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695 results for “model output”
Processed model outputs for "South Asian summer monsoon enhanced by the uplift of Iranian Plateau in Middle Miocene"
<p>This dataset contains processed model outputs from modeling experiments performed in Zuo et al. (2024), including a set of 12 experiment with different CO2 concentrations and topography during the Middle Miocene. Due to space limitations, we provide the summer(JJA) mean climatology data for each experiment and some data that can be used to reproduce the figures in this paper. The raw data can be obtained by contacting the corresponding author.</p> <p><strong><span>Table 1. </span></strong><span>Simulations performed with CESM1.2 in this study.</span></p> <table> <tbody> <tr> <td> <p><span>experiment</span></p> </td> <td> <p><span>Geolography</span></p> </td> <td> <p><span>vegetation</span></p> </td> <td> <p><span>CO2</span></p> <p><span>(ppm)</span></p> </td> <td> <p><span>IP</span></p> </td> <td> <p><span>HM</span></p> </td> </tr> <tr> <td> <p><span>piControl</span></p> </td> <td> <p><span>Modern</span></p> </td> <td> <p><span>Modern</span></p> </td> <td> <p><span>280 </span></p> </td> <td> <p><span>Modern</span></p> </td> <td> <p><span>Modern</span></p> </td> </tr> <tr> <td> <p><span>MMIO</span></p> <p><span>(IP100HM80)</span></p> </td> <td> <p><span>M.Miocene*</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>400</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> </tr> <tr> <td> <p><span>IP0HM0</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>400</span></p> </td> <td> <p><span>0</span></p> </td> <td> <p><span>0</span></p> </td> </tr> <tr> <td> <p><span>IP50HM0</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>400</span></p> </td> <td> <p><span>50%</span></p> </td> <td> <p><span>0</span></p> </td> </tr> <tr> <td> <p><span>IP100HM0</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>400</span></p> </td> <td> <p><span>100%</span></p> </td> <td> <p><span>0</span></p> </td> </tr> <tr> <td> <p><span>IP0HM100</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>400</span></p> </td> <td> <p><span>0</span></p> </td> <td> <p><span>100%**</span></p> </td> </tr> <tr> <td> <p><span>IP50HM100</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>400</span></p> </td> <td> <p><span>50%</span></p> </td> <td> <p><span>100%</span></p> </td> </tr> <tr> <td> <p><span>IP100HM100</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>400</span></p> </td> <td> <p><span>100%</span></p> </td> <td> <p><span>100%</span></p> </td> </tr> <tr> <td> <p><span>MMIO280</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>280</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> </tr> <tr> <td> <p><span>MMIO560</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>560</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> </tr> <tr> <td> <p><span>MMIO800</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>800</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> </tr> <tr> <td> <p><span>MMIO1000</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> <td> <p><span>1000</span></p> </td> <td> <p><span>M. Miocene</span></p> </td> <td> <p><span>M.Miocene</span></p> </td> </tr> </tbody> </table> <p><span>*M.Miocen</span><span>e</span><span>: Middle Miocene</span></p> <p><span>** 100% of the height of modern HM.</span></p>
Outputs from fitted models across the cross-validation scenarios for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts and, code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains the outputs from fitted models across the cross-validation scenarios.</p> <p>In the folder <em>Ouputs_cross_validation</em>, each species is represented by a .RData file, numbered from 1 to 77 (excluding 7, which corresponds to <em>Bombycilla garrulus</em>; see the preprint for details). This dataset is specifically used to generate Figure 1, which shows the AUC of various cross-validation scenarios. To reproduce this figure in R, place all the files in the <em>Results/Fitted_models</em> folder and run the <em>Models_Outputs.R</em> script located in the <em>Results</em> folder of Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052.</a></p> <p>Note: These data have been separated due to memory requirements (23.14GB).</p>
DYAMOND-II Model Outputs for "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations"
<p>This dataset contains outputs from the DYAMOND Phase-II simulations, focusing on Global Storm-Resolving Models (GSRMs) at various Atmospheric Radiation Measurement (ARM) sites. The dataset supports the analysis presented in the manuscript "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations."</p> <p>Included are high-resolution model outputs from nine GSRMs, detailing simulations of atmospheric processes at six ARM sites, including variables of clouds, temperature, humidity, wind, and surface fluxes. This dataset allows for a direct comparison between GSRM simulations and ARM field observations, facilitating the evaluation of PBL-coupled and decoupled clouds. For further inquiries or assistance regarding the dataset, please contact the corresponding author at su10@llnl.gov.</p>
Model outputs used in "Drivers of cross-shelf exchange in the East Auckland Current system"
<p>These simulations were developed during the work published in in Santana et al. (2023) (https://gmd.copernicus.org/articles/16/3675/2023/). The DA run is refeered as ASFUVTS (7-day assimilation window) and the NoDA run is the non-assimilative run in Santana et al., (2023). The DA run assimilates observations presented and analysed in Santana et al. (2020) (https://www.nature.com/articles/s41598-021-89222-3).</p> <p>We used the Regional Ocean Modeling System (ROMS), a primitive-equation, hydrostatic, and free-surface ocean model that solves the Reynolds-averaged form of the Navier–Stokes equations. ROMS is a fully nonlinear, finite-difference model that uses terrain-following (sigma) vertical coordinates and horizontal orthogonal or curvilinear Arakawa C grid (Shchepetkin and McWilliams, 2003, 2005; Haidvogel et al., 2008). The model domain (290 x 150) is rotated 52.14° clockwise to better resolve the NZNES and spans 332 km offshore at the widest point (near North Cape). The domain has horizontal resolution of approximately 2 km, which roughly captures coastline variability and still resolves the continental shelf and slope without large computational cost. The model has 30 vertical sigma layers and model bathymetry was interpolated from the 250 m resolution bathymetric data set built by the National Institute of Water and Atmospheric Research (NIWA - https://niwa.co.nz/our-science/oceans/bathymetry). We use a vertical discretisation scheme that increases the resolution near the surface and bottom by applying stretching function type 4 and transformation equation option 2 (Shchepetkin and McWilliams, 2005, 2009). The vertical resolution is higher at the upper 200 m (from 4 to 30 layers). On the slope (depth<1000 m), the vertical resolution is higher than 66 m and in the open ocean the thickest level is 233 m (3838.6 m depth). Baroclinic modes are resolved using a time step of 180 s, while the barotropic time step is 6 s.</p> <p>Model surface forcing is from the Japanese atmospheric 55-year reanalysis for driving ocean models (JRA55-do, Tsujino et al., 2018). A previous study demonstrated that JRA55-do had the highest correlation with observed winds in comparison to other atmospheric forcing datasets in the Southwest Pacific (Taboada et al., 2019). Atmospheric forcing fields of wind speed, net shortwave radiation, downward longwave radiation, relative humidity, temperature, rain, and pressure are specified every 3 h and used to compute the surface fluxes of stress, heat and freshwater using the bulk flux parameterisation of Fairall et al., (2003). The model uses initial and boundary conditions of SSH, temperature, salinity, and velocities from HYCOM-NCODA (Chassignet et al., 2009) versions 91.1 and 91.2 which cover period of simulations generated here. de Souza et al., (2021) analysed the performance of four global reanalysis that assimilate SSH, SST and Argo data on the New Zealand waters. The authors found that HYCOM-NCODA (8 km resolution) had higher SSH and SST variability than low-resolution (25 km) satellite surface observations and other reanalyses with similar grid-spacing. HYCOM-NCODA had velocity standard deviation similar to that observed at M4 and M5, even though GLORYS (8 km global ocean reanalysis, Lellouche et al., 2018) better represented temperature and salinity profiles in the region. Annual average discharge from several rivers are included as lateral forcing in the model. The boundary forcing is applied daily using Chapman (1985) condition for free surface, Shchepetkin condition (Mason et al., 2010) for barotropic velocities and mixed radiation-nudging (Marchesiello et al., 2001) for baroclinic velocities, temperature and salinity. A 5-day nudging coefficient is applied towards the lateral boundaries. The model aims to simulate continental shelf, slope and rise regions, including the offshore extent of the EAuC and its eddy variability. This and the upcoming studies using the simulations analysed here focus on intra-annual variability and tides are not included as forcing.</p>
Model simulation output for New Configuration for Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model
<p>Simulation output from the new configuration model used in the manuscript Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model</p>
Raw model output presented and analyzed in: "Ancient Stratified Thermochemical Piles due to High Intrinsic Viscosity"
<h3>Summary</h3> <p>This archive contains the raw model output for all cases presented in the manuscript titled "Ancient Stratified Thermochemical Piles due to High Intrinsic Viscosity".</p> <p>The output files are archived in hdf5 file format. The content of these files (one for each case) is as follows:</p> <ul> <li>Temperature, viscosity and compositional fields, as well as the time-series of the lower heat-flux (from 4 billion years of model evolution onwards)</li> <li>Age field at 5 billion years of model evolution</li> <li>Time</li> </ul> <p>The file mesh.h5 contains the x and y meshes of the models (called 'xmesh' and 'ymesh', respectively).</p> <p>All data provided can be used to reproduce the results and figures in the manuscript, as detailed in the main and supplementary text. </p> <p> </p> <p> </p>
MOM6 Model Output from "Improved Upper Ocean Vertical Mixing in the Equatorial Oceans in NOAA/GFDL's OM4 Model"
<p>Datasets produced during the MOM6 experiments described by "Improved Upper Ocean Vertical Mixing in the Equatorial Oceans in NOAA/GFDL's OM4 Model".</p> <p>These datasets are needed to reproduce the figures in the version of this manuscript as submitted to ESS.</p> <p>The location of the notebooks for creating figures from these datasets is indicated in the manuscript.</p>
WRF Model Output for a Simulation of Hurricane Laura (2020), 2.25km grid, 2020-08-26-00Z through 2020-08-26-10Z
<p>This is WRF model output for a simulation of Hurricane Laura (2020). The tar file contains gzipped netcdf files each with 6 model outputs at 20 minute intervals.</p>
WRF Model Output for a Simulation of Hurricane Laura (2020), 2.25 km grid, 2020-08-26-12Z to 2020-08-26-22Z
<p>This is WRF model output for a simulation of Hurricane Laura (2020). The tar file contains gzipped netcdf files each with 6 model outputs at 20 minute intervals.</p>
Scherrenberg et al. (2024) supplement (Climate of the past): Ice-sheet model code, and output of Northern Hemisphere ice-sheet evolution of the past 800 kyr
<p>Supplement to Scherrenberg et al. (2024), article in Climate of the Past.</p> <p>This data-set contains ice-sheet model (IMAU-ICE) code (see IMAU_ICE_Code.zip; see https://github.com/IMAU-paleo/IMAU-ICE/tree/main for the most recent version of the model), the model output and configuration files (see Data_output.zip), and scripts to create figures (see Scripts_and_Figures.zip).</p> <p>Please note that additional input fields are required to run IMAU-ICE and to produce the figures. See Scherrenberg et al., (2024) for more information or contact the corresponding author.</p> <p>Citation: M.D.W. Scherrenberg, C.J. Berends, R.S.W. van de Wal: Late Pleistocene glacial terminations accelerated by proglacial lakes, climate of the past, special issue "icy landscapes of the past", 2024</p>
OpenFoam model output for "A Fuel Cell Power Supply System Equipped with Artificial Gill Membranes for Underwater Applications"
<p>Dataset of numerical experiments carried out with OpenFOAM v 10 as used in the manuscript "A Fuel Cell Power Supply System Equipped with Artificial Gill Membranes for Underwater Applications" by Lucas Merckelbach and Prokopios Georgopanos.</p> <p> </p>
Simulation data output used in Griffiths and Phelps lightning initiation model, revisited
<p>Simulation data output used in the paper titled "Griffiths and Phelps Lightning Initiation Model, Revisited" submitted for publication in JGR by A. Attanasio, P. R. Krehbiel, and C. L. da Silva.</p> <p>The model simulates the collective dynamics of a system of positive streamers using the framework first proposed by Griffiths and Phelps [1976]. The README.txt file contains details about the data structure.</p> <p>C. L. da Silva, Jan/29/2019</p>
Model outputs
<p>Model data for Fig.2-4</p>
Morphodynamic modeling of sediment pulse dynamics: Raw code and output
<p>Fortran source code and raw output files (.csv) for a 1D morphodynamic model simulating sediment pulse evolution.</p> <p>This material is provided as is, with absolutely no warranty expressed or implied. Any use is at your own risk.</p>
Supporting Model Code and Output for https://doi.org/10.5194/gmd-2019-78, 2019
<p>Model code and timeseries output for Travis et al. 2019 (GMD) https://doi.org/10.5194/gmd-2019-78, 2019.</p> <p> </p>
BCC-GEOS-Chem v1.0 model output (2012-2014)
<p>The file includes the model output of the BCC-GEOS-Chem v1.0.</p>
A Weighting Function Model for Unsteady Open Channel Friction: Raw code and output
<p>Matlab source code and Raw Origin Lab file (.opj) for a weighting function model simulating unsteady open channel friction slopes. This material is provided as is. Methods (formulas) and Figures are explained further in the related article "A Weighting Function Model for Unsteady Open Channel Friction" by Junwei Zhou; Weimin Bao; Geoffrey R. Tick; Qing Cao; and Fanghong Ye.</p>
PACMAN-P Model Outputs for TRAPPIST-1b and e
Open the record for dataset details and reuse information.
STIM model output for "Aurorally-driven Supersonic Gravity Waves in Saturn's Atmosphere"
<p>Saturn Thermosphere-Ionosphere Model (STIM) output corresponding to the figures shown in the paper "Aurorally-driven Supersonic Gravity Waves in Saturn's Atmosphere".</p>
Simulation output of the reference setup in "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" contains the simulation output of the 20 reference runs. Additional data is available upon request from the corresponding author.</p>
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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.