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342 results for “Solar wind”
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>
Historical Weather, Load, Wind, and Solar Data for the Salt River Project
<p>We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:</p><ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>
Solar Wind properties measured with instruments on the Advanced Composition Explorer (ACE)
<p>Combined ACE/SWEPAM, ACE/Mag, and ACE/SWICS data set<br> ACE/MAG and ACE/SWEPAM data are taken from the ACE Science center (https://izw1.caltech.edu/ACE/ASC/) and binned to the 12-minute time resolution of SWICS.<br> The SWICS data is based on the PHA data and analyzed as described in Berger (2008).<br> This data set is used in the following two publications:<br> Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023, submitted), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>Contact: Verena Heidrich-Meisner, CAU Kiel heidrich@physik.uni-kiel.de</p> <p>We thank the science teams of ACE/SWEPAM, ACE/MAG as well as<br> ACE/SWICS for developing, maintaining and calibrating the instruments and for providing the respective level 2 and level 1 data products.<br> This work was supported by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) as SOHO/CELIAS 50 OC 2104.</p> <p>Data products description:<br> year: year of observation (int)<br> time: day of year in current year as float<br> yeartime: time in years as float (UTC)<br> vsw: solar wind proton speed in km/s, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> dsw: solar wind proton density in cm^{-3}, measured by ACE/SWEPAM (level 2 from ACE Science Center)and rebinned to 12 minute time resolution<br> tsw: solar wind proton temperature in K, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> B: magnetic feld strength in nT, measured by ACE/MAG (level 2 from ACE Science Center)<br> colage: proton-proton collisional age computed as 6.4* 1e8 * dsw /(vsw* tsw**(3/2)) in K^{3/2} s^2 cm^3 km^{-1}<br> dO7_6: ratio of the O7+ to O6+ charge state densities, measured by ACE/SWICS, derived directly from PHA (pulse height analysis) data<br> eO7_6: estimate of the relative error of dO7_6 based on the counting statistics<br> ldO7_6: decadic logarithm of dO7_6<br> elO7_6: estimate of the relative error of the decadic logarithm dO7_6 based on the counting statistics<br> mcsFe: mean charge state of Fe, based on SWICS PHA of Fe8+, Fe9+, Fe10+, Fe11+, and Fe12+ in units of the elementary charge e. At least 10 counts distributed over Fe8+, Fe9+, Fe10+, Fe11+ and Fe12+ are required<br> emcsFe: estimate of the relative error of dO7_the mean Fe charge state in e (assumes 10% relative error for each Fe charge state)<br> cor_hole: coronal hole wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> sec_rev: sector reversal plasma wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> stream_belt: streamer belt wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> ICME: interplanatery coronal mass ejections time periods (with a six hour safety margin before and after each ICME) from the Jian (2006,2011) and Richardson & Cane (2014, 2018) ICME lists. Entries are 0 or 1, 1 of the data point is assigned to this type.<br> totalCountsFe: number of counts in ACE/SWICS distributed over Fe8+-Fe12+<br> The data set is restricted to data points where valid data points are available for all listed data products. Only for the mean charge state of Fe invalid data points are indicated with nan (not a number)</p> <p>References:<br> Berger, L. 2008, PhD thesis, Kiel, Christian-Albrechts-Universität, Diss., 2008<br> Gloeckler, G., Cain, J., Ipavich, F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 497–539<br> McComas, D., Bame, S., Barker, P., et al. 1998b, in The Advanced Composition Explorer Mission (Springer), 563–612<br> Smith, C. W., L’Heureux, J., Ness, N. F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 613–632</p> <p>Xu, F. & Borovsky, J. E. 2015, Journal of Geophysical Research: Space Physics, 120, 70<br> Heidrich-Meisner, V., Berger, L., & Wimmer-Schweingruber, R. F. 2020, Astronomy & Astrophysics, 636, A103<br> Jian, L., Russell, C., & Luhmann, J. 2011, Solar Physics, 274, 321<br> Jian, L., Russell, C., Luhmann, J., & Skoug, R. 2006, Solar Physics, 239, 393<br> Richardson, I. G. 2004, Space Science Reviews, 111, 267<br> Richardson, I. G. 2018, Living reviews in solar physics, 15, 1</p> <p>Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>year/1 time/day of year yeartime/UTC vsw/km/s dsw/cm^{-3} tsw/K B/nT colage/(K^{3/2} s^2 cm^3 km^{-1}) dO7_6/1 eO7_6/1 ldO7_6/1 elO7_6/1 mcsFe/e emcsFe/e cor_hole/bool sec_rev/bool stream_belt/bool ICME/bool totalCountsFe/1</p>
The winds of young Solar-type stars in the Hyades - Quiet Sun model
<p>This is the quiet Sun model from my MNRAS paper "The winds of young Solar-type stars in the Hyades"(https://doi.org/10.1093/mnras/stab1696). Please see the paper for a full description.</p>
Dataset and code for "Classification of Solar Wind With Machine Learning"
<p>Matlab software and data from http://www.mlspaceweather.org/ for the paper</p> <p>https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JA024383</p>
Influence of He$^{++}$ and shock geometry on interplanetary shocks in the solar wind: 2D Hybrid simulations
<p>After protons, alpha particles (He$^{++}$) are the most important ion species in the solar wind, constituting typically about 5\% of the total ion number density. Due to their different charge-to-mass ratio protons and He$^{++}$ particles are accelerated differently when they cross the electrostatic potential in a collisionless shock. This behavior can produce changes in the velocity distribution function (VDF) for both species generating anisotropy in the temperature which is considered to be the energy source for various phenomena such as ion cyclotron and mirror mode waves. How these changes in temperature anisotropy and shock structure depend on the percentage of He$^{++}$ particles and the geometry of the shock is not completely understood. In this paper we have performed various 2D local hybrid simulations (particle ions, massless fluid electrons) with similar characteristics (e.g., Mach number) to interplanetary shocks for both quasi-parallel and quasi-perpendicular geometries self-consistently including different percentages of He$^{++}$ particles. We have found changes in the shock transition behavior as well as in the temperature anisotropy as functions of both the shock geometry and He$^{++}$ particle abundance: The change of the initial $\theta_{Bn}$ leads to variations of the efficiency with which particles can escape to the upstream region facilitating or not the formation of compressive structures in the magnetic field that will produce increments in perpendicular temperature. The regions where both temperature anisotropy and compressive fluctuations appear tend to be more extended and reach higher values as the He$^{++}$ content in the simulations increases.</p> <p> </p>
Some Similarities and Differences between the Observed Alfvénic Fluctuations in the Fast Solar Wind and Navier-Stokes Turbulence
<p>Three text data sets are uploaded: wind tunnel data (modane1.txt), solar-wind magnetic-field data (Flat1maginterp.txt) and solar-wind velocity data (Flat15interp3DP.txt)</p>
Precipitating Solar Wind Hydrogen at Mars: Improved Calculations of the Backscatter and Albedo with MAVEN Observations
<p>These files contain the derived data products used in the paper, including the penetrating and backscatter energy spectra and directional fluxes. See Readme.txt for a description of the data that is stored in each file.</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>
Wind and SOLAR RES predicted production data for Crete and Peloponnese - ONENET WP8
<p>WP8 aimed at the development and implementation of a web based app that enhances Active Power Management necessary for coordination of a TSOs and DSOs, using AI methods and cloud calculation engines that was tested in Peloponnese and Crete regions. Full description of the scope and results of WP8 Greek demo can be found in the relative deliverable <em>D8.2: Development and implementation of the “F-Channel” platform</em> (https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D8.2_V1.0.pdf). For purpose of this project similar, historical weather data in 1 hour resolution have been used in order to obtain behavior patterns of climatic parameters (daily, monthly, season) throughout region of interest. For this purpose various ERA5 climatic datasets has been used and AI algorithms applied in combination with terrain orography data. Modeled results was <strong>compared </strong>with <strong>operational data </strong>from TSO/DSO and appropriate model calibration has been provided based on deep learning AI algorithms.</p> <p>The data for Wind power plant modelled production is given in file <<a href="../api/records/10848817/draft/files/wind_res_onenet_wp8.csv/content" target="_blank" rel="noopener noreferrer">wind_res_onenet_wp8.csv</a>> in the following columns <time> ; <aa> ; <pw> ; <ws> ; <wp_name> . Columns are related to: Hourly time, wind park code, power [MWh], wind speed [m/s] and wind park name, respectively.</p> <p>The data for Solar power plant modelled production is given in file <<a href="../api/records/10848817/draft/files/solar_onenet_wp8.csv/content" target="_blank" rel="noopener noreferrer">solar_onenet_wp8.csv</a>> in the following columns <time> ; <name> ; <pw> ; <ta> ; <ghi> . Columns are related to: Hourly time, solar park name, power in MWh, ambient temperature and Global Horizontal irradiance [W/m2], respectively.</p>
The impact of solar wind magnetic field fluctuations on the magnetospheric energetics
<p>This dataset provides the results and analysis tools of the manuscript by Ala-Laht et al. "The impact of solar wind magnetic field fluctuations on the magnetospheric energetics". In addition, relevant SWMF simulation input files are included. See ReadMe.txt for information.</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>
103 anti-correlated structures identified in the solar wind
<p>This is the entire table listing the 103 anti-correlated structures identified.</p> <p>Each row represents a structure, the listed information are:<br>['No.', 'Year', 'ST1', 'ET1', 'ST2', 'ET2','ST3', 'ET3', 'ST4', 'ET4']<br>ST1: start time for the Fe signal based on the weak solution, in DOY<br>ET1: end time for the Fe signal based on the weak solution, in DOY<br>ST2: start time for the O signal based on the weak solution, in DOY<br>ET2: end time for the O signal based on the weak solution, in DOY<br>ST3: start time for the Fe signal based on the strong solution, in DOY<br>ET3: end time for the Fe signal based on the strong solution, in DOY<br>ST4: start time for the O signal based on the strong solution, in DOY<br>ET4: end time for the O signal based on the strong solution, in DOY</p> <p> </p>
Solar wind plasma, magnetic field parameters and geomagnetic storm index SYM-H from 2000 to 2020
<p>SYM-H index is used to quantify the intensity of geomagnetic storm. Its temporal variation is related to the solar wind plasma and magnetic field parameters. This dataset offers time series of solar wind density, solar wind velocity and solar wind magnetic field, SYM-H index. The python and matlab code files for processing and plotting data are also included. </p>
On the variability of the slow solar wind: New insights from the modelling and PSP-WISPR observations.
<p>We analyse the signature and origin of transient structures embedded in the slow solar wind, and observed by the Wide-Field Imager for Parker Solar Probe (WISPR) during its first 10 passages close to the Sun. WISPR provides a new in-depth vision on these structures, which have long been speculated to be a remnant of the pinch-off magnetic reconnection occurring at the tip of helmet streamers.<br> We pursue the previous modelling works of Reville (2020b, 2022) that simulate the dynamic release of quasi-periodic density structures into the slow wind through a tearing-induced magnetic reconnection at the tip of helmet streamers. Synthetic WISPR white-light (WL) images are produced using a newly developed advanced forward modelling algorithm, that includes an adaptive grid refinement to resolve the smallest transient structures in the simulations. We analyse the aspect and properties of the simulated WL signatures in several case studies, typical of solar minimum and near-maximum configurations.<br> Quasi-periodic density structures associated with small-scale magnetic flux ropes are formed by tearing-induced magnetic reconnection at the heliospheric current sheet and within 3-7Rs. Their appearance in WL images is greatly affected by the shape of the streamer belt and the presence of pseudo-streamers. The simulations show periodicities on the ~90-180min, ~7-10hr and ~25-50hr timescales, which are compatible with WISPR and past observations.<br> This work shows strong evidence for a tearing-induced magnetic reconnection contributing to the long-observed high variability of the slow solar wind.</p>
Maps of solar wind plasma precipitation onto Mercury's surface: a geographical perspective
<p>Data Archive to accompany: "Maps of solar wind plasma precipitation onto Mercury’s surface: a geographical perspective." Federico Lavorenti, Elizabeth A. Jensen, Sae Aizawa, Francesco Califano, Mario D’Amore, Deborah Domingue, Pierre Henri, Simon Lindsay, Jim M. Raines, and Daniel Wolf Savin. Submitted 2023 May to Planetary Science Journal.</p> <p>The files contained in this archive comprise the values shown in Figures 3 & 5.</p>
Solar wind plasma, magnetic field parameters and geomagnetic storm index SYM-H from 2000 to 2020
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>
Supplementary data for: "Global Venus-solar wind coupling and oxygen ion escape"
<p>Supplementary data for paper "Global Venus-Solar wind coupling and oxygen ion escape", submitted to Geophysical Research Letters. See README file for description of the data in each file.</p>
Data and results related to "Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements and Effects on Capacity Adequacy - Energy"
<p>The file includes data used for the analysis and results coming from the study (which was focused on the Italian "Nord" bidding zone). In particular:</p> <p>(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.</p> <p>(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).</p> <p>(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements</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.