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Dataset results
1,028 results for “simulation model”
Micelle size screening - yFis1 simulation - 50 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Micelle size screening - eYqjD simulation - 50 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Micelle size screening - Magaining 2 simulation - 50 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Micelle size screening - Magaining 2 simulation - 45 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Micelle size screening - eYqjD simulation - 45 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Micelle size screening - Magaining 2 simulation - 40 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Earth system model simulation results (1980-2014) for snow analysis
<p>This dataset contains monthly output in 1984-2014 from simulations using E3SM. To extract them, you should first collect the files together and run <code>zip -F sd_simulation_output_sliced.zip --out sd_simulation_output.zip, then unzip sd_simulation_output.zip</code>.</p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p> <p>Please cite the paper as follows:</p> <p>Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118</p> <p> </p>
Model Simulation Results
<p>mHM model runs</p>
Evolutionary adaptation of trees and modelled future larch forest extent in Siberia. Code and simulation data
<p>Code and datset used for the publication: "Evolutionary adaptation of trees and modelled future larch forest extent in Siberia" 2023 Gloy et al.</p>
An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation (dataset)
<p>5 subjects (4 PD and 1 ET) IMU and sEMG raw data supporting the conclusions of the article "An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation"</p> <p>name standard:<br>pacientnumber_age_sex_disease_arm_timesincediagnose.mat</p> <p>Xs, Xs1 ... Xs17 are the time vectors for sEMG and IMU. </p> <p> </p> <p><strong><em>If you use any part of this data for your research, please cite our paper:</em></strong></p> <pre>@article{pinheiro2024opensim, title={An OpenSim-based closed-loop biomechanical wrist model for subject-specific pathological tremor simulation}, author={Pinheiro, Wellington C and Ferraz, Henrique B and Castro, Maria Claudia F and Menegaldo, Luciano L}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, year={2024}, publisher={IEEE} }</pre> <p> </p> <p>Contact: wellington@peb.ufrj.br</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high-resolution simulations with the Weather Research and Forecasting Model
<p>This archive includes data and ipython notebooks to create the figures for the manuscript "Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high-resolution simulations with the Weather Research and Forecasting Model" submitted to Journal of Climate in August 2022.</p> <p>Model output from WRFwiso and iCAM is in data.zip (saved as monthly means)</p> <p>Notebooks and python modules are in scripts.zip</p> <p>Required python packages (all included in environment.yml):</p> <ul> <li>numpy</li> <li>matplotlib</li> <li>netcdf4</li> <li>basemap</li> <li>scipy</li> <li>wrf-python</li> <li>windspharm</li> <li>metpy</li> <li>intergrid</li> <li>cmocean</li> </ul> <p>Version 1 is the original upload from the first submission.</p> <p>Version 2 includes small updates of the data and scripts from the revision.</p>
data shown in manuscript "WRF-Comfort: Simulating micro-scale variability of outdoor heat stress at the city scale with a mesoscale model"
<blockquote> <p>data shown in manuscript "WRF-Comfort: Simulating micro-scale variability of outdoor heat stress at the city scale with a mesoscale model"</p> </blockquote>
Anonymized data for paper "Automatic Bug Fixing in the Era of Large Language Models: Interactive Simulation of Programmer Behavior" submitted to ICSE 2024
<p>The project includes the BFP benchmark used in the submitted ICSE 2024 paper titled "Automatic Bug Fixing in the Era of Large Language Models: Interactive Simulation of Programmer Behavior"</p>
Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum
<p>Simulation data (TS, FSNT, and FLNT) and apap cloud feedback analysis for CESM2 LGM simulation</p> <p><strong>Simulation boundary condition files in 1-degree resolution: boundary_condition_files.zip</strong></p> <p><strong>Please cite: </strong></p> <p>Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., & DiNezio, P. (2021). Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum. <em>Geophysical Research Letters</em>, <em>n/a</em>(n/a), e2020GL091220. https://doi.org/10.1029/2020GL091220</p>
Dataset for the publication: Factors associated with risky drinking decisions in a virtual reality alcohol prevention simulation: A structural equation model
<p>This dataset includes the data from the publication <em>Factors associated with risky drinking decisions in a virtual reality alcohol prevention simulation: A structural equation model. </em>The first table sheet lists the data of the variables; the second table sheet describes the coding of the variables. </p>
Jet feature data from PAMIP model simulations
<p>Author: Yvonne Anderson</p> <p>Contact: ee22ya@leeds.ac.uk</p> <p>Dataset created: 21/08/2023</p> <p>Paper title: Minimal influence of future Arctic sea ice loss on North Atlantic jet stream morphology</p> <p> </p> <p><strong>Dataset information</strong></p> <p>CSV files contain arrays of daily jet feature data for all ensemble member winters for a given model.</p> <p>Dimensions of the arrays are (number of ensemble members, 90 winter days).</p> <p><strong>Filename structure</strong></p> <p>Filenames of CSV files can be interpreted as: timeperiod_jetfeature_model.csv</p> <p><strong>Example filename structure</strong></p> <table> <thead> <tr> <th scope="col">Time period</th> <th scope="col">Jet feature</th> <th scope="col">Model</th> <th scope="col">Example filename</th> </tr> </thead> <tbody> <tr> <td>Present-day</td> <td>Latitude</td> <td>AWI-CM-1-1-MR</td> <td>present-day_jet_latitude_AWI-CM-1-1-MR.csv</td> </tr> <tr> <td>Future</td> <td>Speed</td> <td>HadGEM3-GC31-MM</td> <td>future_jet_speed_HadGEM3-GC31-MM.csv</td> </tr> </tbody> </table> <p><strong>Jet feature description</strong></p> <p>Jet feature data are for the largest mass jet region found on each day of winter, where jet mass is the area weighted jet speed.</p> <p>The jet features and corresponding units contained in the csv files are as follows:</p> <table> <thead> <tr> <th scope="col">Jet feature</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <td>Latitude</td> <td>°</td> </tr> <tr> <td>Speed</td> <td>ms<sup>-1</sup></td> </tr> <tr> <td>Mass</td> <td>ms<sup>-1</sup></td> </tr> <tr> <td>Tilt</td> <td>°</td> </tr> <tr> <td>Area</td> <td>m<sup>2</sup></td> </tr> </tbody> </table> <p><strong>Time periods</strong></p> <p>Time periods are present-day and future, which refer to simulations forced by present-day and future sea ice concentrations, from which the jet features have been extracted.</p> <p><strong>Models</strong></p> <p>Models are AWI-CM-1-1-MR, CanESM5, FGOALS-f3-L, HadGEM3-GC31-MM, IPSL-CM6A-LR and MIROC6 from the Polar Amplification Model Intercomparison Project (PAMIP; https://doi.org/10.5194/gmd-12-1139-2019)</p> <p><strong>Spatial and temporal information</strong></p> <p>Arrays contain daily jet feature data that has been constrained to the North Atlantic region (0-60 &deg; W, 15-75 &deg; N) and to the winter period (December, January and February)</p> <p><strong>Prior processing</strong></p> <ul> <li>Original dataset: netcdf files of daily zonal wind data from Polar Amplification Model Intercomparison Project simulations forced by present-day and future sea ice concentrations</li> <li>850 hPa wind speed data was extracted and regridded to 2.81 ° x 2.81 ° resolution</li> <li>Constrained to North Atlantic region and winter period</li> <li>Wind speed data was filtered using a 10-day Lanczos filter with a 61 day window</li> <li>Jet feature data was extracted for each day in ensemble member winters and saved to numpy arrays</li> </ul> <p><strong>Example code for loading jet variables from csv file</strong></p> <p>To generate a numpy array of jet variable arrays contained in the csv file:</p> <pre><code class="language-python">loaded_jet_variable_arrays = np.genfromtxt((path_to_file/filename.csv'), delimiter=',')</code></pre> <p>To combine arrays for all ensemble member winters, which allows plotting of daily jet feature distributions:</p> <pre><code class="language-python">jet_variable_array_all_winters = np.concatenate(loaded_jet_variable_arrays)</code></pre> <p> </p> <p> </p>
Super-droplet simulations of a 1D rain shaft model
<p>Super-droplet simulations of a one-dimensional rain shaft model, generated by using the Python-based code PySDM v2.15. The updraft speed and the temperature profile are prescribed, as described in<br> "Shipway, B. J., & Hill, A. A. (2012). Diagnosis of systematic differences between multiple parametrizations of warm rain microphysics using a kinematic framework. Quarterly Journal of the Royal Meteorological Society, 138(669), 2196-2211".<br> The data were generated for five surface pressure levels, ranging from 988 hPa to 1012 hPa, with updraft amplitudes ranging from 1 to 4 m/s and an initial aerosol number density varying from 10 to 1000 per cubic centimeter.</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.