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3,592 results for “Grid”
Dutch grid map layers for biodiversity surveys
<p>This dataset contains map layers in GeoPackage format with the following grids used in biodiversity surveys in the Netherlands:</p> <table> <tbody> <tr> <td><strong>Layer name</strong></td> <td><strong>Size (m)</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>IvonGrid Kwartierhok</td> <td>1250x1042 </td> <td>Used between 1902 and 1950 by IVON botanical surveys.</td> </tr> <tr> <td>IvonGrid Uurhok</td> <td>5000x4168 </td> <td>Used between 1902 and 1950 by IVON botanical surveys. Each square contains 16 'kwartierhok' squares with corresponding first four characters in column name.</td> </tr> <tr> <td>RdGrid Kilometerhok</td> <td>1000</td> <td>Used since 1950 in biodiversity surveys. </td> </tr> <tr> <td>RdGrid Uurhok</td> <td>5000</td> <td>Used since 1950 in biodiversity surveys. Each square contains 25 kilometer squares.</td> </tr> </tbody> </table> <p> </p> <p>The RdGrid maps are available in the WGS84 and RD New (EPSG:28992) spatial reference systems, as they are aligned to rounded RD coordinates. IvonGrid is only available in WGS84.</p> <p><strong>Grid square codes</strong></p> <p>Kilometerhok (1x1 km) and Uurhok (5x5) grids have corresponding grid square codes. A code consists of three concatenated numbers for map sheat (1-62), 5 km square (11-58) and 1 km square (11-55). E.g. a 5 km square code looks like 4017. In literature and on collection labels this often written as 40.17 or 40-17.</p> <p><strong>Maintanance</strong></p> <p>This dataset is being curated by FLORON Plant Conservation Netherlands since 1988 for use in botanical surveys (previously by Theo Peterbroers, Bart Vreeken and Ruud Beringen).</p> <p><strong>History</strong></p> <p>IVON stands for "Instituut voor Vegetatie-onderzoek Nederland", an NGO founded in 1930 was most active until the 1950's and then succeeded by FLORON in 1988. More information about the repartition of the IVON grid can be found in Atlas van de Nederlandse Flora part 1 (Mennema et al. 1989). The original map was hand drawn on topographical maps by J.W.C. Goethart en W. J. Jongmans (Van Ooststroom 1956) originally following the Bessel 1841 projection (EPSG:7004). The grid cells are equal in size, although not rounded to kilometers. The size was probably chosen to fit maps that were commercially available at that time.</p> <p> </p> <p><strong>References</strong></p> <p>Mennema, J., Quene-Boterenbrood, Plate, C. L., Van der Meijden, R., & Weeda, E. J. (1989). Atlas van de Nederlandse flora. Kosmos, Amsterdam.</p> <p>van Ooststroom, S.J. (1956). Het I.V.O.N. – Werk. Correspondentieblad ten dienste van de floristiek en het vegetatie-onderzoek van Nederland 1: 2–3.</p>
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
<p>Summary:</p> <p>There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.</p> <p>The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>How to cite this dataset:</p> <p>Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. <em>Earth System Science Data</em>, <em>14</em>(9), pp.4397-4418.</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>Update history:</p> <p>I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)</p> <p>Version 1.03.1: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.</p> <p>Version 1.02: New data summary, add Netcdf data format</p> <p>Version 1: Initial dataset for peer-review, CSV format only</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.</p> <p>SPAM2010: </p> <p>Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.</p> <p>GAEZv4:</p> <p>FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021</p> <p>The dataset of Ricciardi et al.'s:</p> <p>Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.</p> <p>The global dominant field size dataset:</p> <p>Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.</p> <p>GLC-Share:</p> <p>Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.</p> <p>CAAS-IFPRI cropland extent map:</p> <p>Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.</p>
Gridded Surface Emission Data for Delhi-NCR
<p>The gridded (~400mt) surface chemistry data for Delhi-NCR with domain size 70km X 65 km which includes megacity Delhi and surrounding regions by including 17 different types of Anthropogenic sources of air pollutants like PM<sub>2.5</sub>, PM<sub>10</sub>, CO, NO<sub>x</sub>, VOC, SO<sub>2</sub>, BC and OC for the base year 2020. Unit is Tons/Grid/yr. The files are given in .shp file format suitable for GIS software.</p>
A Standardized European Hexagon Gridded Dataset Based on OpenStreetMap POIs
<p>Point of interest (POI) data refers to information about the location and type of amenities, services, and attractions within a geographic area. This data is used in urban studies research to better understand the dynamics of a city, assess community needs, and identify opportunities for economic growth and development. POI data is beneficial because it provides a detailed picture of the resources available in a given area, which can inform policy decisions and improve the quality of life for residents. This paper presents a large-scale, standardized POI dataset from OpenStreetMap (OSM) for the European continent. The dataset's standardization and gridding make it more efficient for advanced modeling, reducing 7,218,304 data points to 988,575 without significant resolution loss, suitable for a broader range of models with lower computational demands. The resulting dataset can be used to conduct advanced analyses, examine POI spatial distributions, conduct comparative regional studies, enhancing understanding of the economic activity, distribution, attractions, and subsequently, economic health, growth potential, and cultural opportunities. The paper describes the materials and methods used in generating the dataset, including OSM data retrieval, processing, standardization, and hexagonal grid generation. The dataset can be used independently or integrated with other relevant datasets for more comprehensive spatial distribution studies in future research.</p>
Standardized reference grids for spatial analyses at various grain sizes
<p><strong>Description:</strong><br> These Reference grids have been created for the <a href="https://naturaconnect.eu/">NaturaConnect project</a> and are based on an intersection of the<a href="https://www.eea.europa.eu/data-and-maps/data/eea-coastline-for-analysis-1/gis-data/europe-coastline-shapefile"> European Coastline delineation</a> and the <a href="https://gadm.org/">GADM database</a>.<br> Thee reference grids have been created in a way so that they are fully consistent with the EEA reference grid (https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2), meaning that for example two 5km gridded cells fully match a 10km grid cell in width.</p> <p><strong>Filestructure:</strong><br> ReferenceGrid_Europe_{format}_{grain}</p> <ul> <li> format is either "frac" for fractional data (which has been multiplied with 10000 to save in integer format) or binary (0,1).</li> <li> grain is provided as layers in 100m, 1000m, 5000m, 10000m, 50000m spatial resolution. Alternative aggregations can be provided on request.</li> </ul> <p><strong>File format:</strong><br> The layers are gridded geoTiff files and can be loaded in any conventional Graphical Information System (GIS) or specific analytical programming languages (e.g. R or python). In addition external pyramids (.tfw) have been precreated to enable faster rendering.</p> <p><strong>Geographic projection:</strong><br> We use the <a href="https://epsg.io/3035">Lamberts-Equal-Area Projection</a> by default for all layers in NaturaConnect. This is an equal-area (but distorted shape) projection and commonly used by European institution with a focus on the European continent. For global layers the <a href="https://epsg.io/54009">equal-area World Mollweide projection</a> is used.<br> <br> <strong>Sourcecode:</strong><br> The code to reproduce the layers has been made available in the "code" file.<br> </p>
Statistical blending of global-gridded climatological products: an approach to inverse hydrological model
<p>The growing use of global-scale environmental products in hydro-climatic modeling (with different assumptions, resolutions, and precisions) has increased the variety of their applications and the complications of their uncertainties and evaluations. Researchers have recently turned to statistical blending (fusion) of these products to achieve optimal modeling while avoiding difficulties. The proposed statistical blending in this study includes five large-scale and satellite precipitation (Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA5-Land of ECMWF (ERA), Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission (TRMM), and Terra) and evapotranspiration (Global Land Evaporation Amsterdam Model (GLEAM), SSEBop, Moderate Resolution Imaging Spectroradiometer (MODIS), Terra, and ERA) products committed in three modeling scenarios. The blending procedures are organized using a conceptual water balance model to achieve the best precipitation and evapotranspiration results for the conceptual production of streamflow using hydrological inverse modeling. Based on the results, the proposed blending procedures of precipitation and evapotranspiration improved the performance of the model using different statistical metrics. In addition, the results show the conformity of the pattern and behavior of the blended precipitation calculated using the moving least square method in the study area. This happened by changing the estimation based on <em>in situ</em> values, particularly in cold months considering the orographic/snow effects. The combining method provides a good fusion procedure to improve the realistic estimation of precipitation and evapotranspiration in ungagged watersheds as well<strong>.</strong></p>
Gridded global organic matter reactivity (RCM parameter a, in years)
<p>Gridded data product for the globally extrapolated RCM parameter a (in yrs) and its respective reactivity k (in 1/yrs from k = nu/a). This represents a combination of the two datasets presented in the main text in Fig. 9. The deep-sea extrapolation uses data from Seiter, Hensen, and Zabel (2005), while the shallow ocean (SFD<1000m) uses data from Jørgensen, Wenzhöfer, Egger, and Glud (2022). The area South-Est of Australia remains empty as in Seiter et al. (2005) and for reasons given in the main text.</p>
Gridded products of global river methane concentrations, flux rates and emissions
<p><strong>Information on the products on this repository</strong></p> <p>These data is created using the R scripts with the random forest models and upscaling procedures found in: https://github.com/rocher-ros/RiverMethaneFlux.</p> <p>Raw files to reproduce this product can be found in https://doi.org/10.5281/zenodo.7733604</p> <p>The results of this analysis are published in the article "Global Methane emissions form rivers and streams" (in Nature) (https://doi.org/10.1038/s41586-023-06344-6).</p> <p>Main author is Gerard Rocher-Ros, for which correspondence can be sent to g.rocher.ros@gmail.com</p> <p>Units of the variables in the product are:<br> -River methane concentration: mmol CH4 m-3<br> -River methane diffusive flux rates: mmol CH4 m-2 d-1 (of river area)<br> -River methane diffusive emissions: Mega grams of C-CH4 (for each pixel).</p> <p>The spatial resolution of the product is 0.25 degrees (which corresponds to around 27 km). The files are in WGS84.</p> <p>There are four main products in this folder, packed as geotiff files, and described below.</p> <p>+ The file "river_methane_yearly.tiff" contains three layers:<br> - Yearly average river CH4 concentrations (ch4_conc_avg)<br> - Yearly average river CH4 diffusive flux rates (ch4_flux_avg)<br> - Yearly total river CH4 diffusive emissions (ch4_emissions_year)</p> <p>+ The file "river_methane_concs_monthly.tiff" contains twelve layers, with the modelled river methane concentrations for each month.</p> <p>+ The file "river_methane_flux_monthly.tiff" contains twelve layers, with the modelled river methane flux rates for each month.</p> <p>+ The file "river_methane_emissions_monthly.tiff" contains twelve layers, with the total river methane emissions for each month.</p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-16, and the abundance values range from log10(VMR) = -3.5 to -1.5 in steps of 0.5 and 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input H2O value (abundance case), the retrieved H2O value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for H2O. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input H2O': str})</p>
ONFIRE Dataset: Monthly Gridded Burned Area data
<p>The ONFIRE Dataset presents a 1° x 1° lat-long gridded database detailing monthly burned areas (BA) stemming from national fire data across five regions: Australia, Canada, Chile, Europe, and the United States. Each grid cell encapsulates the center's latitude and longitude, coupled with the total square meters burned within the month. The dataset spans varying periods per region, starting from 1950 in Australia, 1959 in Canada, 1985 in Chile, 1980 in Europe, and 1984 in the US, extending up to 2021.The ONFIRE DATASET is available in netCDF4, RData, and ASCII formats for accessibility and ease of integration.</p>
Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE
<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, <em>Climate of the Past,</em> 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul> <p> </p> <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul> <p> </p> <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul> <p> </p> <ul> <li><strong>Methodology:</strong> The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>
Dataset to Study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets
<p>We publish the dataset used to study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets (as part of chapter 7 of deliverable D3.3 of the OneNet project).</p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to two distribution networks: the Matpower systems 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Injections and loads of the nodes are adapted to create an anticipated imbalance in the interconnected system, resolved by flexibility. In addition, the lines’ upper limits are adjusted to create anticipated congestion in the networks. The interconnected system is fully represented in "Network_case_A_B_C.xlsx" (upward balancing need) and "Network_case_D.xlsx" (downward balancing need).<br>Upward and downward flexibility bids are randomly generated and allocated to the nodes. </p> <p>7 bids lists are available in this dataset. </p> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Downscaled climate grids at 30m for a variety of bioclimatic variables over the San Joaquin Experimental Range, CA: 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Downscaled climate grids at 30m for a variety of bioclimatic variables over the Teakettle Experimental Forest, 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Downscaled climate grids at 30m for a variety of bioclimatic variables over the Tejon Ranch, CA: 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Flow accumulation grid generated from 10 meter DEM, Andrews Experimental Forest, 1998
Flow accumulation grid generated from 10 meter DEM, Andrews Experimental Forest. This grid is useful for determining the area of land that drains to a point. The user selects a point on the grid, and the value of that point represents the area (in 100 square meters) that drain to the point. This grid can also be used for generating watershed boundaries and stream networks.
Average monthly and annual precipitation spatial grids. (1971-2000 and 1980-1989), Andrews Experimental Forest
These files are spatially gridded precipitation of average monthly and annual precipitation for the climatological periods 1971-2000 and 1980-89, Andrews Experimental Forest. The original 1980-89 grids were updated for a greater time span and also the extent of the coverage is increased. Interpolation of point station measurements to a spatial grid was done using the PRISM model, developed by Christopher Daly of the PRISM Group at Oregon State University. PRISM interpolation accounts for the effects of elevation on the spatial patterns of precipitation. Grid resolution is 100 meters. Station data used in the interpolation were obtained from current and historic rain gauge stations within the forest. These grids represent the first significant effort to map climatological precipitation in the Andrews Forest. Further information on PRISM can be found at http://prism.oregonstate.edu/
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), Andrews Experimental Forest
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), adjusted for the effects of solar radiation and sky view factors, Andrews Experimental Forest. Maps were created using PRISM (Parameter-elevation Regressions on Independent Slopes Model), developed by Dr. Christopher Daly at Oregon State University’s PRISM Climate Group in 2010 (prism.oregonstate.edu). Grids were exported into ASCII format from GRASS GIS software; values are in degrees C x 100. Spatial resolution is 50 meters. Two sets of temperature values are available: (1) values derived from an interpolation of point station temperature values accounting for elevation; and (2) values from (1), adjusted for effects of solar radiation exposure and sky view factors. Radiation exposure and sky view factors were calculated from a two-stream solar radiation model that accounts for elevation, slope, aspect, and shading from adjacent pixels on a 50-m digital elevation model. Temperature data were obtained from selected benchmark and reference stand climate stations within the HJ Andrews, as well as National Weather Service Cooperative (COOP) and USDA NRCS Snow Telemetry (SNOTEL) stations in the vicinity. Due to the sparseness of the station data outside the Andrews, values outside the Andrews are considered to have high uncertainty. Temperature values assume an open site with no canopy cover, so are not appropriate for describing temperatures within the forest canopy. See MS033 for radiation grids used to make the radiation adjustments.
Radiation spatial grids, Andrews Experimental Forest, 1995-2000
This database contains 50m gridded mean monthly radiation data over the HJ Andrews. The IPW (Image Processing Workbench) model was used to create the grids with parameters specified by the user to approximate climatic conditions at the HJ Andrews. Cloudiness was accounted for by varying proportions of direct and diffuse radiation for each month. Values are theoretical and not directly based upon observed radiation values. For further information, see the project website at http://andrewsforest.oregonstate.edu/lter/research/component/climate/smithjw/hja/.
Flow direction grid at 1 kilometer resolution for North Slope drainage basins, Alaska
We derived and evaluated a 1 kilometer spatial resolution flow direction grid for the terrestrial drainage of the North Slope of Alaska. The region is resolved by 182,722 grid cells and the associated connectivity. It is bounded by the Brooks Range and Beaufort Sea coast, and extends from the northern Chukchi Sea coast eastward to the small rivers near 140 degrees West. The dataset is provided in raster and tabular format, with the latter including coordinates, river basin identifier, and downstream reach and direction for each grid cell in the region. Over three dozen river basins are identified by name in an associated lookup table. This new mapping resolves the terrestrial drainages for rivers exporting freshwater, nutrients, and other materials to Elson, Simpson, Jago, Kaktovik, and other coastal lagoons at a resolution that captures important processes linked to surface and subsurface hydrological flows. Our analysis suggests that the mapping exhibits notable similarity in basin area boundaries relative to the benchmark USGS National Hydrography Dataset.
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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.