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300 results for “land change”
Concordant and opposing effects of climate and land-use change on avian assemblages in California’s most transformed landscapes
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Agriculture land-use change seasonally rewires stream food webs: A case study from headwater streams in the Lake Erie watershed
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Reptile diversity patterns under climate and land use change scenarios in a subtropical montane landscape in Mexico
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Data and code from: Breakdown in seasonal dynamics of subtropical ant communities with land-cover change
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Land Change Trajectories across Four Watersheds in the Upper Little Tennessee River Basin
These data were collected to reconstruct spatially explicit land use/land cover change trajectories that were temporally consistent and at very high accuracies for a selection of watersheds in Macon County, NC: Cartoogechaye, Coweeta, Skeenah and Watauga. Buildings (points) and roads (lines) were digitized from historic maps and aerial photographs and aligned where necessary for temporal consistency. Land cover was simultaneously classified across all years for each 25x25m pixel (1/16 ha) in order to maximize temporal consistency.
Data from: Land-use change interacts with climate to determine elevational species redistribution
Climate change is driving global species redistribution with profound social and economic impacts. However, species movement is largely constrained by habitat availability and connectivity, of which the interaction effects with climate change remain largely unknown. Here we examine published data on 2798 elevational range shifts from 43 study sites to assess the confounding effect of land-use change on climate-driven species redistribution. We show that baseline forest cover and recent forest cover change are critical predictors in determining the magnitude of elevational range shifts. Forest loss positively interacts with baseline temperature conditions, such that forest loss in warmer regions tends to accelerate species' upslope movement. Consequently, not only climate but also habitat loss stressors and, importantly, their synergistic effects matter in forecasting species elevational redistribution, especially in the tropics where both stressors will increase the risk of net lowland biotic attrition.
Data from: Thermal tolerance and the importance of microhabitats for Andean frogs in the context of land-use and climate change
<p><span>1. Global warming is having impacts across the Tree of Life. Understanding species' physiological sensitivity to temperature change and how they relate to local temperature variation in their habitats is crucial to determining vulnerability to global warming. </span></p> <p><span>2. We ask how species' vulnerability varies across habitats and elevations, and how climatically-buffered microhabitats can contribute to reduce their vulnerability. </span></p> <p><span>3. We measured thermal sensitivity (critical thermal maximum – CT<sub>max</sub>) of 14 species of <i>Pristimantis</i> frogs inhabiting young and old secondary, and primary forests in the Colombian Andes. Exposure to temperature stress was measured by recording temperature in the understory and across five microhabitats. We determined the frogs' current vulnerability across habitats, elevations and microhabitats accounting for phylogeny and then ask how vulnerability varies under four warming scenarios: +1.5⁰C, +2⁰C, +3⁰C and +5⁰C. </span></p> <p>4. We found that CT<sub>max</sub> was constant across species regardless of habitat and elevation. However, species in young secondary forests are expected to become more vulnerable because of increased exposure to higher temperatures. Microhabitat variation could enable species to persist within their thermal temperature range as long as regional temperatures do not surpass +2°C. The effectiveness of microhabitat buffering decreases with a 2-3°C increase, and is almost null under a 5°C temperature increase.</p> <p><span>5. Microhabitats will provide thermal protection to Andean frog communities from climate change by enabling tracking of suitable climates through short distance movement. Conservation strategies, such as managing landscapes by preserving primary forests and allowing regrowth and re-connection of secondary forest would offer thermally buffered microhabitats and aid in the survival of this group. </span></p>
Land Use and Land Cover Change 2000-2016 in Mozambique
<p>This repository includes land use and land cover maps of Mozambique for 2000, 2005, 2010 and 2016 years.</p> <p>The methodology is based on remote sensing methodology and include satellite image collection and compositing (annual cloud-free and shadow free Landsat images for 2000, 2005, 2010 and 2016), delineation of a large collection of training plots based on National Land Cover Classification system level 1, supervised classification using a machine learning algorithm (Random Forest) and post-processing steps.</p> <p>The LULCC map for 2016 show area statistics of 45.0% (35.8 Mha) of dry forest, 37.0% (29.3 Mha) of grassland and fallow, 13.7% (10.8 Mha) of cropland 2.0% (1.6 Mha) of wetlands, 1.3% (1 Mha) of other categories (rocks, sands, or bare soils), 0.3% (271,000 ha) of Mangroves, and 0.1% (673.1 ha) of urban areas. The deforestation over the 2000-2016 period is estimated to have been 207,272 ha per year.</p> <p>The methodology and statistics are presented in the report included in this repository. Theses maps are outputs from the study "An Analysis of Land Use Changes and Land Degradation in Mozambique" conducted by Nitidae and CIRAD in the LAUREL project.</p> <p> </p>
30 Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel using Landsat Timeseries
<p>30m resolution historically consistent land cover and cover fraction maps over the Sudano-Sahel for the period 1986-2015. These land cover / cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification / regression model, while historical consistency is achieved using the Hidden Markov Model.</p> <p>Validated land cover / cover fraction maps covering the full Sudano-Sahel are provided for 2015 (2015_Sahel.zip), while historical maps are available for four focus areas. The extent of the areas are displayed in 11_study_area.jpeg</p> <p>Each of the zip files contains 14 GeoTIFF files for the respective period and area:</p> <ul> <li>Landsat_LC30_epochYYYY_AREA_bare-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_crops-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif [# overpasses that are used as input for the creation of the maps for this region / epoch]</li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif [temporally cleaned discrete classification map using the Hidden Markov Model; legend see below] </li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification.tif [original discrete classification map; legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_forest-type-layer.tif [legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_grass-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_moss-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_shrub-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_snow-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_tree-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_urban-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-permanent-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-seasonal-coverfraction-layer.tif [0-100%]</li> </ul> <p>Discrete classification legend:</p> <ul> <li>0: Unknown. No or not enough satellite data available.</li> <li>20: Shrubs. Woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall. The shrub foliage can be either evergreen or deciduous.</li> <li>30: Herbaceous vegetation. Plants without persistent stem or shoots above ground and lacking definite firm structure. Tree and shrub cover is less than 10 %.</li> <li>40: Cultivated and managed vegetation / agriculture. Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type.</li> <li>50: Urban / built up. Land covered by buildings and other man-made structures.</li> <li>60: Bare / sparse vegetation. Lands with exposed soil, sand, or rocks and never has more than 10 % vegetated cover during any time of the year.</li> <li>70: Snow and ice. Lands under snow or ice cover throughout the year.</li> <li>80: Permanent water bodies. Lakes, reservoirs, and rivers. Can be either fresh or salt-water bodies.</li> <li>90: Herbaceous wetland. Lands with a permanent mixture of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water.</li> <li>100: Moss and lichen.</li> <li>111: Closed forest, evergreen needle leaf. Tree canopy >70 %, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>112: Closed forest, evergreen broad leaf. Tree canopy >70 %, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>113: Closed forest, deciduous needle leaf. Tree canopy >70 %, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>114: Closed forest, deciduous broad leaf. Tree canopy >70 %, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>115: Closed forest, mixed.</li> <li>116: Closed forest, not matching any of the other definitions.</li> <li>121: Open forest, evergreen needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>122:Open forest, evergreen broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>123: Open forest, deciduous needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>124: Open forest, deciduous broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>125: Open forest, mixed.</li> <li>126: Open forest, not matching any of the other definitions.</li> <li>200: Oceans, seas. Can be either fresh or salt-water bodies.</li> </ul> <p>Forest type legend:</p> <ul> <li>0: Unknown</li> <li>1: Evergreen needle leaf</li> <li>2: Evergreen broad leaf</li> <li>3: Deciduous needle leaf</li> <li>4: Deciduous broad leaf</li> <li>5: Mix of forest types</li> </ul> <p>More detail on the classification algorithm and the resulting maps can be found in the accompanying paper: </p> <p>Souverijns, N.; Buchhorn, M.; Horion, S.; Fensholt, R.; Verbeeck, H.; Verbesselt, J.; Herold, M.; Tsendbazar, N.-E.; Bernardino, P.N.; Somers, B.; Van De Kerchove, R. Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series. <em>Remote Sens.</em> <strong>2020</strong>, <em>12</em>, 3817. https://doi.org/10.3390/rs12223817</p> <p>Please note that a quality layer is available for each of the historical areas / periods (Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif). In case a value of 4 or lower is achieved here, the discrete land cover classification / cover fraction for this period / area is highly uncertain. Take this into account when analysing the maps. Furthermore, take note that there is a large difference between the temporally cleaned (Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif) and original discrete land cover classification (Landsat_LC30_epochYYYY_AREA_discrete-classification.tif). We recommend to use the temporally cleaned version in combination with the quality layer.</p>
Data from: Anthropogenic land-use change intensifies the effect of low flows on stream fishes
1. As ecosystems experience simultaneous disturbances, it is critical to understand how multiple stressors interact to affect ecological change. Land-use change (LUC) and extreme flow events are two important stressors that could interact to affect fish populations. 2. We evaluated the individual and interactive effects of discharge and LUC associated with oil and natural gas development (ONGD) on populations of two stream fishes over a seven-year period. We used repeated-state (i.e., abundance trends) and rate (i.e., colonization and persistence) responses to advance our understanding of flow-ecology relationships in a multiple-stressor framework. 3. Overall, fish abundance, colonization, and persistence declined in association with discharge. The effect of LUC associated with ONGD differed between species, with the abundance of Mottled Sculpin declining and Mountain Sucker increasing. We found both synergistic and antagonistic interactions between discharge and LUC. LUC intensified the effect of low flows for one species and lead to greater variability in responses to flows for the other species. These differences between species' responses are likely due to differences in their physiological tolerances and behavioral adaptations to disturbance. 4. Synthesis and applications. Our research provides empirical evidence for the complex interactions that can arise between discharge and anthropogenic LUC. Management efforts (e.g., silt fences, vegetation replanting, and in-stream restoration) to mitigate the effects of anthropogenic alterations and promote high-quality refuge habitats could help mitigate the effect of hydrologic extremes. Further development of flow-ecology relationships in a multiple-stressor framework will help guide management of stream fishes, and provide a better understanding of the mechanisms underlying flow-ecology relationships for different species.06-Sep-2019
Agricultural intensification and land use change: assessing country-level induced intensification, land sparing and rebound effect
<p><span><span><span><span><span><span><span><span><span><span><span>In the context of growing societal demands for land based products, crop production can be increased through expanding cropland or intensifying production on cultivated land. Intensification can allow sparing land for nature, but it can also drive further expansion of cropland, i.e. a rebound effect. Conversely, constraints on cropland expansion may induce intensification. We tested those hypotheses by investigating the bidirectional relations between changes in cropland area and intensity, using a global cross-country panel dataset over 1961-2016. We used a cointegration approach with additional tests to disentangle long and short-run causal relations between variables, and total factor productivity and yields as two measures of intensification. Over the long run we found support for the induced intensification thesis for low income countries. In the short run, intensification resulted in a rebound effect in middle-income countries, which include many key agricultural producers strongly competitive in global agricultural commodity markets. This rebound effect manifested for commodities with high price-elasticity of demand, including rubber, flex crops (sugarcane, palm oil and soybean), and tropical fruits. Over the long run, strong rebound effects remained for key commodities such as flex crops and rubber. Staple cereals such as wheat and rice manifested significant land sparing. In low-income countries, intensification driven by increases in total factor productivity was associated with a stronger rebound effect than yields increases. Agglomeration economies may drive yields increases for key tropical commodity crops. Our study design could allow addressing other complex long and short run causal dynamics in land and social-ecological systems.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from study: Sixty-seven years of land-use change in southern Costa Rica
<p>This is the GIS data and imagery used for analyses in the article<br /> <em>Sixty-seven years of land-use change in southern Costa Rica</em> by Zahawi<br /> et al. currently in revision at PLOS One.<br /> <br /> This study required the orthorectification of historic aerial photographs, as well as forest cover mapping and landscape analysis of 320 km<sup>2</sup> around the Las Cruces Biological Station in San Vito de Coto Brus, Costa Rica. The imagery and GIS data generated were used to account for forest cover change over five different time periods from 1947 to 2014.<br /> <br /> The datasets supplied include GIS files for:</p> <ul> <li>Extent of the study area (shapefile).</li> <li>Forest cover mapped for each time period (geotiff).</li> <li>Imagery of the mosaics generated with the orthorectified historic aerial photographs (geotiff).</li> <li>Age in studied time periods of the current forest patches (shapefile).</li> <li>Connectivity lines inside the studied area (shapefiles).</li> </ul> <p>All files are in Costa Rica Transverse Mercator 2005 (CRTM05) projected coordinate reference system. For transformation between coordinate systems please refer to http://epsg.io/5367</p> <p>Aerial photographs for the years 1947, 1960, 1980 and 1997 were acquired from the Organization for Tropical Studies GIS Lab and the Instituto Geográfico Nacional of Costa Rica. The orthorectification process was done first on the 1997 set of images and used the current 1:50,000 and 1:25,000 Costa Rican cartography to identify geographical reference points. The set of 1997 orthophotos was used as a reference set to orthorectify remaining years with the exception of 1947 images. The orthorectification process and all other geospatial analyses were done on the CRTM05 spatial reference system and the resulting orthophotos had a 2m cell size. The largest Root Mean Square error (RMSE) of the orthorectification of these three time slices of aerial photographs was 15 m.</p> <p>Given the lack of information on flight parameters, and the expansive forest coverage in 1947 photographs, images were georeferenced and built into a mosaic using river basins and the few forest clearings that had a similar shape in the 1960 flyover. The 1947 set of images did not cover the whole study area, having empty areas without photographs that represented ˜12.1% of the analysis extent. Nonetheless, these areas were classified as forested given that forest was present in these same areas in the 1960 imagery.</p> <p>Forest mapping was done by visual interpretation of orthophotos and Google imagery. The areas were considered forested if tree crowns were easily identified when viewing the images at a scale of 1:10,000. In areas where it was difficult to discern the type of land cover, a scale of 1:5,000 was used. This was done to eliminate agroforestry systems such as shaded coffee areas (with trees planted in rows) or very early stages of forest regeneration from the forest land-cover class. The analysis was done only in areas that were cloud free in the five time slices. This resulted in the elimination of 134 ha (~0.4%) from of the original area outlined above. Polygons were drawn over the different areas using QGIS and were transformed into raster files of 10 m cell size.</p>
Integrative assessment of climate change-related impacts and risks on urban land
<p>Shapefile data set estimating trends of mean annual terrestrial surface air temperature (°C) and mean annual total precipitation (mm) and several heat indicators for urban land, characterised by clusters of local spatial autocorrelation in regard to the age of urban area and the coefficient of variation of urban area extent over time.</p>
Dataset for Bukovsky et al. (2021): "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections"
<p>This dataset contains derived data and model data necessary for reproducing the results found in "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections" by Melissa S. Bukovsky, Jing Gao, Linda O. Mearns, and Brian C. O'Neill. This dataset contains data not otherwise available in other public archives, as noted in Bukovsky et al. (2021, Earth's Future; preprint available at https://doi.org/10.1002/essoar.10504141.2). That is, this dataset contains data from the land-use change simulations that are not part of NA-CORDEX (na-cordex.org), but which are complementary to those published in the NA-CORDEX archive.</p>
Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
<p>The dataset contains six files from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) land cover transition data derived from LandSat satellite imagery for years 1986 and 2016. The files include data on household and plot features, prices of land use outputs, a diagonal block matrix of variables for mixed effects analysis, beliefs and opinions on land use change, and land cover transitions. The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and is representative of the region. It can be useful for studies on land use policies, environmental protection, and the drivers and impacts of land use land cover change in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17.</p>
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
<p>The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km<sup>2 </sup>of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.</p>
Land Cover Change Hotspots from 2001 to 2021
<p><span>Land cover change hotsposts based on the harmonized global land cover maps from the ESA Climate Change Initiative, WorldCover, and Copernicus Global Land Cover Service from 2001 to 2021 at 0.1x0.1 degree grid</span></p>
GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change
<p>We complied the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) from 632 papers documented in Web of Science till the June 2024. This database comprises 1,187 sites with 5,805 records at multiple sample depths.<br>This dataset (in csv formats) is associated to the "GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change" by Chen et al. (2025). The README file includes the full explanation of all the columns.<br>Manuscript citation: Chen, S., Shuai, Q., Arrouays, D., Chen, Z., Dai, L., Hong, Y., Hu, B., Huang, Y., Ji, W., Li, S., Liang, Z., Ma, Y., Richer-de-Forges, A.C., Schillaci, C., Su, Y., Teng, H., Wang, N., Wang, X., Wang, Y., Wang, Z., Wang, Z., Xu, D., Xue, J., Ye, S., Zhang, X., Zhou, Y., Zhu, P., Shi, Z. , 2025. GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change. In preparation.<br>When using the data, please cite repositories as well as the original manuscript.<br>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Anthropogenic land‐use change shapes bird diversity along the eastern Himalayan altitudinal gradient
<p>Globally, the conversion of natural forest into agricultural land and human settlement has altered avian diversity and structure often leading to functional and/or phylogenetic homogenisation. While the effects of land-use change on avian functional and phylogenetic diversity is well studied in the tropics, it is poorly understood and scarcely studied in the Himalayas, let alone in the eastern Himalayan bird communities.</p> <p>This dataset comprises observations of 336 bird species from a replicated point-count transect survey conducted between 2019 and 2020 in Bhutan. We used a multispecies occupancy model to estimate occupancy probability while accounting for detection probability. The detection-corrected z-matrix was used to calculate functional and phylogenetic diversity. </p> <p>Our study shows that bird community occupancy along the elevational gradient is negatively associated with human land use (agriculture and settlement). Bird assemblages were functionally and phylogenetically clustered at higher elevations. Agriculture and settlement harboured higher functional and phylogenetic diversity whereas forests had phylogenetically diverse communities within functionally convergent traits.</p> <p>The higher functional and phylogenetic diversity in agriculture and settlement suggests that bird diversity offers an opportunity for a broad range of ecosystem services. Protection of forests abutting human settlements and agriculture will help preserve higher phylogenetic diversity. We recommend that agricultural practices that safeguard and improve bird-friendly habitats should be promulgated. Educational programmes on the importance of the roles of birds should be implemented and integrating bird conservation with farm production will help conserve bird diversity in human-dominated landscapes. To enhance the conservation value of working landscapes in the Himalayas, avitourism can be explored further. </p>
Existing land uses constrain climate change mitigation potential of forest restoration in India
<p>The datasets were developed as part of the publication "Existing land uses constrain climate change mitigation potential of forest restoration in India". Please refer to the manuscript for processing details.<br> <br> ForestBioclimaticEnvelop_ProjectionUTM is the bioclimatic envelope of forests developed. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system.</p> <p>FinalOpportunity_AfterExclusions_ProjectionUTM is the feasible area of opportunity, after all exclusions of land uses and covers that cannot naturally regenerate to forests. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system.</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.