Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “Solar cycle”
Simulations for impacts of large-scale Saharan solar farms on the global terrestrial carbon cycle
<p>Four experiments were conducted to investigate the impact of a large scale Saharan solar farm on the global carbon cycle. Here a fully coupled Earth System model (ESM) EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of Photovoltaic (PV) solar panels over the Sahara desert. These experiments considered solar panel coverage at 5, 20 or 50% of a region of the Sahara Desert. The dataset contains selected output data from the simulations. Simulations were run for 200 years. The last 60 years were used for analysis after the model reached an equilibrium. </p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Power et al. (2023).</p>
Relative phase between tidal and solar cycles influences the heating of a coral atoll lagoon
Open the record for dataset details and reuse information.
STEG data of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies
<p>Figure_7_data: laboratory data of TEG output power working at low temperature differences. Data used in Figure 7 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies.</p> <p>Figure_9_data: experimental data of TEG temperature differences from July 24 to July 31, 2017. Data used in Figure 9 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies.</p> <p>Figures_11_14_data: input and output data of the STEG model. One-year cycle data. Used to obtain figures 11 to 14 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies</p>
SERPENTINE catalog of solar cycle 25 multi-spacecraft solar energetic particle events
<p>This catalog contains multi-spacecraft solar energetic particle (SEP) events, which were observed with the new heliospheric spacecraft fleet in solar cycle 25. It has been created within the European Union’s Horizon 2020 project <em>SERPENTINE</em> (Solar energetic particle analysis platform for the inner heliosphere). The catalog comprises key SEP characteristics observed by five different observer locations as provided by <em>Solar Orbiter, Parker Solar Probe, STEREO A, Wind</em> and <em>SOHO</em> (at the Lagrangian point 1), and <em>BepiColombo</em>. It focuses on large events, which show energetic proton increases above 25 MeV observed at least at two spacecraft. The catalog provides not only key parameters of the proton event but also the same parameters for 1 MeV and 100 keV electrons, respectively.</p> <p>For more information, and if you use this catalog, please refer to the corresponding publication:</p> <blockquote> <p>The solar cycle 25 multi-spacecraft solar energetic particle event catalog of the SERPENTINE project<br>N. Dresing, A. Yli-Laurila, S. Valkila, J. Gieseler, D. E. Morosan, G. U. Farwa, Y. Kartavykh, C. Palmroos, I. Jebaraj, S. Jensen, P. Kühl, B. Heber, F. Espinosa, R. Gómez-Herrero, E. Kilpua, V.-V. Linho, P. Oleynik, L. A. Hayes, A. Warmuth, F. Schuller, H. Collier, H. Xiao, E. Asvestari, D. Trotta, J. G. Mitchell, C. M. S. Cohen, A. W. Labrador, M. E. Hill and R. Vainio<br>A&A, 687 (2024) A72<br>DOI: <a href="https://doi.org/10.1051/0004-6361/202449831">10.1051/0004-6361/202449831</a>, arXiv:<a href="https://arxiv.org/abs/2403.00658">2403.00658</a>. </p> </blockquote> <p>The csv files provided here are archived versions of the dynamic catalog available at <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/">https://data.serpentine-h2020.eu/catalogs/sep-sc25/</a></p> <p>Provided are two semicolon-separated csv files containing the same data. The only difference is that the file <em>sep-sc25_extended_header.csv </em>includes an extensive header.</p> <ul> <li>Field descriptions <ul> <li>id: ID</li> <li>science_case: Science case</li> <li>date: Event date [UTC]</li> <li>flare_time: Flare time [UTC]</li> <li>flare_lat: Flare Carrington latitude [deg]</li> <li>flare_lon: Flare Carrington longitude [deg]</li> <li>flare_class: Flare class (GOES)</li> <li>flare_comments: Flare Comments</li> <li>radio_type2: Radio type II bursts</li> <li>decametric_type2_start: Decametric type II burst start time [UT]</li> <li>decametric_type2_stop: Decametric type II burst end time [UT]</li> <li>decametric_type2_freq_range: Decametric type II frequency range [MHz]</li> <li>radio_type2_start: Metric radio type II burst start time [UT]</li> <li>radio_type2_stop: Metric radio type II burst end time [UT]</li> <li>radio_type2_freq_range: Frequency range of metric type II burst [MHz]</li> <li>radio_imaging_available: Metric radio imaging available</li> <li>radio_comments: Radio comments</li> <li>cme_id: Associated CMEs</li> <li>solar_mach_link: Solar-Mach link</li> </ul> </li> </ul> <ul> <li>S/C codes <ul> <li>BepiC: BepiColombo</li> <li>L1: L1 (SOHO/Wind)</li> <li>PSP: Parker Solar Probe</li> <li>STA: STEREO A</li> <li>SolO: Solar Orbiter</li> </ul> </li> </ul> <ul> <li>S/C related field descriptions <ul> <li>{sc}_sc_lat: S/C Carrington latitude [deg]</li> <li>{sc}_sc_lon: S/C Carrington longitude [deg]</li> <li>{sc}_dist: S/C radial distance [au]</li> <li>{sc}_ip_shock_id: Associated IP shocks</li> <li>{sc}_p25MeV_onset_date: S/C protons 25 MeV onset date [UTC]</li> <li>{sc}_p25MeV_onset_time: S/C protons 25 MeV onset time [UTC]</li> <li>{sc}_p25MeV_onset_time_formatted: S/C protons 25 MeV onset time [UTC] (Formatted)</li> <li>{sc}_p25MeV_onset_averaging: S/C protons 25 MeV averaging used for onset [min]</li> <li>{sc}_p25MeV_onset_sector: S/C protons 25 MeV sector used for onset</li> <li>{sc}_p25MeV_peak_date: S/C protons 25 MeV peak date [UTC]</li> <li>{sc}_p25MeV_peak_time: S/C protons 25 MeV peak time [UTC]</li> <li>{sc}_p25MeV_peak_time_formatted: S/C protons 25 MeV peak time [UTC] (Formatted)</li> <li>{sc}_p25MeV_peak_flux: S/C protons 25 MeV peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_p25MeV_peak_flux_formatted: S/C protons 25 MeV peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_p25MeV_peak_averaging: S/C protons 25 MeV averaging used for peak [min]</li> <li>{sc}_p25MeV_peak_sector: S/C protons 25 MeV sector used for peak</li> <li>{sc}_p25MeV_injection_date: S/C protons 25 MeV inferred injection date [UTC]</li> <li>{sc}_p25MeV_injection_time: S/C protons 25 MeV inferred injection time [UTC]</li> <li>{sc}_p25MeV_pathlength: S/C protons 25 MeV path length used for inferred injection time [au]</li> <li>{sc}_p25MeV_sw_speed: S/C protons 25 MeV onset solar wind speed [km/s]</li> <li>{sc}_p25MeV_comments: S/C protons 25 MeV comments</li> <li>{sc}_e100keV_onset_date: S/C electrons 100 keV onset date [UTC]</li> <li>{sc}_e100keV_onset_time: S/C electrons 100 keV onset time [UTC]</li> <li>{sc}_e100keV_onset_time_formatted: S/C electrons 100 keV onset time [UTC] (Formatted)</li> <li>{sc}_e100keV_onset_averaging: S/C electrons 100 keV averaging used for onset [min]</li> <li>{sc}_e100keV_onset_sector: S/C electrons 100 keV sector used for onset</li> <li>{sc}_e100keV_peak_date: S/C electrons 100 keV peak date [UTC]</li> <li>{sc}_e100keV_peak_time: S/C electrons 100 keV peak time [UTC]</li> <li>{sc}_e100keV_peak_time_formatted: S/C electrons 100 keV peak time [UTC] (Formatted)</li> <li>{sc}_e100keV_peak_flux: S/C electrons 100 keV peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e100keV_peak_flux_formatted: S/C electrons 100 keV peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e100keV_peak_averaging: S/C electrons 100 keV averaging used for peak [min]</li> <li>{sc}_e100keV_peak_sector: S/C electrons 100 keV sector used for peak</li> <li>{sc}_e100keV_injection_date: S/C electrons 100 keV inferred injection date [UTC]</li> <li>{sc}_e100keV_injection_time: S/C electrons 100 keV inferred injection time [UTC]</li> <li>{sc}_e100keV_pathlength: S/C electrons 100 keV path length used for inferred injection time [au]</li> <li>{sc}_e100keV_sw_speed: S/C electrons 100 keV onset solar wind speed [km/s]</li> <li>{sc}_e100keV_comments: S/C electrons 100 keV comments</li> <li>{sc}_e1MeV_onset_date: S/C electrons 1 MeV onset date [UTC]</li> <li>{sc}_e1MeV_onset_time: S/C electrons 1 MeV onset time [UTC]</li> <li>{sc}_e1MeV_onset_time_formatted: S/C electrons 1 MeV onset time [UTC] (Formatted)</li> <li>{sc}_e1MeV_onset_averaging: S/C electrons 1 MeV averaging used for onset [min]</li> <li>{sc}_e1MeV_onset_sector: S/C electrons 1 MeV sector used for onset</li> <li>{sc}_e1MeV_peak_date: S/C electrons 1 MeV peak date [UTC]</li> <li>{sc}_e1MeV_peak_time: S/C electrons 1 MeV peak time [UTC]</li> <li>{sc}_e1MeV_peak_time_formatted: S/C electrons 1 MeV peak time [UTC] (Formatted)</li> <li>{sc}_e1MeV_peak_flux: S/C electrons 1 MeV peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e1MeV_peak_flux_formatted: S/C electrons 1 MeV peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e1MeV_peak_averaging: S/C electrons 1 MeV averaging used for peak [min]</li> <li>{sc}_e1MeV_peak_sector: S/C electrons 1 MeV sector used for peak</li> <li>{sc}_e1MeV_injection_date: S/C electrons 1 MeV inferred injection date [UTC]</li> <li>{sc}_e1MeV_injection_time: S/C electrons 1 MeV inferred injection time [UTC]</li> <li>{sc}_e1MeV_pathlength: S/C electrons 1 MeV path length used for inferred injection time [au]</li> <li>{sc}_e1MeV_sw_speed: S/C electrons 1 MeV onset solar wind speed [km/s]</li> <li>{sc}_e1MeV_comments: S/C electrons 1 MeV comments</li> <li>{sc}_ep_ratio: Ratio of Electrons (~1MeV) / Protons (25-40 MeV)</li> </ul> </li> </ul> <div> </div> <div><strong>CHANGELOG:</strong></div> <div> <ul> <li>2025-06-05 <ul> <li>Updated peak fluxes and peak times of PSP 1 MeV electrons, as well as PSP's e/p ratios (the previous flux values are erroneous!)</li> </ul> </li> <li>2024-09-06 <ul> <li>Flare information has been updated</li> <li>Updated cme_id info corresponding to updated CME catalog</li> <li>(Zenodo version only: change file format from "semicolon separated" to "comma separated, with fields containing commas surrounded by quotes")</li> </ul> </li> <li>2024-05-24<br> <ul> <li>Events 35, 36: flare times (GOES) were incorrectly provided as peak times. They have been changed to start times (like for all flares).</li> <li>All 8 events where SOLO/STIX has been used for flare determination had been time shifted to the Sun. Now they are shifted to 1 AU to be consistent with the GOES times.</li> </ul> </li> </ul> </div>
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>
Derivative products: "Very Local Interstellar Medium Revealed by Complete Solar Cycle of Interstellar Neutral Helium Observations with IBEX" by Swaczyna et al.
<p>Derivative products from: Swaczyna et al. 2022, "Very Local Interstellar Medium Revealed by Complete Solar Cycle of Interstellar Neutral Helium Observations with IBEX", ApJS, under review</p>
How stable are solar-type cycles over half a century? Chromospheric S-indices Mt. Wilson vs. TIGRE
<p> <br>by Klaus-Peter Schröder (1); Jürgen Schmitt (2); Marco Mittag (2)<br>Departamento de Astronomia, Universidad de Guanajuato, Mexico; Hamburger<br>Sternwarte, University of Hamburg, Germany</p> <p>Since autumn 2013, the robotic 1.2m telescope TIGRE (formerly the Hamburg Robotic<br>Telescope) is operating in Guanajuato (Mexico, and see poster by Gonzalez et al.). Regularly<br>using the same calibration stars for S-indices as did the Mount Wilson project (see Baliunas et<br>al. 1995), our R=20,000 HEROS echelle spectra have provided a decade of chromospheric<br>activity monitoring of the same ca. 100 solar-type stars, including a smaller number with<br>pronounced cycles (see Schröder et al. 2013) of periods of o(10yrs), which were already<br>observed at Mount Wilson since the 1970ies. Hence, on a timescale of half a century, we can<br>now use those same stars, first shown by Olin C. Wilson and his group to perform solar-type<br>activity cycles, to probe the stability of these cycles. This is motivated by the solar cycle<br>variability, which is evident, when comparing the last two, relatively weak maxima monitored by<br>TIGRE, with the Mount Wilson data from the 1970ies and 1980ies. Hence, we here show that<br>this is not a singular behavior. Strong cycles seem to be less variable, while weaker cycles like<br>the solar are natural to change between stronger and weaker performance. In the case of the<br>Sun this behavior is consistent with an o(century)-period as of the semi-regular Gleissberg<br>cycle, as well as with the occurrence of grand-minima.</p> <p><br>Theme(s): The Sun and Cool Stars in the Time Domain, Cool Stars as Stellar Systems</p>
Empirical models of "Magnetosheath Jets Over Solar Cycle 24: An Empirical Model"
<p>The empirical models of jet observations rates as a function of solar wind parameters used in the article "Magnetosheath Jets Over Solar Cycle 24: An Empirical Model" by L.Vuorinen, A. T. LaMoury, H. Hietala, and F. Koller. Please contact L. Vuorinen (lakavu@utu.fi) for additional information.</p>
Data containing alitude profiles of Joule heating and Pedersen conductivity from EISCAT and TIE-GCM used for the research work "A comparative assessment of the distribution of Joule heating in altitude as estimated in TIE-GCM and EISCAT over one solar cycle"
<p>This archive contains the data used for the research work with title "A comparative assessment of the distribution of Joule heating in altitude as estimated in TIE-GCM and EISCAT over one solar cycle". The data represent Joule heating and Pedersen conductivity altitude profiles for the duration of the solar cycle 24 between the years 2009 and 2019. The information is separated by Magnetic Local Time, Kp index and altitude. There are data from the EISCAT incoherent scatter radar in Matlab format and from a TIE-GCM simulation executed at the Department of Electrical and Computer Engineering, Democritus University of Thrace in NetCDF format. </p>
Geomagnetic Induced Currents (GICs) estimations for Portugal mainland - Top 8 geomagnetic storms of solar cycle 24
<p>Geomagnetic Induced Currents, or GICs, are electric currents induced in conductive infrastructures (such as power lines and pipelines) due to changes in the Earth's magnetic field. GICs can cause power outages, damage transformers and other electrical equipment, and interfere with the operation of pipelines.</p> <p>We present here the GIC estimation for all the high voltage lines (150, 220 and 400 kV) over mainland Portugal for the eight most intense geomagnetic storms during solar cycle 24. The induced electric field is calculated from geomagnetic series obtained at Coimbra or San Fernando magnetic observatories, according to the Nearest Neighbour method and from 31 magnetotelluric soundings (https://doi.org/10.5281/zenodo.7147543).</p> <p>The GIC peak and standard deviation values for each substation are listed in the dataset. The criteria used for the definition of the geomagnetic storm duration in order to compute standard deviation values are based on the Ebre Observatory tables (https://www.obsebre.es/en/rapid) and the Hutchinson et al. (2011) criteria for the storm beginning and end, respectively.</p>
The correspondence of Pc5 waves at high latitude and in the solar wind during the solar cycle 23: Data of figures
<p>This deposit contains the data used in the publication in Journal of Geophysical Research: Space Physics, currently titled "The correspondence of Pc5 waves at high latitude and in the solar wind during the solar cycle 23". The paper associated with the data has been submitted on 19.12.2018, and this upload has been done to correspond with the data policy of the journal. The data will be made available through Zenodo after the paper has been accepted for publication or as required by the journal.</p> <p>The steps required to produce this derived data set have been described in detail in the publication above.</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.