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1,028 results for “simulation model”
Ice-sheet model simulation ensembles (produced Fall 2018)
<p>Ice-sheet model simulation ensembles over the last interglacial and a future high emissions scenario (RCP8.5), and varied over two model parameters (CREVLIQ and CLIFVMAX). The data was pickled as a pandas dataframe with python 3, and can be retrieved by loading using pickle with the same version.</p>
IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations: Boundary conditions and outputs.
<p>Inputs, boundary conditions, and ouputs files of the experiments described in Sepulchre et al. manuscript "<em>IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations</em>" submitted for publication to Geoscientific Model Development:</p> <p><a href="https://www.geosci-model-dev-discuss.net/gmd-2019-332"><strong>https://www.geosci-model-dev-discuss.net/gmd-2019-332</strong></a></p> <p>The 90Ma_IPSLCM5A2_inputs.tar tarball contains the input and boundary files used to run the 3,000-year Cretaceous experiment.</p> <p>The output_files.tar tarball contains the netcdf output files of the preindustrial, historical and Cretaceous simulations analyzed in the manuscript. Diagnoses are presented through a Jupyter notebook that can be retrieved and played interactively <strong><a href="https://doi.org/10.5281/zenodo.3549652"><strong>here</strong></a>.</strong></p> <p><strong> </strong></p>
Datasets for the publication "Simulation of snow management in Alpine ski resorts using three different snow models"
<p>Snow model simulation results used for the paper "Simulation of snow management in Alpine ski resorts using three different snow models".</p> <p>The following results are available for each of the nine ski resorts:</p> <ul> <li><em><resort></em>_swe_<em><date></em>.tif or <em><resort></em>_swe.geojson: Spatially distributed SWE outputs for 2016-12-24 and 2017-12-24 in GeoJSON (for the two French resorts) or GeoTIFF (for all other resorts) format. Unit: kg m<sup>-2</sup></li> <li><em><resort></em>_point_depth.csv, <em><resort></em>_point_swe.csv: Time series of snow depth (in m) and SWE (in kg m<sup>-2</sup>) for the respective points of interest. The column names correspond to the snow management configurations as shown in Fig. 5 of the paper.</li> </ul>
Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations
<p>This dataset contains NetCDF files necessary to replicate results from the 2024 paper "<em>Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations</em>"</p> <p>The dataset contains NetCDF files with 1 year of zonally-averaged NASA Ames Mars Global Climate Model (MGCM) fields with 5-sol binning for each of the simulations presented in the paper: </p> <ul> <li>a "low-resolution" simulation with no parameterization for gravity waves</li> <li>a "high-resolution" simulation with no parameterization for gravity waves</li> <li>a "low-resolution" simulation with parameterizations for orographic and non-orographic gravity waves</li> </ul> <p>Also included are:</p> <ul> <li>a file describing the coordinates for the MGCM's vertical grids used in the study </li> <li> atmospheric fields not provided in the other NetCDF files and necessary to replicate figures 3 and supplemental figure FS2 from the paper.</li> <li>a README.txt detailing the content of each file in the dataset</li> </ul>
Eutrophication indicators in the Baltic Sea 1970-2100 and nutrient loads to three coastal systems. BALTSEM model simulations and observations.
<p>Dataset and model code accompanying manuscript: </p> <p>Ehrnsten, E. Humborg C., Gustafsson, E. and Gustafsson B. G. 2024. Disaster avoided: current state of the Baltic Sea without human intervention to reduce nutrient loads. Resubmitted to Limnology & Oceanography Letters 2024-09-13. </p> <p> </p> <p>This repository contains the following files:</p> <p> </p> <p>1_Data_description.pdf</p> <p>Description of data sets and details on model forcing and data collection methods.</p> <p> </p> <p>Eutrophication_indicators1970-2021_BALTSEM_and_observations.xlsx</p> <p>Eutrophication indicators in the Baltic Sea: BALTSEM model simulation output from real load and no reduction scenarios as well as observations 1970-2021.</p> <p> </p> <p>BALTSEM_output_future_1970-2100.xlsx</p> <p>BALTSEM model simulation output 1970-2021 with observed nutrient loads (Real loads scenario) and statistics of 100 model runs 2022-2100 with present (2021) nutrient loads. The 100 runs represent statistical variations in forcing and boundary conditions to account for uncertainty in future weather and sea level conditions.</p> <p> </p> <p>NPloads_BS_M_C.xlsx</p> <p>Nitrogen and phosphorus loads from the Baltic Sea, Mississippi and Changjiang catchments 1950-2021 collected from several published sources.</p> <p> </p> <p>baltsem9.5_carbon.tar.gz</p> <p>Copressed folder with model code for BALTSEM 9.5 as well as forcing data used in the simulations. Information on folder contents and a user guide to run the model simuations can be found in the file BALTSEMGettingStartedCarbon.pdf</p>
MITgcm simulations of sea level response to freshwater injected at the surface and at depth in southern high latitudes: Model output and analysis code
<p>Model output (netcdf) and python code (included in both py and ipynb formats) to create the figures in Eisenman et al. (2024).</p> <div> <p>See https://eisenman-group.github.io for further details.</p> </div>
Dataset from the Towing Tank Test of the B.I.O. Hespérides ship model, conducted at the CEHINAV Towing Tank, in the presence of simulated broken ice
<p>This data set presents a collection of measurements carried out at CEHINAV Towing Tank. The colletion includes several rar files described below:</p> <ul> <li><strong>Towing_test.rar</strong>: Raw data (ASCII files) containing the following information: <ul> <li>Time (Chanel 1)</li> <li>Velocity (Chanel 2)</li> <li>Resistance (Chanel 4)</li> <li>Laser position bow (Chanel 5)</li> <li>Laser position stern (Chanel 6)</li> <li>Laser position longitudinal (Chanel 7)</li> <li>Absolute carriage position (Chanel 8)</li> </ul> </li> <li><strong>Towed_self_propulsion</strong>: Raw data (ASCII files) containing the followig information: <ul> <li>Time (Chanel 1)</li> <li>Velocity (Chanel 2)</li> <li>RPM engine (Chanel 3)</li> <li>Resistance (Chanel 4)</li> <li>Laser position bow (Chanel 5)</li> <li>Laser position stern (Chanel 6)</li> <li>Laser position longitudinal (Chanel 7)</li> <li>Thrust (Chanel 8)</li> <li>Torque (Chanel 9)</li> <li>Absolute carriage position (Chanel 11)</li> </ul> </li> <li><strong>Figures_coverage</strong>: PNG files with images of block coverage tested and files containing the relative position during the test.</li> <li><strong>HESPERIDES_model.msd</strong>: Maxurf file with the geometry of the model tested.</li> </ul> <p> </p> <p>The tests were conducted in february and november of 2020.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part06
<p>This dataset is part six of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part05
<p>This dataset is part five of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part02
<p>This dataset is part two of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part03
<p>This dataset is part three of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part04
<p>This dataset is part one of the? FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-Part01
<p>This dataset is part one of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
SIMULATION of: A Unified Model for WaveParticle Duality and Quantum Entanglement.pdf
<p>This Google Colab notebook contains the simulations used to validate the Fasano Symbiosis model, as presented in the paper "A Unified Model for Wave-Particle Duality and Quantum Entanglement." The simulations explore how entangled particles interact and propagate information through their combined wave function. The notebook provides an interactive environment where users can run the simulations and explore the impact of different energy interactions on particle wavelengths, quantum entanglement, and wave function collapse. The results from these simulations align with the theoretical predictions discussed in the paper and offer insights into quantum communication and cryptography.</p>
Simulated semivisible jets of the "Aachen" model
<p>Dataset published with "<a href="https://arxiv.org/abs/2312.03067">Semi-visible jets, energy-based models, and self-supervision</a>", Favaro L. et al.</p> <p>It consist of a QCD dijets background file "qcd_constit.h5" and a signal file "aachen_constit.h5". The signal model has been proposed in "<a href="https://arxiv.org/abs/1907.04346">Strongly interacting dark sectors in the early Universe and at the LHC through a simplified portal</a>", Bernreuther E. et al. </p> <p>The signal model has a dark mediator with mass m_Z'=2TeV and dark quarks with mass m_qd=500MeV charged under dark SU(3)_d. The hadronization of the dark sector consist of dark pions with mass m_pi=4GeV and dark rhos with mass m_rho=5GeV.</p> <p>Simulation:</p> <ul> <li>Madgraph + Pythia + Delphes simulation;</li> <li>(E, px, py, pz) of the first 200 constituents, pT ordered;</li> <li>most jets have less than 70 constituents, the remaining ones are zero-padded;</li> <li>Fastjet reconstructed fat jets using anti-kt algorithm with R=0.8;</li> <li>kinematic cuts: 100<pT<300, |\eta<2| ;</li> <li>semivisible jets are matched to the parton level dark quark;</li> <li>pheno parameters: invisible fraction r_inv=0.75, m_pi=4GeV, m_rho=5GeV.</li> </ul> <p>Other publications which use this dataset:</p> <p>[<a href="https://arxiv.org/abs/2206.14225">1</a>] "A Normalized Autoencoder for LHC triggers", Dillon B. et al., arXiv:2206.14225</p> <p>[<a href="https://arxiv.org/abs/2202.00686">2</a>] "What's anomalous in LHC jets", Buss T. et al., arXiv:2202.00686</p>
Data for "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model"
<p>Data to accompany:</p> <p>O’Neill, J.F., Edwards, T.L., Martin, D.F., Shafer, C., Cornford, S.L., Seroussi, H.L., Nowicki, S., Adhikari, M., Gregoire, L.J.. (2024). "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere". <em>The Cryosphere</em>. DOI: 10.5194/egusphere-2024-441 (preprint)</p> <p>Zipped directories called ismip6_<em>expname</em>_8km containing NetCDFs of output data from each experiment, on an 8 km EPSG3031 polar stereographic common grid for ISMIP6. Variable names are the same as those used for ISMIP6 i.e: land ice mass (lim), land ice mass above floatation (limnsw), floating area (iareaf), grounded area (iareag), thickness (lithk), x component of mean velocity (xvelmean), y component of mean velocity (yvelmean), basal mass flux (libmassbffl), acabf (surface mass balance), sftflf (floating ice mask), sftgrf (grounded ice mask), sftgif (ice mask), dlithkdt (ice thickness imbalance), base (elevation at base of ice sheet) and orog (surface elevation of ice sheet). These latter two are only included for the experiments plotted in Figure 11 in "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere".</p> <p> </p> <p>Also included are csv data for summary variables, masked regionally, and by sectors detailed in the main paper. Please contact J ONeill with any questions or requests. </p>
Ocean model simulations in cold-water coral ecosystems off the coasts of Angola and Namibia in the Southeast Atlantic: Setup, boundary conditions and model results.
<p>The dataset contains all essential data for the setup of high-resolution local area model implementations using the ROMS-AGRIF model version 3.1 in two cold-water coral regions off the coasts of Angola and Namibia in the Southeast Atlantic. The data include computational grids, initialization fields (temperature, salinity), and boundary conditions (temperature, salinity, currents, and sea surface height) for each model area. It also includes model output, which has been used in different studies of the local oceanography of the region .</p> <p>Initialization, forcing and output data for each ROMS-AGRIF model implementation are provided in two compressed archive data files:</p> <ul> <li>Angola Margin: Angola_Model_Setup1.7z</li> <li>Namibia Margin: Namibia_Model_Setup1.7z</li> </ul> <p>The data set description is provided in the file:</p> <ul> <li>DataSet_Description_Angola_Namibia_Model.pdf</li> </ul> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Simulated datasets from modelling demographic events and migration patterns
<p>These are datasets generated from multi-state model (MSM) project on understanding demographic events and migration patterns in two urban slums of Nairobi City in Kenya at the African Population and Health Research Center (APHRC). The project focuses on using MSM techniques to analyze residence demographic events in Nairobi urban slums, with an emphasis on key events such as:</p> <ul> <li>Births</li> <li>Deaths</li> <li>Migration (in-migration and out-migration)</li> <li>Changes in residence status (exit and entry)</li> </ul> <p>The primary aim of these datasets is to allow those who want to understand and model the demographic transitions in Nairobi's informal settlements, identifying factors that influence residence changes over time. </p>
Occupant Simulation Data based on Honda Accord 2024 Simplified Passenger Model and Full-factorial Sampling with 243 samples and VIRTHUMAN 5, 50, 95 Percentiles
<p>Database with 729 Honda Accord 2014 passenger occupant simulations featuring VIRTHUMAN. </p>
North Atlantic average sea-surface temperature in a CMIP6 multi-model ensemble of historical and ssp585/ssp245 simulations
<p>This dataset contains North Atlantic average sea-surface temperatures and derived indices calculated for a multi-model ensemble of historical and future scenario (ssp585, ssp245) simulations contributing to the Coupled Model Intercomparison Project phase 6. A detailed description of the dataset is provided by Zanchettin, D., and Rubino, A., Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability, Communications Earth & Environment, 2024, doi:10.1038/s43247-024-01804-x.</p> <p><br>The data are provided as netcdf files.</p> <p>The name of each file is structured as {model}_r{realization}_historical_{scenario}.nc where {model} is the model name, {realization} is a number corresponding to the historical realization, and {scenario} is either of the two scenarios considered (ssp585 and ssp245).</p> <p>Each file contains data for the following one dimensional variables:</p> <ul> <li>year: the sequence of years for which the data are provided</li> <li>NASST: annual-average spatially averaged North Atlantic sea-surface temperature</li> <li>state: slowly variable component of NASST obtained from a dlm decomposition of NASST</li> <li>strend: stochastic trend of NASST obtained from a dlm decomposition of NASST</li> <li>AMV: Atlantic Multidecadal Varibility index obtained as difference between NASST and state</li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
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.