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1,028 results for “modelling & simulation”
Simulated results from an agent-based model examining inequality and innovation in social networks
<p>Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity, and agent behavior as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a group's ability to solve a difficult exploration task. We explore how size, connectivity, and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, population size has a positive effect on innovation, but that large and small populations perform similarly per capita; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation; and that the highest performing agents tend to occupy more central network positions. Moreover, we show that every network factor which facilitates innovation leads to a proportional increase in inequality of performance, creating "genius effects" among otherwise "dumb" agents in both idealized and real-world networks.</p>
Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations Figures
<p>Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations Figures</p>
NorSand4AI: A Comprehensive Triaxial Test Simulation Database for NorSand Constitutive Model Materials
<p>Dataset of the homonymous paper: a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests. Two datasets are provided: the first one considers a nested Latin Hypercube Sampling of input parameters encompassing 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160000 triaxial test results (NorSandTXL_H5.zip). The second one considers nested quasi-Monte Carlos sampling techniques (Sobol and Halton) of input parameters encompassing 2048 soil types, each subjected to 42 initial test configurations, resulting in a total of 172032 triaxial test results (NorSand_2048_42_Drained.zip and NorSand_2048_42_Undrained.zip). Each simulation dataset comprises a 4000 by 10 matrix that can be used for general multivariate forecasting benchmarks, in addition to direct geotechnical and soil science applications.</p>
Application of a Comprehensive Hygroscopicity Framework for Simulating Aerosol Water Uptake in a Large-scale Climate Model
<p>This dataset was generated from 18-month simulations of the NCAR Community Atmosphere Model (CAM); the atmospheric component of the Community Earth System Model version 2 (CESM2). The dataset can be used to reproduce the analysis and figures presented in the upcoming JGR publication.</p>
Simulation data for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly"
<p>This is the original simulation data sets for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly".</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>
Code and data for RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project
<p>Model configuration code and post-processed data for simulations with SAM6.11.2 (Khairoutdinov and Randall, 2003) and CAM6 (https://github.com/ESCOMP/CESM/releases/tag/release-cesm2.1.3) needed to reproduce figures in the protocol paper for RCEMIP-II (Wing et al., 2023):</p> <p>Wing, A. A., Silvers, L. G., and Reed, K. A.: RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-235, in review, 2023.</p> <p>SAM6.11.2 data (SAM6.11.2-lambda6000.zip and SAM6.11.2-lambda6144.zip):</p> <ul> <li>lambda6000: simulations with wavelength 6000 km</li> <li>lambda6144: simulations with wavelength 6144 km</li> <li>Each simulation, for a given mean SST ($SST) and delta SST ($dT) has the following data files <ul> <li>crh_avg_$SST_$dT.mat: column relative humidity averaged over the short (y) dimension, as a function of x and time.</li> <li>mockwalker2048x128x74_3km_12s_$SST_$dT.nc: domain-averaged 0D (function of t) and 1D (function of z and t) data <ul> <li>The "long" simulations, which have a domain that is twice as long as normal, instead have files with names mockwalker4096x128x74_3km_12s_$SST_$dT.nc</li> <li>The "wide" simulations, which have a domain that is twice as wide as normal, instead have files with names mockwalker2048x256x74_3km_12s_$SST_$dT.nc</li> <li>The "longwide" simulations, which have a domain that is twice as long and twice as wide as normal, instead have files with names mockwalker4096x256x74_3km_12s_$SST_$dT.nc</li> </ul> </li> <li>SAM_CRM_MW_$SST_$dT_1D_cldfrac_avg.nc: domain cloud fraction profile (function of z and t) following cfv2 definition of Stauffer and Wing (2022)</li> </ul> </li> </ul> <p>SAM6.11.2 configuration files (SAM6.11.2-lambda6000-config.zip and SAM6.11.2-lambda6144.zip):</p> <ul> <li>lambda6000: simulations with wavelength 6000 km</li> <li>lambda6144: simulations with wavelength 6144 km</li> <li>Each simulation, for a given mean SST and delta SST has the following configuration files <ul> <li>snd: Initial sounding</li> <li>prm: Namelist parameters</li> <li>grd: Vertical grid</li> <li>domain.f90: Domain size and number of subdomains</li> <li>simpleocean.f90: SST specification</li> </ul> </li> </ul> <p>CAM6 data (CAM6.zip):</p> <ul> <li>Each simulation, for a given mean SST ($SST) and delta SST ($dT) has the following data files <ul> <li>MockWalk54_humidity_HCF_$dT_$SST.nc: column relative humidity averaged over 4 longitude points, as a function of latitude and time.</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rlut_avg.nc: domain-averaged longwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rsut_avg.nc: domain-averaged upwelling shortwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rsdt_avg.nc: domain-averaged downwelling shortwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_1D_cldfrac_avg.nc: domain-averaged cloud fraction profile (function of z and t)</li> </ul> </li> </ul> <p>CAM6 configuration files (CAM6-MW295dT1p25-config.tar, CAM6-MW300dT1p25-config.tar, CAM6-MW305dT1p25-config.tar): Contains model initialization and configuration files for simulations with delta SST = 1.25 K. Simulations with other delta SST values need only change the delta SST parameter. </p>
Data and scripts for the submission "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models"
<p>Dataset and scripts used to generate Figures for "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models", submitted to the <strong><em>Journal of Advances in Modeling Earth Systems</em></strong> (JAMES).</p> <p>Scripts: Python and NCL</p> <p>Data: Netcdf, PNG, Python pickled objects</p>
Simulation data and surrogate model for the DTU 10MW reference wind turbine including down-regulation, power boosting and individual blade control
<p>This contribution provides the simulated data and surrogate models for the DTU 10 MW reference wind turbine in an onshore configuration simulated with FAST v8.16.00. The dimensions include mean wind speed, turbulence intensity, and power level, as well as the application of an individual blade control (IBC) loop. Down-regulation up to 50% is considered using two controller trajectories. The <em>constTSR</em> trajectory considers only pitching for down-regulation, maintaining a constant tip speed ratio, and the <em>lin70</em> trajectory considers both pitch and rotational speed reduction to achieve down-regulation. Power boosting is performed up to 130% power level by following the optimal Cp trajectory until the requested power level is reached.</p> <p>The regression is done with two methods: a spline-based interpolation and a Gaussian Process Regression (GPR). The raw data, smoothened data, and the trained GPR models are provided along with scripts for generating the surrogate model's predictions with both methods. A short description of the simulation parameters and variables considered is given in the supplementary pdf file.</p> <p>The dataset is part of the doctoral thesis 'Wind Turbine Operational Optimization Considering Revenue and Fatigue Objectives' by Vasilis Pettas at the University of Stuttgart (<a href="http://dx.doi.org/10.18419/opus-13959">http://dx.doi.org/10.18419/opus-13959</a>) and the journal publication 'Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities' <a href="https://doi.org/10.3390/en17061284">(https://doi.org/10.3390/en17061284</a>). Detailed analysis of the controller design and validation of the surrogate models can be found in these publications. </p>
Model America - Arizona extract from ORNL's AutoBEM - (Archetypes with simulation results)
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Two sets of data are provided for 2,555,153 buildings located within the boundary of Arizona in the United States:</p> <ol> <li><strong>Data (420KB *.csv) - Arizona 527 building archetypes based on different Climate Zones (2B-136, 3B-133, 4B-128, 5B-130) with simulation results.</strong></li> <li><strong>Models (38.8MB *.zip by Climate Zone) – OpenStudio and EnergyPlus building energy models named according to ID.</strong></li> </ol> <p> </p> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
Model America - Summer 2020 Arizona Building Energy Simulation Results from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Data is provided for 2,555,152 buildings located within the boundary of Arizona in the United States:</p> <p><strong>Data (1.48GB *.csv) - Arizona 2,555,152 building information data with simulation results separated by county. (Simulation results are for June 1st-August 31st, 2020)<br></strong></p> <p><strong>Building Information Data Fields:</strong></p> <ul> <li>ID</li> <li>CZ</li> <li>Centroid</li> <li>State_Abbr</li> <li>Footprint2D</li> <li>Height,Area2D</li> <li>BuildingType</li> <li>NumFloors</li> <li>Area</li> <li>Standard</li> <li>NumWalls</li> <li>WWR_surfaces</li> </ul> <p><strong>Energy Simulation Data Fields:</strong></p> <ul> <li>Electricity_Facility[kBTU]</li> <li>NaturalGas_Facility[kBTU]</li> <li>Heating_Electricity[kBTU]</li> <li>Cooling_Electricity[kBTU]</li> <li>Heating_NaturalGas[kBTU]</li> <li>Heating_Total[kBTU]</li> <li>WaterSystems_Electricity[kBTU]</li> <li>Lighting_Electricity[kBTU]</li> <li>Equipment_Electricity[kBTU]</li> <li>Fans_Electricity[kBTU]</li> <li>Pumps_Electricity[kBTU]</li> <li>HeatRejection_Electricity[kBTU]</li> <li>HeatRecovery_Electricity[kBTU]</li> <li>Surface_Outside_Face_Heat_Emission[GJ]</li> <li>Zone_Exfiltration_Heat_Loss[GJ]</li> <li>Zone_Exhaust_Air_Heat_Loss[GJ]</li> <li>Heat_Rejection_Energy[GJ]</li> <li>Anthropogenic_Emissions[GJ]</li> </ul> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"
<p>Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"</p>
Evaluation data for: Adaptive sampling by citizen scientists improves species distribution model performance: a simulation study
<p>All of the evaluation data for the simulations in the paper: Adaptive sampling by citizen scientists improves species distribution model performance: a simulation study. We considered the impact of five adaptive sampling methods on the performance of species distribution models (SDMs), please see the paper for more information. Contained in this repository are the evaluation metrics (AUC, mean square error (MSE) and correlation) for SDMs before and after adaptive sampling has taken place. The MSE and correlation evaluation metrics were calculated against the true distributions of the species. These files are those with "combined_outputs" in the titles. The repository also contains the observations of all the species in the simulations both before and after adaptive sampling (the files with "all_observations" in the title.</p> <p>These datasets are to be used with the plotting and evaluation scripts in the GitHub repository associated with the paper.</p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 3 and the representative concentration pathway (RCP) used was RCP 7.0. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Fig. 3 in The Challenges of Incorporating Realistic Simulations of Marine Protists in Biogeochemically Based Mathematical Models
Fig. 3. The six stages of selective grazing, redrawn from Montagnes et al. (2008b).
Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest
<p>Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30-year period that spanned repeated hurricanes. We used a non-linear matrix model of soil C pools and fluxes ("soilR") and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times and younger mean ages of soil C in the particulate, occluded, and mineral-associated soil organic matter pools at 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the thirty years. Between 1988 and 2018, the occluded C stock increased, and d<sup>13</sup>C in all pools decreased, while changes in particulate and mineral-associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris-deposited C, and possible microbial scavenging of old and young C in the particulate and mineral-associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, and eventually lead to net losses of C from tropical forest soils.</p>
Supporting Data for "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of Timothy M. Merlis, Kai-Yuan Cheng, Ilai Guendelman, Lucas Harris, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, Gabriel A. Vecchi, and Stephan Fueglistaler (2024): "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change".</p>
Simulation model of group polarization in emergencies
<p>In this study, the risk structure framework of online public opinion group polarization of public health emergencies in the ternary space is combined, as well as the causes and constraints of group polarization distributed in the same space. The number of online users with extreme opinions can indicate the risk of group polarization in accordance with the idea of phase change space. Moreover, a simulation model of group polarization of online public opinion of public health emergencies is developed. The system dynamics simulation of the model is carried out by using the software Anylogic and predicts the evolution trend of the model from the perspective of simulation. The research results indicate that the model built in this study can effectively simulate the formation and dissemination of extreme opinions in the online public opinion of public health emergencies, and the vital factors or constraints on the group polarization include emotional guidance, heat reduction, as well as and life pressure.</p>
Data associated to the manuscript "Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model</p> <p>Camille Bacon, Patrice Simon, Mathieu Salanne and Alessandra Serva</p> <p><em>ChemRxiv, </em>10.26434/chemrxiv-2024-9577m, 2024</p> <p>The folder <em>input_files</em> contains typical MetalWalls input files used to perform the simulations.</p> <p>The folder <em>raw_data</em> contains the processed data used to plot all the figures of the paper.</p>
Automatic message sequence chart creation from simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm
<p><span>These videos show the creation of two message sequence charts from simulation runs of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with three constituting processes. </span></p> <p><strong><span>Message Sequence Chart of <span> </span>Parametric Model With 3 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The number of constituting processes is parametric in the model and was set to three. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 3 Processes via Step-By-Step Simulation Run_SuppInfo.mp4:</span></strong><span> This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm in a step-by-step simulation run with our updated extension server of the CPN tools version 4.0.0. The number of constituting processes is parametric in the model and was set to three. We manually fired our selected enabled transition of the model to create this video. </span></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.