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406 results for “Forest Cover”
Historical forest cover in the Kivu Rift (1958)
<p>If you use this dataset, please refer to our work:<strong> Depicker, A., Jacobs, L., Mboga, N. <em>et al.</em> Historical dynamics of landslide risk from population and forest-cover changes in the Kivu Rift. <em>Nat Sustain</em> (2021). https://doi.org/10.1038/s41893-021-00757-9</strong></p> <p>This .tif file represents the forest cover in the Kivu Rift in 1958, covering parts of the eastern DRC, western Rwanda, and western Burundi. The forest data was derived from 1 m resolution panchromatic historical aerial photographs (conserved at the Royal Museum for Central Africa) and resampled to a 30 m resolution.</p> <p>The data range from 0 to 1, representing the % of forest cover. Data smaller than 0 can be considered NoData values.</p> <p>This work was funded by the Belgian Science Policy Office (BELSPO) through the PAStECA project (BR/165/A3/PASTECA) entitled `Historical Aerial Photographs and Archives to Assess Environmental Changes in Central Africa' (http://pasteca.africamuseum.be/).</p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
Understanding the Influences of Forest Type, Cover Board Type and Weather on Salamanders
Salamanders are vital bioindicators that function to support a terrestrial forest ecosystem. The continuous loss of amphibian species and their habitat can have profound impacts on terrestrial systems. In terrestrial environments, salamanders use natural cover for refuge, foraging, and maintaining moisture; however, artificial cover has commonly been used to survey and conserve these species. The objective of this study was to assess terrestrial salamander preference for natural versus artificial coverboards in relation to forest stands in two successional stages located within the James H. Barrow Biological Field Station (Hiram, Ohio). Ten artificial (particle board, 30 x 33 cm) and ten natural (white ash, 30 x 30 cm) coverboards were placed in two 50 m parallel transects arranged 2 m apart within transitional and mature forests. Surveys were conducted weekly between the second week of September and the second week of November from 2018 to 2021. Average weakly precipitation and max temperature were recorded. Both abundance and species richness were significantly higher under natural coverboards and in the transitional forest. There were also correlation between species richness and abundance with daily max temperature and weakly precipitation. 678 individuals across five species were found: Eastern Red-Backed Salamander, Spotted Salamander, Four-Toed Salamander, Red-Spotted Newt, and Northern Two-Lined Salamander. Eastern Red-Backed Salamanders were the most abundant species within both mature and transitional forests. Natural coverboards may be a better method to survey terrestrial salamanders because artificial coverboards are comprised of wood chippings, wax and adhesives which may alter soil permeability for less favorable conditions.
Dimensions, cover, volumes, mass and nutrient stores of Coarse Woody Debris (bark and wood from logs, snags, and stumps) from forests plots in the western United States and Mexico, 1977 to 2005
These data provide an inventory of the mass and nutrients stored within various forest types by coarse woody debris (CWD). CWD inventoried includes standing dead trees (greater than 10 cm diameter at breast height) and dead and downed wood (greater than 10 cm large end and >1 long). The majority of measurements are from permanent sample plots associated with the Andrews LTER permanent sample plots network. This includes clusters of plots at and near the H. J. Andrews Experimental Forest (OR), Cascade Head Experimental Forest (OR), Mount Rainier National Park (WA), Olympic National Park (WA), and Fraser Experimental Forest (CO). There are also data from plots in the Yucatan in Mexico; however, the live tree data for these plots is not available. The species of CWD inventoried are primarily those found in the Pacific Northwest; the dominants being Douglas-fir, western hemlock, mountain hemlock, western redcedar, Pacific silver fir, noblel fir, lodgepole pine, ponderosa pine, sitka spruce, and Englemann spruce. The majority of measurements were made in the 1975 to 1995 period; however plots are periodically remeasured and the intent to eventually remeasure all the plots except those in Mexico. In each plot measurements of log and snag dimensions (length and diameters), as well as decay class and species were recorded in the stands (td01201 file). These dimension data are combined with data on density and nutrient content for each species and decay class (td01202 file) and plot area and slope (td01203 file) to calculate CWD volume, cover, biomass and nutrient storage (td01204 file). When data on density and nutrient concentrations is not known, a list of substitutions is used (td01206). The adjust tree diameters if measurements are taken at the base of the dead tree, taper regressions are used (td01205).
Canopy cover in mature black spruce forest, mature mixed forest, and early successional forest in Bonanza Creek Experimental Forest
This dataset contains measurements of canopy cover in three habitats commonly used by snowshoe hares in Bonanza Creek Experimental Forest.
Horizontal cover in mature black spruce forest, mature mixed forest, and early successional forest in Bonanza Creek Experimental Forest
This dataset contains measurements of horizontal cover (i.e. visual obstruction) in three habitats commonly used by snowshoe hares in Bonanza Creek Experimental Forest.
Structural cover at snowshoe hare predation sites in Bonanza Creek Experimental Forest from 2008 to 2012
This dataset contains the horizontal and canopy cover measured at predation sites of snowshoe hares that were collared in Bonanza Creek Experimental Forest.
SBC LTER: Reef: Coefficients for estimating biomass from body size or percent cover for kelp forest species
These data provide coefficients to estimate the biomass of macroalgae, invertebrates and fish from field measurements of body size or percent cover. We developed quantitative relationships between mass and length or mass and percent cover, and conversion factors for transforming wet mass into dry mass, shell free and decalcified dry mass, and ash-free dry mass for taxa of benthic macroalgae and macroinvertebrates common to giant kelp forests in southern California. We also compiled literature-based relationships between mass and total length for reef fish common to giant kelp forests in southern California.
SBC LTER: Reef: Kelp Forest Community Dynamics: Kelp Forest Data to support "Estimating biomass of benthic kelp forest invertebrates from body size and percent cover"
These data describe quantitative relationships between wet mass and length or wet mass and percent cover, and conversion factors to transform wet mass into dry mass, shell-free and decalcified dry mass, and ash-free dry mass for 84 species of benthic macroinvertebrates common to giant kelp forests in southern California. Data are based on organisms collected from sites in the Santa Barbara Channel between April 2010 and May 2014. These measurements are intended to facilitate the conversion of invertebrate abundance into common metrics of biomass, for quantitative studies of community dynamics, trophic interactions, energy flow and biodiversity. Converting numerical abundance (i.e., organism density) to biomass requires information on the relationship between individual size and biomass. For colonial and small aggregating taxa that are numerous and indistinct, measures of abundance are usually proportional (e.g., percent cover). Hence, converstions are taxa-specific, based on either size or cover, and a variety of metrics of species biomass are included, e.g., wet mass, shell-free wet mass, ash-free dry mass. Data are published in Reed, D. C, J. C. Nelson, S. L. Harrer, and R. J. Miller, Estimating biomass of benthic kelp forest invertebrates from body size and percent cover data. Marine Biology. DOI: 10.1007/s00227-016-2879-x. From the paper abstract: The inability to compare different measures of species abundance (such as density and percent cover) or different metrics of species biomass (such as wet mass and ash-free dry mass) hampers quantitative studies of community dynamics, trophic interactions, energy flow and biodiversity. This has been especially problematic for the dynamic and highly productive communities inhabiting shallow reefs in temperate seas where varied metrics are commonly used to characterize the abundance and biomass of different suites of species. Regressions for all 84 species were highly significant and regression fits were very good for mos
Database for study on Vietnam's forest cover changes 2005-2016
<p>The database provided here forms the basis of a study which is published in the journal World Development (year 2020) under the title ' Vietnam’s forest cover changes 2005-2016: veering from transition to (yet more) transaction?'. The database contains two hundred province-level data variables provided by government agencies. The database includes potentially relevant indicators of the Vietnamese provinces’ terrain and geography (G variables), forestland and tree plantation cover/changes (F), population density/changes and ethnic composition (P), labor and poverty (L), development of infrastructure (S) and hydro-electric capacities (H), agricultural cultivations/changes and productivity (A), industrial wood processing capacities (W), illicit logging (C), forestland tenure/contracts (T), payments for forest ecosystem service (PFES) funding/coverage (E), as well as various indices of governance, institutional and public administration, and socio-economic performance (I). Each variable contains 63 data (i.e. 63 Vietnamese provinces) and is described in detail in the file, including data type and summary statistics. The variables were mostly accessible directly from government sources, or indirectly from NGOs or via international publications (the sources are indicated in the file).</p>
Loss and fragmentation of fire-resistent primary forest cover in Sumatra and Kalimantan
<p>Here we share primary forest loss and fire occurrence in Sumatra and Kalimantan covering 2001 through 2019 period. The datasets include primary forest cover fraction and active fire detection counts at 1km spatial resolution and annual time step.</p> <p>For details on the datasets see included README file and the following open access publication:</p> <p>Nikonovas <em>et al</em>., Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan,<em> Communs Earth and Environ., <strong>1</strong>, (2020).</em></p> <p>Usage Notes</p> <p>Contact Tadas Nikonovas (tadas.nik@gmail.com) for questions on usage or additional details.</p> <p> </p> <p>Acknowledgements</p> <p>This study forms part of the Towards a Fire Early Warning System for Indonesia (ToFEWSI) project (Oct. 2017- Oct. 2021), which is funded through the UK’s National Environment Research Council – Newton Fund on behalf of UK Research & Innovation (NE/P014801/1), Indonesia Endowment Fund for Education and the Indonesian Science Fund (Principal Investigators: Allan Spessa (UK) and Muhammad Ali Imron (Indonesia)). The ToFEWSI project is developing a suite of climate, hydrological- and agent-based models to predict the incidence of peat forest fires in Indonesia, plus new evidence-based proposals for managing fires in Indonesia.</p> <p> </p>
Mapping canopy cover in African dry forests from combined use of Sentinel-1 and Sentinel-2 data: 2018 maps for Tanzania
<p>The monitoring of tropical forests has benefited from the increased availability of high-resolution earth observation data. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. The availability of time series of remote sensing images at 10-meters is changing this paradigm.</p> <p>In the context of REDD+ national reporting requirements, we investigated a methodology that is reproducible and adaptable in order to ensure user appropriation. The overall methodology consists of three main steps: (i) the generation of Sentinel-1 (S1) and Sentinel-2 (S2) layers, (ii) the collection of an ad-hoc training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows are compared in terms of their capability to capture the canopy cover of forests in East Africa. Two types of maps are derived from these mapping approaches: i) binary tree cover/no tree cover (TC/NTC) maps, and ii) maps of canopy cover classes. The method is applied at scale, over Tanzania and one final map for each workflow is shared. Two big data computing platforms are combined to exploit the important volume of satellite data available over a yearly period.</p> <p>The reference dataset (training and validation), the three best maps and the codes to produce the S1 and S2 composites on Google Earth Engine are shared here.</p> <p>The folder “reference_dataset.zip” contains the expert based training and validation dataset. The point shapefile corresponding to the center of the plot as well as the 3x3 and 5x5 polygon shapefile are shared together with qml layer file for each type of shapefile.</p> <p>Three maps (binary TC-NTC “pixel” RF, forest type “pixel” RF and “window” ETC) are shared. A 40 km buffer from national boundaries is kept in order to let users refine their area of interest. The qml style file are also shared.</p> <p>In the “script.zip” folder, the javascript codes to generate the S1 and S2 mosaics are shared.</p>
Data for: Freeze tolerance influenced forest cover and hydrology during the Pennsylvanian
<p><span>Global forest cover affects the Earth system by altering surface mass and energy exchange. Physiology determines plant environmental limits and influences geographical vegetation distribution. Ancient plant physiology, therefore, likely affected vegetation-climate feedbacks. We combine climate modeling and ecosystem-process modeling to simulate arboreal vegetation in the late Paleozoic ice age. Using GENESIS V3 GCM simulations, varying <i><span>p</span></i>CO<sub><span>2</span></sub>, <i><span>p</span></i>O<sub><span>2</span></sub>, and ice extent for the Pennsylvanian, and fossil-derived leaf C:N, maximum stomatal conductance, and specific conductivity for several major Carboniferous plant groups, we simulated global ecosystem processes at a 2-degree (longitude, latitude)</span><span> resolution with </span><i>Paleo</i>-BGC<span>. Based on leaf water constraints, Pangaea could have supported widespread arboreal plant growth and forest cover. However, these models do not account for the impacts of freezing on plants. According to our interpretation, freezing would have affected plants in 89% of unglaciated land during peak glacial periods, and 65% during the warmer interglacials. Comparing forest cover, minimum temperatures, and paleo-locations of Pennsylvanian-aged plant fossils from the Paleobiology Database supports restriction of global forest extent due to freezing. Many genera were limited to </span>25% <span>of unglaciated land where temperatures remained above −</span>4°C<span>. Freeze-intolerance of Pennsylvanian arboreal vegetation had the potential to alter surface runoff, silicate weathering, CO<sub><span>2</span></sub><span> levels, and</span> climate forcing. As a bounding case, we assume total plant mortality at </span>−4°C <span>and estimate that contracting forest cover increased net global surface runoff by up to 6.1%. Repeated freezing likely influenced freeze- and drought-tolerance evolution in lineages like the coniferophytes, which became increasingly dominant in the Permian and early Mesozoic.</span></p>
Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks
<p>This Data set contain 29 table of weighted interaction network between plants (columns) and frugivore birds from the Brazilian Atlantic Forest used in the manuscript "Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks" published in Oikos Journal.</p>
Input data for: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes input data used in the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard.</strong> Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>For this article, data have been processed with a R/GRASS script. The development version of this script is available on GitHub at https://github.com/ghislainv/deforestation-maps-Mada. The last release of this script is archived on Zenodo: [DOI: 10.5281/zenodo.1118484].</p>
Output data from: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes output data from the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard</strong>. Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>This repository includes Madagascar forest cover (forXXXX.tif), forest density (fordensXXXX.tif), distance to forest edge (dist_edge_XXXX.tif) and forest fragmentation index (fragXXXX.tif) for the years 1953, 1973, 1990, 2000, 2005, 2010 and 2014. Data are available as GeoTIFF raster files at 30m resolution in the UTM 38S projection (EPSG:32738).</p>
Land Use and Land Cover Mapping of Katanino Forest Reserve, Zambia (2019–2023)
<h1><strong>Overview</strong></h1> <p>The land use and land cover maps encompass the Katanino Forest Reserve in the Copperbelt province, Zambia. These maps categorize the area into two classes: forest and non-forest. They were derived from NICFI, Sentinel-2, and Sentinel-1 mosaics, resulting in a spatial resolution of 4.77 meters, covering the period from 2019 to 2023. </p> <h1><strong>Maps Accuracy</strong></h1> <p>The overall accuracy of the final annual maps (2019–2023) ranged from 0.90 to 0.94. The user’s and producer’s accuracies are detailed in Table 1.</p> <p>Table 1: Land use and land cover maps validation, including overall, producer (PA) and user (UA) accuracies values for each class.</p> <table> <tbody> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2019</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2020</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2021</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2022</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2023</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> </tr> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> </tr> <tr> <td> <p><span>Forest</span></p> </td> <td> <p><span>0.87</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.83</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.91</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>1</span></p> </td> </tr> <tr> <td> <p><span>Non Forest</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.86</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.82</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.89</span></p> </td> <td> <p><span>1</span></p> </td> <td> <p><span>0.87</span></p> </td> </tr> <tr> <td> <p><strong><span>Overall Accuracy</span></strong></p> </td> <td> <p><strong><span>0.92</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.90</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.94</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> </tr> </tbody> </table> <h1><strong>Files descripion</strong></h1> <ul> <li>KAT_2019.tif: 2019 land use and land cover map</li> <li>KAT_2020.tif: 2020 land use and land cover map</li> <li>KAT_2021.tif: 2021 land use and land cover map</li> <li>KAT_2022.tif: 2022 land use and land cover map</li> <li>KAT_2023.tif: 2023 land use and land cover map</li> <li>qgis_style.qml: QGIS style file</li> <li>KAT_training_samples(.shp, .shx, .dbf, .prj): training samples with class labels</li> <li>KAT_validation_samples(.shp, .shx, .dbf, .prj): validation samples with class labels</li> </ul> <p> </p>
MODIS tree cover change of North American boreal forests 2000-2019
<p>The published files are two maps of North American boreal forest tree cover trends between 2000 and 2019. Pixel values are annual trends in tree cover expressed as % change per year. The trends are based on annual tree cover estimates from the MODIS Vegetation Continuous Field version 6 product. We quantified tree cover trends per pixel through Theil-Sen's slope estimation using the 'zyp' package in R. We followed the Yue-Pilon pre-whitening method to account for temporal autocorrelation. We created a trend map for all data points within the boreal biome boundary following Gauthier et al. 2015, Science (<a href="https://doi.org/10.1126/science.aaa9092">DOI: 10.1126/science.aaa9092</a>) and added a 120km buffer around it (tcchange_all_points_clipped.tif). We also produced a map where we masked out non-significant trends based on a Mann-Kendall-test (tcchange_significant_points.tif). Both maps have a spatial resolution of around 1,000m.</p> <p>The map forms the key results in our manuscript: Rotbarth et al. 2023. 'North American boreal forests: Northern expansion is not compensating for southern declines'. Nature Communications</p>
Satellite_Observed Changes in Forest Cover over Northern China from 1996_2020
<p>This dataset is associated with a research article entitled "Satellite_Observed Changes in Forest Cover over Northern China from 1996_2020".</p> <p>As an important part of the land surface, forest is an important factor affecting global carbon, water cycle and climate change; Fractional Forest Cover (FFC) represents the proportion of forest canopy cover area to the whole pixel observed from the vertical direction. It is an important parameter for monitoring forest resources and is related to forest structure or attributes, such as forest area or forest distribution density. It is a variable representing forest cover on a continuous scale.</p> <p>Based on the remote sensing images of Gaofen-2 and Landsat-8, a model for estimating the FFC in the three-north regions of China is constructed based on the ensemble machine learning method. From 1996 to 2020, the annual FFC products with a resolution of 30 meters covering the three northern regions of China with long time series have been generated. The data values contained in the FFC range from 0 to 100, the "Nodata" value is set to -99, and the data file is provided in Geo-Tiff format.</p>
Data from: Past forest-cover explains current genetic differentiation in the Carpathian newt (Lissotriton montandoni), but not in the smooth newt (L. vulgaris)
<p class="MsoNormal"><strong>Aim:</strong><strong><span> </span></strong><span>Current genetic variation and differentiation are expected to reflect the effects of past rather than present landscapes due to time lags, i.e., the time necessary for genetic diversity to reach equilibrium and reflect demography. </span>Time lags can affect our ability to infer landscape use and model connectivity, and also obscure the genetic consequences of recent landscape changes<span>. In this work, we test if past forest-cover better explains contemporary patterns of genetic differentiation in two closely related but ecologically distinct newt species – <em>Lissotriton montandoni</em> and <em>L. vulgaris</em>. </span></p> <p class="MsoNormal"><span><strong>Location: </strong></span><span><span>Carpathian Mountains and foothills.</span></span></p> <p class="MsoNormal"><span><strong>Methods: </strong>Genetic differentiation between populations was related with landscape resistance optimized with tools from landscape genetics, for multiple timeframes, using forest-cover data from 1963 to 2015. Analyses were conducted for </span><span><span>pairs of populations at distances from 1 to 50 km.</span></span></p> <p class="MsoNormal"><span><strong>Results:</strong></span><span><strong><span> </span></strong></span><span><span>We </span></span><span>find evidence for a time lag in <em>L. montandoni</em>, with forest-cover from 40 years ago (ca. 10 newt generations) better explaining current genetic differentiation. In <em>L. vulgaris</em>, current genetic differentiation was better predicted by present land-cover models with lower resistance given to open-forests. This result may reflect the generalist ecology of<em> L. vulgaris</em>, its lower effective population sizes and exposure to habitat destruction and fragmentation.</span></p> <p class="MsoNormal"><strong>Main conclusions:</strong><span> <span>Our study provides evidence for time lags in <em>L. montandoni</em>, showing that the genetic consequences of landscape change for some species are not yet evident. Our findings highlight the interspecific variation in time lag prevalence, and demonstrate that current patterns of genetic differentiation should be interpreted in the context of historical landscape changes.</span></span></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.