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75 results for “Soil variables”
Soil biogeochemical variables collected on the Arctic Long Term Ecological Research (ARC LTER) experimental plots in moist acidic and dry heath tundra, Arctic LTER, Toolik Field Station, Alaska 2017.
**Note: Versions 1 and 2 had the wrong data files.** Soil nutrients (total Carbon and Nitrogen, inorganic nutrients (ammonium ion (NH4), nitrate anion (NO3-), phosphate anion (PO43-)); organic nutrients (extractable organic carbon (EOC), extractable total nitrogen (ETN), extractable organic phosphorus (EOP)), microbial biomass, and extracellular enzyme activity on soils sampled from the Arctic LTER Dry Heath (organic soils only) and Moist Acidic Tundra (organic and mineral soils) herbivore exclosures and control plots at Toolik Lake, AK in July 2017.
Hubbard Brook Experimental Forest: Watershed 3 – One year of resin-extracted solutes from variably saturated soils
Hbr363: WS3 One year of resin-extracted solutes from variably saturated soils The Lateral Weathering Study looks at spatial patterns of mineral weathering processes at Hubbard Brook Experimental Forest. This project is characterizing mineral and elemental depletion/enrichment, soil morphology and chemistry, solute transport, and groundwater chemistry along hydropedological gradients. This dataset provides the total elemental mass of inorganic solutes (Ca, Na, Mg, Al, Fe, Mn, P, and S) as well as dissolved organic carbon (DOC) that were extracted off resins installed into shallow groundwater wells (~30-100cm) in Watershed 3. Resin packs were deployed for a total of one year (August 2019-2020) with four consecutive deployment periods, to avoid overloading resin ion capacity. Total mass for each solute was accounted for an entire resin pack, which was 5cm in height and 5cm in diameter, containing approximately 90 g of resin. Resin packs were installed in three different topographic positions along three transects (sites = 9), to characterize solute mass fluxes through different hydropedological units.
Soil water content measurements in plots with experimentally altered precipitation variability at the Jornada Basin LTER site, 2011-ongoing
This dataset contains soil moisture data from a study at the Jornada Experimental Range (JER) in southern New Mexico. The study was designed to assess the effect of interannual variability in precipitation on average aboveground net primary productivity (ANPP) in Chihuahuan Desert grasslands. The study began in 2009 and has five precipitation treatments (see Methods). While the study began in 2009, contains 50 plots (10 per treatment) and is ongoing, these data have only been collected since July 2011 in a subset of 20 plots (4 per treatment). This dataset is intended to provide information about the amount of water in surface and deep soil layers, as well as verify that experimental precipitation manipulations are effective. This is an ongoing dataset that will be updated yearly.
Multiple biogeochemical variables were measured for organic and mineral soils on Arctic LTER experimental plots in moist acidic and non-acidic tundra, Arctic LTER Toolik Field Station, Alaska 2013.
Measures of soil nutrient content (available N and P, Extractable N and P, Total C, N and P), and microbial biomass and activity (exoenzyme activity) were measured for organic and mineral soils on Arctic LTER experimental plots at Toolik field station in moist acidic and non-acidic tundra (organic soils only).
Soil biogeochemical variables collected on the Arctic LTER experimental plots in moist acidic, moist non-acidic, wet shrub and shrub tundra, Arctic LTER Toolik Field Station, Alaska 2015
We investigated the effect of long-term warming on multiple soil and microbial carbon, nitrogen, and phosphorus pools, and microbial extracellular enzyme activities, with a particular focus on phosphorus, in Alaskan tundra plots underlain by permafrost
Soil and plant biogeochemical and soil temperature variables collected at brown lemming (Lemmus trimucronatus) and tundra vole (Microtus oeconomus) structure sties near Nome, Toolik Lake, and Utqaigvik, Alaska, summer 2018-2020
Soil and plant sampling analysis under small mammal-built structures and controls sites from near the Team Vole fences: Nome, Toolik, Utqiagvik, AK 2018-2020.
Dryland soil mycobiome response to long-term precipitation variability at the Jornada Basin LTER site, 2013-2019
This dataset contains data and code for the paper "Dryland soil mycobiome response to long-term precipitation variability depends on host type" published in Journal of Ecology in 2022. Data were collected at the Jornada Basin LTER site in southern New Mexico, USA. Soil samples were sent to the University of Georgia, for DNA extraction and sequencing, performed at the Georgia Genomics and Bioinformatics Core in Athens, GA. Sequencing data are archived at NCBI under project identifier PRJNA884111.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) precipitation and transpiration variable outputs (precipitation, total, potential and actual evapotranspiration), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of precipitation, total evapotranspiration and actual evapotranspiration Outputs of snow water equivalent, snow melt, and runoff, as well as the model configuration file, as well as model inputs are archived separately on the Environmental Data Initiative.
Global Soil Bioclimatic variables at 30 arc second resolution
<p>Soil temperature layers were calculated by adding monthly soil temperature offsets to monthly air-temperature maps from CHELSA (date range 1979-2013) (Karger et al. 2017, Sci Data). These soil temperature layers were then used to calculate annual means, temperature ranges, standard deviation, warmest and coldest months and quarters. Wettest and driest quarters were identified for each pixel based on CHELSA monthly values. A quarter is a period of three months (1/4 of the year).</p> <p>When using any of these layers, please cite: Lembrechts et al., Global maps of soil temperature (2021). Global Change Biology. DOI: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16060">10.1111/gcb.16060</a></p> <p>For each Soil Bioclim layer, two depth intervals are available: 0 - 5 cm and 5 - 15 cm.</p> <p>We have followed the generally accepted definitions of BIO 1 - BIO 11: </p> <ul> <li>SBIO1 = Annual Mean Temperature</li> <li>SBIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))</li> <li>SBIO3 = Isothermality (BIO2/BIO7) (×100)</li> <li>SBIO4 = Temperature Seasonality (standard deviation ×100)</li> <li>SBIO5 = Max Temperature of Warmest Month</li> <li>SBIO6 = Min Temperature of Coldest Month</li> <li>SBIO7 = Temperature Annual Range (BIO5-BIO6)</li> <li>SBIO8 = Mean Temperature of Wettest Quarter</li> <li>SBIO9 = Mean Temperature of Driest Quarter</li> <li>SBIO10 = Mean Temperature of Warmest Quarter</li> <li>SBIO11 = Mean Temperature of Coldest Quarter</li> </ul> <p>These layers are also publicly available as Google Earth Engine assets. These are acessible via:</p> <ul> <li><a href="https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v1_0_5cm">https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm</a></li> <li><a href="https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v1_5_15cm">https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v2_5_15cm</a></li> </ul> <p>Also available are monthly maps of soil temperature, for two depth intervals (0-5 cm and 5-15 cm). For example, the soil temperature map for month 1 (January) at 0-5 cm is named soilT_1_0_5cm.tif'. </p> <p>To mask pixels by the proportion of extrapolation, the files 'PCA_int_ext_0_5cm.tif' and 'PCA_int_ext_5_15cm.tif' can be used. </p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences VIII - Soils 2015
This data set was collected as a part of Brian Houseman's MS Thesis, Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences (December 2017). Data include soil characteristics (temperature, moisture, depth, N, C, P, and pH) that were measured in 2015. Data were collected on study plots established across two burn scars (2004 Boundary Fire and 1971 Wickersham Dome Fire) within the Yukon-Tanana Uplands ecoregion of interior Alaska.
Hourly time series of soil and atmosphere variables at the experimental site of El Cautivo, Tabernas Desert, Almeria, Spain (February 2018 to December 2019)
<p>Measurements were performed along a hypothetical succession of biological soil crusts. Main studied variables were the soil-atmosphere CO2 and water vapor fluxes. This dataset was used by Lopez-Canfin et al. (2022) and Kim and al. (2024) at the time of publication.</p>
Carbon stock increase during post-agricultural succession in central France: no change of the superficial soil stock and high variability within forest stages
Open the record for dataset details and reuse information.
VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model
<p>## VICGlobal: soil, vegetation, and elevation band input files for the VIC hydrological model</p> <p>Date updated: June 28, 2021</p> <p>Authors and affiliations: Jacob Schaperow (1), Dongyue Li (1,2)<br> 1. Department of Civil and Environmental Engineering, UCLA<br> 2. Department of Geography, UCLA<br> Author contact info: jschap@g.ucla.edu</p> <p>The current version, v1.6d improves upon v1.6c by splitting the image parameters by continent, reducing file sizes.</p> <p>v1.6c is the same as v1.6, except that the image mode parameters have been updated to reflect the changes made to the classic mode parameters (e.g. r0 and rmin are different, and albedo, fcanopy, and LAI are calculated based on snow-free values).</p> <p>## Overview</p> <p>VICGlobal is a dataset that can be used to run the Variable Infiltration Capacity (VIC) hydrological model over regional to continental scales. The dataset is at 1/16 degree resolution and has latitudinal coverage from -60 to 85 degrees. All files are referenced to the WGS84 ellipsoid and datum (EPSG code 4326).</p> <p>The vegetation parameter file uses the IGBP classification and use partial land use types. The vegetation parameter rooting depths and root fractions are based on the method of Zeng (2001). The vegetation library file is largely the same as that of Livneh et al. (2013; 2015); however, the monthly average LAI, canopy fraction, and albedo values for each land cover type are calculated based on MODIS observations from 2017, using the method of Bohn and Vivoni (2019).</p> <p>There are two vegetation libraries: one for the northern hemisphere, and one for the southern hemisphere, in order to account for the seasonality of LAI, canopy fraction, and albedo.</p> <p>WARNING: although it appears small in compressed form, the image driver parameter input file, VICGlobal_params.nc, is about 140 GB when unzipped. Users are encouraged to use the image mode parameters that are already split by continent. For example, the parameter file for Africa is about 19 GB.</p> <p>A data descriptor is in preparation for submission to Nature Scientific Data (https://www.nature.com/sdata/).</p> <p>Other VIC input datasets (coverage limited to North America):<br> * Bohn and Vivoni MOD-LSP dataset: https://zenodo.org/record/2559631</p> <p>## List of contents</p> <p>Inputs for VIC-4 or the VIC-5 Classic Driver<br> * Soil parameter file<br> * Vegetation parameter file<br> * Elevation band file<br> * Vegetation library files (one each for the northern and southern hemispheres)</p> <p>Inputs for the VIC-5 Image Driver<br> * Parameter file (global)<br> * Domain file (global)<br> * Parameter files for each continent<br> * Africa<br> * Australia<br> * Eurasia (except Kamchatka)<br> * Kamchatka<br> * North America<br> * Oceania (New Zealand and nearby islands)<br> * South America<br> * Domain files for each continent<br> * GeoTiffs with continent masks</p> <p>Matlab codes for subsetting the VICGlobal parameters to a region of interest are also provided.</p> <p>## References</p> <p>* Bohn and Vivoni (2019). MOD-LSP, MODIS-based parameters for hydrologic modeling of North American land cover change. https://www.nature.com/articles/s41597-019-0150-2</p> <p>* Livneh et al. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. https://www.nature.com/articles/sdata201542</p> <p>* Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P. and Lettenmaier, D. P.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions, J. Clim., 26(23), 9384–9392, doi:10.1175/JCLI-D-12-00508.1, 2013.</p> <p>* Zeng (2001). Global Vegetation Root Distribution for Land Modeling. Journal of Hydrometeorology. https://doi.org/10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2</p>
High-resolution large weighing lysimeter measurements with meteorological and soil-hydrological variables from a Mediterranean Savanna
<p>Raw and processed lysimeter weighing and flux data at the instrumental site ES-LMa of six large high-precision weighing lysimeters in a Mediterranean Savanna ecosystem for the period from 2019-06-01 to 2020-05-31. Additionally, meteorological and radiometric data are provided. Additionally, code for the lysimeter processing is provided.</p> <p>Reproducible workflow of the article <strong>Paulus et al. 2022: Resolving seasonal and diel dynamics of non-rainfall water inputs in a Mediterranean ecosystem using lysimeters. HESS, https://doi.org/10.5194/hess-2021-519</strong></p> <p>Variables, units and detailed description are found in the README.html file.</p>
Soil nitrous oxide (N2O) and carbon dioxide (CO2) flux from a Central Iowa crop field and accompanying soil edaphic and climatic variables.
To quantify the magnitude of soil nitrous oxide flux and the drivers of nitrous oxide emissions in a representative central Iowa corn-soybean agricultural system, we measured greenhouse gas emissions (N2O and CO2) from 2017 to 2019 (primarily using custom automated chambers) along with soil chemical and physical parameters across a topographic gradient in a typically managed agricultural field near Ames, Iowa, USA. More details can be found in the associated manuscript, Lawrence et al. (2021).
High frequency soil sensor data for SOM input - Complex drivers of riparian soil oxygen variability revealed using self-organizing maps
The provided datasets contain the original (non-normalized) high-frequency soil and meteorological observations that were fed to the Self-Organizing Map (SOM) in order to identify ranges of values associated with low and high soil O2 conditions. For the Champlain Valley (CV) site we used the natural breaks algorithm to subset the data into high and low O2 datasets. O2 values were consistently low at the Green Mountains (GM) site, so we ran a single SOM for all O2 values at this site. The original values were then range-normalized before they were fed to the SOM.
McMurdo Dry Valleys Polygon Study: Variability in soil biogeochemistry and biodiversity
In the Antarctic Dry Valleys, soil polygons are prominent features of the landscape and may be key units for scaling local ecological information to the greater region. We examined polygon soils in each of the 3 basins of Taylor Valley, Antarctica. Our objectives were to characterize variability in soil biogeochemistry and biodiversity at local to regional scales, and to test the influence of soil properties upon invertebrate communities. We found that soil biogeochemical properties and biodiversity vary over multiple spatial scales from fine (less than 10 m) to broad (greater than 10 km) scales. Differences in biogeochemistry were most pronounced at broad scales among the major lake basins of Taylor Valley corresponding to differences in geology and microclimate, while variation in invertebrate biodiversity and abundance occurred at landscape scales of 10-500 m, and within individual soil polygons. Variation in biogeochemistry and invertebrate communities across these scales reflects the influence of physical processes and landscape development over ecosystem structure in the dry valleys. The development of soil polygons influences the spatial patterning of soil properties such as soil organic matter, salinity, moisture, and invertebrate habitat suitability. Nematode abundance and life history data indicate that polygon interiors are more suitable habitats than soils in the troughs at the edges of polygons. These data suggest that physical processes (i.e. polygon development) and biogeochemistry are an important influence on the spatial variability of biotic communities in dry valley soil ecosystems.
SGS-LTER CPER Hillslope Soil Spatial Variability on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-1984
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83515. CPER Hillslope Soil Spatial Variability - Pedons were characterized along three parallel transects, spaced at approximate 40 m intervals perpendicular to a hillslope at the CPER. Pedons were described at 7 landscape positions along each transect: summit, shoulder, upper backslope, middle backslope, lower backslope, footslope, and toeslope. Pedons were described by genetic horizon according to the standards of the National Cooperative Soil Survey. Analyses included: particle size; organic C; total N; organic and total P. Bulk Density was estimated using particle size and organic C data, according to: Rawls, W.J. 1983. Estimating soil bulk density from particle size analysis and organic matter content. Soil Sci. 135: 123-125.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.