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1,028 results for “simulation model”
Stellar models with calibrated convection and temperature stratification from 3D hydrodynamics simulations
<p>MESA T-tau data file associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.478.5650M/abstract">Stellar models with calibrated convection and temperature stratification from 3D hydrodynamics simulations</a></p>
Novel representation of leaf phenology improves simulation of Amazonian evergreen forest photosynthesis in a land surface model
<p>This dataset contains the LAI, Litterfall and GPP etc. of the four Amazon FLUX sites (BR-Sa1, BR-Sa3, BR-Ma2 and GF-Guy) simulated using the improved ORCHIDEE model and the corresponding FLUXNET eddy-covariance or ground-measured data. The more detial please the ReadMe.pdf in the zip.</p> <p>Data is organized with netCDF4(.nc).</p> <p><br> If you want to know more detail please contact: chenxzh73@mail.sysu.edu.cn</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>
Simulation data and code for "Modeling radiation belt dynamics using a positivity-preserving finite volume method on general meshes"
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Future forest flux (e.g., GPP, NPP, NEP) simulated by the optimized InTEC model under four SSP-RCP scenarios
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Simulated of PRACE Using a Transparent Uterine Cavity Model
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Models, scripts, simulated data, and results from the article "Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework."
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Intercomparison of Two Model Climates Simulated by a Unified Weather-Climate Model System (GRIST)
<p>Part I and other scripts.</p>
DATA for Evaluating Scale-Aware Boundary Layer Similarity Functions and Their Mechanisms in Tropical Cyclone Modeling Using Idealized Large-Eddy Simulations
<p>data.xlsx has the data to make Figs.1-4</p> <p> </p>
Anatomical models and scripts for conducting simulations of conduction delays in the ventricular conduction system in human post myocardial infarction using MonoAlg3D
<p>The repository contains:<br><br>1. Video example (example_LBBB.mp4). Simulation of accelerating sinus rhythm (120 to 171 bpm) in myocardial infarction (MI) and left bundle branch block (LBBB) conditions leading to arrhythmia.</p> <p>2. Collection of anatomical models (ventricles and conduction system) used to create the population of models in the study. It includes variability in the infarct size (none, small, large) and multiple conduction delay conditions (LBBB, RBBB and left ventricle) in different sizes, locations and combinations. Models can be visualised in Paraview using the script paraview-alg-plugin.py. The file simulation_plan.xlsx provides further details on the population of models.</p> <p>3. Simulation scripts, defining the scenario and model conditions of the simulations are described in these files, to be used in MonoAlg3D (<span><a href="https://github.com/LLRiebel/MonoAlg3D_C-2023">https://github.com/LLRiebel/MonoAlg3D_C-2023</a></span>). </p>
Initial conditions and output of IndividualDisplacements.jl simulation on the ECCO4 model grid
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Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
Introduction The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. Methods:1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. Results:The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Conclusions: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
Data from: Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees
Stochastic simulation studies of animal breeding have mostly relied on either the infinitesimal genetic model or finite polygenic models. In this study, we investigated the long-term effects of the chosen model on honeybee breeding schemes. We implemented the infinitesimal model, as well as finite locus models, with 200 and 400 gene loci and simulated populations of 300 and 1000 colonies per year over the course of 100 years. The selection was of a directly and maternally influenced trait with maternal heritability of h²_m = 0.42, direct heritability of h² d = 0.27, and a negative correlation between the effects of r_md = −0.18. Another set of simulations was run with parameters h²_m = 0.53, h²_d = 0.34, and r_md = −0.53. All models showed similar behavior for the first 20 years. Throughout the study, we observed a higher genetic gain in the direct than in the maternal effects and a smaller gain with a stronger negative covariance. In thelong-term, however, only the infinitesimal model predicted sustainable linear genetic progress, while the finite locus models showed sublinear behavior and, after 100 years, only reached between 58% and 62% of the mean breeding values in the infinitesimal model. While the infinitesimal model suggested a reduction of genetic variance by 33% to 49% after 100 years, the finite locus models saw a more drastic loss of 76% to 92%. When designing sustainable breeding strategies, one should, therefore, not blindly trust the infinitesimal model as the predictions may be overly optimistic. Instead, the more conservative choice of the finite locus model should be favored.
Data and Codes for Evaluating and Improving Scale-Awareness of a Convective Parameterization Closure Using Cloud-Resolving Model Simulations of Convection
<p>Provide necessary fields averaged over different subdomain sizes from 64 km to 4 km (see 64 to 4 .7z files) processed from the output of CRM simulation of MC3E case (for TWP-ICE case, please get the processed data and associated codes from http://doi.org/10.5281/zenodo.4542461). Also, associated codes for calculation of important fields (like dCAPEls, dCAPEe, Msa and so on) are also provided in code.7z. Please see all "note.txt" files in code.7z to know how to use these codes.</p>
Codes and datasets associated with the paper "Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework"
<p>Here, you will find some of the codes, images, and datasets utilized in the article: </p> <p>Basu (2017). "Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework", Optical Engineering, 56(7), 071505 (https://doi.org/10.1117/1.OE.56.7.071505)</p> <p>WRF codes: namelist.wps, namelist.input, myoutfields.txt</p> <p>NCL codes: d02_terrain.ncl, wrf_SurfaceASTD_d02.ncl</p> <p>RADAR loop: KGRR.gif</p> <p>MATLAB codes: Plot_Buoy.m</p> <p>Note: buoy datasets are available publicly from https://www.ndbc.noaa.gov/ </p>
Simulations of Typhoon In-Fa (2106) and air-sea interactions using a coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system
<p>The Observation data supporting the result of our manucript submitted to JGR-Ocean.</p> <p> </p>
Shallow Convection Datasets Simulated by Different Large Eddy Models
<p>The datasets contain four shallow convection cases (RICO, BOMEX, ATEX, and ARM-SGP) simulated by three large eddy models (SAM, WRF, UCLA-LES). For each convection case, there are nineteen two-dimensional variables, including winds, temperature, and humidity. Three types of plumes are provided, i.e., convective core refers to rising plumes that have positive buoyancy and contain condensed water, and convective updraft defined as plumes containing liquid water and upward vertical velocity, and cloud defined as grid points containing liquid water. Conditionally sampled variables such as in-cloud temperature, moisture and vertical velocity are also provided, which are indispensable for calculating entrainment rate. These datasets will be used as a reference to help users verify and improve parameterization schemes of shallow convection.</p> <p>Table 2 List of the LES output variables</p> <table align="center"> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Long name</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Zonal wind</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>V</p> </td> <td> <p>Meridional wind</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p>Vertical velocity</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>W2</p> </td> <td> <p>Variance of vertical velocity</p> </td> <td> <p>m<sup>2</sup>/s<sup>2</sup></p> </td> </tr> <tr> <td> <p>QT</p> </td> <td> <p>Total water</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>QV</p> </td> <td> <p>Water vapor</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>QC</p> </td> <td> <p>Cloud condensate</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>P</p> </td> <td> <p>Pressure</p> </td> <td> <p>hPa</p> </td> </tr> <tr> <td> <p>Z</p> </td> <td> <p>Height</p> </td> <td> <p>m</p> </td> </tr> <tr> <td> <p>A_cor</p> </td> <td> <p>Core fraction</p> </td> <td> <p>100%</p> </td> </tr> <tr> <td> <p>A_upd</p> </td> <td> <p>Updraft fraction</p> </td> <td> <p>100%</p> </td> </tr> <tr> <td> <p>A_cld</p> </td> <td> <p>Cloud fraction</p> </td> <td> <p>100%</p> </td> </tr> <tr> <td> <p>QT_cor</p> </td> <td> <p>Mean qt in core</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>QT_upd</p> </td> <td> <p>Mean qt in updraft</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>QT_cld</p> </td> <td> <p>Mean qt in cloud</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>W_cor</p> </td> <td> <p>Mean w in core</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>W_upd</p> </td> <td> <p>Mean w in updraft</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>W_cld</p> </td> <td> <p>Mean w in cloud</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>QTFLUX</p> </td> <td> <p>Total water flux</p> </td> <td> <p>W/m<sup>2</sup></p> </td> </tr> </tbody> </table>
Open Research Data for "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields"
<p>The (1) NOAA GFDL T-SHiELD outputs, (2) processed ASOS data, and (3) observation-based, theory-driven wind profiles data used in the manuscript "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields". </p>
Model simulations utilizing the latest urban underlying surface and anthropogenic heat data
<p>Based on numerical simulations utilizing the latest urban underlying surface and anthropogenic heat data over the Yangtze River Delta urban agglomeration, we find that LU change and AH emission can result in opposite effects on summer precipitation. The related model simulations are included in this dataset.</p>
CTIPe model simulations
<p>Simulation output such as atomic oxygen to molecular nitrogen ratio (O/N2), peak electron density (NmF2), and total electron content (TEC) from the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model for 2019-2021. The data files are in support of the publication “Ionospheric response to solar EUV radiation variations using GOLD observations and the CTIPe model”</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.