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”
mixQTL sample data
<p># Introduction</p> <p>Sample data for mixQTL (https://github.com/liangyy/mixqtl).</p> <p>Data is derived from the GEUVADIS project (https://www.internationalgenome.org/data-portal/data-collection/geuvadis)</p> <p><br> # Disclaimer</p> <p>The data is provided "as is", and the authors assume no responsibility for errors or omissions. <br> The User assumes the entire risk associated with its use of these data. <br> The authors shall not be held liable for any use or misuse of the data described and/or contained herein. <br> The User bears all responsibility in determining whether these data are fit for the User's intended use. </p> <p>The information contained in these data is not better than the original sources from which they were derived,<br> and both scale and accuracy may vary across the data set. <br> These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics<br> appropriate for applications that potential users of the data may contemplate. <br> <br> The user is responsible to comply with any data usage policy from the original GWAS studies;<br> refer to the list of traits described [here](https://www.biorxiv.org/content/10.1101/814350v1)<br> to identify their respective Consortia's requirements.</p> <p><br> THE DATA IS PROVIDED WITHOUT WARRANTY OF ANY KIND,<br> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.<br> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,<br> WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br> OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE DATA.</p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Attica (Greece) on 23-26 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides sample datasets for the assessment of the fire danger during the Attica (Greece) wildfires occurred on 23-26 July 2018:</p> <ul> <li> <p>ECMWF_EFFIS_20180723_1200_en.tar<br> (ensemble forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723_1200_hr.tar<br> (deterministic forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_hr_e5.tar<br> (deterministic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_en_e5.tar<br> (probabilistic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_e5.tar<br> (probabilistic and deterministic reanalysis based on ERA5 issued for 2018-07-23/26, global coverage, FWI only)</p> </li> <li> <p>bbox.tar, containing 1 index (FWI) for the bounding box:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions in the period 23-26 July 2018</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts (in the period 14-26 July 2018) generated using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> </ul> </li> <li> <p>lon_min = 23, lon_max = 25, lat_min = 37, lat_max = 39</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Sample Sentinel-1 SAR data for sea ice type retrieval
<p>Sample Sentinel-1 SAR data for sea ice type retrieval processed with thermal noise removal (<a href="https://ieeexplore.ieee.org/document/8126233">https://ieeexplore.ieee.org/document/8126233</a>).</p> <p>Original data is available at ESA Scientific Hub <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#%21/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> <li> <p>Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
M4DB Sample Data Set
<p>A sample data set for the MicroMagnetic-Metadata DataBase (M4DB).</p>
Sample data for analysis of period/frequency gradient and phase gradient in spreadouts, ex vivo models of somitogenesis
<p>Here are timelapse imaging (as .tif) of a dynamic Notch signaling reporter (i.e. LuVeLu) in spreadouts, ex vivo models of somitogenesis. Also here are the corresponding period and phase wavelet movies, generated using a wavelet analysis workflow developed by Gregor Mönke. These sample data are used to run an accompanying Python script, available at: <a href="https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/FrequencyPhase_GradientSlopeAnalysis">https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/FrequencyPhase_GradientSlopeAnalysis</a></p>
A sample image data for ImageJ Bleach Correction Plugin
<p>This sample image data is for testing the ImageJ Bleach Correction plugin. Data was acquired by Boryana Petrova in the Christian H. Haering Lab (EMBL) in 2011 and provided as a sample for development to Kota Miura</p> <p><a href="https://imagej.net/Bleach_Correction">https://imagej.net/Bleach_Correction</a></p> <p>For more details, see "README.md".</p>
The laboratory data of the measurements carried out on an n-decane saturated limestone sample
<p>This supporting information provides the numerical results of the laboratory experiments conducted on an n-decane saturated limestone sample with varying dead fluid volume, which correspond to the data presented in manuscript "The Effect of Boundary Conditions on the Elastic Moduli Measurements at Low Frequencies" submitted to Journal of Geophysical Research: Solid Earth.</p>
Data from: Spatial and temporal patterns of environmental DNA detection to inform sampling protocols in lentic and lotic systems.
<p>The development of efficient sampling protocols for the capture of environmental DNA (eDNA) could greatly help improve accuracy of occupancy monitoring for species that are difficult to detect. However, the process of developing a protocol in situ is complicated for rare species by the fact that animal locations are often unknown. We tested sampling designs in lake and stream systems to determine the most effective eDNA sampling protocols for two rare species: the Sierra Nevada yellow-legged frog (<i>Rana sierrae</i>) and the foothill yellow-legged frog (<i>R. boylii</i>). We varied water volume, spatial sampling, and seasonal timing in lakes and streams; in lakes we also tested multiple filter types. We found that filtering 2 L versus 1 L increased the odds of detection in streams 5.42X (95% CI: 3.2-9.19X) in our protocol, from a probability of 0.51 to 0.85 per technical replicate. Lake sample volumes were limited by filter clogging and we found no effect of volume or filter type. Sampling later in the season increased the odds of detection in streams by 1.96X for every 30 days (95% CI: 1.3 - 2.97X) but there was no effect for lakes. Spatial autocorrelation of the quantity of yellow-legged frog eDNA captured in streams between 100 and 200 m, indicating that sampling at close intervals is important.</p>
VOC analysis data set for polyfilament analysis of antibacterial PE, PA and PLA fibre samples with natural additive rosin and silver
<p>This data is a detail report related to the VOC measurements described and analyzed in the work<br> Weathering and safety of antibacterial polymer-rosin polyfilaments. The detailed description of the<br> method, samples preparations and main outcomes are given in the work. Here, Tables 1-7 indicate the<br> average and (range) concentration of emitted compounds from each polyfilament fibre sample analyzed<br> at different temperatures for their VOC emissions.</p>
VOC chromatogram data set for polyfilament analysis of antibacterial polyfilament fibre samples with natural additive rosin and silver
<p>This data is a detail report related to the VOC measurements described and analyzed in the paper Weathering of antibacterial melt-spun polyfilaments modified by pine rosin. The detailed description of the method, sample codes, samples preparations and main outcomes are given in the manuscript. Figures (1-7) are the direct graphs from the device software as ‘chromatograms’ (TIC, total ion chromatograms) for each polyfilament fibre sample analyzed at different temperatures. The graphs indicate the measured (computed) ion count (abundance, arbitrary units) as a function of time (minutes).</p> <p> </p>
Cray-HPC Data Sample
<p>Sample of console and job logs of Cray system shared in the SC 18 <a href="https://dl.acm.org/doi/abs/10.5555/3291656.3291668">paper</a> (Doomsday: Predicting Which Node Will Fail When on Supercomputers). </p> <p>If you use this data, please cite the following paper that first released the logs:</p> <pre>@inproceedings{DBLP:conf/sc/DasMHRB18, author = {Anwesha Das and Frank Mueller and Paul Hargrove and Eric Roman and Scott B. Baden}, title = {Doomsday: predicting which node will fail when on supercomputers}, booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, {SC} 2018, Dallas, TX, USA, November 11-16}, pages = {9:1--9:14}, publisher = {{IEEE} / {ACM}}, year = {2018} } </pre>
Supporting publication for 'Prevalence sample-based guidance for reporting 2020 data'
<p>These two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer examples on how prevalence data can be reported using SSD2.</p>
Data from: Dense infraspecific sampling reveals rapid and independent trajectories of plastome degradation in a heterotrophic orchid complex
Heterotrophic plants provide excellent opportunities to study the effects of altered selective regimes on genome evolution. Plastid genome (plastome) studies in heterotrophic plants are often based on one or a few highly divergent species or sequences as representatives of an entire lineage, thus missing important evolutionary-transitory events. Here we present the first infraspecific analysis of plastome evolution in any heterotrophic plant. By combining genome skimming and targeted sequence capture, we address hypotheses on the degree and rate of plastome degradation in a complex of leafless orchids (Corallorhiza striata) across its geographic range. Plastomes provide strong support for relationships and evidence of reciprocal monophyly between C. involuta and the endangered C. bentleyi. Plastome degradation is extensive, occurring rapidly over a few million years, with evidence of differing rates of substitution among the two principal clades of the complex. Genome skimming and targeted sequence capture differ widely in coverage depth overall, with depth in targeted sequence capture datasets varying immensely across the plastome as a function of GC content. These findings will help fill a knowledge gap in models of heterotrophic plastid genome evolution, and have implications for future studies in heterotrophs.
Data from: Overcoming the challenge of small effective sample sizes in home-range estimation
Technological advances have steadily increased the detail of animal tracking datasets, yet fundamental data limitations exist for many species that cause substantial biases in home‐range estimation. Specifically, the effective sample size of a range estimate is proportional to the number of observed range crossings, not the number of sampled locations. Currently, the most accurate home‐range estimators condition on an autocorrelation model, for which the standard estimation frame‐works are based on likelihood functions, even though these methods are known to underestimate variance—and therefore ranging area—when effective sample sizes are small. Residual maximum likelihood (REML) is a widely used method for reducing bias in maximum‐likelihood (ML) variance estimation at small sample sizes. Unfortunately, we find that REML is too unstable for practical application to continuous‐time movement models. When the effective sample size N is decreased to N ≤ urn:x-wiley:2041210X:media:mee313270:mee313270-math-0001(10), which is common in tracking applications, REML undergoes a sudden divergence in variance estimation. To avoid this issue, while retaining REML's first‐order bias correction, we derive a family of estimators that leverage REML to make a perturbative correction to ML. We also derive AIC values for REML and our estimators, including cases where model structures differ, which is not generally understood to be possible. Using both simulated data and GPS data from lowland tapir (Tapirus terrestris), we show how our perturbative estimators are more accurate than traditional ML and REML methods. Specifically, when urn:x-wiley:2041210X:media:mee313270:mee313270-math-0002(5) home‐range crossings are observed, REML is unreliable by orders of magnitude, ML home ranges are ~30% underestimated, and our perturbative estimators yield home ranges that are only ~10% underestimated. A parametric bootstrap can then reduce the ML and perturbative home‐range underestimation to ~10% and ~3%, respectively. Home‐range estimation is one of the primary reasons for collecting animal tracking data, and small effective sample sizes are a more common problem than is currently realized. The methods introduced here allow for more accurate movement‐model and home‐range estimation at small effective sample sizes, and thus fill an important role for animal movement analysis. Given REML's widespread use, our methods may also be useful in other contexts where effective sample sizes are small.
Data from: Population genomics and demographic sampling of the ant-plant Vachellia drepanolobium and its symbiotic ants from sites across its range in East Africa.
The association between the African ant plant, Vachellia drepanolobium, and the ants that inhabit it has provided insight into the boundaries between mutualism and parasitism, the response of symbioses to environmental perturbations, and the ecology of species coexistence. We use a landscape genomics approach at sites sampled throughout the range of this system in Kenya to investigate the demographics and genetic structure of the different partners in the association. We find that different species of ant associates of V. drepanolobium show striking differences in their spatial distribution throughout Kenya, and these differences are only partly correlated with abiotic factors. A comparison of the population structure of the host plant and its three obligately arboreal ant symbionts, Crematogaster mimosae, Crematogaster nigriceps, and Tetraponera penzigi, shows that the ants exhibit somewhat similar patterns of structure throughout each of their respective ranges, but that this does not correlate in any clear way with the respective genetic structure of the populations of their host plants. A lack of evidence for local coadaptation in this system suggests that all partners have evolved to cope with a wide variety of biotic and abiotic conditions.
Data from: Landscape genetic inferences vary with sampling scenario for a pond breeding amphibian
A critical decision in landscape genetic studies is whether to use individuals or populations as the sampling unit. This decision affects the time and cost of sampling and may affect ecological inference. We analyzed 334 Columbia spotted frogs at 8 microsatellite loci across 40 sites in northern Idaho to determine how inferences from landscape genetic analyses would vary with sampling design. At all sites, we compared a proportion available sampling scheme (PASS), in which all samples were used, to resampled datasets of 2-11 individuals. Additionally, we compared a population sampling scheme (PSS) to an individual sampling scheme (ISS) at 18 sites with sufficient sample size. We applied an information theoretic approach with both restricted maximum likelihood and maximum likelihood estimation to evaluate competing landscape resistance hypotheses. We found that PSS supported a low-density forest model (0.87) and ISS supported this model as well as additional models when testing hypotheses of landcover types that create the greatest resistance to gene flow for Columbia spotted frogs. Increased sampling density and study extent, seen by comparing PSS to PASS, showed a change in model support from a model of only low-density forest to a model of only high-density forest. As number of individuals increased, model support converged at 7 individuals for ISS to PSS. ISS may be useful to increase study extent and sampling density, but may lack power to provide strong support for the correct model with microsatellite datasets. Our results highlight the importance of additional research on sampling design effects on landscape genetics inference.
Data from: Improved transcriptome sampling pinpoints 26 ancient and more recent polyploidy events in Caryophyllales, including two allopolyploidy events
• Studies of the macroevolutionary legacy of polyploidy are limited by an incomplete sampling of these events across the tree of life. To better locate and understand these events, we need comprehensive taxonomic sampling as well as homology inference methods that accurately reconstruct the frequency and location of gene duplications. • We assembled a dataset of transcriptomes and genomes from 169 species in Caryophyllales, of which 43 were newly generated for this study, representing one of the densest sampled genomic-scale datasets available. We carried out phylogenomic analyses using a modified phylome strategy to reconstruct the species tree. We mapped phylogenetic distribution of polyploidy events by both tree-based and distance-based methods, and explicitly tested scenarios for allopolyploidy. • We identified twenty-six ancient and more recent polyploidy events distributed throughout Caryophyllales. Two of these events were inferred to be allopolyploidy. • Through dense phylogenomic sampling, we show the propensity of polyploidy throughout the evolutionary history of Caryophyllales. We also provide a framework for utilizing transcriptome data to detect allopolyploidy, which is important as it may have different macro-evolutionary implications compared to autopolyploidy.
Data from: Estimating diversification rates on incompletely-sampled phylogenies: theoretical concerns and practical solutions
<p>Molecular phylogenies are a key source of information about the tempo and mode of species diversification. However, most empirical phylogenies do not contain representatives of all species, such that diversification rates are typically estimated from incompletely sampled data. Most researchers recognize that incomplete sampling can lead to biased rate estimates, but the statistical properties of methods for accommodating incomplete sampling remain poorly known. In this point of view, we demonstrate theoretical concerns with the widespread use of analytical sampling corrections for sparsely sampled phylogenies of higher taxonomic groups. In particular, corrections based on "sampling fractions" can lead to low statistical power to infer rate variation when it is present, depending on the likelihood function used for inference. In the extreme, the sampling fraction correction can lead to spurious patterns of diversification that are driven solely by unbalanced sampling across the tree in concert with low overall power to infer shifts. Stochastic polytomy resolution provides an alternative to sampling fraction approaches that avoids some of these biases. We show that stochastic polytomy resolvers can greatly improve the power of common analyses to estimate shifts in diversification rates. We introduce a new stochastic polytomy resolution method (TACT: Taxonomic Addition for Complete Trees) that uses birth-death-sampling estimators across an ultrametric phylogeny to estimate branching times for unsampled taxa, with taxonomic information to compatibly place new taxa onto a backbone phylogeny. We close with practical recommendations for diversification inference under several common scenarios of incomplete sampling.</p>
Data from: Sampling effects drive the species-area relationship in lake zooplankton
<p><b>The Island Species-Area relationship (ISAR) describes how the numbers of species increases with increasing size of an island (or island-like habitat, such as lakes), and is one of the oldest laws in ecology. Despite its conceptual importance, there remains a great deal of ambiguity regarding the ISAR and its underlying processes. We compiled data from sampled zooplankton assemblages from several hundred lakes in North America and Europe to examine the influence of the three main hypothesized mechanisms leading to ISARs - passive sampling, disproportionate effects, and habitat heterogeneity. We compiled data on lake zooplankton assemblages that reported sample-level and lake level species richness estimates, as well as relative abundance data. In both North American and European lakes, we found a consistent and strong increase in total species richness with increasing lake area. However, when we compared the number of species standardized by number of individuals, there was no relationship between lake area and sample-level species richness or an estimate of species relative abundances, calculated as the Probability of Interspecific Encounter (PIE; a measure of evenness). This was true even when multiple samples were taken across lakes and combined, reducing the likelihood that habitat heterogeneity was driving the results. Overall, our results suggest that the ISAR of zooplankton in these lakes was most likely determined by sampling effects rather than disproportionate effects or habitat heterogeneity leading to more species in larger lakes. Understanding the mechanisms driving ISAR results such as ours can also help us develop predictions for biodiversity change when the area of these habitats changes. </b></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.