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4,230 results for “Energie”
Dataset of future district heating energy demand in a Finnish municipality
<p>******************* Please view the README.md file for detailed documentation of data. ********************</p> <p>Title: Impact of climate change, energy efficiency and population on long-term heat demand scenarios in districts: Datasets and Supplementary Materials Version: 1.0</p> <p>Date of Release: 28/10/2020 Identifier: doi:10.5281/zenodo.4139299 Permalink: http://dx.doi.org/10.5281/zenodo.4139299</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J. & Ruusunen, M. Impact of climate change, energy efficiency and population on long-term heat demand scenarios in districts <em>Under Review, </em> <strong>2020</strong></p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README file. Contact information: Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi or jeannicolas.louis@gmail.com</p> <p>Dates of data modelisation: 2013 - 2030 - 2050</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: Hourly, heating season.</p> <p>Types: Input data (all input configuration data are freely available, but dataset related to the district heating network and buildings are not distributed and not shareable for copyright reasons), power, temperature</p> <p>Format: All data are stored in .mat file format (MatLab file). </p> <p>This directory contains the following datasets and supplementary materials: A summary of all the files has been compiled and stored in the "READ ME.md" or "READ ME.html" file</p>
Supplementary material for the publication: J.D. Nixon, K. Bhargava and E. Gaura, Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps, 2020
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, we deployed a Standalone Solar System for a Community Hall in Nyabiheke camp, Rwanda, and a PV-battery Microgrid in Kigeme camp, Rwanda. The microgrid supplies power to a playground and two nursery buildings. It powers a total of 20 CPE (each with 3 LEDs) and 10 sockets. The standalone system at Hall powers 7 CPE (with 3 LEDs each) and 4 sockets. The aim of the study was to (a) understand the energy consumption behaviour, light usage and other enabled uses within the set location in each camp (b) create an evidence base on the value of energy and its benefits in displaced contexts (c) identify best practice in the construction, control and operation of the respective systems as a shared energy resource.</p> <p>The system data used for the performance analysis for this study (July 2019 and March 2020) is deposited here along with the metadata. The results from analysis are presented in a paper titled '<strong>Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps</strong>' (currently under submission). The scripts for analysis can be found at our Github account <a href="https://github.com/cogent-computing">Cogent Labs</a> under HEED-Microgrid and HEED-Hall repositories.</p>
Associated Data: RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features
<p>Additional digital data to "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features" (ChemRxiv preprint:<a href="https://doi.org/10.26434/chemrxiv.12636704.v1">https://doi.org/10.26434/chemrxiv.12636704</a>).</p> <p>Associated code can be found at: <a href="https://github.com/HITS-MCM/RASPDplus">https://github.com/HITS-MCM/RASPDplus</a></p> <p>Files:</p> <ul> <li>weights.tar.gz: contains the model weights of one random dataset split and its associated crossvalidation folds. Used for standard RASPD+ evaluation.</li> <li>additional_model_replicates.tar.gz: contains the remaining models trained on the full set of descriptors.</li> <li>external_test_sets.tar.gz: contains the descriptor tables for all external test sets used</li> <li>dude.tar.gz: contains the descriptor tables for and several identifier lists for evaluation on the Directory of Useful Decoys - Enhanced (DUD-E)</li> <li>run_outputs.tar.gz: Performance metric data and predicted values created during the model training and evaluation runs. Basis for the figures and metrics in the manuscript.</li> </ul> <p> </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>
Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in 'winning' hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.
<p>Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.</p>
Eddy Kinetic Energy and SST gradients global datasets and trends. Additionally, this dataset includes ocean basins and ocean processes masks.
<p>This dataset includes the post-processed data used for the paper titled "Mesoscale kinetic energy response to changing oceans". The original data was obtained from AVISO+ SSH altimetry and NOAA optimal interpolated sea surface temperature (OISST):</p> <p>AVISO+ SSH: https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global/gridded-sea-level-heights-and-derived-variables.html</p> <p>NOAA-OISST: https://www.ncdc.noaa.gov/oisst</p> <p>From satellite observations of sea surface height (SSH) and sea surface temperature (SST) over the satellite record (1993 - 2019), EKE and SST gradients are derived. </p> <p>Then the fields are then temporally smoothed using a running average of 12 months. Trends and the significance of each field are finally computed with linear regression and a modified Mann–Kendall test (https://github.com/josuemtzmo/xarrayMannKendall).</p> <p>Geographical regions consist of the following ocean basins: the Southern Ocean, the Indian Ocean, the Pacific Ocean, and the Atlantic ocean. These ocean basins were expert-defined to capture ocean processes at all scales (ocean_basins_and_dynamical_masks.nc).</p> <p>Dynamical regions (Fig. 5d): the Antarctic Circumpolar Current (ACC), the boundary currents and their extensions, the tropics, the subtropical ocean gyres, and the remaining regions (ocean_basins_and_dynamical_masks.nc).</p> <p>Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/EKE_SST_trends</p>
Mining API Interactions to Analyze SoftwareRevisions for the Evolution of Energy Consumption (MSR'2021 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our MSR'2021 paper <em>Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption</em>.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file <em>msr_2021_dataset.csv</em> and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>sampleNr - a number identifying the group this sample relates to</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>durationAdjusted - duration after alignment between method trace and energy profile</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>`package-names` - per package uAPI profile</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>The files <em>joule_anova_posthoc_result.csv</em> and <em>uAPI_anova_posthoc_result.csv</em> contain the results of the ANOVA and Tukey HSD posthoc analysis to determine accuracy and F1-score of the presented approach.</p> <p> </p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
A global atlas of extreme wind speeds for wind energy applications
<p>Here we present a global, homogenized and geospatially explicit digital atlas of the sustained fifty-year return period wind speed (<em>U<sub>50</sub></em>)<em><sub> </sub></em>and associated confidence intervals based on ERA5 reanalysis output at 100 m a.g.l.. Four different approaches are used to derive <em>U<sub>50</sub></em> estimates using 40 years of hourly disjunct 20-minute sustained wind speeds. All rely on use of the Gumbel distribution to fit extreme wind speeds but differ in how the distribution parameters are derived. Resulting values of <em>U<sub>50</sub></em> are compared to reference wind speeds <em>U<sub>ref</sub></em> derived using five times the mean wind speed as specified in the International Electrotechnical Commission (IEC) wind turbine design standards. An observationally derived dataset used in evaluation of the atlas is also included, along with a MATLAB script used in deriving the <em>U<sub>50</sub></em> estimates.</p> <p>Associated publication is: Pryor S.C. and Barthelmie R.J. (2021): A global assessment of extreme wind speeds for wind energy applications. <em>Nature Energy</em> DOI: 10.1038/s41560-020-00773-7</p>
Optimised household consumption profiles through a smart building energy mangement system TABEDE
<p>In the context of the TABEDE project (<a href="https://www.tabede.eu/">https://www.tabede.eu/</a>) several synthetic profiles simulating the consumption and generation of residential buildings, whose appliances were controlled by our proposed Energy Management System (i.e., the TABEDE solution), were simulated. Their construction process was characterised by the following:</p> <ul> <li>Consumption profiles were generated via a bottom-up approach capable of emulating the consumption of individual household appliances. These last ones correspond to the most used appliances in the UK, which were randomly distributed among the buildings based on their average utilisation rate and ownership observed in residential buildings in the country.</li> <li>The physics in terms of heat exchange between neighbouring buildings and the environment were considered, together with the size of the buildings and their physical characteristics. A total of 66 houses and apartments, according to 8 type or building archetypes were created.</li> <li>PV generation profiles were generated according to the meteorological condition of the simulated day.</li> </ul> <p>Together with this, the profiles feature how the TABEDE solution optimised the flexible part of the consumption (i.e., appliances that were controllable by the solution and whose consumption could be shifted in time without sacrificing user comfort) to minimize the electricity bill of the buildings.</p> <p>The information contained in the actual database features the following variables:</p> <ul> <li>TABEDE penetration: percentage of buildings owning the TABEDE solution. Buildings with TABEDE will observe their flexible consumption being optimised.</li> <li>PV penetration: percentage of buildings with a PV system installed on them.</li> <li>Simulation day: one day in summer (19/06/2019) featuring the highest solar radiation of the year, and a day in winter (19/12/2019) with the lowest.</li> <li>Batteries: whether the PV systems is installed alongside household batteries.</li> </ul> <p>Details on the formulation can be found in: <a href="https://urldefense.com/v3/__https:/www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/__;!!La4veWw!khYBEaeJY85mX5yQUrp0PwoXcg5U10dEdgZ296hONYGyBS5xg91Z8MoDUQy34a4f9Lo$">https://www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/</a></p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
Aldeghi et al. Files for absolute free energy calculations in gromacs.
<p>These are the files for performing absolute free energy calculations using gromacs as reported in "Accurate calculation of the absolute free energy of binding for drug molecules. Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC.<br /> Chem Sci. 2016 Jan 14;7(1):207-218. DOI: 10.1039/C5SC02678D"</p> <p> </p> <p>The files should prove useful for anyone wishing to try out their own methodology for comparison purposes or even just to repeat the work on a known dataset. The data is presented as a zip archive that should unpack into a directory called "Aldeghi-et-al-chemical-science-2016". There are four sub-directories in there and a README.txt file which should explain the details of the data.</p> <p> </p>
RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties
<p>We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.</p>
SDDF Energy Dataset
<p>This conformational energy dataset, developed as part of the Smart Distributed Data Factory (SDDF) project, contains over 2.75 million molecular conformations based on drug-like molecules sourced from the <strong>ENAMINE</strong> database. Energies were calculated using <strong>DFT</strong> with the ωB97x density functional and the 6–31G(d) basis set. The conformations were generated from SMILES using RDKit, MMFF94 optimization, and molecular dynamics (MD) simulations, providing a diverse set of molecular structures and energy states.</p> <ul> <li><strong>RDKit Conformations:</strong> 1,123,693</li> <li><strong>RDKit + MMFF94 Optimized:</strong> 1,151,936</li> <li><strong>MD-Generated:</strong> 483,279</li> </ul> <p>This dataset serves as a benchmark for energy prediction models, with training (638,617 examples), validation (134,732 examples), and test subsets (24,890 examples) created using a strict scaffold-based split to ensure no overlap and less than 70% similarity between the training and test sets.</p> <p>Dataset contents:</p> <ul> <li><em>data.tar.gz</em>: contains the conformations in Structured Data File format, grouped into separate folders based on the molecule ID. Each conformation's label is provided within its SDF file as a property named "energy".</li> <li><em>INDEX.smi</em>: specifies the molecule IDs and their corresponding SMILES.</li> <li><em>SOURCES.csv</em>: specifies the conformation generation method for each conformation.</li> <li><em>SDDF_train.tsv</em>, <em>SDDF_validation.tsv</em>, and <em>SDDF_test.tsv</em> specify the molecule IDs and conformations for each subset of the benchmark.</li> </ul> <p>A detailed description is provided in the accompanying paper.</p>
Dataset from: A quiet public? Procedural justice in Portuguese wind energy governance
<p>This dataset accompanies a journal article related with public participation in wind and solar energy in Portugal. It contains a database of web scraped public consultation processes related with wind power plants and decentralized solar power plants until 2023. It also contains the R Markdown files that were used to analyze the scraped data. The results of this analyzes, and their discussion, can be found in the associated article.</p>
O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data release
<p>Raw data and codes used in M. Gacesa, R. J. Lillis, and K. J. Zahnle, "O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy", MNRAS 491, 5650-5659 (2020).</p> <ul> <li>v1.1 includes <strong>differential cross section</strong> data for inelastic scattering: O(3P)+CO2(v=0,j=ji) -> O(3P)+CO2(v=0,jf) and energy transfer to the internal degrees of freedom calculated as in Gacesa & Kharchenko, Geophys. Res. Lett. 39, L10203 (2012).</li> </ul> <p>These files are distributed under GNU General Public License v3.0 and include NO liability or warranty of any kind. No support is provided by the authors. We cannot promise to answer any questions related to this dataset nor to prepare different products for you.</p> <p>Please cite this work as: Marko Gacesa, Lillis, Robert J., & Zahnle, Kevin J. (2019). O(3P)+CO_2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data pre-release (Version v0.9-beta) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3256699">http://doi.org/10.5281/zenodo.3256699</a></p>
Great Britain's primary substation service areas and annual domestic energy statistics
<p>This geospatial data is a combination of Great Britain's 4436 primary substation service areas which have been parsed into a single shapefile for energy systems analysis. The original component datasets were provided by the six distribution network operator (DNO) companies in Great Britain (National Grid Electricity Distribution, Electricity North West Ltd, Scottish and Southern Electricity Networks, UK Power Networks, Scottish Power Energy Networks and Northern Power Grid). Attribution is given to the original data owners at each of these six DNOs and the resulting dataset from this work has been created and published under an open licence with each DNO's permission. </p><p>The data is available to download as two geojson files in the WGS84 coordinate system. One is a streamlined version which just contains the polygons along with a unique primary identifier (UPID), primary substation name, DNO licence area and local authority. The other contains the polygons along with richer energy data which was aggregated to the primary substation level from publicly available Department for Energy Security and Net Zero, Office for National Statistics and National Grid ESO datasets. This data is also available to download in tabular form as a csv file. The meter numbers and consumption values are the means of those reported from 2015-2020. The substation polygons were those as received or publicly available as of the time period of this study (2021-22).</p><p>The pre-print manuscript of the methodology used to create this dataset can be found on arXiv at:</p><p>https://doi.org/10.48550/arXiv.2311.03324</p><p>Funding to support this work was received from the Engineering and Physical Sciences Research Council (EP/W008726/1) under the Gas Net New project and the Alan Turing Institute's Science of Cities and Regions Programme. Thanks are also given to the contributors of QGIS and the Geopandas Python library, both of which were used in this analysis. </p>
IonSolv-Aq Dataset for: Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes
<p>This repository includes datasets and supplementary materials for the manuscript "Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes" by Jonathan W. Zheng and William H. Green. <strong>Citations should refer directly to the manuscript:</strong></p> <blockquote> <p>Zheng, J. W., & Green, W. H. (2023). Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes. <em>The Journal of Physical Chemistry A</em>, <em>127</em>(48), 10268-10281.</p> </blockquote> <p>This compilation includes experimental and computed solvation free energies for the compounds in the IonSolv-Aq dataset, as well as .xyz files for all conformers used in the corresponding work. The lower-quality set of data described in the manuscript is also available in the "extra-anion-data.zip" archive file.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Global energy use and carbon emissions from irrigated agriculture
<p>This repository contains supporting data for: "<strong>Global energy use and carbon emissions from irrigated agriculture"</strong></p><p>Email: qinjingxiu17@mails.ucas.ac.cn and duanweili@ms.xjb.ac.cn</p><p>The dataset contains:</p><p>-Global energy consumption and CO2 emissions from irrigation . </p><p>-Global CO2 emissions from groundwater degassing . </p><p>-Energy consumption and CO2 emissions with different irrigation and pumping systems and irrigation water sources. </p><p>-Global energy consumption and CO2 under drip and sprinkler scenarios. </p><p>-Global energy consumption and CO2 under mix electricity scenarios. </p><p>-Energy units: Terajoule (TJ); CO2 emissions units: (Tonnes CO2)</p><p>-Files are uploaded in .tif raster data. </p>
Dataset: Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay
<p><i><strong>"Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el cálculo del Índice de Pobreza Energética Multidimensional (MEPI) para el caso de estudio. </p><ol><li>MEPI_CarmenSoler_Data_2018_CHILECON2023.xlsx</li></ol><p>En el archivo, podrán encontrar los extraídos de los resultados de la encuesta realizada en el 2018 por un equipo de investigadores paraguayos (En el artículo podrán encontrar más información). Además de los datos, podrán ver todos los pasos y cálculos llevados a cabo para obtener los resultados obtenidos. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </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.