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.
154
datasets available to search
ShareScore release 0.9.0
Dataset results
154 results for “Raster”
Input raster datasets for Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)
<p>Input raster datasets used to create an Ecological Condition Model (ECM) for open pine ecosystems in the Apalachicola Regional Restoration Initiative area of the eastern Florida Panhandle. Our goal was to develop an ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities.</p> <p> </p> <p> </p>
Output raster datasets from an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the output results for a spatial water yield model applied to an 11 county area in the state of Florida panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in this datasets associated publication. All data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p> <p>The output raster datasets contained here are water yield estimate informed with 2018 pine basal area, binary depth to water table, and average aridity index input rasters. These rasters have 10m spatial resolution, the raster extent covers 11 counties in the panhandle of Florida, the units are in centimeters of water yield per year. The water yield outputs consist of ten rasters representing the current water yield using the mean aridity raster, the water yield expected from the three pine tree thinning scenarios: 7m/hectare ba, 11 m/hectare, and 18 m/hectare, taken from the mean aridity index. Then rasters representing the water yield expected from the three thinning scenarios under maximum, and minimum aridity indexes.</p> <p> </p> <p>These ten outputs are listed here:</p> <p>"wy_current_mean" Based on 2018 BA conditions; Mean ARID</p> <p>"wy_18_mean" BA reduced to 18m2ha-1; Mean ARID</p> <p>"wy_11_mean" BA reduced to 11m2ha-1; Mean ARID</p> <p>"wy_7_mean" BA reduced to 7m2ha-1; Mean ARID</p> <p>"wy_ 18_max" BA reduced to 18m2ha-1; Maximum ARID</p> <p>"wy_ 11_max" BA reduced to 11m2ha-1; Maximum ARID</p> <p>"wy_7_max" BA reduced to 7m2ha-1; Maximum ARID</p> <p>"wy_ 18_min" BA reduced to 18m2ha-1; Minimum ARID</p> <p>"wy_ 11_min" BA reduced to 11m2ha-1; Minimum ARID</p> <p>"wy_ 7_min" BA reduced to 7m2ha-1; Minimum ARID</p> <p> </p> <p>Water yield was estimated for 2018 using the following datasets to inform the model in the Current Water Yield Calculation tool:</p> <ul> <li>Leaf area index modeled from a 2018 pine species basal area raster,</li> <li>Depth to water table data provided by Florida Geological Survey and reclassified as a binary raster,</li> <li>Average aridity index raster generated with precipitation data from PRISM Climate Group and MODIS PET data.</li> </ul> <p>For detailed information on how the above inputs were developed, please see the associated publication:</p> <p>Vernon, J., St. Peter, J., Crandall, C., Awowale, O.E., Medley, P., Drake, J., & Ibeanusi, V. (2023). Spatial application of southern pine water yield for prioritizing forest management activities. ISPRS International Journal of Geo-Information, 12(2), 34. <a href="https://doi.org/10.3390/ijgi12020034">https://doi.org/10.3390/ijgi12020034</a> </p>
Input raster datasets for an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the inputs for a spatial water yield model applied to an 11 county area in the state of Florida's panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in the associated publication. The five input datasets required for this water yield analysis are: 1) a model of pine species basal area, named "ARSA_PineBA_10m" 2) a binary depth to water table raster named "DTW_cm_binary2" , and 3) three spatial aridity index raster dataset named "Aridity_Min", "Aridity_Max" and "Aridity_Mean", created from potential evapotranspiration, and precipitation raster datasets. The min max and mean codifiers relate to the range of aridity values found in our dataset of 7 year temporal range, from MODIS PET and PRISM percipitation yearly data. All input and output data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p>
Statewide 2-km raster of year since last widlfire
This raster contains the year since last burn from 1942-2010 at 2-km cell size for the state of Alaska. The raster matches SNAP statewide climate rasters in extent and cell size. The geotiff raster is in the Alaska Albers NAD83 coordinate system as an unsigned 16-bit integer with valid pixel values ranging from 1942 through 2010.
Statewide 2-km raster of number of fires within each 2-km pixel since 1942
This raster contains the count of wildfires from 1942-2010 within each 2-km cell for the state of Alaska. The raster matches SNAP statewide climate rasters in extent and cell size. The geotiff raster is in the Alaska Albers NAD83 coordinate system as an unsigned 4-bit integer with valid pixel values ranging from 1 through 9 as the number of fires within each pixel from 1942 through 2010.
McMurdo Dry Valleys GIS Raster Layers
Basic raster layers from the MCM-LTER spatial data holdings have been exported and symbolized. The dataset files offered here include: 30m DEM made from USGS Topo map SPOT Satellite Image 39-558 LANDSAT 7 Satellite Image Note - the SPOT and LANDSAT layers are not MCM-LTER data products.  These resources were updated last in 2007, for more up-to-date layers, and potentially, higher resolution layers, please visit the Polar Geospatial Center and other affine geospatial data clearinghouses.Â
Wetland Areas - Ipswich Watershed - Idrisi Raster File.
This map shows the location of wetland areas inside of the Ipswich River watershed sudy area.
Land use and cover (LUC) rasters of the São Lourenço River Basin (2002 - 2014)
<p>The LUC dataset of São Lourenço river basin, a major Pantanal wetland contribution area as provided by the 4<sup>th</sup> edition of the <a href="https://www.embrapa.br/pantanal/bacia-do-alto-paraguai">Monitoring of Changes in Land cover and Land Use in the Upper Paraguay River Basin - Brazilian portion - Review Period: 2012 to 2014</a> (Embrapa Pantanal, Instituto SOS Pantanal, and WWF-Brasil 2015). For the development of the <a href="https://reginalexavier.github.io/OpenLand/index.html">OpenLand R package</a> (tests and <a href="https://reginalexavier.github.io/OpenLand/articles/openland_vignette.html">vignettes</a>), the original multi-year shape file was clipped to the extent of São Lourenço basin, transformed into a 5-layer <a href="https://rdrr.io/cran/raster/man/stack.html"><code>RasterStack</code></a> and then saved as .RDA file which can be loaded into <a href="https://www.r-project.org/">R</a> (R Core Team, 2019). Five LUC maps (2002, 2008, 2010, 2012 and 2014) compose the time series. The study area of approximately 22,400 km<sup>2</sup> is located in the Cerrado Savannah biom in the southeast of the Brazilian state of Mato Grosso.</p> <p>The category names and colors to be associated with the pixel values follow the conventions given by Instituto SOS Pantanal and WWF-Brasil (2015) <a href="https://www.embrapa.br/documents/1354999/1529097/BAP+-+Mapeamento+da+Bacia+do+Alto+Paraguai+-+estudo+completo/e66e3afb-2334-4511-96a0-af5642a56283">(access document here, page 17)</a>. The Portuguese legend acronyms were maintained as defined in the original dataset.</p> <p><strong>The original legend from SOS Pantanal</strong></p> <pre><code class="language-markdown"> _______________________________________________________________________________________________ |Pixel Value |Legend | Class | Use | Category | Colour| |------------|--------|---------------|-------------------|-----------------------------|-------| |2 | Ap | Anthropogenic | Anthropogenic Use | Cattle farming |#FFE4B5| |3 | FF | Natural | NA | Forest formation |#228B22| |4 | SA | Natural | NA | Park savanna |#00FF00| |5 | SG | Natural | NA | Gramineous savanna |#CAFF70| |7 | aa | Anthropogenic | NA | Anthropogenized vegetation |#EE6363| |8 | SF | Natural | NA | Wooded savanna |#00CD00| |9 | Agua | Natural | NA | Water bodies |#436EEE| |10 | Iu | Anthropogenic | Anthropogenic Use | Urban areas |#FFAEB9| |11 | Ac | Anthropogenic | Anthropogenic Use | Crop farming |#FFA54F| |12 | R | Anthropogenic | Anthropogenic Use | Reforestation |#68228B| |13 | Im | Anthropogenic | Anthropogenic Use | Mining areas |#636363| </code></pre> <p> </p>
Raster and original working data for the paper Holocene matters: landscape history accounts for current species richness of vascular plants in forests and grasslands of eastern Central Europe
<p>Aim: Current species-richness patterns are sometimes interpreted as a legacy of landscape history, but historical processes shaping the distribution of species during the Holocene are frequently omitted in biodiversity models. Here, we test their importance in modelling current species richness of vascular plants in forest and grassland vegetation.<br> Location: Western Carpathians and adjacent regions.<br> Taxon: Vascular plants.<br> Methods: Numbers of all species and of habitat specialists were extracted from plot records of forest and grassland vegetation. For each plot, environmental and historical data were derived from thematic maps. Historical data related to the persistence of (i) temperate taxa during the Late Glacial and Early Holocene, (ii) open-landscape taxa during the Middle Holocene, and (iii) taiga species during the Late Holocene were based on 112 fossil pollen profiles. Boosted regression trees were used to model spatial patterns in species richness.<br> Results: Historical variables always appeared among the best predictors of current species richness. In light forests, species richness highly mirrored both the Late Glacial (12.5% contribution) and Middle-Holocene (8.6%) landscape history. The latter factor became an important predictor also for species richness of steppe grasslands (8.3%) along with temperature seasonality (11.9%). Species richness of dark coniferous forests was best predicted by the Late-Holocene occurrence of taiga forests (14.8%), which had an even stronger effect on the richness of habitat specialists (20.5%). <br> Main conclusions: Landscape changes since the Last Glacial Maximum are important predictors of current plant species richness. The historical effects were found to be habitat-specific and, because they may interact with recent environmental conditions and anthropogenic pressures, they often show a non-linear relationship with species richness. We provide one possible direction of incorporating past landscape changes into the models of species richness.</p>
A global Aridity Index raster from 2003 to 2022
<p>An annually global gridded AI dataset is generated with a resolution of 0.05°×0.05°, covering a period from 2003 to 2022.</p> <p>Detail information refers to : Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022, International Journal of Digital Earth, 18:1, 2473639, DOI: 10.1080/17538947.2025.2473639</p> <p>To link to this article: https://doi.org/10.1080/17538947.2025.2473639<br> </p>
Dissolved calcium and pH raster layers for freshwater environments in Canada and the USA
<p>Calcium concentration and pH are key parameters that are linked to multiple chemical and biological processes in freshwater environments. This dataset presents high-resolution (10 x 10 km) interpolated raster layers for these two variables across Canada and the continental USA. These layers were generated via spatial interpolation (Kriging with a fixed zero nugget) after comparison of multiple interpolation methods, using water quality data for lakes and rivers compiled from multiple sources. This is the first time that such data have been made available at this scale and resolution, providing a valuable resource for research, including projects evaluating risks from environmental change, pollution, and invasive species. </p>
Raster-based dataset for spatio-temporal analysis of forest fires in the Amazon rainforest from 2001 to 2020
<p>Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associated study to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides three sets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world. </p>
Drainage network for a region in central Buenos Aires province, 30m resolution raster, EPSG:5347
<p>This drainage network was obtained using the Copernicus-30 Global Digital-Surface-Model, tiles (S37-38, W60) with QGIS and PCraster plugin: local direction drainage and flow accumulation algorithms. It helps to clarify the surrounding drainage pattern at cities like Azul, Cacharí and Tandil, for flood alleviation schemes.</p>
Supplement - Physiographic Variable Raster Data
<p>To link physiographic variables to lithology and trail characteristics (Objective 2), we created process domain maps and used principal component analysis considering physiographic variables such as slope, topographic position index (TPI), and concavity as a function of lithology and trail type. All metrics were calculated using ArcGIS Pro and lidar collected 2013 by McKim & Creed Inc. for City of Boulder. High-resolution lidar data can be useful in determining different types of processes, ranging from slow creep to landslides (Booth et al. 2009; Booth et al. 2013) and variables like slope highlight areas where mass movements are likely to occur. Similarly, concavity, or landscape curvature, indicates where advective versus diffusive processes occur, correlated to convex versus concave landscapes, respectively (Dietrich and Perron 2006; Sweeney et al., 2015). Concave processes are dominated by diffusive movement (slope-dependent transport; Dietrich and Perron 2006). These processes are competing against one another on the hillslope and give rise to diffusion-dominated ridges and advection-dominated valleys (Dietrich and Perron 2006; Sweeney et al., 2015). The TPI is a landform classification used to determine roughness indices like valleys and ridges in the study area. The TPI was calculated at different resolutions (5-m, 10-m, 50-m, 100-m) to see if different hillslope attributes were identifiable at the different scales. To calculate the TPI, the mean for each resolution was subtracted from the Digital Elevation Model (DEM). Derived physiographic variables and GIS data products can be found in this repository. </p>
A compilation of environmental geographic rasters for SDM covering France
<p>This dataset is a compilation of geographic rasters from multiple environmental data sources. It aims at making the life of SDM users easier. All rasters cover the metropolitan French territory, but have varying resolutions and projections. Each directory inside the main directory "<strong>0_mydata</strong>" contain a single environmental raster. Punctual extraction of raster values can be easily done for large sets of WGS84-(longitude,latitude) points coordinates and for multiple rasters at the same time through the R function <strong>get_variables</strong> of script <a href="https://github.com/ChrisBotella/SamplingEffort/blob/master/_functions.R">_functions.R</a> from Github repository: <a href="https://github.com/ChrisBotella/SamplingEffort">https://github.com/ChrisBotella/SamplingEffort</a>. All data sources are accessible on the web and free of use, at least for scientific purpose. They have various conditions of citations. Anyone diffusing a work using the present data must reference along with the present DOI, the original source data employed. Those source data are described in the paragraphs below. We provide the articles to cite, when required, and webpages for access.</p> <p><strong>Pedologic Descriptors of the ESDB v2: 1 km × 1 km Raster Library :</strong> The library contains multiple soil pedology (physico-chemical properties of the soil) descriptors raster layers covering Eurasia at a resolution of 1 km. We selected 11 descriptors from the library. They come from the PTRDB. The PTRDB variables have been directly derived from the initial soil classification of the Soil Geographical Data Base of Europe (SGDBE) using expert rules. For more details, see [1, 2] and [3]. The data is maintained and distributed freely for scientific use by the European Soil Data Centre (ESDAC) at <a href="http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster">http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster</a>. The 11 rasters are in the directories <strong>"awc_top", "bs_top", "cec_top", "dimp", "crusting", "erodi", "dgh", "text", "vs", "oc_top", "pd_top"</strong>.</p> <p><strong>Corine Land Cover 2012, Version 18.5.1, 12/2016 :</strong> It is a raster layer describing soil occupation with 48 categories across Europe (25 countries) at a resolution of 100 m. This data base of the European Union is freely accessible online for all use at <a href="http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012">http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012</a>. The raster of this variable is in the directory "<strong>clc</strong>".</p> <p><strong>Hydrographic Descriptor of BD Carthage v3: </strong>BD Carthage is a spatial relational database holding many informations on the structure and nature of the french metropolitan hydrological network. For the purpose of plants ecological niche, we focus on the geometric segments representing watercourses, and polygons representing hydrographic fresh surfaces. The data has been produced by the Institut National de l’information Géographique et forestière (IGN) from an interpretation of the BD Ortho IGN. It is maintained by the SANDRE under free license for non-profit use and downloadable at:<br> <a href="http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX">http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX</a><br> From this shapefile, we derived a raster containing the binary value raster proxi_eau_fast, i.e. proximity to fresh water, all over France.We used qgis to rasterize to a 12.5m resolution, with a buffer of 50m, the shapefile COURS_D_EAU.shp on<br> one hand, and the polygons of SURFACES_HYDROGRAPHIQUES.shp with attribute NATURE=“Eau douce<br> permanente” on the other hand.We then created the maximum raster of the previous ones (So the value of 1 correspond to an approximate distance of less than 50m to a watercourse or hydrographic surface of fresh water). The raster is in the directory named "<strong>proxi_eau_fast</strong>".</p> <p><strong>USGS Digital Elevation Data :</strong> The Shuttle Radar Topography Mission achieved in 2010 by Endeavour shuttle measured elevation at three arc second resolution over most of the earth surface. Raw measures have been post-processed by NASA and NGA in order to correct detection anomalies. The data is available from the U.S. Geological Survey, and downloadable on the Earthexplorer (<a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a>). One may refer to <a href="https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects">https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects</a> for more informations. the elevation raster is in the directory named "<strong>alti</strong>".</p> <p><strong>Potential Evapotranspiration of CGIAR-CSI ETP : </strong>The CGIAR-CSI distributes this worldwide monthly potential-evapotranspiration raster data. It is pulled from a model developed by Antonio Trabucco [4, 5]. Those are estimated by the Hargreaves formula, using mean monthly surface temperatures and standard deviation from WorldClim 1:4 (<a href="http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description">http://www.worldclim. org/</a>), and radiation on top of atmosphere. The raster is at a 1km resolution, and is<br> freely downloadable for a nonprofit use at: <a href="http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description">http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description</a>. This raster is in the directory "<strong>etp</strong>".</p> <p><strong>Bioclimatic Descriptors of Chelsea Climate Data 1.1:</strong> Those are raster data with worldwide coverage and 1 km resolution. A mechanistical climatic model is used to make spatial predictions of monthly mean-max-min temperatures, mean precipitations and 19 bioclimatic variables, which are downscaled with statistical models integrating historical measures of meteorologic stations from 1979 to today. The exact method is explained in the reference papers [6] and [7]. The data is under Creative Commons Attribution 4.0 International License and downloadable at (<a href="http://chelsa-climate.org/downloads/">http://chelsa-climate.org/downloads/</a>). The 19 bioclimatic rasters are located in the directories named "<strong>chbio_X</strong>".</p> <p><strong>ROUTE500 1.1:</strong> This database register classified road linkages between cities (highways, national roads, and departmental roads) in France in shapefile format, representing approxi-mately 500,000 km of roads. It is produced under free license (all uses) by the IGN. Data are available online at <a href="http://osm13.openstreetmap.fr/~cquest/route500/">http://osm13.openstreetmap.fr/~cquest/route500/</a>. For deriving the variable “<strong>droute_fast</strong>”, the distance to the main roads networks, we computed with qGis the distance raster to the union of all elements of the shapefile ROUTES.shp (segments).</p> <p><strong>References : </strong></p> <p>[1] Panagos, P. (2006). The European soil database. GEO: connexion, 5(7), 32–33.</p> <p>[2] Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L. (2012). European Soil Data<br> Centre: Response to European policy support and public data requirements. Land Use Policy,<br> 29(2),329–338.</p> <p>[3] Van Liedekerke, M. Jones, A. & Panagos, P. (2006). ESDBv2 Raster Library-a set of rasters<br> derived from the European Soil Database distribution v2. 0. European Commission and the<br> European Soil Bureau Network, CDROM, EUR, 19945.</p> <p>[4] Zomer, R., Bossio, D., Trabucco, A., Yuanjie, L., Gupta, D. & Singh, V. (2007). Trees and<br> water: smallholder agroforestry on irrigated lands in Northern India.</p> <p>[5] Zomer, R., Trabucco, A., Bossio, D. & Verchot, L. (2008). Climate change mitigation: A<br> spatial analysis of global land suitability for clean development mechanism afforestation and<br> reforestation. Agriculture, ecosystems & environment, 126(1), 67–80.</p> <p>[6] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler,<br> M. (2016). Climatologies at high resolution for the earth’s land surface areas. arXiv preprint<br> arXiv:1607.00217.</p> <p>[7] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler, M.<br> (2016). CHELSEA climatologies at high resolution for the earth’s land surface areas (Version<br> 1.1).</p>
MX Raster Data For Compression
<p>This data set, contains 6 raster experiment data sets, performed on and around a large lysozyme crystal sample using various beam intensities. These data are used to investigate several compressions (ratio and speed). </p> <p> </p>
Inundation Maps - Polyphytos open surface water reservoir - WQeMS raster products
<p>Within this dataset, inundation maps of the Polyphytos open surface water reservoir in Greece for the years 2021 and 2022 are available in GeoTIFF raster format. They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project as intermediate products. Each raster file in the dataset is named according to the date in which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps.</p>
Zonal and raster data for i-SoMPE WP1 R Project
<p>This data is necessary to run the i-SoMPE WP1 R Project available on GitLab and Zenodo.</p>
Raster classification and mapping of ecological units of Southern California
Open the record for dataset details and reuse information.
Raster and original working data for the paper Holocene matters: landscape history accounts for current species richness of vascular plants in forests and grasslands of eastern Central Europe
Open the record for dataset details and reuse information.
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.