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95 results for “Wind Energy”
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Wind energy production in forests conflicts with tree - roosting bats
<p>Many countries are investing heavily in wind power generation,<sup>1</sup> triggering a high demand for suitable land. As a result, wind energy facilities are increasingly being installed in forests,<sup>2,3</sup> despite the fact that forests are crucial for the protection of terrestrial biodiversity.<sup>4</sup> This green-green dilemma is particularly evident for bats, as most species at risk of colliding with wind turbines roost in trees.<sup>2</sup> With some of these species reported to be declining,<sup>5-8</sup> we see an urgent need to understand how bats respond to wind turbines in forested areas, especially in Europe where all bat species are legally protected. We used miniaturized global positioning system (GPS) units to study how European common noctule bats (<em>Nyctalus noctula</em>), a species that is highly vulnerable at turbines,<sup>9</sup> respond to wind turbines in forests. Data from 60 tagged common noctules yielded a total of 8129 positions, of which 2.3% were recorded at distances <100 m from the nearest turbine. Bats were particularly active at turbines <500 m near roosts, which may require such turbines to be shut down more frequently at times of high bat activity to reduce collision risk. Beyond roosts, bats avoided turbines over several kilometers, supporting earlier findings on habitat loss for forest-associated bats.<sup>10</sup> This habitat loss should be compensated by developing parts of the forest as refugia for bats. Our study highlights that it can be particularly challenging to generate wind energy in forested areas in an ecologically sustainable manner with minimal impact on forests and the wildlife that inhabit them.</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>
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>
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>
Database of Participatory Practices and Social Innovations in Wind Energy Developments
<p>Inês Campos was responsible for designing the database, collecting data, and analyzing data. Flávio Oliveira also collaborated in the design of the database and data collection. </p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Result data related to "Cost-potential curves of onshore wind energy: the role of disamenity costs"
<p>This dataset estimates the impact of incorporating disamenity costs of wind onshore in Europe (in addition to technology cost). The data haset has been generated and used for the publication:</p> <blockquote> <p>Ruhnau, O., Eicke, A., Sgarlato, R., Tröndle, T., Hirth, L., 2022. Cost-potential curves of onshore wind energy: the role of disamenity costs. Environmental and Resource Economics. DOI: <a href="https://doi.org/10.1007/s10640-022-00746-2">10.1007/s10640-022-00746-2</a></p> </blockquote> <p>The corresponding code is published on <a href="https://github.com/timtroendle/wind-onshore-cost-potential">GitHub</a>.</p> <p>The dataset includes:</p> <ol> <li>Maps that exhibit the population count within a predefined distance (e.g., "population-within-1km.tif") and the resulting disamenity costs ("disamenity-cost.tif")</li> <li>Tables that summarize the engineering and disamenity costs faced at each potential turbine location in the EU (e.g., "turbines-AT.csv")</li> </ol>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts
<p>The data table lists the calculated present burden (IST-Belastungsgrad) based on the year 2014 and the maximum possible burden (MAX-Belastungsgrad) on the population caused by the further expansion of wind energy. The so-called burden level is calculated accounting for the area occupied by wind turbines, the total area of a district and the population density. Additionally the table holds data on possible future burden levels based on two scenarios for the year 2050. Each district can be identified by its key, "Regionalschlüssel", and corresponding geo data (EPSG: 25832) of the administrative area provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>The dataset was created in the context of the interdisciplinary research project VerNetzen and is described in detail in the final project report: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 98-118, 143-145.</p> <p><strong><em>Deutsch:</em></strong></p> <p>Die Tabelle enthält u.a. den derzeitigen Belastungsgrad, festgestellt für das Jahr 2014, und den maximal möglichen Belastungsgrad je Landkreis. Der Belastungsgrad ist ein Indikator für die durch den Zubau von Windenergie betroffene Bevölkerung und berechnet sich aus der Gesamtfläche eines Landkreises, der für die Windenergie genutzten Fläche und der Bevölkerungsdichte. In der Tabelle sind ebenfalls mögliche zukünftige Belastungsgrade auf Grundlage zweier Projektszenarien für das Jahr 2050 enthalten. Die jeweiligen Landkreise können mit dem Regionalschlüssel oder den geographischen Daten (EPSG: 25832) des Bundesamtes für Kartographie und Geodäsie zugeordnet werden: © GeoBasis-DE / BKG 2014 (Daten verändert).</p> <p>Der Datensatz ist im Kontext des interdisziplinären Forschungsprojekts VerNetzen entstanden und ist ausführlich im Projektabschlussbericht beschrieben: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S.98-118, S.143-145.</p> <p> </p> <p> </p>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts - auxiliary values
<p>The table contains the population and the size of the total area for each German district as of 2013. Furthermore it contains the size of those areas per district, that potentially could be used for wind energy.</p> <p>The data on the population is provided by the Federal Statistical Office and the statistical Offices of the Länder: © Federal Statistical Office and the statistical Offices of the Länder, Regionaldatenbank Deutschland, December 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (data was changed). The total district area is derived from geo data provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>For further information on potential areas see VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 105-109.</p> <p><em><strong>Deutsch:</strong></em></p> <p>Die Tabelle umfasst die Bevölkerungsanzahl und Flächengröße je deutschem Landkreis für das Jahr 2013. Außerdem ist die Größe jener Fläche angegeben, die potentiell für die Windenergie genutzt werden könnte.</p> <p>Die Bevölkerungszahlen werden von den Statistischen Ämtern des Bundes und der Länder zur Verfügung gestellt: © Statistische Ämter des Bundes und der Länder, Regionaldatenbank Deutschland, Dezember 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (Daten geändert). Die Landkreisflächen werden auf Grundlage von Geodaten des Bundesamtes für Kartographie und Geodäsie berechnet: © GeoBasis-DE / BKG 2014 (Daten geändert).</p> <p>Für weitere Informationen bzgl. der Potentialflächen siehe VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S. 105-109.</p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </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 3, 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>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</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). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
Source data for "Halving the North Sea's offshore wind energy carbon footprint"
<p>This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993. </p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>wind</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>1506</span></span><span><span> datapoints from </span></span><span><span>28</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.</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>
Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design
<p>Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087.</p> <p>Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. </p> <p>Additional details can be found from the published article: <a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a></p>
Wind shadows from U.S. east coast offshore wind energy lease areas.
<p>Georeferenced data layers describing whole wind farm wakes (wind shadows) for use in planning and development along the U.S. east coast based on WRF simulations performed using the accompanying namelist. Full details of the analysis are provided in: Pryor and Barthelmie: Wind shadows impact planning of large offshore wind farms</p> <p> </p> <p>This work is supported by the U.S. Department of Energy (DoE) (DE-SC0016605). The research used computing resources from the National Science Foundation: Extreme Science and Engineering Discovery Environment (XSEDE) (allocation award to SCP is TG-ATM170024) and National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
Dataset: Global X Wind Energy ETF (WNDY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset of historical hourly information of four european wind farms for wind energy forecasting and maintenance
<p><strong>If you use this dataset please cite this paper: Sánchez-Soriano, J.; Paniagua-Falo, P.J.; Gómez Muñoz, C.Q. Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance. Data 2025, 10, 38. <a href="https://doi.org/10.3390/data10030038" target="_blank" rel="noopener">https://doi.org/10.3390/data10030038</a></strong></p> <p>For an electric company, having an accurate forecast of the expected electrical production and maintenance from its wind farms is crucial. This information is essential for operating in various existing markets such as Iberian Energy Market Operator - Spanish Hub (OMIE in its Spanish acronym), Portuguese Hub (OMIP in its Spanish acronym), and Iberian electricity market between the Kingdom of Spain and the Portuguese Republic (MIBEL in its Spanish acronym), among others. The accuracy of these forecasts is vital for estimating the costs and benefits of the handling of electricity. This article explains the process of creating the complete dataset, which includes the acquisition of the hourly information of four European wind farms as well as a description of the structure and content of the dataset which amounts to 2 years of hourly information. The wind farms are in three countries, two from Auvergne-Rhône-Alpes (France), Aragon (Spain) and the Piemonte region (Italy). The presented dataset is available and accessible to improve the forecasting and management of wind farms, especially for the detection of faults and the elaboration of a preventive maintenance plan.</p> <p>The full description of the characteristics of the dataset, as well as its components, format and methodology, can be found here: "Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance". Data 2025, 10, 38. <a href="https://doi.org/10.3390/data10030038" target="_blank" rel="noopener">https://doi.org/10.3390/data10030038</a></p>
Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate
<p>This repository includes raw datasets, Python scripts, and output data products associated with the MRes project '<span>Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate</span>', by Josh Abrahams, University of Leeds. </p>
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
<p>Data for the Wind Energy Science paper "Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization".</p> <p>The data includes a file describing the wind direction distribution. The i<sup>th</sup> probability value corresponds to the probability of the wind coming between direction i and i+1.</p> <p>The other data files, corresponding to the wind farm layouts, provide the x,y coordinates of the wind turbines. </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.