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1,028 results for “simulation model”
FESOM model data used in a study on simulated signatures of Greenland melting in the North Atlantic
<p>FESOM (v1.4) model data used in the paper 'Simulated signatures of Greenland melting in the North Atlantic: a model comparison with Argo floats, satellite observations, and ocean reanalysis’ (submitted to JGR Oceans for review) by Stolzenberger et al.</p> <p>The data set consists of monthly simulated potential temperature, salinity and sea surface elevation fields for high and low resolution, including (GF) and excluding (NGF) Greenland freshwater forcing, for the North Atlantic (NA, 50°N-86°N), and for the time period 1993-2016.</p> <p>Furthermore, steric height changes from the inversion output is available as time series for the time period 2002-2016.</p>
Simulated dataset of dryland vegetation model
<p>This file contains simulated output from a drylands vegetation model used to produce figs 3, S3, S4 and S6 in the paper "Remotely-sensed slowing down in spatially patterned dryland ecosystems" authored by Michiel P. Veldhuis, Ricardo Martinez-Garcia, Vincent Deblauwe, Robert M. Pringle, Corina E. Tarnita & Vasilis Dakos </p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>7_experiments.zip contains modified model code and output data of each experiment in this study.</li> <li>off-line test.zip contains off-line test code and output data.</li> <li>plot_scripts.zip are the NCL scripts used for figures in the paper.</li> </ul>
Data from: Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography
<p>This dataset contains GPS coordinates of occurrences from 22 species from Sulawesi, Indonesia. It was used in the manuscript "Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography" to demonstrate the utility of the fauxcurrence R package (<a href="https://github.com/ogosborne/fauxcurrence">https://github.com/ogosborne/fauxcurrence)</a>.</p>
Mars ISSM simulations model files 1
<p>These are the simulation model files for:</p> <ul> <li>'MARS.DEPO_00011': for DEPO_00008-00011 simulation</li> <li>'MARS.DEPO_00012': for DEPO_00012 simulation</li> <li>'MARS.DEPO_00018': for DEPO_00017-00018 simulation</li> <li>'MARS.DEPO_00019': for DEPO_00019 simulation</li> <li>'MARS.DEPO_00024': for DEPO_00020-00024 simulation</li> <li>'MARS.DEPO_00026': for DEPO_00025-00026 simulation</li> <li>'MARS.DEPO_00036': for DEPO_00036 simulation</li> <li>'MARS.DEPO_00037': for DEPO_00037 simulation</li> <li>'MARS.DEPO_00038': for DEPO_00038 simulation</li> </ul>
Mars ISSM simulations model files 2
<p>These are the simulation model files for:</p> <ul> <li>'MARS.DEPO_00011': for DEPO_00008-00011 simulation</li> <li>'MARS.DEPO_00012': for DEPO_00012 simulation</li> <li>'MARS.DEPO_00018': for DEPO_00017-00018 simulation</li> <li>'MARS.DEPO_00019': for DEPO_00019 simulation</li> <li>'MARS.DEPO_00024': for DEPO_00020-00024 simulation</li> <li>'MARS.DEPO_00026': for DEPO_00025-00026 simulation</li> <li>'MARS.DEPO_00036': for DEPO_00036 simulation</li> <li>'MARS.DEPO_00037': for DEPO_00037 simulation</li> <li>'MARS.DEPO_00038': for DEPO_00038 simulation</li> </ul>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>Figs&Table are the NCL scripts used for figures and table in the paper.</li> <li>Model_Results contains output data of each experiment in this study.</li> <li>Mods_Scripts contains modified model code.</li> <li>Offline_Code contains off-line test code.</li> </ul>
Data for modeling and simulation of the Beijing-Shijiazhuang section of the South-to-North Water Diversion Project
<p>Data we used to calculate and analysis the main contribution in our manuscript of Digital Twin for large-scale water diversion projects.</p>
Simulation results of the technology diffusion model for electrolysis capacity
<p>This upload contains the pre-run simulation output of the technology diffusion model for the article:</p> <p>Odenweller, A., Ueckerdt, F., Nemet, G. F., Jensterle, M., and Luderer, G.: "Probabilistic feasibility space of scaling up green hydrogen supply".</p> <p>You may use the files to run the model code (available on GitHub) in reproduction mode, which reproduces all figures of the article without having to run the time-consuming simulation yourself. All files are provided in the rds format.</p> <p>Contents of this upload:</p> <ul> <li>01_conventional_growth_parameters.rds - Parameters of the distributions in the conventional growth case</li> <li>02_conventional_growth_sample.rds - Sample of the distributions in the conventional growth case</li> <li>03_conventional_growth_results.rds - Simulation results of model in the conventional growth case</li> <li>04_unconventional_growth_parameters.rds - Parameters of the distributions in the unconventional growth case</li> <li>05_unconventional_growth_sample.rds - Sample of the distributions in the unconventional growth case</li> <li>06_unconventional_growth_results.rds - Simulation results of model in the unconventional growth case</li> </ul>
Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning
<p>Data and code underlying the findings reported in the paper titled "Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning."</p>
Model outputs and observation data for "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)"
<p>Model outputs and observation data for paper "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)".</p>
Model data and namelists for Sterzinger et al. (2022) - "Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations"
<p>Model data and namelists for "<a href="https://acp.copernicus.org/preprints/acp-2022-36/">Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations</a>"</p> <p>Horizontally averaged data is provided in NetCDF4 files (oliktok.nc, ascos.nc, summit.nc) for all output variables. Horizontally averaged vertical momentum flux is provided in a separate file for each simulation (*_vert_momentum_flux.nc files).</p> <p>Info on variables is provided by the RAMS model variable guide PDF <a href="https://vandenheever.atmos.colostate.edu/vdhpage/rams/docs/RAMS-VariableList.pdf">available here</a>.</p> <p>Model namelists are provided for each simulation (*_RAMSIN files). ASCOS initialization sounding info is provided within the ASCOS_RAMSIN file - initialization soundings are provided in SOUND_IN files.</p>
Tool and python programs for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model"
<p>Here is the content:</p> <p> * file dir_list which contains information about each file's content</p> <p> * the tool is used to alter a data file by keeping a specific number of significant digits for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model'</p> <p> * pythons program and its associated data to create each figure and table in the paper (data for Table 07 is not included due to size is larger than 50GB)</p>
Massive He star progenitor models for CCSN simulations
<p>He star progenitor models calculated by K. Takahashi.<br> Reference: K. Takahashi, T. Takiwaki, and T. Yoshida, 2022, submitted.</p>
Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model
<p>This dataset includes a complete set of raw data, metadata and saved session data which is necessary for re-producing figures in a paper entitled "Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model" submitted to the Journal of Geophysical Research - Atmosphere.</p>
Data for "Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation"
<p>The datasets for the manuscript "Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation".</p> <p>model: WRF3.1.1</p> <p>location: Southern Great Plains</p> <p>time: from 2100 UTC 23 May to 0600 UTC 24 May</p> <p>time interval: 6 minutes</p> <p>domain size: 512km x 512km</p> <p>vertical layer: 500hpa</p> <p>variables: P, PB, PH, PHB, U, V, W, T, QCLOUD, QICE, QVAPOR</p> <p>calculated data: mse, up_only(only consider updraft), up_down(consider both updrafts and downdrafts)</p> <p> </p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Article Data
<p>NetCDF datatset of presented results from the publication titled "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model" in the Journal of Geophysical Research - Atmospheres, Paper #2021JD036214R.</p>
Bering10K BEST_NPZ ROMS model: Carbon dioxide removal simulation output
<p>This dataset accompanies our manuscript:</p> <p>Wang H, Pilcher DJ, Kearney KA, Cross JN, Shugart OM, Eisaman MD, Carter BR. Simulated impact of ocean alkalinity enhancement on atmospheric CO2 removal in the Bering Sea. Submitted to Earth's Future, in review</p> <p>The dataset includes selected output variables (temperature, salinity, and carbonate system variables) from two 10-year simulations of the Bering10K ROMS application with BEST_NPZ biogeochemistry. The simulations represent a control run and a point source alkalinity enhancement; see paper for further details. The source code used to run this this simulation is available on Github (https://github.com/beringnpz/roms-bering-sea) and archived at DOI: 10.5281/zenodo.7062782.</p>
Model output dataset used in "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China"
<p>This dataset accompanies the paper by Zhang et al. "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China".</p> <p>Three heavy precipitation events were modeled using the WRF v3.9 model:</p> <p>(1) The_21_July_Beijing_Rainstorm_Simulation<br> (2) The_30_July_Ningxia_rainstorm_Simulation<br> (3) The_19_June_Jiangxi_rainstorm_Simulation</p> <p>Furthermore, three cases were designed for each heavy precipitation event: (1) control experiment (DEFAULT), using the default M-O option (<em>C<sub>zil</sub></em> ~ 0); (2) constant <em>C<sub>zil</sub></em> (CZIL0.01, CZIL0.05, CZIL0.1, CZIL0.3, CZIL0.5 and CZIL0.8), with <em>C<sub>zil</sub></em> values of 0.01, 0.05, 0.1, 0.3, 0.5, and 0.8; (3) a dynamic canopy-height dependent <em>C<sub>zil</sub></em> (NEWCZIL).</p> <p>Plain Language Summary for this paper:<br> Over recent decades, the frequent occurrence of heavy precipitation events has caused devastating ecological and socioeconomic impacts, such as agriculture losses, infrastructure damage, and casualties. High-resolution atmospheric modeling at a convection-permitting grid spacing (≤4 km) provides valuable applications for predicting heavy precipitation. Precipitation can be strongly affected by the energy and moisture exchanges between land surface and atmosphere. However, the representation of land-atmosphere interactions in atmospheric models and the responses of precipitation to land-atmosphere exchange efficiency remain great uncertainties. This study performed 3-km high-resolution atmospheric modeling with a dynamic vegetation-type-dependent land-atmosphere exchange scheme for three typical heavy precipitation events that occurred over areas with different dominant land-cover types. The results showed that land-atmosphere exchange efficiency mainly affected the precipitation intensity as well as the onset and peak time of precipitation. The dynamic exchange scheme modifies the efficiency of land-atmosphere exchanges to match local land cover conditions and could reproduce well the field observations, especially the intensity and location of the heaviest rainfall which usually serve as the most concerned variable in a major rainstorm event. Our findings show that the dynamical scheme could help achieve more accurate precipitation simulations.</p>
Model simulations for " Potential impacts of LUCC and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China"
<p>Experiment_1.rar, Experiment_2.rar, and Experiment_3.rar are the model simulations from experiment 1, experiment 2, and experiment 3, respectively. All the simulations are original from the CLM5 model in netcdf format.</p> <p>More details on these data can be found in the paper "Potential impacts of LUCC and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China". </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.