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858 results for “Energy Data”
Solar and meteorological data collected from the Plaine des Palmistes Parc National station (La Réunion) by the ENERGY-Lab at the University of La Reunion between December 2018 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Radio Telescope Bras D'Eau station (Mauritius) by the ENERGY-Lab at the University of La Reunion between November 2015 and March 2023
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Bras Panon Moreau station (La Réunion) by the ENERGY-lab at the University of La Reunion between November 2010 and September 2014
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Saint Paul Le Carat station (La Réunion) by the ENERGY-Lab at the University of La Reunion between October 2022 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Saint Joseph Marie station (La Réunion) by the ENERGY-lab at the University of La Reunion between September 2013 and June 2015
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »
<p>This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).</p> <p>It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.</p> <p>Five separate datasets are included:</p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Algeria<br>Angola<br>Benin<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Republic<br>Chad<br>Congo Republic<br>Democratic Republic of the Congo<br>Djibouti<br>Egypt<br>Equatorial Guinea<br>Eritrea<br>Eswatini<br>Ethiopia<br>Gabon<br>The Gambia<br>Ghana<br>Guinea<br>Guiné-Bissau<br>Côte d'Ivoire<br>Kenya<br>Lesotho<br>Liberia<br>Libya<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Morocco<br>Mozambique<br>Namibia<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>Somalia<br>South Africa<br>South Sudan<br>Sudan<br>Togo<br>Tunisia<br>Uganda<br>Tanzania<br>Zambia<br>Zimbabwe</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <span><a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></span></p> <p><strong>See also</strong></p> <p>Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a></p>
Data for "Microscopic origin of the effect of substrate metallicity on interfacial free energies"
<p>Data related to the article "Microscopic origin of the effect of substrate metallicity on interfacial free energies"</p> <p>Laura Scalfi, Benjamin Rotenberg, arXiv:2105.06799 [physics.chem-ph]</p> <p> </p>
Library of price and performance data of domestic and commercial technologies for low-carbon energy systems
<p>This library consists of extensive price and performance data of commercially available technologies for low-carbon energy systems on the UK market, including domestic and commercial applications. All information is obtained from published manufacturer datasheets and pricelists. The library contains useful information for energy-system and technology modellers in their efforts to capture the techno-economic characteristics of different technology options, minimise uncertainties and suggest reliable system- and technology-design strategies.</p> <p>The data analysis shows how technology characteristics vary with component choice, technology size and to capture the spread of values found between different suppliers, which can be used to determine uncertainty bounds on key performance indicators. Fitting techniques can be used to determine relationships arising from the collected data, infer values for which data are not available and quantify the related variability in technology characteristics.</p> <p>This work was conducted by members of the <a href="https://www.imperial.ac.uk/clean-energy-processes/">Clean Energy Processes (CEP) Laboratory</a> and is part of Project 2 of the <a href="https://www.imperial.ac.uk/energy-futures-lab/idles/">Integrated Development of Low-Carbon Energy Systems (IDLES)</a> project. IDLES brings together researchers across Imperial College London and partner organisations and companies to provide the evidence needed to facilitate a cost-effective and secure transition to a low-carbon future. The overarching aim of Project 2 is: (i) to characterise current and new/future technologies in terms of cost and performance to provide evidence for whole-energy system modelling; and (ii) to extend the capabilities of whole-energy-system models so that they can, apart from optimising energy network infrastructures, provide information to manufacturers about the optimal choice of materials, components and the design of key technologies.</p> <p>The library will be updated regularly as more data regarding existing and new technologies are collected.</p>
The global wave energy spectra data
<p>The data were downloaded from ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/GLOBAL/2017_ECMWF/ef, which are derived by Wavewatch model.</p>
Code for data and figures published in "Solar energy as an early just transition opportunity for coal-bearing states in India"
<p>The following code and data were used to generate the figures in the article "Solar energy as an early just transition opportunity for coal-bearing states in India". The article was published in Environmental Research Letters (<a href="https://iopscience.iop.org/article/10.1088/1748-9326/ac5194">https://iopscience.iop.org/article/10.1088/1748-9326/ac5194</a>)</p> <p>The code is written in R. Before running the Rmd file, create a folder called "Data" and store all the files there, except the Rmd file.</p>
Supplementary Data: A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors
<p>This dataset is the supplementary dataset for the case study used in the journal article (<a href="https://doi-org.tudelft.idm.oclc.org/10.1016/j.apenergy.2022.118572">https://doi.org/10.1016/j.apenergy.2022.118572</a>):</p> <p>A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors</p> <p> </p> <p>Before using the dataset, please</p> <p>1. refer to the article for the details of the data used in the case study,</p> <p>2. read README.txt for the structure of the dataset.</p> <p> </p> <p>Please also kindly cite the journal article when using the dataset.</p>
Life cycle-based environmental impacts of energy scenarios - additional data
<p>This data set documents additional results of the paper "Life cycle-based environmental impacts of energy system transformation strategies for Germany: Are climate and environmental protection conflicting goals?" (Tobias Naegler and co-authors, published in Energy Reports (2020), https://doi.org/10.1016/j.egyr.2022.03.143). It shows life cycle-based environmental impacts for 10 different transformation strategies for the German energy and transport system.</p>
Harmonized remodeled energy system transformation strategies for Germany - additional data
<p>This dataset compares 10 different scenarios for the transformation of the German energy system by 2050. These scenarios were used in the <a href="https://www.innosys-projekt.de">InNOSys project</a> as a starting point for a multidimensional impact assessment and evaluation of different transformation strategies (see also <a href="https://www.mdpi.com/2071-1050/13/9/5217">https://www.mdpi.com/2071-1050/13/9/5217</a>).<br> As a source of inspiration for these scenarios, 10 different transformation strategies were used, as published for Germany in 2012-2018. However, for the present document, the original scenarios were re-modeled in a harmonized way.</p> <p>An additional documentation of the scenarios is also available on ZENODO. </p>
Data for Horowitz et al. (2022). The Energy System Transformation Needed to Achieve the U.S. Long-Term Strategy
<p>Data repository for Horowitz, et al. 2022, "The Energy System Transformation Needed to Achieve the U.S. Long-Term Strategy"</p> <p>All data shown in the paper is included here. Data includes results from:</p> <p>1. U.S. LTS GCAM scenarios (labeled "GCAM")<br> 2. U.S. LTS NEMS scenarios (labeled "NEMS")<br> 3. Princeton's Net-Zero America scenarios (labeled "NZA")<sup>a</sup><br> 4. Williams et al. (2020) scenarios (labeled "Williams")<sup>b</sup></p> <p><br> Results are included in the following files:</p> <p>1. waterfall.csv - Figure 1. Emissions decomposition by scenario. GCAM only.<br> 2. clean_fuels.csv - Figure 2. Clean fuel (bioliquids, biogas, and blue/green hydrogen) consumption. All models.<br> 3. coal.csv - Figure 2. Primary energy consumption of coal without CCS. All models.<br> 4. ZEV_stock.csv - Figure 2. Zero-emission vehicle percentage of light-duty vehicle stock. All models.<br> 5. ghg_by_type.csv - Figure 3. Greenhouse gas emissions by gas/source. GCAM only.</p> <p> </p> <p>Reference:</p> <p>a) Larson, E., Greig, C., Jenkins, J.,Mayfield, E., Pascale, A., Zhang, C., Drossman, J., Williams, R., Pacala, S., Socolow, R., et al. (2021). Net-zero America: Potential pathways, infrastructure, and impacts. (Princeton University).</p> <p>b) Williams, J. H., Jones, R. A., Haley, B., Kwok, G., Hargreaves, J., Farbes, J., & Torn, M. S. (2021). Carbon-Neutral Pathways for the United States. AGU Advances, 2, e2020AV000284. <a href="https://doi.org/https://doi.org/10.1029/2020AV000284">https://doi.org/https://doi.org/10.1029/2020AV000284</a>.</p>
Data and code: Climate policy accelerates structural changes in energy employment
<p>The file contains code to create the figures used in main text and supplementary information of the paper <strong>Climate policy accelerates structural changes in energy employment</strong>.</p> <p>To run the RMD file and see the resulting figures, press Knit on R studio (requires the package knitr), or else see the attached HTML file, already created through such a process.</p>
Energy Efficiency Technologies Costs - Support File for Data Manipulation Starter Data Kits
<p>This file can be used to manipulate the Energy Efficiency Technologies Costs data for the Starter Data Kits. </p>
Data for the paper: Water-Food-Energy nexus: Learning from global cities using machine learning algorithms
<p>This data respository contains the datasets used to produce the "<strong>Water-Food-Energy nexus: Learning from global cities using machine learning algorithms" </strong>article. </p>
What energy infrastructure to support 1.5°C scenarios? - Scenario data
<p>This excel file summarizes the main assumptions considered for the analysis " What energy infrastructure to support 1.5°C scenarios?", prepared by Artelys on behalf of the European Climate Foundation in 2020. The report is available here: https://www.artelys.com/wp-content/uploads/2020/12/Artelys-2050EnergyInfrastructureNeeds.pdf</p>
Scenario data for article: Effects of the energy transition on environmental impacts of cobalt supply: A prospective Life Cycle Assessment study on future supply of cobalt
<p>This dataset contains the background data for the paper '<a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13258">Effects of the energy transition on environmental impacts of the cobalt supply: A prospective Life Cycle Assessment study on the future cobalt supply</a>' as published in the Journal of Industrial Ecology.</p> <p><strong>Please note that an easier to use version of this data for LCA is available through the Premise (<a href="https://www.sciencedirect.com/science/article/pii/S136403212200226X">Sacchi et al. 2022</a>) Community Scenarios <a href="https://github.com/premise-community-scenarios/cobalt-perspective-2050">here</a>.</strong> This version is slightly adapted to fit into the Premise architecture and is compatible with ecoinvent v3.8 cutoff.</p> <p>This repository contains:</p> <ul> <li>Python code + readme to model the variables, generate presamples packages and generate LCA results based on those. (code folder)</li> <li>Input and output data for Variables 1-3 (files 1&2)</li> <li>Presamples excel sheets for each variable/scenario combination (file 3)</li> <li>Summarized LCA results (the full results can be generated through running the code provided) (file 4)</li> <li>Full LCA results used for the contribution analysis (file 5)</li> <li>Underlying data for each of the figures (file 6)</li> </ul> <p>We refer to the paper (linked above) for more information on the study.<br> </p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p> <p><strong>Access: </strong>Open access</p> <p> </p> <p>[Changelog]</p> <p>2023-03-23 - 1.3.1: Add link to Premise Community scenario page.<br> 2022-05-18 - 1.3.0: Fix minor error in data files '4 - LCA results' and '6 - Figure data' in demand amounts for total impacts.<br> 2022-04-06 - 1.2.1: Included link to article after publication<br> 2022-03-30 - 1.2.0: Included underlying figure data<br> 2022-01-24 - 1.1.1: Opened repository after paper acceptance<br> 2021-11-26 - 1.1.0: Update of code to comply with peer-review<br> 2021-07-12 - 1.0.0: Set-up of repository</p>
Global demand 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>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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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.