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858 results for “Energy Data”
Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand)
<p>A Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand). </p>
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts
<p>Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts</p>
Baseline data for Citizen Energy Responsible Behaviour in 27 Member States
<p>This dataset contains all the information on the levels that each country in Europe should have in the labelling system to assess citizens energy behaviours fully described in the document AURORA D1.1 Near-Zero Emissions Citizens Label https://doi.org/10.5281/zenodo.7594879</p> <p>The zip file contains one document for each member state. Reference values are extracted from the same datasources individuated in D1.1 for the 5 selected countries fully described in the document.</p>
Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems
<p>Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.</p>
Simulation data for the office cell building energy model with the attached overhang
<p>Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".</p>
A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument: Data Repository
<p>This repository contains the synthetic catalogue for the DESI Bright Galaxy Survey produced wit the N-body code <em>gevolution</em>, which is analysed in the manuscript "<a href="https://arxiv.org/abs/2306.04213">A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument</a>", as well as the raw data of the analysis results. The catalogue "catalogue.csv.bz2" is in the CSV format and can be directly read using the pandas library of python, for example. The columns in the catalogue contain the following information:</p> <p>0. Column index<br> 1. Comoving coordinate x (in units of Mpc/h)<br> 2. Comoving coordinate y (in units of Mpc/h)<br> 3. Comoving coordinate z (in units of Mpc/h)<br> 4. Observed redshift<br> 5. Cosine of the observed polar angle measured with respect to the axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with <span class="math-tex">\(\phi_1 = \pi/4\)</span>, then rotating along the new y axis with <span class="math-tex">\(\theta_2 = \mathrm{arccos}(1/\sqrt{3})\)</span> and setting the final rotation angle to zero, <span class="math-tex">\(\phi_3 = 0\)</span>; hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix)<br> 6. Observed azimuthal angle phi measured with respect to axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with <span class="math-tex">\(\phi_1 = \pi/4\)</span>, then rotating along the new y axis with <span class="math-tex">\(\theta_2 = \mathrm{arccos}(1/\sqrt{3})\)</span> and setting the final rotation angle to zero, <span class="math-tex">\(\phi_3 = 0\)</span>; hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix)<br> 7. Logarithm of the luminosity in units of solar luminosity <span class="math-tex">\(L_\odot\)</span><br> 8. Observed flux (in units of <span class="math-tex">\(L_\odot/\mathrm{Mpc}^2\)</span>)<br> 9. Number of particles in each object, plus a uniform noise between 0 and 1. This quantity is the proxy of the mass that was used to assign luminosity to the objects.<br> 10. Flag that identifies the selected objects within each redshift bin. The Flag is 0 for objects not included in the catalogue, and equal to the mean redshift of the bins <span class="math-tex">\(\bar{z} = 0.25, 0.35, 0.45\)</span> for the selected objects. <br> 11. Flag that identifies the bright and faint objects for case 1 (50% bright, 50% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects.<br> 12. Flag that identifies the bright and faint objects for case 2 (90% bright, 10% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects.<br> 13. Flag that identifies the bright and faint objects for case 3 (50% bright, 50% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects.<br> 14. Flag that identifies the bright and faint objects for case 4 (90% bright, 10% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects.</p> <p>The example script "example-script.ipynb" demonstrates how to query the catalogue to extract e.g. the redshift distribution of the objects for the different cases considered in Table 4 of the manuscript.</p> <p>Additionally, the measured dipole data vectors with the jackknife covariance matrices (<span class="math-tex">\(\mathrm{cov}^\mathrm{JK}_{ij}\)</span>), as well as the theoretical data vectors with the theoretical measurement covariance (<span class="math-tex">\(\mathrm{cov}^\mathrm{th}_{ij}\)</span>) and the theoretical prediction covariance (<span class="math-tex">\(\mathrm{cov}^\mathrm{pred}_{ij}\)</span>) are provided within this repository:</p> <ul> <li>In the measurements.tar.gz archive, the measured data for the flux-limited case can be found in the /flux-limit subdirectory, while the data for the case without flux-limit is in /no-flux-limit. The data vectors are named "dipole_<redshift bin>_<% of bright galaxies>.txt. The first column in each of those files is the separation bin <span class="math-tex">\(d\)</span> in <span class="math-tex">\(\mathrm{Mpc}/h\)</span>, the second column is the mean two-point correlation function dipole of the 100 jackknife subsamples, and the third column is the square root of the diagonal part of the jackknife covariance matrix (<span class="math-tex">\(\mathrm{cov}^\mathrm{JK}_{ij}\)</span>). The corresponding jackknife covariance matrices are named "cov_<redshift bin>_<% of bright galaxies>.txt.</li> <li>In the theory.tar.gz archive, the theoretical predictions are found in /flux-limit for the case with flux limit and in /no-flux-limit for the case without flux limit. The theoretical data vectors are named "dipole_<% of bright galaxies>B_z<redshift bin>_gevol.dat". The first column in each of those files is the separation bin <span class="math-tex">\(d\)</span> in <span class="math-tex">\(\mathrm{Mpc}/h\)</span>, the second column the theoretical two-point correlation function dipole and the third column is the square root of the diagonal part of the theoretical prediction covariance matrix (<span class="math-tex">\(\mathrm{cov}^\mathrm{pred}_{ij}\)</span>) . The theoretical measurement covariance matrices are named "covariance_Lp6_<% of bright galaxies>B_z<redshift bin>_gevol.dat", and the theoretical prediction covariance matrices are named "covtheo_<% of bright galaxies>B_z<redshift bin>_gevol.dat". </li> </ul> <p>The example script also demonstrates how to use these data files to reproduce plots of the dipole measurement vs the theoretical prediction like in Figures 6, 7, C1 and C2. The archives need to be unpacked before using the example script to access the data.</p>
Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release
<p>This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey.</p> <p>A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers.</p> <p>The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean <span class="math-tex">\((g-r, r-i, r-z)\)</span> colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude.</p> <p>Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code.</p> <p>The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.</p>
Data Set Accompanying "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding Thermodynamics of Mixed Surfactant Systems"
<p>This data set accompanies "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding<br> Thermodynamics of Mixed Surfactant Systems" by Colin K. Egan and Ali Hassanali. It includes example GROMACS<br> input files for all simulations analyzed in the paper, as well as example data sets and analysis scripts. See<br> https://doi.org/10.26434/chemrxiv-2023-h11k5 for the preprint manuscript.</p>
Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers
<p>This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861). </p>
Data used for modeling in Energy-water-land-CCUS nexus model: carbon dioxide opportunities based on optimized regional development
<p>In this dataset, the data used for modeling technologies in an energy-water-land-CCUS nexus model in Khark Island in Iran, and the main sources for gathering them are presented.</p>
Net-zero 1.5 °C sectorial pathways for G20 countries: energy and emissions data to inform science-based decarbonization targets
<p><span>This data for global, regional (EU-27), and country-specific (G20 member countries) energy and emission pathways required to achieve a defined carbon budget of under 450 Gt/CO2, developed to limit the mean global temperature rise to 1.5°C, over 50% likelihood. The data were calculated with the 1.5°C sectorial pathways of the One Earth Climate Model—an integrated energy assessment model devised at the University of Technology Sydney (UTS). </span></p> <p><span>The data consist of the following six zip-folder datasets (refer to Section 2 for an explanation of the data):</span></p> <p><span>1. </span><span>Appendix folder: Each file contains one worksheet, which summarizes the overall 1.5°C scenario.</span></p> <p><span>2. </span><span>Sector folder (XLSX): Each file contains one worksheet, which summarizes the industry sectors analysed.</span></p> <p><span>3. </span><span>Sector folder (CSV): The data contained are the same as those described in point 2.</span></p> <p><span>4. </span><span>Sector emissions folder: Each file contains one worksheet, which summarizes the total annual emissions for each industry sector.</span></p> <p><span>5. </span><span>Scope emissions folder (XLSX): Each file contains one worksheet, which summarizes the total annual emissions for each industry sector—with the additional specificity of emission scope. </span></p> <p><span>6. </span><span>Scope emissions folder (CSV): The data contained are the same as those described in point 5.</span></p>
Supporting data for "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer"
<p>This repository contains the supporting data for the publication: "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer".</p>
Data from: Lizard richness in mainland China is more strongly correlated with energy and climatic stability than with diversification rates
<p><strong><span>Aim</span></strong><span>: Contemporary environmental, historical, and evolutionary factors are increasingly used to decipher the drivers of spatial patterns of species richness. Evidence of such correlations for Chinese reptiles is scarce and poorly understood. We therefore explored the validity of the environmental capacity, historical climatic stability, and diversification rates hypotheses on Chinese lizard richness.</span></p> <p><span><strong>Location</strong>:</span><span> Mainland China</span></p> <p><span><strong>Taxon</strong>:</span><span> Squamata: Sauria</span></p> <p><strong><span>Methods</span></strong><span>: We mapped the distribution ranges of all 237 lizards in mainland China using a combination of different datasets. We used current environmental conditions (ambient energy, environmental productivity, and habitat heterogeneity), historical climate stability indices (long-term: since ~3.3 Ma and short-term: since the Last Glacial Maximum), and mean tip diversification rates to test whether current environmental conditions, historical climate change, and diversification rates drive contemporary richness patterns of lizards in China. We applied piecewise structural equation models (pSEM) to jointly evaluate our hypotheses, considering direct and indirect effects.</span></p> <p><strong><span>Results</span></strong><span>: Chinese lizards showed latitudinal diversity gradients. We found consistent support for contemporary climatic and environmental factors' relationships with richness. Richness was also positively correlated with short-term climatic stability, but less so with long-term stability. Diversification rates were only seldom found to be positively correlated with lizard richness.</span></p> <p><span><strong>Main conclusions</strong>:</span><span> Our results support the</span> <span>environmental capacity and historical climate hypotheses, which link high richness to highly productive warm and stable regions (and low richness to cold and unstable regions). We conclude that post-speciation dispersal and short-term climatic oscillations quickly swamp the long-term signal of diversification rates and climatic fluctuations, creating strong current climate-richness associations.</span></p>
Data for "Absolute binding free energy calculation based on the fragment molecular orbital method and its application in designing novel SHP-2 allosteric inhibitors"
<p>Data for publication "Absolute binding free energy calculation based on the fragment molecular orbital method and its application in designing novel SHP-2 allosteric inhibitors".All structures of complex and input files for FMO , FMO/SMD , FMO/PCM , and COSMO calculation are provided .</p>
Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis
<p>This dataset was collected to understand how Norwegian households responded to the electricity price shock due to the European energy crisis. It consists of consumer characteristics and their self-reported responses to the extraordinarily high electricity prices which were collected by a survey of 4,446 consumers. The consumer characteristics contain information about socio-demographics such as income, age, education, number of residents, residence type, residence size, and how conscious the respondents are about their electricity consumption. Furthermore, major electricity-consuming appliances are identified, such as whether the residents have an electric vehicle and how they heat their homes, and if they have a variable electricity tariff. In addition, hourly metered electricity consumption data covering October 2020 to March 2022 from a subset of 1,136 residential consumers of the surveyed households and the total hourly residential electricity consumption per Norwegian bidding area from July 2019 to July 2022as well as the hourly day-ahead electricity prices are included in the dataset. These data are interesting to researchers that aim to gain insight into the electricity consumption behaviour of the residential sector and the impact of different socio-demographic variables.</p> <p>A detailed description is available as a data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923007667">Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis - ScienceDirect</a></p> <p>Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a></p> <p>Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a></p>
Supplementary Data for "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"
<p>Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation".</p><p>Includes:</p><ul><li>All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models.</li><li>Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes.</li><li>Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).</li></ul>
Case study result data set for the submitted article "Implications of hydrogen import prices for the German energy system in a model-comparison experiment"
<p>The data set contains result data for the German energy system in a long term scenario (scenario year 2045) as described in the publication "Implications of hydrogen import prices for the German energy system in a model-comparison experiment". The results have been generated with the models REMod of Fraunhofer Institute for Solar Energy Systems ISE, Enertile of Fraunhofer Institute for Systems and Innovation Research ISI, and SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE.</p><p><strong>Abbreviations:</strong></p><ul><li>BEV - Battery Electric Vehicles</li><li>CC - Combined Cycle</li><li>CCGT - Combined Cycle Gas Turbine</li><li>CHP - Combined heat and power</li><li>CO2 - Carbon dioxide</li><li>con - consumption</li><li>FC - Fuel Cell</li><li>FCEV - Fuel Cell Electric Vehicle</li><li>gen - generation</li><li>H2 - Hydrogen</li><li>HT - High temperature</li><li>ICE - Internal Combustion Engine</li><li>LDV - Light-Duty Vehicle</li><li>LT - Low temperature</li><li>med - medium</li><li>OC - Open Cycle</li><li>OCGT - Open Cycle Gas Turbine</li><li>PHEV - Plug-In Hybrid Vehicles</li><li>PS - Pumped Storage</li><li>PV - Photovoltaics</li><li>ST - Steam turbine</li><li>SynFuel - Synthetic fuel</li><li>w/ - with</li><li>w/o - without</li><li>yr - year</li></ul>
Data from: Separating the impact of individual land surface properties on the terrestrial surface energy budget in both the coupled and uncoupled land–atmosphere system
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Data for: Bibliographic synthesis of biodiversity-relevant criteria for solar energy siting
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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.