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6,381 results for “spatial”
Global peatland, bare rock and bare sand extent at 100 m to 1 km spatial resolution based on multisource data
<p>Ensemble estimate of the global distribution of <a href="https://en.wikipedia.org/wiki/Peatland">peatlands</a> / extent (<strong>peatland.extent_wri.gfw.peatgrids_p</strong>). This is a simple average from three (3) sources of data:</p> <ol> <li><a href="https://data.globalforestwatch.org/datasets/gfw::global-peatlands/about">WRI Global Peatlands extent map</a> at 30-m (250-m effective);</li> <li><a href="https://doi.org/10.5281/zenodo.12559238">PEATGRIDS</a> at 1-km;</li> <li><a href="https://globalpeatlands.org/new-online-global-peatland-map-asian-peatlands-story-map-presenting-best-peatlands-mapping">Global Peatlands Map 2.0</a> produced by the Global Peatlands Initiative;</li> </ol> <p>The average between the three sources is an extent map with value 0–100%. The refence period is 2000–2020, although probably most of data is based on pre 2010. For more details about the source data please refer to the cited references below.</p> <p>Bare rock and bare sand estimates are based on the following two sources of data:</p> <ol> <li><a href="https://land.copernicus.eu/en/products/global-dynamic-land-cover">Copernicus GLC land cover</a> at 100-m for 2015 and 2019;</li> <li><a href="https://lcz-generator.rub.de/global-lcz-map">Local Climate zones</a> map at 100-m for 2018;</li> </ol> <p>Two classes are considered: (1) probability of occurrence of bare rock (<strong>bare.rock_glc.gfz_p</strong>), (2) probability of occurrence of bare sand i.e. shifting sand (<strong>bare.soil.sand_glc.gfz_p</strong>). We recommend using only the 1-km data for spatial modeling.</p> <p>The time-series of bare areas (<strong>bare.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. </p>
Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain - Datasets and Python notebooks
<p>This dataset and the associated Python notebooks and R-code are related to the publication "Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain".</p>
Data supporting the study "An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021))
<p>Data supporting the figures and findings presented in the study <strong>"An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021), <em>Atmos. Chem. Phys..</em></strong></p>
Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean
<p>Koptekin et al. (2022) "<strong><em>Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean</em></strong>", Current Biology <a href="https://doi.org/10.1016/j.cub.2022.11.034">https://doi.org/10.1016/j.cub.2022.11.034</a></p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta.</p> <p><strong>DESCRIPTION</strong><br> These data were compiled as predictors of the distribution of riparian landbird species and groups of waterbird species, to facilitate projecting the probability of species or group presence across a given landscape. They were used to identify Priority Bird Conservation Areas and in analyses of the impacts of scenarios representing habitat restoration and perennial crop expansion on suitable habitat. These data are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta</li> <li>Dybala KE, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7672193.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7672193)</p> <p><strong>PROGRESS</strong><br> Complete</p> <p><strong>UPDATE FREQUENCY</strong><br> None planned</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from WorldClim (representing 1970-2000), National Hydrography Dataset (published 2020), and Point Blue's Water Tracker (representing 2013-2019).</p> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>bio_1: </strong>annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>bio_12:</strong> total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>streamdist: </strong>square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li><strong>pwater_fall:</strong> mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li><strong>pwater_win:</strong> mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> <p><strong>Literature Cited</strong></p> <ul> <li>Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 37:4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a> </li> <li>Reiter ME, Elliott NK, Barbaree B, Moody D. 2018. An automated open surface water tracking system for California’s Central Valley. Report to the U.S. Fish and Wildlife Service. Petaluma, California: Point Blue Conservation Science. Available from: <a href="https://data.pointblue.org/apps/autowater/ ">https://data.pointblue.org/apps/autowater/ </a></li> <li>[USGS] United States Geological Survey. 2020. National Hydrography Dataset Best Resolution (NHD) for Hydrologic Units (HU) 4 - 1802, 1803, 1804, 1805. Reston (VA): U.S. Geological Survey. Available from: <a href="https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products ">https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products </a></li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong><br> N/A</p> <p><strong>COORDINATE REFERENCE SYSTEM</strong><br> WGS 84 / UTM zone 10N (EPSG:32610)</p> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics
<p>These data comprise three layers describing the spatial and temporal distribution of cloud-free optical observations over the tropics. The test datasets shared here are derived from the combination of Landsat and Sentinel-2 satellite data and represent a portion of the full datasets. The test data correspond to year 2020 over Mesoamerica. </p> <ul> <li>Spatial data: the Meso2020_validObs contains one band 'valid_obs' indicating the number of cloud-free observations at the pixel level. </li> <li>Temporal data: the maximumWaitDate2020 contains two bands 'max' and 'maxDay' corresponding to the number of maximum consecutive days without data in a year and final date in which the maximum number of consecutive days without data occurred, respectively.</li> </ul>
Spatial immunophenotyping of the tumor microenvironment in non-small cell lung cancer
<p>A dataset with spatial immune cell information on a lung cancer cohort from Uppsala University Hospital, Sweden, with anonymized clinical data. For more information please refer to the 'readme' file and the original study (https://doi.org/10.1016/j.ejca.2023.02.012).</p>
Billiards with Spatial Memory
<p>Mathematical billiards with self-avoiding particles are studied. </p> <p>The particles self-trap and show chaotic motion (see Figure 1 of the pre-print and the dataset here).</p> <p>In a triangular billiard, the self-trapping locations form a complex pattern with a wide long-tailed distribution of trajectory lengths (see Figure 2 of the pre-print and the dataset here).</p> <p>The final pattern depends on the geometry of the polygon (see Figure 3 of the pre-print and the dataset here).</p> <p>Each subfolder includes the description of the raw data.</p> <p>Link to Pre-print: https://arxiv.org/abs/2307.01734</p>
Dataset used for the analysis described in "Spatial patterns and controls on wind erosion in the Great Basin"
<p>This data set contains AERO model outputs and associated Bureau of Land Management Assessment, Inventory, and Monitoring calculated values for functional plant group cover estimates for monitoring plots across the Great Basin. Versrion 2 (V2) includes MLRA number and sampling year column ("sample_yr") that were omitted in previous version.</p>
MeteoEurope1km - TMAX (1991–2000): daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period
<p>MeteoEurope1km is the daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period. The dataset consists of five daily variables:</p> <ul> <li><strong>TMAX - maximum temperature</strong> (<strong>1991–2005 period</strong>, 2006–2020 period)</li> <li>TMIN - minimum temperature (1991–2005 period, 2006–2020 period)</li> <li>TMEAN - mean temperature (1991–2005 period, 2006–2020 period)</li> <li>SLP - mean sea level pressure</li> <li>PRCP - total precipitation</li> </ul> <p>Daily gridded temperature data were interpolated using the Regression Kriging, with digital elevation model (DEM) and topographic wetness index (TWI) as covariates.<br> Daily gridded sea level pressure data were interpolated using Ordinary Kriging.<br> Daily gridded precipitation data were interpolated using Indicator and Ordinary Kriging methodology in two steps:</p> <ol> <li>Indicator Kriging - prediction of precipitation occurence</li> <li>Ordinary Kriging - prediction of total daily precipitation for locations where precipitation occurs (1. step).</li> </ol> <p>File naming convention of the MeteoEurope1km files is <em>var_day_yyyymmdd_proj.tif</em> (e.g. <em>tmax_day_20201231_3035.tif</em>), where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection EPSG code - 3035</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> All dataset files are available as Cloud-Optimized GeoTIFFs (COGs).<br> Use the R <a href="https://github.com/AleksandarSekulic/Rmeteo">meteo</a> package, <em>europe1km</em> function to make a point query and obtain the values for a specific location and a specific period.</p>
The recovery of plant community composition following passive restoration across spatial scales, Cedar Creek Ecosystem Science Reserve, 1983-2016
1. Human impacts have led to dramatic biodiversity change which can be highly scale-dependent across space and time. A primary means to manage these changes is via passive (here, the removal of disturbance) or active (management interventions) ecological restoration. The recovery of biodiversity, following the removal of disturbance is often incomplete relative to some kind of reference target. The magnitude of recovery of ecological systems following disturbance depend on the landscape matrix, as well as the temporal and spatial scales at which biodiversity is measured. 2. We measured the recovery of biodiversity and species composition over 27 years in 17 temperate grasslands abandoned after agriculture at different points in time, collectively forming a chronosequence since abandonment from one to eighty years. We compare these abandoned sites with known agricultural land-use histories to never-disturbed sites as relative benchmarks. We specifically measured aspects of diversity at the local plot-scale (α-scale, 0.5m2) and site-scale (γ-scale, 10m2), as well as the within-site heterogeneity (β-diversity) and among-site variation in species composition (turnover and nestedness). 3. At our α-scale, sites recovering after agricultural abandonment only had 70% of the plant species richness (and ~30% of the evenness), compared to never-ploughed sites. Within-site β-diversity recovered following agricultural abandonment to around 90% after 80 years. This effect, however, was not enough to lead to recovery at our γ-scale. Richness in recovering sites was ~65% of that in remnant never-ploughed sites. The presence of species characteristic of the never disturbed sites increased in the recovering sites through time. Forb and legume cover declines in years since abandonment, relative to graminoid cover across sites. 4. Synthesis. We found that, during the 80 years after agricultural abandonment, old-fields did not recover to the level of biodiversity in remnant never-plough
Invasion dynamics of quagga mussels within a Southern California reservoir and its spatially intermittent watershed
Since its discovery in Lake Mead, Nevada in 2007, the invasive quagga mussel (Dreissena rostriformis bugensis) spread throughout the lower Colorado River drainage and into connected Southern California water systems. In December 2013, quagga mussels were found in Lake Piru, California, a reservoir with no connection to the Colorado River drainage. An initial “boom” period occurred in the first year after colonization. High densities and settlement rates continued for three years while lake water levels were low and relatively stable, despite periodic removals of mussels from lake infrastructure. Mussels were initially restricted to hard substrates but were regularly found on soft sediments within two years of colonization. Storms in 2017 dramatically increased the lake level and deposited substantial sediment, which eliminated mussels on soft sediments and reduced the overall mussel population. Reproduction and juvenile settlement rebounded within 6 months, despite the low population of adult mussels in the lake. Environmental conditions, particularly fill status and water temperature, rather than adult density, appear to be the primary driver of veliger abundance in this system, while recruitment was primarily explained by veliger abundance. Elevated water releases from the reservoir increased the flux of veligers downstream and led to mussel recruitment >15 km downstream. Sustained establishment of quagga mussels downstream has not occurred in the Santa Clara River and seems unlikely due to the unstable habitat conditions. However, periodic downstream colonization increases the likelihood for the infestation to spread and impact agricultural and municipal water systems that receive water from the river.
Temporal and spatial changes of the abundance and species composition of phytoplankton in the California Current from samples collected aboard CalCOFI cruises from summer 1996 through 2022.
The abundances of 385 taxonomic categories of phytoplankton (species where possible) are presented for the 26.5 -year period beginning with summer, 1996 and concluding with autumn 2022. There were four cruises per year. Samples were water samples collected from the second depth, which was designed to sample the mixed layer when a mixed layer existed, generally between 5m - 15m. Before counting, samples from single stations were pooled into four regions: NE (northern inshore), SE (southern inshore), Alley (the region of the California Current) and Offshore (Central Pacific). Pooled samples were enumerated with an inverted microscope. The species data are presented by seven major taxonomic categories followed by the sums of those major taxa. The species codes are defined in the table metadata.
SMP01 Spatial variation of soil microbial processes under Cornus drummondii shrubs of varying size at Konza Prairie, 2017
Soil was collected from multiple locations under individual Cornus drummondii shrub islands of varying size to measure within-shrub heterogeneity in soil microbial processes. Soil chemistry (Total C, Total N, extractable inorganic N, extractable P, and organic matter cotent), microbial biomass C and N, and potential extracellular enzymatic activity of β-glucosidase, phosphatase, NAG-ase, and LAP-ase were measured. Potential carbon mineralization and the isotopic composition of respired soil carbon was measured over a 77-day laboratory incubation.
SPW01 Spatial and physical characteristics of bison wallows on Konza Prairie since 2011
The objective of this study was to characterize spatial and physical attributes of bison wallows at the Konza Prairie Biological Station in northeastern Kansas. We used aerial imagery from two different years (2011 and 2019) to assess the abundance and spatial distribution of wallows in relation to fire frequency, elevation, and slope. We also recorded physical characteristics for a randomly selected subset of wallows (n = 966) and analyzed these data in relation to the same landscape features. Results indicate that wallows are more abundant on areas characterized by combinations of more frequent burning, higher elevations, and little or no slope. Wallows were smaller in areas burned more often and shallower at higher elevations, particularly when located on grazing lawns. Terrestrial plants were found in approximately 72.1% of the wallows sampled, and their prevalence increased with increasing slope. We found some quantity of aquatic plants in approximately 7.1% of the sampled wallows. The probability of finding aquatic vegetation in wallows was higher on grazing lawns and in areas burned less frequently, particularly every 20 years.
Frog spatial distribution data (El Verde + Bisley)
Most Puerto Rican Eleutherodactylus are terrestrial frogs that breed for prolonged periods of time in more or less continuous habitat. Because their life cycle lacks a free living larvae stage, reproductive behavior is not tied to bodies of water and they do not have the large aggregations typical of many aquatic breeders. For these reasons, assessing their population status requires examining fairly large areas of habitat. I began systematically sampling the anuran community on a 12 ha grid at the Bisley watersheds in 1989 and a second 16 ha grid at El Verde in 1993. These efforts have provided a comprehensive data set that can be used to evaluate future changes in the anuran community in this forest. Count of frogs, various frog predators, and various frog preys were taken at regular intervals on the grids. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Spatial and temporal differences in shrimp numbers (1 year, 20 pools)
We added woody debris to stream pools in three streams in an experiment designed to increase cover for freshwater shrimp. We trapped four species of freshwater shrimp during 4 months following wood additions. Stream pool morphology was estimated using maximum depth, surface area, and volume. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
MCR LTER: Coral Reef: Turbinaria CHN from a spatially explicit sampling campaign in lagoons of Moorea, French Polynesia
Nutrients are important for ecosystem structure and community dynamics. To quantify time-integrated patterns of nutrient regimes in tropical lagoon ecosystems, concentrations of nitrogen, hydrogen, and carbon were measured from tissues of the macroalga Turbinaria ornata collected in lagoons around Moorea, French Polynesia in 2016, 2017, and annually starting in 2019. These sampling periods initially corresponded with distinct seasonal shifts in rainfall and wave forcing and later focused on the rainy season. Results showed that N enrichment was highest nearshore in fringing reef habitats, as well as in bays and at reef passes.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/344/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
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.