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695 results for “model output”
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from CARAIB spring wheat simulations
<p>This data set contains output data from simulations with the model CARAIB for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE winter wheat simulations
This data set contains output data from simulations with the model APSIM-UGOE for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE spring wheat simulations
This data set contains output data from simulations with the model APSIM-UGOE for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES soybean simulations
This data set contains output data from simulations with the model JULES for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES rice simulations
This data set contains output data from simulations with the model JULES for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE rice simulations
This data set contains output data from simulations with the model APSIM-UGOE for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES spring wheat simulations
<p>This data set contains output data from simulations with the model JULES for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').</p>
Model output: Unraveling the mechanisms that cause cyclic channel-shoal dynamics of ebb-tidal deltas: a numerical modeling study
<p>Output from model runs for Lenstra et al., “Unraveling the mechanisms that cause cyclic channel-shoal dynamics of ebb-tidal deltas: a modeling study”. The dataset contains two types of model output, namely (1) the default model runs and (2) the sensitity runs with waves+tides, waves only, and tides only.</p> <p>The files covering the default model runs contain:</p> <p>ModeledDays - time vector for the depth series [days]</p> <p>XT - east-west location of grid points high resolution domains [m]</p> <p>YT - north-south location of grid points high resolution domains [m]</p> <p>depth - depth series for the high resolution domains [m]</p> <p>XSea - east-west location of grid points outer sea domain [m]</p> <p>YSea - north-south location of grid points outer sea domain [m]</p> <p>depthSea - depth series for the outer sea domain [m]</p> <p>XBasin - east-west location of grid points basin domain [m]</p> <p>YBasin - north-south location of grid points basin domain [m]</p> <p>depthBasin - depth series for the basin domain [m]</p> <p>ST_Days - time vector for the sediment transport series [days]</p> <p>ST_Inlet - tidally-averaged total sediment transport through the inlet (positive seaward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>ST_Up - tidally-averaged total sediment transport through the cross-section at the updrift coast (positive eastward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>ST_Down - tidally-averaged total sediment transport through the cross-section at the downdrif coast (positive eastward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>The files covering the sensitivity runs contain for the high resolution domains:</p> <p>XT - east-west location of grid points [m]</p> <p>YT - north-south location of grid points [m]</p> <p>TA_STX - east-west component of the tidally-averaged total sediment transport [m<sup>3</sup>/s/m]</p> <p>TA_STY - north-south component of the tidally-averaged total sediment transport [m<sup>3</sup>/s/m]</p> <p>TA_U - east-west component of the tidally-averaged flow velocities [m/s]</p> <p>TA_V - north-south component of the tidally-averagedf low velocities [m/s]</p> <p>M2 - amplitude of the semi-diurnal tidal flow velocities (not for wave only runs) [m/s] </p>
3-km high resolution model outputs using the WRF and WRF-Hydro model for HRB
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Heihe river basin (HRB). Model results are used for investigating the effect of lateral terrestrial water flow on regional climate modeling. The analysis results were published in a peer-reviewed journal at https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018JD030174</p> <p>Two experiments use the following model configuration: 3km horizontal resolution with 350*350 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Leibniz Supercomputing Center (LRZ) SuperMUC. </p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrfout_selvar_P_ET_R_DRA" provides P, ET, surface runoff, drainage, and filename with "wrfout_selvar_T_Q2_SM_SH2O_AWS_CONV" provides T, specific humidity, soil moisture, and liquid water, atmosphere water storage and convergence. Tagged precipitation from upper HRB is also provided.</p>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
Numerical model code, input files and output data for publication "Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current"
<p>Contains numerical model data (code, input files, selected output, matlab diagnostic routines) to supplement publication ``Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current'', by Naveiro Garabato and co-authors. All numerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations, and reproduction of diagnostics shown in plots in the above-referenced paper.</p>
NEMURO- Model OUTPUTS
<p>The data sets provided are model outputs (NEMURO-North Pacific Ecosystem Model for Understanding Regional Oceanography) for central Arabian Sea. </p>
Model output: CICE experiments with varying floe and wave physics
<p>Model output from CICE experiments with varying floe size distribution and wave physics</p> <p> </p> <p>See manuscript below for further details:</p> <p>Roach, L., C. Bitz, C. Horvat, and S. Dean (2019), Advances in modelling interactions between sea ice and ocean surface waves. Journal of Advances in Modeling Earth Systems (in review)</p>
RNA Pol III input data and output models
<p>This repository contains the input experimental data used in a tutorial on modeling of RNA Polymerase III and the largest cluster of output models.</p>
Model outputs for "Multi-grid algorithm for passive tracer transport in NEMO ocean circulation model"
<p>Model outputs used to write "Multi-grid algorithm for passive tracer transport in NEMO ocean circulation model" publication.</p>
Model output data for Xueyi Jing et al., "A Scheme of Sea Surface Upward Longwave Radiation with Sea Spray Layer Effect"
<p>CESM Model output data used in "A Scheme of Sea Surface Upward Longwave Radiation with Sea Spray Layer Effect" .</p> <p> </p> <p>Seasonal files were derived from monthly outputs by CDO. "F2000_f09_CTL*******" are results for control run, "F2000_f09_LW_Test1*******" are results for sensitivity experiment. </p>
The netCDF output data of Parallel Princeton Ocean Model based on OpenACC
<p>This dataset represents the output results from the simulated seamount case, where the outputs vary depending on whether parallel (p) or serial (s) execution is used, as well as the different simulation durations and resolutions applied.</p>
Groundwater model outputs for coastal aquifer system
<p>Model outputs for 9 predevelopment coastal groundwater models, and one development scenario for each of these models. This data has been used to generate results for a manuscript which has been submitted for publication.</p>
CESM2 model output from Eisenman & Armour (2024): Part 3
<p>Description of data: See README.txt</p> <p>Citation for data: I. Eisenman and K. Armour (2024). The radiative feedback continuum from Snowball Earth to an ice-free hothouse. Nature Communications, https://doi.org/10.1038/s41467-024-50406-w</p> <p>See also https://eisenman-group.github.io for links to other fields from these simulations.</p>
CESM2 model output from Eisenman & Armour (2024): Part 2
<p>Description of data: See README.txt</p> <p>Citation for data: I. Eisenman and K. Armour (2024). The radiative feedback continuum from Snowball Earth to an ice-free hothouse. Nature Communications, https://doi.org/10.1038/s41467-024-50406-w</p> <p>See also https://eisenman-group.github.io for links to other fields from these simulations.</p>
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.