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695 results for “model output”
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: output files (1 of 2)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes daily-mean output of all (ocean circulation, sea ice, and biogeochemistry) modules for September 2013, when the simulation was initialized. Similar files for January 2015 are available at <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample input files for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a>, <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a>, and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p>
Model input data, simulation output data and processed data presented in "Modelling the three-dimensional, diagnostic anisotropy field of an ice rise"
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WRF model output used in an ideal model experiment demonstrating the applicability of a channel-synthesizing method for reducing uncertainties in satellite radiance transfer modeling
<p>This repository stores the WRF model output used in the paper "A novel channel-synthesizing method for reducing uncertainties in satellite radiance transfer modeling" submitted to Geophysical Research Letters.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from PROMET rice simulations
This data set contains output data from simulations with the model PROMET 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, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the ERA-Interim (Dee et al. 2011) 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').
InVEST model inputs and key outputs for the SESMO journal manuscript "Where should livestock graze?"
<p>Archives of InVEST model data related to optimization of livestock- and water-related ecosystem services. (SESMO journal manuscript "Where should livestock graze?")</p> <p>1. InVEST inputs: Includes all the inputs to run the open source models InVEST (Seasonal Water Yield and Sediment Delivery Ratio)</p> <p>2. InVEST marginal values: key outputs derived from the InVEST models that are used in the optimizer (cf. manuscript). Includes baseflow marginal values (MV_SWY) and sediment marginal values (MV_SDR)</p> <p>3. InVEST outputs figures: key outputs derived from the InVEST models and optimizer that were used to create the figures in the manuscript</p>
Model outputs: Quantitative assessment of fire and vegetation properties in historical simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project
<p>This dataset contains the fire model outputs of various variables used in the initial FireMIP benchmarking paper.</p>
MODFLOW model source code, files, and output
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Experimental moisture content and water saturation and modeled output NAPL-phase saturation
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Output of all the models tested on the real time jiggled seismic events
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Model files and output seismograms for Seismic Wavefield Modelling of Enceladus: challenges and opportunities presented by a 3D ice shell (Dapré & Irving)
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Initial conditions and output of IndividualDisplacements.jl simulation on the ECCO4 model grid
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Support Data Operational Efficiency of Pharmaceutical Companies in China Based on Three Stage DEA with Undesirable Outputs Model.
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Model outputs of ENETWILD density model for wild boar based on hunting yield data. August 2021
<p>These maps are models obtained from density hunting yield data of the ENETWILD project based on available raw data. These are frequently updated in order to improve results.</p> <p>Objectives:<br> - To evaluate whether an approach based on density data is capable to correct overpredictions of previous reports for high-resolution predicted patterns when raw data are collected at different spatial resolution.<br> - Downscaling to 10x10 km grid >>> file " sp_DensityModel_10km_20210621_MESS_crs3035.tif"<br> - Downscaling to 2x2 km grid >>> file "sp_DensityModel_2km_20210621_MESS_MaxPred50_crs3035.tif”<br> <br> Model settings and predictors: <br> - Assuming cells as municipality in 10x10 km grid downscaling.<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling.</p> <p>Conclusions guiding future methodological steps:<br> - To explore approaches to manage spatial autocorrelation at European scale to improve the predictive performance of the results<br> - To compile complete data for each country for modeling temporal dimension of wild boar patterns.</p> <p>For further details and methodological approach see the report:</p> <p>ENETWILD-consortium, S. Illanas, S. Croft, G. C. Smith, J. Fernández-López, J. Vicente, J. A. Blanco-Aguiar, R. Pascual-Rico, M. Scandura, M. Apollonio, E. Ferroglio, O. Keuling, S. Zanet, F. Brivio, T. Podgorski, K. Plis, R. C. Soriguer, P. Acevedo. Update of model for wild boar abundance based on hunting yield and first models based on occurrence for wild ruminants at European scale. EFSA supporting publication 2021: EN-6825. 30 pp. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.2903%2Fsp.efsa.2021.EN-6825&data=04%7C01%7C%7C371a629e88d2444ab27f08d966f4158f%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637654020801719420%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=4XGKExGdoJYsJ%2B8xvdTrVxVzQHPRN8Aem%2BMX7zSd8f4%3D&reserved=0">https://doi.org/10.2903/sp.efsa.2021.EN-6825</a></p>
Model code and outputs of "Modeling the Dispersal of the San Francisco Bay Plume over the Northern and Central California Shelf"
<p>- "model_code.rar" contains the model source code.</p> <p>- "model_grid.nc" contains the grid file.</p> <p>- "model_outputs.nc" contains the model outputs.</p>
Model code and output data for Tang et al. 2022 (doi:10.3389/fenvs.2022.1013875)
<p>flex_extract.tar: The code used to prepare inputdata for flexpart and flexdust.</p> <p>FLEXDUST_default_20210301-20210331.nc: The output of default FLEXDUST.</p> <p>FLEXDUST_updated_20210311-20210320.nc: The output of updated FLEXDUST.</p> <p>flexpart_1h_default.nc: The output of forward FLEXPART simulation using default FLEXDUST.</p> <p>flexpart_1h_erode_topoTc_true_moreveg0.1_76FVThresh_noblock_KOK_AN05.nc: The output of forward FLEXPART simulation using updated FLEXDUST.</p>
Empirical Models output - RECCAP2 Program
<p>This data set contains the Empirical models' output data provided under RECCAP2 Program</p>
model output
<p>model output-NOSIP case</p>
Model output after parameter optimization for NEMO4.0-SI3
<p>Using the default configuration of NEMO4.0-SI3 (version 4.0.7 revision 15731), the 2003-2009 seasonal mean Antarctic sea ice concentration budget was obtained after adjusting the following three parameters: rn_pstar=3.298e+04, rn_cio=0.006094, Cd_ice=0.0009563.</p> <p>Both files are in npz format and can be read using python. ''Benchmark_budget_1to12_NEMO4-SI3-optim.npz'' file contains 4 fields: 'dadt_seasonal', 'adv_seasonal', 'div_seasonal' and 'res_seasonal', each of size [145, 145, 4], corresponding to lon, lat and season. The meaning of each field can be found in Nie et al. (2023), noting that here is the seasonal average for the years 2003-2009.</p> <p>The ''Klonlat.npz'' file contains 3 fields, 'Klon' (longitude), 'Klat' (latitude) and 'K_A' (grid area), all of size [145, 145].</p>
STET modeling output files for 2017 Sept solar flare study at Mars
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Selected modeled output variables from ROMS 4DVAR model fits for the P1706 and P1604 CCE-LTER process cruises
Values for Kz and w were extracted from a three-dimensional physical model (9 km horizontal resolution, 42 vertical layers) developed using the Regional Ocean Modeling System (ROMS) four-dimensional variational data assimilation system (4DVAR) (Moore et al., 2011), then depth-interpolated linearly to calculate values at the MLD. Initial and boundary conditions were taken from the California Current System Reanalysis product produced by the University of California, Santa Cruz. Surface forcing was taken from the 9-km resolution Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) (Doyle, Jiang, Chao, & Farrara, 2009; Hodur, 1997). We assimilated satellite remote sensing products (sea surface height from Ssalto/Duacs and sea surface temperature from AVHRR) and in situ temperature and salinity data vertical profiles collected during CTD measurements on our cruise. Assimilated data were used to update model initial conditions and surface forcing to generate a dynamically-consistent month-long model fit covering the duration of each cruise and producing 8-hr averaged outputs. For detailed descriptions of our ROMS 4DVAR approach, see (Song et al., 2012) and (Miller, Song, & Subramanian, 2015). Kz and w were interpolated to CTD cast locations from the 9-km ROMS 4DVAR solution using bilinear 2D interpolation from the four points comprising the nearest neighbors to the cast coordinates.
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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.