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858 results for “Energy Data”
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>
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>
Data accompanying the manuscript "Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales"
<p>This dataset contains time series of wind energy production aggregated over France and Europe, obtained from a 1000-year climate simulation from the CESM model (version 1.2.2, Hurrel et al. 2013), coupled to a simple energy model to compute grid-point capacity factor from surface wind. Wind power is then computed by multiplying the capacity factor by the installed capacity, taken from 5 e-Highway scenarios (X5, X7, X10, X13 and X16), and integrated over the regions of interest. More details about the climate simulation, wind energy model and installed capacity scenarios can be found in the associated manuscript, "Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales" (Cozian et al. 2023).</p><p>The data is organized into 10 files for France and 10 files for Europe. In each case, the 10 files correspond to 10 batches of 100 years each, with 3-hourly output. Each file contains 5 time series corresponding to the 5 installed capacity scenarios.</p><h4>References</h4><ul><li>Hurrell J W, Holland M M, Gent P R, Ghan S, Kay J E, Kushner P J, Lamarque J F, Large W G, Lawrence D, Lindsay K, Lipscomb W H, Long M C, Mahowald N, Marsh D R, Neale R B, Rasch P, Vavrus S, Vertenstein M, Bader D, Collins W D, Hack J J, Kiehl J and Marshall S (2013). The community earth system model: A framework for collaborative research. Bulletin of the American Meteorological Society, 94, 1339–1360. <a href="https://doi.org/10.1175/BAMS-D-12-00121.1">https://doi.org/10.1175/BAMS-D-12-00121.1</a></li><li>e-Highway 2050 (2015). Europe's future secure and sustainable electricity infrastructure. <a href="https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/results">https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/results</a></li><li>Cozian B, Herbert C and Bouchet F (2023). Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales. <a href="https://doi.org/10.48550/arXiv.2311.13526">https://doi.org/10.48550/arXiv.2311.13526</a></li></ul>
Supplementary Data for Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay
<p>This dataset contains the interpolation tables for use with the DM21cm code release as part of "Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay." For details on usage, see the public github repository at: https://github.com/yitiansun/DM21cm. </p>
Data for: Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics
<p>Dataset for <em>Bound impurities in a one-dimensional Bose lattice gas: </em><em>low-energy properties and quench-induced dynamics</em> [<a href="https://scipost.org/SciPostPhysCore.7.3.049">SciPost Phys. Core 7, 049 (2024)</a>].</p>
Data Repository - Distinct Roles of Direct and Indirect Electrification in Pathways to a Renewables-dominated European Energy System
<p>This is the data repository to reproduce the scenario analysis of the paper "<a href="https://www.cell.com/one-earth/fulltext/S2590-3322(24)00037-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS259033222400037X%3Fshowall%3Dtrue">Distinct roles of direct and indirect electrification in pathways to a renewables-dominated European energy system</a>".</p> <p>The source code for the REMIND version used in this study is available at <a href="https://github.com/fschreyer/remind/tree/ElecH2_prod">https://github.com/fschreyer/remind/tree/ElecH2_prod</a>. The scenario config file that was used to start the specific model runs of the paper and that inlucdes all scenario-specific model settings can be found in the repository under <a href="https://github.com/fschreyer/remind/blob/ElecH2_prod/config/21_regions_EU11/scenario_config_ElecH2.csv">./config/21_regions_EU11/scenario_config_ElecH2.csv</a>. The repository is a fork with slight changes relative to the main release version available at <a href="https://github.com/remindmodel/remind/tree/v3.2.1">https://github.com/remindmodel/remind/tree/v3.2.1</a> and <a href="https://doi.org/10.5281/zenodo.7852740">https://doi.org/10.5281/zenodo.7852740</a>. The model documentation can be found at <a href="https://rse.pik-potsdam.de/doc/remind/3.2.0">https://rse.pik-potsdam.de/doc/remind/3.2.0</a>. </p> <p>Model output data as well as other data that were used in the study are stored in data.zip. Moreover, we added a PlotsData.zip file, which contains the data shown in the figures of the paper. The R script to produce the figures and analysis of the paper can be found in ElecH2paper_Plots.Rmd. We publish a comprehensive dataset of our model output which includes more data than what is needed to reproduce the figures of the paper. Those data can be helpful to compare and contextualize our scenarios or use them for further analyses. However, due to the scope and complexity of our modeling framework, these data need to be used with care. The data used for the analysis of this study have been thoroughly validated. However, we cannot always perform such validation for the whole dataset and data need to treated with caution in particular at high regional or sectoral resolution and with respect to aspects that were not in the focus of the study as there maybe artefacts or limitations of our modeling approach. Please contact us in case you would like to use our scenarios for further analyses. We welcome open and constructive exchange on our data. </p> <p> </p> <p>Contact:<br>Felix Schreyer<br>Potsdam Institute for Climate Impact Research<br>felix.schreyer@pik-potsdam.de</p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Data from: Solar energy resource availability under extreme and historical wildfire smoke conditions
<p>The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in <em>Nature Communications</em>. Data are the final processessed and merged datasets sourced from the following publicly available data products:</p> <ul> <li>National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (<a href="https://nsrdb.nrel.gov/)">https://nsrdb.nrel.gov/)</a>. <ul> <li>Bulk download in July 2023 via AWS: <a href="https://registry.opendata.aws/nrel-pds-nsrdb/">https://registry.opendata.aws/nrel-pds-nsrdb/</a></li> <li>Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth</li> </ul> </li> <li>National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product. <ul> <li>Access: <a href="https://www.ospo.noaa.gov/Products/land/hms.html#maps">https://www.ospo.noaa.gov/Products/land/hms.html#maps</a></li> <li>Variables: smoke plume locations</li> </ul> </li> <li>National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006). <ul> <li>Access: <a href="https://lpdaac.usgs.gov/products/mcd19a2v006/">https://lpdaac.usgs.gov/products/mcd19a2v006/</a></li> <li>Variables: aerosol optical depth and cloud mask</li> </ul> </li> <li>NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1) <ul> <li>Access: <a href="https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp">https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp</a></li> <li>Variables: cloud optical depth</li> </ul> </li> </ul> <p>A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al. </p> <p>Associated code scripts are located in the linked code repository.</p>
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS
<p>This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.</p> <p>This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834</p>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Data from PV module energy rating standard IEC 61853-3 intercomparison
<p>This is the data from PV module energy rating standard IEC 61853-3 intercomparison.</p> <p>Details can be found in:</p> <p>M. R. Vogt, S. Riechelmann, A. M. Gracia-Amillo, A. Driesse, A. Kokka, K. Maham, P. Kärhä, R. Kenny, C. Schinke, K. Bothe, J. C. Blakesley, E. Music, F. Plag, G. Friesen, G. Corbellini, N. Riedel-Lyngskær, R. Valckenborg, M. Schweiger, W. Herrmann, „PV module energy rating standard IEC 61853-3 intercomparison and best practice guidelines for implementation and validation”, accepted IEEE JPV. DOI (identifier) 10.1109/JPHOTOV.2021.3135258</p>
Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya (Data Sets and Codes)
<p>This repository contains AWS datasets for the modelling periods considered in the analysis presented in the research paper, together with ablation measurements, pre-processed forcing data, T&C model codes, outputs and scripts for analysing outputs. When previously published elsewhere, references and links to the full, original datasets are provided under References.</p> <p>Matlab scripts for executing the T&C model are provided and should work stand-alone on any machine with a Matlab version 2019b or later installed.</p>
Replication data for: Energy flow analysis of an industrial ammonia refrigeration system
<p>This dataset includes energy data acquired from a pelagic fish processing plant, including data from an industrial ammonia refrigeration system that provides cooling and freezing. In addition, production data is included. Data from the system was analysed within the KSP project PCM-STORE (308847) supported by the Research Council of Norway and industry partners. PCM-STORE aims at building knowledge on novel PCM technologies for low-temperature thermal energy storage. Collecting and analysing data is an important part of evaluating the potential for reduction of CO2 emissions and increasing energy efficiency. Many processing plants measure and log data, but it is not often published. This dataset includes specific energy demand, peak power demand, power demand for different sections of the plant, ambient temperatures, and production volumes. The data was collected in 2021. The included graphics show the refrigeration system and some resulting tables and graphs. Production follows a seasonal cycle throughout the year, with no (or very low) production in the spring (Mar-May), and peak production in the autumn (Sep-Nov). The cycle is linked to the seasonal availability of fish. Annual SEC numbers (200-247 kWh/tonnes) were found to be in line with other Norwegian pelagic plants. A strong dependency between SEC and volume throughput were also found, where months of low production resulted in high SEC values and vice versa. Knowledge about the processes indicates that a fillet production is more energy intensive compared to round production, due to more energy demand from the fillet sections, higher mass (fish and brine) in each box and higher requirement of hot water for cleaning. This dataset is related to the conference paper "Energy flow analysis of an industrial ammonia refrigeration system and potential for a cold thermal energy storage" presented at the 15th IIR Gustav Lorentzen Conference on Natural Refrigerants, Trondheim, Norway 13-15 June 2022.</p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers
<p>The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events.</p> <p>The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/</p> <p>The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling" to be published in the journal Animal - Open Space. </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>
Dataset: Spatial Data Starter Kit for OnSSET Energy Planning in Kitui County, Kenya
<p>This is a set of openly-available data pre-processed to facilitate county-level energy planning using the Open Source Spatial Electrification Tool (OnSSET) in Kitui County, Kenya. It provides a ready-to-use starter kit of data inputs for county-level OnSSET analysis. The work to identify these data is submitted for publication - publication details will be added here as soon as possible upon release. These data are contained in spatial data files used to create the input for OnSSET in Kitui, and a prepared CSV data input for OnSSET in Kitui (<em>kitui_OnSSET_data</em>). The following spatial data files are included in the dataset:</p> <ul> <li>kitui_admin: A vector (.geojson) file containing the administrative boundaries of Kitui county. </li> <li>kitui_clusters: A vector (.geojson) file locating population clusters generated in data processing for OnSSET.</li> <li>kitui_demand: A raster (.tif) file containing merged health, agriculture, commercial, and residential demands for Kitui county in kWh.</li> <li>kitui_elevation: A raster (.tif) containing elevation information.</li> <li>kitui_GHI: A raster (.tif) file containing global horizontal irradiance data for Kitui county.</li> <li>kitui_hydro: A vector (.geojson) file containing the locations of hydropower stations in Kitui county. Note that there are none, and that this is expected.</li> <li>kitui_night_lights: A raster (.tif) file capturing the light emitted from Kitui county at night.</li> <li>kitui_power_stations: A vector (.geojson) file showing the locations of power stations in Kitui county.</li> <li>kitui_roads: A vector (.geojson) file showing the main roadways in Kitui county.</li> <li>kitui_transformers: A vector (.geojson) file showing transformer locations in Kitui county.</li> <li>kitui_transmission_lines: A vector (.geojson) file locating transmission lines in Kitui county. </li> <li>kitui_travel_hours: A raster (.tif) file showing travel time to the nearest market center in Kitui county.</li> <li>kitui_wind_100: A raster (.tif) file of wind speeds at 100 m in Kitui county.</li> </ul> <p>This dataset has been produced through work undertaken in the Climate Compatible Growth Programme.</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.