .csv files each containing article texts from newspapers published on the Shared Research Repository. </p>
SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs
<p>SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs.</p>
Sample data for Galaxy genome assembly tutorials
<p>Datasets for assembly tutotorials.</p>
Hyperspectral data of sediment samples collected from Vigo fieldwork Sept. 2023
<h2>Abstract</h2> <p>Raw hyperspectral data of 9 sediment samples in HDF5 file format. The samples were collected by UPORTO and IGME from Vigo Campaign fieldwork in Sept. 2023 but they were scanned at Ecotone lab in Trondheim by Ecotone UHI in February 2024. The data was scanned for both dry and wet sediments.</p> <p>This depositry contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Hyperspectral data of sediment samples collected from Vigo fieldwork Sept. 2023</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Raw hyperspectral data of 9 sediment samples. The samples were collected by UPORTO and IGME from Vigo Campaign fieldwork in Sept. 2023 but they were scanned at Ecotone lab in Trondheim with Ecotone UHI in February 2024. The data was scanned for both dry and wet sediments.</p> <p>This depositry contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Raw hyperspectral data, sediments, sand, mineral resource</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p><a href="https://doi.org/10.5281/zenodo.13381973">https://doi.org/10.5281/zenodo.13381973</a></p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Mineral resources</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>16.02.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>HDF5</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>2 mm (with UHI about 2 m away)</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>no</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>no</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone As</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone As</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone As, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
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.