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1,103 results for “moisture”
Microbial and soil moisture impacts of compost amendments and rainfall pulses in a degraded dryland soil, Arizona, 2021-2023
Compost, an organic soil amendment, has been proposed to increase soil carbon storage and water-holding capacity in drylands, and this management strategy may be particularly impactful in degraded drylands with low soil organic content. Compost additions and rainfall variability may interact to affect soil moisture, which is an important catalyst for soil microbial activity. This dataset is from a study that investigated how variable compost application amounts and simulated rainfall pulses affect soil moisture, microbial activity, and carbon content in a laboratory incubation study. Soils were amended with different amounts of compost (0, 0.35, and 0.70 g cm -2) and water pulses (5, 10, and 15 mm) in a full-factorial design. Each treatment received the same cumulative amount of water throughout the incubation, but pulses occurred at different frequencies (every 5, 10, and 15 days). Soil moisture content and microbial respiration were measured daily. Soil carbon content was measured at the end of the experiment.
Marcell Experimental Forest 10-minute soil temperature and moisture, 2008 - ongoing
This data set is a record of soil temperature and volumetric water content (soil moisture) on two upland mineral soil hillslopes at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. These data are collected as part of the long-term monitoring program at the S2 catchment, which has 6.5 ha of upland mineral soil that surrounds a central 3.2 ha peatland. Soil temperature and moisture are recorded every 10 minutes since 2008 at S2S, a north-facing hillslope that is south of the S2 bog. Measurements at S2N, a south-facing hillslope that is north of the S2 bog, began during 2009. Measurements were recorded at two depths at each of three different relative positions (downslope, mid-slope, and upslope) on the two hillslopes. The MEF is operated and maintained by the USDA Forest Service, Northern Research Station.
Summer soil temperature and moisture at the Anaktuvuk River Severely burned site from 2010 to 2013
Soil moisture and temperature were recorded at the Anaktuvuk River burn area during the summers from 2010 to 2013. Six sensors were deployed and measured temperature on half-hourly intervals over the summer and into the fall depending on battery function. Sensors were place in a hexagonal shape around a central data logger. Note that over time sensor depths changed due to frost heave and other environmental factors. All data contained should be treated as suspect where sensors may have been at surface. These sensors were removed August 20, 2013, no replacement sensors were installed.
Summer soil temperature and moisture at the Anaktuvuk River Moderately burned site from 2010 to 2013
Soil moisture and temperature were recorded at the Anaktuvuk River burn area during the summers from 2010 to 2013. Six sensors were deployed and measured temperature on half-hourly intervals over the summer and into the fall depending on battery function. Sensors were place in a hexagonal shape around a central datalogger. Note that over time sensor depths changed due to frost heave and other environmental factors. All data contained should be treated as suspect where sensors may have been at surface. These sensors were removed August 20, 2013, no replacement sensors were installed.
Summer soil temperature and moisture at the Anaktuvuk River Unburned site from 2010 to 2013
Soil moisture and temperature were recorded at the Anaktuvuk River burn area during the summers from 2010 to 2013. Six sensors were deployed and measured temperature on half-hourly intervals over the summer and into the fall depending on battery function. Sensors were place in a hexagonal shape around a central datalogger. Note that over time sensor depths changed due to frost heave and other environmental factors. All data contained should be treated as suspect where sensors may have been at surface. These sensors were removed August 23, 2013, no replacement sensors were installed.
Air temperature, relative humidity, soil temperatures and soil moisture for Arctic Long Term Experimental Research (ARC LTER) heath experimental plots, Toolik Field Station, North Slope Alaska for 2001-2024-09-22.
Air temperature and relative humidity at 3 meters, soil temperatures at 2 depths, 5 and 10 cm, canopy temperatures and soil moisture at 10 cm were measured in an Arctic Long Term Experimental Research (ARC-LTER) heath tundra site (DHT89) at Toolik Lake Field Station, North slope, Alaska. Only control and nutrient addition (nitrogen plus phosphorus ) treatments plots soils were measured. Note: In version 1 the moisture columns were mixed up. The fractional volumetric water columns were actually the period frequency of the wave of the sensor (Campbell Scientific CS616). Version 3 adds calculated percent moisture corrected for organic soil.
Model estimates of runoff, dissolved organic carbon, soil temperature and moisture for Elson Lagoon watershed, Alaska, 1981-2020
This dataset contains model estimates of dissolved organic carbon (DOC) yield (mg C/m^2) and runoff (mm), for surface and subsurface flows, soil temperature (degree C), and soil moisture (% of soil volume) for grid cells spanning the Elson Lagoon watershed in northwest Alaska. Daily air temperature, precipitation, and wind speed data from Utqiagvik airport were used for meteorological forcings for the daily simulation by the Permafrost Water Balance Model (PWBM) from 1981 to 2020. The DOC and runoff data files are organized by grid cell and month. The soil temperature and soil moisture files are organized by grid cell and day of year (DOY), and contain values for the first eight model soil layers, with centers of the layers at 1, 3, 8, 13, 23, 33, 45, 55 cm depth. The estimates are most useful for analyses of the dynamics of the watershed’s surface and subsurface runoff and DOC yield. Leachate DOC concentrations can be obtained using the gridded runoff and yield values. A manuscript describing the data and associated analysis has been accepted for publication in Environmental Research Letters (Rawlins et al., 2021).
Bonanza Creek LTER: Hourly Soil Moisture (VWC) at Various Depths from 2000 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
A collection of soil moisture measurements collected with from CPCRW sites. Moisture is recorded from a range of depths: 10, 20, and 40 cm.
Bonanza Creek LTER: Hourly Soil Moisture (VWC) at Various Depths from 2002 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska
A collection of soil moisture from upland and floodplain sites. Moisture is recorded from a range of depths: 5, 10, 20, and 50 cm. Data was recorded using Campbell Scientific dataloggers. The sensors in use are CS615 water content reflectometers. The period average is recorded and converted to volumetric water content using the Topp's equation.
Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Gravimetric Soil Moisture
The influence of inorganic nitrogen (N) inputs on decomposition is poorly understood. Some prior studies suggest that N may reduce the decomposition of substrates with high concentrations of lignin via inhibitory effects on the activity of lignin-degrading enzymes, although such inhibition has not always been demonstrated. The purpose of E145 was to study the effects of nitrogen (N) addition on decomposition of seven substrates ranging in initial lignin concentrations (from 7.4 - 25.6%) over five years in eight different grassland and forest sites in central Minnesota.
Hubbard Brook Experimental Forest: Watershed 3 soil moisture transect, 2007
Soil moisture was measured along a slope transect in Watershed 3 as part of a study examining hydrologic connectivity between hillslopes and the stream. Soil moisture sensors were located nearby shallow groundwater wells (available from https://doi.org/10.6073/pasta/210b60a3d2f5ee2bb2635ee2fb33b637) along a topographically defined landform sequence (footslope–backslope– shoulder) in a sub-catchment of Watershed 3. The study was part of Joel Detty's M.S. thesis. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Shrub influence on soil moisture, nutrients, temperature and species composition, 2019 - 2020.
Shrubification, the expansion and densification of shrubs, is occurring in arctic and alpine zones across the globe (Myers-Smith et al., 2011). This alteration is primarily driven by warming temperatures (Elmendorf et al., 2012b, 2012a), and can have major consequences for the existing vegetation (Anthelme et al., 2007; Pajunen et al., 2011; Venn et al., 2014) and for nutrient pools (Sturm et al., 2005; DeMarco et al., 2014) due to the abiotic and biotic effects of shrubs. Shrubs accumulate snow which insulates the ground during the winter and provides more moisture later in the season (Liston et al., 2002). During the summer, shrubs provide shade and wind protection. Additionally, shrubs can increase the soil nitrogen (N) pool through their high input of plant material into the soil (DeMarco et al., 2014). These small-scale climatic and soil alterations have important consequences for plant community dynamics in the arctic and alpine. References: Anthelme, F., Villaret, J.-C., and Brun, J.-J., 2007: Shrub encroachment in the Alps gives rise to the convergence of sub-alpine communities on a regional scale. Journal of Vegetation Science, 18(3):355–362. DeMarco, J., Mack, M. C., and Bret-Harte, M. S., 2014: Effects of arctic shrub expansion on biophysical vs . biogeochemical drivers of litter decomposition. Ecological Society of America, 95(7):1861–1875. Elmendorf, S. C., Henry, G. H. R., Hollister, R. D., Björk, R. G., Bjorkman, A. D., Callaghan, T. V., Collier, L. S., Cooper, E. J., Cornelissen, J. H. C., Day, T. A., Fosaa, A. M., Gould, W. A., Grétarsdóttir, J., Harte, J., Hermanutz, L., Hik, D. S., Hofgaard, A., Jarrad, F., Jónsdóttir, I. S., Keuper, F., Klanderud, K., Klein, J. A., Koh, S., Kudo, G., Lang, S. I., Loewen, V., May, J. L., Mercado, J., Michelsen, A., Molau, U., Myers-Smith, I. H., Oberbauer, S. F., Pieper, S., Post, E., Rixen, C., Robinson, C. H., Schmidt, N. M., Shaver, G. R., Stenström, A., Tolvanen, A., Totland, Ø., Troxler, T., Wahren, C. H
SEV-LTER Mean - Variance Experiment Plains Grassland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And How does micro-environmental variation among plots influence how treatments alter soil moisture profiles over three soil depths? This data package includes sensor data from the Mean x Variance experiment in the Plains grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM, which is dominated by the grass species Bouteloua gracilis (blue grama).
SEV-LTER Mean x Variance Experiment Desert Grassland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
Soil Moisture at Three Different Dune Elevations on the Hog Island, Northampton County, VA 2023
In summer 2023, soils were collected from swale grasslands (embryonic, Intermediate [swale 1] and Inland [swale 2]) on southern Hog Island. They were kept in plastic bags to quantify soil moisture content, which was determined by weighing cores to obtain water mass before and after drying at 105 deg_C for 72 hours. For details, see: Woods, N.N., Zinnert, J.C. Shrub encroachment of coastal ecosystems depends on dune elevation. Plant Ecol 225, 1047-1057 (2024). https://doi.org/10.1007/s11258-024-01453-2
SM2RAIN test dataset with ASCAT and SMAP satellite soil moisture (plus ERA5 evapotranspiration)
<p>Are you looking for a research contest?</p> <p>Here [SM_RAIN_EVAP_1009points.nc] you can find a 5-year dataset at 1009 points in Italy, the United States, India and Australia of co-located in space and time:</p> <ol> <li>satellite soil moisture (from ASCAT, Wagner et al., 2013, doi:10.1127/0941-2948/2013/0399)</li> <li>evapotranspiration (from ERA5 reanalysis by ECMWF: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview)</li> <li>ground-based rainfall.</li> </ol> <p>and a ~3-year dataset at the same points including soil moisture from SMAP (April-2015 --> December 2017) [SM_SMAP_ASCAT_ETERA5_Pobs_1009opints.nc]</p> <p>The dataset can be used for testing multiple approaches for rainfall estimation from soil moisture, as done in <a href="https://www.linkedin.com/feed/hashtag/?keywords=%23SM2RAIN">#SM2RAIN</a> algorithm (<a href="http://hydrology.irpi.cnr.it/research/sm2rain/">http://hydrology.irpi.cnr.it/research/sm2rain/</a>).</p> <p>The global dataset we have developed is available here: <a href="https://zenodo.org/record/3635932">https://zenodo.org/record/3635932</a></p> <p>The NetCDF file contains all the data, and the figures (PNG files) represent an example of the results we have obtained in the paper and of the new dataset including SMAP.</p> <p><strong>Reference</strong><br> Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. <em>Earth System Science Data</em>, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>.</p> <p>For clarifications and support contact me at <a href="mailto:luca.brocca@irpi.cnr.it?subject=SM2RAIN%20test%20dataset">luca.brocca@irpi.cnr.it</a> </p>
BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. </p> <p> </p> <p>Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. </p> <p> </p> <p>The file name convention for the Level 3 NetCDF files is as follows.</p> <p> </p> <p>uas_L3_yyyymmdd_hhmmss_vx.x.nc</p> <p>where</p> <p>L3 = Level 3 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>x.x = version number </p> <p>Time is the flight start time in UTC.</p> <p>Version number description is provided in the NetCDF global attributes.</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. </p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.</p> <p><strong>December 2023 update</strong>: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
SM2RAIN-ASCAT (2007-2022): global daily satellite rainfall from ASCAT soil moisture
<p><strong>SM2RAIN-ASCAT is a new global scale rainfall product</strong> obtained from ASCAT satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014; 2019</em>). The SM2RAIN-ASCAT rainfall dataset (in mm/day) is provided over a regular grid at 0.1-degree sampling (3600x1801) on a global scale. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The SM2RAIN method was applied to the ASCAT soil moisture product (<em>Wagner et al., 2013</em>) for the period from January 2007 to December 2022 (16 years), for version 2.1.2n.</p> <p>The rainfall dataset is provided in NetCDF format. A total of 16 NetCDF files, one per year, are provided. The quality flag provided with the dataset has been used to mask out low quality data, as well as the areas characterised by complex topographic, frozen soil, and presence of tropical forests. In addition to the daily accumulated rainfall value, also the rainfall noise (mm/day) is provided for every day.</p> <p><strong>Version 2.1.2 should not be used due to an error in the precipitation data. Version 2.1.2n with respect to version 2.1 is calibrated anew and extended to December 2022.</strong></p> <p>A GeoTIFF version of the dataset (v1.5) is available here: <a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a></p> <p>A monthly version at 0.25- and 0.5-degree resolution (v1.4) is available here: <a href="https://doi.org/10.5281/zenodo.4570191">https://doi.org/10.5281/zenodo.4570191</a></p> <p>A sample dataset that can be used for testing SM2RAIN algorithm is available here: <a href="../record/2580285#.XLrYDugzbIU">https://zenodo.org/record/2580285</a></p> <p>Details on the dataset development and its assessment with ground and reanalysis observations are provided as:</p> <p><strong>Brocca, L.</strong>, Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. <em>Earth System Science Data</em>, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>.</p> <p><strong>Simple Python and Matlab codes for the extraction of SM2RAIN-ASCAT rainfall at one and multiple station(s)\location(s) are available at (note that reader for versions <1.3 are not usable for version >1.3): </strong><a href="https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader">https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader</a></p> <p><strong>The SM2RAIN code in Python is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rain">https://github.com/IRPIhydrology/sm2rain</a><br><strong>The SM2RAIN code in Matlab is available here</strong>: <a href="https://github.com/IRPIhydrology/SM2RAIN_Matlab">https://github.com/IRPIhydrology/SM2RAIN_Matlab</a><br><strong>The SM2RAIN code in R is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rainR">https://github.com/IRPIhydrology/sm2rainR</a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li>EUMETSAT Global SM2RAIN project (contract n° EUM/CO/17/4600001981/BBo)</li> <li>EUMETSAT "Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF)" CDOP 3 (EUM/C/85/16/DOC/15).</li> </ul>
Datasets for "A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models"
<p>This repository contains the post-processed model outputs from HAMSTER v1.2.0 as used in the following paper: </p> <p>Keune, J., Schumacher, D. L., and Miralles, D. G.: A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models, Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, 2022.<br> <br> The data set contains (1) global validation statistics for the three fluxes (evaporation, precipitation, sensible heat), and (2) the climatological source regions of precipitation and heat for Denver, Beijing and Windhoek. The former are found in the directory 'validation/global', and the latter are found in the directories '1001' (Denver), '3001' (Beijing) and '5002' (Windhoek). Multiple experiments were performed to assess the uncertainty of the source regions. Thus, multiple files exist, that show the same variables but for multiple experiments (indicated by the names "ALLPBL", "RH-10-20", "SOD08-SCH19", "SCH20", "FAS19" in the file name). For the moisture source regions, the uncertainty of the attribution methodology was assessed; these are indicated by the different folders, i.e. 'linear_upscaled' and 'random2_upscaled'. For each city and each experiment, the climatologically averaged source regions ('_mean.nc') and the climatologically averaged individual backward day contributions ('_bwmean.nc') are provided. Data sets are in the netCDF format and contain metadata following the CF convention.</p>
Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC)
<p><strong>Related paper:</strong> <a href="https://hess.copernicus.org/preprints/hess-2022-121/">Forkel et al. (2022)</a></p> <p>The VOD2LFMC dataset contains estimates of leaf moisture content as defined as live-fuel moisture content (LFMC) derived from passive microwave satellite observation of vegetation optical depth (VOD). LFMC is defined as the fresh mass of a leaf over the dry mass and is expressed in %:</p> <p><span class="math-tex">\(LFMC = {m_{fresh}-m_{dry}\over m_{dry}}*100\%\)</span></p> <p>LFMC was estimated from the <a href="https://doi.org/10.5281/zenodo.2575599">VODCA version 1</a> dataset of Ku-band VOD using the model approach “B” as described in Forkel et al. (2022).</p> <p>The file VOD2LFMC-B_v01_2000-2017.zip contains (unzipped ~ 57 GB):</p> <ul> <li>daily global data per month netCDF files</li> <li>a README file</li> <li>Ancillary file VOD2LFMC-B_v01_support-by-obs.nc</li> </ul> <p>Grid, time and variable definitions:</p> <ul> <li> <p>Grid-name: Geographic Lat/Lon</p> </li> <li> <p>Pixel-size: 1/4 degrees</p> </li> <li> <p>Size-x: 1440</p> </li> <li> <p>Size-y: 557</p> </li> <li> <p>Time period: February 2000 – July 2017</p> </li> <li> <p>Temporal resolution: daily</p> </li> <li> <p>Variable: Live-fuel moisture content (LFMC) in %</p> </li> <li> <p>Valid-range: 0-400%</p> </li> </ul> <p> </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.