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3,339 results for “heterogeneities”
Data from: Biotic pressures and environmental heterogeneity shape beta-diversity of seedling communities in tropical montane forests
<p>Many theories have been proposed to explain the high diversity of plants in the tropics. However, we lack an understanding of the processes that drive plant diversity and community assembly at different spatial scales. Here, we applied beta-diversity partitioning to test how biotic and abiotic factors are associated with seedling beta-diversity in a tropical montane forest in Southern Ecuador. We recorded seedling communities on 81 subplots at nine plots located at three elevations along a 2000-m elevational gradient. We measured biotic pressures (i.e. herbivory and fungal pathogen attacks) and environmental conditions (i.e. soil moisture and canopy closure) at all subplots and related them to species turnover and richness differences in seedling communities within and between elevations. We found that species turnover increased with differences in biotic dissimilarity within elevations, while differences in species richness within elevations increased with increasing environmental dissimilarity. Between elevations, species turnover increased with increasing environmental dissimilarity. Our findings show that species turnover and changes in species richness are related differently to abiotic and biotic factors, and that the importance of these factors for shaping seedling diversity is scale-dependent. Our study contributes to better understand the processes driving seedling beta-diversity and the assembly of plant communities in highly diverse tropical montane forests.</p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </p>
Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
<p>Python figures in pickle format</p> <p>matplotlib version 3.5.1 </p>
The heterogeneous rheological properties of rock mass caused the formation of deep tension fractures
<p>Date for the paper —The heterogeneous rheological properties of rock mass caused the formation of deep tension fractures.</p>
CDOM spectral slope (S275-295) as tracers of water masses, CDOM heterogeneity, and 14C-DOC in an oligotrophic marginal sea
<p>The present study is focused on the CDOM vertical profiles in the northern South China Sea. The results suggest humic-like FDOM is controlling the variation of the spectral slope of CDOM (<em>S</em><sub>275-295</sub>). In addition, our results suggest <em>S</em><sub>275-295</sub> could be used as tracers of water mass, CDOM diversity and radiocarbon age of dissolved organic carbon in oligotrophic ocean.</p>
Climatic forcing and individual heterogeneity in a resident mountain bird: legacy data reveal effects on reproductive strategies
<p><span>Optimization of clutch size and timing of reproduction </span><span>have substantial effects on lifetime reproductive success in vertebrates, and b</span><span>oth </span><span>individual quality and </span><span>environmental variation may impact </span><span>life history strategies</span><span>. </span><span>We tested hypotheses related to maternal investment and timing of reproduction</span><span>, using 17 years (1978-1994) of individual-based life history data on willow ptarmigan (<em>Lagopus l. lagopus</em>, </span><span>n=290 breeding females with n=319 breeding attempts</span><span>) in central Norway. We analysed whether climatic variation and individual state variables (age and body mass) affected number of offspring and timing of reproduction, and individual repeatability in strategies</span><span>. The results suggest that willow ptarmigan share a common optimal clutch size that is largely independent of measured individual states. While we found no clear direct weather effects on clutch size, higher spring temperatures advanced onset of breeding, and early breeding was followed by an increased number of offspring. Warmer springs were positively related to maternal mass, and mass interacted with clutch size in production of hatchlings. Finally, clutch size and timing of reproduction were highly repeatable within individuals, indicating that individual quality guided trade-offs in reproductive effort. Our results demonstrate how climatic forcing and individual heterogeneity in combination influenced life history traits in a resident montane keystone species.</span></p>
Physiological significance of the heterogeneous distribution of zeaxanthin and lutein in the retina of the human eye
<p>cross-sections of the human eye retina recorded with the FLIM technique and Raman microscopy</p>
Data from: Demographic consequences of an extreme heatwave are mitigated by spatial heterogeneity in an annual monkeyflower
<p>This repository includes three different datasets as well as associated readme files. One dataset contains fitness data for individuals planted within a common garden experiment planted near the Browder Ridge Trailhead, Oregon USA in 2019. The second dataset contains fecundity data from 12 populations in the Central Oregon Cascades in 2018 and 2019. The final dataset contains demographic and phenological data from eleven populations in the Central Oregon Cascades in 2019. </p>
Analysis scripts and PFLOTRAN input files for "Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface"
<p>Supporting files for manuscript <em>Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface </em>(preprint published at https://arxiv.org/abs/2305.12575).</p> <p>Contents:</p> <ul> <li>PFLOTRAN input files (*.in) for the simulations that were used in the manuscript.</li> <li>The corresponding heterogeneous permeability fields for those simulations (*.h5).</li> <li>The CO2 property database for use with the PFLOTRAN MPHASE module.</li> <li>Python scripts for generation of the random fields and quantification of uptake rate.</li> <li>Jupyter notebooks for generation of summary figures.</li> <li>Excel spreadsheet summarizing the output of each of the simulations.</li> </ul>
STL files: Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing
<p>STL files for paper titled modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing</p>
Winter range shifts and their associations with species traits are heterogeneous in eastern North American birds
<p>Many species' distributions are shifting in response to climate change. Many distributional shifts are predictably poleward or higher in elevation, but heterogeneity in the rate and direction of shifts both within and between species appears to be common. We found high heterogeneity in the trajectory of winter range shifts for 65 species of birds across eastern North America and in the different traits and trait interactions associated with these shifts across the spatial scales we examined. We used data from the Christmas Bird Count to quantify the trajectory of winter latitudinal center of abundance range shifts over four decades (1980–2019) for 65 species of songbirds and woodpeckers in North America, both across eastern North America (ENA) as a whole and for the Atlantic (ATL) and Mississippi (MISS) flyways separately. We then used linear models and AICc model selection to test whether species traits could explain variation in range shifts or flyway discrepancies. Across ENA, most species showed northward latitudinal range shifts, but some showed no latitudinal shift while others shifted southwards. Amongst ATL and MISS, we documented both within- and between-species differences in the rate and direction of latitudinal shifts, complicating the results from across ENA. No single trait emerged as a dominant driver of range shift differences at the ENA and flyway scales. Migration strategy interacted with insectivory to explain variation at the largest spatial scale (ENA), whereas frugivory and mean winter latitude explained much of the variation in ATL and MISS, respectively. Exploring heterogeneity in range shifts within and between species, and in the associations between range shifts and life history traits, will help us better understand the mechanisms that mediate differing responses to environmental change and predict which species will be better able to adapt to those changes. </p>
Supporting data to Heterogeneity in spatiotemporal variability of High Mountain Asia's runoff and its underlying mechanisms
<p>This dataset includes (1) the boundary of 12 basins over the southern High Mountain Asia, (2) their climate and catchment properties, (3) significance of interannual, interdecadal and multidecadal variability of runoff and corresponding climate variables, and (4) the decomposed timeseries of runoff and its related atmospheric drivers. Please see the publication for more details.</p>
Supporting data to Heterogeneity in spatiotemporal variability of High Mountain Asia's runoff and its underlying mechanisms
<p>This dataset includes (1) the boundary of 12 basins over the southern High Mountain Asia, (2) their climate and catchment properties, (3) significance of interannual, interdecadal and multidecadal variability of runoff and corresponding climate variables, and (4) the decomposed timeseries of runoff and its related atmospheric drivers. Please see the publication for more details.</p>
Supplementary material for "A Rapid, Open-Source CCT Predictor for Low Alloy Steels, and its Application to Compositionally Heterogeneous Material"
<p>The complete collection of measured, modelled and analysed data associated with the work "A Rapid, Open-Source CCT Predictor for Low Alloy Steels, and its Application to Compositionally Heterogeneous Material" and includes:</p> <ol> <li>Measured and analysed dilatometry data.</li> <li>Optical micrographs of as-cooled microstructures.</li> <li>Microhardness measurements of the as-cooled samples.</li> <li>PAG size analysis.</li> <li>Modelled CCT data.</li> <li>SA-540 EPMA data.</li> <li>Modelled results from adapting the model to consider SA-540 chemical heterogeneity.</li> <li>Full chemical analysis for each alloy examined.</li> <li>Modelled Thermo-Calc CCT data.</li> <li>Modelled JMatPro CCT data.</li> </ol>
Subsurface redox interactions regulate ebullitive methane flux in heterogeneous Mississippi River deltaic wetland
<p>This dataset is model outputs archive from a model study about 'Subsurface redox interactions regulate ebullitive methane flux in heterogeneous Mississippi River deltaic wetland' (manuscript is Under Review). Plotting scripts are also included in this data file. There are four model results. 'Ebull' model output are ebullition flux, 'Jswi' model output are diffusion flux across soil-water interface, and 'Methane' files are porewater concentration results in each soil layers, and 'LtranR' model outputs for redox species exchange rates across different soil layers. The model source codes can be found at <a href="https://zenodo.org/record/8084441">https://zenodo.org/record/8084441</a> and <a href="https://zenodo.org/record/8087566">https://zenodo.org/record/8087566</a>.</p>
Local and landscape environmental heterogeneity drive ant community structure in temperate semi-natural upland grasslands
<p>Environmental heterogeneity is an important driver of ecological communities. Here, we assessed the effects of local and landscape spatial environmental heterogeneity on ant community structure in temperate semi-natural upland grasslands of Central Germany. We surveyed 33 grassland sites representing a gradient in elevation and landscape composition. Local environmental heterogeneity was measured in terms of variability of temperature and moisture within and between grassland sites. Grassland management type (pasture vs. meadows) was additionally included as a local environmental heterogeneity measure. The complexity of habitat types in the surroundings of grassland sites was used as a measure of landscape environmental heterogeneity. As descriptors of ant community structure, we considered species composition, community evenness, and functional response traits. We found that extensively grazed pastures and within-site heterogeneity in soil moisture at local scale, and a high diversity of land cover types at the landscape scale affected ant species composition by promoting nest densities. Ant community evenness was high in wetter grasslands with low within-site variability in soil moisture and surrounded by a less diverse landscape. Fourth-corner models revealed that ant community structure response to environmental heterogeneity was mediated mainly by worker size, colony size, and life history traits related with colony reproduction and foundation. We discuss how withinsite local variability in soil moisture and low-intensity grazing promote ant species densities, and highlight the role of habitat temperature and humidity in affecting community evenness. We hypothesize that a higher diversity of land cover types in a forest-dominated landscape buffers less favorable environmental conditions for ant species establishment and dispersal between grasslands. We conclude that spatial environmental heterogeneity at local and landscape scales plays an important role as a deterministic force in filtering ant species and, along with neutral processes (e.g. stochastic colonization), in shaping ant community structure in temperate semi-natural upland grasslands.</p>
Data from: Virus community structuring is shaped by habitat heterogeneity and resource utilisation strategies
<p><span>After decades of disconcerted research, we still recognise large gaps in the understanding of mechanisms that govern disease dynamics in complex biological communities. To determine how spatial structuring of plant communities caused by anthropic disturbance affects resource utilisation traits of viruses, we combine high-throughput, network, and metacommunity approaches. We find that the disturbance gradient corresponded to network modules and habitat specificity exhibited by a majority of viruses. Communities were connected through key hub species of either generalist viruses or high potential host reservoirs. Spatial dependencies were evident in regression models of species richness and correlations between metacommunity structure and both host range and transmission mode at finer spatial resolutions. We propose that virus community assembly is influenced by variation in niche opportunities. Distinctions in virus community composition caused by resource compartmentalisation can be used to track ecological traits important in forecasting transmission risk.</span></p>
Drug-Cell Line-Gene heterogeneous graph from NCI60
<p>Dataset for "Attention-Guided Gene Importance Assessment in Drug-Cell-Gene Heterogeneous Network for Drug Response Deciphering". </p> <p># information</p> <p>- drug_cell_gene.csv.gz: Adjacency matrix of drugs, cell lines, and genes from NCI60. <br> - train.csv: training data. Contains drug name and cell line name<br> - val.csv: validation data. Contains drug name and cell line name</p> <p>- test.csv: test data. Contains drug name and cell line name</p> <p>- train_values.npy: train value. Contains drug response values (Z-score)<br> - val_values.npy: validation value. Contains drug response values (Z-score)</p> <p>- test_values.npy: test value. Contains drug response values (Z-score)</p>
Data files for 'Tan et al., (2023). Structural heterogeneity-controlled rupture process of the 2021 Mw 7.1 Fukushima, Japan earthquake revealed by joint inversion of seismic and geodetic data'
<p>slip model.dat: rupture model of the 2021 Mw 7.1 Fukushima earthquake</p> <p>In 'slip model.dat', each row contains the moment rate function of each sub-fault. The numbers of the sub-faults are given in the first two columns.</p>
Data from: Drivers of geomorphic heterogeneity in unconfined non‐perennial river corridors
<p>River corridors along non-perennial stream networks are known to provide diverse physical and ecological functions that are thought to be related to geomorphic heterogeneity. Geomorphic heterogeneity refers to the spatial and temporal variability of geomorphic units, which are geomorphic patches within the river corridor formed by a given set of processes. While studies on the magnitude and drivers of geomorphic heterogeneity are emerging in perennial streams, similar studies in ephemeral streams are lacking. Given the ubiquity of non-perennial streams globally, we aim to answer questions regarding the magnitude and drivers of geomorphic heterogeneity in non-perennial river corridors as well as how geomorphic units and heterogeneity reflect processes related to flood disturbance. Geomorphic units were mapped in 30 unconfined river corridors within six non-perennial watersheds in Utah and Arizona, USA. Landscape heterogeneity metrics – Shannon's Diversity Index, Shannon's Evenness Index, and patch density – were used to quantify geomorphic heterogeneity within each reach. Additionally, variables that may potentially constrain or drive heterogeneity were quantified, including floodplain shape, grain size, large wood abundance, and proxies for flood disturbance. While heterogeneity positively correlated with metrics for morphology and disturbance, statistical models suggest that morphologic context, particularly floodplain width, was a more important predictor for estimating geomorphic heterogeneity. Still, geomorphic units reflected aggradation processes indicative of a range of flood energies, suggesting a strong tie between heterogeneity and disturbance. Results suggest that geomorphic heterogeneity may be resilient to changes in flood disturbance frequency or magnitude, but future studies investigating long-term temporal heterogeneity are needed.</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.