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2,495 results for “continentality”
Net Ecosystem Carbon Balance of Grazing Lands across the continental United States, 2013-2023
Grazing lands underpin U.S. beef production, store roughly one-third of global soil organic carbon, deliver multiple ecosystem services, and are closely tied to the prosperity and resilience of rural communities. In this study, we calculated net ecosystem carbon balance (NECB), the net status of grazing lands as a carbon sink or source, by integrating carbon uptake from photosynthesis, and carbon loss through ecosystem respiration, enteric fermentation and manure from livestock. Our objective was to synthesize multiple years of annual NECB of grazing lands measured by eddy covariance towers from 16 pastures across seven USDA Long-term Agroecosystem Research Network (LTAR) sites and enteric fermentation and manure emissions derived from the stocking rates. We evaluated annual NECB against mean annual precipitation (MAP), mean annual temperature (MAT), vegetation, soil, fire history, grazing pressure index (GPI) and fertilization history. We found: (1) grazing lands were a carbon sink or neutral in most sites, and NECB was not significantly different between grasslands and shrublands, mesic and xeric conditions, and fertilized and unfertilized sites; (2) NECB increased with precipitation and temperature, but decreased with a higher GPI; and (3) precipitation, temperature, and GPI interacted such that temperature had a positive effect when MAP was greater than 700 mm and GPI had a negative effect when MAP was less than 1000 mm. Thus, most grazing lands in our study function as a carbon sink unless coupled with water deficit, low temperature, or heavy grazing. NECB is most sensitive to precipitation when water was limited with high interannual variability. Future work to improve our understanding of NECB on grazing lands should directly measure enteric fermentation and ecosystem emissions in different systems and add measurements on carbon loss through wind erosion and leaching.
Continental Europe Digital Terrain Model geomorphometry derivatives at 30 m, 100 m and 250 m
<p>Digital Terrain Model geomorphometry derivatives based on the DTM for Continental Europe using the <a href="https://epsg.io/3035">EPSG:3035</a> projection system. Processed using <a href="http://www.saga-gis.org/">SAGA GIS</a>, <a href="https://grass.osgeo.org/grass78/">GRASS 7 GIS</a> and <a href="https://gdal.org/programs/gdaldem.html">GDAL</a> at 3 standard spatial resolutions: 30-m, 100-m and 250-m. Derivatives include:</p> <ul> <li>devmean = deviation from mean value derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.4.0/statistics_grid_1.html">SAGA GIS</a>,</li> <li>downlocal / down = downslope local and general curvature derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.1.1/ta_morphometry_26.html">SAGA GIS</a>,</li> <li>hillshade = hillshading derived using using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>mnr = Module Melton Ruggedness Number derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.4/ta_hydrology_23.html">SAGA GIS</a>,</li> <li>northerness/easterness = derived using <a href="https://grass.osgeo.org/grass78/manuals/addons/r.northerness.easterness.html">GRASS 7 GIS</a>,</li> <li>openp / openn = openness positive negative derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.5/ta_lighting_5.html">SAGA GIS</a>,</li> <li>slope = slope in percent derived using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>topidx = a topographic index (wetness index) derived using <a href="https://grass.osgeo.org/grass76/manuals/r.topidx.html">GRASS 7 GIS</a>,</li> <li>tpi = Topographic Wetness Index derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.1.3/ta_hydrology_20.html">SAGA GIS</a>,</li> <li>vbf = Multiresolution Index of Valley Bottom Flatness derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.6/ta_morphometry_8.html">SAGA GIS</a>,</li> </ul> <p>Detailed processing steps can be found <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers"><strong>here</strong></a>. Read more about the processing steps <a href="https://opendatascience.eu/building-continental-europe-digital-terrain-model-30-m-resolution-using-machine-learning"><strong>here</strong></a>.</p> <p>Derivatives were chosen aiming to support soil and vegetation mapping projects. The slope.percent map at 30-m has been converted from 0-100% scale to 0-200% (Byte format) to help decrease the file size.</p>
Data for: "Continental-scale patterns in diel flight timing of high-altitude migratory insects"
<p>This dataset contains the proportional migratory insect intensity and traffic data used in Haest <em>et al.</em> (2024) to quantify patterns in diel flight periodicity of migratory insects between 50-500m above ground level during March-October 2021 using a network of seventeen vertical-looking radars across Europe. Please see the Materials and Methods section in Haest <em>et al.</em> (2024) for more details on the dataset. </p>
Data and code for: Rachel A Reeb, J Mason Heberling, & Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. AoB PLANTS, plae058
<p>Data and Analysis Code for: </p> <p>Rachel A Reeb, J Mason Heberling, Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. <em>AoB PLANTS</em>, plae058. <a href="https://doi.org/10.1093/aobpla/plae058">https://doi.org/10.1093/aobpla/plae058</a></p> <p>Includes two R markdown files ("climate_data_extraction_code.rmd" is the script for data extraction and cleaning and "Data_Analysis_V2.rmd" is the analysis script), the associated datasets (in .csv format), and the metadata file ("readme.txt").</p>
MOD-LSP: MODIS-Based Parameters for Variable Infiltration Capacity (VIC) Model over the Continental US, Mexico, and Southern Canada
<p>The MOD-LSP project contains MODIS-based land and surface (soil and vegetation) parameters for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994), release 5.0 and later (Hamman et al., 2018). The MOD-LSP spatial domain covers the continental United States, Mexico, and southern Canada; the associated domain files can be found in the <a href="https://zenodo.org/record/2564019">PITRI archive</a> (Bohn et al. 2018). This spatial domain and 0.625° (6 km) grid resolution are compatible with the gridded daily meteorological forcings of Livneh et al. (2015) ("L2015" hereafter) (http://ciresgroups.colorado.edu/livneh/data/daily-observational-hydrometeorology-data-set-north-american-extent), which can be disaggregated to hourly time step via the MetSim tool (Bennett et al. 2018) using the <a href="https://zenodo.org/record/2564019">aforementioned PITRI domain files</a> (Bohn et al. 2018).</p> <p>These parameters have two main purposes: (1) to improve upon previous widely-used parameters over the region (e.g., L2015) with updated, higher-resolution land cover maps and spatially explicit observations of surface properties; and (2) to expand from a single parameter set corresponding to one point in time to a series of parameter sets that account for temporal variability at seasonal to decadal scales.</p> <p>A detailed description of methods, the data sources and purposes of different VIC parameter sets within MOD-LSP, and how to use them with VIC, can be found in the MOD-LSP User Guide.pdf, included here. The scripts that were used to create the MOD-LSP parameters are archived on <a href="https://zenodo.org/record/3364149">Zenodo and GitHub</a> (Bohn 2019).</p> <p>If you wish to present or publish results that use these parameter sets, please cite the following paper:</p> <p>Bohn, T. J., and E. R. Vivoni, 2019b: MOD-LSP, MODIS-based land surface properties for assessing land cover variability and change over North America. Sci. Data, 6, 144, doi: 10.1038/s41597-019-0150-2.</p> <p>In addition, if you use the domain files associated with the PITRI precipitation disaggregation to accompany the MOD-LSP parameter files in VIC simulations, please cite the following paper:</p> <p>Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling. J. Hydrometeorol., 20, 297–317, doi:10.1175/JHM-D-18-0203.1.</p> <p>Contents:</p> <ul> <li>MOD-LSP User Guide v1.0.pdf - Explains how parameters were generated and how to set up the files for input in VIC simulations.</li> <li>global_param.template - Template for global_parameter file, which lists the locations of the other input files and sets various simulation options. The template contains placeholders for some filenames and simulation options, which must be replaced with real values by the user.</li> <li>params.$DOMAIN.L2015.nc - VIC-5 compliant NetCDF parameter files with values taken from the L2015 project for domain $DOMAIN (which is one of "CONUS_MX" or "USMX").</li> <li>params.CONUS_MX.MOD_IGBP.mode.2000_2016.nc - VIC-5 compliant NetCDF parameter file over the CONUS_MX domain, with land cover fractions taken from the MODIS MCD12Q1.006 product and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the climatological mean of MODIS observations over the period 2000-2016.</li> <li>params.USMX.NLCD_INEGI.$LCID.2000_2016.nc - VIC-5 compliant NetCDF parameter file over the USMX domain, with land cover fractions taken from the NLCD_INEGI dataset, from year = $LCID, and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the climatological mean of MODIS observations over the period 2000-2016.</li> <li>params.USMX.NLCD_INEGI.$LCID.$YYYY_$YYYY.nc - VIC-5 compliant NetCDF parameter file over the USMX domain, with land cover fractions taken from the NLCD_INEGI dataset, from year = $LCID, and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the MODIS observations from a single year $YYYY.</li> <li>veg_hist.$DOMAIN.$LCTYPE.$LCID.2000_2016.nc - timeseries of monthly land surface properties (LAI, Fcanopy, albedo) from MODIS observations spanning years 2000-2016, over domain $DOMAIN, aggregated over land cover classification $LCTYPE from year $LCID.</li> </ul>
Diet composition for small pelagic fishes across the Northeast U.S. Continental Shelf for NES-LTER, ongoing since 2013
These data represent the diet composition of small pelagic fishes assessed by the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) project. The six species of fish in this dataset represent a subset of the species collected in bottom trawls conducted by the NOAA Fisheries Northeast Ecosystems Surveys from Cape Hatteras to the Gulf of Maine. Sampling occurred in the Spring and Fall seasons. Fish were frozen and stomach content analyses were conducted by the Fisheries Oceanography and Larval Fish Ecology Lab at the Woods Hole Oceanographic Institution. Data are counts and length measurements for prey items examined under a dissecting microscope. Prey species were matched to the lowest taxonomic level in the Integrated Taxonomic Information System (ITIS) for scientific name and taxonomic serial number. The dataset was supplemented with geospatial and temporal information from NOAA Fisheries trawl databases.
Three-dimensional thermal structure of East Asian continental lithosphere
<p>This data set includes 3 supplement data files and 1 readme file for 3D thermal structure of East Asian continental lithosphere. </p>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
Soil pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: soil pH in H2O;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Soil clay content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: clay.tot = clay content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Global continental and ocean basin reconstructions since 200 Ma
<div>Description of Resources - Seton et al. (2012)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Seton, M., Müller, R. D., Zahirovic, S., Gaina, C., Torsvik, T., Shephard, G., Talsma, A., Gurnis, M., Turner, M., Maus, S., Chandler, M. (2012). Global continental and ocean basin reconstructions since 200 Ma. Earth-Science Reviews, 113(3), 212-270. doi:<a href="https://doi.org/10.1016/j.earscirev.2012.03.002" target="_blank" rel="noopener">10.1016/j.earscirev.2012.03.002</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div> </div> <div>The files associated with this data collection allow for the visualisation and/or manipulation of the global plate motion model presented by Seton et al. (2012), they include:</div> <div>• <strong>Rotations </strong>- Global rotation model that contains the reconstruction poles that describe the motions of the continents and oceans.</div> <div>* Seton_etal_ESR2012_2012.1.rot (410 KB)</div> <div> </div> <div>• <strong>Plate IDs </strong>- A list of all the plate IDs used in the rotation and geometry files and their corresponding plate names.</div> <div>* Seton_etal_ESR2012_PlateIDs.pdf (102 KB) </div> <div> </div> <div>• <strong>Coastlines </strong>- Geometries of the present-day coastlines.</div> <div>* Seton_etal_ESR2012_Coastline_2012.1.gpml (17.5 MB)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.shp (3.3 MB inc. auxiliary files, datum-WGS 1984)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.txt (2.7 MB)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.kml (4.4 MB)</div> <div> </div> <div>• <strong>Continent-ocean boundaries </strong>(COBs) - Locations of the boundaries between oceanic and continental crust for plates involved in this study.</div> <div>* Seton_etal_ESR2012_COB_2012.1.gpml (410 KB)</div> <div>* Seton_etal_ESR2012_COB_2012.1.shp (152 KB inc. auxiliary files, datum-WGS 1984)</div> <div>* Seton_etal_ESR2012_COB_2012.1.txt (86 KB)</div> <div>* Seton_etal_ESR2012_COB_2012.1.kml (176 KB)</div> <div> </div> <div>• <strong>Plate polygons and boundary geometries</strong> - Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are valid at 1 Myr intervals (0-200 Ma). The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation file. </div> <div>* Seton_etal_ESR2012_PP_2012.1.gpml (29.5 MB)</div> <div> </div> <div>Note:</div> <div>Paleo age grids used by Seton et al. (2012) are released in Müller et al. (2013) [Müller, R. D., Dutkiewicz, A., Seton, M., & Gaina, C. (2013). Seawater chemistry driven by supercontinent assembly, breakup, and dispersal. Geology, 41(8), 907-910. doi: <a href="https://doi.org/10.1130/G34405.1" target="_blank" rel="noopener">10.1130/g34405.1</a>]</div> <div> </div> <div>The paleo age grids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Seton_etal_2012_ESR/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Seton_etal_2012_ESR/</a></div>
Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale
<p>Continental Europe surface lithology based on <strong><a href="http://www.europe-geology.eu/onshore-geology/geological-map/onegeologyeurope/">EGDI / OneGeology map</a></strong> at 1:1M scale produced by <a href="https://egdi.geology.cz/record/basic/5729ffdf-2558-48fc-a5d2-645a0a010855">GEOZS, Slovenia</a>. European datasets harvested from national WFS for geologic units or national geological units datasets, based on OneGeology and <strong><a href="https://inspire.ec.europa.eu/codelist/LithologyValue/">INSPIRE Lithology</a></strong> and Geochronologic Era URI codelists. Layers include:</p> <ul> <li>EGDI_GE_GeologicUnit_EN_1M_Surface_LithologyPolygon_v2_250m_epsg.3035.tif = original EGDI surface lithology map;</li> <li>dtm_surface.lithology_egdi.1m_c_250m_s_20000101_20221231_eu_epsg.3035_v20240530.tif = gap filled surface lithology map;</li> </ul> <p>Missing values in the original EGDI lithology map have been imputed by training a random forest classifier model based on parameters derived from DTM and soil regions map from Die Bundesanstalt für Geowissenschaften und Rohstoffe (BGR). By generating 1 million random points, geographically balanced over the whole pan-EU land area, each class in the map was covered properly. Classes whose number of samples is less than 10 were discarded from the model training. The hyperparameter tuning of the model was carried out via a Bayesian approach with a criteria to maximize accuracy of 5k-fold cross validation. The tuned random forest model achieved an accuracy of 47% (Kappa=0.43) for the testing data, 20% of the generated sample points. The lithology of Turkey, on the other hand, was digitised from the available geology map produced by the General Directorate of Mineral Research and Exploration (MTA). The available raster map was post-processed and classified as 20 lithology classes using the k-means algorithm. These classes were harmonized with the classes in the EGDI lithology map.</p> <p>Acknowledgment: GEOZS, Continental Shelf Department at the Ministry for Transport and Infrastructure.</p>
Parameters for PITRI Precipitation Temporal Disaggregation over continental US, Mexico, and southern Canada, 1981-2013
<p>This dataset contains parameter values for the Precipitation Isosceles Triangle (PITRI) precipitation disaggregation method (Bohn et al., 2019) over the CONUS+Mexico domain (southern Canada, the continental US, and Mexico; 14.65 - 53° N latitude, 65-125° W longitude), at 1/16° (6 km) spatial resolution. There are two parameters: "dur" (mean event duration [minutes]) and "t_pk" (mean time of peak precipitation intensity [minutes from beginning of day]). In each land grid cell, each parameter has 12 climatological mean monthly values for the period 1981-2013.</p> <p>This dataset contains 2 NetCDF-format files:</p> <ul> <li>domain.CONUS_MX.L2015.nc - this contains parameters over the entire CONUS+Mexico domain, using the land mask of the Livneh et al. (2015) daily meteorology dataset.</li> <li>domain.USMX.L2015.nc - this contains the same parameters, but clipped to exclude Canada (to be consistent with datasets that cover only that part of the domain).</li> </ul> <p>These files are structured as input "domain" files for 2 applications:</p> <ul> <li>MetSim meteorology simulator (https://github.com/UW-Hydro/MetSim/releases/tag/2.0.0_alpha; Bennett et al., 2018). The PITRI algorithm has been implemented as an option in MetSim. To use this algorithm within MetSim, set the "prec_type" option to "triangle" or "mix" in the configuration file. The "mix" option is a blend of the "uniform" (previous) method and the "triangle" method that fixes biases in snow accumulation rates yielded by the "triangle" method in some climates. "mix" uses the "uniform" method on days for which minimum daily temperature falls below 0 C, and "triangle" method on all other days.</li> <li>Variable Infiltration Capacity (VIC) model, release 5.0 and later (Liang et al., 1994; Hamman et al., 2018; https://github.com/UW-Hydro/VIC). VIC does not use the PITRI parameters, but does use the other variables such as mask, elevation, area, etc. For VIC to use the output of MetSim (disaggregated meteorological fields) as input, VIC needs to use the same domain file as was used in MetSim.</li> </ul> <p>Algorithm details can be found in the following paper, which should be cited if you use this dataset:</p> <p>Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling, Journal of Hydrometeorology 20(2), 297-317, doi: 10.1175/JHM-D-18-0203.1.</p>
MIRCA-BC-USMX: Irrigated and planted fractions over the continental United States and Mexico for years 1992, 2002, and 2012
<p>The MIRCA-BC-USMX project contains a spatially explicit mean annual cycle of monthly planted and irrigated fractions at 0.0625 degree (6 km) spatial resolution over the continental United States and Mexico for years 1992, 2002, and 2012.</p> <p>These fractions were generated by (1) reconciling the MIRCA2000 Global Monthly Irrigated and Rainfed Crop Areas dataset (Portmann et al., 2010) with the cropland and pasture classes of year 2001 of the harmonized NLCD_INEGI land cover dataset (Bohn and Vivoni, 2019b); (2) bias-correcting the irrigated and planted fractions to match state-by-state total irrigated and planted areas from government records in the United States (USDA, 2016) and Mexico (SADER, 2014; SAGARPA, 2016).</p> <p>These fractions have been added to land surface parameter files for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994) version 5.1 (Hamman et al., 2018), extended to include the irrigation module of Haddeland et al. (2006), available on <a href="https://github.com/tbohn/VIC/tree/feature/irrig.imperv.deep_esoil">GitHub</a>. The parameter files were taken from the MOD-LSP project, available on <a href="https://zenodo.org/record/2612560">Zenodo</a> (Bohn and Vivoni, 2019a). The VIC 5 image driver requires a "domain" file to accompany the parameter file. This domain file is also necessary for disaggregating the daily gridded meteorological forcings to hourly for input to VIC via the disaggregating tool <a href="https://github.com/UW-Hydro/MetSim">MetSim</a> (Bennett et al., 2018). We have provided a domain file compatible with the meteorological forcings of Livneh et al (2015) and the MIRCA-BC-USMX parameters, on <a href="https://zenodo.org/record/2564019">Zenodo</a> (Bohn et al., 2019a,b).</p> <p>Contents:</p> <ul> <li>Input Files <ul> <li>county_codes.csv - table mapping the numerical codes for counties with the county names used by the US Census Bureau and USDA. This was created by parsing this information from US Census tables from years 1990, 2000, and 2010 and USDA tables from years 1992, 2002, and 2012 and manually reconciling discrepancies across years. Thus the names may not match county names in the original files exactly from year to year, but rather represent my own naming convention. However these discrepancies were rare.</li> <li>mun_us.0.01_deg.asc.tgz and mun_mx.0.0.01_deg.asc.tgz - gzipped tar archives containing mun_us.0.01_deg.asc and mun_mx.0.01_deg.asc, which are ascii-format ESRI grid files created by rasterizing publicly available shapefiles of US and Mexican counties/municipios. These have 0.01 degree (1 km) spatial resolution and pixels have numerical values equal to the codes in county_codes.csv.</li> </ul> </li> <li>Output Files <ul> <li>fplant_firr_bc.$LCYEAR.nc, where $LCYEAR is one of ("s1992","2001", or "2011") - NetCDF-format files at 0.0625 degree (6 km) resolution containing 12 monthly maps each of bias-corrected "fplant" (planted area fraction) and "firr" (irrigated area fraction) for a specific historical year.The value of $LCYEAR indicates the snapshot of the NLCD_INEGI harmonized land cover classification with which fplant and firr were reconciled (so that these area fractions would not exceed the total agricultural/pastoral area given by NLCD_INEGI). Values of fplant and firr were bias corrected so that state-wide total areas matched government records from USDA (USDA, 2014) and SAGARPA (SADER, 2014; SAGARPA, 2016). For $LCYEAR = ("s1992", "2001", "2011"), the agricultural census year used in the bias correction was (1992, 2002, 2012).</li> <li>fplant_firr_bc.2011.mun_mx.nc - same as fplant_firr_bc.2011.nc, but bias-corrected at the municipio level in Mexico. County-level bias correction was not possible in the US due to lack of sufficient resolution USDA records. Similarly, municipio-level records were not available in Mexico prior to year 2003.</li> <li>params.USMX.NLCD_INEGI.$LCYEAR.$YEAR1_$YEAR2.with_irrig.nc - VIC 5 image driver-compliant input parameter files into which fplant and firr of the given $LCYEAR have been inserted. $YEAR1 and $YEAR2 indicate the first and last years of MODIS data used to estimate the annual cycle of monthly LAI, fcanopy, and albedo (independent of the values of fplant and firr).</li> <li>params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.mun_mx.nc - same as params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.nc but with fplant and firr bias-corrected at the municipio level in Mexico.</li> </ul> </li> </ul> <p>These parameters were created with scripts archived on <a href="https://github.com/tbohn/MIRCA-BC-USMX/releases/tag/v1.1">GitHub</a> (Bohn, 2019).</p>
11 May 2024 Superstorm Ionospheric Observations in the Continental US
<p><strong>May11_150km.mat: </strong> MATLAB save file - Ionospheric total electron content data from worldwide GNSS receivers, processed by MIT Haystack Observatory through the Millstone Hill Geospace Facility. Data is line of sight and conversion assumes a non-standard 150 km ionospheric pierce point. </p> <p>Direct permanent link: Anthea Coster, MIT/Haystack Observatory. (2024) Data from the CEDAR Madrigal database. Available from https://w3id.org/cedar?experiment_list=experiments4/2024/gps/11may24&file_list=los_20240511.001.h5</p> <p>GPS TEC data products and access through the Madrigal distributed data system are provided to the community by the Massachusetts Institute of Technology under support from US National Science Foundation grant AGS-1952737. Data for the TEC processing is provided from the following organizations: UNAVCO, Scripps Orbit and Permanent Array Center, Institut Geographique National, France, International GNSS Service, The Crustal Dynamics Data Information System (CDDIS), National Geodetic Survey, Instituto Brasileiro de Geografia e Estatística, RAMSAC CORS of Instituto Geográfico Nacional de la República Argentina, Arecibo Observatory, Low-Latitude IonosphericSensor Network (LISN), Canadian High Arctic Ionospheric Network, Institute of Geology and Geophysics, Chinese Academy of Sciences, China Meteorology Administration, Centro di Ricerche Sismologiche, Système d'Observation du Niveau des Eaux Littorales (SONEL), RENAG: REseau NAtional GNSS permanent - https://doi.org/10.15778/resif.rg, GeoNet - the official source of geological hazard information for New Zealand, Finnish Meteorological Institute, SWEPOS - Sweden, Hartebeesthoek Radio Astronomy Observatory, TrigNet Web Application, South Africa, Australian Space Weather Services, RETE INTEGRATA NAZIONALE GPS, Estonian Land Board, TU Delft, Western Canada Deformation Array, EUREF Permanent GNSS Network, GeoDAF: Geodetic Data Archiving Facility, African Geodetic Reference Frame (AFREF), Kartverket - Norwegian Mapping Authority, Geoscience Australia, IGS Data Center of Wuhan University, Pacific Northwest Geodetic Array, Nevada Geodetic Laboratory, Earth Observatory of Singapore, National Time and Frequency Standard Laboratory - Taiwan, and Korea Astronomy and Space Science Institute.</p> <p><strong>teczero.mat:</strong> MATLAB save file - 5-minute averaged background median TEC map, derived from May11_150km.mat file.</p> <p><strong>TECmaps0.m:</strong> MATLAB script - plotting script, employing teczero.mat, and used for Figure 3 and Figure 4B of manuscript.</p> <p><strong>gps_map.mat:</strong> MATLAB save file - source data for Figure 4A of manuscript: median vertical TEC map over 2-minute interval 0207-0208 UTC on 2024-05-11. </p> <p><strong>Fig4a_map.m: </strong>MATLAB script - plotting script, employing gps_map.mat, and used for FIgure 4A of manuscript.</p> <p><strong>cntr.m</strong>: MATLAB script - coastline plotting function.</p> <p><strong>burst.m</strong>: MATLAB script - plots individual LOS TEC data showing TEC bursts. Saves to "bursts.mat". Inputs "May11_150km.mat".</p> <p><strong>bursts.mat</strong>: MATLAB save file - individual TEC burst results.</p> <p><strong>1 Missouri_Skies_-_All-Sky_Fisheye_Missouri_Skies_Fishey_2030UT_2127UT.mp4: </strong>MP4 image file. Fisheye lens image of aurora from Missouri during 2024-05-11 storm. Used in Figure 2 of manuscript.</p> <p><strong>{amt,att,bmt,bot,het,hmt,nmt,omt,pat,sum,vmt}240511.g.001.hdf5</strong> - MagStar magnetometer measurements in HDF5 self-documenting file format, used for magnetometer figures in manuscript. Downloaded from the Madrigal database system. Direct permanent link: Jenn Gannon, Paul E. Meade, Eframir Franco-Diaz, Computational Physics. (2024) Data from the CEDAR Madrigal database. Available from https://w3id.org/cedar?experiment_list=experiments/2024/het/11may24&file_list=het240511g.001.hdf5. (Replace "het" with appropriate 3-letter site name from above list).<br><br></p>
Data set: Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes
<h1>Repository for "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes"</h1> <p>---</p> <p>These data scripts were used to perform analyses included in the research paper "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes" </p> <p>Main questions for the study:</p> <p>1. What are the main axes of morphological variation?<br>2. Does variation in morphology among species correlate with their current environments? <br>3. Are lineages that occupy ecologically similar habitats morphologically convergent? <br>4. Is speciation predominantly allopatric or sympatric? <br>5. Do sister species have greater morphological and ecological niche overlap than expected relative to non-sister species pairs?</p> <h2>## Data structure</h2> <p>Contents in the data folder is archived as a zip and can be downloaded from Zenodo (for all versions see https://zenodo.org/doi/10.5281/zenodo.10397830). Once you unzip the zipped files, you will see three folders and some files that are no in any folders. </p> <p>/data/ - files that were manually created and the phylogeny</p> <p>/data/script_generated_data/ - A combination of processed data needed to run the analyses </p> <p>/data/dorsal/ - photographs of the head from the dorsal view. These photos were used for digitising landmarks and semilandmarks. </p> <p>/data/worldclim2_30s/ - cropped and merged annual temperature from WorldClim2 (Fick and Hijmans 2017), soil bulk density from <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData.html">Soil and Landscape Grid of Australia</a>, and Global Aridity Index from Zomer et al. (2022). <br><br>/DREaD/ - contains some files required to replicate DREaD analysis</p> <h2>## Code/Software</h2> <p>All scripts can be run using open source software. Scripts should be run in order to create necessary files that will be saved in /data/script_generated_data/ for further scripts. R is required to run R scripts (.R).</p> <h3>### /Code</h3> <p> - utility/*.R - scripts for custom functions. These are sourced in other scripts.<br> - DREaD/*.R - scripts associated with DREaD analyses<br> - 00_linear_measurement_shaperatio.R - script used to account for sexual dimorphism and calculate conventional PCA. Addresses Q1.<br> - 01_model_fitting.R - script used to address Q2 and plot visualisations.<br> - 02_convergence.R - this script calculates Ct1-4 and C5 scores. Addresses Q3.<br> - 02_convergence_model_fitting.R - this script evaluates fit of different evolutionary models to traits. Addresses Q3.<br> - 02_convergence_test_simulations.R - simulation studies to show that our phylogeny has sufficient power to detect convergence.<br> - 03_niche_enmtools_bias_account.R - calculates ecological niche models (ENMs) for each species using MAXENT. Runs Age-Overlap Correlation tests for geography and ENMs. Partially addresses Q4.<br> - 03_DREaD_Blindsnakes_AS.R - script to run DREaD analysis. <br> - 03_morpho_niche_overlap_plots.R - Runs Age-Overlap Correlation tests for body shape and snout shape. Plots AOCs. Partially addresses Q4. <br> - 04_pairwise_distance_test.R - Binomial tests between sister and non-sister pairs for ENMs and Geographic Range. Partially addresses Q5<br> - 04_morpho_pairwise.R - Binomial tests between sister and non-sister pairs for body shape and snout shape. Partially addresses Q5</p> <h2>## Contact</h2> <p>Should you have questions about these scripts or would like to request raw data, please do not hesitate to contact Sarin Tiatragul (contact information can be found in the paper) or on Github (https://github.com/stiatragul/blindsnakemorphoevo)</p> <h2>## References</h2> <p><a name="ref-fickWorldClim2017"></a>Fick, S. E., and R. J. Hijmans. 2017. <a href="https://doi.org/10.1002/joc.5086">WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas</a>. International Journal of Climatology 37:4302–4315.</p> <p><a name="ref-zomerVersion2022"></a>Zomer, R. J., J. Xu, and A. Trabucco. 2022. <a href="https://doi.org/10.1038/s41597-022-01493-1">Version 3 of the global aridity index and potential evapotranspiration database</a>. Scientific Data 9:409.</p>
Indicative distribution map for Ecosystem Functional Group M3.1 Continental and island slopes
<p>This archive contains indicative distribution maps and profiles for <strong>M3.1 Continental and island slopes</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Ensemble of ice shelf basal melt rates and ocean properties for tipped-over continental shelves
<p><strong>Summary</strong><strong>:</strong></p> <p>This dataset contains the reference and tipped states from several model configurations developed at the <a href="https://www.awi.de/en/">Alfred Wegener Institute (AWI)</a> and the <a href="https://www.ige-grenoble.fr/?lang=en">Institut des Géosciences de l’Environnement (IGE)</a>. They were gathered here in the context of the <a href="https://www.tipaccs.eu">TiPACCs European project</a> and constitute a useful ensemble of reference and tipped ocean–ice-shelf simulations that <strong>can be used to feed ice-sheet simulations or to train melt parameterizations</strong>.</p> <p>The simulations produced by AWI are based on the <a href="https://fesom.de">FESOM</a> global ocean–sea-ice model using either Z- or Sigma- coordinates and all show a cold-to-warm tipping point for Filchner-Ronne Ice Shelf. The two sets of simulations produced by IGE are based on the <a href="https://www.nemo-ocean.eu">NEMO</a> ocean–sea-ice model. They include a global configuration showing a cold-to-warm tipping point for Ross Ice Shelf, and regional Amundsen Sea configuration showing a warm-to-warmer transition (likely not a proper tipping point). </p> <p>The files include 3-dimensional and sea-floor ocean temperatures and salinities, ice-shelf melt rates, as well as topographic and grid data. All variables are interpolated onto the common 8km stereographic grid that was used to provide ocean forcing in ISMIP6 (<a href="https://doi.org/10.5194/tc-14-2331-2020">Nowicki et al. 2020</a>).</p> <p>We provide the reference state and the anomaly, so that the tipped state is:</p> <ul> <li><em>Tipped = Reference + Anomaly</em></li> </ul> <p>To have an overview of the reference and tipped states, have a look at these figures:</p> <ul> <li><em>figure_ref_and_anomalies_1.pdf</em></li> <li> <p><em>figure_ref_and_anomalies_2.pdf</em></p> </li> <li> <p><em>figure_seafloor_temp_zooms.pdf</em></p> </li> </ul> <p> </p> <p>_______________________________________________</p> <p><strong>Detailed Data Description</strong><strong>:</strong></p> <p> </p> <ul> <li><strong>reference_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.1007/s10236-013-0642-0">Timmermann and Hellmer (2013)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.5194/os-13-765-2017">Timmermann and Goeller (2017)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>contact: Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, Z-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: ERA Interim</li> <li>provided average: 2008-2017 (10-year mean), i.e. model year 30-39</li> <li>more: same mesh as <a href="https://doi.org/10.5194/tc-13-2317-2019">Gürses et al. (2019)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>contact: Pierre Mathiot <a href="mailto:pierre.mathiot@univ-grenoble-alpes.fr">pierre.mathiot@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-4.0, eORCA025.L121 (Global, 1/4°, 121 vertical levels)</li> <li>atmospheric forcing: JRA55do</li> <li>provided average: 2<sup>nd</sup> cycle of 1989-1998 (10-year mean); we first run 1979-2018, and we redo 1979-1998 starting from the 2018 state.</li> <li>more: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>contact: Nicolas Jourdain <a href="mailto:nicolas.jourdain@univ-grenoble-alpes.fr">nicolas.jourdain@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-3.6, AMUXL12.L75 (Amundsen, 1/12°, 75 vertical levels)</li> <li>atmospheric forcing: MAR (<a href="https://doi.org/10.5194/tc-14-229-2020">Donat-Magnin et al. 2020</a>)</li> <li>provided average: 1989-2009 (21-year mean)</li> <li>more: similar model set-up as <a href="https://doi.org/10.1016/j.ocemod.2018.11.001">Jourdain et al. (2019)</a>.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_high_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_low_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing south of 60°S HadCM3 A1B starting 2050, otherwise ERA Interim starting 1979</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_medium_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: ERA Interim modified with a strong imprint of the seasonal cycle of HadCM3 A1B 2070-2089</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: manipulated ERA Interim with prolongued summer and shorter, milder winter south of 50°S, additional modification of winds in Weddell Sea region</li> <li>provided average: model year 108-117 (10-year mean), i.e. 2008-2017 of 3<sup>rd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</li> <li>perturbation of the model parameters: Different iceberg distribution and different sea-ice–ocean drag and snow conductivity on sea-ice, leading to less sea-ice production in the eastern Ross Sea.</li> <li>More: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</li> <li>perturbation of atmospheric forcing: MAR forced by the CMIP5 multi-model anomaly under the RCP8.5 scenario (<a href="https://doi.org/10.5194/tc-15-571-2021">Donat-Magnin et al. 2021</a>).</li> <li>provided average: 2080-2100 (21-year average)</li> </ul> </li> </ul> <p> </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.