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52 results for “Wind power”
Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains capacity factors timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000--2016. It has been generated using <a href="https://www.renewables.ninja">Renewables.ninja</a> and is based on MERRA-2 reanalysis data. For each of the ~2700 onshore location, it contains one time series for onshore wind turbines and five time series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops. For each of the ~2800 offshore location there is one timeseries for offshore wind turbines.</p> <p>Two GeoTIFF files contain spatial information of onshore and offshore locations. For each of the three technologies -- onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDF files are linked through unique IDs for all locations.</p> <p>This data serves as input data to euro-calliope, a model of the European electricity system.</p> <p>The following parameters have been used to generate the timeseries:</p> <pre><code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code></pre> <p>CHANGELOG:</p> <p>Version 3 (2022-05-18)</p> <p>* Update spatial scope to include Iceland and its offshore EEZ.<br> * Update temporal scope to include 2017 and 2018.</p> <p>Effect of increasing spatial scope is a slight change in the spatial position of the data points.</p> <p>Version 2 (2020-06-18)</p> <p>* Add time series for rooftop PV with different orientations.</p>
Wind power plants layouts according to arbitrary reference points, Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array
<p><strong>If this dataset helps your research, please cite it and the papers further below (according to which OWPP you study).</strong></p> <p>This dataset contains the layouts of the Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array offshore wind power plants (OWPPs), which can be utilized in a variety of studies. </p> <p>The X and Y coordinates, in kilometers, were written according to arbitrary reference points. The positions of wind turbine generators (WTGs) and substations (SS) for the OWPPs came from the sources below.</p> <p><strong>Thanet</strong>: WTGs from [1] (page 7), SS based on [3] (page 9).<br><strong>West of Duddon Sands</strong>: WTGs from [2] (page 5), SS based on [3] (page 9).<br><strong>Ormonde</strong>: WTGs and SS from [4] (page 2).<br><strong>Westermost Rough</strong>: WTGs and SS from [5] (page 4).<br><strong>Horns Rev 1</strong>: WTGs and SS from [6] (page 6).<br><strong>Horns Rev 2</strong>: WTGs and SS from [7] (page 5).<br><strong>Anholt</strong>: WTGs and SS from [8] (page 3).<br><strong>London Array</strong>: WTGs from [9] (page 15), SS based on [10] (page 2).</p> <p>From the OWPPs' layout figures [1-9], I used Graph Grabber 2.0.2* to extract the data points. Then, based on visual inspection of layouts in [1-9], I utilized 2D projections to align WTGs that should be aligned. References [1], [2], and [9] do not provide the SS positions. Thus, I carefully overlapped the layouts with other layouts from [3] and [10] to approximate the SS locations.</p> <p>Regarding the arbitrary reference points for the coordinates, although the values in the X and Y axes differ from [1-9], note that the distances among the WTGs are the same from [1-9]. Furthermore, there are no axes in [1,5]. Instead, distances are given, which are enough to obtain the layout. Values in meters were converted to kilometers.</p> <p>As seen in the attachments, the coordinates can be obtained via either "h5" or "csv" files, which can be easily read by Matlab, Julia, and Python, among others. <strong>The name of the datasets in the "h5" file are</strong>: "Thanet", "WDS", "Ormonde", "WMR", "HornsRev1", "HornsRev2", "Anholt", and "LondonArray".</p> <p><strong>Except for the London Array OWPP, the first row in all data matrices represents the SS coordinates, whereas the subsequent rows represent the WTGs. London Array has 2 substations, thus the first and second rows represent their coordinates. In all matrices, the first and second columns are the X and Y coordinates, respectively.</strong></p> <p>*<a href="https://www.quintessa.org/software/downloads-and-demos/graph-grabber-2.0.2">Graph Grabber 2.0.2 | Downloads And Demos | Software | Quintessa Limited | Scientific and Mathematical Consultancy</a></p>
Wind Speed vs Spanish Power Prices
<p>Average, min and max daily OMIE power prices (Spanish market) with corresponding wind average speed and maximum speed for each day. Units: €/MWh (Power Price), km/h (wind speed).</p>
Power production from the U.S. east coast offshore wind lease areas
<p>The accompanying file include information regarding the set up of WRF simulations of power production and wake extents from offshore wind lease areas along the U.S. east coast, and also data presented in figures in the "Wind power production from the U.S. east coast offshore lease areas" paper and the associated MATLAB data processing code.</p> <p>The US Department of Energy Office of Science (DE-SC0016605), the US Department of Energy Office of Energy Efficiency and Renewable Energy and New York State Energy Research and Development Authority via the National Offshore Wind Research and Development consortium (147505) funded this research. This research was enabled by computational resources supported by the U.S. National Science Foundation via the Extreme Science and Engineering Discovery Environment (XSEDE) (award TG-ATM170024) and ACI-1541215, and those of the 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>
Wind speed and power potential for Switzerland
<p>When using the provided data, please cite the following article:</p> <p>Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859.</p> <p> </p> <p><strong>Summary: </strong></p> <p>This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017.</p> <p>Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 monitoring stations over the country. The data are then interpolated using a spatio-temporal machine learning model, allowing the estimation of wind speed and its uncertainty at unsampled locations. Then, the modelled spatio-temporal wind speed field is used to estimate the wind power. This is computed based on the characteristic parameters of an Enercon E-101 wind turbine at 100 meters hub height. The latter indicates the distance from the turbine platform to the rotor of an installed wind turbine, showing how high the turbine stands above the ground without considering the length of the turbine blades.</p> <p>The hourly estimations of wind speed are then averaged over each of the ten years studied, for each 250 x 250 spatial location, while wind power data are summed over each year for each spatial unit. Advantages and limitations of the proposed method are discussed in Amato et al. (2021).</p> <p><strong>Data description: </strong></p> <p>The hourly estimation of wind speed and power for Switzerland from 2008 to 2017 are available under request. Here we share the annual values. For both wind speed and power, the data are available over 660697 spatial units of 250 x 250 meters each, covering the entire Swiss territory. Check details in the file Data_description.pdf.</p> <p>Data are provided in the pickle format, see <a href="https://docs.python.org/3/library/pickle.html#module-pickle">https://docs.python.org/3/library/pickle.html#module-pickle</a>.</p>
Modified pool system based on the IEEE RTS-96 system incl. 39 wind power producers
<p>This is the data-set associated with the numerical simulations in the paper "A. Papakonstantinou, P. Pinson, <em>Population Dynamics for Renewables in Electricity Markets: A Minority Game View</em>". The paper will be presented in 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) in Oct. 16-20, 2016 in Beijing, China.</p> <p>We modify the original data-set [1] by adding the marginal costs for conventional generation introduced by [2] and flexible generators capable of providing up and down regulation following [3]. The cost of up-regulation is assumed to be 10% higher than the day-ahead cost and the cost of down-regulation 9% less than the day-ahead ahead costs.</p> <p>Furthermore, regarding stochastic generation, we assume zero marginal and cost free spilling action, while load shedding induces a cost of 1000 EUR/MWh. Finally, we assume that the total demand is at 80% of the conventional generation [3], while the total capacity of the 39 stochastic producers is at 30% of the demand.</p> <p>Within the data file the specific data used for the analysis in the paper are under pes_input().</p> <p>[1] IEEE RTS Task Force of APM Subcommittee, “The ieee reliability test system-1996.” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 1010–1020, 1999.</p> <p>[2] D. Kirschen. Unit commitment data for modernized ieee rts-96. Accessed: 10-03-2016. [Online]. Available: http://www.ee.washington.edu/research/real/library.html</p> <p>[3] A. J. Conejo, M. Carri ́on, and J. M. Morales, Decision Making Under Uncertainty in Electricity Markets. Springer, 2010.</p> <p> </p> <p> </p> <p> </p> <p> </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>
Figures and data: Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
<p><strong>Figures from the publication <em>Combining wake redirection and derating strategies in a load-constrained wind farm power maximization.</em></strong></p> <p> </p> <p>*.fig files can be opened in <code>Matlab</code></p> <p>*.csv files can be opened through a standard text editor (e.g.,<code> Notepad++</code>), imported and visualized in <code>Matlab</code> through the functions <code>>>readmatrix()</code> and <code>>>readtable()</code></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>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
List of wind power projects in Vietnam
<p>Historical inventory of wind power projects in Vietnam updated 2022-05-04 "post FIT race"</p> <p>This release is complete for projects with COD or PPA at the end of the FIT period.</p> <p>The number of records is 548, of which 360 refer to active projects phases, and 215 have investment costs.</p> <p>Each record describes a wind project phase. The table includes projects at all stages of the lifecycle. Some have been mentioned years ago and are now of historical interest only. Some have just a signed exploration MoU. All records are referenced to one or several public sources.</p> <p>Fields: Record status, Project stage, Project name, Owner, Location (hamlet, commune, province, region, longitude, latitude), Location type (onshore/nearshore/offshore), Capacity (MWp), Connection plan, Turbines, Investment (VND or USD),</p> <p>dates (MoU, Investment Licence,, Groundbreaking), Note and sources.</p> <p>The dataset also include:</p> <ul> <li>Descriptive data paper.</li> <li>A CSV export of the table.</li> <li>Python module to read the data from the CSV and python script to print the descriptive statistics.</li> </ul>
Bias correction of simulated Brazilian wind power generation based on reanalysis data
<p>Available data:</p> <p>- Brazilian wind power generation time series derived from MERRA-2 reanalysis data with wind speed and wind power bias correction.</p> <p>- Wind speed correction factors derived from INMET wind speeds (http://www.inmet.gov.br/portal/) as well as wind power correction factors dervied from ONS wind power generation time series are also provided.</p> <p>- Simulation of about 38 years of wind power generation with fixed capacity.</p> <p>Data used for validation:</p> <p>- Historical wind power generation data, which were used for validation of simulated time series, can be found at the ONS homepage (http://ons.org.br/Paginas/resultados-da-operacao/historico-da-operacao/geracao_energia.aspx).</p> <p> </p> <p>Other Links:</p> <p>- Information on this will soon be found here: https://refuel.world/</p> <p>- Code for generating time series, validation and analysis: https://github.com/KatharinaGruber/BrazilWind</p> <p>- Master thesis belonging to data: https://doi.org/10.5281/zenodo.1471221</p>
Bias-corrected simluated wind power generation time series for Brazil
<p>Simulated and bias corrected wind power generation time series data sets for Brazil, its North-East and South, seven states and seven wind parks.</p> <p>The data sources, generation and validation of the datasets are described in the article "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil", preprint available on arXiv: arxiv.org/abs/1904.13083, final version DOI: <a href="https://doi.org/10.1016/j.energy.2019.116212">10.1016/j.energy.2019.116212</a></p> <p>Code for generating the datasets is available at github.com/KatharinaGruber/BrazilWindpower_biascorr</p> <p> </p> <p>The files "comp_*" contain comparisons of simulated and observed wind power generation time series with daily resolution for all regions.</p> <p>"comp_noc.RData" is for comparison of interpolation methods and contains time series generated with Nearest Neighbour interpolation (NN), Bilinear Interpolation (BLI) and Inverse Distance Weighting (IDW).</p> <p>"comp_wmsa.RData" is for comparison of wind speed mean approximation methods and contains time series generated with Nearest Neighbour interpolation (NN - no correction applied), mean approximation with measured data (IN) and mean approximation with the Global Wind Atlas (GWA).</p> <p>"comp_wsc.RData" is for comparison of spatiotemporal wind speed correction methods and contains time series generated with mean approximation with the Global Wind Atlas (wmsa) and combined mean approximation with the Global Wind Atlas and hourly and monthly mean approximation with measured data (wschm).</p> <p> </p> <p>The files "statpowlist_*" contain hourly simulated wind power generation time series for three interpolation methods (NN, BLI, IDW), two mean approximation methods (wsmaIN - measured data (INMET), wsmaWA - Global Wind Atlas) as well as for spatiotemporal (hourly and monthly) wind speed bias correction (wschm) for each wind park available in The Wind Power dataset.</p>
Datasets and Codesets for "Wavelet Decomposition and Neural Networks: A Potent Combination for Short Term Wind Speed and Power Forecasting"
<p>This is the datasets and codesets used in the paper:</p> <p>A. E. Kio, J. Xu, N. Gautam, and Y. Ding, 2024, “Wavelet decomposition and neural networks: A potent combination for short term wind speed and power forecasting,” Frontiers in Energy Research, section of Wind Energy, Vol. 12, pp. 1277464. </p> <p>The PDF file, "Reproducibility Report," explains how to reproduce the results in the tables and figures.</p>
Dataset on PowerWorld Software Power Flow Calculations on an Underground Distribution Feeder for Inserting Renewable Distribution Generation from Biogas, Photovoltaic and Small Wind Sources
<p>This Dataset brings all the information, details and source files used for power flow studies of the USP-105 underground feeder of the distribution medium voltage network in the University of São Paulo campus, which has received several embedded DG sources, namely a biogas plant, photovoltaic units and a small wind turbine.</p> <p>The power flow simulations were realized using the PowerWorldTM Simulator, v.23</p> <p>The files types on the Dataset are: </p> <p>.pwb, .pwd and tsb: Powerworld software input files for the simulations</p> <p>.csv: where a semicolon symbol (;) is used as a column separator, while a dot symbol (.) represents the decimal separator. The first row of each CSV file corresponds to the header row to help identify data.</p>
Evidence of absence regression: a binomial N-mixture model for estimating fatalities at wind power facilities
Open the record for dataset details and reuse information.
On the Fourier Contribution of Strong Current Sheets to the High-Frequency Magnetic Power Spectral Density of the Solar Wind
<p>These two tab-delimited files list the Universal Times at which the WIND spacecraft and the MMS spacecraft encounter strong current sheets in the solar wind.</p>
Machine-learning-long-term-wind-power-time-series
<p>In this research work we assess how time-series generated by machine learning models (MLM) compare to Renewables.ninja in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. With this archive, we want to make three machine learning derived time-series available in the Feather format for everyone interested.</p>
High-resolution wind power generation time series for Germany in the period 2000-2015
<p>High-resolution wind power generation time series for Germany in the period 2000-2015. A paper describing the applied methodology can be found in this repository as well.</p> <p>The final temporal resolution is hourly and the spatial resolution NUTS 3. The data is stored in csv and hdf5 files.</p> <p>More information on the data set, e.g. missing time stamps and versioning, can be found in readme.txt.</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.