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136 results for “Model Versioning”
SMODE L4 ROMS Model Output Version 1
This dataset contains output data files for the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) Regional Ocean Modelling System (ROMS) model runs. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Model output is limited to the SMODE field campaign region and uses 1hr ERA5 atmospheric forcings. ROMS model output files are provided in NetCDF format. Users should note that these files are very large and are encouraged to do analysis in the cloud rather than downloading.
Metop-B ASCAT Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles.Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include: New leaf growth and old leaf litterfall Sunlight interception by leaves and penetration to the ground Precipitation routing to leaves and soil Snow accumulation and melting Drainage and runoff of soil water Evaporation of water from soil and wet leaves Transpiration of soil water through leaf stomata Photosynthetic fixation of carbon from CO2 in the air Uptake of nitrogen from the soil Distribution of carbon and nitrogen to growing plant parts Decomposition of fresh plant litter and old soil organic matter Plant mortality Fire The model uses a daily time-step. This means that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions.A companion file with more information about Biome-BGC and its components is available at ftp://daac.ornl.gov/data/model_archive/BIOME_BGC/biome_bgc_4.1.1/comp/BiomeBGC_v411_release.pdf .Biome-BGC, Version 4.1.1 was developed and is maintained by the Numerical Terradynamic Simulation Group, School of Forestry, The University of Montana, Missoula, Montana, USA. Additional information can be found on there web site at: http://www.ntsg.umt.edu/.
GLAS/ICESat 1 km Laser Altimetry Digital Elevation Model of Greenland, Version 1
The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and land Elevation Satellite (ICESat) provides global measurements of elevation, and repeats measurements along nearly-identical tracks; its primary mission is to measure changes in ice volume (mass balance) over time. This digital elevation model (DEM) of Greenland is derived from GLAS/ICESat laser altimetry profile data and provides new surface elevation grids of the ice sheets and coastal areas, with greater latitudinal extent and fewer slope-related effects than radar altimetry.This DEM is generated from the first seven operational periods (from February 2003 through June 2005) of the GLAS instrument. It is provided on polar stereographic grids at 1 km grid spacing. The grid covers all of Greenland south of 83° N. Elevations are reported as centimeters above the datums, relative to both the WGS 84 ellipsoid and the EGM96 geoid, in two separate elevation data files. A data quality map of the interpolation distance is distributed in addition to the elevation data. ENVI header files are also provided.The data are in 4-byte (long) signed integer binary files (big endian byte order) and are available via FTP.
SCATSAT-1 Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations aboard SCATSAT-1, representing the first science quality release of these data funded under the MEaSUREs program. This product from SCATSAT-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. There are typically 14 orbits per day, and the thumbnail preview shows coverage for the first ten orbits in an example day. Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
CLPX-Model: Rapid Update Cycle 40km (RUC-40) Model Output Reduced Data, Version 1
The Rapid Update Cycle, version 2 at 40km (RUC-2, known to the Cold Land Processes community as RUC40) model is a Mesoscale Analysis and Prediction System (MAPS) data set that uses the Model Output Reduced Data Set (MORDS) version. This data set has been subsetted for use in the Cold Land Processes Field Experiment (CLPX).
Metop-A ASCAT Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
WLDAS Noah-MP 3.6 Land Surface Model L4 Daily 0.01 degree x 0.01 degree Version D1.0 (WLDAS_NOAHMP001_DA1) at GES DISC
The Western Land Data Assimilation System (WLDAS), developed at Goddard Space Flight Center (GSFC) and funded by the NASA Western Water Applications Office, provides water managers and stakeholders in the western United States with a long-term record of near-surface hydrology for use in drought assessment and water resources planning. WLDAS leverages advanced capabilities in land surface modeling and data assimilation to furnish a system that is customized for stakeholders’ needs in the region. WLDAS uses NASA’s Land Information System (LIS) to configure and drive the Noah Multiparameterization (Noah-MP) Land Surface Model (LSM) version 3.6 to simulate land surface states and fluxes. WLDAS uses meteorological observables from the North American Land Data Assimilation System (NLDAS-2) including precipitation, incoming shortwave and longwave radiation, near surface air temperature, humidity, wind speed, and surface pressure along with parameters such as vegetation class, soil texture, and elevation as inputs to a model that simulates land surface energy and water budget processes. Outputs of the model include soil moisture, snow depth and snow water equivalent, evapotranspiration, soil temperature, as well as derived quantities such as groundwater recharge and anomalies of the state variables.
CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)
The CENTURY model, Version 4, is a general model of plant-soil nutrient cycling that is being used to simulate carbon and nutrient dynamics for different types of ecosystems including grasslands, agricultural lands, forests and savannas. CENTURY is composed of a soil organic matter/ decomposition submodel, a water budget model, a grassland/crop submodel, a forest production submodel, and management and events scheduling functions. It computes the flow of carbon, nitrogen, phosphorus, and sulfur through the model's compartments.
CLPX-Model: Local Analysis and Prediction System: 4-D Atmospheric Analyses, Version 1
The Local Analysis and Prediction System (LAPS), run by the NOAA's Forecast Systems Laboratory (FSL), combines numerous observed meteorological data sets into a collection of atmospheric analyses.
MAPSS: Mapped Atmosphere-Plant-Soil System Model, Version 1.0
MAPSS (Mapped Atmosphere-Plant-Soil System) is a landscape to global vegetation distribution model that was developed to simulate the potential biosphere impacts and biosphere-atmosphere feedbacks from climatic change. Model output from MAPSS has been used extensively in the Intergovernmental Panel on Climate Change's (IPCC) regional and global assessments of climate change impacts on vegetation and in several other projects.
S-MODE NCOM Model Output Version 1
This dataset contains model output from the Navy Coastal Ocean Model (NCOM) run during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. NCOM model output consists of daily files during the deployment dates of the pilot campaign in Fall 2021, IOP1 in Fall 2022, and IOP2 in Spring 2023. Data consists of ocean variables such as salinity, sea water temperature, water depth, and surface wind stress, and are available in netCDF format.
Antarctic 1 km Digital Elevation Model (DEM) from Combined ERS-1 Radar and ICESat Laser Satellite Altimetry, Version 1
This data set provides a 1 km resolution Digital Elevation Model (DEM) of Antarctica. The DEM combines measurements from the European Remote Sensing Satellite-1 (ERS-1) Satellite Radar Altimeter (SRA) and the Ice, Cloud, and land Elevation Satellite (ICESat) Geosciences Laser Altimeter System (GLAS). The ERS-1 data are from two long repeat cycles of 168 days initiated in March 1994, and the GLAS data are from 20 February 2003 through 21 March 2008. The data set is approximately 240 MB comprised of two gridded binary files and two Environment for Visualizing Images (ENVI) header files viewable using ENVI or other similar software packages. The data are available via FTP.
Antarctic 5-km Digital Elevation Model from ERS-1 Altimetry, Version 1
This data set provides a Digital Elevation Model (DEM) for Antarctica to 81.5 degrees south latitude, at a resolution of 5 km. Approximately twenty million data points were used to generate this data set. Data points were derived from ERS-1 radar altimetry during the geodetic phase from March 1994 to May 1995.
GLAS/ICESat 500 m Laser Altimetry Digital Elevation Model of Antarctica, Version 1
The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and land Elevation Satellite (ICESat) provides global measurements of elevation, and repeats measurements along nearly-identical tracks; its primary mission is to measure changes in ice volume (mass balance) over time. This digital elevation model (DEM) of Antarctica is derived from GLAS/ICESat laser altimetry profile data and provides new surface elevation grids of the ice sheets and coastal areas, with greater latitudinal extent and fewer slope-related effects than radar altimetry.This DEM is generated from the first seven operational periods (from February 2003 through June 2005) of the GLAS instrument. It is provided on polar stereographic grids at 500 m grid spacing. The grid covers all of Antarctica north of 86° S. Elevations are reported as centimeters above the datums, relative to both the WGS 84 ellipsoid and the EGM96 geoid, in two separate elevation data files. A data quality map of the interpolation distance is distributed in addition to the elevation data. ENVI header files are also provided.The data are in 4-byte (long) signed integer binary files (big endian byte order) and are available via FTP.
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 1 and the representative concentration pathway (RCP) used was RCP 2.6. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Incremental Model Transformations with Triple Graph Grammars for Multi-version Models Evaluation Data
<p>Java abstract syntax graphs for two software development projects in multi-version model and snapshot encoding.</p>
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1) - MONICA model source code and data
<p>Zip file consisting of the data used in the manuscript "Evaluation and optimisation of the soil carbon turnover routine in the MONICA model".<br> The data consists of 11 German long term experimenting field sites, formatted in MONICA readable input files. Each consisting of one dataset for weather information (.met), and three .json files describing the management, site and soil properties of each treatment. The validation datasets consist of soil temperature and soil moisture measurement and are distinguishable by the .vals file type/format.</p> <p>For further information please refer to the manuscript.</p>
Transcultural Validation of the French Version of the " Patients' Endorsement of a Biopsychosocial Model of Chronic Pain " Scale
ClinicalTrials.gov study NCT06698406. IPD Sharing: NO. Countries: 1. Publications: 0.
Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
This data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.
ScienceDex guides
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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.