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506 results for “crop data”
Sample data for "A weakly supervised framework for high resolution crop yield forecasts"
<p>This dataset includes sample data for the United States to run the weakly supervised framework as described in the paper titled <em>A weakly supervised framework for high resolution crop yield forecasts</em>, accessible at </p> <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2205.09016">https://doi.org/10.48550/arXiv.2205.09016</a></td> </tr> </tbody> </table> <p> </p> <p>The updated paper (including results from the US) is published in Environmental Research Letters:</p> <p><a href="https://doi.org/10.1088/1748-9326/acf50e">https://doi.org/10.1088/1748-9326/acf50e</a></p> <p> </p> <p>The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting/tree/weaksup.</p> <p> </p> <p>Data</p> <p>1. County data (county-data.zip) for county-level strongly supervised models:</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p> <p>* CSSF_COUNTY_US.csv: Crop productivity indicators including total above-ground production (kg ha<sup>-1</sup>), total weight of storage organs (kg ha<sup>-1</sup>), development stage (0-2). Source: de Wit et al. (2022).</p> <p>* METEO_COUNTY_US.csv: Meteo data including maximum, minimum, average daily air temperature (℃); sum of daily precipitation (PREC) (mm); sum of daily evapotranspiration of short vegetation (ET0) (Penman-Monteith, Allen et al., (1998)) (mm); climate water balance = (PREC - ET0) (mm). Source: Boogaard et al. (2022).</p> <p>* REMOTE_SENSING_COUNTY_US.csv: Fraction of Absorbed Photosynthetically Active Radiation (Smoothed) (FAPAR). Source: Copernicus GLS (2020).</p> <p>* SOIL_COUNTY_US.csv: Soil water holding capacity. Source: WISE Soil Property Database (Batjes, 2016).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p> </p> <p>2. 10-km grid data (grid-data.zip) for grid-level strongly supervised models:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level (similar to county data above).</p> <p>* METEO_GRIDs_US.csv: Meteo data at 10km grid level (similar to county data above).</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level (similar to county data above).</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level (similar to county data above).</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021), Lobell et al. (2020).</p> <p> </p> <p>3. County labels and 10-km grid inputs (dscale-US.zip) for weak supervision:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level.</p> <p>* METEO_GRIDs_US.csv: Meteo indicators at 10km grid level.</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level.</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level.</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p>
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </p>
Data from: Carbon and Water Balances in a Watermelon Crop Mulched with Biodegradable Films in Mediterranean Conditions at Extended Growth Season Scale
<p><span>Abstract</span></p> <p><span>The uploaded data are relative to the investigation around (i) the carbon source/sink nature and, further, (ii) the water and carbon balances, of a drip-irrigated and mulched watermelon. The crop was cultivated under the semi-arid climate of the Apulia region, in south Italy.</span></p> <p><span>The used mulching films were biodegradable as indicate by the producer; plants and some non-standard fruits were left on the soil as green manure after harvesting, thus, the experiment spanned from planting to the subsequent crop (6 months of continuous measurement from June to November 2023). </span></p> <p><span>The results detailed in the original publication indicate that mulching films contribute to carbon sequestration in the soil (+19.3 gC m<sup>−2</sup>). However, this mulched watermelon represents a net carbon source, with a net biome exchange, as loss from ecosystems, equal to +230 gC m<sup>−2</sup>. This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 gC m<sup>−2</sup>). </span></p> <p><span> </span></p> <p><span>Methods</span></p> <p><span>Site and crop</span></p> <p><span>The field site was at the CREA-AA Research Unit experimental farm located in southern Italy (Rutigliano–Bari, 41 01’ N, 17°01’ E, altitude 147 m a.s.l.)., characterized by a Mediterranean semi-arid climate (average annual rainfall of 535 mm). The soil is classified as Lithic Rhodoxeralf, with a clay texture, stable structure, shallow profile (0.6–1.1 m) and rapid drainage due to an underlying cracked limestone subsoil. The SOC content averages around 12.0 g kg<sup>−1</sup>. The field capacity and the permanent wilting point volumetric water contents are 0.36 and 0.21 m<sup>3</sup> m<sup>−3</sup>, respectively; with a bulk density of 1.15 Mg m<sup>−3</sup>, the available soil water ranges from 80 to 140 mm.</span></p> <p><span>The studied watermelon crop (seedless var. Lion king), followed a broccoli cabbage crop harvested in April and partially incorporated (0.81 kg m<sup>−2</sup> of fresh biomass in a soil layer depth of 0.30 m, corresponding to 0.69 kgH2O m<sup>−2</sup>) as green manure on 25 May 2023. Main tillage at medium depth ploughing (0.30 m) and seedbed preparation were performed between 25 and 30 May 2023; the biodegradable film mulch (model PC 100 d8, BASF, Italy, 1 m width) was applied on 1 June 2023. On the same day, driplines (2.1 Lh<sup>−1</sup> emitters, 0.60 m apart) and the main organic fertilization (Orga-Kem 6.11.8 + 11CaO, 300 kg ha<sup>−1</sup>) were also applied. The watermelon plants were transplanted on 9 June at a spacing of 2.70 m between rows and 1 m between plants, covering an area of about 4.0 ha, with a density of approximately 3200 plants ha<sup>−1</sup>. Every 6 rows, the inter-row distance was 5 m to facilitate machinery passage. The first irrigation was performed the day before planting. Crop management adhered to the usual treatments in the area including mechanical weed removal every 4 weeks, irrigation around three times per week to maintain optimal soil water conditions and monthly fertigation (ammonium sulphate 50 kg ha<sup>−1</sup>, magnesium nitrate 30 kg ha<sup>−1</sup>, calcium nitrate 60 kg ha<sup>−1</sup>, mycorrhizae 20 kg ha<sup>−1</sup>). The scalar harvest of marketable fruits occurred between 28 and 31 August 2023. After harvesting, on 25 September 2023, the fresh plant residues (0.6 kg m<sup>−2</sup> of fresh biomass, corresponding to 0.49 kgH2O m<sup>−2</sup>), unharvested fruits (4.0 kg m<sup>−2</sup> of fresh material, corresponding to 3.7 kgH2O m<sup>−2</sup>) and the mulching film were chopped by a tractor shredder and ploughed in two steps, on 2 and 13 October 2023, to a soil depth of 0.30 m. Measurements concluded at the end of November 2023, when tillage for the new winter crop commenced.</span></p> <p><span> </span></p> <p><span>Measurements of H<sub>2</sub>O and CO<sub>2</sub> fluxes; partitioning in evaporation, transpiration, photosynthesis and respiration</span></p> <p><span>The eddy covariance technique was employed to monitor water vapor (H<sub>2</sub>O) and carbon dioxide (CO<sub>2</sub>) fluxes. The equipment comprised a three-dimensional sonic anemometer (uSonic 3 Scientific, Metek GmbH, 25337 Elmshorn, Germany) and a fast response open-path infrared gas analyzer (LI-7500, Li-COR Inc., Lincoln, NE, USA). The three wind components, sonic temperature and atmospheric concentrations of CO<sub>2</sub> and H<sub>2</sub>O were continuously measured at 1.5 m above the crop canopy, with the sensor height adjusted to follow crop growth, reaching a maximum of 1.75 m. </span></p> <p><span>Data were recorded at a frequency of 10 Hz on a dedicated computer using the MeteoFlux software (Servizi Territorio, S.n.c., Cinisello Balsamo, Italy) and were stored on an hourly scale. Post-processing and computation of hourly fluxes of H<sub>2</sub>O (mmol m<sup>−2</sup> s<sup>−1</sup>) and CO<sub>2</sub> (</span>μ<span>mol m<sup>−2</sup> s<sup>−1</sup>) were conducted using EddyPro software, v7.0.9 (</span><a href="http://www.licor.com/eddypro"><span>http://www.licor.com/eddypro</span></a><span>), applying 60 min block averaging, double coordinate rotation, the statistical test, the maximum cross-covariance method, and the WPL density correction.</span></p> <p><span>H<sub>2</sub>O and CO<sub>2</sub> fluxes were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method. This method utilizes the Monin–Obukhov similarity theory to separate stomatal (photosynthesis, Fp, and transpiration, Ft) from non-stomatal (respiration, Fr, and evaporation, Fe) processes (Palatella et al., 2014). the H<sub>2</sub>O and CO<sub>2</sub> EC fluxes were partitioned using an adaptation of the code in Phyton provided by (Skaggs et al., 2018) and downloaded from <span> </span></span><a href="https://github.com/usda-arsussl/fluxpart"><span>https://github.com/usda-arsussl/fluxpart</span></a><span> (V0.2.10).</span></p>
Data from Sand aggradation alters biofilm standing crop and metabolism in a low-gradient Lake Superior tributary
We conducted a comparative study of biofilm standing crop and metabolism in the Salmon Trout River, a tributary of Lake Superior where watershed disturbances have led to 3-fold increases in streambed fine sediments, predominately sand, in the past decade. We compared biofilm standing crop and metabolism rates using light–dark chambers in reaches where substrate consisted of predominately exposed rock or sand substrates. This data archive includes rates of primary production and respiration, biomass measurements from chambers, and benthic standing crop and water chemistry data collected from the same river sites over the course of a summer. All data were published in Journal of Great Lakes research in 2015, https://doi.org/10.1016/j.jglr.2015.09.004
Data for 'Local food crop production can fulfil demand for less than one-third of the population'
<p><strong>This dataset is supplement to the following publication (<em>please cite that when using the data</em>):</strong></p> <p>Kinnunen et al. 2020. Local food crop production can fulfil demand for less than one-third of the population. Nature Food 1: 229–237. http://doi.org/10.1038/s43016-020-0060-7</p> <p> </p> <p><strong>Data description</strong></p> <p><strong><em>Distance to food:</em></strong> Globally optimized distance between crop production and consumption. The optimization creates a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. Data is in two formats: NetCDF (dist_food_netcdf.zip) and multi-band geotiff (dist_food_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (dist_food_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (dist_food_halfLoss.nc / .tif)</li> <li>halving the yield gap (dist_food_halfYieldGap.nc / .tif)</li> <li>both of these measures together (dist_food_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: km</p> <p> </p> <p><em><strong>Foodsheds:</strong></em> The data contains global foodsheds which are areas that are connected by food flows between raster cells. The food flows are from a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. In addition to normal foodsheds (values>0), there are two special cases: ridge-cells (value: -99) and unconnected single cells (value: -50). Ridge-cells are raster cells connected to multiple foodsheds, while being able to satisfy their own demand locally. Unconnected single cells are not connected to any other foodshed. Each positivie value is a crop specific id, signifying a connected foodshed area. </p> <p>Data is in two formats: NetCDF (foodsheds_netcdf.zip) and multi-band geotiff (foodsheds_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (foodsheds_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (foodsheds_halfLoss.nc / .tif)</li> <li>halving the yield gap (foodsheds_halfYieldGap.nc / .tif)</li> <li>both of these measures together (foodsheds_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: -</p> <p> </p>
Data for "Crop Diversification in Viticulture with Aromatic Plants: Effects of Intercropping on Grapevine Productivity in a Steep-Slope Vineyard in the Mosel Area, Germany"
<p>This dataset is corresponding to an open-access article named "Crop Diversification in Viticulture with Aromatic Plants: Effects of Intercropping on Grapevine Productivity in a Steep-Slope Vineyard in the Mosel Area, Germany" published in Agriculture (https://www.mdpi.com/2077-0472/11/2/95; <a href="https://doi.org/10.3390/agriculture11020095">https://doi.org/10.3390/agriculture11020095</a>), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Seed mass data for crop species and wild progenitors
<p>Data supporting a comparison of seed masses in crop species and their wild progenitors.</p> <p>The data files which fed into the analysis reported in the paper are:</p> <ul> <li>Grass_crops_raw.csv</li> <li>Legume_crops_raw.csv</li> <li>Veg crops combined.csv</li> <li>Beet_seeds_dissected.csv - mass of true seeds of beet; the data in the main 'Veg crops' file are for beet seed capsules, which are easier to collect and weigh.</li> <li>Cassava_EMBRAPA_*.csv - extra seed mass data from cassava, analysed separately from the main data, and described in supplementary material 2.</li> </ul> <p>Scripts used in processing the data (*.py) and a Makefile controlling some processing steps are included.</p> <p>There is also some extra data collected on other species of vegetable crops: <em>Brassica </em>spp., Chicory & Endive (<em>Cichorium </em>spp.), Leek (<em>Allium ampeloprasum</em>), and on fibre crops: Kenaf & Roselle (<em>Hibiscus </em>spp.), Jute (<em>Corchorus olitorius</em>), Hemp (<em>Linum</em> spp.). The data available for these was not sufficient to include them in our analysis, but they are provided here anyway.</p>
Supplementary data: Winter cover cropping: Effect on soybean and synergistic implications on soil microbiome
<p>Supplementary data: (i) Agronomic and quality data of soybean (2 varieties) grown in 2 years (2020 & 2021) in two management systems (organic & low-input) with different cover crops; (ii) Soil microbiome analysis of the soybean field trials.</p>
Crop-specific salinity and irrigation data for river sub-basin water scarcity analyses in the US and AU
<p>This dataset contains observed monthly and annual salinity (EC) data for surface water (river) respectively groundwater, spatially averaged over sub-basins within the Central Valley, CA and the Murray Darling basin, AU, used for salinity-inclusive water scarcity assessments. The data also includes crop-specific irrigated area, irrigation withdrawals and salinity thresholds and other parameters specified, as well as example codes for analyses related to the manuscript: Thorslund et al., <em>Salinity impacts on irrigation water-scarcity in food bowl regions of the US and Australia.</em></p>
2022 Rice Crop-type Data for Western Tanzania
<p>Rice Crop-type data from Katavi Region Tanzania was collected by the NASA Harvest Program at the University of Maryland, the Sokoine University of Agriculture, and Flamingoo Food Limited under the Optimizing Crop Yield Data Collection for Supply Chain Enhancement project (more at: https://cropanalytics.net/optimizing-yield-data/) funded by ENABLING CROP ANALYTICS AT SCALE (ECAAS) is a multi-phase initiative that aims to catalyze the development, availability, and uptake of agricultural ground and remote sensing data and applications in smallholder production systems more at (https://cropanalytics.net/)</p>
Extra data to accompany code in GitHub burntfields_punjab, both used in Walker et. al. 2022, Detecting crop burning in India using satellite data
<p>Supplementary data files to accompany GitHub code 'burntfields_punjab' supporting Walker et. al. (2022) Detecting crop burning in India using satellite data [<a href="https://arxiv.org/abs/2209.10148">available here</a>] and Jack et. al. (2024) Money (not) to burn: Payments for ecosystem services to reduce crop residue burning).</p> <p>Includes custom Sentinel-2 cloud masks and data from Sentinel-2 Spectral Mixture Analysis to highlight Char (burning) based on general concept and methods from Daldegan et. al (2019). Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone. Remote Sensing of Environment 232, 111340. </p> <p>Note: Bands in weekly BASMA layers are: 0 = green vegetation, 1 = Non-productive vegetation and bare soil, 2 = Char (burned).</p> <p>further details are provided at: <a href="https://github.com/klwalker-sb/burntfields_punjab">https://github.com/klwalker-sb/burntfields_punjab</a> (archived at: <a href="https://doi.org/10.5281/zenodo.11225292" target="_blank" rel="noopener">DOI: 10.5281/zenodo.11225292</a>)</p>
Assessment of the condition of winter crops before winter dormancy on the basis of Planet data; season 2018
<p>NDVI determined on the basis of images of Planets from the dates 07.09.2018 and 14.10.2018, were used to study the assessment of the winter crop before winter dormancy. Available data from the September and October dates were used to assess the degree of development and density of plants.</p>
Assessment of the condition of winter crops on the basis of Planet data; season 2017/2018
<p>NDVI determined on the basis of images of Planets from the dates 17.10.2017 and 13.04.2018, were used to study the assessment of wintering of crops. Acquisition of data before and after winter rest allows to assess the condition of winter crops. Available data come from the research area of the Kujawsko-Pomorskie voivodeship.</p>
Assessment of the condition of winter crops before winter dormancy on the basis of Sentinel-2 data; season 2018
<p>NDVI determined on the basis of images of Sentinel-2 from the dates 15 and 18.10.2018, were used to study the assessment of the winter crop before winter dormancy. Data were used to assess the degree of development and density of plants. The data was used to study the correlation with Planet.</p>
Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services"
<p>Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services" (Qiu et al. 2025), including source data, R scripts, and output results.</p>
Data base of cycles 1 and 2 of biometric variables of fuzzy model for assessing the development of the radish crop
<p>This data represent the fuzzy model developed of a Rule-Based System (RBS) to evaluation the development of the radish crop in two production cycles, for the irrigation depth at 100% of evapotranspiration. This RBS represents the function <span class="math-tex">\(f:\mathbb{R}\rightarrow\mathbb{R}^{10}\)</span>, where the domain is represented by the Days After Sowing (DAS), and counterdomain is represented by the ten biometric variables, denominated: Number of Leaves (NL), Root Length (RL), Bulb Diameter (BD), Bulb Length (BL), Green Root Weight (GRW), Green Leaf Weight (GLW), Green Bulb Weight (GBW), Dry Root Weight (DRW), Dry Leaf Weight (DLW). </p>
Global Crop Type Validation Data Set for ESA WorldCereal System
<p>This dataset was created by using a new IIASA tool, called “Street Imagery validation” (<a href="https://svweb.cloud.geo-wiki.org/">https://svweb.cloud.geo-wiki.org/</a>) where users could check street level images (e.g., Google Street Level images, Mapillary etc.) and identify the crop type where it is possible. The advantage of this tool is that there are plenty of georeferenced images with dates, going back in time. The disadvantage is that users need to check plenty of images where only few will clearly show cropland fields that are mature enough to be identified. To make the data collection more efficient, we provided our experts with preliminary maps of points in agricultural areas where street level images are available for the year 2021. Then, the experts checked those locations in an opportunistic way. The dataset is completely independent from all the existing maps and the reference datasets.</p> <p>There are 3 main data records uploaded:</p> <ol> <li>sv_croptype_poly.zip – an archive with a shapefile containing all the collected polygons with crop type information. Not all the polygons correspond to actual field boundaries.</li> <li>sv_croptype_validations.csv – a table with crop type observations with centroid coordinates in WGS84</li> <li>sv_worldcereal_validation.csv – a table with a subset of crop type observations used in validation of WorldCereal crop type maps for 2021.</li> </ol> <p>Fields:</p> <ul> <li>"id" – unique observation identifier;</li> <li>"imgSource" – source of imagery used for visual inspection;</li> <li>"imgLoc" – image location;</li> <li>"svImgDate" – image date;</li> <li>"imageIdKey" – image unique identifier;</li> <li>"submitedAt" – date of submission of crop type observation;</li> <li>"cropType" - crop type observation;</li> <li>"irrType" – irrigation type;</li> <li>"x", "y" – centroids of submitted polygons in WGS84.</li> </ul>
Data of yield in a mandarin crop derived from Diverfarming project
<p>Crop yield data, auxiliary data and methods metadata from a mandarin crop studied in Diverfarming project</p>
Raw Data for Publication "Desmodium Volatiles in "Push-Pull" Cropping Systems and Protection Against the Fall Armyworm, Spodoptera frugiperda"
<p>This repository contains all raw and processed data related to the publication titled "Desmodium Volatiles in "Push-Pull" Cropping Systems and Protection Against the Fall Armyworm, Spodoptera frugiperda" written by Daria M. Odermatt, Frank Chidawanyika, Daniel M. Mutyambai, Bernhard Schmid, Luiz A. Domeignoz-Horta, Collins O. Onjura, Amanuel Tamiru and Meredith C. Schuman.</p> <p>The data is subdivided in four sections:</p> <ol> <li>Volatile sampling of Desmodium intortum, D. incanum, and maize headspaces</li> <li>Oviposition bioassays comparing moth egg-laying preferences on maize vs. Desmodium (direct and indirect exposure)</li> <li>Choice assays evaluating moth behavior in response to maize alone vs. maize with Desmodium volatiles</li> <li>No-choice assays evaluation moth attraction toward maize alone, maize + D. intortum and maize + D. incanum</li> </ol> <p>More detailed information is available in the README files located within each folder.</p>
Data from: Deciphering host-parasitoid interactions and parasitism rates of crop pests using DNA metabarcoding
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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.