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269 results for “vegetation cover”
Vegetation and Ground Cover of Permanent Plots for Upper Phillips Creek Marsh, Nassawadox, VA 1990-2014
Juncus roemerianus, the black needlerush, is a commonly found salt marsh plant. It exists especially in areas of little tidal inundation and low salinities. As such, it may play an important role in the accretion of marshes where sediment sources are not readily accessible. Biogenic accretion of marshes is a slow process representing the net result of production and decomposition. To study the process effectively requires long-term observations and experiments. We will initiate research at this site by selecting locations for study, collecting preliminary data on the locations, and designing long-term experiments. Plots for long-term observations will allow for analyses of changes in dominance of cover by plant species. We will choose plots to include the interface between J. roemerianus and other species and will follow changes at the interface. Also attached is an unpublished manuscript (PDF/A format) containing details of the data and some analyses.
Vegetation cover fractions for the City of Berlin dervided from Landsat 5, Landsat 7 and Landsat 8 data between 1988 and 2018
<p>Fractional vegetation cover dataset used in the study "Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series".</p> <p>Landsat satellite imagery (Landsat-5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper) and Landsat-8 OLI (Operational Land Imager)) was acquired for seven years between 1988 and 2018. Imagery was pre-processed and a regression-based unmixing approach was performed in order to generate fraction maps of vegetated and non-vegetated surfaces. For complete method description please see:</p> <p>Wellmann, T., Schug, F., Haase, D., Pfulgmacher, D., van der Linden, S. (2020). Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series. <em>Landscape and Urban Planning</em></p>
Automated vegetation cover estimation from close-range photogrammetric point clouds in mountain terrain for comparison of vegetation location properties - Dataset
<p>Vegetation cover data of the used plots, showing values for manually digitized, in-situ, and photogrammetric methods.</p>
Digital orthoimagery for fractional vegetation cover at site Alpha from Morgan et al Kuiseb River study
<p>Contains high-resolution digital orthoimagery (RGB and NIR) and derived products for site Alpha, from the following manuscript:</p> <p>Morgan et al. (in prep), Spatiotemporal analysis of vegetation cover change in a large ephemeral river: multi-sensor fusion of unmanned aerial vehicle (UAV) and Landsat imagery.</p>
A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types
<p>Dataset accompanying manuscript <em>"A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types". </em>Datasets contain a wall-to-wall map of vegetation types covering the study area of terrestrial Norway, produced using three methods for assembling individual predictions from Distribution models (<em>probability-based method</em>, <em>performance-based method</em> and <em>prevalence-based method</em>). </p>
Fig. 1. Projection cover during vegetation period Fig. 2 in P H E N O L O G Y O F H E R B A C E O U S V Eg E Tat I O N I N Br Oa Dl Eaved Fo Res T O F K Am Sa Bo Tanic Al - Zoological Reserve
Fig. 1. Projection cover during vegetation period Fig. 2. Phenological spectrum of herbs in a) in a) 2009, b) 2010. 2009, b) 2010.
Global monthly percentage of vegetation cover (MODIS FCover MODV1B product: Eurasia, Africa, Oceania)
<p>Monthly Global FCover product generated from MODIS data. Dataset represent monthly gap-filled FCover estimates the period 2000-2015 over Eurasia, Africa and Oceania. FCover was estimated using linear spectral mixture analysis and interpolated using empirical orthogonal functions algorithm to take advantage of all non-missing available pixels in both the spatial and temporal dimensions to gap-fill missing satellite observations. The global product of vegetation cover (as percentage of cover) based on MODIS images with monthly variation can be used as a critical support for several indicators related to ecologically based modelling.</p>
Vegetation cover fraction in each town block across Japan
<p>Although the percent of green space is the most frequently used index for quantifying urban green, it is currently unavailable in most cities in Japan. Here we provide an open dataset of the index for each town block across the country using Sentinel-2 satellite images in Google Earth Engine. The dataset is calibrated and validated with airborne datasets obtained in nine cities in Tokyo.</p> <p> </p> <p><strong>日本全国の町丁目別の緑被率の公開</strong></p> <ul> <li>都市の緑を定量化する指標としては、緑被率が最もよく使われていますが、現在、日本のほとんどの都市では緑被率を利用することができません。ここでは、Sentinel-2の衛星画像をGoogle Earth Engineを用いて、日本全国の町丁目別の緑被率をオープンデータとして提供しています。このデータセットは、東京都内の9区の航空機データ(各区のWEBサイトで公開)を用いて校正・検証されています。</li> <li>ライセンスに関してデータは<a href="https://creativecommons.org/licenses/by/4.0/deed.ja">CC BY 4.0</a>で公開しています。</li> <li>校正・検証に関しての詳細は、<a href="https://doi.org/10.3130/aijt.28.521">日本建築学会技術報告集</a>に掲載しています。 </li> <li>「FRAC_VEG」が緑被率です。</li> <li>境界データは「平成27年国勢調査」を用いています。</li> </ul> <p> </p> <p><strong>データ概要</strong></p> <p>年:2020</p> <p>空間分解能:町丁目(平成27年国勢調査)</p> <p> </p> <p><strong>引用方法</strong></p> <p>・データの利用のみの場合</p> <p>Kiyono Tomoki, Fujiwara Kunihiko, & Tsurumi Ryuta. (2021). Vegetation cover fraction in each town block across Japan (1.0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5553516">https://doi.org/10.5281/zenodo.5553516</a></p> <p>・上記以外の場合(この取り組みそのものや,アルゴリズム等への言及)</p> <p>清野友規, 藤原邦彦, 鶴見隆太. Google Earth Engineを用いた町丁目別緑被率オープンデータ(全国版)の作成と評価, 日本建築学会技術報告集, 2022, 28 巻, 68 号, p. 521-526, https://doi.org/10.3130/aijt.28.521</p> <p> </p> <p><strong>問い合わせ</strong></p> <p>意見・要望・感想などお気軽にお問い合わせください。</p> <p>鶴見隆太(日建設計総合研究所) tsurumi.ryuta@nikken.jp</p> <p> </p> <p><strong>更新情報</strong></p> <p>v1.0.1 2021/10/7</p> <p>・エクセルでの文字化けを解消(文字コード:BOM付UTF-8)</p> <p>・町丁目のユニーク識別子であるKEY_CODE(11桁)を追加</p> <p>v0.1.0</p> <p>・初期バージョンをリリース</p> <p> </p> <p><strong>PI</strong></p> <p>Kiyono Tomoki</p>
Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products
<p>These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include:</p> <p>1. Measured data of global fractional vegetation cover from 2013-2021 </p> <p>2. Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).</p>
Vegetation Density Across NYC: Analysis of Land Cover Data (2017) within 200 meter Buffers of Points
<p><strong>Summary:</strong></p><p>This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable. </p><p>The contained files were developed by the NY Cities Program of <a href="https://www.nature.org/newyork">The Nature Conservancy</a> and the <a href="https://nyc-eja.org/">NYC Environmental Justice Alliance</a> through the <a href="https://medium.com/gage-nyc/introducing-the-just-nature-nyc-partnership-513612e8c3b4">Just Nature NYC Partnership</a>. Additional context and interpretation of this work is available in a <a href="https://medium.com/gage-nyc/looking-at-cooling-benefits-of-plants-through-nyc-vegetation-data-ccdeb33cbe17">blog post</a>.</p><p> </p><p><i>References:</i></p><p>Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. <i>Urban Climate</i> 31:100583. <a href="https://doi.org/10.1016/j.uclim.2020.100583">https://doi.org/10.1016/j.uclim.2020.100583</a></p><p> </p><p><strong>Files in this Repository:</strong></p><p>Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, <a href="https://epsg.io/2263">EPSG 2263</a>):</p><p>Points with unique identifiers (<i>fid</i>) and data on proportion tree canopy cover (<i>prop_canopy</i>), proportion grass/shrub cover (<i>prop_grassshrub</i>), and proportion total vegetation cover (<i>prop_veg</i>) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage):</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip</i></p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg</i> </p><p>Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data)</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif</i></p><p>Computer Code:</p><p>Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database.</p><p><i>nyc_point_buffer_vegetation_overlay.sql</i></p><p> </p><p><strong>Data Sources and Methods:</strong></p><p>We used two openly available datasets from the City of New York for this analysis:</p><p>Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at <a href="https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page">https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page</a> </p><p>Six-inch resolution land cover data for New York City as of 2017, available at <a href="https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns">https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns</a> </p><p>All data were used in the New York State Plane Coordinate System, Long Island Zone (<a href="https://epsg.io/2263">EPSG 2263</a>). Land cover data were used in a polygonized form for these analyses.</p><p>The general steps for developing the data available in this repository were as follows:</p><p>Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions</p><p>Delete any points that do not overlap the areas in the Borough Boundaries dataset.</p><p>Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020).</p><p>Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles.</p><p>Relate the results from the overlay analysis back to the associated points.</p><p>Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data."</p><p>All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in <i>nyc_point_buffer_vegetation_overlay.sql</i>. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the <a href="https://gdal.org/programs/gdal_rasterize.html">gdal_rasterize</a> function.</p>
Estimated wetland vegetation cover comparing reference and muskrat disturbed areas
Biota that act as ecosystem engineers are increasingly recognized as effective components of habitat restoration as they can continuously alter and shape their environments without human intervention. Muskrats are considered ecosystem engineers and are relatively common in North American wetlands. While muskrat impacts to large areas have been documented, few studies have quantified the impact of muskrats on ecosystem biodiversity. We conducted a field study in wetlands along the Upper St. Lawrence River (New York, USA) to investigate the effects of muskrat herbivory and structure building on plant biodiversity in cattail-invaded wetlands. The data collected are separated into two main groups, i.e. “reference” vegetation surveys and “muskrat” vegetation surveys. “Reference” surveys were conducted for the partial grant fulfillment funded by the New York Department of Environmental Conservation [contract AM10165] and the National Fish and Wildlife Foundation special project administered by the United States Fish and Wildlife Service, Partners for Fish and Wildlife Program, New York Field Office, Cortland, NY [grant number #2005-0129-055/58859]. “Muskrat” surveys were conducted for the Masters project by Z.X. Kua. The five datasets include 1) Reference quadrat plant taxa estimated cover (n = 1912), 2) Reference quadrat cattail measurements (n = 737), 3) Muskrat-disturbed quadrat plant taxa estimated cover (n = 1886), 4) Muskrat-disturbed quadrat cattail measurements (n = 582), and 5) Wetland plant taxa lookup table for vegetation survey (n = 135).
Soil Moisture and vegetation cover patterns after logging and burning an old-growth Douglas-fir forest in the Andrews Experimental Forest, 1960-1983
This soil moisture study was initiated in 1960 to investigate the effects of patch clearcut logging and slash burning (1962-63) in an old-growth Douglas-fir forest in the Oregon Cascade Range. Since soil moisture and vegetation sampling continued regularly until 1980, this is a unique data set that represents nearly two decades of post-treatment information. Plant cover exerts a profound influence on soil moisture levels through its effects on interception, infiltration, evaporation, and transpiration. In the Douglas-fir forests of the Pacific Northwest, clearcut logging and slash burning are common practices that can dramatically alter plant cover and soil moisture. Logging can increase soil moisture by temporarily reducing cover and associated water use, and burning may further augment soil moisture levels by suppressing the survival and regrowth of vegetation. Indeed, part of the rationale for slash burning in the region is to control shrubs and other vegetation that would otherwise compete with conifer seedlings for available moisture, light, and nutrients. Within a few years after burning, however, invading vegetation may deplete soil moisture to levels comparable to forested areas. Such observations point to the value of long-term information to better understand dynamic soil moisture and plant cover responses to forest practices.
Eight Mile Lake Research Watershed, Thaw Gradient Extended sites: Vegetation cover and abundance.
This data set contains meausrements of vegetation cover and abundance (using a braun-branquet score) at 229 sites surrounding an eddy covariance tower. Eight Mile Lake Research Watershed, Thaw Gradient Extended sites.
Eight Mile Lake Research Watershed, Thaw Gradient Extended sites: Vegetation data from land cover classes from an upland watershed undergoing permafrost thaw.
This data set contains meausrements ofpercent of ground cover (vegetation, water, bare soil) from sites throughout the wathershed within certain land cover types identified by an unsupervised landcover classification. The purpose was to see how land cover classes differed in soil properties and if we could detect diffences in classes undergoing permafrost thaw that results in thermokarst.
Interior Alaska managed sites:vegetation community composition and ground cover measured one time in either summer 2012 or 2013
This dataset contains number of hits by different vegetation types and ground cover measured using a pin flag using the point intercept method.
Saddle catchment vegetation cover for Distributed Hydrology Soil Vegetation Model (DHSVM), 2 meter, 2019
The Saddle Catchment of the Niwot Ridge LTER is subject to observed and modeled hydrologic connectivity and nuances that is influenced by the variable vegetation across the area. This file was specifically intended to update the Distributed Hydrology Soil Vegetation Model (DHSVM) input file used to simulate vegetation within the Saddle Catchment, as an ongoing method for evaluating modeled hydrologic connectivity. Thus, high resolution vegetation information was used from existing National Ecological Observatory Network (NEON) datasets. NEON has developed a suite of vegetation datasets including a 1m spatial resolution Airborne Observation Platform (AOP) Total Biomass data product. This product is an orthorectified (UTM projection) raster product derived from NEON AOP Imaging Spectrometer (NIS) reflectance data (DP3.30016.001). This vegetation layer was chosen because it visually reflected the differences in vegetation across the Saddle Catchment at a much higher resolution than previous DHSVM vegetation files. Using this gridded biomass product, different vegetation types were manually assigned and translated into to DHSVM vegetation types/codes. The assigning and translating of vegetation types was done by consulting NWT LTER individuals who specialize in the vegetative and soil properties within the Saddle Catchment and using available point-vegetation data from the Saddle Catchment sensor node network. The final product was upscaled to a 2m resolution using R (to match all other DHSVM input files).
SGS-LTER Long-Term Monitoring Project: Vegetation Cover on Small Mammal Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1999 -2006, ARS Study Number 118
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83458. The abundance and diversity of small mammals in shortgrass steppe is strongly influenced by the structure and composition of vegetation. Vegetation structure provides cover from predators and harsh abiotic conditions. Plant species composition affects the types of seeds and herbaceous material available to granivores and herbivores, and influences arthropod populations, which are important prey for the omnivorous species that dominate in shortgrass steppe. Both vegetation structure and plant community composition are sensitive to the availability of precipitation as well as the activity of large mammalian herbivores. In 1999, we began measuring vegetation structure and plant community composition on the three grassland and three shrubland trapping webs where we live-trap small mammals. Vegetation measurements are made once each year, usually in mid-July. Percent canopy cover of each plant species was estimated visually in 30 0.10-m2 Daubenmire quadrats on each web. To estimate habitat structure, we measured the height of grass, forb and shrub plants adjacent to each quadrat, the density of half-shrubs, small mammal mounds and burrows, harvester ant mounds and the dimension
Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution
<p>Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail <a href="https://github.com/Envirometrix/PNVmaps"><strong>here</strong></a>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included.</p> <p>Produced for the needs of the <a href="https://naturemap.earth/"><strong>NatureMap</strong></a> which is project run by the <strong>International Institute for Applied Systems Analysis</strong> (IIASA), the <strong>International Institute for Sustainability</strong> (IIS), the <strong>UN Environment Programme World Conservation Monitoring Centre</strong> (UNEP-WCMC), and the <strong>UN Sustainable Development Solutions Network</strong> (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI).</p> <p>Maps will also be made available via: <a href="https://OpenLandMap.org">OpenLandMap.org</a>. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.</p> <p>If you discover a bug, artifact or inconsistency in the predictions, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://github.com/Envirometrix/PNVmaps/issues">https://github.com/Envirometrix/PNVmaps/issues</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>pnv = theme: potential natural vegetation,</li> <li>potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"),</li> <li>probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS),</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: period 2000-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Rangeland Analysis Platform - vegetation cover 1988
<p>Rangeland Analysis Platform vegetation cover 1988</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1990
<p>Rangeland Analysis Platform vegetation cover 1990</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
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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.