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210 results for “Energy modeling”
Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Energy Modelling for DRCONGO: Sand files
<p>Dataset used as part of the training organized in April 2023 in Windhoek (Namibia) by CCG (Climate Compatible Growth) in OSeMOSYS (open source energy modeling system) to run 3 scenarios for Democratic Republic of Congo.</p>
Structure Databases: Analysis and Augmentation of Guest-Host Interaction Energy Models as CHA and AEI Zeolite Crystallization Phase Predictors
<p>Structures and energies associated with the paper: Analysis and Augmentation of Guest-Host Interaction Energy Models as CHA and AEI Zeolite Crystallization Phase Predictors</p>
A prototyping software engineering approach for designing and implementing model-based cloud mobile application for rationalized energy consumption
<p>The dataset used in this study comprises four files containing household consumers' energy consumption records. These records are collected at hourly intervals, providing detailed information on the energy usage patterns of the households. The dataset serves as a valuable resource for analyzing energy consumption trends, developing energy management strategies, and exploring the potential for energy efficiency improvements in residential settings.</p> <p> </p> <p> </p> <p> </p> <p> </p>
SESMG Model Definitions: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the files is one SESMG model definition used for the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". Further information can be found in this publication. The file names indicate to which sensitivity analysis of the study the individual model definition belongs to. Used acronyms: "ng" = natural gas.</p>
SESMG Model Results: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the folders contains SESMG results for a sensitivity analysis of the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". More information can be found in this publication. Each folder contains two subfolders. The "cost-minimum" subfolder contains the results for financially optimized systems, and the "emission-minimum" subfolder contains the results for GHG emission-optimized systems. Within these subfolders, the results for different gradations of the respective sensitivity parameters are stored in separate sub-subfolders. The 01_reference_total_ghg_emissions folder has a slightly different structure. Since the results are not separated into financially and emissions-optimized scenarios, the results of different gradations are stored directly in the main folder of this sensitivity analysis.</p>
Replication Package for "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code"
<p>This repository contains the replication package for the paper <em>"</em>An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code" by Max Hort, Anastasiia Grishina, and Leon Moonen, accepted for publication in the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023).</p> <p>The paper is deposited on arXiv, will be available later at the publisher's site (<a href="https://ieeexplore.ieee.org/Xplore/home.jsp">IEEE</a>), and a copy is included in this repository.</p> <p>The replication package is archived on Zenodo with DOI: <a href="https://doi.org/10.5281/zenodo.8058667">10.5281/zenodo.8058667</a>. The data is distributed under the CC BY 4.0 license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you build on this data or code, please cite this work by referring to the paper:</p> <pre><code>@inproceedings{hort2023:sharing, title = {An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code}, author = {Max Hort and Anastasiia Grishina and Leon Moonen}, booktitle = {17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)}, year = {2023}, publisher = {IEEE} note = {To appear. Pre-print on arXiv.} }</code></pre>
Data and model code: Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050
<p>Dataset for <em>Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050</em></p>
Energy Balancing Modeling and Mobile Technology to Support e-Weight Loss
ClinicalTrials.gov study NCT02857595. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Energy benefits and emergent space use patterns of an empirically parameterized model of memory-based patch selection
Open the record for dataset details and reuse information.
Data from: Modeling central metabolism and energy biosynthesis across microbial life
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SLABCC: Total energy correction code for charged periodic slab models
<p>Test set for SLABCC (SLAB Charge Correction) including the input files, input parameters and the expected output.<br> The geometries correspond to the positively charged Cl-vacancy on the surface of the NaCl slab with different vacuum thickness. The compiler type/compilation flags, linked libraries, and the hardware architecture may influence the optimization results but the effects on the correction energies should be negligible.</p> <p>The latest version of SLABCC can be downloaded from <a href="https://github.com/MFTabriz/slabcc">https://github.com/MFTabriz/slabcc</a></p> <p> </p>
The potential of sector coupling in future European energy systems soft linking between the Dispa-SET and JRC-EU-TIMES models - Dataset
<p>Supporting dataset and Dispa-SET version used within "The potential of sector coupling in future European energy systems soft linking between the Dispa-SET and JRC-EU-TIMES models" paper.</p>
Data archive for paper "WRF‐TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model"
<p><strong>WRF-TEB data archive</strong></p> <p>This archive contains data and tools to reproduce results as included in <a href="https://doi.org/10.1029/2019ms001961">Meyer et al. (2020)</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li><a href="https://sylabs.io/">Singularity</a> version >= 3.</li> </ul> <p><strong>Usage</strong></p> <p>To run all models and plotting scripts included in integration test and meteorological evaluation, run the following command from your command-line interface.</p> <pre><code>NPROC=8 TYPE=evaluate tools/singularity/run.sh</code></pre> <p>where <code>NPROC=8</code> is the maximum number of processes to use. The output can be found in the <code>work/</code> folder.</p> <p><strong>HPC</strong></p> <p>If you want to use this in an HPC environment, use <code>tools/hpc</code> as a template. As an example, to run the evaluation on Imperial HPC using PBS (Portable Batch System), use:</p> <pre><code>qsub -v REPO_ROOT=$(pwd),TYPE=evaluate tools/hpc/job_imperial.sh</code></pre> <p><strong>Copyright and License</strong></p> <p>Copyright and licensing information are included at the top of source files or as separate files in folders.</p> <p><strong>References</strong></p> <p>Meyer, D., Schoetter, R., Riechert, M., Verrelle, A., Tewari, M., Dudhia, J., Masson, V., Reeuwijk, M., & Grimmond, S. (2020). WRF‐TEB: implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model. Journal of Advances in Modeling Earth Systems. <a href="https://doi.org/10.1029/2019ms001961">https://doi.org/10.1029/2019ms001961</a></p>
Supporting model data for paper: Measuring the impact of a new snow model using surface energy budget process relationships
<p>Supporting model data for paper: Measuring the impact of a new snow model using surface energy budget process relationships which has been submitted to the Journal of Advances in Modelling Earth Systems: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002144</p> <p>The experiment id h3hh corresponds to simulations with the ECMWF IFS with a single layer snow model. h3eg corresponds to the experimental 5-layer snow model.</p> <p>The timeseries are made by concatenating hourly data from day2 of forecasts initialised at 00UTC each day between Dec 1st 2013 and 1 June 2014.</p>
Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model - 1/2 degree WaveWatchIII configuration files
<p>This dataset contains the mesh and model configuration information for a WaveWatchIII run using a 1/2 degree structured grid.</p> <ul> <li>glo_30m.bot <ul> <li>Bottom depth file for a 1/2 degree structured grid</li> </ul> </li> <li>glo_30m.mask <ul> <li>Mask file for a 1/2 degree structured grid</li> </ul> </li> <li>obstructions_local.glo_30m.in <ul> <li>local obstructions file for use with UOST source term switch</li> </ul> </li> <li>obstructions_shadow.glo_30m.in <ul> <li>shadow obstructions file for use with UOST source term switch</li> </ul> </li> <li>ww3_grid.inp <ul> <li>Input file for the ww3_grid pre-processing program. This file specifies many of the model configuration settings.</li> </ul> </li> <li>ww3_shel.inp <ul> <li>Input file for the ww3_shel program.</li> </ul> </li> </ul>
Energy-dependent protein folding: modeling how a protein folding machine may work
<p>MD trajectories of all-atom MD simulations for peptides P1-P5. </p>
Model data for "Factors Modulating Variability of Eddy Kinetic Energy in the Southern Ocean from Idealized Simulations" "
<p>This dataset contains the all the idealized simulations with different topographic features.</p>
UPAYA MENINGKATKAN AKTIVITAS BELAJAR DAN HASIL BELAJAR PESERTA DIDIK KELAS VIII SMP NEGERI 1 SANANAMELALUI MODEL PEMBELAJARAN BERBASIS MASALAH PADA SUB POKOK USAHA DAN ENERGI
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Mechanical data of rotary shear experiments, temperature measurements, and temperature numerical models for the manuscript: "Mechanical energy dissipation during seismic dynamic weakening in calcite-bearing faults"
<p>All data included in this data repository is ancillary to the manuscript "Energy dissipation during dynamic weakening in calcite-bearing fault rocks", submitted to Journal of Geophysical Research: Solid Earth. </p><p>The data consists in time series of high velocity friction experiments run with SHIVA (INGV, Rome), time series acquired from a two-color pyrometer (UC3M), the synchronization of the two, and numerical models. The data format is .mat, proprietary to Matlab, but they can be easily accessed with Python (see <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">link</a>). Each .mat contains vector of the measured variables when opened from Matlab, or dictionaries when opened using the scipy.loadmat() function. </p><p>SHIVA and PYRO red data (calibrated data), fin data (synchronized data), and shivaRED vect data (numerical model data) are included in this data repository in separate folders. Numerical models are grouped in subfolder by type of model (the relation fin data to model is 1:n). We included the scripts to convert SHIVA raw data into SHIVA red data (<a href="https://github.com/aretu/shivaUNIX">link to shivaUNIX</a>), SHIVA and PYRO red data into fin data (/scripts/syncing2021.m), to obtain numerical models from fin data (<a href="https://github.com/aretu/shivaRED">link to shivaRED</a>), and to plot data (/scripts/making plots.ipynb).</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.