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87 results for “energy simulation”
Simulated heating energy demand for two residential neighbourhoods
<p>The large-scale and comprehensive artificial dataset introduced in this research reflects the energy demands of two neighbourhoods and with some reasonable limitations mimics monitoring campaigns otherwise collected on-site from buildings in use. The monitoring campaigns are created using white-box simulation models for single-family houses representing typical neighbourhoods in Flanders. The datasets are generated using Dymola and the IDEAS package embedded in TEASER. Each house varies in geometry, size, envelope properties, occupancy schedules, and installed gas heating systems. In this research, two datasets are created, one reflecting the properties of a low-performing building stock dating before the introduction of the EPBD (2006), and the other reflecting properties of a well-performing stock built after 2006. The envelope properties for older houses are allocated using EPC data grouped in four construction periods, while for newly built houses the properties are based on EPB reports, both were collected in Flanders. The datasets include heavy-weight houses in a detached, semi-detached, or terraced typology. Furthermore, the houses are simulated as one or two-zone buildings, depending on the number of floors which range from one to three floors. In the simulations, a natural infiltration model is implemented as well as a stochastic occupant behaviour model mimicking gains from occupants and appliances. Due to the complexity of the large-scale simulation, the heating system is post-processed in a data-driven approach and the heat source for both datasets are gas-fired heating systems. In total six system configurations are considered including condensing and non-condensing boilers with three types of domestic hot water (DHW) sub-systems (no integrated DHW, direct and with a storage tank). For all configurations, a variable production efficiency is considered dependent on the load ratio. The urban-scale simulation is carried out at a 10-minute frequency for the weather data assuming the location of Heverlee (Belgium) in the year 2016.<br>The original purpose of this dataset was the development of statistical tools for the assessment of the heat loss coefficient of the building fabric. However, the generated artificial datasets provide a large spectre of usually difficult-to-measure inputs suitable to assess the importance of different components in the overall energy balance. Even though the original work looked into individual building behaviour, the datasets can be also used from an urban perspective for energy planning purposes.</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Figures
<p>This entry contains the sources for the figures included in the body of the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes, as well as those found in the supplementary information.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1477004</p> <p> </p> <p> </p>
Generalised Oscillator Strengths for the simulation of EELS spectra, with a broader coverage of high energy and minor edges
<p>This deposit contains a tabulated set of generalised oscillator strengths, which are required to compute the double differential cross sections for the inelastic scattering of fast electrons by atoms, i.e. for the simulation of EELS spectra.</p> <p>These tabulated values are calculated self-consistently within the local density approximation using the exchange correlation potential after Perdew [1]. For this a modified version of a program by Hamann is used [2]. Using this atomic potential the wave function of the ejected free electron is calculated, which is normalised by matching it to spherical Bessel and Neumann functions at large distances from the core [3]. The remaining integral constitutes a spherical Bessel transform. Using the convolution theorem this integral is solved with the fast Fourier transformation routine as done in [4]. A further discussion is available along with the code (see below), or more in-depth (but in German) in the <a href="https://www.uni-muenster.de/imperia/md/content/physik_pi/kohl/abschlussarbeiten/lsegger-bsc-arbeit.pdf">Thesis of L. Segger</a>.</p> <p><strong>This updated version offered here greatly expands the number of available edges</strong>, but is otherwise identical to the earlier version uploaded at <a href="https://zenodo.org/record/6599071">https://zenodo.org/record/6599071</a>.</p> <p> </p> <p>The data offered here is in the GOSH file format, a file format developed for the distribution of such datasets. A description of the file format as used here is included in the file `gosh.md`, while an up to date version can be found at:</p> <p><a href="https://gitlab.com/gguzzina/gosh">https://gitlab.com/gguzzina/gosh</a></p> <p>The code used to compute the GOS is publicly available, along with a discussion of the approach and methods, at:</p> <p><a href="https://github.com/Br0Fi/goscalc">https://github.com/Br0Fi/goscalc</a></p>
Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic (data).
<p>Data for the "Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic".</p>
Supplementary material for "Song et al., Modelling Simul. Mater. Sci. Eng., 2021: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies"
<p>This zip archive contains supplementary material in the form of datasets and jupyter notebooks that are used in the following publication:</p> <ul> <li>authors: Hengxu Song, Nina Gunkelmann, Giacomo Po, and Stefan Sandfeld</li> <li>journal: Modelling Simul. Mater. Sci. Eng.</li> <li>year: 2021</li> <li>title: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies</li> </ul>
M2 internal tide modal energy terms from a global HYCOM simulation
<p>This data set contains modal energy terms from a forward global HYCOM simulation (22.1) with realistic tide and atmospheric forcing as discussed in <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a> (On the interplay between horizontal resolution and wave drag and their effect on tidal baroclinic mode waves in realistic global ocean simulations, 2020, MC Buijsman, GR Stephenson, JK Ansong, BK Arbic, JAM Green, ... Ocean Modelling 152, 101656). <strong>Please cite this article when using these data. </strong></p> <p>This is a 4-km simulation with 41 layers. All data is on the native tripole grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>The data set contains</p> <ol> <li>The time-mean and depth-integrated M2 mode 1-5 energy terms: x (eastward) and y (northward) fluxes, KE, APE, conversion, flux divergence, and the intermodal energy conversion (topographic mode coupling) term. The terms are computed for a two-week time series starting on GMT 01-Sep-2016 01:00:00. For details see the paper.</li> <li>Positive seafloor depth, and latitude and longitude coordinates</li> </ol> <p>The M2 mode-1 SSH of the same simulation can be found here: https://doi.org/10.5281/zenodo.5514226</p> <p><a href="https://sites.google.com/site/maartenbuijsman/">https://sites.google.com/site/maartenbuijsman/</a></p>
Demonstration Cases - Simulation data of energy consumption of residential building typologies
<p>The dataset is about the energy analysis for retrofit strategies of 5 building typologies and the EDEA project located in 3 climates zones in Europe: South (Madrid), Central (Berlin) and North (Helsinki).<br> The dataset includes:<br> (1) Open Document Spreadsheet (.ods) file with the results of Heating Consumption (kWh/m2·year) and Cooling Consumption (kWh/m2·year) for the five buildings, in three locations and for several scenarios:<br> - Locating external new insulation in walls and roof.<br> - Replacing Windows.<br> - Combination strategies: locating new insulation layers and replacing the existing windows.<br> - Installing solar protection devices.</p>
Binding Affinity Prediction Workflow - Simulation Input Files and Absolute Binding Free Energies
<p>The Binding Affinity Prediction (BAP) workflow calculates absolute binding free energies for protein-ligand complexes by taking their crystal structures, converting them into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks, and analysing the resulting trajectories with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the free-energy estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the BAP workflow was run on the PDBbind 2020 (http://www.pdbbind.org.cn/index.php) refined set. This entry contains the MD simulation input files (BAPSimulationInputFiles.tar.gz) and the ABFE estimates (BAPBindingFreeEnergyEstimates.csv) obtained from four 250 ns trajectories for each complex. The MD simulations for more than 4000 complexes were run on the Leonardo supercomputer while the implicit-solvent calculations were carried out on Galileo, both operated by Cineca (Italy). The MD trajectories will be stored at Cineca for approx. 1 year after publication of this entry; contact Cineca's user support if you are interested in the trajectories.</p> <p>The README file describes how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Binding-Affinity-Prediction-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
The role of particle, energy and momentum losses in 1D simulations of divertor detachment
<p>Source code, inputs, simulation outputs, analysis scripts and figures used in the paper</p> <p>"The role of particle, energy and momentum losses in 1D simulations of divertor detachment" by B D Dudson, J Allen, T Body, B Chapman, C Lau, L Townley, D Moulton, J Harrison, B Lipschultz.</p> <p>Questions to benjamin.dudson@york.ac.uk .</p> <p>This version is before submission to journal.</p> <p> </p> <p> </p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</p>
Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions
<p>This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions."</p> <p>The dataset is composed of the following files:</p> <p>* pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space</p> <p>* s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above</p> <p>* view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view"</p> <p>* s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue.</p> <p>* s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above.</p> <p>* viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg"</p> <p>* 0readme -- brief instructions on how to view the trajectories</p> <p>* colors.vmd -- utility script for VMD</p> <p>* covmacros.vmd -- VMD script to define coronavirus spike subdomains</p> <p>* fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles</p> <p>* diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations</p> <p>* pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes</p> <p>Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br> </p>
Datasets for "Hydrodynamic and hydromagnetic energy spectra from large eddy simulations"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Hydrodynamic and hydromagnetic energy spectra from large eddy simulations" Haugen & Brandenburg. If anything turns our to be incomplete, please email brandenb@nordita.org.</pre>
Intelligent Energy Systems Ontology: Local flexibility market and power system co-simulation demonstration
<p>The Intelligent Energy Systems Ontology (IESO) provides semantic interoperability within a society of multi-agent systems (MAS) developed in the scope of power and energy systems (PES). It leverages the knowledge from existing and publicly available semantic models developed for specific PES subdomains to accomplish a shared vocabulary among the agents of the MAS community, overcoming heterogeneity among the reused ontologies. IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through the simulation of the management of a rural distribution network, considering the validation of the grid’s technical constraints. This dataset publishes files demonstrating: i) a snapshot of the initial semantic knowledge base (KB); ii) queries to the KB to get services inputs; iii) conversions between syntactic and semantic models; <br> iv) constraints validations; v) automatic conversion of units of measure.</p>
Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for orbital and ionization energies) results discussed in the paper titled "Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations" by Richard Asamoah Opoku, Céline Toubin, and André Severo Pereira Gomes.</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for ionization energies) results discussed in the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes.</p> <p>In each archive file there is a README explaining how to use the bundled scripts to process the data.</p>
Brazil - Future weather files for building energy simulation
<p>This dataset contains future weather files for energy simulation of buildings of the state capitals of Brazil plus the Federal District. The weather data is provided in EPW format, commonly used in <a href="https://energyplus.net/">EnergyPlus inputs</a>. The periods of 2010, 2050, and 2090 are considered for generating the Typical Meteorological Years (TMY) for each location. Additionally, different climate model projections were used to enable an analysis of uncertainties in the simulation results. <strong>In this updated version, we have included the complete individual years used to develop the TMYs. This data is provided in CSV format and has been bias-corrected.</strong></p> <p>The future climate data was derived from various regional climate model projections from the <a href="https://cordex.org/">Coordinated Regional Downscaling Experiment (CORDEX) project</a>. These projections are part of the CORDEX-CORE experiment, which includes three GCMs (HadGEM2, MPI-ESM, and NorESM1) as driving models, and two nested RCMs (regcm and remo) for dynamical downscaling, totaling an ensemble of six members at a spatial resolution of approximately 25km. The <a href="https://doi.org/10.1007/s10584-011-0148-z">representative concentration pathways </a>RCP 8.5 and RCP 2.6 were the available scenarios for the region, and both were considered for developing the weather files. The future climatic variable values were interpolated and formatted to an hourly resolution, commonly used in building energy simulation tools. Subsequently, different bias correction methods were applied to specific climate variables based on historical weather series. These data series consist of hourly data measured at weather stations located in each city between the years 2001 and 2021. The historical series files were made available by Dru Crawley and Linda Lawrie and served as the basis for developing the TMYx weather files available on the <a href="https://climate.onebuilding.org/">OneClimate Building website</a>.</p> <p>It is crucial to recognize that the historical data is based on measurements taken at airports, which are often situated far from urban centers. As a result, urban overheating was not factored into these developed weather files. It is also important to note that the developed weather files represent a typical meteorological year for each period, and do not include the most extreme periods, such as heatwaves and atypical summers.</p> <p>The weather files were developed specifically for use with the EnergyPlus engine. Therefore, climatic variables not used by the engine (e.g., precipitation and ceiling height), even if available in EPW format, should not be considered for other studies.</p> <p>It is important to emphasize the higher internal operative temperature results obtained when using the REGCM model compared to the REMO model in Building Energy Simulations. <strong>Caution is recommended when using files developed using a single combination of climate models, especially with weather files based on the REGCM model.</strong></p> <p><strong>Suggestions for corrections can be sent to matheus.bracht@posgrad.ufsc.br</strong></p>
Simulation systems of: "Free energies of membrane stalk formation from a lipidomics perspective"
<p><strong>Simulation systems of: </strong></p> <p>Free energies of membrane stalk formation from a lipidomics perspective</p> <p>Chetan S. Poojari, Katharina C. Scherer, Jochen S. Hub</p> <p>Nature Communications, 12, 6594 (2021), <a href="https://doi.org/10.1038/s41467-021-26924-2">https://doi.org/10.1038/s41467-021-26924-2</a></p> <p> </p> <p><strong>First published as a preprint manuscript in BioRxiv as:</strong></p> <p>Free energies of stalk formation in the lipidomics era</p> <p>Chetan S. Poojari, Katharina C. Scherer, Jochen S. Hub,</p> <p>BioRxiv, https://www.biorxiv.org/content/10.1101/2021.06.02.446700v1, 2021</p> <p>The archive contains</p> <ul> <li>starting conformations of double-membrane systems</li> <li>topologies</li> <li>MD parameter files</li> </ul> <p>Running the simulations requires a modified version of GROMACS, which implements the chain coordinate available at GitLab:</p> <p><a href="https://gitlab.com/cbjh/gromacs-chain-coordinate">https://gitlab.com/cbjh/gromacs-chain-coordinate</a></p>
Simulated industrial CT dataset for deep learning with dual-energy tomograms and ground truth material maps for copper and iron
<p>We use this dataset for training and evaluation of a deep learning model to discriminate multi-material systems with X-ray CT.</p> <p>The dataset consists of:</p> <ul> <li>inputs: dual-energy tomograms as binary files without a header (tensor <strong>shape for numpy: 2x128x128 @float32</strong>) <ul> <li>simulated spectra are 250kVp and 450kVp both prefiltered using 2mmCuSn</li> </ul> </li> <li>outputs: the material maps a.k.a. ground truths for the training (same shape as inputs) <ul> <li>sampled with a delaunay algorithm and randomly filled with iron and copper fractions</li> </ul> </li> </ul> <p>The <strong>dataset is normalized to [0, 1]</strong>, so you have to multiply by the mass densities of copper and iron to obtain effective fractions in g/cm^3.</p>
Free Energy Differences from Molecular Simulations: Exact Confidence Intervals from Transition Counts
<p>Supporting data for <strong>Free Energy Differences from Molecular Simulations: Exact Confidence Intervals from Transition Counts</strong></p> <p>Molecular simulations make it possible to predict equilibrium constants and corresponding free energy differences. For a system that exists in two states A and B, the equilibrium constant K can be predicted as K = t_B / t_A, where<br> t_B and t_A are times spent in states B and A, respectively. The free energy can be calculated as Delta G = -kT log(K). Here we propose a new method for calculation of confidence intervals for K and Delta G. The ratio of the true<br> value of K and estimated K follows the F-distribution with degrees of freedom df1 = number of B to A transitions and df2 = number of A to B transitions. This makes it possible to calculated the confidence interval of K solely from<br> the number of transitions.</p> <p>The code in the directory errors was used to calculate Table 1 of the article. The code in the directory type1error was used to generate 10000 first time passage times for a transition from A to B and B to A as random numbers with<br> exponential distribution. This was done for different combinations of number of transition and values of K. Number of confidence intervals not spanning the predefined value of K (type 1 errors) was expected to be 5 % for 95-% confidence intervals. This was in agreement with the result.</p> <p>The code in the directory type1errorodd was used to run similar experiment as type1error, but with number of A to B transitions higher than B to A by one. The code in the directory threestates was used to run similar experiment as<br> type1error and type1errorodd but for a system with three states A, B and C. The directory glycerol contains a trajectory, evolution of values of torsion angles and the code for analysis of the simulation of glycerol in water.</p> <p>The directory ffmp contains evolution of values of RMSD from the native structure, manual assignments of folded and unfolded states and the code for analysis of simulations of fast folding miniproteins (original data from Lindorf-Larsen et al. Science 2011, 334(6055) 517-520).</p> <p>The directory se contains the code for calculation of standard errors numerically and by the method presented in the article.</p> <p>The directory parallel presents the code for calculations supporting our method to calculate rate and equilibrium constants in parallel simulations.</p> <p>Codes written in R were executed using R version 3.4.4 by running:<br> <em>$ R –no-save < code.R > code.log</em></p> <p>File md5sums contains md5sum codes for all files.</p> <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.