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342 results for “solar wind”
Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2060-2079
<p>Please see the parent record at <a href="https://doi.org/10.5281/zenodo.10214348">https://doi.org/10.5281/zenodo.10214348</a>.</p>
Mirror of "ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials"
<h2>Mirrored from Joint Research Centre Data Catalogue</h2><p><a href="https://data.jrc.ec.europa.eu/collection/id-00138#datasets">https://data.jrc.ec.europa.eu/collection/id-00138#datasets</a></p><blockquote><p>This collection contains datasets from ENSPRESO, an EU-28 wide, open dataset for energy models on renewable energy potentials, at national (NUTS0) and regional levels (NUTS2) for the 2010-2050 period. Within ENSPRESO, ENergy Systems Potential Renewable Energy SOurces, technical potentials are provided for wind, solar and biomass, based on coherent GIS-based land-restriction scenarios. For wind, resource evaluation also considers setback distances as well as high resolution geo-spatial wind speed data. For solar, potentials are derived from irradiation data and available area for solar applications. For biomass, agriculture, forestry and waste sectors are considered. The temporal resolution for wind and solar is both annual and year fractions (timeslices as used by JRC-EU-TIMES). ENSPRESO complements the EMHIRES collection, that provides meteorologically derived power time series at high temporal and spatial resolution. ENSPRESO can impact the results of any energy model by improving its analyses of the competition and complementarity of energy technologies.</p></blockquote><p><a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:RUIZ%20CASTELLO%20Pablo">RUIZ CASTELLO Pablo</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:NIJS%20Wouter">NIJS Wouter</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:TARVYDAS%20Dalius">TARVYDAS Dalius</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:SGOBBI%20Alessandra">SGOBBI Alessandra</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ZUCKER%20Andreas">ZUCKER Andreas</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:PILLI%20Roberto">PILLI Roberto</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:CAMIA%20Andrea">CAMIA Andrea</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THIEL%20Christian">THIEL Christian</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:HOYER-KLICK%20Carsten">HOYER-KLICK Carsten</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:DALLA%20LONGA%20Francesco">DALLA LONGA Francesco</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:KOBER%20Tom">KOBER Tom</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BADGER%20Jake">BADGER Jake</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:VOLKER%20Patrick">VOLKER Patrick</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ELBERSEN%20Berien">ELBERSEN Berien</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BROSOWSKI%20Andre">BROSOWSKI Andre</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THR%C3%84N%20Daniela">THRÄN Daniela</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:JONSSON%20Klas">JONSSON Klas</a></p><h3>How to cite</h3><p>Ruiz Castello, P., Nijs, W., Tarvydas, D., Sgobbi, A., Zucker, A., Pilli, R., Camia, A., Thiel, C., Hoyer-Klick, C., Dalla Longa, F., Kober, T., Badger, J., Volker, P., Elbersen, B., Brosowski, A., Thrän, D. and Jonsson, K., ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials, European Commission, 2019, JRC116900.</p><p>European Commission</p><p>JRC116900</p><h3>Remarks</h3><p>The originator of this mirror requires stable and reliable URLs due to an integration of the dataset into an automated workflow. The data catalogue has frequent outages.</p>
Large-scale green grabbing for wind and solar PV development in Brazil
<h2>Large-scale green grabbing for wind and solar PV development in Brazil</h2><p>This repository contains the R code and parts of the data used for the analysis in the paper "Large-scale green grabbing for wind and solar PV development in Brazil" by Michael Klingler, Nadia Amelie, Jamie Rickman, and Johannes Schmidt, available as <a href="https://eartharxiv.org/repository/view/5824/">pre-print</a>.</p><p>Due to data sharing limitations, we cannot provide all data in the repository. Partly this data is not available publically at all (i.e. Bloomberg data, data by the instituto socio ambiental), partly the data has to be downloaded manually (CAR).</p><p>We still provide a repository which at least allows to understand the procedures we used during the analysis.</p><h3>Land tenure data set</h3><p>The procedures used to form our final land tenure data set can be found in land-tenure-data/processing.txt It is a mix of analyses in Python and in QGis.</p><h3>Analysis of land tenure data and park ownership/investment information</h3><p>The R-code to analyze the owernship relationships between windpark areas and investors/owners can be found in src/. All required libraries will install automatically.</p><p>The first two scripts cannot be executed due to data limitations. They create the sankey diagrams linking park areas to onwers and investors:</p><p>- 1.1-figures-results-1-wind.R</p><p>- 1.2-figures-results-1-solar.R</p><p>These three scripts are used to analyze the land tenure types prevailing on parks and comparing them to random areas. They should run with the provided data sets:</p><p>- 2-random-sampling-areas.R</p><p>- 3-intersection-parks-land-tenure.R</p><p>- 4-figures-land-tenure.R</p><p>This script validates our data against an independent data source. However, it cannot be run as it needs the proprietary Bloomberg database:</p><p>- 5-validation.R</p>
Planning resource adequacy of wind- and solar-based electricity systems: Input data and results files
<p>This record contains the input data and raw results files for the study titled "<a href="https://www.sciencedirect.com/science/article/pii/S2666792424000234">Planning reliable wind- and solar-based electricity systems</a>."</p> <p>Tyler H. Ruggles, Edgar Virgüez, Natasha Reich, Jacqueline Dowling, Hannah Bloomfield, Enrico G.A. Antonini, Steven J. Davis, Nathan S. Lewis, Ken Caldeira, "Planning reliable wind- and solar-based electricity systems," Advances in Applied Energy, 2024, https://doi.org/10.1016/j.adapen.2024.100185.</p> <p>Additionally, csv files are provided to recreate the associated figures in the paper in the "Figures_files.zip" file.</p> <p>The input data contains wind and solar generation availability profiles and electricity demand profiles for the contiguous US. The profiles cover the years 1950-2022 and are calculated from the ERA5 dataset. The study only used the satellite era data from the year 1979 onward. Input profiles are presented at 4 resolutions: hourly, 2-hour, 3-hour, and 4-hour resolution.</p> <p>The results files contain some keys indicating the modeling scenario used: "SWB" = "Solar+Wind+Battery"; "SWBNG" = "Solar+Wind+Battery+Natural Gas generation"; and "SWBPGP" = "Solar+Wind+Battery+Power-to-H2-to-Power Loop". The files can be grouped into multiple categories:</p> <ol> <li>The main analysis including the initial energy system optimization results and the secondary system performance testing results. <ol> <li>Initial optimization results are found in zip files titled "Initial_Optimization_Jan29v1_*.zip"</li> <li>The testing of the optimized systems are found in the zip file "Lost_Load_Decade_Testing_Jan29v1.zip"</li> </ol> </li> <li>A secondary analysis compared systems optimized on a single year of data and tested on a single other year of data. Those results are in "Matrix_Figure_NYrs1_Aug04v1.zip"</li> <li>A supplementary analysis compared the modeled results using input data with the 4 different time resolutions. These results can be found in the zip files titled "DeltaT_Test_July08v1dt*.zip"</li> </ol>
Solar wind parameters and geomagnetic indices for severe (SYM-H < -100nT) geomagnetic storms over two solar cycles
<p><em><strong>Severe storms data set (1996-2022)</strong></em></p> <p><em>Lotz, Grant, Davel (2024).</em></p> <p><strong>Changes from previous version:</strong></p> <ul> <li>Previous version had errors in calculation of `HER_eh` and `ABK_eh`. The mistake is corrected in this version.</li> <li>This version includes storms from 1996-2022. Previous version had 1996-2019.</li> </ul> <p>This data set is curated from 1-minute time resolution OMNI [4] and INTERMAGNET [7] data, spanning the period 1996 - 2022.</p> <p>It consists of all geomagnetic storms that reached minimum SYM-H (Symmetric-H index) of < -100 nT. We classified these as "severe" storms. There are 115 such events in the period 1996 - 2018. No severe storms detected between 2007-2010 or in 2019. Each file in the set contains the events in that year (YYYY) in the format "severe_YYYY.pickle". The geomagnetic storms are identified by the procedure described in [1], with thresholds -100 nT and -20 nT (see [1] for further detail).</p> <p>The data sets are Pandas "dataframes" [2], saved in the Python "pickle" format [3]. Each dataframe contains 23 variables, listed in the table below, with their description and unit.</p> <p>Fourteen of the parameters are solar wind plasma and magnetic field parameters from the High Resolution OMNI data set [4].</p> <p>There are 7 geomagnetic variables:</p> <ul> <li>one is the global Sym-H index,</li> <li>four are H-component geomagnetic observations from Hermanus, South Africa (HER: 34.4 S, 19.22 E) and Abisko, Sweden (ABK: 68.35 N, 18.82 E) and their time derivatives</li> <li>two are the e_h index [5] calculated at HER and ABK</li> </ul> <p>Finally, we list the time shift applied to each data point to shift to the bow shock nose (described for OMNI at [6]) and a string identifier for each geomagnetic storm.</p> <p> </p> <table> <tbody> <tr> <td><strong>#</strong></td> <td><strong>Name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>1</td> <td>time_shift</td> <td> <p>Time shift applied to shift solar wind parameters to bow shock</p> </td> <td>s</td> </tr> <tr> <td>2</td> <td>Bt</td> <td>Magnitude of interplanetary magnetic field (IMF)</td> <td>nT</td> </tr> <tr> <td>3</td> <td>Bx</td> <td>X-component of IMF</td> <td>nT</td> </tr> <tr> <td>4</td> <td>By_GSE</td> <td>Y-component of IMF (GSE coordinates)</td> <td>nT</td> </tr> <tr> <td>5</td> <td>Bz_GSE</td> <td>Z-component of IMF (GSE coordinates)</td> <td>nT</td> </tr> <tr> <td>6</td> <td>By_GSM</td> <td>Y-component of IMF (GSM coordinates)</td> <td>nT</td> </tr> <tr> <td>7</td> <td>Bz_GSM</td> <td>Z-component of IMF (GSM coordinates)</td> <td>nT</td> </tr> <tr> <td>8</td> <td>Vsw</td> <td>Bulk solar wind speed</td> <td>km/s</td> </tr> <tr> <td>9</td> <td>Vx</td> <td>X-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>10</td> <td>Vy</td> <td>Y-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>11</td> <td>Vz</td> <td>Z-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>12</td> <td>Np</td> <td>Proton number density in solar wind plasma</td> <td>#/cc</td> </tr> <tr> <td>13</td> <td>Temp</td> <td>Temperature of solar wind plasma</td> <td>K</td> </tr> <tr> <td>14</td> <td>Pd</td> <td>Dynamic or flow pressure of solar wind plasma</td> <td>nPa</td> </tr> <tr> <td>15</td> <td>E_field</td> <td>Solar wind electric field</td> <td>mV/km</td> </tr> <tr> <td>16</td> <td>SymH</td> <td>Symmetric-H index of the geomagnetic field</td> <td>nT</td> </tr> <tr> <td>17</td> <td>HER_H</td> <td>H-component of the geomagnetic field at Hermanus</td> <td>nT</td> </tr> <tr> <td>18</td> <td>HER_dHdt</td> <td>Time derivative of HER_H</td> <td>nT/min</td> </tr> <tr> <td>19</td> <td>HER_eh</td> <td>e_h index at HER</td> <td>nT/min</td> </tr> <tr> <td>20</td> <td>ABK_H</td> <td>H-component of the geomagnetic field at Abisko</td> <td>nT</td> </tr> <tr> <td>21</td> <td>ABK_dHdt</td> <td>Time derivative of ABK_H</td> <td>nT/min</td> </tr> <tr> <td>22</td> <td>ABK_eh</td> <td>e_h index at ABK</td> <td>nT/min</td> </tr> <tr> <td>23</td> <td>Storm_ID</td> <td>String identifier of the geomagnetic storm</td> <td>-</td> </tr> </tbody> </table> <p> </p> <p><em><strong>References</strong></em></p> <ol> <li>Lotz, S. I., & Danskin, D. W. (2017). <em>Space Weather</em>, 15, 1347– 1356. <a href="https://doi.org/10.1002/2017SW001662">https://doi.org/10.1002/2017SW001662</a></li> <li><a href="https://omniweb.gsfc.nasa.gov/ow_min.html">https://omniweb.gsfc.nasa.gov/ow_min.html</a></li> <li><a href="https://pandas.pydata.org/">https://pandas.pydata.org/</a></li> <li><a href="https://docs.python.org/3/library/pickle.html">https://docs.python.org/3/library/pickle.html</a></li> <li>Wintoft, P., Wik, M., & Viljanen, A. J. Space Weather Space Clim., 5, A7 (2015). <a href="http://dx.doi.org/10.1051/swsc/2015008">http://dx.doi.org/10.1051/swsc/2015008</a></li> <li><a href="https://omniweb.gsfc.nasa.gov/html/ow_data.html#time_shift">https://omniweb.gsfc.nasa.gov/html/ow_data.html#time_shift</a></li> <li><a href="https://www.intermagnet.org/index-eng.php">http://www.intermagnet.org</a></li> </ol>
Global mixing and transport of solar wind and magnetospheric ions in the nonlinear Kelvin-Helmholtz region across the terrestrial dayside magnetopause
<p>Data of figs 1-6 of the manuscript "Global mixing and transport of solar wind and magnetospheric ions in the nonlinear Kelvin-Helmholtz region across the terrestrial dayside magnetopause".</p>
Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity: Raw Data
<p>This dataset contains all GenX model input and results data relevant to the working paper ‘Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity.’ Data for each modeled scenario is contained within a folder in the main directory ('Final_Outputs'), using the naming convention p1_2030_case[number]. Scenarios correspond to the following table:</p> <table> <tbody> <tr> <td><strong>Case Number</strong></td> <td><strong>Scenario</strong></td> <td><strong>VRE Cost</strong></td> <td><strong>Forced Battery Capacity (GW)</strong></td> </tr> <tr> <td>1</td> <td>Fixed Interconnection</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>2</td> <td>Fixed Interconnection</td> <td>Low</td> <td>5</td> </tr> <tr> <td>3</td> <td>Fixed Interconnection</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>4</td> <td>Fixed Interconnection</td> <td>Low</td> <td>15</td> </tr> <tr> <td>5</td> <td>Optimized Interconnection</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>6</td> <td>Optimized Interconnection</td> <td>Low</td> <td>5</td> </tr> <tr> <td>7</td> <td>Optimized Interconnection</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>8</td> <td>Optimized Interconnection</td> <td>Low</td> <td>15</td> </tr> <tr> <td>9</td> <td>Co-Located Storage</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>10</td> <td>Co-Located Storage</td> <td>Low</td> <td>5</td> </tr> <tr> <td>11</td> <td>Co-Located Storage</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>12</td> <td>Co-Located Storage</td> <td>Low</td> <td>15</td> </tr> <tr> <td>13</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>14</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>15</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>16</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>15</td> </tr> <tr> <td>17</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>18</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>19</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>20</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>15</td> </tr> <tr> <td>21</td> <td>Co-Located Storage</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>22</td> <td>Co-Located Storage</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>23</td> <td>Co-Located Storage</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>24</td> <td>Co-Located Storage</td> <td>Mid</td> <td>15</td> </tr> </tbody> </table> <p>The 'fixed interconnection' scenario describes the scenario where the capacity of interconnection for each solar photovoltaic (PV) or wind site is fixed to assumed values. The 'optimized interconnection' scenario enables the model to independently size the renewable energy to interconnection and grid connection capacity. The 'co-located storage' scenario enables any solar PV or wind resource and storage resource to be sited behind a grid connection point while optimizing the interconnection buildout for each site. These scenarios are further explained in the respective working paper. Renewable energy cost sensitivity tags include ‘low’ for assumed low projected VRE and battery costs in 2030 and ‘mid’ for assumed mid projected VRE and battery costs in 2030. Various storage discharge capacities are forced into the system as a percentage of peak demand and range from 3.75-15 GW. Within each case folder, all of the input files (.csv), result files directly outputted by the model (in the 'Results' folder), and setting files (GenX and solver settings in the 'Settings' folder) can be found. All model outputs are described in detail in the GenX documentation. The code can be found on the GenX GitHub repository: https://github.com/GenXProject/GenX.jl. This work has not yet been peer-reviewed.</p>
Dataset for analysis of the value of energy for wave, wind and solar power
<p>Data and code for: Vrana, Til Kristian, & Svendsen, Harald G. (2021). Quantifying the Market Value of Wave Power compared to Wind&Solar - a case study. The 9th Renewable Power Generation Conference - RPG Dublin Online 2021 (RPG 2021), (https://doi.org/10.1049/icp.2021.1383)</p>
Demodulation Combining 1D-CNN and Bi-LSTM Network over Strong Solar Wind Turbulence Channel
<p>The data in this dataset is derived MATLAB simulation dataset by us to illustrate the GMSK signal demodualtion over strong solar wind turbulence using deep learing.<br> Disclaimer<br> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.<br> Description of the dataset<br> One file per is provided as a csv file with the following features:<br> train_dataset: Including data_kb2 and data_awgn. data_kb2 is the GMSK modulated fading signal data and lable data under the influence of strong solar wind turbulence, data_awgn is the GMSK modulated signal data and lable data under the influence of Gaussian white noise only.In which per mod_data and lable_data have 21000 csv file, respectively. And per csv file has 800 data. Among them ,the lable is the original binary number. The train_dataset is used to train neural network demodulator model.<br> test_dataset:Including test_data and test_lable.The test_data and test_lable have 6 file respectively. And they are used to test the trained neural network demodulator model.<br> test_data:<br> kb2_Tb0d5_m1d4: GMSK data at the BTb=0.5 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d5_m1d2:GMSK data at the BTb=0.5 , solar wind turbulence scintillation index m=1.2.<br> kb2_Tb0d3_m1d4:GMSK data at the BTb=0.3 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d3_m1d4: GMSK data at the BTb=0.3 , solar wind turbulence scintillation index m=1.2.<br> Awgn_Tb0d5:GMSK at data the BTb=0.5 under the influence of Gaussian white noise only.<br> Awgn_Tb0d5:GMSK data at the BTb=0.3 under the influence of Gaussian white noise only.<br> test_lable:<br> kb2_Tb0d5_m1d4: GMSK lable data at the BTb=0.5 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d5_m1d2:GMSK lable data at the BTb=0.5 , solar wind turbulence scintillation index m=1.2.<br> kb2_Tb0d3_m1d4:GMSK labe data at the BTb=0.3 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d3_m1d4: GMSK labe data at the BTb=0.3 , solar wind turbulence scintillation index m=1.2.<br> awgn_Tb0d5:GMSK lable data at the BTb=0.5 under the influence of Gaussian white noise only.<br> awgn_Tb0d5:GMSK labe data at the BTb=0.3 under the influence of Gaussian white noise only.<br> </p>
Dataset for for manuscript "Solar wind magnetic holes can cross the bow shock and enter the magnetosheath"
<p>This data set contains event times and basic properties of magnetic holes analysed in the manuscript "Solar wind magnetic holes can cross the bow shock and enter the magnetosheath".</p>
era5 monthly solar radiation, rain, 100m wind
<p>era5 monthly solar radiation, rain, 100m wind</p>
Extreme power shortage events of wind-solar supply systems for individual countries
<p><span>Raw data of extreme power shortage events in wind-solar supply system in the paper entitled “Climate change impacts on the power shortage events of wind-solar supply systems worldwide during 1980–2022” on Nature Communications.</span></p>
Solar Wind - Venus Interaction during the Solar Maximum & Solar Minimum 1 Periods: A Newly Developed Multi-Fluid MHD Model
Open the record for dataset details and reuse information.
A wave detection procedure for electromagnetic cyclotron waves AND results (data) in case of the solar wind
<p>A detection procedure (produced via IDL) for electromagnetic cyclotron waves is presented,which is used to find low frequency waves in the solar wind over a period of 7 years. The procedure and the relevant results (data) are described in a manuscript entitled "Statistical study of low frequency electromagnetic cyclotron waves in the solar wind at 1 AU", which has been submitted to Journal of Geophysical Research - Space Physics for publication. More information about the procedure and data can be accessed by writing to the following address: zgqisp@163.com.</p>
Dataset for optimal Wind+Hydrogen+Other+Battery+Solar (WHOBS) electricity systems for European countries
<p>Data outputs from optimisation of Wind+Hydrogen+Other+Battery+Solar (WHOBS) electricity systems for European countries.</p> <p>Input data and code can be found here:</p> <p>https://github.com/PyPSA/WHOBS</p> <p>The summary of the results with metadata can be found here:</p> <p>https://github.com/PyPSA/WHOBS/tree/master/results-181002</p> <p>if you don't want to download 6 GB.</p> <p>To load the data, install PyPSA and do e.g. for Germany (DE) in 2030:</p> <p>network = pypsa.Network("DE-2030.nc")</p> <p>Note that in these files, in some hours batteries discharge and charge at the same time. This is because the model wants to dump energy and this behaviour has the same cost and effect as curtailing wind and solar. In the latest version of the github code, wind and solar have tiny marginal costs (0.02 and 0.01 EUR/MWh respectively) to prevent this behaviour. With these costs, curtailment is preferred.</p>
Proton temperature anisotropy constraint associated with alpha beam instability in the solar wind
<p>Simulation data and code repository for the paper "Proton temperature anisotropy constraint associated with alpha beam instability in the solar wind". This repository contains the particle-in-cell hybrid simulation code based on CAM-CL scheme and the simulation results. The parameter files are also included in the archived tar file. Another package contains the files for the data used in the paper.</p> <p> </p>
Input data and code related to "Utilizing curtailed wind and solar power to scale up electrolytic hydrogen production in Europe"
<p>Datasets and code for the submitted article: "Utilizing curtailed wind and solar power to scale up electrolytic hydrogen production in Europe" </p> <p>by Alissa Ganter<sup>1,2</sup>, Tyler H. Ruggles<sup>2</sup>, Paolo Gabrielli<sup>1</sup>, Giovanni Sansavini<sup>1,*</sup>, Ken Caldeira<sup>2</sup></p> <p><sup>1</sup> Institute of Energy and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland</p> <p><sup>2</sup> Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA</p> <p><sup>*</sup> Corresponding author: email - sansavig@ethz.ch</p> <p>All rights lie with the authors. Refer to the README.docx for a description of the datasets and their usage in the article.</p>
The Dayside Ionopause of Mars: Solar Wind Interaction, Pressure Balance, and Comparisons with Venus
<p>These files contain the derived data products used in the paper. See Readme.txt for a description of the data stored in each file.</p>
Database for "A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves"
<p>Database for <strong>"A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves" </strong>submitted to Space Weather by Murphy et al. </p> <p>The repository contains 2 datasets:</p> <ul> <li>The power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> <li>The power spectral density of the azimuthal electric field from THEMIS and Van Allen Probes along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> </ul> <p>Both datasets are provided as an IDL save file and as an HDF5 file.</p> <p>The IDL data can be loaded using: </p> <pre><code>filename='electric_field_psd.sav' filename='magnetic_field_psd.sav' restore, filename, /verbose</code></pre> <p>The HDF5 file can be opened and investigate using (small changes will be required to store each variable):</p> <pre><code class="language-python">import h5py filename = 'magnetic_field_psd.h5' # magnetic field data filename = 'magnetic_field_psd.h5' # electric field data with h5py.File(filename, "r") as f: # loop through all keys (data) #print key and key attribute and size for i in f.keys(): print(f"{i} - {f[i].attrs['attributes']}, shape - {f[i].shape}") ds_obj = f[i] # returns as a h5py dataset object ds_arr = f[i][()] # returns as a numpy array</code></pre> <p>Below is a description of unique and common variables in each file. The psd variables have a shape [f,t], indicating the first dimension is frequency and the second is time, the f_mhz variable has shape [f], and all time series have shape [t]; here [f] and [t] denotes the number of elements in frequency and time arrays. </p> <p>------------------------------------</p> <p><strong>Magnetic field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>magnetic_field_psd.h5</li> <li>magnetic_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd <ul> <li>Power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES</li> <li>Units - nT<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Electric field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>electric_field_psd.h5</li> <li>electric_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd <ul> <li>Power spectral density of the azimuthal electric field from THEMIS, Van Allen Probes, and GOES</li> <li>Units - (mV/m)<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Common Data in the Magnetic and Electric Field Datasets (variable in file):</strong></p> <ul> <li>probe <ul> <li>Corresponding satellite of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>t <ul> <li>Time stamp of each time series; number of seconds since 1970 (UNIX time), [t]</li> <li>Units - s</li> <li>Shape [t]</li> </ul> </li> <li>f_mhz <ul> <li>Frequency of psd data, [f]</li> <li>Units - mHz</li> <li>Shape [f] - (19)</li> </ul> </li> <li>ae <ul> <li>OMNI AE index of each time stamp</li> <li>Units - nT'</li> <li>Shape [t]</li> </ul> </li> <li>al <ul> <li>OMNI AL index of each time stamp, units - nT</li> <li>Shape [t]</li> </ul> </li> <li>au <ul> <li>OMNI AU index of each time stamp </li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_t <ul> <li>OMNI IMF B of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_x <ul> <li>OMNI IMF Bx (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_y <ul> <li>OMNI IMF By (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_z <ul> <li>OMNI IMF Bz (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>dst <ul> <li>OMNI Dst of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>kp - <ul> <li>OMNI Kp (Kp*10) of each time stamp</li> <li>units - NA</li> <li>Shape [t]</li> </ul> </li> <li>l_sh <ul> <li>L-shell of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>ls_t05 <ul> <li>L* from TS05 of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>mlt <ul> <li>Magnetic Local Time of each time stamp</li> <li>Unit - hour</li> <li>Shape [t]</li> </ul> </li> <li>n <ul> <li>OMNI Solar Wind Proton Density of each time stamp</li> <li>Units - n/cc</li> <li>Shape [t]</li> </ul> </li> <li>pdyn <ul> <li>OMNI Solar Wind Dynamic Pressure (flow pressure) of each time stamp</li> <li>Units - nPa</li> <li>Shape [t]</li> </ul> </li> <li>symh - b'OMNI Sym-H of each time stamp, units - nT', shape - (477205,)</li> <li>v_t <ul> <li>OMNI Solar wind V of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_x <ul> <li>OMNI Solar wind Vx (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_y <ul> <li>OMNI Solar wind Vy (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_z <ul> <li>OMNI Solar wind Vz (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p>
New Evidence on the Origin of Solar Wind Microstreams/Switchbacks
<p>Here is the supplement material (high-quality animations) for a paper "New Evidence on the Origin of Solar Wind Microstreams/Switchbacks" by Kumar et. al (2023), ApJ Letters</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.